GOME Assimilated and Validated Ozone and NO 2 Fields for Scientific Users and for Model Validation

Transcription

1 GOA GOME Assimilated and Validated Ozone and NO 2 Fields for Scientific Users and for Model Validation February January 2003 KNMI / ESA Assimilated GOME total ozone 25 Sep 2002 million square km Ozone Hole Area w.r.t. 200 DU in the Southern Hemisphere GOME Assimilated Ozone KNMI / ESA Aug 01 Sep 01 Oct 01 Nov 01 Dec 31 Dec [DU] Final Report European Commission, Fifth Framework Programme Environment and Sustainable Development, Call identifier: EESD-ENV-99-2 Key Action 2: Global Change, Climate and Biodiversity 2.4: European component of the global observing system Sub action 2.4.1: Better exploitation of existing data and adaption of existing observing systems Contract number: EVK2-CT Preparation date: April 2003

2 Partners KNMI Dept. of Climate Research and Seismology, Royal Netherlands Meteorological Institute, Netherlands Prof. Hennie Kelder (project co-ordinator), Dr. Henk Eskes (project leader), Folkert Boersma, Dr. Arjo Segers, Dr. Roeland van Oss, Dr. Ronald van der A, Pieter Valks UIO Department of Geophysics, University of Oslo, Norway Prof. I.S.A. Isaksen, Michael Gauss LAP-AUTH Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Greece Prof. C.S. Zerefos, Dr. Dimitris Balis IUP Institut für Umweltphysik, University of Heidelberg, Germany Prof. U. Platt, Dr. Thomas Wagner, Dr. Mark Wenig, Steffen Beirle NILU Norwegian Institute for Air Research, Norway Dr. Georg Hansen, Aasmund Fahre Vik ESA-ESRIN European Space Research Institute, Italy Dr. Claus Zehner EU European Commission, DG RTD, Brussels, Belgium Dr. Riccardo Casale Web page

3 Contents Executive summary 1 1 Summary Context of the work Objectives of the GOA project Outline of methodology and innovation Highlights Progress to date Conclusions Introduction to the GOA project Objectives of GOA Context Brief presentation of consortium Work achieved Overview Deliverables and milestones reached: Results Progress by work package Co-ordination activities Six year validated data set of total ozone The TM3DAM ozone assimilation model Generation of a 6 year total ozone data set i

4 4.2.3 Validation of the retrieved and assimilated total ozone products User interface to the GOME total ozone data sets Assimilated and validated 3D ozone fields based on GOME ozone profiles and columns Objectives Preparation of the ozone profile assimilation Implementation of the TM3PAS assimilation model Review of the ozonesonde Standard Operational Procedures and real-time quality control Generation of ozone profiles for the period of one year Assimilation of GOME ozone profiles Validation of the ozone fields using Umkehr profiles Validation of the ozone fields using homogenised sonde profiles Tropospheric ozone columns derived from assimilation Tropospheric ozone estimates based on GOME data Comparison between the Oslo CTM2 and the MOZAIC ozone data Retrieval and assimilation of NO 2 columns from GOME Introduction Generation of NO 2 slant column densities Combined retrieval, modelling and assimilation approach for GOME NO Deriving tropospheric NO 2 vertical columns Validation of GOME NO 2 Vertical Column Densities Critical evaluation of the different aspects of the retrieval Generation of tropospheric and stratospheric NO 2 columns with the combined retrieval-assimilation approach Comparison of the results with an independent chemistry-transport model CTM modelling studies and validation with GOME observations and measurement campaigns Introduction Comparison of the Oslo CTM2 with GOME total ozone CTM model runs CTM model comparison and comparison with GOME ozone and NO 2 data ii

5 4.5.5 Requirements for the GOME derived ozone and NO 2 fields and for future space borne observations Future stratospheric ozone changes and changes in the chemical oxidation in the troposphere Data user feedback Deviations from the work plan Conclusions, progress towards project objectives Annexes 69 A Conference attendance B Project meetings and visits C Publicity material D Published material (include pre-prints or offprints) E References F Definitions, acronyms, abbreviations G Summary of the GOA project workshop H Conference proceedings contribution, Ozone symposium, Goteborg, I Total ozone validation report J Documentation on the total ozone assimilation approach K Documentation on the ozone profile assimilation approach L Ozone profile validation report M Documentation on the nitrogen dioxide retrieval - assimilation approach N Comparison between GOME and the CTM2 and TM3 models iii

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7 Executive summary Introduction Ozone and NO 2 are two key species determining the chemistry of both the lower and middle atmosphere. Long-term and high quality data sets of these two compounds are crucial to quantify and understand changes in the atmospheric composition, and to monitor the impact of international agreements such as the Montreal and Kyoto protocols. The key issue of the Montreal protocol is the reduction of man-made chlorine and bromine containing compounds that are responsible for the destruction of the stratospheric ozone layer. The Kyoto protocol deals with reductions of the emissions of greenhouse gases such as carbon dioxide and methane. Observations of ozone and NO 2 are also crucial to validate the performance of chemistry-transport and general circulation models. These models provide analyses and predictions of anthropogenic changes in the chemical composition of the atmosphere, in relation to stratospheric ozone depletion and the oxidation capacity of the atmosphere, affecting chemically active greenhouse gases. Ozone has a strong impact on the atmosphere and on life on Earth. The stratospheric ozone layer is a shield against harmful UV radiation from the sun. Ozone is itself a greenhouse gas. On the other hand, ozone is a reactive gas and plays a central role in the chemical reactions, thereby influencing the lifetime of other greenhouse gases such as methane. Ozone near the Earth s surface (smog) is harmful to animals, plants and human health. The presence of nitrogen oxides (NO, NO 2 ) and other ozone precursors determines the amount of photochemical smog. NO x also plays a central role in the oxidation of reactive greenhouse gases and greenhouse gas precursors. Despite the importance of ozone and NO 2 there is only a limited amount of observational information on these compounds, especially in the troposphere. The large spectral range in combination with the nadir viewing geometry of the Global Ozone Monitoring Experiment (GOME) spectrometer on the European ERS-2 satellite allow for the retrieval of two unique products, namely (a) vertical profiles of ozone, with explicit tropospheric information, and (b) NO 2 total columns and tropospheric columns. The satellite instrument has the additional advantage that it scans practically the entire globe. Measurements of the global distribution of nitrogen oxides provides direct information on the location of the major air pollution areas and the amount of NO (NO 2 ) emitted. Deriving reliable quantitative tropospheric and stratospheric information from GOME, and from future instruments like SCIAMACHY on ENVISAT and OMI on EOS-AURA, is a major challenge and the central theme of the GOA project. Objectives of the GOA project The European project GOA GOME Assimilated and Validated Ozone and NO 2 Fields for Scientific Users and for Model Validation has provided long-term value-added GOME data sets to scientific users and policy makers. Detailed quality estimates and validation data sets of the ozone and nitrogen dioxide products have been generated, based on an extended set of ground based and ozone sonde observations, 1

8 2 GOA Final Report April, 2003 and the error statistics obtained from the data assimilation. The data sets have been confronted with the output from global chemistry-transport models to improve their modelling capability of current and future changes of ozone and chemically active greenhouse gases. Innovation Several new techniques have been developed and were applied in the GOA project: The novel technique of chemical data assimilation has been used. This technique combines the dynamical information in atmospheric models with the chemical observations of GOME. In this way the sequence of satellite observations is translated into global maps of the ozone and NO 2 distribution. A new combined retrieval, modelling and assimilation approach has been developed for GOME NO 2. The retrieval carefully addresses the large sensitivity of the NO 2 retrieval to clouds, albedo and the a-priori profile shapes. Tropospheric and stratospheric NO 2 column data sets have been generated, together with the corresponding averaging kernels and detailed error estimates. The combination of GOME stratospheric ozone profiles, GOME total ozone and a state-of-the-art chemistry-transport model results in estimates of the tropospheric ozone column. Furthermore, tropical tropospheric ozone columns have been derived by exploiting the available cloud information - the cloud slicing technique. Products The GOA project has delivered the following products: A new and tested algorithm for the combined retrieval and assimilation of GOME NO 2. A seven-year data set of assimilated total ozone fields based on GOME ozone columns. Three-dimensional ozone distributions based on the assimilation of GOME ozone profiles, for the year An extensive validation data set of the above products. Tropospheric NO 2 and tropospheric ozone column estimates. A detailed data product based on the new retrieval approach is available for the year 1997, and NO 2 retrievals and images are available for the period A web interface to these data sets and the validation reports, for atmospheric scientists and other users. Further development and a critical evaluation of the modelling capabilities of two state-of-the-art chemistry-transport model through the confrontation with the GOME data. Partners and web site The GOA consortium consists of the Department of Climate Research and Seismology, Royal Netherlands Meteorological Institute (Netherlands); the Department of Geophysics, University of Oslo (Norway); the Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (Greece); the Institut für Umweltphysik, University of Heidelberg (Germany); the Norwegian Institute for Air Research (Norway); and the European Space Research Institute of ESA (Italy). More information can be found on the project web site: This web site provides access to the GOME ozone and nitrogen dioxide data sets generated during the project.

9 Chapter 1 Summary 1.1 Context of the work Ozone and NO 2 are key species in the chemistry of the troposphere and stratosphere. Data sets of these compounds are crucial to validate the performance of chemistry-transport models (CTM s), and the analysis and prediction of anthropogenic changes in the chemical composition of the atmosphere of gases of particular importance for stratospheric ozone depletion and the oxidation capacity of the atmosphere, affecting chemically active greenhouse gases. The large spectral range in combination with the nadir viewing geometry of the Global Ozone Monitoring Experiment (GOME) spectrometer allow for the retrieval of two unique products, namely (a) vertical profiles of ozone, with explicit tropospheric information, and (b) NO 2 total columns, including the tropospheric column. Deriving reliable quantitative tropospheric information from GOME, and future instruments like SCIAMACHY on ENVISAT and OMI on EOS-AURA, is a major challenge for the future and was investigated in detail in the GOA project. 1.2 Objectives of the GOA project The GOA project provided long-term series of high-level assimilated GOME data to scientific users. Detailed quality estimates and validation data sets of the products have been provided, based on an extended set of ground based and ozone sonde observations, and the error statistics obtained from the data assimilation. The data sets were confronted with output from global chemistry-transport models to improve their modelling capability of current and future changes of ozone and chemically active greenhouse gases. 1.3 Outline of methodology and innovation The approach used in GOA consists of the following steps: 3

10 4 GOA Final Report April, 2003 The retrieval of high-quality ozone and NO 2 products from the GOME measurements. The generation of synergistic products from the GOME retrieved products and model information on the evolution of the chemical species by means of data assimilation. The extensive validation of these GOME derived products. The provision of an internet interface to these value-added GOME products for scientific and other users. The confrontation of chemistry-transport models with the GOME measurements, to improve their capability to describe the real world. Several innovative tools have been developed and were used to reach these goals: The novel technique of chemical data assimilation has been used. This technique combines dynamical information with the chemical observations of GOME, resulting in four-dimensional ozone and NO 2 data sets. The combination of GOME stratospheric ozone profiles, GOME total ozone and a state-of-the-art chemistry-transport model results in estimates of the tropospheric ozone column. This approach has not been used before. GOME total column observations of total ozone have been combined with GOME derived cloud fraction and cloud top heights to derive tropical tropospheric ozone columns. A combined retrieval, modelling and assimilation approach has been developed for GOME NO 2. This approach has been used to generate a detailed stratospheric and tropospheric NO 2 column data set for 1997 which is available for download from the GOA web site. A detailed error analysis has been developed for the combined assimilation, modelling and retrieval approach. A new algorithm for Umkehr (99 code) has been applied to extend the ground-based retrieval of ozone profiles, especially at high altitudes where ozone sondes are becoming less accurate. Recent developments in the understanding of parameters affecting the accuracy of ozone sonde profiles have been applied to all available ozone sonde measurements for the GOME period. 1.4 Highlights Highlights of the GOA project are: Generation of a seven-year data set ( ) of assimilated ozone distributions based on GOME ozone column measurements (The ESA GOME Data Processor version 3 ozone column product). Validation results have been produced for the KNMI GOME fast-delivery total ozone product, based on the WMO network of total ozone measuring stations. The validation results have been compared with the delta validation results for the GOME Data Processor (GDP) versions 2.7 and 3.0. The assimilated total ozone distributions for the period have been validated against the WMO network of total ozone measuring stations, and the results have been compared with the delta validation of the GDP version 3.0 ozone columns.

11 Summary 5 Development of an improved fast GOME ozone profile retrieval algorithm. The approach is built around analytic expressions for the radiation transfer extended with look-up tables for the polarisation, and is based on the latest knowledge of GOME calibration and degradation aspects. Generation of one year of retrieved ozone profiles from GOME based on this new retrieval algorithm. Development of an improved data assimilation approach for GOME ozone profiles. The approach accounts for the retrieval averaging kernels and covariance matrices. Generation of a one year data set of three-dimensional assimilated ozone fields, based on this data assimilation approach and the new ozone profile retrieval code. Validation of the retrieved ozone profiles with ground based lidar and sonde observations. Validation of the assimilation model and assimilated 3D ozone fields with ozone profiles from ozone sondes and lidar. Improvement of the Heidelberg GOME NO 2 DOAS algorithm by analysing the influence of the daily measured solar reference spectrum. Development of a stratospheric tropospheric separation algorithm for NO 2 columns (University of Heidelberg). Analysis and correction of the influence of clouds on the retrieved NO 2 vertical column densities. Development of a new combined retrieval and modelling approach for the determination of tropospheric and stratospheric NO 2 columns based on the GOME measurements. In this approach the usual simplified a-priori profile information is replaced by profile shapes taken from the TM3 chemistrytransport model. The radiative transfer calculation is based on a new set of albedo maps, detailed cloud information from the FRESCO algorithm and temperature information from the ECMWF model. A new assimilation approach to estimate the stratospheric contribution to the total column has been implemented and used. The resulting data product contains very detailed error estimates and averaging kernel information. A detailed error model for individual tropospheric NO 2 column estimates, accounting for all aspects of the retrieval. Development of a new Oslo CTM2 version, featuring improved parameterizations of heterogeneous chemistry in the stratosphere, dry and wet deposition, boundary layer mixing, and improved vertical resolution. Oslo CTM2 model runs for 1997 and November 1999 to April 2001 with the new 40-layer version of the model; daily retrieval of 3-D global distributions of ozone and NO 2. KNMI-TM3 model runs for the year Detailed model validation of the KNMI TM3 and Oslo CTM2 models using GOME measurement data of the GOA project. Both monthly-mean and daily data have been used. Access to the main GOA data products is provided via the GOA project web page. Documentation and validation reports on the data products are also provided on the GOA web page, together with the data products.

12 6 GOA Final Report April, Progress to date The GOA activities can be grouped in tasks that largely coincide with the main work packages: 1) Coordination. 2) Assimilation of 6 years of GOME ozone column data, and the validation of these ozone fields; 3) The retrieval, assimilation and validation of ozone profile information derived from the GOME data; 4) The development of improved schemes to determine tropospheric and stratospheric columns of NO 2 from the GOME measurements; 5) Modelling of tropospheric and stratospheric ozone and NO 2, and the intercomparison of the model results with the GOME derived observations of these compounds; 6) The creation of an internet interface to these data sets, and the involvement of users of these products. Co-ordination: All coordination deliverables have been generated. One annual report, quarterly reports and a final report (this document) were produced. A user requirements document was generated at the start of the project, and was updated in Two glossy brochures have been produced. A website was installed in the first months of the project. In January 2003 a successful workshop was organised for scientists and users (Annex G). The assimilation of 7 years of total ozone GOME observations was completed in January Several improvements have been made to the ozone assimilation model TM3DAM. The processing for the GOME period was based on the new GDP3 data product which has become available in November The year 2002 will be processed in the first month after delivery of this report. In addition, a data assimilation analysis run based on the KNMI fast-delivery ozone columns (November 1999-present) was performed. An extensive intercomparison with ground-based observations of total ozone has been performed. The biases with respect to Brewer and Dobson data have been mapped as a function of latitude and time of year. This is done for (a) the total ozone data sets from the KNMI fast-delivery retrieval, (b) the DLR GOME Data Processor versions 2.7 and 3.0, and (c) the assimilated ozone fields for the period The TM3DAM software has been extended and prepared for the assimilation of GOME ozone profile information. The approach accounts for the averaging kernels and covariance matrix resulting from the retrieval. An improved fast retrieval scheme has been developed at the KNMI, and a one-year profile data sets has been generated in the second half of the second year of the GOA project. The TM3DAM ozone distributions have been compared with lidar and sonde profiles available for the year 2000, to provide a first estimate of the model forecast errors. The retrieved profiles and the assimilated fields have been validated with ozone sonde and lidar profiles, and a validation report was produced (Annex K). Tropospheric ozone estimates have been generated, but more work is needed to understand and improve the approach. A new cloud slicing technique was used to estimate tropical tropospheric ozone columns, and these data sets are available via the GOA web site. The Oslo CTM2 model has been compared with ozone as retrieved from the GOME measurements. The GOME NO 2 DOAS retrieval has been improved, and a data set has been created. These data sets use a fixed solar reference to avoid large spurious NO 2 variations that are related to the GOME instrument. The NO 2 troposphere-stratosphere separation algorithm has been improved, and the effects of clouds are accounted for explicitly. Global maps for stratospheric and tropospheric NO 2 have been created for the period. A software package has been completed for the combined modelling, retrieval and assimilation of GOME NO 2. In this approach the usual climatological a priori information is replaced by model estimates of

13 Summary 7 the vertical profile of NO 2. Details of the retrieval have been improved: the temperature dependence of the cross section is accounted for, and a new surface albedo data set is constructed, based on GOME and TOMS radiance measurements. An assimilation approach for the separation between the stratospheric and tropospheric contributions to the observed slant columns was developed during GOA. Model improvements for the Oslo CTM2 and the KNMI TM3 chemistry-transport models have been implemented. Model runs have been performed for different years within the GOME period. The model fields in 1997 have been intercompared, and the ozone and NO 2 columns have been compared with the GOME data. The data sets created by the GOA project, the validation reports and documentation are available via the project web page, This website will be maintained in the coming years. 1.6 Conclusions Ozone data assimilation provides a detailed description of special events, such as the evolution of low-ozone events (mini ozone holes), the spectacular breakup of the 2002 ozone hole, excursions of the ozone holes and dynamical features in the ozone distribution. A good correspondence is found between the small-scale structures in the total ozone field as generated with the assimilation and as seen with the Total Ozone Mapping Spectrometer (TOMS). The TM3DAM assimilation model is able to predict new GOME observations with model-gome residuals of the order of 3%. This is a satisfactory result, given that this 3% represents a sum of GOME measurement and retrieval noise, a representativeness error and model forecast errors. The new ERA-40 reanalysis meteorology of the ECMWF provides ozone analyses which are of poorer quality than the operational meteorological data sets. GOME (both KNMI-fd and DLR GDP2.7), underestimates on the average the ground based measurements, which is in good agreement with the previous studies conducted. In both data sets it is evident a seasonal dependence of the differences between GOME and ground based total ozone. Both data sets reveal a SZA dependence for angles greater than 70. Although both KNMI and DLR data sets exhibit the negative maximum differences in autumn of both hemispheres, the KNMI positive maximum differences present a three-month shift compared to the corresponding DLR positive maximum differences A bias correction in the total ozone data as a function of month and latitude has been applied to the operational production of assimilated total ozone fields based on KNMI-fd data, which resulted to ozone fields with a reduced latitudinal dependence. Assimilated GOME total ozone data using GDP3.0 are consistent with the level-2 data and reveal similar characteristics concerning their seasonal and latitude dependence. Both level-2 and assimilated ozone data underestimate ozone additionally by 2-3% over stations that are close to deserts. Differences have been identified in the latitude dependence of the assimilated ozone data, for the pre-1999 and post 1999 period, that depend on the wind fields used.

14 8 GOA Final Report April, 2003 The ozone profile retrieval is very sensitive to detailed calibration and precise modelling (including polarization). These dependencies have been studied by several groups involved in GOME ozone profile retrieval, and important new calibration and forward modelling developments on these aspects have occurred in the past year. Various data sets derived in the frame of the GOA project were validated by means of European ozonesonde and lidar data: (1) KNMI assimilated GOFAP ozone profiles (March July 2000), (2)TM3DAM model profiles considering GOME total ozone (1 November December 2000), (3) GOME derived (level-2) ozone profiles for 4 European sites (all 1997), (4) Assimilated GOME profiles (level-3, with TM3DAM) for 10 sites (all 2000). The validations using different reference techniques are consistent with each other. On a single profile basis, the quality varies considerably from case to case, irrespective site location and season. While in many cases dynamical structures are re-produced very well, ozone-depleted layers in the polar vortex are not captured. On a statistical basis, high latitude profile data sets show the most significant improvement relative to the a-priori profile (about factor 2 from about 200 to 5 mbar pressure level). At mid-latitudes average improvement is much less pronounced and limited to altitudes below 100 mbar. The quantitative NO 2 retrieval of individual pixels depends strongly on the treatment of clouds, profile shape, albedo, solar reference, stratosphere subtraction approach and other aspects. The sensitivity of GOME NO 2 on these aspects has been mapped. Two independent approaches have been developed to improve the retrieval of NO 2. The model validation with GOME data revealed generally good agreement. However, the models underestimate NO 2 peaks in highly polluted areas. This is primarily due to limited horizontal resolution, but probably also to an underestimation of emission sources in some cases in the Southern Hemisphere. Comparison with GOME reveals an overestimation of Oslo CTM2 total ozone columns at high latitudes, but shows very good agreement regarding the horizontal pattern of total ozone distribution. Comparison with MOZAIC aircraft measurements has shown that the Oslo CTM2 overestimates ozone and underestimates NO x in the upper troposphere.

15 Chapter 2 Introduction to the GOA project 2.1 Objectives of GOA GOA aims to extend and improve the data products of GOME. GOA will generate and distribute a five year data set of assimilated fields of ozone and NO 2 based on GOME observations. These fields will be validated with other observations obtained during measurement campaigns and from monitoring networks. This data set of two key chemicals of the atmosphere will be compared with output from global chemistry-transport models (CTM s) to improve their modelling capability of current and future changes of tropospheric and stratospheric ozone and chemically active greenhouse gases. The GOA objectives are: To generate a 5-year data set of ozone fields (level-4 products) based on the measurements (available level-2 data) of the GOME spectrometer on board of the ESA ERS-2 satellite. To validate these ozone fields with an extensive set of independent ground based and satellite observations. Improve and monitor the quality of the ground based observations. To provide these fields to the scientific community by means of a web site and cd-rom. To estimate the tropospheric ozone content by using total column ozone data and ozone profile retrievals for GOME in a single assimilation. To improve the GOME NO 2 product by using position and time dependent model-predicted profiles of NO 2 for the determination of the air-mass factor in the DOAS retrieval of NO 2. To validate this set of NO 2 fields with independent ground based and satellite observations. To provide assimilated NO 2 (NO x ) fields (target year 1997) to the scientific community. To estimate the tropospheric NO 2 column based on the assimilation and by exploiting the differences in spatial distribution of stratospheric and tropospheric NO x. Comparison with model results. To identify NO x emission source strengths, by performing model studies and compare with the GOME NO 2 observations. To use this extensive combined data set of ozone and NO 2 to validate the performance of chemistrytransport models concerning the modelling of the oxidation capacity, affecting chemically-active 9

16 10 GOA Final Report April, 2003 greenhouse gases, and the modelling of the seasonal and year to year variation in stratospheric ozone. These objectives aim first of all at the key action better exploitation of existing data, focusing on observations of the European spectrometer GOME on board of the ESA ERS-2 satellite. 2.2 Context GOA: focus on two key atmospheric species: Ozone and NO 2 are key species determining the chemistry of both the lower and middle atmosphere. Data sets of these compounds are crucial to validate the performance of chemistry-transport models (CTM s), and the prediction of anthropogenic changes in the chemical composition of the atmosphere of gases of particular importance for stratospheric ozone depletion (subject of the Montreal protocol) and the oxidation capacity of the atmosphere, affecting chemically active greenhouse gases (subject of the Kyoto protocol). Stratospheric ozone: Ozone depletion due to heterogeneous chemistry related to the release of man made CFC s is the subject of the Montreal protocol. The seasonal and year to year variation of stratospheric ozone will be studied in this project, based on the continuous series of GOME observations over a period of 5 years. Comparing stratospheric models with observed ozone column densities, ozone profiles and NO 2 in the stratosphere will help limit the existing uncertainties in the model simulation of transport processes and in the chemistry connected to heterogeneous processes. The need for tropospheric observations: NO x and ozone strongly affect the oxidation capacity of the troposphere, enabling the removal of chemically active pollutants and greenhouse gases, such as CH 4, that are the subject of the IPCC reports and the Kyoto convention. Despite the central role of NO x and ozone in atmospheric chemistry, only a limited amount of observational data is available for the troposphere at this moment. The European GOME spectrometer, a unique instrument: The large spectral range in combination with the nadir viewing geometry of GOME allow for the retrieval of two unique products, namely (a) vertical profiles of ozone, with explicit tropospheric information, and (b) NO 2 total columns, including the tropospheric column. However, deriving reliable quantitative tropospheric information from GOME and future instruments like SCIAMACHY on ENVISAT and OMI on EOS-AURA is a major challenge for the future and will be investigated in detail in the GOA project. Global maps: Data assimilation combines models of the evolution of the atmosphere with observations of different atmospheric constituents and at different times and places. As a result the sequence of measurements provided by the satellite instrument, are converted in global, synoptic maps of ozone and it s uncertainty. The ozone maps are very convenient for scientific studies and for use in a CTM or for comparisons with other CTM results as well as for comparisons with other monitoring instruments. A five-year data base of assimilated fields will be generated in this project. These will be provided to the scientific community by means of the GOA web site. Tropospheric column estimates based on data assimilation:

17 Introduction to GOA 11 Data assimilation is a valuable tool to produce coherent data sets based on the retrieved ozone observations. Transport models driven by meteorological analyses have been shown to provide accurate predictions of the structures of ozone in the lower stratosphere. The assimilation of both GOME stratospheric profiles and GOME total ozone observations results in an alternative estimate of the tropospheric contribution to the total ozone column. This approach will be developed and validated in the GOA project, using state-of-the-art chemistry-transport models. A-priori profile assumptions will lead to large errors in the retrieval: The retrieval of NO 2 columns from GOME is very sensitive to the assumed vertical profile of NO 2. Since tropospheric pollution is very localised, these profiles depend strongly on the geolocation as well as on time. New retrieval approaches have been developed by the University of Heidelberg and by the KNMI. Based on state-of-the-art knowledge of emissions, chemistry and transport, a CTM provides a first guess for the NO 2 profile and this is subsequently used in the KNMI approach to determine the so-called airmass factor in the retrieval of NO 2. The intercomparison of the two highly independent approaches will provide information on the uncertainties related to the different aspects in the retrieval. The approaches will be further developed and validated, and the results will be compared with an independent CTM. Estimates of the tropospheric and stratospheric contribution to the NO 2 column will be provided to users via the GOA web site. Retrieval of ozone: Ozone columns derived with the DOAS approach is one of the official ESA GOME products, and has shown a steady improvement in the last years. However, there are still substantial dependencies on for instance season and solar zenith angle. These dependencies limit the use of these data for trend studies. It is a major challenge to improve these products, and to quantify the accuracy by means of extensive validation studies. These issues are addressed in the GOA project. The retrieval of ozone profiles are still a subject of very active research. There are fundamental physical differences between the ozone profile retrieval and ozone column retrieval approaches, and the ozone information is extracted from different parts of the spectrum. GOME ozone profile retrieval is still under development, and issues like the calibration of the GOME spectra have to be addressed with much care. Because of these differences between the column and profile products, separate work packages are defined to address the retrieval, assimilation and validation (WP 2 and 3). Relation to ESA and EUMETSAT programmes: Both ESA and EUMETSAT will be involved in the proposal. These are the leading European agencies responsible for remote sensing of the atmosphere from space. ESA is running a Data User Programme, with the intention to stimulate the use of observations from European satellite instruments. EUMETSAT is also stimulating the use of ozone products by meteorological and scientific users. The ozone SAF is responsible for the validation and generation of products from the future GOME-2 instruments on METOP 1-2-3, the successors of GOME. The techniques developed in this project will be of direct relevance to the Data User Programme and to the O 3 -SAF. Confronting models with observations: The main purpose of WP 5 is to demonstrate the usefulness of the provided ozone and NO 2 data, both column and profiles in the troposphere and stratosphere, for model studies of chemical processes and long term changes due to man made emissions. For global model calculations with 3D CTMs it is important to have a good global coverage in the observed distribution which will be used for model validation. Data for remote regions in the troposphere, and for the lower stratosphere will in particular

18 12 GOA Final Report April, 2003 be included in the comparisons. These are regions where there currently only are partly coverage by observation, and where there are large uncertainties in calculated distribution, variation and changes. The calculations will therefore lead to less uncertainties in the model calculations, and thereby more accurate predictions. These improved data sets will serve as input to ozone and climate assessments. Chemistry transport models: Two CTMs will be used in GOA. The KNMI TM3 model is used for the assimilation of the GOME data. For NO 2 assimilation the model will run with a resolution of 4 degrees (and later 2.5 degrees) and will contain a gas phase chemistry scheme including non-methane hydrocarbons. In the case of ozone the model will be run with a simple description of ozone chemistry, but with an increased horizontal resolution (1 degree and 2 degrees). The model is driven by accurate meteorological input from the European Centre for Medium-Range Weather Forecasts. For the assimilation an extended optimal interpolation scheme and a 4D-Var assimilation scheme will be used. The Oslo CTM2 model contains extensive chemistry both for the stratosphere and for the troposphere, and is thus particularly suited for model studies of chemical compounds (e.g. ozone distribution and changes) in the upper troposphere and in the lower stratosphere. It is a global 3D CTM extending from the surface to 10 hpa, it uses ECMWF meteorological input and runs with resolutions ranging from T21 to T63. The model uses observed surface distribution of the precursors of ozone depleting substances (CFCs, N 2 O, bromine source gases) and methane, and estimates of emissions (natural and anthropogenic) for the sources of the more short lived source gases CO, NO x, and hydrocarbons. MOZAIC: Measurement of Ozone by airbus in-service Aircraft (MOZAIC) is an European Union Environment Research project involved with the observation of ozone. MOZAIC I and II will provide additional information on ozone at selected routes and at certain heights and will complement the data set of GOME and ground-based observations, and this data will be used in the GOA project. 2.3 Brief presentation of consortium The contractors are: Royal Netherlands Meteorological Institute (KNMI), Netherlands Department of Geophysics, University of Oslo (UIO), Norway Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (AUTH), Greece University of Heidelberg (IUP), Germany Norwegian Institute for Air Research, Oslo (NILU), Norway European Space Agency (ESA), Italy The goal of the GOA project was to provide quality-monitored assimilated ozone and NO 2 fields, based on the observations of GOME, and to confront and improve chemistry-transport models with this data to enhance the understanding of the processes affecting the atmospheric composition. The partners mentioned above form a balanced group to achieve this goal:

19 Introduction to GOA 13 The KNMI has used its experience in the assimilation of ozone using CTM s to provide daily threedimensional ozone distributions based on GOME data from 1995 to 2000 (2002 at the end of the project). Together with AUTH and NILU the analysis has been extended and validated with a large set of ozone sondes and ground-based observations (Dobson, Brewer, SAOZ, Umkehr). AUTH and NILU have extensive experience with quality control of ozone and other data. ESA provided advice on the presentation of the results to users. NILU provided: 1) A central data collection facility for all ozone soundings for the GOA project. 2) In real-time quality assurance/quality control (QA/QC) of the soundings and make these data available to GOA for assimilation purposes. 3) Real-time diagnosis and feed-back to ozone sonde stations of the QA/QC results. 4) An indexed, well documented and quality controlled experimental data set for the period for the validation of the GOME profile data. The experience of IUP on the retrieval of NO 2 for GOME has been combined with the experience of the KNMI on the modelling and assimilation of NO x. An improved estimate of especially the tropospheric NO 2 column followed from this collaboration. A preliminary comparison with independent groundbased data was made. Ozone and NO x are the two key species for both tropospheric and stratospheric chemistry. This GOME observational data set have been compared with global CTM s by both UIO and KNMI to improve their modelling capability. UIO has a large experience in the modelling of stratospheric and tropospheric chemistry. They performed long term simulations and compared the results with the GOME data to identify shortcomings in model aspects such as transport, source and sink contribution and source strengths. They provided feedback on modelling aspects of the assimilation. UIO has specified the requirements on the provided ozone and NO 2 fields for model improvement. The communication between the research institutes and the leading agencies on remote sensing in Europe have benefited both. Especially, it contributed to the ESA-ESRIN Data Users Programme (DUP) and the EUMETSAT ozone SAF. In particular, the GOME Ozone Fast Delivery Service and the Tropospheric Emission Monitoring Internet Service (TEMIS), part of the ESA-DUP, have benefited from the products of GOA.

20 Chapter 3 Work achieved 3.1 Overview The work achieved during the two years of the GOA project is detailed in section 4. This section is organised according to the work packages as described in the GOA Description of Work. The objectives of these work packages were: Co-ordination of the GOA activities (WP1) Assimilation of 5 years (now 7 years) of GOME data using the TM3 chemistry-transport model of the KNMI. Validation of the assimilated fields and error estimates with independent ground-based and space observations. Creating an internet interface for user access to the data (WP2). Combined assimilation of GOME (stratospheric) profiles and GOME total ozone. Validation of the three-dimensional assimilated ozone fields with ozone sondes and Umkehr profiles. Estimates of the tropospheric ozone column and validation of these columns (WP3). Development and testing of a new combined retrieval-assimilation approach for NO 2 columns from GOME. Generating a long term data set of total NO 2 and tropospheric and stratospheric NO 2 columns (WP4) CTM modelling studies and validation with GOME observations and measurement campaigns (WP5) Data user feedback (WP6) 3.2 Deliverables and milestones reached: Results A listed summary of the main achievements for the six GOA work packages: Project management related results: A web site has been set up for the GOA project ( in the first month of the project (D1.6) 14

21 Work achieved 15 A private web site has been set up for the GOA members, to exchange and discuss model, observations and validation results. A brochure has been produced in month 3 of the project. Copies were sent to the EU (150 in total) and all partners (D1.5). A updated brochure listing the main GOA results has been produced at the end of the project. Copies will be sent to the EU (150 in total), the partners (D1.5), listed users and to scientific institutes to promote the GOA work. A User Requirements Document was produced during the first months of the project, based on researchers and organisations that were contacted by the GOA partners (D1.4). This document has been updated at the end of the project. An interim report was produced (D1.1) A final report was produced (this report, D1.2). Six quarterly reports have been produced in the years 2001 and 2002 (D1.3). Two papers were written to promote the GOA activities, and the GOA activities were presented at several international conferences. A workshop was organised in January More information can be found in Annex G, and on the GOA web site (D1.7). Three working visits have been organised between partners. A cd-rom with the final report and a selection of the GOA data sets will be produced after completion of this report. Science related results: The total ozone data assimilation model TM3DAM has been set up for the routine assimilation of the GOME total ozone data set. (WP2.1) ECMWF reanalysis data (ERA-40) has been transferred to the KNMI and has been preprocessed for the assimilation runs. Studies have been performed to investigate the quality of these data sets compared to the operational model. (WP2.1, WP3) GOME GDP3.0 data ( ) and KNMI fast delivery data ( ) have been collected and prepared for the assimilation. (WP2.1) A 7-year data set ( ) of assimilated ozone fields is produced, based on the GDP3 ozone columns. A second data set ( ) was produced based on the Fast Delivery ozone data. (WP2.2, D2.1) Two scientific papers were written with a detailed description of the TM3DAM model, the assimilation aspect, assimilation results and forecast performance. A validation data set ( ) has been produced for the ground based stations involved in the extracted as input for WP 2.3 (Milestone 2 of WP2). Validation results have been generated for: 1) the KNMI fast-delivery level-2 data, 2) the GDP2.7 ozone column retrieval, 3) the GDP3 ozone column retrieval, and 4) the assimilated ozone fields. A total ozone data set from November 1999 until the present is accessible via the GOA web page. The data sets are provided including documentation on the validation results, the data contents/format and the assimilation approach (WP2.4, D2.3).

22 16 GOA Final Report April, 2003 Total ozone validation results are available as a validation report on the GOA web site. This includes validation of the KNMI fast-delivery level-2 data, the GDP2.7 ozone column retrieval, the GDP3 ozone column retrieval, and the assimilated ozone fields. (D2.2). The data assimilation software has been prepared for the assimilation of ozone column information from GOME. The assimilation scheme accounts for averaging kernels and retrieval covariances. (WP3.1, D3.1) Ozone profiles have been generated based on the GOME Fast Delivery ozone profile retrieval. An improved retrieval software has been further developed (The OPERA algorithm, based on the LIDORT RTM). The production of improved profiles for the period of one year (2000) has been completed (WP3.4 & 3.1, D3.2 & D3.3). The data sets described in WP3 will be based on these improved ozone profiles. A one year assimilation run was completed with the new assimilation scheme (TM3-PAS) and the OPERA profiles (year 2000). (WP3.3, D3.2, D3.3) Tropospheric ozone estimates have been generated with the profile assimilation model. Tropical tropospheric ozone estimates have been generated with the cloud slicing technique, and are available (period ) via the GOA web site. (WP3.5, D3.4) Validation data sets for the Umkehr ground stations have been extracted from the OPERA retrieval data set and from the 3D assimilated fields. A validation study will be completed in May (WP 3.6) The ozone sonde data collected by NILU has been homogenised. (D3.5) The OPERA and assimilation profiles have been validated with a large set of ozone sonde measurements and lidar observations. A validation report has been produced (Annex L). (WP3.7, D3.6) The tropospheric ozone model results from the Oslo CTM-2 have been compared with MOZAIC data. (WP3.7) The data base of 3D assimilated ozone fields is accessible in the form of images from the GOA web site. (WP3.8) Calculations of GOME NO 2 SCDs have been performed based on a fix solar reference for the years (WP4.1). Global maps of stratospheric and tropospheric NO 2 have been created ( ) (WP4.2). A software package has been completed to derive NO 2 vertical columns based on the GOME measurements (WP 4.3, D4.1). The generation of NO 2 columns for the year 1997 was completed. (WP4.3) Different tropospheric NO 2 maps have been calculated with constant albedo, calculated albedo and cloud influence correction (WP4.2, WP4.4). All sources of error (due to clouds, albedo, profile shape, the DOAS fit and the stratospheric background) have been investigated and quantified. (WP4.4) A detailed data product based on the new retrieval approach is available for the year 1997, and NO 2 retrievals and images are available for the period (WP4.6, D4.2, D4.3) An intercomparison between the Oslo CTM-2 model, the TM3 model and GOME tropospheric NO 2 has been performed. (WP3.8, WP4.7, WP5.1) The NO 2 data sets are available via the GOA web site (data files and images), together with a description of the retrieval approach and documentation on the data format. (D4.5) Model runs were completed. (WP5.2)

23 Work achieved 17 A detailed intercomparison has been performed between the Oslo CTM2 and the KNMI TM3 models, year (WP5.3) Intercomparison results between GOME ozone and NO 2 and the CTM modelling results (Oslo CTM2 and KNMI TM3 model) have been generated. (WP5.3) Estimates of uncertainties in CTM model calculated distributions are made, based on the comparisons with observations and definition of measurement requirements. Two different sets of surface emission data have been studied, leading to a more accurate implementation of emission sources in the Oslo CTM2 model (WP5.4). A model validation of the Oslo CTM2 against MOZAIC data was performed for the years 1996 and 1997 (WP5.7). Calculations of future changes in ozone and other gases were performed, based on the improved model formulation of the Oslo CTM2 (WP5.6). The model intercomparison has provided a basis for requirements for measurements (WP5.5). The validation against GOME data has given valuable input for further improvement of the Oslo CTM2, especially in the troposphere and the middle stratosphere. ESA has provided advice on GOME and the new GDP3.0 data product. Users have been contacted, and feedback on the data products has been provided. (WP6, D6.1)

24 Chapter 4 Progress by work package 4.1 Co-ordination activities The co-ordination activities for GOA can be categorised as follows: Promotion of the GOA project During the project several activities have taken place to promote GOA and to make people aware of the activities and products generated by the project. A web site was set up in the first weeks of the project, containing project information. The web page serves as the interface to the data sets generated within GOA. The web page stresses the link with the other projects that are part of the EU GATO cluster. A glossy leaflet was produced and distributed during the first few months and near the end of the project. The updated version of the leaflet lists the main products of GOA. Copies were sent to the EU, the GOA partners and interested colleagues. The GOA project was presented at several conferences such as the 26th European Geophysical Society meeting in Nice, March, and the Sixth European Symposium on Stratospheric Ozone, Goteborg, Sweden, 2-6 September Additionally, two papers describing the GOA project were written for the Proceedings of the EUMETSAT / Ozone SAF meeting in Halkidiki, Greece, May 2001, and the Proceedings of the Sixth European Symposium on Stratospheric Ozone. At the end of the project a cd-rom is produced which includes the final report, the web site, a subset of the ozone and NO 2 data sets produced and validation results for these data sets. User involvement In the first months all GOA partners have contacted potential user of the GOA data sets. These persons/institutes were asked to fill out a short form to express which data products are of interest, why they are interested, and what kind of demands they have (on content, format, accessibility). Based on this user feedback the User Requirements Document was written. During the project contacts with these users were maintained, and several people have expressed their interest in the GOA data sets. The users have been invited to the workshop, which was organised in January During the workshop the GOA achievements have been presented. At the end of the project and updated User Requirements Document is produced. The promotion material mentioned above is distributed to potential users. Furthermore, documentation how to use the data is provided together with the GOA data sets. 18

25 Progress by work package 19 Interaction between partners A private web site has been set up to enable the exchange of documents and results among the partners of GOA. A kick-off meeting (at the KNMI) and four progress meetings (in Thessaloniki, Heidelberg, Göteborg, and De Bilt) were organised. Minutes of these meetings are available. Two additional meeting of the KNMI and Heidelberg team, and a meeting of the UIO and KNMI teams have taken place to discuss technical issues related to the GOA work. An exchange of data sets has occurred between all partners. The final data sets and the corresponding validation reports is a combined effort of the GOA team. The partners have regular contact via . Project status Six quarterly reports, minutes of the GOA meetings, a summary of the workshop, one annual report and a final report (this report) were produced.

26 20 GOA Final Report April, Six year validated data set of total ozone In the frame of this work package an assimilation of 6 years of GOME total ozone data (period ) has been performed based on the ozone assimilation model TM3DAM of the KNMI. These data were validated and error estimates with independent ground-based observations were determined The TM3DAM ozone assimilation model Model setup The six year data base of assimilated ozone fields are generated with the KNMI tracer-transport and assimilation model called TM3DAM. The modelling of the transport, chemistry and the aspects of the ozone data assimilation are described in more detail in a recent paper [Eskes et al., 2003]. Here we will only provide a brief overview of the model setup. An introduction to the data assimilation approach is also available on the GOA website, and is included in this report as annex. The three-dimensional advection of ozone is described by the flux-based second order moments scheme of Prather. The model follows the new ECMWF vertical layer definition, operational from the end of 1999 until the present. The 60 hybrid layers between 0.1 hpa and the surface have been reduced to 44 by removing 16 layers in the lower and middle troposphere, and above 300 hpa the layers in the model coincide with the ECMWF layers. The horizontal resolution of the model version is 2.5 by 2.5 degree. The model is driven by 6-hourly meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. Ozone chemistry is described by two parametrizations. One is a linearization of the gas-phase ozone chemistry, in which ozone is depending on a source-sink, the ozone amount, temperature and UV radiation. A second parametrization scheme accounts for heterogeneous ozone loss. This second scheme introduces an additional chlorine activation tracer which is formed when the temperature drops below the critical temperature of polar stratospheric cloud formation. The total ozone data is assimilated in TM3DAM based on a parametrised Kalman filter technique. This approach produces detailed and realistic time- and space-dependent forecast error distributions. These error estimates should be interpreted as the precision of the assimilation model: it is a measure of how well the short-range model forecast is able to predict new GOME observations. The error modelling approach has been discussed in [Eskes et al., 2003]. On top of this forecast precision estimates, separate comparisons with ground based observations are needed to estimate biases in the retrieved columns and the assimilated fields. These validation issues are the subject of the next sections. model development and preparation The TM3DAM model development was part of previous projects. The model is now used operationally at the KNMI to produce daily ozone analyses and forecasts. This work was partly performed in the context of the ESA Data User Programme project GOFAP. During the GOA product this ozone assimilation and forecast system was maintained, and several reanalysis runs were performed. These results are provided on the web site with the address fd. The GOA activities consist of a re-analysis assimilation run from 1995 until 2002 based on GOME total ozone data.

27 Progress by work package 21 During the GOA project several model improvements and adjustments were implemented (WP 2.1). These include: The fast delivery ozone column retrieval results are now written in an extended format, including information about retrieval aspects such as cloud parameters and the measurement geometry. With this information it is possible to perform additional quality screening on the data. One conclusion of the first validation efforts is the large deviation observed for large solar zenith angles. Extra quality control is added to TM3DAM to filter out unrealistic cloud results and pixels with solar zenith angles larger than 80 degree. Observation minus forecast error statistics was produced for the year 2000, and a detailed modelling of the covariance matrix and the model error growth is based on this. The covariance modelling aspects are discussed in a recent paper [Eskes et al, 2003]. The TM3DAM code is installed on the new supercomputer of the KNMI. The implementation of the model was improved during the project. The calculation of mass fluxes based on the ECMWF wind fields was revisited. This is a delicate point, and unrealistic, large vertical fluxes can easily occur due to the necessary interpolations from the ECMWF to the TM3DAM model grids. A new, improved approach was developed and implemented [Segers et al, 2002] and is now used. The assimilation run from is based on the ECMWF ERA-40 reanalysis meteorology. Preparations had to be made to handle the large data volume involved. The download of ERA-40 meteorological fields to the KNMI for the GOME period was completed in the second year of the project. A few studies were performed to investigate the ERA-40 meteorological data sets (see discussion below). At the end of 1999 the ECMWF has extended their operational forecast model to 60 vertical layers with a top at 0.1 hpa, in the mesosphere. This new model version provides a very detailed description of the stratosphere, compared to the pre-1999 model version. This old operational version (before 1999) had a top level at 10 hpa, and only 5 layers between 10 and 100 hpa. Because of this crude stratospheric resolution it was initially decided within GOA to base the long assimilation run on the ERA-40 reanalysis data set, which is created by the 60 layer ECMWF model version. The ECMWF was producing this reanalysis data set during the GOA project. This dependence on the results of the ERA-40 project has lead to some delay during the GOA project. The transfer of the ERA-40 meteorological fields to the KNMI, and the preparation of these fields for the assimilation runs was completed in the second half of Experiences with the ERA-40 meteorological data Several experiments have been conducted to study the behaviour of the ERA-40 data set in comparison with the meteorological fields of the operational ECMWF model. The main differences between these tow data sets are the lower resolution of ERA-40, and the use of the 3D-Var (ERA-40) vs. 4D-Var (operational) data assimilation scheme. The experiments were: Age of air experiments: Multi-year tracer transport model runs were performed driven by the wind fields of the ERA-40 reanalysis.

28 22 GOA Final Report April, 2003 Figure 4.1: The monthly mean observation minus forecast difference for April, using ERA-40 (left, 1998) or operational (right,2001) wind fields. Assimilation of GDP3.0 ozone columns. A free model run (year 1995) with ozone was performed with the TM3DAM model to study the ability of the model to produce realistic ozone. The assimilation run with ERA-40 wind fields was compared with an assimilation run with wind fields from the operational ECMWF model. These experiments led to similar conclusions: The age of air experiments show that the residual Brewer- Dobson circulation in the stratosphere is too fast with the ERA-40 fields; tracers have a residence time in the mid-latitude stratosphere at 20 km altitude of approximately 2 year, in contrast to measurements that indicate that the age of air is approximately 4 years at these latitudes. The model run without assimilation shows a negative ozone column bias in the tropical stratosphere, and a positive bias at mid-latitudes of about 50 DU. This is consistent with too much transport of ozone from the tropics to mid-latitudes. The results of the two assimilation runs with ERA-40 and operational meteorological input is shown in figure 4.1. This figure shows the difference between the GOME observations and the model forecast (OMF) during the assimilation. The two panels show a striking difference between the two wind fields in the tropics. Despite the weak variability of ozone at low latitudes, the ERA-40 model run shows significant departures from the observations. These OMF values are up to twice as large as for the run with the operational wind fields. Again this signals that there is a problem with the stratospheric circulation in ERA-40. Also the longitude-averaged monthly-mean bias is generally larger for the reanalysis winds. For the operational ECMWF winds these are generally below 1%. The use of ERA-40 meteorological fields generally shows larger differences between the new GOME observations and the short-range forecast of the model. Especially in the tropics the model performance is worse with the reanalysis winds. Due to this experience with the ERA-40 data set the GOA team has decided to base the ozone assimilation run on both the ECMWF ERA-40 meteorology before November 1999 and on the ECMWF operational meteorology after October 1999, when the 60-level version of the model with detailed stratosphere became operational. This choice has the advantage that the assimilated fields are of better quality from November There is also a considerable disadvantage: because of the model change in 1999 the data set is not uniform, and changes observed in the assimilated fields over the GOME period may be (partly) related to the model change in 1999.

29 Progress by work package 23 Figure 4.2: The monthly mean observation minus forecast difference as a function of longitude and latitude, comparing GDP3.0 (top) with the KNMI fast-delivery ozone columns (bottom).

30 24 GOA Final Report April, 2003 million square km Ozone Hole Area w.r.t. 200 DU in the Southern Hemisphere GOME Assimilated Ozone KNMI / ESA Aug 01 Sep 01 Oct 01 Nov 01 Dec 31 Dec Figure 4.3: The development of the area of the South Pole ozone hole in the eight-year period , as derived from the assimilated GOME ozone fields. Especially in the tropics/subtropics this is an issue. Experiences with the GOME GDP3.0 ozone columns Two assimilated ozone field data sets have been created during GOA: one based on the new GOME GDP3.0 product (period ), and one based on the fast-delivery ozone column retrieval (FD) of the KNMI (period ). The assimilated fields resulting from the assimilation of the fast-delivery columns have been compared with the results based on the GDP3.0 ozone columns. It was found that at high latitudes the OmF values were often larger for the GDP3.0 than for the FD. At low and mid latitudes the differences were much smaller. An example of this is given in figure 4.2. The red and dark blue grid cells in the top figure (GDP3.0) are clearly not present in the OmF map of the FD assimilation. Furthermore, the OmF patterns at high latitudes for the GDP3.0 case are nearly uncorrelated to the patterns of the GDP3.0 case. The difference between the maps therefore demonstrates internal inconsistencies in the GDP3.0 total column data set. Apparently the retrieval has problems related to surface albedo and/or snow cover. These problems are largely absent in the FD data set which is much more consistent. On the other hand the delta validation has shown that the average biases are much reduced in GDP3.0 as compared to the GDP2.7 ozone columns (see annex). The FD product has problems at high solar zenith angles and high ozone columns (this report) where a significant negative bias is observed. Clearly there is still room for improvement of the level-2 ozone columns retrievals.

31 Progress by work package 25 Figure 4.4: The split-vortex on September 25, Generation of a 6 year total ozone data set During the GOA project a re-analysis assimilation run was conducted for the period November 1999 until March 2002 (WP 2.2). This run was based on the KNMI Fast Delivery retrieved GOME ozone columns. Validation data sets have been drawn from this run, with a focus on the year These data sets include all Brewer-Dobson-M124 station locations (see section 4.2.3). After completion of the reanalysis run based on the new GDP3.0 ozone columns a new validation data set was generated for the period This data set consists of daily local-noon total ozone values for all the GAW stations (see annex, and description of wp2.3 below). One example drawn from this data set is shown in Fig It shows the evolution of the area covered by the South pole ozone hole for the period of eight years, from 1995 to An advantage of the assimilation is the extra detail provided in the early stages of the formation of the ozone hole. Due to the absence of light no reliable measurements can be made at the pole. However, measured low ozone values at the edge of the polar vortex are forcing the model and will influence the model state over most of the polar region. One interesting feature is the ozone hole development in the year This was a quite exceptional case, with ozone depletion occurring very early and rapidly, and a record breaking large extent of the hole in the first week of September. After this the ozone recovered fast, and the hole disappeared mid-november. This in contrast to the more normal ozone hole years 2001 and In

32 26 GOA Final Report April, 2003 Figure 4.5: The low-ozone event of 30 November these years the area with ozone depletion was more constant during September-October, and the recovery took place in early December. Clearly the most exceptional year was On September 25, the polar vortex and the ozone hole split into two parts. This event is unique, as such a SOuthern Hemisphere September vortex instability has never before occurred in the available measurements since the end of the 50s. The assimilated total ozone distribution is shown in Fig One of these remaining parts was unstable, and mixed with mid-latitude air. The other part moved back to the pole and formed a small ozone hole. This recovery is seen as an increase of the ozone hole are in early October. A second remarkable feature of the 7-year total ozone data set is the occurrence of low-ozone events over the Northern Atlantic Ocean and Western Europe. These events occur in the late-autumn and winter period. The frequency of these events shows an increase in recent years, and the lowest ozone column values since the late 70s have been observed on November 30, This event is shown in Fig The data set has also been compared with the Brewer measurements performed at De Bilt. This comparison is shown in Fig Clearly the assimilated fields nicely follows the monthly-averaged Brewer column measurements. However, there is also a clear offset between the assimilated GOME and ground-based observation, in particular in the period October to February. The validation of the assimilated ozone fields is discussed in more detail in the next section and in the Annex.

33 Progress by work package 27 DS overpass Dobson units GOME Brewer Brewer-GOME Year Figure 4.6: Comparison between the GOME GDP3.0 assimilated ozone fields (black) and the Brewer observations at De Bilt (red) Validation of the retrieved and assimilated total ozone products In the frame of WP2 LAP-AUTH was responsible to validate the assimilated total ozone fields for the period by comparison with total ozone data archived at World Ozone Data Center, in Toronto, Canada. The ground based data records were based on the WMO Global Atmosphere Watch (GAW), which include total ozone data sets obtained by Dobson, Brewer spectrometers and filter ozonometers (M-124) from all available observations from about 140 observing stations during the period from July 1995 to December Validation of GDP2.7 and KMNI Fast Delivery total ozone During the first year of the project two sets of operational level-2 GOME total ozone data have been validated, the KNMI fast delivery and the GDP2.7 from DLR. As a general picture GOME underestimates on the average the ground based measurements, which is in good agreement with the previous studies conducted. It is characteristic that the 66,67 % of the monthly percentage differences between GOME processed by DLR and Dobson total ozone for the Northern Hemisphere lies in the range between -7,5 to 0 %, while for the Southern Hemisphere the corresponding percentage is 71,21%. For the monthly percentage differences between GOME processed by KNMI and Dobson total ozone for the Northern Hemisphere the 56,67 % lies in the range between -2,5 to 0%, while for the Southern Hemisphere the corresponding percentage is 76,67%. In both data sets it is evident a seasonal dependence of the differences between GOME and ground based total ozone. Although both KNMI and DLR data sets exhibit the negative maximum differences in autumn

34 28 GOA Final Report April, 2003 Figure 4.7: Contour plots of the monthly percentage differences. Upper panel: KNMI fast delivery. Lower panel: DLR GDP 2.7.

35 Progress by work package 29 of both hemispheres, the KNMI positive maximum differences present a three-month shift compared to the corresponding DLR positive maximum differences. In order to investigate in a more thorough way this seasonal dependence, in combination with a its latitudinal distribution, we illustrated in figure 4.7 the contour plots of the monthly percentage differences between GOME processed by DLR and KNMI for the two Hemispheres divided in 10 latitude by 12 months blocks. In each block the solid lines present the mean values of the monthly percentage differences of GOME from all ground stations that belong in a certain latitudinal belt and for a given month throughout the examined time period. Based on these plots an attempt for bias correction in the total ozone data as a function of month and latitude has been applied to the operational production of assimilated total ozone fields based on KNMI-fd data, which resulted to ozone fields with reduced latitudinal dependence. Validation of GDP3 and assimilated ozone fields During the second year of the project the new GDP3.0 data set became available, which has been used as input for the final assimilated ozone fields, produced by the GOA project. These fields have been validated with the same reference data set used for the validation of fd and GDP2.7. A detailed report on the validation results can be found in Annex I of the current report, where a summary of ESA s validation campaign of GDP3.0 level-2 is presented, as well as results from the comparisons between level-2, assimilated and ground-based data are presented. In the appendix it is demonstrated that the assimilated GOME total ozone data using GDP3.0 are consistent with the level-2 data and reveal similar characteristics concerning their seasonal and latitude dependence. However the assimilated total ozone data show bias close 1% relative to the level-2 data almost at all latitudes. Differences have been identified in the latitude dependence of the assimilated ozone data, for the pre-1999 and post 1999 period, that depend on the wind fields used. The use of ERA-40 fields yields similar results with the level-2 data, while the use of ECMWF operational winds introduces in the tropics changes of 2%. Both level-2 and assimilated ozone data underestimate ozone additionally by 2-3% over stations that are close to deserts. The large differences observed in the high latitudes of the Southern Hemisphere are attributed in the accuracy of the ground-based ozone data used as a reference User interface to the GOME total ozone data sets An internet interface has been constructed and has been linked to the GOA web site (WP 2.4). This provides access to the assimilated fields for the period The year 2002 will be added soon. The following data sets are provided: Daily images for the global ozone distribution and for the Northern and Southern Hemisphere, in PNG format. Total ozone fields in ASCII format (based on the NASA-TOMS data files). Total ozone fields in HDF version 4 format, every 6 hours. Forecast precision error distribution, in HDF format, every 6 hours. Monthly-mean ozone distributions and ozone variability, in GIF and HDF format. These data sets are complemented with a validation report, documentation on the data sets, and documentation on the TM3DAM assimilation code.

36 30 GOA Final Report April, 2003 Figure 4.8: Data access to the assimilated ozone fields on the GOA web site. Figure 4.8 shows the web page which provides access to these data sets. Additionally, ozone forecasts and a data base of assimilated total ozone fields based on the KNMI Fast- Delivery ozone column retrieval can be accessed from the GOME fast-delivery site ( fd).

37 Progress by work package Assimilated and validated 3D ozone fields based on GOME ozone profiles and columns Objectives The main objective of WP3 is to produce 3-dimensional global ozone fields by assimilating GOME ozone profile data with the KNMI TM3DAM chemical transport model. In order to quality-assess these fields in the best possible way, both TM3 results and GOME results have been extensively validated against various types of measurements: ozone sondes, Umkehr profiles from Brewer measurements and - in excess of the work plan - a limited set of lidar measurements. From the validated model / GOME data sets, estimates of tropospheric ozone have been calculated and compared with results from an independent chemistry transport model Preparation of the ozone profile assimilation As preparation to the assimilation of ozone profiles, an extensive comparison of the TM3DAM modelled ozone profiles (see section 4.2.1) with available ground-based observations (lidar and sondes) was started. These data sets provided important input on the model forecast errors before GOME profile information is added, and has been used to identify weak points in the modelling of the ozone distribution. It has also provided feedback to the ozone column assimilation work. As an extension to the work plan, we have compared the TM3DAM model profiles with all available lidar observations made in the year 2000 at Lauder, New Zealand. Comparisons with measurements made in Andøya and Sodankylä are discussed below. In Fig. 4.9 a comparison is shown between two Lidar ozone profile measurements in Lauder and corresponding model profiles. The 2 May case was chosen because this profile shows a strong departure from climatology. This particular profile is very well reproduced by the model. In other cases the comparison shows larger differences, e.g. the plot of May 6. However, despite the quantitative differences the model qualitatively correctly predicts the observed ozone peak at 13 km altitude. In general the model is observed to predict the position of the steep ozone rise above the tropopause quite well, but quantitative differences are observed at/near the ozone maximum. It is anticipated that the quantitative agreement will improve when reliable GOME profile information is added to the model Implementation of the TM3PAS assimilation model For assimilation of ozone profiles, the TM3 Profile ASsimilation model (TM3PAS) has been developed. TM3PAS is an extension of the existing assimilation model for total ozone columns, TM3DAM. A short overview of TM3DAM was presented in In the first year of GOA, the TM3DAM software was extended to assimilate GOME ozone profiles (WP3.1). A detailed description of TM3PAS is available as annex K of this report. In summary, the activities consisted of: Isolation of the ozone tracer model TM3 in TM3DAM, for usage in both assimilation schemes. By this, changes in the ozone model will be incorporated automatically in both TM3DAM as well as TM3PAS.

38 32 GOA Final Report April, 2003 Figure 4.9: Comparison between Lauder ozone lidar profiles (black/blue) and TM3DAM model ozone profiles (red) on 2 and on 8 May For comparison the climatological ozone distribution of May is also shown (brown). Construction of an observation operator for GOME profiles. This operator computes predicted GOME ozone profiles based on the modelled ozone distribution. The observation operator accounts for the layer definitions in the retrieval and in the model, the a-priori information used in the retrieval and the averaging kernel matrix resulting from the retrieval. Construction of a three-dimensional covariance model, in order to fully explore the vertical information provided in the profiles. The three dimensional covariance has been build by adding vertical correlations to the two dimensional (horizontal) covariance model of TM3DAM. Model runs have been performed for the period November December An inter-comparison data base of ozone profiles at 158 ground stations, and a set of simulated GOME profile retrievals has been generated based on these runs Review of the ozonesonde Standard Operational Procedures and real-time quality control Objectives of WP 3.2: Review and update of the ozonesonde Standard Operational Procedures (SOP) and development of sounding support software, to improve the homogeneity of the ozone soundings for the GOME period. Real time quality assurance / quality control and dissemination of ozonesonde data. Post real time quality control of ozonesonde data. Organisation of a database for ozonesonde data consisting of European and World Ozone Data Centre (WODC) sonde data. NILU is together with many other institutions involved in an ongoing reviewing process of the ozonesonde SOPs. This process is organised and steered by the World Meteorological Organisation (WMO). The present review was started with a meeting in Geneva in May 2001, but so far no obligatory changes in the SOPs have been adopted. As a consequence, the reviewing process probably will not have a major

39 Progress by work package 33 impact on the data quality of ozonesonde soundings used in the GOA project. It should also be noted that neither the review of SOPs nor the use of sounding support software is binding for any station not included in the GOA consortium. For these reasons, and because of the leave of the GOA PI at NILU shortly after the start of the project, efforts at NILU related to WP 3.2 concentrated on QA/QC of existing and new ozonesonde data sets and the build-up of a functional ozonesonde database. The NADIR database at NILU has gathered all ozonesonde soundings performed during the European ozone campaigns SESAME ( ) and THESEO (1998-today). These cover the whole lifetime of the GOME instrument, which started operations in autumn The ozonesonde files are stored in the (ASCII-based) NASA Ames format. This format gives room to a large amount of additional information of technical and scientific nature, which is of limited use in applications as in the frame of the GOA project, and makes the files difficult to read and not suited for meta databases. The responsibility for the quality of the stored data has generally been with the data delivering institution; only in some cases, e.g., the GOME core validation campaign, NILU performed a data format compatibility control. In the frame of WP 3.2 software was developed to transfer the NASA Ames files into a simpler ASCIIbased format (CREX) allowing the easy transformation into file formats suited for conditional databases, such as HDF, which will be used in the ENVISAT validation. The reformatting was combined with (formal) data quality assessment routines, checking both data format, data gaps and maximum sounding altitude reached as a quality criterion. The software is designed such that the NADIR ozonesonde data directories are searched for new files every 15 minutes. If the sonde is of non-standard format or the residual ozone column is larger than 15% of the total ozone column or the total column is < 180 DU, the file is rejected and stored separately for further investigations. A list of such files is stored in a dedicated log file. The software was put into a test mode operation shortly after the start of the GOA project, but stopped after the former NILU PI left. In spring 2002 it was restarted in order to work up all soundings since 1999 (taken in the frame of the THESEO campaign); it is running operationally now on a near-real time basis, mainly as a service to the European Centre for Medium Range Weather forecast (ECMWF). In a second step all data from 1995 to 1999 (assigned to the SESAME campaign) were reformatted to the CREX format and quality-checked; these are now available in a dedicated catalogue in the NADIR database at NILU. While the format and the number of variables of the operational ozonesondes (since 1999) was determined by the requirements of the main user, the European Centre for Medium Range Weather forecast, the earlier data contain an additional variable, the geopotential height. This facilitates the comparison with various other datasets, e.g., lidars which measure the geometric height. A list of all sonde data files available in CREX format and the quality check report is available from NILU on request (45 page document). An additional option to be discussed is to refine the data quality check with respect to total ozone. The current criterion (180 DU) excludes scientifically interesting low ozone periods, but does not identify data files with systematically wrong values, e.g. in case of using wrong pump constants. Such cases could be identified by comparing total ozone with independent sources, such as data from the KNMI TM3 assimilation model.

40 34 GOA Final Report April, Generation of ozone profiles for the period of one year In the GOA description of work a distinction is made between a fast stratospheric profile retrieval approach (WP3.1) and a more accurate but computationally intensive profile retrieval code (WP3.4). During the first year of the GOA project and based on new algorithm developments it was decided to combine these two work packages. The retrieval algorithm to generate the high-quality profiles will be used also for WP3.1, i.e. to generate one year of data. Since the start of the GOA project, the high quality algorithm has been improved, mainly in processing speed, such that it supersedes the old algorithm both in speed and accuracy. KNMI has developed an improved algorithm for the retrieval of ozone profiles from GOME spectral measurements. This algorithm (OPERA) will also be used for SCIAMACHY and the future missions OMI and GOME-2. KNMI is responsible for the delivery of ozone profiles for these two instruments. The algorithm uses an on-line radiative transfer model (RTM) to simulate the earthshine measurement in the wavelength range nm [Oss and Spurr, 2002]. The ozone profile and auxiliary parameters, such as surface albedo, are iteratively adjusted to achieve an optimal agreement between the measured and the simulated spectrum. Due to the limited amount of profile information in the earthshine spectrum, an addition constraint is used to regularise the inversion (Optimal Estimation). For this constraint we used the Fortuin-Kelder ozone climatology as a-priori information on the profile. For the RTM the algorithm uses LIDORT, which can be used to efficiently and accurately generate earthshine radiances and the Jacobian derivatives of these radiance with respect to any atmospheric parameter that influences it. The algorithm has been completed in The following problems that determine the accuracy of the GOME profiles have been solved: Quality of the GOME spectral measurements (Level 1): several calibration steps have been improved to increase the quality of the ozone profiles: improvement of the wavelength calibration, improvement of the correction for the instrument polarisation sensitivity, correction for the degradation of the instrument. The effect of these errors, before and after correction, on the ozone profile have been quantified for a range of realistic conditions [van der A, 2002]. Adjustments to the RTM: correction for the error due to scalar approximation (not taking the polarisation of light into account), Ring effect (wavelength shifts due to (inelastic) rotational Raman scattering), corrections for the sphericity of the atmosphere. The ozone profile retrieval algorithm is a few order of magnitude slower than the ozone column (DOAS) algorithm, due to the larger wavelength region and the on-line radiative transfer modelling. The GOA goal to process one year of data has prompted the following activities to decrease the processing time: algorithm efficiency: minimum number of streams (discrete polar angles in the RTM) and minimum number of atmospheric layers. The algorithm performs a separate single (fast) and multiple (slow) scattering calculation. For the single scattering computations a large number of layers and phase function moments are chosen to minimize the number of layers and streams for the multiple scattering part. Distribution of the processing over multiple processors. The distributed GOME ozone profile processing has been organised in a straightforward manner: different workstations are scheduled to process different sections of the one-year period data set. Processing is mainly done at night time and during weekends to increase the number of available workstations.

41 Progress by work package 35 A one-year data set (2000) of GOME ozone profiles has been generated using OPERA. The data set contains every third 12sec-pixel of GOME. This pixel measures 960 km (East-West) by 100 km (North- South) and corresponds to one (12-sec) Band1a-integration of GOME. Band 1a contains the part of the UV spectrum that contains ozone profile information. There are about sec-pixels in a single GOME orbit, of which 60 have been processed. There are 14 GOME orbits per day. GOME profiles that co-locate with ground based profile measurements have been supplied to the GOA partners for validation purposes. The full dataset has been used as input for the ozone profile assimilation (WP3.4). Software has been written to extract temperature profiles at the GOME measurement locations from the ECMWF meteorological archive. The temperature profiles have not been used in the one-year set of ozone profiles produced for assimilation, but will be used for future retrievals Assimilation of GOME ozone profiles In this work (WP3.4), the previously described set of ozone profiles retrieved from GOME has been assimilated. The ozone model in TM3PAS has been initialized with a zonal constant ozone field following climatology, valid for October From this month on, the driving ECMWF meteorological fields are available at 60 levels up a top of 0.1 hpa. Starting from this initial condition, a free running TM3 model (thus without any assimilation), simulated the ozone distribution over the rest of 1999 and whole 2000, the year for which GOME ozone profiles are available. Investigation of the simulated ozone showed that a free running TM3 simulates to much ozone at higher latitudes. At the southern hemisphere for example, TM3 simulates ozone in a range of DU, while DU is what is seen in the 5 year assimilated set produced in the project. The same holds for the northern hemisphere, where the free running TM3 simulates high ozone values (above 450 DU) over a much larger area than expected. The origin of this overestimation is probably found in transport errors in the driving meteorological model. Global meteorological assimilation models such as the ECMWF model are characterised by a too strong circulation from the equator to the poles, which is hardly visible from day to day but accumulates during long term simulations. As a result, ozone rich air from the equator is transported to the poles too fast. The ozone field from the free run valid for January formed the initial condition for the assimilation of GOME ozone profiles. The full set of ozone profiles available for 2000 has been assimilated using the TM3PAS system described in the annex. The assimilated ozone fields have been made available to the scientific community through the GOA website, in the form of images of horizontal patterns (total ozone, ozone mixing ratios at fixed pressure levels) and a vertical cross-section of zonal averaged ozone. Similar as the TM3DAM column assimilation, the TM3PAS profile assimilation is able to compensate for the ozone growth at higher latitudes visible in the free running TM3 model. The total ozone patterns of both assimilation schemes are comparable after a few weeks of spin up, where TM3PAS suffers from a less accurate initial state. To see how the GOME profiles are used to adjust the ozone values, the 3D fields produces with TM3PAS have been compared with the fields from the free running model. The zonal average of the difference is plotted in figure 4.11, as observed for April The figure shows that the assimilation reduces the amount of ozone at higher latitudes especially around the stratospheric

42 36 GOA Final Report April, 2003 Figure 4.10: Example of increased total ozone at high latitudes for a free running model. In comparison with total ozone from for example the TM3DAM column assimilation, the ozone at higher latitudes is about 50 DU too high. ozone maximum, in absolute as well as relative sense. This is not a surprise, since the GOME instrument is most sensitive to the ozone in this slab of air. The sensitivity of the instrument has been incorporated in the assimilation automatically through the averaging kernels (see section 4.3.5). Increments of ozone are only visible around the equator at lower model levels (about 50% in comparison with the free model run). The only location with large increments in absolute amounts is around the north pole, caused by assimilation of a rather small amount of biased GOME ozone profiles, suffering from large solar zenith angles. For a first validation of the assimilation, the total ozone columns in TM3PAS have been compared with total ozone products from GOME. In total, three different total ozone products are available: derived from the KNMI-OPERA ozone profiles used for the assimilation, the KNMI-NRT total ozone product, and ESA s GDP v3 column. The later set has been analysed to produce the 7 years re-analysis in the project (section 4.2.1). Figure 4.12 shows the bias between the model ozone fields and the three column products, either for the free model run (bias only), and for the assimilation run (bias plus and minus standard deviation), observed for April The biases between GOME and the free model run differ strongly with the latitude. TM3 s overestimation of ozone outside the tropics is clearly visible in the negative biases. Although all column data has been retrieved from the same instrument, the differences between the products is substantial. The products differ more from each-other near the poles, indicating that all retrievals suffer from problems with large solar zenith angles. The bias between TM3 and the KNMI- OPERA product is in general the smallest, which can be expected from the fact that this product is based on retrieval of the complete profile. After assimilation of these profiles, the bias becomes almost zero. A standard deviation between model and GOME-OPERA total column of about 10 DU (southern hemisphere) to 15 (northern hemisphere) remains after assimilation. A substantial bias with the other

43 Progress by work package 37 Figure 4.11: Difference between assimilated and free model run in terms of zonal averaged ozone: absolute (colours), and relative to the free run (contours). products remains, especially toward the poles. In following sections, the assimilated ozone fields are validated against independent data: with ozone sondes in section 4.3.8, and with Umkehr measurements in section Validation of the ozone fields using Umkehr profiles The ozone profile data sets have been generated in the second half of the second year of the GOA project. As a result there was not enough time to finish this task, but the work is well advanced at the time of writing. Two data sets have been extracted as preparation to this workpackage, namely: 1) A set of collocated OPERA ozone profiles has been generated for the Umkehr stations. 2) A set of collocated profiles from the assimilation of the OPERA ozone profiles has also been generated. Both data sets, generated by KNMI, are now being analysed by LAP-AUTH. The results are expected in May Validation of the ozone fields using homogenised sonde profiles Objectives: The main objective is to validate both directly GOME-derived ozone profiles (level-2 data) and higherlevel assimilated ozone profile data with ozonesonde profiles for selected ozonesonde launch sites and parts of the 7-year record of GOME data. In addition to the tasks described in the Document of Work, NILU included ozone lidar data from a Norwegian site (ALOMAR) in the validation exercise. Although this data set is very limited, it adds value to the validation by means of ozonesondes, since lidar profiles

44 38 GOA Final Report April, 2003 Figure 4.12: Comparison between modelled and from GOME retrieved total ozone products (KNMI s OPERA and NRT and ESA s GDP 3.0), for either a free running model, or after assimilation of OPERA profiles. reach altitudes of up to 45 km. The validation of GOME ozone profiles derived at KNMI was performed for two periods, the years 1997 and The 1997 data set was used to validate ozone profiles from GOME pixels (level-2 data). The sites included in the validation are listed in Table 4.1, including the number of single profile comparisons. The emphasis was on three ozonesonde stations Ny-Ålesund, Sodankylä, and de Bilt and the ALOMAR ozone lidar station, for which GOME profiles were selected from a radius of about 700 km. Additional GOME data sets were provided for Athens, Eureka, and Yakutsk, while Kiruna, Hohenpeissenberg, Lindenberg, and Uccle could be included due to their short distance to the main validation sites. The main validation results are summarised below. More detail can be found in the profile validation report in the annex, which is also available as web page on the GOA web site. The comparison was performed using partial ozone columns between fixed pressure levels, as given in the GOME algorithm, 42 in total ranging from the surface to 0.1 hpa. The ozonesonde and lidar profiles were integrated for these given pressure intervals. Both the original partial columns of the ground-based /balloon-borne measurements and profiles convoluted with the averaging kernel functions of the GOME profile retrieval algorithm were used in the validation of the GOME profiles. Moreover, the a-priori profiles used in the GOME retrieval algorithm were compared with the validation data set, in order to be able to estimate the improvement or gain of information achieved from the GOME measurements. Figure 4.13 shows two examples of single profile comparisons from Lindenberg and Ny-Ålesund, respectively. The Lindenberg example (left panel) shows a very good agreement between ground-based and GOME-derived profile, resulting obviously from information gain in the satellite measurement. On the other hand, profiles with a pronounced ozone depletion as at Ny-Ålesund on 28 March 1997 (right

45 Progress by work package 39 Station name Longitude Latitude no. of comparisons Andøya (ozonesonde) Andøya (ozone lidar) Athens De Bilt Eureka Hohenpeissenberg Kiruna Lindenberg Ny-Ålesund Sodankylä Uccle Yakutsk Table 4.1: List of stations used in the 1997 validation panel), are not well produced in the GOME profile. One should note that this discrepancy problem can be explained by the averaging kernel functions of the retrieval algorithm, since the sonde profiles convolved with these functions do not reproduce the depleted layers, either. Instead, they consequently produce a more or less pronounced intermediate minimum at around 15 km altitude which is not seen in any of the sonde and lidar measurements. A preliminary review of all single comparisons indicates that it is the more pronounced, the larger the deviation of the real profile from the climatology is. On the other hand, the algorithm does a very good job in many cases at mid-latitudes, in particular in situations with a very high tropopause, such as the Lindenberg example in Figure 4.13 shows. On a statistical basis (as far as this is significant with at most 40 single profile comparisons), at all stations the GOME partial columns overestimate the sonde partial columns by in the order of 5 to 10% throughout most of the altitude range compared, if one uses the average-kernel function convolved profiles in the comparison above the tropopause; the standard deviations range from 8% to 15%, but increase towards the tropopause. From about 5 km altitude to the tropopause GOME values are generally 5 15% smaller, most pronounced at high latitudes, while at mid-latitudes there is a significant overestimation in the mid-troposphere followed by a negative deviation above. Taking the average of the absolute values of percentage deviations (square root of the squares of percentual deviations), which is a measure of the variability irrespective sign of the deviation, one finds a decrease of the variability by about 50% from about 10 to 22 km at mid-latitudes, and from 12 to 28 km at high-latitude stations. At higher and lower altitudes, the retrieved profiles yield negligible improvements. Typical high and mid-latitude results are shown in Figure 4.14, from de Bilt (upper panels) and Sodankylä (lower panels). For the year 2000, GOME profiles assimilated with the KNMIs TM3DAM model (level-3 data) were validated using a selected number of sites. Also in this case, both ozonesonde and ALOMAR lidar data were used. The stations included in this validation effort are given in Table??. In this case, the number of single comparisons was considerably higher, since the main limiting factor is the number of soundings, except around winter solstice (when the assimilation model cannot fill the measurement gaps at the highest latitudes). In this data set, the validation parameter was ozone number

46 40 GOA Final Report April, 2003 Figure 4.13: Comparison of ozone partial columns normalised per km altitude as measured with an ozonesonde at Lindenberg (left; green dotted curve/ triangles) and at Ny-Ålesund (right), and derived from GOME (black line/diamonds). Yellow dashed line: ozonesonde partial column profile convolved with the GOME algorithm averaging kernel functions. For comparison: ozone climatology used in the GOME retrieval algorithm (thin blue line). density as a function of pressure. In the case of ozonesonde soundings, this is straight-forward since pressure is measured. With respect to the lidar data set, pressure is taken from the closest ECMWF data point on the geographical grid and in time below typically 30 km, while it is derived from the (lidar-) measured temperature and atmospheric density above; the transition altitude depends on the instrument alignment quality determined from the overlap between measured profile and ECMWF profile. As in the case of the GOME level-2 ozone profiles, the agreement between assimilated ozone profiles and ground-based measurements is highly variable on a single-profile basis. In many cases, the retrieved and then assimilated profiles reproduce also detailed structures in a very convincing way. One such example is given in the left panel of Figure 4.15 where a sounding from Payerne in November 2000 is shown. In other cases the retrieved profile deviates strongly from the measured profile, although the latter is very close to the climatological profile. At high latitude sites the situation is similar, with a mixture of well reproduced and not reproduced dynamical features in the ozone layer. There is, however, an additional problem under ozone depletion conditions: the combined GOME retrieval TM3DAM assimilation model approach is not capable of reproducing ozone depletion, even if it is very pronounced as in the year The right panel of Figure 4.15 shows an example from March 2000 from Ny-Ålesund with at most 60% ozone depletion in the 40-to-60 mbar pressure region. The depleted layer is not seen in the assimilated data. It appears as if the assimilated data still suffer the same setback as the pure TM3DAM data set analysed in an earlier phase of the project, where (GOME) total ozone was used to scale the profile, and

47 Progress by work package 41 Figure 4.14: Relative average deviations between convolved ozonesonde profiles and GOME profiles (left panels): red solid line; standard deviation: yellow lines; single profiles: black dots. Right panels: average of absolute values (signs removed) of percentage deviations between measured and GOME algorithm profiles with standard deviations: a-priori profiles (green) vs. retrieved profiles (red).

48 42 GOA Final Report April, 2003 Station name Longitude Latitude no. of comparisons Andøya (ozonesonde) Andøya (ozone lidar) De Bilt Lerwick Lindenberg Legionowo Ny-Ålesund Ørlandet Payerne Sodankylä Uccle Yakutsk Table 4.2: List of stations used in 2000 validation thus the ozone overestimation in the depleted layer was compensated with an underestimation at lower altitudes. On a statistical basis, there is a noticeable difference between high and mid-latitude stations. At high latitude stations, one of which is shown in Figure 4.16, left panel, there is a general improvement over the whole height range compared to the a-priori profile, except around 200 ± 50 mbar, where the deviation changes from positive to negative, but does not decrease absolutely. Considering the different distribution of soundings throughout the year, also the results of the different techniques (ozonesondes, lidar) show a satisfactory agreement in the over lap area. The results from the lidar validation reveal, however, a larger standard deviation of the single-profile results than the sonde validation below, say, 100 mbar. This may be due to the larger uncertainties of lidar data in the lowermost height region. Only above 5 mbar, the standard deviations increase rapidly in the lidar comparison due to the increasing uncertainty in the g-b data set there. In the 200-to-5-mbar range the average deviations are reduced from typically 10 to less than 3%. On the other hand, the standard deviation of the single profile comparisons is 10% at best and over large parts of the altitude profile closer to 20%. Consequently one has to state that the information gain from the GOME data is limited, and great care should be used before applying the data in analyses of processes with limited vertical extent such as ozone depletion in the Arctic. At mid-latitude stations, such as Payerne (Figure 4.16, right panel) an improvement compared to climatological profiles is only obvious between 500 and 150 mbar. Below and above, the deviations between the climatological and the sonde profiles are comparable the deviation between the retrieved GOME and the balloon measurement. However, in the height region where an improvement is given the scatter from the single profile comparisons is significantly larger than the improvement. One has, of course, to consider that in most profiles this region is close to the tropopause, i.e. rather small vertical offsets of the ozone tropopause cause large relative deviations. Comparing these results to those reported earlier (using only assimilated profiles with GOME total ozone as normalisation parameter), there is an improvement during the first months; the spin-up effects are

49 Progress by work package 43 Figure 4.15: Ozone profiles retrieved from GOME and assimilated with the KNMI TM3DAM model tool (solid profile) and a-priori climatological profile used in the model (dotted line). Red profile: ozonesonde profiles (diamonds: ozone densities at the model pressure levels). Left panel: Payerne sounding 20 November 2000; right panel: Ny-Ålesund sounding 11 March not as visible anymore. In general, the altitude dependence of the deviation is reduced using GOME data. However, the problem with extracting information on ozone depletion at high altitudes remains, and thus severely limits the geophysical applicability of the data. The high variability in single-profile agreement also underlines that one rather should use the GOME data in statistical analyses, but not single-day / few-profile investigations Tropospheric ozone columns derived from assimilation The developed ozone profile assimilation is able to produce fields of tropospheric total ozone columns. Since the GOME ozone profiles are most accurate in the stratosphere (where the ozone maximum resides), and the assimilation is most sensitive to this part of the profile, the quality of the assimilated stratospheric ozone is probably large. If the assimilated stratospheric part is subtracted from a total ozone column (for example a GOME total ozone product, or the total ozone in the assimilation model), the result is the tropospheric column. The quality of such a tropospheric column is expected to be larger than a tropospheric column retrieved directly from GOME, since the instrument is less sensitive to ozone at lower altitudes. Figure 4.17 shows an example of a tropospheric ozone column extracted directly from the assimilation. Experiments showed that the tropospheric column is quite sensitive to the definition of the tropopause. In the figure this is visible as the noisy pattern of large ozone values between tropics and extra-tropics, where the tropopause decreases strongly with the latitude. The definition and quality of the tropospheric columns should be subject of further study, before this product could be made available to the scientific community.

50 44 GOA Final Report April, 2003 Figure 4.16: Average relative deviation between GOME derived assimilated ozone profiles and groundbased measurements (bold red line) and its standard deviations (thin red lines) for Sodankylä (left) and Payerne ozonesondes (right). Black dots: single profiles included. For comparison: average deviation between climatology used as a-priori profile in the GOME retrieval and ground-based measurements (green bold line) The figure represents a preliminary result, and more development work is needed. These activities will be continued after GOA, and the results will be validated with tropospheric ozone measurements and model results Tropospheric ozone estimates based on GOME data In addition to the workpackage, tropospheric ozone estimates have been derived from the GOME data based on the convective-cloud-differential technique, the KNMI fast-delivery ozone columns and the FRESCO cloud retrieval scheme. This method gives promising results that compare well with sonde observations. The GOA web site provides access to these tropical tropospheric ozone data sets. The retrieval of tropical tropospheric ozone columns (TTOCs) from GOME data and the interpretation of the observed variability in the TTOC, is one of the innovative KNMI activities. The TTOCs are determined with the so-called convective-cloud-differential (CCD) method, by subtracting the ozone column in the stratosphere from the total ozone column [Valks, 2003]. This method uses GOME ozone measurements over highly reflecting, high-altitude clouds to obtain an above-cloud ozone column. In certain regions, such as the tropical western Pacific, these high-reflectivity clouds are mostly associated with strong convection and cloud tops in the upper troposphere. The GOME instrument is able to determine the cloud fraction and cloud top pressure with the Fast Retrieval Scheme for Clouds from the Oxygen A- band (FRESCO). The tropospheric ozone columns (below the 200 hpa level) are derived at cloud-free pixels by subtracting the above-cloud ozone column from the GOME total ozone column. This assumes that the ozone column above the 200 hpa level is independent of longitude, which has good validity in the tropics.

51 Progress by work package 45 Figure 4.17: Example of tropospheric ozone column derived from the TM3PAS assimilated ozone fields. The troposphere is defined as the model levels where the vertical temperature gradient is at least - 2 K/km. With the GOME-CCD method, monthly-averaged tropospheric ozone columns have been calculated on a 2.5 by 5 degree grid for the tropical region (usually between 20N and 20S) for the whole GOME period from July 1995 to present. The images and data are available on the internet via the GOA project web site. Figure 4.18 shows the tropical tropospheric ozone column for October The month October is at the end of biomass burning season, when large-scale fires occur over Southern Africa and South-America. Clearly visible are the high tropospheric ozone columns over the Atlantic Ocean, with values of Dobson Units (DU). These large-scale ozone increases over the southern Atlantic occur primarily in the middle and upper troposphere. The phenomenon can be attributed to a complex interaction of biomass burning, lightning NO x emissions and large-scale transport. The GOME-CCD method provides valuable information to untangle these intriguing processes. The accuracy of the GOME-CCD method has been assessed by comparing the derived tropospheric ozone columns with ozone sonde measurements from 7 sites of the recently started SHADOZ network, in which Paramaribo takes part. Figure 4.19 shows the comparison for the Brazilian station Natal. There is good agreement between the GOME tropospheric ozone values and the sonde measurements. Clearly visible is the strong yearly increase in tropospheric ozone during the biomass-burning season, starting in July and ending in October. Figure 4.19 also shows the comparison for Paramaribo, which is very close to the equator. Here, the influence of biomass burning is smaller, and the seasonal variation in the tropospheric ozone column can be explained by taking into account the migration of the ITCZ over Paramaribo, twice a year, between Dec-Feb and Apr-Aug5). In the wet seasons, the tropospheric ozone columns are fairly low, due to convective uplift of humid and ozone poor air. The increase in the tropospheric ozone values during the long dry season (Aug-Dec) under the influence of subsidence, is found in both the GOME ozone values and the sonde measurements.

52 46 GOA Final Report April, 2003 GOME tropospheric ozone (below 200 hpa) October 2001 latitude (deg) longitude (deg) DU Figure 4.18: Tropical tropospheric ozone column (below 200 hpa) for October 2001 derived from GOME observations. The high tropospheric ozone values of Dobson Units over the Atlantic Ocean are clearly visible Comparison between the Oslo CTM2 and the MOZAIC ozone data In coordination with the TRADEOFF project, comparisons with MOZAIC data have been made for 1997 with the new 40-layer version of the Oslo CTM2 including tropospheric and stratospheric chemistry (work package 3.8 and 5.7). In addition the old 19-layer version of the Oslo CTM2 was validated against MOZAIC data from An overestimation of ozone in the troposphere was detected and is probably due to too high ozone precursor emissions. This has been partly remedied now and first calculations have indicated considerably less ozone.

53 Progress by work package 47 Natal, Brazil (5S,35W) Ozone column [DU] GOME CCD Sonde Paramaribo, Surinam (6N,55W) Ozone column [DU] GOME CCD Sonde Month Figure 4.19: Tropospheric ozone columns for Natal (a) and Paramaribo (b) for the period July 1998 Dec The asterisks denote the integrated ozonesonde measurements with 1σ error bars (if there was only one sonde measurements in a particular month, the error bar is omitted). The diamonds denote the TTOCs derived with the GOME-CCD method. 4.4 Retrieval and assimilation of NO 2 columns from GOME Introduction An important step in filling the gap in our knowledge of tropospheric NO x has been made by the GOME. The prime advantage of satellite spectrometers like GOME is their capability of providing a full global mapping of the atmospheric composition [Borrell 2001]. After cloud filtering, GOME provides global coverage NO 2 maps roughly every week. Column amounts of NO 2 can be derived from the detailed spectral information provided by GOME in the wavelength range nm. Good signal to noise ratio s (of about 20) are obtained for NO 2 with the Differential Optical Absorption Spectroscopy (DOAS) retrieval technique [Leue 2001, Wenig 2001]. This is related to the absence of strong other absorbers (e.g. ozone) in this spectral interval. GOME has also demonstrated the ability to observe boundary layer NO 2 : on top of a stratospheric background enhanced column NO 2 amounts are observed that correlate well with known industrialised areas. NO 2 plumes originating from biomass burning events have been

54 48 GOA Final Report April, 2003 detected by GOME. Furthermore, there are signatures of lightning-produced NO 2 in the GOME data set [Beirle et al., 2003]. Motivation for the GOA NO 2 activities A remaining major challenge is the derivation of high quality quantitative tropospheric NO 2 column amounts for individual ground pixels based on the satellite data. Tropospheric retrieval is characterised by large uncertainties, related to clouds, the surface albedo, the trace gas profile, the stratospheric column of NO 2, the temperature profile and aerosols: The largest uncertainties are due to clouds, as they will shield near-surface NO 2 from the view of the satellite. The retrieval depends very sensitively on the presence of clouds, and even small cloud fractions (between 5 to 20% cloud cover) have a major impact. High quality observations of the cloud properties (at least cloud fraction and cloud top height) are necessary for a quantitative retrieval. The surface albedo directly influences the sensitivity of GOME for boundary layer NO 2. High quality albedo maps in the relevant spectral range are essential. Profiles of NO 2 are characterised by a large range of variability. At emission areas the NO 2 concentration will peak at the surface, while downstream of such areas the pollution plume will peak at higher altitudes. The profile of NO 2 will be determined by aspects like the distribution of emission sources, the stability and height of the boundary layer, wet removal of nitric acid, deep convection and long-range transport by the wind. All these aspects are strongly varying in time and space. The NO 2 columns measured by GOME consist of comparable stratospheric and tropospheric contributions. The stratospheric background has to be quantified carefully in order to derive the tropospheric column. Atmospheric dynamics is well known to generate significant variability in stratospheric tracer amounts, consistent with for instance HALOE observations of NO 2. A standard approach applied to GOME is based on the assumption that stratospheric NO 2 is zonally uniform, or at least has only a small longitudinal variation. Such simplification makes the retrieval of small tropospheric NO 2 columns practically impossible. The cross-section of NO 2 is depending on temperature. Based on temperature profiles from meteorological analyses (e.g. the ECMWF model) and predicted NO 2 profiles from TM3 this can be effectively accounted for. Another source of uncertainty are aerosols. Thick aerosol layers influence the radiation field and the sensitivity of GOME for near-surface NO 2. Apart from these issues there is an other GOME instrumental aspect which has a strong influence on the quantitative accuracy of the slant column estimates. This is related to spurious spectral structures which are most probably caused by the diffuser plate in the GOME instrument [Richter and Wagner, 2001]. These spurious structures lead to large, irregular offsets in the slant columns derived with the DOAS technique. In the GOA project, the approach chosen to avoid these irregular jumps in the reflectance spectra is to use a fixed solar reference spectrum, thereby however allowing a fixed but likely small bias in the total columns. GOA innovation Within the GOA project two independent approaches were developed and both techniques were applied to estimate tropospheric and stratospheric vertical columns. The first approach (University of Heidel-

55 Progress by work package 49 berg) is measurement-based and avoids as much as possible the use of a-priori information. The main motivation in this first approach is to generate data sets which are independent from model results. A study of GOME small-pixel data (40 x 80 km 2 ) resulted in a highly resolved tropospheric NO 2 climatology. The second approach (developed at KNMI) is a new combined modelling, retrieval and assimilation approach. Here the main idea is to integrate the GOME measurements and model results to provide the most optimal a-priori information possible for the retrieval and thus allowing quantitative estimates of both total and tropospheric columns. The KNMI approach includes a detailed error analysis to yield realistic uncertainty estimates of the individually retrieved columns. The detailed differences between these two independent approaches were studied and find their origin in the observation operator (or averaging kernel), for which a formalism has been developed in the GOA project [Eskes and Boersma, 2003]. The critical retrieval issues discussed above were explicitly addressed during the GOA project Generation of NO 2 slant column densities The approach developed at the KNMI focuses on the air-mass factor calculation, and is based on the slant columns calculated by the Heidelberg group. During the first year of GOA, a slant column data set has been generated for the whole GOME period. KNMI has coupled the combined modelling and retrieval approach to this data set. As a second option the slant column observations from the GDP version 2.7 were used for comparison. For the retrieval of NO 2 slant column densities (SCD) the differential optical absorption spectroscopy (DOAS) technique has been applied (WP4.1). For this evaluation the spectral range from 431 nm to 452 nm was used. A detailed description of the retrieval can be found in [Wenig, 2002; Leue et al., 2001; Velders et al., 2001]. Ideally, a daily direct sun light spectrum is used as the reference spectrum in the DOAS analysis. With this procedure almost all instrumental variations cancel out and the residual structures of the DOAS fitting procedure are small. GOME measures one solar reference spectrum per day. This solar reference however seems to contain some variations which affect the resulting NO 2 SCDs in such a strong way that it causes problems in the assimilation approach. The resulting time series of NO 2 SCD shows a dominating periodic structure (figure 4.20). Since this structure is similar for different parts of the Earth, this effect is systematic. The reason for this prominent structure in the retrieved SCD is assumed to be the diffuser plate adding a spectral structure similar to the NO 2 cross section to the solar reference [Richter and Wagner, 2001; Richter et al., 2002]. To avoid this effect, one fix solar reference (i.e. June, 1st, 1997) was used for all DOAS fits. The result of this procedure is shown in figure 4.21: The prominent structures in the time series have vanished. Hence, the fixed solar reference is used for the evaluation of all data. Although there is a degradation of the instrument the use of a solar reference does not result in significantly increased fit errors. This method provides time series of NO 2 SCDs for a long time ( ) with constant conditions and are therefore very useful for the assimilation scheme. For recent data, i.e. 2002, it cannot be excluded that the use of a fixed solar reference causes long term trends in the SCD, since the fit coefficient of the solar spectrum has decreased systematically over the last few months. The reason for this is assumed to be the degradation of the instrument. To avoid this

56 50 GOA Final Report April, 2003 Figure 4.20: Time series of mean NO 2 vertical column densities (VCD) for different regions. Prominent structures appear every year in different regions of the world. [Wenig, 2002] problem, a new fitting algorithm was developed, that enables the fixing of single fit parameters (i.e. to set the fraunhofer fit coefficient as 1) Combined retrieval, modelling and assimilation approach for GOME NO 2 The KNMI retrieval of NO 2 is based on a combined retrieval / modelling approach which matured over the last year (WP4.3). An overview of this approach is available as a web page on the GOA website, and is included as annex in this report (see also Martin et al., [2002], who developed a similar approach). The chemistry-transport model TM3, driven by high-quality meteorological fields, provides best-guess profiles of NO 2, based on the latest emission inventories, atmospheric transport, photochemistry, lightning modelling and wet/dry removal processes [Houweling, 1998] [Velders 2001]. These model forecast fields are collocated with the GOME observations, and the radiative transfer modelling in the retrieval is performed based on the model trace gas profile and temperature profiles. The temperature dependence of the cross section of NO 2 [Burrows 1999b] is accounted for. The retrieval is coupled to cloud top height and cloud fraction retrievals derived from the GOME data with the Fresco algorithm [Koelemeijer, 2001], and the retrieval is coupled to a high quality albedo map that is based on the strong points of the long-term TOMS [Herman and Celarier, 1997], and multispectral GOME [Koelemeijer et al., 2002] datasets. A simplified overview of the approach is given in figure 4.22.

57 Progress by work package 51 Figure 4.21: Time series of mean NO 2 VCDs using a fixed solar reference. The periodic structures have vanished. [Wenig, 2002] The building blocks of the system shown in Figure 4.22 are: GOME NO 2 slant column densities, as retrieved by IUP [Leue, 2001]. The chemistry-transport model TM3 [Houweling, 1998]. GOME cloud retrievals from the Fresco algorithm [Koelemeijer, 2001]. Albedo maps, based on TOMS and GOME measurements [Herman, 1997], [Koelemeijer, 2003]. The multiple scattering radiative transfer model DAK [Stammes, 2001]. The computation is coupled to monthly albedo maps. These were constructed from TOMS monthly Lambertian equivalent reflectivity (LER) maps [Herman and Celarier, 1997] and similar GOME maps [Koelemeijer, 2002]. The advantage of the TOMS maps is the long measurement series involved (from November 1978 to 1992) and the smaller ground pixel which reduces the problems related to residual cloudiness. The advantage of GOME is the explicit detailed spectral dependence. The procedure for constructing the maps as shown in figure 4.23 is the following: first we calculate the ratio between the 440 nm and 380 nm albedos from the GOME data. The TOMS LER at 360/380 nm is subsequently multiplied with this GOME ratio to obtain an estimate of the LER at 440 nm. Two air-mass factor computations were performed, one for clear sky, and one for a 100% cloud cover.

58 52 GOA Final Report April, 2003 GOME NO2 slant column data Assimilation GOME Cloud top height Cloud fraction Chemistry-transport model Radiative transfer modelling (AMF) NO2 profile shape Temperature profile Stratospheric NO2 Tropospheric NO2 column Figure 4.22: The combined modelling, retrieval, assimilation approach. The clouded case is modelled as a Lambertian surface at the altitude retrieved by the FRESCO algorithm. The clear and clouded parts are subsequently intensity-weighted with the FRESCO cloud fraction to obtain the air-mass factor. A separate Rayleigh scattering calculation, following [Vermote and Tanré, 1992], is added for the clear and cloudy intensity estimates. The temperature dependence of the NO 2 cross section is accounted for a posteriori, based on the model temperature (ECMWF model profile) and (TM3) NO 2 profile. An aerosol correction has not (yet) been added: this is non-trivial, since the effect will strongly depend on the aerosol type, optical thickness and vertical profile. Secondly, aerosols have partly the same effects as clouds, and the FRESCO algorithm is sensitive to both clouds and aerosols. Sensitivity studies have been performed to study these effects of aerosols on the air-mass factor and the cloud retrieval. The slant column produced by the observation operator is the result of a combined radiative transfer and chemistry-transport modelling. The slant columns following from the DOAS fit, on the other hand, are basically a pure observational quantity, independent of a priori information. This is an important benefit of the observation operator or averaging kernel approach: comparing modelled with measured slant columns (instead of comparing vertical columns) is the most pure way of comparing model and observation. A scientific paper on the theory and use of the observation operator has been published within the framework of the GOA project [Eskes and Boersma, 2003]. Apart from this paper, documentation has been written on the use and applicability of the averaging kernel, which can be found on the GOA web site. The kernels are included in the KNMI GOA data product. The differences between the modelled and measured slant columns were used to force the modelled stratospheric NO 2 column to be consistent with the GOME observation (slant column data assimilation). This stratospheric model state was used as a reference to estimate the column for the troposphere only. The advantage of this approach is that slant column variations related to stratospheric dynamics can now be accounted for with the tracer transport model, thereby allowing the retrieval of small tropospheric column values. An additional advantage is the statistical estimate of the uncertainty of the stratospheric

59 Progress by work package 53 Figure 4.23: Lambertian equivalent reflectivity map at 440 nm, based on TOMS and GOME measurements. column that is an important input in the error analysis Deriving tropospheric NO 2 vertical columns For trace gases, which are located both in the stratosphere and the troposphere (like NO 2 and BrO), the separation of the tropospheric and the stratospheric fraction is possible under several assumptions. If the life time in the troposphere is small (in the order of days) significant tropospheric trace gas amounts can be found only close to their sources. This is in particular true for tropospheric NO 2 observations and it is possible to determine the stratospheric column over the oceans (far away from the tropospheric NO 2 sources). In contrast, it can be expected that the stratospheric variability (especially in longitudinal direction) is small compared to the spatial gradients in the troposphere. Thus the stratospheric column can be determined far away from the tropospheric sources (e.g. over the ocean) and subtracted from the total columns observed over the continents (where significant tropospheric concentrations exist). Such methods were especially applied to GOME observations of tropospheric NO 2 [Leue et al., 2001; Velders et al., 2001; Richter and Burrows, 2002; Beirle et al., 2002; Wenig et al., 2003b]. Basically two different approaches were used: The application of image sequence techniques to take the longitudinal variation into account [Leue, 1999; Leue et al., 2001; Wenig, 2001; Wenig et al., 2003]. The different steps of the method are illustrated in Fig. 4.24:

60 54 GOA Final Report April, 2003 Figure 4.24: Different steps in the determination of tropospheric [Wenig et al., 2003] (see text). NO 2 VCD s observations from GOME

61 Progress by work package 55 Figure 4.25: Global mean of tropospheric NO 2 VCD, using narrow viewing mode pixels only ( ), corrected for seasonal effects. [Beirle et al., 2003c] 1) First the NO 2 VCD over a period of several days are derived from the GOME observations (Fig a) 2) In the next step the clear sky minimum over this period is determined (taking into account the cloud fraction determined from PMD measurements, Fig.4.24c) to account for possible contributions of lightning produced tropospheric NO 2 (Fig b) 3) The continents are masked out (Fig. 4.24d) and the gaps are interpolated in longitude direction (Fig f). This intermediate step represents the stratospheric contribution. 4)To extract the tropospheric NO 2 VCD from a single day measurement (Fig e) the stratospheric columns are subtracted. The result (Fig.4.24 g) has then to be corrected for the tropospheric AMF. 5)This tropospheric AMF (Fig i) is calculated with respect to the respective ground albedo (Fig h) of a given measurement. 6) Using this AMF the tropospheric NO 2 VCD is determined (Fig j). A second method simply subtracts the total NO 2 VCD over a selected latitudinal cross section over the Pacific from the total column measurements over the continents [Richter and Burrows, 2002; Beirle et al., 2002]. The second method is of course much simpler compared to the image sequence approach. However, for specific applications it has significant advantages because of its stability. Figure 4.25 shows a climatology of tropospheric NO 2 (mean of , corrected for seasonal effects) [Beirle et al., 2003c]. The tropospheric VCDs are received using the reference sector method. No further correction for tropospheric AMF was applied. The high spatial resolution was achieved by only taking pixels in the so called narrow swath mode with a spatial resolution of 80*40 km 2, that is performed every 10th day. The industrialized regions of the world clearly show up. Due to the high spatial resolution, even single cities like Moscow or Madrid stand out imposingly.

62 56 GOA Final Report April, 2003 GOME and GASCOD - NO2 8.00E+15 GOME NO2 VC 7.00E+15 GASCOD NO2 Daily AVG 6.00E+15 NO2 VC [mol/cm^2] 5.00E E E+15 '96 '97 '98 '99 '00 ' E E E+00 01/12/ /10/ /08/ /06/ /04/ /02/ /12/ /10/ /08/ /06/ /04/ /02/ /12/ /10/ /08/ /06/ /04/ /02/ /12/ /03/ /01/ /11/ /09/ /07/ /05/ /03/ /01/ /11/ /09/ /07/ /05/ /03/ /01/ /11/ /09/ /07/ /05/ /03/ /01/1999 Date Figure 4.26: Time series of GOME and GASCOD measurements of NO 2 VCD for Terra Nova Bay, Antarctica Validation of GOME NO 2 Vertical Column Densities To validate the retrieved NO 2 column densities (WP4.5), comparisons with other measurements are necessary. Therefore a comparison of GOME data and measurements from the Gas Analyser Spectrometer Correlating Optical Differences (GASCOD) was performed for Terra Nova Bay (74 26 S, E, Ross Sea) for the years [Bortoli et al., 2000]. Figure 4.26 shows time series of both measurements. The antarctica yearly cycle of NO 2 VCD is clearly reflected in both datasets. Also the quantitative comparison shows a good agreement. Comparisons of NO 2 column densities with other ground measurements are going to follow for other latitudes. Especially for tropical data we will use the DOAS instrument in Surinam (U. Fries, IUP). Data is not available before First looks reveal quite large differences, that are thought to be due to the degradation of the GOME instrument Critical evaluation of the different aspects of the retrieval In the first year of the GOA project the KNMI and Heidelberg teams organised a dedicated meeting to discuss all the aspects of the NO 2 retrieval (WP4.4). During the meeting an action list with critical issues was compiled, and the intercomparison work has started. In the second year, a comparison was performed between Heidelberg and GDP2.7 slant columns, which resulted in an agreement better than 7% between the two datasets, apart for the diffuser problems apparent in the GDP2.7 dataset. The Fresco

63 Progress by work package 57 Figure 4.27: Bias and standard deviation of observation-minus-forecast vertical columns during the first month of assimilation. The bias between the model and the GOME NO 2 data has become very small after 5-6 days (80 tracks). The standard deviation then becomes about 0.2e15 molecules cm 2. cloud scheme has been compared with a newly developed cloud retrieval (HICRU) at the University of Heidelberg. Problems were identified in the Fresco data set over deserts. The GOA workshop, held in January 2003, also resulted in a critical evaluation of the various retrieval aspects. In March 2003, a meeting was held at the University of Heidelberg where one monthly mean tropospheric NO 2 dataset was compared, yielding a reasonable agreement, taking into account the large differences between the retrieval inputs Generation of tropospheric and stratospheric NO 2 columns with the combined retrieval-assimilation approach During the last year of the GOA-project, the development and testing of the combined retrieval-assimilation approach was completed. The main motivation for this new approach was already outlined in the introduction. In short, the approach aims at reducing uncertainties related to the retrieval problems associated with the stratospheric column, clouds, surface albedo and profile shape. In the second year of the GOA project a one-year data set (1997) of quantitative tropospheric and total NO 2 column estimates was produced. This data set will be further extended in the near future. The generation of the 1997 data set required careful analysis of all essential input parameters. Some analyses have been partly described above, for instance, the quality of the slant column data set was established in WP4.3 based on a Heidelberg-GDP2.7 comparison, where the two slant column data sets agreed better than 7%.

64 58 GOA Final Report April, 2003 Statistical error components: slant column stratosphere profile shape cloud fraction cloud pressure retrieval error estimate for individual pixels albedo Figure 4.28: Diagram of the tropospheric NO 2 column error modelling. Individual error sources (on the left) are propagated through the retrieval algorithm to produce an error estimate of the tropospheric column. The uncertainty of all other input parameters was established as part of WP4.5. The estimate of the stratospheric column is essential in the combined retrieval-assimilation approach. Detailed analysis was carried out on the data-assimilation of slant columns into TM3 in order to check the validity of the stratospheric estimate of NO 2. It turned out that assimilating GOME observations of stratospheric NO 2 into TM3 forces the model to the observations after a spin-up time of about six measurement days. Figure 4.27 illustrates that the difference between observation and model forecast approaches zero after approximately 80 tracks have been fed into the assimilation scheme. The statistical error associated with the observation-forecast is less than 10% of the stratospheric column. From a comparison between the GOME LER at 440 nm and the constructed LER at 440 nm we determined the statistical uncertainty of the constructed 440 nm albedo map to be less than The bias was smaller than the original TOMS discrete step size of A second important error source are cloud parameters. FRESCO cloud fraction and cloud top pressure are well validated quantities and we estimated the uncertainty in the cloud parameters from literature [Koelemeijer, 2002] and [Tuinder, 2003]. In order to estimate the impact of profile errors, the profile variability was computed for each month in 1997, and this variability was assumed te be representative of the profile error. Sensitivity studies have been performed to estimate the size of the errors that can be expected due to the neglect of aerosols. Preliminary results indicate that cloud algorithms give slightly higher cloud fractions, but may significantly increase the associated cloud top pressure, thus giving rise to a partly cancellation of errors through the air-mass factor calculation. Individual error contributions to the NO 2 product have been discussed in the literature, but a systematic error analysis on a pixel-by-pixel basis has not become available so far. A quantitative estimate of the NO 2 columns however is in dire need of a realistic quantitative error estimate. The GOA NO data product now includes a realistic total and tropospheric column error based on errors in the DOAS spectral fit, cloud algorithm, the albedo map, the profile shape, and the estimate of the stratospheric column [Boersma and Eskes, 2003]. Figure 4.28 shows a diagram of the error modelling method; statistical errors of all ancillary parameters are input to the retrieval scheme. Figure 4.30 shows the air-mass

65 Progress by work package 59 Figure 4.29: Mean tropospheric NO 2 column for March 1997, derived with the combined retrieval, modelling and assimilation approach factor contribution to the estimated error for March In general, errors for appreciable individual tropospheric columns are in the 20-80% range. Total column errors over most of the globe are estimated to be up to 10% precise, but for regions with strong tropospheric contributions to the total column, these errors may be much larger. The following products are available on the project website ( Data product files in HDF-4 format with NO 2 column information for the individual GOME observations. Each file contains one day of data. Monthly-mean gridded tropospheric NO 2 distributions: PNG and PDF format images and ascii data files. Three-day composite maps of tropospheric NO 2, PNG and PDF format images. Figure 4.31 shows the web page which provides access to these data sets. The data product files are described in detail in the data product specification document, along with software to read the data. This product specification document is available on the GOA web site. The files contain three groups of data, namely: The main data product: tropospheric, stratospheric and total columns of NO 2, error estimates of these columns, the averaging kernel vector and a quality flag.

66 60 GOA Final Report April, 2003 Figure 4.30: Error (in %) in the tropospheric air-mass factor for March 1997 due to uncertainties in the cloud fraction, cloud height, surface albedo and profile shape. Only pixels with a cloud radiance fraction < 0.5 were taken. Note that the air-mass factor errors over the oceans may be small, but that the relative retrieval errors over oceanic regions will be dominated by the errors in the retrieved slant column and stratospheric column estimate. Geolocation data: Latitude and longitude of the corners and centre of the GOME footprint, solar zenith angle, viewing angle, azimuth angle. Ancillary data: this consists of retrieval-specific data, such as the slant column, surface albedo, cloud fraction and top pressure and air-mass factors. We would like to stress here that two features are now available in this GOA NO 2 data set, that have previously not been offered to the scientific community. The first feature is the DOAS averaging kernel, which provides the link between the true NO 2 distribution and the retrieved quantity, i.e. the total or tropospheric column. The averaging kernel (or observation operator) gives users the opportunity to directly relate his or her NO 2 distribution to what the GOME instrument would retrieve, thus allowing a comparison between independent ground measurement and retrieval, or between model prediction and retrieval. The second feature is the quality flag and the error estimate on the tropospheric retrievals. The tropospheric NO 2 columns can be meaningfully retrieved for cloud fractions less than approximately 15%. The quality flag and error estimate are meant to stimulate users to critically use the data product. Retrievals for cloud fractions higher than 15% may be biased by possible unrealistic ghost columns.

67 Progress by work package 61 Figure 4.31: Internet access to the GOME NO 2 data sets on the GOA web site Comparison of the results with an independent chemistry-transport model An NO 2 data base has been generated with the Oslo CTM2 chemistry-transport model (WP4.7). An evaluation shows a reasonably good comparison with the KNMI TM3 model results, and the main emission areas correspond well with the dominant NO 2 hot-spots observed by GOME (see Annex N).

68 62 GOA Final Report April, CTM modelling studies and validation with GOME observations and measurement campaigns Introduction The main purpose of this work package was to demonstrate the usefulness of the provided ozone and NO 2 data, both as column distributions and vertical profiles in the troposphere and stratosphere, for model studies of chemical processes and long term changes due to man made emissions. For global model calculations with 3D CTMs it is important to have a good global coverage in the observed distribution which will be used for model validation. In particular, data for remote regions in the troposphere and for the lower stratosphere were included in the comparisons. These regions are currently only partly covered by observations, and large uncertainties exist in the calculated distributions and changes. Model intercomparisons and model validation help identifying and reducing uncertainties in the model calculations, and thereby allow more accurate predictions. The main objective for the University of Oslo within this work package was to do model experiments for the GOME period. The main tool was the Oslo CTM2, a chemical transport model, which is driven by meteorological data from ECMWF and applies the highly accurate Second Order Moment scheme for advection. The model includes detailed chemistry packages for both the troposphere and the lower stratosphere. Model results were compared with the TM3 CTM of the KNMI. This comparison, and the confrontation with observations from GOME and with the MOZAIC data set have provided some insight in the differences between models and have helped to identify weaknesses in the modelling of (especially tropospheric) chemistry Comparison of the Oslo CTM2 with GOME total ozone Observational data from GOME were interpolated into the Oslo CTM2 grid and compared to modelled distributions in the period of the model runs, i.e. 1997, 2000, including the entire winter season 2000/2001 (Work package 5.1). An example is shown in Figure 4.32 for March Regarding the horizontal distribution of the total ozone column the agreement is very good. Overestimation of ozone occur at high latitudes. This is likely to be due to the overestimation of the partial ozone column in the top layer of the model, which applies climatological ozone (calculated by the Oslo 2D model) without any zonal or vertical variation CTM model runs During the first few months of GOA the vertical resolution of the Oslo CTM2 was improved from 19 to 40 layers. The resulting vertical resolution in the boundary layer is now on the order 100 meters, while in the tropopause region it is about one kilometre, which is double the resolution of the previous model version. The first experiments were run for the periods January to December 1997 and November 1999 to March Three-dimensional monthly-mean data were calculated for NO 2, ozone, and other key components. Also, for each day, 3-dimensional model fields were made available for 10:30 local time - in line with GOME observations - for the entire period modelled so far.

69 Progress by work package 63 Figure 4.32: Total ozone on March 15, 1997, observed by GOME (left) and modelled by the Oslo CTM2 (right). Scale in DU CTM model comparison and comparison with GOME ozone and NO 2 data A detailed comparison with results from TM3 has shown good agreement between the two chemical transport models both in terms of NO 2 and ozone (work package 5.3). Figure 4.33 shows the tropospheric NO 2 column as an example. Regarding tropospheric and stratospheric vertical profiles, various cases have been selected for model inter-comparison and validation against observations, including areas that are heavily polluted by industry or biomass burning and remote, unpolluted areas (both oceanic and continental): 1) 51 degrees N, 0 degrees East (urban, polluted), 2) 0 degrees N, 120 degrees W (unpolluted, tropical marine), 3) 5 degrees N, 20 degrees E (polluted, biomass burning), and 4) 65 degrees S, 0 degrees E (unpolluted, marine, high southern latitudes). Regarding the shape of the vertical profile, there is good agreement between the two models. However, deviations remain in the boundary layer over highly-polluted areas and the lowermost stratosphere. In both cases the Oslo CTM2 yields considerably less NO 2 than the KNMI model. As the emission set and the meteorological data in the models are the same, this disagreement is assumed to be due to different parameterizations of boundary layer mixing and differences in the chemistry schemes. For the new version of the Oslo CTM2 the parameterization of boundary layer mixing has been completely revised and first results were available during the second half of GOA. Vertical profiles were significantly changed in the lower troposphere due to the new parameterization. As for total column NO 2 changes were less significant. Areas of peak column density are somewhat less extended in the new scheme. A new boundary layer mixing scheme has also been added to the KNMI TM3 model. The impact of the different boundary layer schemes is shown in more detail in the Annex on CTM-2 / TM3 model inter-comparisons and validations against GOME data. Figure 4.34 shows a model inter-comparison for the NO 2 vertical profile over London. The shape of the profile agrees well between the models. However, the Oslo CTM-2 calculates somewhat higher NO 2 mixing ratios in the free troposphere and the lower stratosphere. The model inter-comparison has also considered ozone precursors such as CO and CH 4. Some of the results will be presented in the Annex on the CTM-2 / TM3 model inter-comparisons. During the development of the new Oslo CTM2 version developed in the second half of the GOA project,

70 64 GOA Final Report April, 2003 Figure 4.33: Total tropospheric NO 2 column, January 1997, monthly mean. Top-left: GOME, high resolution. Top-right: GOME, low resolution. Bottom-left: KNMI TM3. Bottom-right: Oslo CTM-2. [10 15 molecules cm 2 ]

71 Progress by work package 65 51N,0E Figure 4.34: NO 2 volume mixing ratio vertical profile for the London area, January 1997, monthly mean. Blue line: Oslo CTM2. Black line: KNMI TM3. sensitivity studies for emissions (work package 5.4), different meteorology and chemistry were accomplished. The new model version now features a more detailed implementation of surface emissions. Ozone precursor emissions are given on a 1x1 degree horizontal grid and divided into different regions and numerous source categories. This work has greatly benefited from the EU project POET. Detailed comparisons with GOME data provided through the GOA project show that peak areas are well resolved in the model. Also, the height of the peaks is well resolved. However, the agreement depends on the horizontal resolution of the model. With T21 resolution the model cannot represent the high peaks seen by GOME in 0.4x0.5 degree resolutions. Also, the peaks, especially over Europe seem to be more extended in the CTM2 model, while the peaks in the Southern Hemisphere (South Africa, South America, Eastern Australia) are, in most cases, underestimated. This will be further investigated using the measurement data base that has been developed in the GOA project Requirements for the GOME derived ozone and NO 2 fields and for future space borne observations For NO 2 it is most important to distinguish specific regions and events with enhanced concentrations. Short lifetimes of NO and NO 2 and the resulting small-scale variation necessitate a rather high horizontal resolution in the observations on the order of 100 km. The target should be to provide the vertical distribution in the troposphere with an accuracy of 20% or less in order to determine the mixing ratio in the upper troposphere (role of lightning), and the free troposphere (biomass burning plumes). A vertical resolution of 2 km or better would allow resolving the vertical extent of the plumes and provide information on the role played by convective transport and production from lightning on mid and upper tropospheric NO x levels. In the lower troposphere measurements of NO 2 are helpful but are likely to be beyond reach for spaceborne platforms in the required accuracy and spatial (both vertical and horizontal) resolution within the foreseeable future. Obviously, the diurnal cycle of NO 2 and ozone in the troposphere cannot be detected by a single sunsynchronous polar orbit. Given the high uncertainty in modelled NO 2 distributions, however, measure-

72 66 GOA Final Report April, 2003 ments of NO 2 at one local time in the troposphere would be nonetheless very valuable for model validation and improvement. Ozone is easier to model than NO 2. First comparisons between the two CTMs and the validation against GOME data show, however, that uncertainties remain, albeit much smaller than in the case of NO 2. Due to the short life time of ozone in the lower troposphere the required measurement accuracy is comparable to that for NO 2. Also in the middle and upper troposphere there is still great spatial variability primarily due to convective activity. In the lower stratosphere the spatial variation of ozone is less pronounced so that a lower spatial resolution would suffice. Uncertainties in modelled column ozone are now below 10% so that a measurement has to be much more accurate than 10% in order to help refining the models Future stratospheric ozone changes and changes in the chemical oxidation in the troposphere During the first half of GOA a simulation has been made for the year 2100 with the Oslo CTM2 in order to study tropospheric ozone increase and stratospheric ozone recovery during the 21st century (work package 5.6). Emissions were based on estimates of WMO and IPCC. Three-dimensional distributions were calculated for several chemical key components such as NO x, VOC, NMHC, chlorine, and bromine compounds. The tropospheric ozone increase between 2000 and 2100 as modelled by CTM2 amounts to 21 DU on a global and annual average. NO 2 will increase in both the troposphere and the stratosphere as a result of increasing emissions of NO x compounds and N 2 O. Results from this study are now published in detail along with results from other models and radiative forcing calculations [Gauss et al., 2003].

73 Progress by work package Data user feedback The European Space Agency is involved in the GOA project on two aspects. First they provide feedback on the status of the GOME instrument and on the ESA GOME ozone and NO 2 products. For GOA, the following aspects are of interest: Information on the retrieval aspects and accuracy of the GOME Data Processor (version 2.7) total ozone product, as obtained from the pole to pole validation with ground based observations. Similar validation results for the KNMI Fast Delivery total ozone product. Status of the development and processing of a new GDP version 3 total ozone product. Status of ERS-2 and the GOME instrument. Secondly, ESA provides feedback to the project from the side of the users in the form of requirements from the user community, and with assistance in writing the User Requirements Document. ESA is also involved in the organisation of the workshop and the promotion of the project results. During the project the partners of the GOA project have contacted potential users. These users were asked to fill in a requirement questionnaire, provided by ESA. Based on this feedback the first version of the user requirements document was compiled. During the project the contacts with these users have been maintained, and new users have been added to the list. 4.7 Deviations from the work plan During the two years of the GOA project several activities have taken place in addition to the workplan. A few small modifications include: The overall scedule: First: During the first year of GOA it was decided to base the work on the new GDP3 GOME products, and on the ECMWF ERA-40 reanalysis data set. These products became available only in the second year (e.g. the GDP3 ozone data set became available in November 2002). Second: The development of the ozone profile scheme was non-trivial, and several improvements have occurred during the GOA project. Third, there were some personnel issues. These three issues have caused a delay, and several activities have shifted to 2002 and even This has also caused some stress, especially the validation work. Despite this, almost all deliverables are now available, including the validation reports. The work on the validation of ozone profiles with Umkehr measurements and the validation of the NO 2 columns will be continued/finished in the first half of Task 3.1. The generation of ozone profiles was planned for the first year of GOA. As motivated in section 4.3.5, it was decided to no longer distinguish the two ozone profile products mentioned in WP3.1 (stratospheric profiles) and WP3.4 (high-quality profiles). This decision is based on new insight in GOME calibration issues, and on new developments in the forward radiative transfer modelling. Because of this, most of the work of WP3 was done in the second year of GOA.

74 68 GOA Final Report April, Conclusions, progress towards project objectives Over all, the GOA project has been successful in fulfilling it s aims. The assimilation and validation of GOME ozone columns has been completed as planned (WP2). A data set of assimilated ozone fields, together with the validation results is available for the period GOME ozone profiles have been generated for the period of one year (2000), and have been assimilated in a newly developed assimilation code (TM3PAS). The development of two independent approaches for GOME NO 2 retrieval has been completed. The approaches have been intercompared, and NO 2 data sets have been generated for the period (WP4). The two chemistry-transport models participating in GOA have been further improved. Model runs have been performed for different years within the GOME period, and model-model and model-gome comparisons have been conducted (WP5).

75 Annex A 69 A Conference attendance The GOA activities and results were discussed at the following meetings. Titles and authors of presentations are given: European Geophysical Society XXVI General Assembly, Nice, France, March 2001: The EU GOA project, Hennie Kelder and Henk Eskes. European Geophysical Society XXVI General Assembly, Nice, France, March 2001: Assimilation of GOME ozone column data in a three-dimensional tracer transport model, Henk Eskes, Peter van Velthoven, Ghada El Serafy and Hennie Kelder, (Sollicited Paper). EGS XXVI. General Assembly, Nice/France, March 2001: M.Gauss, J.K.Sundet, and I.S.A. Isaksen - 3D CTM model calculations of chemical and dynamical processes in the lower stratosphere. Ozone profile working group and TROPOSAT workshop, Frascati, Italy, April EUMETSAT / Ozone SAF meeting in Halkidiki, Greece, May 2001: Invited paper on the GOA project, H.M. Kelder, H.J. Eskes, G.Y. El Serafy, I. Isaksen, M. Gauss, C.S. Zerefos, D. Balis, U. Platt, M. Wenig, T. Wagner, G.H. Hansen, and C. Zehner. Research council of Norway, programme conference, Bergen/Norway, November 2001: M.Gauss & I.S.A. Isaksen - 3D CTM model calculations of chemical and dynamical processes in the lower stratosphere. IAMAS 2001, (International Association of Meteorology and Atmospheric Sciences), July, Innsbruck, Austria: Assimilation of GOME ozone data in Chemistry-Transport models, Henk Eskes, Hennie Kelder, Ghada ElSerafy, Ronald van der A and Pieter Valks. Climate Conference 2001, August 2001, Utrecht: A Combined Retrieval/Assimilation Approach to GOME NO 2, Henk Eskes, Folkert Boersma, Ellen Brinksma, Pepijn Veefkind, Michiel van Weele, and Hennie Kelder. NDSC 2001 Symposium, Arcachon, France, September 2001: Assimilation of GOME ozone measurements, Henk Eskes, Peter van Velthoven, Pieter Valks, Hennie Kelder. First meeting of the IGACO panel (WMO), Geneve, Switserland, November Information day on GMES, Brussels, Belgium, 15 Januari ESA - GOME user consultation meeting, Frascati, January 2002: D. Balis. ESA - GOME Delta validation campaign, Frascati, April 2002: D. Balis SPARC Data Assimilation workshop, June, 2002, University of Maryland, Baltimore County (UMBC), USA: Ozone data assimilation and ozone forecasting, Henk Eskes. This was a meeting of the new data assimilation initiative of SPARC (Stratospheric Processes And their Role in Climate). SPARC is part of the World Climate Research Programme (WCRP) which is one of the main international networks in our area of research. The meeting took place at the University of Maryland, near Washington, USA, on June In an oral presentation Henk Eskes has discussed the ozone data assimilation developments that are part of the GOA activities. International Conference on Modelling, Monitoring and Management of Air Pollution, Segovia, Spain, 1-3 July 2002: NO 2 and ozone observations from space and the prospect for chemical forecasting, Hennie Kelder.

76 70 GOA Final Report April, 2003 EOS-AURA meeting, s-gravenhage, Netherlands, 9 September 2002: NO 2 column density retrievals with OMI, F. Boersma. Sixth European Symposium on Stratospheric Ozone, Goteborg, Sweden, 2-6 September 2002: The GOA project, H.M. Kelder, H.J. Eskes, F. Boersma, I. Isaksen, M. Gauss, C.S. Zerefos, D. Balis, U. Platt, M. Wenig, T. Wagner, G.H. Hansen, O.F. Vik, and C. Zehner; Validation of ozone profiles derived from GOME measurements by means of ozonesonde and ozone lidar measurements, Hansen, G., A.F. Vik, and H. Eskes. IGAC conference, September 2002: How accurate can a tropospheric column density be retrieved? F. Boersma. GOA-MAPSCORE-ASSET Workshop, de Bilt, January 2003: Georg Hansen (NILU), A. F. Vik, H. Eskes, and R. van der A, Validation of GOME derived ozone profiles by means of ozonesonde and lidar, ; Hennie Kelder (KNMI), GOA: Advanced Ozone and NO 2 data sets from GOME ; Arjo Segers (KNMI), Assimilation of GOME ozone profiles in a 3D chemistry-transport model; Folkert Boersma (KNMI), The use of TM3 in deriving accurate estimates of GOME tropospheric NO 2 ; Steffen Beirle (Univ.Heidelberg), Global distribution, identification and quantification of sources of tropospheric NO 2 from GOME and SCIAMACHY; Ronald van der A (KNMI), GOME ozone profile retrieval; Dimitris Balis (LAP-AUTH), Validation of GOME total ozone using the WMO network; Henk Eskes (KNMI), A 7 year data set of assimilated and validated GOME ozone fields

77 Annex B 71 B Project meetings and visits GOA kick-off meeting at the KNMI, 5 March First progress meeting, Thessaloniki, 21 September Second progress meeting, Heidelberg, 7-8 March Working visit of the Heidelberg team to the KNMI, November The purpose of this visit was a critical discussion of all aspects of the NO 2 retrieval. Third progress meeting, Goteborg, Sweden, 4 September GOA-ASSET-MAPSCORE Workshop on Chemical Data Assimilation, KNMI, de Bilt, The Netherlands, January Travel expenses have been paid for two invited lecturers, namely Dr. David Lary and Dr. Heinrich Bovensmann. Final GOA meeting, KNMI, de Bilt, the Netherlands, 17 January Working visit of the UIO team to KNMI, to discuss further proceedings of the model intercomparison and validation, and the final report, de Bilt, 31 October Working visit of the KNMI team to the University of Heidelberg, to discuss the GOA NO 2 product and the final report, Heidelberg, 12 March 2003.

78 72 GOA Final Report April, 2003 C Publicity material A GOA folder has been distributed during the first months of the project. An updated folder is produced at the end of the project. A cd-rom is produced at the end of the project. This cd-rom contains the project web site, the final report and a selection of the GOA data sets. The cd-rom will be distributed to colleagues and users of the GOA data products.

79 Annex D 73 D Published material (include pre-prints or offprints) A, van der, R.J., R.F. van Oss, A.J.M. Piters, J.P.F. Fortuin, Y.J. Meijer and H. Kelder, Ozone profile retrieval from recalibrated Global Ozone Monitoring Experiment data, J. Geophys. Res., 107, /2001JD (2002). Balis D.S and R.D. Bojkov, Characteristics of the Antarctic-spring ozone decline from satellite and ground-based measurements from the appearance of the ozone hole up to December 2001, Proceedings of the 6th European Workshop on Stratospheric Ozone, Göteborg, Sweden, in press.. Beirle, S., presentation at the GOA workshop (available from the GOA web page). Beirle. S. J. Hollwedel, S. Kraus, T. Wagner, M. Wenig, W. Walburga Wilms-Grabe, U. Platt: Estimation of NO 2 emissions from lightning and biomass burning: A case study using tropospheric NO 2 -data deriverd from GOME. Air Pollution 2002, WIT Press, 10, , Beirle, S., U. Platt, M. Wenig, and T. Wagner, NO x production by lightning estimated with GOME, Adv. Space Res., submitted, Beirle, S., U. Platt, M. Wenig, and T. Wagner, Identification and quantification of sources of NO x using GOME and SCIAMACHY data, Geophysical Research Abstracts, European Geophysical Society 2003, 5, 14870, Beirle, S., U. Platt, M. Wenig, and T. Wagner, Highly resolved global distribution of tropospheric NO 2 using GOME and SCIAMACHY data, Geophysical Research Abstracts, European Geophysical Society 2003, 5, 14869, Boersma, K. F., H. J. Eskes, and E. Brinksma, Error analysis for tropospheric NO 2 retrievals, preprint Eckhardt, S., A. Stohl1, S. Beirle, N. Spichtinger, P. James, C. Forster, C. Junker, T. Wagner, U. Platt, and S. G. Jennings, The North Atlantic Oscillation controls air pollution transport to the Arctic, submitted to Atmos. Chem. Phys., Eskes, H. J., van Velthoven, P. F. J., Valks, P. J. M. and Kelder, H. M., Assimilation of GOME total ozone satellite observations in a three-dimensional tracer transport model, Q. J. R. Meteorol. Soc., Eskes, H. J., van Velthoven, P. F. J., Valks, P. J. M. and Kelder, H. M., Assimilation of GOME total ozone satellite observations in a three-dimensional tracer transport model, Q. J. R. Meteorol. Soc., Eskes, H. J., and K. F. Boersma, Averaging Kernels for DOAS Total-Column Satellite Retrievals, Atmos. Chem. Phys. Discuss., 3, 116, Gauss, M. et al., Radiative forcing in the 21st century due to ozone changes in the troposphere and the lower stratosphere, J. Geophys. Res., 108(0), XXXX, doi: /2002jd002624, Gauss, M., I. S. A. Isaksen, S. Wong, and W.-C. Wang, Impact of H 2 O emissions from cryoplanes and kerosene aircraft on the atmosphere, J.Geophys. Res., 108(0), XXXX, doi: /2002jd002623, Kelder, H.M., H.J. Eskes, G.Y. El Serafy, I. Isaksen, M. Gauss, C.S. Zerefos, D. Balis, U. Platt, M. Wenig, T. Wagner, G.H. Hansen, and C. Zehner, The GOA project, Proceedings of the EUMETSAT / Ozone SAF meeting in Halkidiki, Greece, May 2001.

80 74 GOA Final Report April, 2003 Kelder, H.M., H.J. Eskes, F. Boersma, I. Isaksen, M. Gauss, C.S. Zerefos, D. Balis, U. Platt, M. Wenig, T. Wagner, S. Beirle, G.H. Hansen, A.F. Vik, and C. Zehner, The GOA project, Proceedings of the EUMETSAT / Ozone SAF meeting in Halkidiki, Greece, May (This proceedings contribution is included as annex) Leue, C., M. Wenig, T. Wagner, O. Klimm, U. Platt, and B. Jähne, Quantitative analysis of NO x emissions from Global Ozone Monitoring Experiment satellite image sequences, J.Geophys.Res., 106, , Oss, R. F., and R. J. D. Spurr, Fast and accurate 4 and 6 stream linearized discrete ordinate radiative transfer models for ozone profile retrieval, J. Quant. Spectrosc. Radiat. Transfer, 75, , Spichtinger, N., M. Wenig, P. James, T. Wagner, U. Platt, A. Stohl, Satellite detection of a continentalscale plume of nitrogen oxides from boreal fires, Geophys. Res. Lett., 29, , Stohl, A., H. Huntrieser, A. Richter, S. Beirle, O. Cooper, S. Eckhardt, C. Forster, P. James, N. Spichtinger, M. Wenig, T. Wagner, J. Burrows, and U. Platt, Rapid intercontinental air pollution transport associated with a meteorological bomb, Atmos. Chem. Phys. Discuss., 3, , Valks, P.J.M., R.B.A. Koelemeijer, M. van Weele, P. van Velthoven, J.P.F. Fortuin, and H. Kelder, Variability in tropical tropospheric ozone: analysis with GOME observations and a global model, J. Geophys. Res., in press, Wenig, M., N. Spichtinger, A. Stohl, G. Held, S. Beirle, T. Wagner, B. Jähne, and U. Platt, Intercontinental transport of nitrogen oxide pollution plumes, Atmos. Chem. Phys., 3, , Wenig. M., S. Kühl, S. Beirle, T. Wagner, B. Jähne, and U. Platt, Retrieval and Analysis of Stratospheric NO 2 from GOME, submitted to J. Geophys. Res., 2003.

81 Annex E 75 E References Borrell, P., J.P. Burrows, U. Platt and C. Zehner, Determining tropospheric concentrations of trace gases from space, ESA Bulletin 107, August D. Bortoli, F. Ravegnani, G. Giovanelli and Iv. Kostadinov, Seasonal and diurnal variations of stratospheric nitrogen dioxide over Terra Nova Bay (Antarctica), Proceedings of the Quadrennial Ozone Symposium, p Sapporo July Burrows, J.P., M. Weber, M. Buchwitz, V. Rozanov, A. Ladstätter-Weibenmayer, A. Richter, R. Debeek, R. Hoogen, K. Bramstedt, K.-U. Eichmann, M. Eisinger, D. Perner, The Global Ozone Monitoring Experiment (GOME): Mission concept and first results, J. Atmos. Sciences, 56, (1999). European Space Agency (ed. J-C- Lambert). ERS-2 GOME GDP 3.0 Implementation and Delta Validation, Fortuin, J. P. F. and Kelder, H. M., An ozone climatology based on ozonesonde and satellite measurements, J. Geophys. Res., 103, (1998). Herman, J. R., and E.A. Celarier, Earth surface reflectivity climatology at nm from TOMS data, J. Geophys. Res., 102, (1997) Houweling, S., Dentener, F. and Lelieveld, J., The impact of non-methane hydrocarbon compounds on tropospheric photo-chemistry, J. Geophys. Res., 103, (1998) Koelemeijer, R.B.A., P. Stammes, J.W. Hovenier, and J.F. de Haan, A fast method for retrieval of cloud parameters using oxygen A-band measurements from Global Ozone Monitoring Experiment, J. Geophys. Res., 106, (2001) Koelemeijer, R. B. A., J. F. de Haan, and P. Stammes, A database of spectral surface reflectivity in the range nm derived from 5.5 years of GOME observations, J. Geophys. Res., 108, 4070, doi: /2002jd (2003) Leue, C., M. Wenig, B. Jaehne, and U. Platt, Quantitative observation of Biomass burning plumes from GOME, Earth observation quarterly, 58, Leue, C., Wenig, M., and Platt U., Retrieval of Tropospheric NO 2 Concentrations from Multispectral Image Sequences, Jaehne, B. and Haushacker, H. and Geisler, P., Handbook of Computer Vision an Applications, Academic Press, 3, Leue, C., Globale Bilanzierung der NO x -Emissionen aus GOME Satelliten-Bildfolgen, PhD thesis, University of Heidelberg, Martin, R.V., K. Chance, D.J. Jacob, T.P. Kurosu, R.J.D. Spurr, E. Bucsela, J.F. Gleason, P.I. Palmer, I. Bey, A.M. Fiori, Q. Li, and R.M. Yantosca, An improved retrieval of tropospheric nitrogen dioxide from GOME, J. Geophys. Res., 107, 4437 (2002) Richter, A. and T. Wagner, Diffuser Plate Spectral Structures and their Influence on GOME Slant Columns, Technical Note (pdf-file, 360 kb, January Richter, A., F. Wittrock, A. Ladstätter-Weißenmayer, and J.P. Burrows, GOME measurements of stratospheric and tropospheric BrO, Adv. Space Res., 29(11), , 2002.

82 76 GOA Final Report April, 2003 Richter, A., and J. P. Burrows, Tropospheric NO 2 from GOME measurements, Adv. Space Res., 29, (2002) Segers, A., P. van Velthoven, B. Bregman, and M. Krol, On the computation of mass fluxes for Eulerian transport models from spectral meteorological fields, preprint, 2002; Bregman, B., A. Segers, P. van velthoven, and M. Krol, On the use of mass-conserving wind fields in chemistry-transport models, preprint Stammes, P., Spectral radiance modelling in the UV-visible range, Proceedings IRS-2000: Current problems in atmospheric radiation, edited by W.L. Smith and Y.M. Timofeyev, pp , A. Deepak Publ., Hampton (2001). Tuinder, O., Global distribution of OH radical production in the troposphere based on satellite data, PhD thesis, Utrecht, Velders, G.J.M., C. Granier, R.W. Portmann, K. Pfeilsticker, M. enig, T. Wagner, U. Platt, A. Richter, and J.P. Burrows, Global tropospheric NO 2 column distributions: Comparing 3-D model calculations with GOME measurements, J.Geophys.Res., 106, (2001) Vermote, E., and D. tanré, Analytic expressions for radiative properties of planar Rayleigh scattering media, including polarization contributions, J. Quant. Spectrosc. Radiat. transfer, 47, , Wagner, T., A. Richter, C. v. Friedeburg, U. Platt, An Advanced Cloud Product for the Interpretation of Tropospheric Data from GOME and SCIAMACHY, Proceedings of the EUROTRAC-2 Symposium 2002, Garmisch-Partenkirchen, Germany, March 11 15, 2002, Transport and Chemical Transformation in the Troposphere, P. Midgley and M. Reuther (Eds.), Margraf Verlag, Weikersheim, Germany, Wagner, T., A. Richter, C. v. Friedeburg, M. Wenig, U. Platt, Case Studies for the Investigation of Cloud Sensitive Parameters as Measured by GOME, TROPOSAT final report, Springer, Heidelberg, Wagner, T., S. Beirle, C. v.friedeburg, J. Hollwedel, S. Kraus, M. Wenig, W. Wilms-Grabe, S. Kühl, U. Platt, Monitoring of trace gas emissions from space: tropospheric abundances of BrO, NO 2, H 2 CO, SO 2, H 2 O, O 2, and O 4 as measured by GOME, Air Pollution 2002, WIT Press, 10, (2002) Wagner, T., K. Chance, U. Frieß, M. Gil, F. Goutail, G. Hönninger, P.V. Johnston, K. Karlsen-Tørnkvist, I. Kostadinov, H. Leser, A. Petritoli, A. Richter, M. Van Roozendael, U. Platt, Correction of the Ring effect and I0-effect for DOAS observations of scattered sunlight, Proc. of the 1st DOAS Workshop, Heidelberg, 13., 14. Sept., Germany, Wenig, M., Satellite Measurement of Long-Term Global Tropospheric Trace Gas Distributions and Source Strengths, Algorithm and Data Analysis, PhD-thesis, University of Heidelberg, 2001

83 Annex F 77 F Definitions, acronyms, abbreviations 3D AMF CCD CFC CTM CTM2 DLR DOAS DU DUP ECMWF EGS ENVISAT EOS-AURA ERA-40 ERS ESA ESRIN EUMETSAT FRESCO GATO GDP GOA GOFAP GOME IPCC IUP KNMI LAP-AUTH LIDORT METOP MOZAIC NADIR NCEP NILU NMHC NO NO 2 OMI OPERA POET QA/QC RTM Three dimensional Air-mass factor (DOAS retrieval) Convective-cloud-differential techniquefor tropospheric ozone Chlorofluorocarbon Chemical Transport Model The OSLO CTM German Aerospace Center Differential Optical Absorption Spectroscopy Dobson Unit ESA Data User Programme European Centre for Medium-Range Weather Forecasts European Geophysical Society ESA s Environmental Earth observation satellite NASA Earth Observing System, Aura mission ECMWF 40-year reanalysis European Remote Sensing Satellite European Space Agency European Space Research Institute, Italy European Organisation for the Exploitation of Meteorological Satellites Fast Retrieval Scheme for Clouds from the Oxygen A-Band Global Atmospheric Observations GOME Data Processor GOME Assimilated and Validated Ozone and NO 2 Fields for Scientific Users and for Model Validation GOME Fast Delivery Service Global Ozone Monitoring Experiment Intergovernmental Panel on Climate Change Institut für Umweltphysik, University of Heidelberg, Germany Royal Netherlands Meteorological Institute Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki, Greece Linearized Discrete Ordinate Radiative Transfer model Meteorological Operational polar satellites of EUMETSAT Measurement of Ozone by airbus in-service Aircraft NILU s Atmospheric Database for Interactive Retrieval National Weather Service - National Centers for Environmental Prediction Norwegian Institute for Air Research Non-methane hydrocarbons Nitrogen monoxide Nitrogen dioxide Ozone Monitoring Instrument Ozone Profile Retrieval Algorithm Precursors of Ozone and their Effects in the Troposphere, EU project quality assurance/quality control Radiative Transfer Model

84 78 GOA Final Report April, 2003 SAF Satellite Application Facility SCIAMACHY SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY SESAME Second European Stratospheric Arctic and Mid-Latitude Experiment SCD Slant Column Density resulting from the NO 2 DOAS fit SOP Standard Operational Procedure SZA Solar Zenith Angle THESEO Third Stratospheric Experiment on Ozone TM3 Tracer Transport model, version 3; the KNMI Chemical Transport Model TM3DAM Ozone Data Assimilation Model based on the TM3 CTM TM3PAS TM3 Profile ASsimilation model TOMS Total Ozone Mapping Spectrometer TTOC Tropical tropospheric ozone columns TRADEOFF 5th framework EU project on aircraft emissions UIO University of Oslo, Norway UV Ultraviolet radiation VCD Vertical Column Density of NO 2 VOC Volatile Organic Compound WMO World Meteorological Organisation WP Work Package

85 Annex G 79 G Summary of the GOA project workshop Workshop on Chemical Data Assimilation January 2003 KNMI, de Bilt, The Netherlands Workshop summary On January 15-17, 2003 a workshop was held at the KNMI in De Bilt, the Netherlands. The central theme of the workshop is chemical data assimilation, with related sessions on satellite observations of atmospheric composition, atmospheric modelling, and the latest results of new satellite missions (e.g. Envisat) and the Vintersol campaign. The workshop was organised by the coordinators of the EU projects GOA, MAPSCORE and ASSET, namely Henk Eskes (GOA), William Lahoz (ASSET), Dominique Fonteyn and John Remedios (MAP- SCORE). The motivation for this workshop can be summarised as follows: To present the results of the GOA project to users of the GOME data sets that will be provided near the end of the project. All users mentioned in the User Requirements Document have been invited to the workshop, and several of them were present. Furthermore the GOA project results were presented to the community of scientists present at the workshop. The workshop was the first in a series of workshops of the ASSET project. It was the MAPSCORE project workshop on (stratospheric) chemical data assimilation. The workshop was intended to stimulate interaction between the different projects of the EU GATO project cluster (Global Atmospheric Observations). This workshop was part of a series investigating an atmospheric ozone GMES system. A written report on the results of the workshop is envisaged for the GMES-GATO network project. The workshop reports and discusses the latest results of the Envisat atmospheric chemistry instruments GOMOS, MIPAS and SCIAMACHY, and the EU VINTERSOL measurement campaign. The workshop summarised the latest developments in the field of chemical data assimilation. This research field is rapidly developing, and several new groups have joined this field. Since the SODA workshop on chemical data assimilation (December 1998) much progress has been made. Two fields can be distinguished where data assimilation is beginning to reach maturity: 1) the assimilation of satellite observations of ozone in numerical weather prediction models, and 2) the assimilation of research satellite observations of atmospheric tracers and aerosols in sophisticated chemistry-transport models. A new development is the preparations that are made for operational chemical forecasts (or chemical weather ).

86 80 GOA Final Report April, 2003 The workshop programme contains the following sessions: Session on European projects Session on Chemical Data Assimilation Session on Satellite and Ground-based Observations Session on Atmospheric Modelling Session on Results of new missions and measurement campaigns The full workshop programme and electronic versions of the presentations in Adobe Acrobat format are available on the GOA web site,

87 H Conference proceedings contribution, Ozone symposium, Goteborg, 2002 THE GOA PROJECT H.M. Kelder 1, H.J. Eskes 1, F. Boersma 1, I. Isaksen 2, M. Gauss 2, C.S. Zerefos 3, D. Balis 3, U. Platt 4, M. Wenig 4, T. Wagner, S. Beirle 4, G.H. Hansen 5, A.F. Vik 5, and C. Zehner 6 (1) Royal Netherlands Meteorological Institute, P.O.Box 201, 3730 AE De Bilt, The Netherlands (2) Department of Geophysics, University of Oslo, P.O.Box 1022, Blindern, N-0315 Oslo, Norway (3) Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (AUTH), Greece (4) Institut für Umweltphysik, University of Heidelberg, Im Neuenheimerfeld 366, D Heidelberg, Germany (5) Norwegian Institute for Air Research, The Polar Environmental Centre, N-9296 Tromsö, Norway (6) European Space Agency, ESA-ESRIN, Via Galileo Galilei, Frascati, Italy ABSTRACT GOA, GOME Assimilated and Validated Ozone and NO 2 Fields for Scientific Users and for Model Validation, is a two-year European Commission Fifth Framework project (Feb 2001-Feb 2003). The aim of the GOA project is to reanalyze five years of GOME data and to provide high-level GOME data sets to the scientific community. The project will focus on ozone and NO 2. A seven-year ( ) assimilated total ozone data base will be generated, together with an extensive validation data set for quantitative quality estimates. Ozone profile information will be generated, assimilated and validated for the period of one year. A new combined retrieval-assimilation approach will be developed for NO 2 and the different aspects of tropospheric column retrieval will be re-examined. A multi-year NO 2 data set will be provided. The assimilation model and GOME data will be confronted with output from global chemistry-transport models to improve their modeling capability of current and future changes of ozone and chemically active greenhouse gases. 1. INTRODUCTION Ozone and NO 2 are key species determining the chemistry of both the lower and middle atmosphere. Data sets of these compounds are crucial to validate the performance of chemistry-transport models (CTM s), and the analysis and prediction of anthropogenic changes in the chemical composition of the atmosphere of gases of particular importance for stratospheric ozone depletion and the oxidation capacity of the atmosphere, affecting chemically active greenhouse gases. Despite the central role of these compounds in atmospheric chemistry, only a limited amount of observational data is available for the troposphere at this moment. The available data are restricted in space and time (e.g. aircraft measurement campaigns, balloons), do not have a global coverage (ground based observations restricted to a couple of stations worldwide) or are difficult to interpret (surface observations).

88 82 GOA Final Report April, 2003 Mean Tropospheric NO IUP Heidelberg heidelberg.de/ Vertical Column Density [10 15 Molecules/cm 2 ] Figure H.1: Six-years mean of tropospheric NO 2 measured with GOME. The troposphere is estimated using a reference sector over the Pacific. No further cloud / air mass factor correction is applied. The industrialized regions of the world can clearly be seen. The large spectral range in combination with the nadir viewing geometry of the Global Ozone Monitoring Experiment (GOME) spectrometer allow for the retrieval of two unique products, namely (a) vertical profiles of ozone, with explicit tropospheric information, and (b) NO 2 total columns, including the tropospheric column. Deriving reliable quantitative tropospheric information from GOME, and future instruments like Sciamachy on Envisat and OMI on EOS-Chem, is a major challenge for the future and will be investigated in detail in the GOA project. The GOA project will provide long-term series of high-level assimilated GOME ozone and NO 2 data to scientific users. Detailed quality estimates and validation data sets of the products will be produced, based on an extended set of ground based and ozone sonde observations, and the error statistics obtained from the data assimilation. The data sets and the assimilation model will be compared with an independent tropospheric chemistry transport model to discover model shortcomings. 2. INNOVATION The GOA proposal is innovative on several aspects: Chemical data assimilation: The GOA proposal will use data assimilation techniques to produce long term quality controlled assimilated three-dimensional ozone and NO 2 fields based on the GOME observations. Such long term consistent data sets are not available at this moment. This will be an important step towards a unified description of the chemical state of the atmosphere. New approach to the retrieval of NO 2 : The GOME NO 2 data shows pronounced features which are clearly related to tropospheric pollution by the industrialized countries and by biomass burning (see figure H.1). This shows that GOME is well

89 Annex H Estimate of Ozone Hole Area in Southern Hemisphere -- FD Assimilated Ozone 1999 FD area 2000 FD area 2001 FD area 2002 FD area million square km Aug 01 Sep 01 Oct 01 Nov 01 Dec 31 Dec Figure H.2: Spectacular breakup of the 2002 ozone hole, as observed by GOME. Left: Ozone distribution on 25 September. Right: The ozone hole area in 1999, 2000, 2001 and 2002, showing the anomalous ozone hole development in capable of observing NO x close to the surface. However, quantitative estimates of NO 2 are complicated by the presence of the stratospheric background, sensitivity to vertical profile, clouds and surface albedo. With GOA a new algorithm for NO 2 retrieval will be developed, tested and used to provide more quantitative estimates of the NO 2 amounts in the troposphere and stratosphere. This approach uses a state-of-the-art chemistry transport model to generate the best available estimate of the vertical distribution which is subsequently used for a realistic retrieval. Estimates of tropospheric ozone from space: There is a strong demand for global data sets on tropospheric ozone. GOME is unique in the sense that ozone profile information can be retrieved, including information on the lower stratosphere and troposphere. The GOA project will combine GOME ozone profiles and columns, and will assimilate these simultaneously using a transport model. As a result improved estimates of the tropospheric contribution to the ozone column will be derived. Umkehr 99 improvement: The new algorithm for Umkehr (99 code) will be used to extend the ground-based retrieval of ozone profiles, especially at high altitudes where ozonesondes are becoming less accurate or at altitudes beyond balloon burst. Inclusion of Umkehr profiles extends the ground-based sample not only in the vertical, but also in space and time, by significantly increasing the ground-based stations and number of observations. Ozonesonde post flight analysis and homogenization: Recent developments in the understanding of parameters effecting the accuracy of ozone sonde profiles will be applied to all available ozone sonde measurements for the GOME period. These QC routines will particularly focus on the sonde s electrochemical cell time response, the sonde s internal temperature measurement ( box temperature ), and general telemetry issues. Comparisons between CTMs and GOME data:

90 84 GOA Final Report April, 2003 Figure H.3: Total tropospheric NO 2 column, January 1997, monthly mean. Left: KNMI TM3 chemistrytransport model. Right: Oslo CTM2 chemistry-transport model. Scale: molecules cm 2 The new data sets generated by GOME will provide critical tests for CTMs, and will initiate adjustments and improvements of their modeling capacity, and can lead to a better exploitation of chemical data in environmental studies (ozone depletion, greenhouse gases). Such data sets will lead to better estimates of natural and anthropogenic emissions. The data sets are also important input for the assessment of the impact of UV on human health and agriculture. This will lead to more realistic predictions of the influence of chemical pollution and greenhouse gases on the future climate. 3. OBJECTIVES AND DELIVERABLES The GOA objectives are: To generate a 7-year data set of total ozone fields (level-4 products) based on the measurements (available level-2 data) of the GOME spectrometer on board of the ESA ERS-2 satellite. To validate these ozone fields with an extensive set of independent ground based and satellite observations. Improve and monitor the quality of the ground based observations. To provide these fields to the scientific community through the GOA web site. To estimate the tropospheric ozone content by using total column ozone data and ozone profile retrievals for GOME in a single assimilation. To improve the GOME NO 2 product by using position and time dependent model-predicted profiles of NO 2 for the determination of the air-mass factor in the DOAS retrieval of NO 2. To validate this set of NO 2 fields with independent ground based and satellite observations. To provide assimilated NO 2 (NO x ) fields to the scientific community. To estimate the tropospheric NO 2 column based on the assimilation and by exploiting the differences in spatial distribution of stratospheric and tropospheric NO x. Comparison with model results. To use this extensive combined data set of ozone and NO 2 to validate the performance of chemistrytransport models concerning the modeling of the oxidation capacity, affecting chemically-active green-

91 Annex H 85 Latitude (GOME-GROUND)/GOME - KNMI fast delivery Month Figure H.4: First validation results. Left: Comparison between the modelled ozone profiles with TM3 and the ALOMAR lidar, Nov Dec 2000 (red line: climatology). Right: Contour plots of the monthly percent differences between the KNMI fast delivery GOME total ozone retrieval and groundbased Brewer-Dobson total ozone observations. house gases, and the modeling of the seasonal and year to year variation in stratospheric ozone. To identify NO x emission source strengths, by performing model studies and compare with the GOME NO 2 observations. To compare model and GOME data with the Measurement of Ozone by airbus in-service Aircraft (MOZAIC) data base. GOA documentation The GOA web site: GOA folder. GOA Description of Work, KNMI, De Bilt, the Netherlands, June 15, GOA First Annual Report, KNMI, De Bilt, the Netherlands, March 2002.

92 86 GOA Final Report April, 2003 I Total ozone validation report Validation of assimilated total ozone fields D. Balis and C. Zerefos Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (Greece) balis@auth.gr Last update: April 2003 This document summarises the main validation results for the GOME total ozone retrievals and assimilated ozone fields. This text is also available as a web page that is part of the documentation of the data products on the GOA web site. Summary The characteristics of the observed differences between assimilated GOME and ground based total ozone data can be summarized as follows: Assimilated GOME total ozone data using GDP3.0 are consistent with the level-2 data and reveal similar characteristics concerning their seasonal and latitude dependence. The assimilated total ozone data show a 1% bias relative to the level-2 data almost at all latitudes. There are differences in the latitude dependence of the assimilated ozone data that depend on the wind fields used only over the tropics. The use of ERA-40 fields yields similar results with the level-2 data, while the use of ECMWF operational winds introduces in the tropics changes of ±2% Both level-2 and assimilated ozone data underestimate ozone additionally by 2-3% over stations that are close to deserts. The large differences in the high latitudes of the Southern Hemisphere are attributed in the accuracy of the ground-based ozone data used as a reference (Marambio and Arrival Heights) GOME GDP3.0: Overview of the ESA delta validation campaign The assimilated total ozone fields produced by KNMI in the frame of the GOA project are based on the new GDP3.0 version of GOME total ozone data that became available in late The GDP3.0 total ozone product has been validated by IASB, NASA and LAP-AUTH in the frame of ESA s delta validation campaign held in early 2002 using 2000 orbits specially selected for this purpose. Comparison have been performed between GOME total ozone data and observations form the NDSC and WMO-GAW network of ground-based stations as well as with ozone data from TOMS. The results of this campaign are described in detail in the final report issued by ESA (2002). The key finding of this campaign can be summarized as follows:

93 Annex I 87 Figure I.1: Spatial distribution of all WMO total ozone observing stations. GOME gives a consistent global picture of the total ozone field and results in temporal and spatial structures similar to those from other sensors. The validation studies do not reveal any long-term drift of quality. The agreement of GDP level-2 total ozone data product with the other sources of ozone data varies with both latitude and season. At Northern middle latitudes, the average agreement is within ±2-3%. At higher latitudes, a solar zenith angle (SZA) dependent difference appears. In addition a dependence of the GDP data product on the ozone column values has been identified. The average deviation of GOME from ground-based data does not exceed ±2-4% for SZA below 70. At lower sun elevation, the average error ranges from -8% to +5% depending on the season. Lowest total ozone values are overestimated by GOME by 5-10% during ozone hole conditions. Compared to GDP 2.7, GDP 3.0 includes a new determination of effective absorption temperature derived by spectral analysis, better atmospheric databases, and AMFs determined iteratively using a neural network trained on column- and latitude-classified atmospheric profiles and measurement parameters. All upgrades result in a reduction by about 30-50% of the amplitude of the GOME total ozone dependence on the SZA, the latitude, the season, and the ozone column amount. The remaining dependence is attributed to the limited treatment of the atmospheric profile shape effect in GDP and to the partial unsuitability of the particular spectral analysis when the atmosphere becomes optically thick. GDP 3.0 AMFs are determined using column- and latitude-classified atmospheric profiles, which therefore may differ from the actual, highly variable atmospheric profile shape. Under very occasional circumstances of high ozone values (> 500 DU) at low sun elevation (SZA > 85 ), the iterative AMF algorithm might not converge to a realistic AMF value, leading to unrealistically high ozone values (e.g. 700 DU) that could be identified and filtered out easily. WMO Global Atmosphere Watch ozone stations In the frame of GOA project the assimilated total ozone fields for the period have been validated by comparison with total ozone data archived at World Ozone Data Center, in Toronto, Canada.

94 88 GOA Final Report April, 2003 The ground based data records were based on the WMO Global Atmosphere Watch (GAW) which include total ozone data sets obtained by Dobson, Brewer spectrometers and filter ozonometers (M-124) from all available observations from about 130 observing stations during the period from July 1995 to December The spatial distribution of these stations is shown in figure 1. These data records are available from the World Ozone and Ultraviolet Data Center in Toronto, which is operated by Environment Canada as part of the World Ozone Meteorological Organization (WMO) Global Atmosphere Watch programme. According to the currently available data submission format there is one total ozone measurement per day expressed in Dobson Units (DU), referred to either direct sun, zenith sky or other type of observation. Quality of the ground-based measurements The error of individual total ozone measurements for well maintained Dobson and Brewer measurements is 0.3-1% under conditions of high sun, clear sky and low ozone, 2%-3% at high and moderate sun elevation (> 20 and 5%-7% at lower sun elevation and in polar winter. (e.g. Lambert et. al., 1999, Fioletov et. al. 1999). The respective errors for well calibrated and well-kept filter ozonometer (M-124) are about 3% for direct sun and 5% for zenith sky observations [e.g. Bojkov and Fioletov 1994]. Despite the similar performance between the Brewer and Dobson stations, small differences within ±0.6% are introduced due to the use of different wavelengths and a different temperature depedence for the ozone absorption coefficients [Kerr et. al. 1988]. The temperature sensitivity of the ozone absorption cross sections in the Huggins bands affects the performance of the Dobson and Brewer instruments. In particular, the atmospheric temperature seasonal changes is followed by a seasonal variation of the Dobson or Brewer ozone data, where the contribution of the systematic offset is less than 1% [Rooozendael et. al., 1998]. On the other hand, the performance of the filter ozonometer instruments, which consist almost one third of the WMO stations is less accurate than that of Dobson and Brewer instruments. This is due to the fact that the operation of filter ozonometer is totally different from Dobson and Brewer spectrophotometers in combination with their different geographical distribution, as filter ozonometers are mainly distributed over the former USSR region. In particular M-83 and M-124 filter ozonometers exhibit higher standard deviations than Dobson and Brewer instruments when compared to spaceborne instrument measurements [ Fioletov et. al. 1999]. Validation of the assimilated total ozone fields: approach As a comparison measure between assimilated GOME and ground based total ozone data, we calculated the monthly mean values of the daily percent differences of (GOME - ground/gome) for the total of 142 stations. As an indicator of the relative variability we calculated the standard deviations of the percent differences as well as the rms differences. As it is shown in figure I.1 the ground stations of the WMO network do not present a picture of a uniform distribution over the globe. From the total of 142 ground stations used the 122 stations are located in the Northern Hemisphere, while only the 20 ground stations are located in the Southern Hemisphere and thus the non-uniform geographical distribution of the ground based stations introduces certain limitations in our ability to examine the spatial distribution of the differences between GOME and ground stations for certain conditions (e.g over the oceans).

95 Annex I 89 Validation of the assimilated total ozone fields: results In figure I.2 there are shown the mean monthly differences between the two GOME data sets (assimilated and GDP3.0) and the corresponding Dobson and Brewer ground based total ozone measurements for the Northern and the Southern Hemisphere. The level-2 data shown correspond only to the 2000 validation orbits, while the assimilated data correspond to all processed orbits. The percent differences between the GOME level-2 and the Dobson-Brewer data series ranges from -3.5 (in autumn) to almost 0 (in spring), for the Northern Hemisphere, while for the Southern hemisphere mostly due to fewer ground-based observations the validation results of the level-2 data have more noise but however they indicate similar amplitude of the observed seasonality. It has to be mentioned here that this amplitude is reduced by almost 50% relative to the differences found between GDP2.7 and ground observations and are consistent with the ones presented in the GDP3.0 validation report and also by Bramstedt et al This seasonal behavior is similar in phase and amplitude also when comparing the assimilated GDP3.0 total ozone data with ground-based observations. There is an evidence for an additional negative bias of less than 1% in the assimilated data, which however can be also attributed to the fact that different samples are compared. As is shown in figure I.2 there is no evidence for a trend in the differences found between GOME and ground observations in the Northern Hemisphere. Concerning the situation in the Southern Hemisphere there is no trend obvious till the end of 2000 and there is an indication for an increase thereafter. However, since less ground-based stations have updated time series till the end of 2001, this indication must be considered as a preliminary result. The seasonal dependence is not uniform with latitude. In figure I.3 we present contour plots of the monthly percent differences between satellite and ground based observations of total ozone for 10 latitude belts, both for GDP3.0 level-2 data and assimilated data. The GDP3.0 plot is based only on the 2000 validation orbits, while the plot that corresponds to the assimilated ozone is based on all processed orbits. The isopleths present the mean values of the monthly percent differences of GOME from all ground stations that belong in a certain latitudinal belt and for a given month throughout the examined time period. Both datasets reveal similar characteristics concerning the latitudinal dependence of the seasonality in the GOME ozone data. However there are some differences in the 20 S-20 N belt (especially in the SH) where the assimilated data are 1% lower than the level-2 data. The assimilated ozone data utilized two distinct analyses for the wind fields applied. For the period the ERA-40 reanalysis wind field data have been used, while for the post 1999 period the operational ECMWF fields were applied. As it can be seen in the upper panel of figure I.4, the used of different wind fields results to consistent biases and variability versus latitude between themselves and level-2 data, except for the belt 20 S-20 N. The use of the operational ECMWF wind fields introduces less bias (close to 0%) in the 0-10 N belt and more negative bias (close to 5%) in the 0-10 S belt. This fact indicates that the choice of the wind fields that are used for the assimilation is crucial for the homogeneity in time of the assimilated ozone data. The large differences found in the high latitudes of the SH are mostly due calibration uncertainties in the records of certain ground based stations (Arrival Heights and Marambio). The low biases found in all datasets at 30 N correspond to ground-based stations that are close or in desert areas and therefore indicate possible problems in how GDP3.0 treats desert aerosols.

96 90 GOA Final Report April, 2003 Figure I.2: Monthly percent differences between GOME and ground based total ozone measurements for both hemispheres. Solid lines correspond to the assimilated data, dashed lines to level-2 data.

97 Annex I 91 Figure I.3: Month-latitude cross sections of the percent differences between GOME and ground based total ozone (upper panel) for the assimilated ozone data and (lower panel) for the level-2 data (validation orbits only).

98 92 GOA Final Report April, 2003 Figure I.4: Percent differences between GOME and ground based total ozone (upper panel) and rms differences (in DU) (lower panel) as a function of latitude for the datasets indicated.

99 Annex I 93 In the lower panel of figure I.4 there are shown the rms difference between GOME and ground observation of total ozone. The level-2 data show small rms differences in the tropics (about 5 DU) and about 15 DU in the middle and high latitudes. The corresponding rms differences for the assimilated data are similar (although slightly higher) for the middle and high latitudes but are much higher in the 30 S-30 N belt where they reach 15 DU, similar to the values found for the middle latitudes. The rms differences of the assimilated data when using the operational wind fields are slightly lower than the ones that correspond to ERA-40, except for the 0-10 S belt. The high rms values for the arctic are mostly due to limited amount of ground-based data that were used for comparison. References Bojkov R.D., V.E. Fioletov and A.M. Shalamjansky, 1994, Total ozone changes over Eurasia since 1973 based on reevaluated filter ozonemeter data, J. Geophys. Res., 99, 11, Bramstedt et al., Comparison of total ozone from the sattelite instruments GOME and TOMS with measurements from the Dobson network , Atmos. Chem. Phys. Discuss., , 2002 European Space Agency (ed. J-C- Lambert). ERS-2 GOME GDP 3.0 Implementation and Delta Validation, 2002 Fioletov V., J. Kerr, E. Hare, G. Labow, R. McPeters, An assessment of the world ground-based total ozone network performance from the comparison with satellite data, J. Geophys. Res., Lambert et al., 1999, Investigation of pole-to-pole performances of spaceborne atmospheric chemistry sensors with NDSC, J. Atmos. Sci., 56,

100 94 GOA Final Report April, 2003 J Documentation on the total ozone assimilation approach TM3DAM: Assimilated ozone fields based on GOME data Henk Eskes Royal Netherlands Meteorological Institute (KNMI), P.O.Box 201, 3730 AE De Bilt, The Netherlands Tel: , Fax: , eskes@knmi.nl Last update: March 2003 This document provides an introduction to the GOME ozone column data assimilation approach. It is originally a web page that is part of the description of the data products on the GOA web site. What is data assimilation? Data assimilation is a general technique to combine measurements with a model that describe the time evolution of the system. Assimilation improves the model state based on the information contained in the observations. It provides the most probable state of the system, given the uncertainties in the observations and estimated errors of the model forecast. Why data assimilation? There are several motives for using data assimilation in the study of atmospheric chemisty in general and for GOME data in particular: To extend the use of GOME data. Data assimilation converts a sequential list of satellite observations into global maps of ozone, filling the gaps in between the observations. See figure J.1. These global maps are easy to use for intercomparisons with other observations and with modelled ozone distributions. To generate a complete data base of three dimensional daily ozone distributions. To obtain information about the accuracy of the observations, by confronting these observations with the model forecasts, and by comparing the assimilated products with other observations. To obtain information about the performance of the various aspects in the model and possible problems, through the continuous confrontation of the model with new measurements. To provide ozone forecasts [Eskes, 2002]. These provide a basis for UV radiation forecasts and are useful, for instance, for the planning of atmospheric measurement campaigns.

101 Annex J 95 Figure J.1: Top: a collection of 24 hours of GOME data, 30 Nov Bottom: the assimilated ozone field at 12 GMT, based on the GOME data. Apart from the ozone hole at the South pole (blue), very low ozone values are observed above the North Sea. Analysis based on the model shows that the low ozone values are mainly caused by an exceptional atmospheric flow and a transport of ozone-poor air from the subtropics to higher latitudes. The event lasted only a few days, and after that the ozone layer in the Northern Hemisphere returned to normal values. Ozone columns in Dobson units. The TM3DAM model: introduction The model provides an approximate description of the evolution of the three-dimensional distribution of ozone in the atmosphere. This evolution is determined by transport and chemistry. Ozone behaves as a tracer and is transported by the horizontal and vertical winds in the atmosphere. Ozone is also a reactive chemical, and the concentration of ozone is determined by the concentrations of other chemicals, emissions and deposition at the surface and the UV radiation from the Sun. The paper of Eskes et al. [2003] provides a more detailed discussion of the model, assimilation approach and references to related work. The model: transport The model calculates the horizontal and vertical transport of ozone masses. It is driven by the meteorological fields (wind, surface pressure, temperature) from the European Centre for Medium-Range Weather Forecasts (ECMWF). These fields are updated every 6 hours. The model divides the atmosphere in 120 longitude by 90 latitude gridboxes and 44 vertical layers from the surface to 0.1 hpa (the mesosphere). The vertical layers are identical to the ECMWF layers in the stratosphere (where the ozone layer is situated). In the trosphere the amount of layers is reduced with respect to the ECMWF model. The numerical implementation of the transport is based on the second moments advection scheme developed by M. Prather. The ozone distributions are provided on a 1.5 by 1 degree longitude-latitude grid.

102 96 GOA Final Report April, 2003 Figure J.2: Estimated forecast error distribution at 18 GMT, 21 June Scale in Dobson units. Three aspects of the Kalman filter are demonstrated by the figure: 1) new GOME observations lead to a reduction of the uncertainty at the swaths. 2) The forecast error increases with time, from left to right in the figure. 3) The forecast error is transported by the wind field, leading to irregular shapes of the swaths. The uncertainty is high at the South pole due to the lack of GOME observations in June. The model: chemistry Stratospheric ozone chemistry is described by two parametrisations, one for gas-phase chemistry and one for heterogeneous ozone breakdown. These simplified schemes are fast: a more complete treatment of the chemistry would require the explicit modelling of in the oder of 50 chemical species, which is too computationally expensive for our purpose. The gas-phase production and loss of ozone in the stratosphere is described by the chemistry parametrisation developed at Météo France. The implementation is discussed in Eskes [2003]. Ozone breakdown as observed in the antarctic and arctic ozone holes are initiated by heterogeneous chemical processes that take place on liquid and solid cloud particles in the stratosphere, in the presence of polar stratospheric clouds. The heterogeneous chemistry is modelled by a simple scheme that was developed by the Centre for Atmospheric Science, Cambridge University. This scheme introduces an additional 3D tracer field which describes the degree of chlorine activation as a result of the psc formation. In the presence of activation ozone is depleted. GOME observations The GOME instrument measures the sunlight reflected from the Earth s atmosphere and surface in the spectral range nm (UV-visible) [Burrows, 1999]. GOME is part of the ESA ERS-2 polar orbiting satellite. TM3DAM assimilates vertical columns of ozone which are retrieved from the GOME spectra by the fast delivery service at the KNMI [Valks, 2003], or the GOME products of the European Space Agency, provided by the German Aerospace Center [Spurr, 2002]. The pixel size of GOME is 40 by 320 km, and the swath width is 960 km, see J.1. With this swath width GOME obtains global coverage in three days.

103 Annex J 97 Figure J.3: GOME observation minus model forecast statistics for March Solid curve: root-meansquare difference between the GOME ozone column and the model forecast. Dashed curve: average difference or bias between GOME and the forecast. The bias is smaller than 3 DU for all latitudes. Vertical scale in Dobson units. Assimilation scheme The assimilation scheme in TM3DAM is based on the Kalman filter assimilation approach, but the scheme avoids the extreme time and memory consuming aspects of the Kalman filter by fixing the correlations between the model forecast errors [Eskes, 2003]. This correlation is described by a timeindependent function of the distance between the two points, and the shape and correlation length are determined from the forecast performance statistics. The variance (diagonal of the forecast error covariance matrix), however, depends on space and time and the evolution of the variance is described by the Kalman filter equations. As a result the scheme produces a detailed error estimate of the analysed ozone fields (see figure J.2). Forecast skill Fig. J.1 below shows the difference between the GOME total ozone observation and the corresponding model forecast for the ozone column at the same position and time (o-f). The figure shows both the bias and the root-mean-square (rms) difference of (o-f) as a function of the latitude, averaged over all observations in March The rms difference is the sum of three contributions, namely the model forecast error, the observation error (noise) and the representativeness error (mismatch due to the difference between the model grid cells and the field of view of the satellite). The bias between the observations and forecast is smaller than 1 % for all latitudes. Note that the rms is largest for northern midlatitudes. This is related to the large variability of ozone in the Northern hemisphere in winter. A remark about biases In general all observations have biases, or systematic offsets from the truth. Typical differences between the fast-delivery GOME total ozone product and ground-based stations are of the order of 3-5 percent,

104 98 GOA Final Report April, 2003 Figure J.4: A comparison between a GOME assimilated ozone field (left) and independent TOMS observations for the same day (right, taken from the TOMS home page of NASA). To minimise the mismatch in time, the assimilated field is shown at 12 o clock local time. Note that the small scale structures in both plots are very similar. The large-scale differences in ozone values reflect the differences in the instruments, instrument calibration and the retrieval algorithms of total ozone. Date: 31 March Scale in Dobson units. depending on latitude, season and solar zenith angle. In numerical weather prediction models it is common practice to apply a bias correction to observations before the assimilation. In TM3DAM no such bias correction is applied. Through the assimilation the model adopts the ozone level as given by the observations, and in this sense the assimilated fields are higher-level GOME products. This is demonstrated in Fig. J.3 (and figure J.1) which shows that the relative bias between the model and GOME is very small. Given the small bias, the assimilated fields can be compared with independent observations to study the differences with either other satellite instruments or ground-based observations. An example of this is shown in figure J.4. References: Burrows, J.P., M. Weber, M. Buchwitz, V. Rozanov, A. Ladstätter-Weißenmayer, A. Richter, R. Debeek, R. Hoogen, K. Bramstedt, K.-U. Eichmann, M. Eisinger, D. Perner, The Global Ozone Monitoring Experiment (GOME): Mission concept and first results, J. Atmos. Sciences, 56, , R. Spurr, W. Thomas, D. Loyola, GOME Level 1 to 2 Algorithms Description, Technical Note ER- TN-DLR-GO-0025, Deutsches Zentrum für Luft und Raumfahrt, Oberpfaffenhofen, Germany, July 31, Eskes, H.J., P. F. J. van Velthoven, and H. M. Kelder, Global ozone forecasting based on ERS-2 GOME observations, Atmos. Chem. Phys., 2, , 2002 Eskes, H. J., van Velthoven, P. F. J., Valks, P. J. M. and Kelder, H. M., Assimilation of GOME total ozone satellite observations in a three-dimensional tracer transport model, Quarterly Journal of the Royal Meteorological Society, in press, 2003.

105 Annex J 99 GOME fast delivery service, KNMI: fd/. Valks, P. J. M., Piters, A. J. M., Lambert, J. C., and Zehner, C, and Kelder, H., A fast delivery system for the retrieval of near-real time ozone columns from GOME data, International Journal of Remote Sensing, 24, , 2003.

106 100 GOA Final Report April, 2003 K Documentation on the ozone profile assimilation approach The TM3 Profile ASsimilation : TM3PAS Arjo Segers Royal Netherlands Meteorological Institute (KNMI), P.O.Box 201, 3730 AE De Bilt, The Netherlands Tel: , Fax: , Arjo.Segers@knmi.nl Last update: March 2003 Introduction The TM3 Profile ASsimilation model is designed to assimilate ozone profiles measured by satellite instruments into an ozone version of the transport model TM3. The system is based on the existing TM3DAM scheme for assimilation of total ozone columns. For the GOA project, a one-year set of GOME ozone profiles has been assimilated with TM3PAS. First, a description of the profile product is given. This is followed by a description of the assimilation model. The GOME ozone profile product The GOME ozone product provided by ESA consist of total ozone columns only. Recently, the first sets of high quality ozone profiles from the KNMI OPERA retrieval have become available. For the GOA project, a one-year set of ozone profiles retrieved from the GOME instrument has been provided by KNMI. The profiles have been retrieved using the OPERA algorithm (see section 4.3.5). The input to the algorithm consists of radiances collected by GOME. Retrieval of profile information from radiances is very sensitive to noise in the measured spectra, and therefore the OPERA algorithm has been applied to spectra measured over time intervals of 12 seconds. As a consequence, the footprint for which the ozone profile is valid is larger than the footprints of total column products, and the number of profiles that can be retrieved from an orbit is less than the number of total columns. In addition, since the retrieval of profiles is computationally expensive, only one of every three available GOME spectra has been processed for use in the GOA project. The available number of ozone profiles is therefore limited to about per satellite orbit (figure K.1). The profile product consists of a number of elements: a retrieved profile y r defined on 40 vertical layers, an a-priori profile y a, a averaging kernel matrix A, and an error covariance S. The product is related to the true ozone profile y t according to: y r y a = A(y t y a ) + ɛ, ɛ N (o, S) (K.1) The equation reflects that the OPERA algorithm retrieves profiles as the departure from an a-priori profile, which, in this case, is set to the ozone climatology of Fortuin and Kelder [Fortuin, 1998]. The averaging

107 Annex K 101 Figure K.1: Example of footprints of GOME ozone profiles available during a single day. kernel accounts for the fact that the satellite is less sensitive for fast fluctuations in ozone in the vertical, and for ozone in the lower atmosphere. As a result, the retrieved profile is a smoothed version of the true profile; although the retrieved profiles are defined on 40 model levels, the number of independent quantities that can be retrieved is limited to about 5 or 6. Profile assimilation The existing assimilation scheme for total ozone columns, TM3DAM [Eskes, 2003], formed the basis for a new assimilation scheme: TM3PAS, the TM3 Profile Assimilation. The two assimilation models use the same ozone tracer model (TM3), but differ in the actual assimilation parts. For an introduction to the assimilation we refer to section and the annex; what follows is a description of the profile-specific aspects. Observation operator The observation operator projects a TM3 model state on a measurement, here a retrieved profile. The function h(x) is defined to simulate the value of a retrieved profile given the model state: h(x) = V [ y a + A (B C x y a ) ] (K.2) = V [ y a A y a ] + Hx (K.3) The linear projection H = h/ x interpolates the model state x to the vertical layers defined for a pixel: Hx = VA B C x (K.4) The operator C interpolates the ozone concentrations in x to represent the average over the footprint of the considered pixel, for each of the 44 model layers. This is performed by first dividing the footprint in

108 102 GOA Final Report April, 2003 TM3 grid Figure K.2: Projection of TM3 grid cells (regular grid) to the footprint of a GOME pixel. a number of almost square parts (figure K.2), interpolating the four surrounding grid cells to the centre of each square (bilinear interpolation), and finally taking the average value over all parts. Operator B performs the vertical interpolation, projecting the 44 levels in the model on the 40 layers of the GOME profile. Matrix A is the averaging kernel from the profile product. Finally, an operator V is applied which limits the number of vertical layers from 40 to 20 by taking the averages over pairs of layers. The only reason for limiting the number of data points is to decrease the computation time, which grow quadratic with the number of data points. However, since the total degree of freedom in a profile is limited to about 5 or 6, a number of 20 data points per profile is still able to express most of the information in the product. The result of the function h(x) should be compared with a retrieved profile ỹ r that is also averaged over the layers: ỹ r = V y r (K.5) For each profile i retrieved from GOME, a different function h i (x) and operator H i could be defined. To simplify notations, the index are omitted however, and we adopt the convention that profiles y or operators h and H etc. represent data or operations on a large number of pixels, for example all pixels in an orbit. Covariance model The second difference between TM3DAM and TM3PAS concerns the covariance model. In TM3DAM, the covariance matrix defines correlations between 2D fields of total ozone; adjustments of the state toward the measurements are distributed in the vertical relative to the ozone profile in the state. To fully explore the information in the retrieved ozone profiles, TM3PAS contains a full 3D covariance matrix. The covariance between grid cells (i 1, j 1, l 1 ) and (i 2, j 2, l 2 ) is parametrized according to: p(i 1, j 1, l 1 ; i 2, j 2, l 2 ) = σ(i 1, j 1, l 1 ) γ h (i 1, j 1 ; i 2, j 2 ) γ v (l 1 ; l 2 ) σ(i 2, j 2, l 2 ) (K.6) Here, σ represents a 3D standard deviation field. The horizontal correlation function γ h decreases with the horizontal distance between the cells. The function γ h for the profile assimilation is the same as the one used in the column assimilation, and is thus weakly depending on latitude and season. The vertical correlations γ v are derived from an NMC experiment (see, for example [Jeuken, 1999]). During the experiment, the TM3 model was run twice for time periods of three days driven by two

109 Annex K 103 Figure K.3: Example of a-priori (dashed), retrieved (red), and from TM3 state simulated ozone profile (blue). different sets of meteorological fields: either using the 0-72 hour continuous forecast, or using shortrange forecasts of 0-12 hour. The experiment was performed for all days in a month, leading to two sets of about 30 simulated ozone fields; the differences between the sets are a measure of the forecast error in the model. The correlation function γ v has been filled with the sample correlations after 72 hour, based on the idea that each position on earth is covered by a GOME pixel at least one time every three days. Figure K.4 shows examples of the correlations between vertical layers found with this method. The figure shows that correlations vanish if the distance exceeds about 5 layers. Operations on the covariance are applied to the standard deviation field only; the correlations remain the same. Evolution of the covariance in time is for example applied by advection of the standard deviation field: σ(t + t) = ADV[σ(t)] (K.7) In addition, an error growth is implemented following the same parametrization as used in TM3DAM (section 4.2.1): σ 3 := σ 3 + c 3 t (K.8) The constant c is a function of latitude and month, and determined out of observation-minus-forecast data. Representation error The assimilation requires quantification of the difference between measured values and a model state. This representation error is defined as the difference between the observed profile ỹ r and the simulated value h(x t ), where x t is the true state (the true ozone values, defined on the model grid). This difference is assumed to have a normal distribution: v = ỹ r h(x t ) N ((, o), R) (K.9)

110 104 GOA Final Report April, 2003 Figure K.4: Examples of correlations between ozone in different vertical layers, obtained from an NMC experiment. A part of the representation error covariance R is formed by the retrieval error S from Eq. K.1. The remainder is due to the resolution mismatch between the model grid and the GOME pixels. The state of the TM3 model describes the average ozone value in a large grid box, and is therefore not able to represent small scale fluctuations. The footprint of a GOME pixels is however rather small (see figure K.2), and can therefore not be expected to be simulated perfectly by a model state. A representation error is hard to quantify, and is in general obtained by trial and error. For TM3PAS, an adaptive adjustment of the representation error has been implemented. The following parametrization is assumed: R = S + αi (K.10) Kalman-based assimilation schemes use the assumption that the covariance matrix describes the difference between the true x t and the mean state x exactly: P = E [ (x t x)(x t x) T ] (K.11) The difference between observed and mean state is then distributed according to: ỹ r h(x) N ( o, HPH T + R ) (K.12) The value of α in (K.10) is now chosen such that the likelihood of the covariance at the right hand side reaches a maximum given the sample at the left hand side.

111 Annex K 105 Filter equations The assimilation scheme is based on the Kalman filter equations: K = PH T [ HPH T + R ] 1 ( ) x a = x f + K ỹ r h(x f ) (K.13) (K.14) P a = (I KH)P(I KH) T + KRK T (K.15) The matrix inversion is performed by first applying a triangular factorisation: HPH T + R = LL T (K.16) The gain matrix is then solved from: LL T K T = HP (K.17) With the gain available, the mean state is analysed easily. The analysis of the covariance is applied to the standard deviation field only; for a grid cell q, the analysis becomes: (σ 2 q) a = (σ 2 q) f 2k T q (Hp q ) + (L T k q ) T (L T k q ) (K.18) where k q and p q denote the corresponding rows of K and P respectively. References: H. J. Eskes, P. F. J. van Velthoven, P. J. M. Valks and H. M. Kelder, Assimilation of GOME total ozone satellite observations in a three-dimensional tracer transport model, Q. J. R. Meteorol. Soc., in press, J. P. F. Fortuin and H. M. Kelder, An ozone climatology based on ozonesonde and satellite measurements, J. Geophys. Res., 103, , 1998 A.B.M. Jeuken, H. J. Eskes, P. F. J. van Velthoven, H. M. Kelder, and E.V. Hólm, Assimilation of total ozone satllite measurements in a three-dimensional tracer transport model, J. Geophys. Res., 104(D5): , march 1999

112 106 GOA Final Report April, 2003 L Ozone profile validation report Validation of level-2 and assimilated (level-3) height-resolved GOME ozone fields Georg Hansen Norwegian Institute for Air Research (NILU), Department at the Polar Environmental Centre, Tromsø, Norway Georg.H.Hansen@nilu.no Last update: April 2003 This document summarises the main validation results for the GOME ozone profile retrievals and assimilated ozone fields. This text is also available as a web page that is part of the documentation of the data products on the GOA web site. Summary In the frame of the GOA project, two data sets of ozone profiles have been validated: level-2 GOME profiles from 1997 for 4 European stations level-3 assimilated GOME profiles from 2000 for 10 European and 1 Asian station For one station (Andøya), mainly ozone lidar profiles were utilised. The following signatures of the validation were found: The GOME data lead to a significant improvement of the a-priori information throughout the altitude range at high latitudes, while it is limited to the lower and mid stratosphere at middle latitudes GOME level-2 profiles overestimate ozonesonde results by about 5 to 10% in the stratosphere, while in the UTLS region significantly larger, also negative deviations occur In 1997, ozone depletion inside the vortex was not reproduced. Assimilated GOME profiles (level-3) from 2000 confirm the level-2 results from 1997: frequently very good re-production of dynamical features, but failure to reveal Arctic vortex ozone depletion In both data sets, the deviations scatter severely (8 to 30%, depending on location, season, and altitude): main potential use in statistical studies, not single case studies Introduction The main objective is to validate both directly GOME-derived ozone profiles (level-2 data) and higherlevel assimilated ozone profile data with ozonesonde profiles for selected ozonesonde launch sites and parts of the 7-year record of GOME data.

113 Annex L 107 Station name Longitude Latitude no. of comparisons Andøya (ozonesonde) Andøya (ozone lidar) Athens De Bilt Eureka Hohenpeissenberg Kiruna Lindenberg Ny-Ålesund Sodankylä Uccle Yakutsk Table L.1: List of stations used in the 1997 validation In addition to the tasks described in the Document of Work, NILU included ozone lidar data from a Norwegian site (ALOMAR) in the validation exercise. Although this data set is very limited, it adds value to the validation by means of ozonesondes, since lidar profiles reach altitudes of up to 45 km. Moreover, the ALOMAR ozone lidar was involved in another EU project (GODIVA) including validation of GOME ozone profiles, but calculated with the FURM algorithm of the University of Bremen [Hansen et al., 2003]. This offers the opportunity to compare the two GOME ozone profile algorithms. The ALOMAR ozone lidar is also part of the Network for the Detection of Stratospheric Change (NDSC), which has frequently been used for satellite validation purposes in recent years [Hansen et al., 1998; Sugita et al., 2002; Lambert et al., 2003]. The validation of GOME ozone profiles derived at KNMI was performed for two periods, the years 1997 and The validation data set The NADIR database at NILU has gathered all ozonesonde soundings performed during the European ozone campaigns SESAME ( ) and THESEO (1998-today). These cover the whole lifetime of the GOME instrument, which started operations in autumn The ozonesonde files are stored in the (ASCII-based) NASA Ames format. This format gives room to a large amount of additional information of technical and scientific nature, which is of limited use in applications as in the frame of the GOA project, and makes the files difficult to read and not suited for meta databases. The responsibility for the quality of the stored data has generally been with the data delivering institution; only in some cases, e.g., the GOME core validation campaign, NILU performed a data format compatibility control. In the frame of the GOA project software was developed to transfer the NASA Ames files into a simpler ASCII-based format (CREX) allowing the easy transformation into file formats suited for conditional databases, such as HDF, which will be used in the ENVISAT validation. The reformatting was combined with (formal) data quality assessment routines, checking both data format, data gaps and maximum sounding altitude reached as a quality criterion. The software is designed such that the NADIR ozonesonde data directories are searched for new files every 15 minutes. If the sonde is of non-standard

114 108 GOA Final Report April, 2003 Figure L.1: Comparison of ozone partial columns normalised per km altitude as measured with the ALOMAR ozone lidar (green dotted curve/ triangles) and derived from GOME (black line/diamonds). For comparison: ozone climatology used in the GOME retrieval algorithm. format or the residual ozone column is larger than 15% of the total ozone column or the total column is < 180 DU, the file is rejected and stored separately for further investigations. Validation of GOME level-2 data in 1997 The 1997 data set was used to validate ozone profiles from GOME pixels (level-2 data). The sites included in the validation are listed in Table L.1, including the number of single profile comparisons. The emphasis was on three ozonesonde stations Ny-Ålesund, Sodankylä, and de Bilt and the ALOMAR ozone lidar station, for which GOME profiles were selected from a radius of about 700 km. Additional GOME data sets were provided for Athens, Eureka, and Yakutsk, while Kiruna, Hohenpeissenberg, Lindenberg, and Uccle could be included due to their limited distance to the main validation sites. The comparison was performed using partial ozone columns between fixed pressure levels, as given in the GOME algorithm, 42 in total ranging from the surface to 0.1 hpa. The ozonesonde and lidar profiles were integrated for these given pressure intervals. Both the original partial columns of the ground-based / balloon-borne measurements and profiles convoluted with the averaging kernel functions of the GOME profile retrieval algorithm were used in the validation of the GOME profiles. Moreover, the a-priori profiles used in the GOME retrieval algorithm were compared with the validation data set, in order to be able to estimate the improvement or gain of information achieved from the GOME measurements Figure L.1 shows two examples of single profile comparisons of GOME derived profiles and ALOMAR lidar profiles. In both cases, the a-priori profile is changed considerably, i.e., the GOME spectral data yield new information. Above about 20 km altitude, there is significant improvement in the profile. Around and below 20 km, there are still significant deviations to the ground-based profile. This can partially be due to the combination of the horizontal distance between the pixel centre and the lidar site

115 Annex L 109 Figure L.2: Comparison of ozone partial columns normalised per km altitude as measured with an ozonesonde at Ny-Ålesund (left; green dotted curve/ triangles) and at Sodankylä (right), and derived from GOME (black line/diamonds). Yellow dashed line: ozonesonde partial column profile convolved with the GOME algorithm averaging kernel functions. For comparison: ozone climatology used in the GOME retrieval algorithm (thin blue line). Figure L.3: Comparison of ozone partial columns normalised per km altitude as measured with two ozonesondes at Lindenberg (green dotted curve/ triangles) and derived from GOME (black line/diamonds). Otherwise like Figure L.2

116 110 GOA Final Report April, 2003 and the fact that on both occasions the site was inside the vortex, but close to its edge. On the other hand, comparisons between GOME profiles and ozonesonde profiles from the high-latitude stations Sodankylä and Ny-Ålesund under vortex conditions in April 1997 indicate that the GOME profile retrieval has problems to catch layers with ozone depletion. Figure L.2 shows the profiles with the most pronounced depletion at Ny-Ålesund on 28 March 1997, and at Sodankylä on 2 April 1997 at 21 and 17 km altitude, respectively. None of the depleted layers is reproduced. One should note that the problem obviously occurs in the average kernel functions of the retrieval algorithm, since the sonde profiles convolved with these functions do not reproduce the depleted layers, either. Instead, they consequently produce a more or less pronounced intermediate minimum at around 15 km altitude which is not seen in any of the sonde and lidar measurements. A preliminary review of all single comparisons indicates that it is the more pronounced, the larger the deviation of the real profile from the climatology is. On the other hand, the algorithm does a very good job in many cases at mid-latitudes, in particular in situations with a very high tropopause. Figure L.3 shows two extreme examples from the Lindenberg ozonesonde station in Germany. In the left panel the very high tropopause at km and the very low ozone values all the way up to the ozone layer maximum are retrieved very convincingly. The only deficiency is the extremely low value at the very tropopause, which is found in a number of other cases as well. In the right panel, the other extreme is shown: a very low tropopause and high ozone concentrations below the layer maximum. Also in this case the main features are retrieved in a satisfactory way. On a statistical basis (as far as this is significant with at most 40 single profile comparisons), at all stations the GOME partial columns overestimate the sonde partial columns by in the order of 5 to 10% throughout most of the altitude range compared, if one uses the average-kernel function convolved profiles in the comparison above the tropopause; the standard deviations range from 8% to 15%, but increase towards the tropopause. From about 5 km altitude to the tropopause GOME values are generally 5 15% smaller, most pronounced at high latitudes, while at mid-latitudes there is a significant overestimation in the mid-troposphere followed by a negative deviation above. Taking the average of the absolute values of percent deviations (square root of the squares of percentual deviations), which is a measure of the variability irrespective sign of the deviation, one finds a decrease of the variability by about 50% from about 10 to 22 km at mid-latitudes, and from 12 to 28 km at high-latitude stations. At higher and lower altitudes, the retrieved profiles yield negligible improvements. Typical high and mid-latitude results are shown in Figure 3.7-4, from de Bilt (upper panels) and Sodankylä (lower panels). Validation of assimilated GOME profiles in 2000 For the year 2000, GOME profiles assimilated with the KNMIs TM3DAM model (level-3 data) were validated using a selected number of sites. Also in this case, both ozonesonde and ALOMAR lidar data were used. The stations included in this validation effort are given in Table L.2. In this case, the number of single comparisons was considerably higher, since the main limiting factor is the number of soundings, except around winter solstice (when the assimilation model cannot fill the measurement gaps at the highest latitudes). In this data set, the validation parameter was ozone number density as a function of pressure. In the case of ozonesonde soundings, this is straight-forward since pressure is measured.

117 Annex L 111 Figure L.4: Relative average deviations between convolved ozonesonde profiles and GOME profiles (left panels): red solid line; standard deviation: yellow lines; single profiles: black dots. Right panels: average of absolute values (signs removed) of percent deviations between measured and GOME algorithm profiles with standard deviations: a-priori profiles (green) vs. retrieved profiles (red).

118 112 GOA Final Report April, 2003 Station name Longitude Latitude no. of comparisons Andøya (ozonesonde) Andøya (ozone lidar) De Bilt Lerwick Lindenberg Legionowo Ny-Ålesund Ørlandet Payerne Sodankylä Uccle Yakutsk Table L.2: List of stations used in 2000 validation With respect to the lidar data set, pressure is taken from the closest ECMWF data point on the geographical grid and in time below typically 30 km, while it is derived from the (lidar-) measured temperature and atmospheric density above; the transition altitude depends on the instrument alignment quality determined from the overlap between measured profile and ECMWF profile. As in the case of the GOME level-2 ozone profiles, the agreement between assimilated ozone profiles and ground-based measurements is highly variable on a single profile basis. In many cases, the retrieved and then assimilated profiles reproduce also detailed structures in a very convincing way. One such example is given in the left panel of Figure L.5 where a sounding from Payern in November 2000 is shown. Dynamical structures deviating markedly from the average are reproduced. In other cases, such as in the right panel of the Figure, showing a sounding from Uccle in September 2000, the retrieved profile deviates strongly from the measured profile, although the latter is very close to the climatological profile. At high latitude sites the situation is similar, with a mixture of well reproduced and not reproduced dynamical features in the ozone layer. There is, however, an additional problem under ozone depletion conditions: the combined GOME retrieval TM3DAM assimilation model approach is not capable of reproducing ozone depletion, even if it is very pronounced as in the year Figure L.6 shows two examples from March 2000 from Ny-Ålesund (left panel) and Sodankylä (right panel) with at most 60% and 40% ozone depletion in the 40-to-60 mbar pressure region. In neither case the depleted layer is seen in the assimilated data. On a statistical basis, there is a noticeable difference between high and mid-latitude stations. At high latitude stations, two of which are shown in Figure 3.7-5, there is a general improvement over the whole height range compared to the a-priori profile, except around mbar, where the deviation changes from positive to negative, but does not decrease absolutely. Considering the different distribution of soundings throughout the year, also the results of the different techniques shows a satisfactory agreement in the over lap area. The results from the lidar validation show, however, a larger standard deviation of the single-profile results than the sonde validation below, say, 100 mbar. This may be due to the larger uncertainties of lidar data in the lowermost height region. Above 5 mbar, the standard deviations increase

119 Annex L 113 Figure L.5: Ozone profiles retrieved from GOME and assimilated with the KNMI TM3DAM model tool (solid profile) and a-priori climatological profile used in the model (dotted line). Red profile: ozonesonde profiles (diamonds: ozone densities at the model pressure levels). Figure L.6: Comparison between GOME retrieved and assimilated ozone profiles and measured ozonesonde profiles from Ny-Ålesund (left) and Sodankylä (right). Otherwise as Figure L.5.

120 114 GOA Final Report April, 2003 Figure L.7: Average relative deviation between GOME derived assimilated ozone profiles and groundbased measurements (bold red line) and its standard deviations (thin red lines) for Sodankylä ozonesondes (left) and ALOMAR lidar (right). Black dots: single profiles included. For comparison:average deviation between climatology used as a-priori profile in the GOME retrieval and ground-based measurements (green bold line) rapidly in the lidar comparison due to the increasing uncertainty in the g-b data set there. In the 200-to- 5-mbar range the average deviations are reduced from typically 10 to less than 3%. On the other hand, the standard deviation of the single profile comparisons is 10% at best and over large parts of the altitude profile closer to 20%. Consequently one has to state that the information gain from the GOME data is limited, and great care should be used before applying the data in analyses of processes with limited vertical extent such as ozone depletion in the Arctic. At mid-latitudes, the improvement is more dependent on altitude, as can be seen in Figure L.8. At the two selected mid-latitude stations, Payerne and Uccle, which are representative for others, an improvement compared to climatological profiles is only obvious between 500 and 150 mbar. Below and above, the deviations between the climatological and the sonde profiles are comparable the deviation between the retrieved GOME and the balloon measurement. However, in the height region where an improvement is given the scatter from the single profile comparisons is significantly larger than the improvement. One has, of course, to consider that in most profiles this region is close to the tropopause, i.e., rather small vertical offsets of the ozone tropopause cause large relative deviations. Comparing these results to those reported earlier (using only assimilated profiles with GOME total ozone as normalisation parameter), there is an improvement during the first months; the spin-up effects are not as visible anymore. In general, the altitude dependence of the deviation is reduced using GOME data. However, the problem with extracting information on ozone depletion at high altitudes remains, and thus severely limits the geophysical applicability of the data. The high variability in single-profile agreement also underlines that one rather should use the GOME data in statistical analyses, but not single-day /fewprofile investigations.

121 Annex L 115 Figure L.8: Average relative deviation between GOME derived assimilated ozone profiles and groundbased measurements (bold red line) and its standard deviations (thin red lines) for Payerne ozonesondes (left) and Uccle ozonesondes (right). Otherwise like Figure L.7. References Hansen, G., E.P. Shettle and U.-P. Hoppe, Intercomparison of ozone profiles as measured by POAM-II and lidar, Proc. 24th Ann. Europ. Meet. on Atmos. Stud. Opt. Meth., , Hansen, G., K. Bramstedt, V. Rozanov, M. Weber, and J.P. Burrows, Validation of GOME ozone profiles by means of the ALOMAR ozone lidar, Ann. Geophys., accepted, Lambert, J.-C., et al., Coordinated ground-based validation of ENVISAT atmospheric chemistry with NDSC network data: commissioning phase report, Proceedings of the Envisat Validation Workshop, ESA-SP531, Sugita, T., et al., Validation of ozone measurements from the Improved Limb Atmospheric Spectrometer, /2001JD000602, October 2002.

122 116 GOA Final Report April, 2003 M Documentation on the nitrogen dioxide retrieval - assimilation approach Combined retrieval, modelling and assimilation approach to GOME NO 2 Henk Eskes, Folkert Boersma Royal Netherlands Meteorological Institute (KNMI), P.O.Box 201, 3730 AE De Bilt, The Netherlands Tel: , Fax: , eskes@knmi.nl Last update: March 2003 This document provides an introduction to the GOME nitrogen dioxide retrieval - assimilation approach. It is originally a web page that is part of the description of the data products on the GOA web site. Nitrogen oxides and tropospheric chemistry Nitrogen oxides play a central role in tropospheric chemistry, and there are several reasons why an improved knowledge of the global tropospheric distribution of NO 2 (NO + NO 2 ) is important: NO 2 and volatile organic compounds are emitted in large quantities due to human activities such as traffic and industry. In the summer months this mixture produces photochemical smog. The chemical budget of ozone in the troposphere is largely determined by the concentration of NO 2. The knowledge of the ozone distribution and it s budgets is strongly limited by a severe lack of observations of NO and NO 2 in the troposphere. The variability of NO 2 concentrations in the lower troposphere in industrialised areas and near biomass burning sites is very large. The few available point observations of NO 2, on the ground or from aircraft measurements, are therefore difficult to translate to regional scale concentrations. The residence time of NO 2 in the lower troposphere is short. Therefore observations of boundary layer NO 2 contain important information on the emissions of nitric oxide, and the trends in these emissions. The free troposphere is also of great importance for the ozone budget, and for CH 4 and CO oxidation processes. Again these budgets are uncertain due to a limited knowledge of NO 2. The degree of NO 2 transfer from the boundary layer is difficult to model, and NO 2 emissions from lightning are very uncertain. NO 2 concentrations are also enhanced in the upper troposphere due to aircraft emissions. Observing NO 2 from space with GOME An important step in filling the gap in our knowledge of tropospheric NO 2 has been made by the Global Ozone Monitoring Experiment (GOME) instrument on ERS-2 [Burrows, 1999]. The prime advantage

123 Annex M 117 Figure M.1: Tropospheric NO 2 monthly-mean map for August of satellites is their capability of providing a full global mapping of the atmospheric composition. After cloud filtering, GOME provides global coverage NO 2 maps rougly every week. Column amounts of NO 2 can be derived from the detailed spectral information in the wavelength range nm. Good signal to noise ratio s (of about 20) are obtained for NO 2 with the Differential Optical Absorption Spectroscopy (DOAS) retrieval technique [Leue, 2001]. GOME has also demonstrated the ability to observe: ([Richter, 2002], [Martin, 2002], [Velders, 2001]) Boundary layer NO 2 : on top of a stratospheric background enhanced column NO 2 amounts are observed that correlate well with known industrialised areas. NO 2 originating from biomass burning events. There are also signatures of lightning-produced NO 2 in the GOME data set [Beirle, 2003]. Retrieval of tropospheric NO 2 : Challenges A major challenge is the derivation of good quality quantitative tropospheric NO 2 column amounts for individual ground pixels based on the satellite data. The retrieval of tropospheric trace gas species is characterised by large uncertainties, related to clouds, the surface albedo, the trace gas profile, the stratospheric column of NO 2, and aerosols: The largest uncertainties are due to clouds, as they will shield near-surface NO 2 from the view of the satellite. The retrieval depends very sensitively on the presence of clouds, and even small coud fractions (between 5 to 20%) have a major impact. High quality observations of the cloud properties (at least cloud fraction and cloud top height) are necessary for a quantitative retrieval.

124 118 GOA Final Report April, 2003 The surface albedo directly influences the sensitivity of GOME for boundary layer NO 2. High quality albedo maps in the relevant spectral range are essential. Profiles of NO 2 are characterised by a large range of variability. At emission areas the NO 2 concentration will peak at the surface, while downstream of such areas the pollution plume will peak at higher altitudes. The profile of NO 2 will be determined by aspects like the distribution of emission sources, the stability and height of the boundary layer, wet removal of nitric acid, deep convection and long-range transport by the wind. All these aspects are strongly varying in time and space. The NO 2 columns measured by GOME consist of comparable stratospheric and tropospheric contributions. The stratospheric background has to be quantified carefully in order to derive the tropospheric column. Atmospheric dynamics is well known to generate significant variability in stratospheric tracer amounts, consistent with for instance HALOE observations of stratospheric NO 2. A standard approach applied to GOME is based on the assumption that stratospheric NO 2 is zonally uniform, or at least has only a small longitudinal variation. Such simplification makes the retrieval of small tropospheric NO 2 columns impossible in practice. Another source of uncertainty are aerosols. Aerosol layers influence the radiation field and the sensitivity of GOME for near-surface NO 2. The combined modelling-retrieval-assimilation approach The retrieval of NO 2 is based on a combined retrieval, chemistry modelling and assimilation approach. The main motivation for this new approach is to reduce uncertainties related to the retrieval problems listed above. A chemistry-transport model, driven by realistic meteorological fields, provides best-guess profiles of NO 2, based on the latest emission inventories, atmospheric transport, photochemistry, lightning modelling and wet/dry removal processes. These model forecast fields are collocated with the GOME observations, and the radiative transfer modelling in the retrieval is performed based on the model trace gas profile and temperature profiles. The stratospheric NO 2 distribution is obtained from the assimilation of the GOME NO 2 observations with the chemistry-transport model. This stratospheric distribution is employed to derive a tropospheric column by subtracting the modelled (assimilated) stratosphere from the measured column. The retrieval is coupled to cloud top height and cloud fraction retrievals derived from the GOME data (Fresco cloud algorithm). The building blocks of the system are: GOME NO 2 slant column densities, as retrieved by IUP [Leue, 2001]. The chemistry-transport model TM3 [Houweling, 1998]. GOME cloud retrievals from the Fresco algorithm [Koelemeijer, 2001]. Albedo maps, based on TOMS and GOME measurements [Herman, 1997], [Koelemeijer, 2003]. The multiple scattering radiative transfer model DAK [Stammes, 2001].

125 Annex M 119 GOME NO2 slant column data Assimilation GOME Cloud top height Cloud fraction Chemistry-transport model Radiative transfer modelling (AMF) NO2 profile shape Temperature profile Stratospheric NO2 Tropospheric NO2 column Figure M.2: Diagram of the combined retrieval, chemistry modelling and assimilation approach. Innovation Nitrogen dioxide is part of the official ESA GOME data release [Spurr, 2002]. Furthermore several research groups have developed and documented scientific GOME NO 2 products [Richter, 2002], [Martin, 2002], [Leue, 2001]. The motivation for the present approach and the data set provided on this web page is to improve existing products on the following aspects: Averaging kernels. For each pixel the corresponding averaging kernel vector is provided. These kernel provide the relation between the retrieved remote-sensing product and the real atmosphere, and are important for quantitative studies [Eskes, 2003]. These kernels have so far not been provided for DOAS-based retrievals. Error estimates. Individual error contributions to the NO 2 product have been discussed in the literature, but a systematic error analysis on a pixel-by-pixel basis is not available. This data product includes a realistic tropospheric column error based on errors in the DOAS spectral fit, cloud algorithm, the albedo map, the profile shape, and the estimate of the stratospheric column [Boersma, 2003]. Profile shape. The retrieval of tropospheric NO 2 columns is strongly dependent on the assumed profile shape. Because of the large range of variability of profile shapes (depending on sources, wind direction, convective processes and removal processes), a simple climatological profile shape will introduce large errors in the retrieval. In this retrieval more realistic tropospheric profile shapes are obtained from the chemistry-transport model TM3 which explicitly models these aspects. Stratospheric column. The meteorological state of the atmosphere causes the stratospheric column to vary in space and time. Most tropospheric retrievals are based on the assumption that the variation of the stratospheric column with the longitude can be neglected. However, this assumption introduces

126 120 GOA Final Report April, 2003 Statistical error components: slant column stratosphere profile shape cloud fraction cloud pressure retrieval error estimate for individual pixels albedo Figure M.3: Diagram of the error modelling approach. a considerable error in the tropospheric column retrieval. This variability is accounted for by the transport model TM3. Data assimilation is used to make the model stratosphere consistent with the observations of GOME. Temperature dependence. The temperature dependence of the cross section of NO 2 is accounted for based on the temperature profiles of the ECMWF meteorological analyses. Clouds. Explicit corrections are made for both cloud fraction and cloud top height, based on the Fresco algorithm. Albedo. A new surface Lambertian equivalent albedo map relevant for the NO 2 retrieval has been constructed based on the TOMS and GOME multi-year data sets. The retrieval product The following products are available on this website: Data product files in HDF-4 format with NO 2 column information for the individual GOME observations. Each file contains one day of data. Monthly-mean gridded tropospheric NO 2 distributions: PNG and PDF format images and ascii data files. Three-day composite maps of tropospheric NO 2, PNG and PDF format images. The data product files are described in detail in the data product specification document, along with software to read the data. The files contain three groups of data, namely: The main data product: tropospheric, stratospheric and total columns of NO 2, error estimates of these columns, the averaging kernel vector and a quality flag. Geolocation data: Latitude and longitude of the corners and centre of the GOME footprint, solar zenith angle, viewing angle, azimuth angle.

127 Annex M 121 Figure M.4: Examples of DOAS averaging kernels: (a) clear pixel with a surface albedo of 0.02; (b) clear pixel with a surface albedo of 0.15; (c) pixel with an optically thick cloud and cloud top at 800 hpa. Ancillary data: this consists of retrieval-specific data, such as the slant column, surface albedo, cloud fraction and top pressure and air-mass factors. References: Beirle, S., presentation at the GOA workshop (available from this web page) Boersma, K. F., H. J. Eskes, and E. Brinksma, Error analysis for tropospheric NO2 retrievals, preprint Burrows, J.P., M. Weber, M. Buchwitz, V. Rozanov, A. Ladstätter-Weibenmayer, A. Richter, R. Debeek, R. Hoogen, K. Bramstedt, K.-U. Eichmann, M. Eisinger, D. Perner, The Global Ozone Monitoring Experiment (GOME): Mission concept and first results, J. Atmos. Sciences, 56, (1999). Eskes, H. J., and K. F. Boersma, Averaging Kernels for DOAS Total-Column Satellite Retrievals, Atmos. Chem. Phys. Discuss., 3, (2003) Herman, J. R., and E.A. Celarier, Eart surface reflectivity climatology at nm from TOMS data, J. Geophys. Res., 102, (1997) Houweling, S., Dentener, F. and Lelieveld, J., The impact of non-methane hydrocarbon compounds on tropospheric photo-chemistry, J. Geophys. Res., 103, (1998) Koelemeijer, R.B.A., P. Stammes, J.W. Hovenier, and J.F. de Haan, A fast method for retrieval of cloud parameters using oxygen A-band measurements from Global Ozone Monitoring Experiment, J. Geophys. Res., 106, (2001) Koelemeijer, R. B. A., J. F. de Haan, and P. Stammes, A database of spectral surface reflectivity in the range nm derived from 5.5 years of GOME observations, J. Geophys. Res., 108, 4070, doi: /2002jd (2003)

128 122 GOA Final Report April, 2003 Leue,C., M. Wenig, T. Wagner, O. Klimm, U. Platt, and B. Jähne, Quantitative analysis of NOx emissions from Global Ozone Monitoring Experiment satellite image sequences, J.Geophys.Res., 106, (2001) Martin, R.V., K. Chance, D.J. Jacob, T.P. Kurosu, R.J.D. Spurr, E. Bucsela, J.F. Gleason, P.I. Palmer, I. Bey, A.M. Fiori, Q. Li, and R.M. Yantosca, An improved retrieval of tropospheric nitrogen dioxide from GOME, J. Geophys. Res., 107, 4437 (2002) R. Spurr, W. Thomas, D. Loyola, GOME Level 1 to 2 Algorithms Description, Technical Note ER- TN-DLR-GO-0025, Deutsches Zentrum für Luft und Raumfahrt, Oberpfaffenhofen, Germany, July 31, Stammes, P., Spectral radiance modelling in the UV-visible range, Proceedings IRS-2000: Current problems in atmospheric radiation, edited by W.L. Smith and Y.M. Timofeyev, pp , A. Deepak Publ., Hampton (2001). Richter, A., and J. P. Burrows, Tropospheric NO 2 from GOME measurements, Adv. Space Res., 29, (2002) Velders, G.J.M., C. Granier, R.W. Portmann, K. Pfeilsticker, M. enig, T. Wagner, U. Platt, A. Richter, and J.P. Burrows, Global tropospheric NO2 column distributions: Comparing 3-D model calculations with GOME measurements, J.Geophys.Res., 106, (2001)

129 Annex N 123 N Comparison between GOME and the CTM2 and TM3 models Michael Gauss 1, Ivar Isaksen 1 and Henk Eskes 2 1. Department of Geophysics, University of Oslo, Norway. 2. Royal Netherlands Meteorological Institute, De Bilt, the Netherlands. In the future, the measurement database that was established in GOA, will be used extensively for model validation. In this annex we present a selection of results from the model inter-comparison and validation which were accomplished during the GOA project. This effort focused on the GOA target species, NO 2 and ozone, but also on other components important for tropospheric chemistry, such as NO, HNO 3, CO, and methane. Oslo CTM-2 model description Oslo CTM-2 is a global 3-dimensional chemical transport model (CTM) for the troposphere and the stratosphere. The 40-layer version, which has been extensively tested during the TRADEOFF, COZUV, and GOA projects, extends from the surface to 10 hpa, while the new 60-layer version to be used in the proposed work extends up to 0.1 hpa (approximately 64 km). The horizontal resolution can be varied between T21 ( 5.6 x5.6 ), T42 ( 2.8 x2.8 ), and T63 ( 1.9 x1.9 ). The model meteorology is determined by a self-consistent set based on ECMWF IFS forecast data including horizontal winds, temperature, cloud liquid water content, cumulus convection, etc. for different years. Model results can thus be compared readily with observations from the same time. Advective transport uses the concept of Second Order Moments [Prather, 1986], while convection is based on the Tiedtke mass flux scheme [Tiedtke, 1989]. Transport in the boundary layer is treated according to the Holtslag K-profile scheme [Holtslag et al., 1990]. Emissions of source gases (CO, NO x, Methane, non-voc compounds) for different source categories are taken from the GEIA and EDGAR data bases for anthropogenic emissions, and from Mueller [1992] for natural emissions. High-altitude emissions of NO x from lightning and aircraft are included based on Price et al. [1997a/b] and the IPCC-2001 aircraft inventory [IPCC, 2001], respectively. The total lightning emission amounts to 5 Tg/year. Calculation of dry deposition is based on Wesely [1989]. For chemical integrations two separate modules are used covering both tropospheric and stratospheric chemistry. The tropospheric chemistry scheme contains 51 species and has been thoroughly tested in the OSLO CTM-1 model [Berntsen and Isaksen, 1997]. 86 thermal reactions, 17 photolytic reactions, and 2 heterogeneous reactions are integrated by the QSSA method [Hesstvedt et al., 1978] using a numerical time step of 5 minutes. The stratospheric chemistry solver is a well-tested extensive QSSA code developed by Stordal et al. [1985] and updated to include heterogeneous chemistry [Isaksen et al., 1990]. It has been extensively used and validated in the OSLO 2D model [Isaksen et al., 1990] and in a stratospheric 3-D CTM [Rummukainen et al., 1999]. 154 reactions (102 thermal, 45 photolytic, and 7 heterogeneous) involving 57 species and 7 families are integrated in time steps of 10 minutes. The boundary between the tropospheric and the stratospheric chemistry regimes is based on NCEP reanalysis tropopause pressures, which are updated in the model every 6 hours. Photodissociation rates are calculated on-line once every hour following the method described by Wild et al. [2000]. At the upper

130 124 GOA Final Report April, 2003 boundary of the model domain, monthly-mean mixing ratios from the OSLO 2D model are applied. KNMI TM3 model description The chemical tracer model version 3 (TM3) is a global atmospheric model which is used to study the atmospheric composition and changes therein caused by natural and antropogenic changes [Lelieveld and Dentener, 2000; van Velthoven and Kelder, 1996; Dentener et al., 2003; Meijer et al., 2000; Dentener et al., 1999; Houweling et al., 1998]. The TM3 model has a longitude-latitude grid and hybrid sigma-pressure levels up to 10 hpa. The model is used with either a 7.5 x 10, a 3.75 x 5, or a 2.5 x 2.5 degrees horizontal grid and with 19 or 31 layers. The meteorological input data is operational (6-hour forecast) data or reanalysis data from ECMWF. To be used in the TM3 model, the meteorological data is interpolated or averaged from a Gaussian grid to the desired TM3 grid cells [Bregman et al., 2002]. TM3 describes the evolution of 38 species, of which 23 are transported, including their emission, chemical formation and destruction, and physical removal. For advection of the tracers, the model uses the slopes scheme developed by [Russell and Lerner, 1981]. The effect of convective transport on the tracer concentration is diagnosed with a parameterisation [Tiedtke, 1989] that mimics the ECMWF scheme and using the archived windspeeds, pressures, temperatures and specific humidities. Vertical diffusion, which is another important subgrid-scale process, is off-line diagnosed using a parameterisation described in [Louis, 1979]. TM3 contains a tropospheric chemistry module that is a modified CBM-4 scheme [Houweling et al., 1998]. Dry deposition is accounted for by a resistance chain-based parameterisation [Ganzeveld et al., 1998]. The dry deposition velocity is calculated from the aerodynamic resistance, the quasi-laminar boundary layer resistance, and the surface resistance. The wet deposition is based on a parameterisation of Junge and Gustafson [1957] and [Langner and Rodhe, 1991]. The heterogeneous removal of N 2 O 5 on sulfate aerosols has been accounted for by using a parameterisation by [Dentener and Crutzen, 1993]. The annual totals and spatial distributions of the emissions of CO, CH 4, NO x, SO 2, NMHC (inclusing isoprene) and NH 3 are based on 1 x 1 degrees GEIA and EDGAR-v2.0 emission inventories [van Aardenne et al., 2001; Olivier et al., 1996]; Guenther et al., 1995; Yienger and Levy, 1995; Benkovitz et al., 1996]. The lightning parameterisation is based on a linear relation between convective precipitation and lightning intensity [Meijer et al., 2001]. A prescribed vertical profile is used [Pickering et al., 1998] to distribute lightning NO x in the vertical. The top-height of this profile is determined by the top of the convective updraft. The lightning NO x production is scaled to a global production of 5 Tg N per year. Since the modified CBM-4 chemical scheme cannot adequately describe stratospheric chemistry, ozone avove 50 hpa is nudged towards a zonally and monthly mean ozone climatology [Fortuin and Kelder, 1998] scaled with TOMS total ozone measurements. HNO 3 transport from the stratosphere into the upper levels of the TM3 model is accounted for by fixing the O 3 / HNO 3 ratio in the model top layer, based on UARS derived O 3 /HNO 3 ratios at 10 hpa. To account for upper stratospheric CH 4 loss. CH 4 is nudged to UARS CH 4 climatology at 10 hpa.

131 Annex N 125 Oslo CTM-2 / KNMI TM3 model intercomparison The following output has been compared between the models: Monthly-mean total columns of ozone and NO2; zonal-mean distributions of ozone, NO, NO 2, HNO3, CO, and methane; vertical NO 2 profiles at different locations (polluted / remote unpolluted). Figure N.1 shows total ozone columns from the two models in rather good agreement. TM3 calculates somewhat more ozone than CTM2 in mid latitudes, especially in the Southern Hemisphere. Total NO 2 columns (Figure N.2) tend to be larger in CTM2, especially in low latitudes. This is mainly due to the climatological upper layer of CTM2, which yields systematically higher values than TM3. In future versions of CTM2 its stratospheric chemistry will either extend to higher altitudes, or it will be forced in the upper boundary by observed values. The strength and locations of peaks, however, agree very well. Only the peaks due to biomass burning hot spots, in particular in September, are stronger in CTM2. The zonal-mean plots (Figure N.3) reveal that the TM3 model has more methane than CTM2 throughout the entire model domain, less ozone in the free troposphere, and more ozone than CTM2 at the top of the model domain. Also, NO 2 (and NOx, which is not shown in the Figure) tends to be higher in TM3 near the surface. This is confirmed by Figure N.4, which shows vertical NO 2 profiles in the North-Eastern US and a remote marine area which is assumed to be largely unaffected by anthropogenic emissions. The Figure also illustrates the effect of the different boundary layer mixing schemes that have been used in the CTM2. By and large, the vertical structure agrees between the models. Oslo CTM-2 validation against various sets of measurement data Comparisons between the Oslo CTM2 and MOZAIC measurements have revealed an overestimation of tropospheric ozone (see Figure N.5). The overestimation is lower when stratospheric ozone chemistry is switched off in the model and replaced by a pre-defined stratospheric ozone influx of 450 Tg(ozone)/year, based on observations. This approach has been used in the 1996 model simulation, which is included in the figure. However, the aim is to improve the CTM2 version that was used in GOA, i.e. including both tropospheric and stratospheric chemistry, in order to model tropospheric ozone as realistically as possible. Comparisons that have been done in coordination with the TRADEOFF project have shown that the Oslo CTM2 produces to little NOy and NOx in the lower stratosphere and leaves too much N 2 O. On the other hand CO seems to be overestimated. Total ozone and tropospheric NO 2 columns have been validated against GOME data. Examples of total ozone were already shown in the First annual report. The Oslo CTM2 generally calculates larger ozone columns than what is observed by GOME in high latitudes, while the agreement in low latitudes is very good. Overestimations in CTM2 are partly due to the upper layer of CTM2 which contains two-dimensional climatological data, but also to uncertainties in the GOME data (see GOA First Annual report). Here we present a validation regarding tropospheric NO 2 columns (Figure N.6). For these comparisons the GOME data was interpolated into the same resolution as the model (model runs with higher resolution are planned where the resolution of GOME measurements will be exploited in the validation). In general the agreement between GOME and CTM2 is very good. The peaks due to anthropogenic emissions are well resolved, although in some cases underestimated, especially in South Africa and Northern Australia.

132 126 GOA Final Report April, 2003 Over the oceans very small NO 2 columns occasionally lead to negative data in the GOME analysis, while the model yields values between 0 and 0.5e15 molecules/cm2. References: Benkovitz, C.M., M.T. Scholtz, J. Pacyna, L. Tarrason, J. Dignon, E.C. Voldener, P.A. Spiro, J.A. Logan, and T.E. Graedel, Global gridded inventories of anthropogenic emissions of sulfur and nitrogen, JGR 101, 29,239-29,253, Berntsen T. and I. S. A. Isaksen, A global 3-D chemical transport model for the troposphere, 1, Model description and CO and Ozone results, J. Geophys. Res., 102, , Bregman, B., A. Segers, M. Krol, E. Meijer, and P. van Velthoven, On the use of mass-conserving wind fields in chemistry transport models, Atmos. Chem. Phys. Discuss., 2, , Dentener, F.J., and P.J. Crutzen, Reaction of N2O5 on tropospheric aerosols: Impact on the global distributions of NOx, O3 and OH, JGR 93 (D4), , Dentener, F.J., J. Feichter, and A. Jeuken, Simulation of the transport of 222Rn using on-line and off-line global models at different horizontal resolutions: a detailed comparison with measurements, Tellus, 51(B), , Dentener, F., M. van Weele, M. Krol, S. Houweling, and P. van Velthoven, Trends and inter-annual variability of methane emissions derived from global CTM simulations, Atmos. Chem. Phys, 3, 73-88, Fortuin, J.P.F, and H. Kelder, An ozone climatology based on ozonesonde and satellite measurements, JGR 103 (D24), 31,709-31,734, Ganzeveld, L., J. Lelieveld, and G.-J. Roelofs, A dry deposition parameterization for sulfur oxides in a chemistry and general circulation model, JGR 103 (D5), , Guenther, A., et al., A global model of natural volatile organic compound emissions, JGR 100, , Hesstvedt E., O. Hov, I.S.A Isaksen, Quasi steady-state approximation in air pollution modelling: Comparison of two numerical schemes for oxidant prediction, Int. Journal of Chem. Kinetics, Vol. X, , Holtslag, A. A. M., E. I.F DrBruijn and H.-L. Pan, A High resolution air mass transformation model for short-range weather forecasting, Mon. Wea. Rev., 118, , Houweling, S., F. Dentener, and J. Lelieveld, The impact of nonmethane hydrocarbon compounds on tropospheric photochemistry, JGR 103 (D9), 10,673-10,696, Intergovernmental Panel on Climate Change (IPCC), WGI Third Assessment Report, in preparation, Isaksen, I.S.A., Rognerud, B., Stordal, F., Coffey, M.T. and Mankin, W.G., Studies of Arctic stratospheric ozone in a 2-d model including some effects of zonal asymmetries. Geophys. Res. Lett., 17, p , Langner, J., and H. Rodhe, A global three-dimensional model of the tropospheric sulfur cycle, J. Atmos. Chem., 12, , 1991.

133 Annex N 127 Louis, J.-F., A parametric model of vertical eddy fluxes in the atmosphere, Boundary Layer Meteorol., 17, , Mclinden C. A, S. Olsen, B. Hannegan, O. Wild, M. J. Prather and J. Sundet, Stratospheric Ozone in 3-d Models: A simple chemistry and the cross-tropopause flux, J. Geophys. Res., Accepted January, Meijer, E.W., P.F.J. van Velthoven, A.M. Thompson, L. Pfister, H. Schlager, P. Schulte, and H. Kelder, Model calculations of the impact of NOx from air traffic, lightning, and surface emissions, compared with measurements, JGR 105 (D3), , Meijer, E.W., P.F.J. van Velthoven, D.W. Brunner, H. Huntrieser, and H. Kelder, Improvement and evaluation of the parameterisation of nitrogen oxide production by lightning, Phys. Chem. Earth (C), 26 (8), , Mueller, J., Geographical distribution and seasonal variation of surface emissions and deposition velocities of atmospheric trace gases. J. Geophys. Res., 97, , Prather, M. J., Numerical advection by conservation of second-order moments, J. Geophys. Res., 91, , Pickering, K.E., Y. Wang, W.-K. Tao, C. Price, and J.-F. Muller, Vertical distributions of lightning NOx for use in regional and global chemical transport models, JGR 103, 31,203-31,216, Price C., J. Penner and M. Prather, NOx from lightning 1. Global distribution based on lightning physics. J. Geophys. Res., 102, p , 1997a. Price C., J. Penner and M. Prather, NOx from lightning 2. Constraints from the global atmospheric circuit. J. Geophys. Res., 102, p , 1997b. Rummukainen, M., Isaksen, I.S.A., Rognerud, B., and Stordal, F., A global model tool for threedimensional multiyear stratospheric chemistry simulations: Model description and first results. J. Geophys. Res., 104, p , Russell, G.L., and J.A. Lerner, A new finite-differencing scheme for the tracer transport equation, J. Appl. Meteorol., 20, , Stordal, F., Isaksen, I.S.A. and Horntvedt, K., A diabatic circulation two-dimensional model with photochemistry: Simulations of ozone and long-lived tracers with surface sources. J. Geophys. Res., 90, p , Tiedtke, M., A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Monthly Weather Rev., 117, , Van Velthoven, P.F.J., and H. Kelder, Estimates of stratosphere-troposphere exchange: Sensitivity to model formulation and horizontal resolution, JGR 101 (D1), , Wesley, M. L., Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models. Atmos. Environ., 23, , Wild O., X. Zhu and M. J. Prather: Fast-J: Accurate Simulation of In- and Below cloud Photolysis in Tropospheric Chemical Models, J. of Atmos. Chem., 37, No.3, , Yienger, J.J., and H. Levy II, Empirical model of global soil-biogenic NOx emissions, JGR 100, 11,447-11,464, 1995.

134 128 GOA Final Report April, 2003

135 Annex N 129 Figure N.1: Monthly-mean total ozone column (units: DU) modeled by CTM2 (left) and TM3 (right) for different seasons in 1997.

136 130 GOA Final Report April, 2003 Figure N.2: Monthly-mean total NO 2 column (units: 1e15 molecules/cm2) modeled by CTM2 (left) and TM3 (right) for different seasons in 1997.

137 Annex N 131 Altitude [km] Altitude [km] Altitude [km] CTM2 O3 Jan S 60S 30S EQ 30N 60N 85N S 60S 30S EQ 30N 60N 85N CTM2 NO2 Jan CTM2 CH4 Jan S 60S 30S EQ 30N 60N 85N Altitude [km] Altitude [km] Altitude [km] TM3 O3 Jan S 60S 30S EQ 30N 60N 85N TM3 NO2 Jan S 60S 30S EQ 30N 60N 85N TM3 CH4 Jan S 60S 30S EQ 30N 60N 85N Figure N.3: Zonal-mean ozone, NO 2 and CH 4 in January 1997, modeled by CTM2 and TM3. NO 2 is given in pptv, CH 4 and ozone in ppbv.

138 132 GOA Final Report April, N,74W 0N,120W Figure N.4: Modeled vertical monthly-mean NO 2 profiles in a polluted area (Eastern US, 41 N/74 W) and an unpolluted area (Equatorial Pacific, 0 N/120 W) in January Black: TM3, blue: CTM2 with old boundary layer (bulk) mixing scheme, red: CTM-2 with more detailed boundary layer mixing scheme based on Holtslag K-profiles. Figure N.5: Relative difference between the Oslo CTM-2 simulations for 1996 and1997 and MOZAIC aircraft measurements in the New York region. (This figure was generated by Dr. D. Brunner within the TRADEOFF project)

139 Annex N 133

140 134 GOA Final Report April, 2003 Figure N.6: Monthly-mean tropospheric NO 2 columns as modeled in CTM2 and observed by GOME, 1e15 molecules/cm2.