E-AIMS. SST: synthesis of past use and design activities and plans for E-AIMS D4.432

Transcription

1 Research Project co-funded by the European Commission Research Directorate-General 7 th Framework Programme Project No E-AIMS Euro-Argo Improvements for the GMES Marine Service SST: synthesis of past use and design activities and plans for E-AIMS D4.432 Date: September 2013 Author: Emma Fiedler and Alison McLaren (Met Office) Lead: Met Office United Kingdom Co-ordinator: Institut Français de Recherche pour l'exploitation de la Mer - France

2 1. Introduction Argo data are regularly used at the Met Office for validation of output from the operational sea surface temperature (SST) analysis systems OSTIA (Operational SST and Ice Analysis) and GMPE (GHRSST Multi-Product Ensemble, where GHRSST is the Group for High Resolution SST). In this document these two systems are introduced and past validation work using Argo data is described. The plan for validation work within the E-AIMS project is then detailed. There are a number of acronyms given in this document. Where practical these have been defined in the text; else they are included in a glossary at the end. 2. Introduction to OSTIA and GMPE systems The OSTIA (Operational SST and Ice Analysis) system runs daily in NRT (near-real-time) at the Met Office and produces a global, gap-free gridded SST product on a regular latitude-longitude 1/20 o grid. This product also includes surface temperatures of major lakes. A daily analysis of the foundation SST (the SST free of diurnal variability), using an optimal-interpolation-type scheme is produced. The system uses in-situ data from the GTS (drifters, ships and moored buoys) and GHRSST satellite observations (currently from MetOp and NOAA AVHRR, IASI, GOES, SEVIRI and TMI instruments). A background check against the SST analysis for the previous day (with a slight relaxation to climatology) is performed on the SST input data, using a Bayesian scheme which employs pre-defined estimates of the background and observation errors. Satellite data are bias-corrected to a reference dataset comprising in situ data and a high-quality subset of the MetOp AVHRR data. Prior to April 2012, AATSR data was used with in situ as reference data, until the Envisat satellite was decommissioned. Sea ice concentration based on SSMIS, available from OSI-SAF is regridded to the OSTIA grid. Spatial filling using a bi-linear interpolation method is performed on the sea ice data at the North Pole and around coasts. SST under ice of concentration over 50% is relaxed towards freezing, with a timescale which decreases with increasing ice concentration. OSTIA lake ice concentration is produced using a combination of ice concentration from NCEP and a threshold based on the OSTIA lake surface temperature analysis itself. Full details of the OSTIA system are given by Donlon et al. (2012). The OSTIA product grid (1/20 o ) is finer than the actual analysis feature resolution, which is determined by the specification of the background error covariances and correlation length scales, as well as resolution and sampling of the input data. Reynolds et al. (2013) provides a summary of this and Roberts-Jones et al. (2013) details recent developments to the OSTIA system which improved feature resolution. As well as a global SST field, OSTIA products produced on a daily basis also include error estimates of the SST analysis, ice concentration, global bias-correction fields for included satellite instruments and an anomaly to climatology. The OSTIA products are available via the MyOcean project. OSTIA output is used operationally at the Met Office as a boundary condition for numerical weather prediction (NWP). Other operational users of the OSTIA product include ECMWF, and Meteo-France, where its uses include as a first guess for infra-red satellite retrievals of SST. Many other users also access the data through MyOcean on an ad-hoc basis. For example, 119 users have accessed the OSTIA SST product for the year 2013 to date. 1

3 The GMPE system is also run daily at the Met Office and allows intercomparison of various SST analyses from international collaborators in NRT. A median SST is also produced from the contributing analyses, which is made freely available. The current list of analyses included in the GMPE system is: OSTIA (Met Office, UK) GAMSSA (Bureau of Meteorology, Australia) MGDSST (Japan Meteorological Agency, Japan) CMC (Canadian Meteorological Center, Canada) NAVO K10 (Naval Oceanographic Office, USA) OISST.v2:AVHRR (NCDC/NOAA, USA) RTG (NWS/NCEP/NOAA, USA) RSS MW (Remote Sensing Systems, USA) RSS MW/IR (Remote Sensing Systems, USA) FNMOC (Fleet Numerical Meteorology and Oceanography Center, USA) The contributing analyses all use different combinations of various infra-red, microwave and in situ SST datasets. All the input analyses use optimal-interpolation methods to blend these data but methods vary, including quality control, and specification of errors and hence the spreading of the information in the analysis. Within the GMPE system, contributing analyses are regridded to a regular latitude-longitude 1/4 o grid and the median, standard deviation and other information is produced. The land-sea-ice mask is updated daily and taken from the OSTIA product. The GMPE product is also available via the MyOcean project. As a complementary product to the OSTIA foundation temperature, the next generation of SST products currently under development at the Met Office aim to provide estimates of the skin SST at a high enough temporal resolution to resolve the diurnal cycle. The difference between the foundation and the skin SST is illustrated in figure 1. The foundation SST is the SST free of diurnal variability, i.e. the value at the base of any diurnal warm layer. The skin SST is the SST which would be measured by infra-red radiometers. It is affected by diurnal warming due to solar radiation in the daytime (unless the wind is strong enough the mix the surface layer) and also by the cool skin effect whereby molecular diffusion at the surface cools the skin interface. The diurnal skin SST analysis system currently under development at the Met Office aims to provide the diurnal variability of the skin SST which can then be added to the existing foundation SST estimate from the OSTIA system. The system will assimilate infra-red satellite data into cool skin and diurnal warm layer diagnostic models within the NEMO ocean model framework. The initial implementation of this system is expected to have a temporal resolution of 3 hours on a 1/4 o grid. 2

4 Figure 1: Near-surface temperature gradients, from Minnett and Kaiser-Weiss (2012). Numbers on axes are for guidance only, and are not rigorously derived. Work on and output from the OSTIA and GMPE systems make a significant contribution to research and development activities organised through the framework of GHRSST and have relevance for many of the GHRSST working groups, e.g. Diurnal Variability, Climate Data Records, Inter-comparison, SST validation, Inland Waters and High Latitudes. Work on OSTIA also contributes to activities of the MyOcean OSI-TAC (Ocean and Sea Ice Thematic Assembly Center). 3. Past use of Argo data for validation Argo data are not currently assimilated in OSTIA or in any of the other SST analyses which contribute to GMPE and therefore provide a highly valuable independent data set for validation of SST analyses. Near-surface (3-5 m depth) Argo measurements have been shown to provide good estimates of foundation SST, using a triple colocation of Argo data, surface drifters and AATSR satellite data (Merchant and Corlett, 2010, pers. comm., see Martin et al., 2012) and have been used to assess the accuracy of OSTIA and other SST analysis products. Martin et al. (2012) used delayed-mode Argo data, which had undergone quality control procedures at the Met Office using the methods described in Ingleby and Huddlestone (2007), to 3

5 assess the accuracy of SST analyses used in the NRT GMPE system. Each input SST analysis, and the GMPE median, was bi-linearly interpolated from its original grid resolution to the location of the Argo observations valid on the day of the analysis. Means and standard deviations of these matchups were calculated, for the globe and various ocean regions. The Argo assessment demonstrated that the GMPE median was more accurate in terms of the standard deviation than any of the contributing analyses, both globally and in all sub-regions (figure 2). Figure 2: From Martin et al. (2012) (A) Mean (Argo-minus-analysis) and (B) standard deviation differences (K), between each L4 analysis (including the GMPE median), and near surface Argo data (uppermost measurement 3-5 m depth), monthly for 2010, for the globe. Argo data have also been sporadically used to assess the operational GMPE system on a monthly basis using the same method as applied in Martin et al. (2012) (e.g. ) but no continuous or routine validation has been set up. Argo data have also been used to validate long-term SST reanalyses using the GMPE system. A long-term SST reanalysis performed using the OSTIA system, with input data reprocessed as part of the ESA CCI project, was compared to several other long-term SST products using nearsurface Argo data from 2001 as independent validation (Fiedler et al., 2013). Contributing products for the long-term GMPE are: OSTIA CCI (Met Office, UK) OSTIA v1.0 (Met Office, UK) CMC (Canadian Meteorological Center, Canada) MGDSST (Japan Meteorological Agency, Japan) HadISST2 (Met Office, UK) OISST.v2:AVHRR (NCDC/NOAA, USA) In this case it was shown that the GMPE median does not necessarily have the smallest standard deviation and that the CMC and OSTIA CCI reanalyses also perform very well (figure 3). Figure 4 illustrates the spatial variation of the mean error of the GMPE median to Argo data, for

6 Figure 3: From Fiedler et al. (2013), global monthly standard deviation (solid line) and mean error (dashed line) compared to Argo (uppermost measurement 3-5 m depth), for all reanalyses used in long-term GMPE, and GMPE median. Mean error is reanalysis-minus-argo. Argo data are also an important tool for independent assessment of development work for upgrades to the operational OSTIA system, for example, recent improvements to the background error covariances (Roberts-Jones et al., 2013). It is very important to have accurate independent data to perform assessments of this nature. Argo data are also used to independently assess the impact of new data types on the accuracy of the OSTIA analysis before inclusion in the operational system. Argo data also recently provided an independent validation of results when the decommissioning of AATSR meant a change in OSTIA from bias-correction to AATSR, to using a high-quality subset of MetOp AVHRR data (table 1). Table 1: Standard deviation error (K) compared to near-surface Argo data for March 2012 globally and for ocean regions, for OSTIA runs using for bias correction: in situ only, a subset of MetOp AVHRR and in situ, and AATSR and in situ. 5

7 Figure 4: Mean error (GMPE median minus near-surface Argo) on 2 o x 2 o grid, for Design activities and plans for E-AIMS Currently, assessment of OSTIA and GMPE data using Argo has been confined within the framework of particular studies. It is planned that this assessment should be conducted routinely. Summary global and regional statistics from this assessment would be put on external webages and made freely available. The timeliness of these statistics will depend on the use of either NRT or delayed-mode Argo data. The quality of NRT Argo compared to the delayed-mode version will need to be assessed. This would be achieved using bias and standard deviation errors compared to e.g. other in situ datasets (such as drifters, moored buoys) and OSTIA. The need to perform routine quality control of the Argo data at the Met Office will be assessed. A routine validation of OSTIA and GMPE using the Argo data would then be set up. This could be conducted daily in NRT or delayed mode, or monthly depending on the results of the assessment. High vertical resolution temperature data available from certain Argo floats will be used to validate the new OSTIA diurnal cycle product. Before this can be done, a validation method will be developed to use the Argo data closest to the surface to assess the skin SST product. Assessment of the depth and vertical resolution required for the Argo observations in order to observe the diurnal cycle will also take place. Two studies will be carried out on Argo data matchup sampling requirements (both temporal and spatial). These will take the form of a statistical analysis, and tests using the OSTIA system with 6

8 idealised observations. The statistical analysis will involve assessing the number and distribution of matchups required to determine statistically significant errors in SST analyses. A method will be developed to determine this and will have applications for many future studies such as comparing different analysis products or testing developments of individual analysis systems. The idealised observation tests will involve running a control of the OSTIA system to generate a set of idealised observations, with different regional and temporal sampling strategies. Random noise will be added to simulate some degree of observation error. A series of perturbed OSTIA runs using, for example, different input data or different background information will be generated and the accuracy of the runs will be assessed using the idealised observations. The experiment will identify the observation sampling strategies necessary for determining a statistically significant difference between the control and perturbed runs. This will provide information on the minimum suitable sampling needed for assessment of the accuracy of SST analyses. The results from these two studies will be used to quantify the requirements of Argo data for the validation of SST analyses. 5. Summary Near-surface Argo data has been used extensively at the Met Office as a reference dataset for validation, in order to assess the accuracy of SST analyses. The independence of the Argo data from SST analyses make this dataset a very valuable asset. Validation using Argo data will be made routine for the operational Met Office systems OSTIA and GMPE, and the Argo data will be used to validate diurnal cycles in an SST analysis, using the Met Office OSTIA skin temperature diurnal product currently under development. Statistical methods and results from idealised observation experiments will also be used to assess matchup sampling requirements for Argo data in studies of SST analysis accuracy. The results from these studies will be used to quantify and refine Argo data requirements for the validation of SST insitu/satellite analysis products. 7

9 Glossary AATSR Advanced Along Track Scanning Radiometer AVHRR Advanced Very High Resolution Radiometer CCI Climate Change Initiative ECMWF European Centre for Medium Range Weather Forecasting ESA European Space Agency GAMSSA Global Australian Multi-Sensor SST Analysis GHRSST Group for High Resolution Sea Surface Temperature GMPE GHRSST Multi Product Ensemble GOES Geostationary Operational Environmental Satellites GTS Global Telecommunications System HadISST2 Hadley Centre Ice and Sea Surface Temperature (analysis) IASI Infra-red Atmospheric Sounding Interferometer IR Infra-red MGDSST Merged Global Daily Sea Surface Temperature MW Microwave NCDC National Climatic Data Center NCEP National Centers for Environmental Prediction NOAA National Oceanographic and Atmospheric Administration NRT Near Real Time NWP Numerical Weather Prediction NWS National Weather Service OI Optimal Interpolation OSI SAF Ocean and Sea Ice Satellite Application Facility OSI-TAC Ocean and Sea Ice Thematic Assembly Center OSTIA Operational Sea Surface Temperature and Ice Analysis RTG Real Time Global (analysis) SSMIS Special Sensor Microwave Imager/Sounder SEVIRI Spinning Enhanced Visible and Infra-Red Imager SST Sea Surface Temperature TMI Tropical Rainfall Measuring Mission (TRMM) Microwave Imager 8

10 References Donlon, C.J., M. Martin, J.D. Stark, J. Roberts-Jones, E. Fiedler and W. Wimmer (2012), The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), Rem. Sens. Env., 116, Fiedler, E.K. et al. (2013), Comparison of long-term SST reanalyses, in prep. Ingleby, B. and M. Huddleston (2007), Quality control of ocean temperature and salinity profiles historical and real-time data, J. Mar. Syst. 65, Martin, M. et al. (2012), Group for high resolution sea surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE), Deep-Sea Research II 77-80, Reynolds, R.W. et al. (2013), Objective determination of feature resolution in two sea surface temperature analyses, J. Climate 26, Roberts-Jones, J., A. McLaren and M. Martin (2013), Estimating and assessing the impact of background error covariance parameters in the OSTIA system, in prep. 9