Global Land-Atmosphere-Ocean Interface Process Studies by Integrating MERRA Reanalysis with Satellite and In situ Data.

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1 Global Land-Atmosphere-Ocean Interface Process Studies by Integrating MERRA Reanalysis with Satellite and In situ Data Principal Investigator: Co-PIs: Xubin Zeng (Univ. of Arizona) Michael Bosilovich (NASA/GSFC/GMAO) Michael Brunke and Armin Sorooshian (Univ. of Arizona) Proposed Cost: $796,627 (1 January December 2016) Program: NASA NNH12ZDA001N-MAP (Research Theme: MERRA) Proposal Summary Modern Era Retrospective-analysis for Research and Applications (MERRA) is a NASA reanalysis developed under the MAP Program, and it represents an important GSFC/GMAO contribution towards the international reanalysis efforts. MERRA offers both hourly twodimensional fields and 3-hourly three-dimensional atmospheric variables and their tendencies. It also includes the Incremental Analysis Update, the replay capability, the observations assimilated and the associated forecast error and analysis error. Our current MAP project has yielded 11 peer-reviewed publications, 3 submitted manuscripts, several manuscripts in preparation, 8 invited and 13 contributed presentations (from July 2009 to May 2012). Building on these accomplishments and using the unique capabilities of MERRA (including its land-only reprocessing and updated short-term reanalysis), our overall goal is to improve the understanding and parameterization of land-atmosphere-ocean interface processes by integrating the MERRA reanalysis with satellite, surface, and aircraft data. Four research questions will be addressed: (a) how realistic is the terrestrial energy and hydrological cycle of the MERRA? (b) how do the near-surface atmospheric fields affect global energy and water cycle, dynamic vegetation and carbon cycle? (c) what is the diurnal cycle of summertime land-atmospheric boundary layer-convection coupling? and (d) what are the diurnal and seasonal cycles of the ocean surface-stratocumulus interactions? Six specific tasks will be carried out: 1) measurements of near-surface atmospheric fields and land surface fluxes will be combined with soil moisture, stremflow, and snow measurements to evaluate various reanalysis products; 2) MERRA and GEOS5 land surface skin temperature will be evaluated and improved; 3) global hourly near-surface atmospheric fields will be developed by adjusting reanalysis products with in situ and satellite measurements; 4) their impact on land surface energy, water, and carbon cycle and dynamic vegetation will be assessed; 5) land-atmospheric boundary layer-convection interaction over the U.S. Great Plains in summer will be evaluated; and 6) marine stratocumulus-aerosolsradiation-precipitation interaction will also be studied. For the last two tasks, detailed budget analysis will be emphasized. The proposed work will directly address two NASA Earth Science Key Questions: How is the Earth system changing? and What are the sources of change in the Earth system and their magnitudes and trends? Our investigations using MERRA data represent one of the specific themes of the MAP solicitation. Our evaluation and improvement of the GEOS5 model (as used in MERRA) will directly contribute to the mission of GMAO - a core MAP project. Our use of satellite, surface, and aircraft data is also emphasized by the MAP program. The planned GMAO visit of the PI and students will accelerate the transition of our research results (including model improvements) to MERRA and GEOS5. 1

2 Table of Contents Total No. of Pages Cover Page Proposal Summary 1 Table of Contents 1 Scientific/Technical/Management Section 15 References 6 Biographic Sketches 5 Current and Pending Support 6 Budget Justification: Narrative and Details 7 2

3 Scientific/Technical/Management Section 1 Results from prior NASA/MAP support Our current NASA/MAP grant (NNX09AO21G, $732,000, July June 2013, Integrated Evaluations and Applications of the MERRA Reanalysis Data over Northern High Latitudes ) has yielded 11 peer-reviewed publications, 3 submitted manuscripts, and several manuscripts in preparation: (1) Monthly mean temperature (Zeng and Wang 2012): Monthly mean land surface air temperature has been computed as the average of daily maximum and minimum temperatures. This is different from the true monthly mean temperature, which can be very accurately represented using hourly data, as has long been recognized. We argue, from scientific, technological, and historical perspectives, that it is time to compute the true monthly mean using hourly data for the national and international climate data record. (2) Hourly air temperature (two manuscripts in preparation): We have come up with a new idea to develop a global 0.5 hourly air temperature data from based on the CRU TS3.1 in situ data as well as MERRA, ERA-40, ERA-Interim, and NCEP reanalysis products. One of the major findings is that the trend of the amplitude of the monthly averaged diurnal cycle from our hourly data is significantly lower than the diurnal amplitude from CRU. We also recommend that future reanalysis efforts save hourly (rather than 3-hourly or 6-hourly) near-surface two-dimensional fields. (3) Specific humidity inversion climatology (Brunke et al. 2012): We have documented the specific humidity inversions in polar regions and, to a lesser degree, over the subtropical stratus decks using MERRA, CFSR, ERA-40, and NCEP-2 reanalyses as well as rawinsonde data. The mechanism was explored by the water vapor budget analysis. The capability of several climate models in simulating such an inversion was also evaluated. (4) Land-precipitation coupling (Zeng et al. 2010; one manuscript in preparation): A new parameter Γ, representing the ratio of the covariance between monthly or seasonal precipitation and evaporation anomalies from their climatological means, is proposed to estimate the land-precipitation coupling strength. It provides a simple and useful index to quantify the results from global and regional reanalyses, regional and global climate modeling. It has also been used to characterize the results from CMIP5 models. (5) Temperature trend in IPCC AR4 and AR5 models (Sakaguchi et al. 2012a,b): We have quantified the spatial and temporal scale dependence of surface temperature trends simulated by climate models for AR4 and AR5 using in situ temperature data in the 20th century. Two major conclusions are: robust climate signals at 30 or larger spatial scales and 40 years or longer temporal scales can be reproduced by the multi-model ensemble mean; and the progress in model performance from AR4-generation to AR5-generation is relatively small. (6) Reanalysis evaluations (Brunke et al. 2011; Wang and Zeng 2012; Decker et al. 2012; one manuscript in preparation): We have done comprehensive evaluations of various reanalysis products using cruise ship observations over global ocean, flux tower data over North America, weather station and field campaign data over Tibet Plateau, and field campaign data over sea ice. Strengths and weaknesses of each reanalysis were identified. One relevant conclusion is that MERRA is among the best performing for latent and sensible heat fluxes, and wind stress over ocean. 3

4 (7) Surface temperature improvement over arid regions (Zeng et al. 2012a): Over arid regions, two land models Noah and CLM have difficulty in realistically simulating the diurnal cycle of surface skin temperature. Based on theoretical arguments and synthesis of previous observational and modeling efforts, three revisions are developed to address this issue. (8) Monthly river flow forecasting (Zeng et al. 2012b): A simple model is developed for monthly river flow forecasting using the river flow and river basin averaged precipitation in prior month. It is based on water balance, easy to use and reproduce, and robust to calibrate with a short period of data. (9) Atmospheric effect on Earth s surface temperature (Zeng 2010): It is frequently stated that the surface temperature of Earth is 33 C warmer than it would be without the atmosphere and that this difference is due to the greenhouse effect. We find that the atmospheric effect leads to warming of only 20 C. This requires a revision to all of the relevant literature in K-12, undergraduate, and graduate education material and to science papers and reports. (10) Book chapters (Zeng and Dominguez 2012; Niu and Zeng 2012): We finished a book chapter on planetary boundary layer for a textbook and a chapter on land modeling review for the Encyclopedia of Sustainability Science and Technology. We have also given 8 invited and 13 contributed presentations under this project (omitted). In particular, Zeng traveled to give an invited GMAO seminar and interact with various GMAO colleagues involved in the development of GEOS5 and MERRA in November Introduction Observationally-driven modeling and data assimilation related to weather and climate are the focus of the NASA Modeling, Analysis, and Prediction (MAP) program, and the modeling spans the spatial and temporal scales that characterize satellite observations and observations from ground and air based campaigns. One of the two primary projects funded by MAP is related to the GSFC Global Modeling and Assimilation Office (GMAO), and one of the specific research themes in this MAP solicitation is the investigations using the Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis data developed at GMAO (Rienecker et al. 2011). MERRA is a NASA reanalysis for the satellite era using a major new version of the Goddard Earth Observing System (GEOS5) Data Assimilation System developed under the MAP Program. MERRA covers the period of 1979-present, and represents one of NASA s deliverables for the Climate Change Science Program (CCSP). It also represents an important contribution from GSFC/GMAO towards the international reanalysis efforts. In fact, Mike Bosilovich at GMAO, who is the MERRA PI and Co-PI of this proposal, just chaired the WCRP Reanalysis Conference in May 2012 ( Under our current MAP project, we have made significant progress in the comprehensive evaluation of MERRA and other reanalysis products, in value-added data development, and in the application of MERRA and other data to improve our understanding of various processes in the climate system, as has been discussed in Section 1. Even though results from our MAP-funded work are still new, they have already made preliminary (but important) science impacts, as indicated by (1) Our new formulation for roughness lengths for momentum and heat (Zeng et al. 2012a) has been implemented in the NCEP global forcasting system (GFS) to improve the predic- 4

5 tion of land skin temperature over arid regions. (2) The paper of Zeng (2010) requires a revision to all of the relevant literature on atmospheric effect on Earth s surface temperature in K-12, undergraduate, and graduate education material and to science papers and reports. (3) National Weather Service (NWS) held an internal meeting to discuss the recommendation in Zeng and Wang (2012) on taking 24-hr average temperature as the climate data record, and decided to send a formal request to relevant parties within NOAA for further discussion. (4) We have finished the algorithm development and validation as well as production of global 0.5 hourly land surface air temperature dataset from To our knowledge, this is the first such hourly dataset in the world. (5) We have identified the strengths and weaknesses of each reanalysis (including MERRA) for their future improvement. We have also recommended at the WCRP Reanalysis Conference that all future reanalysis saves hourly output of two-dimensional near-surface fields, following the current practice of MERRA and the NCEP CFSR (Saha et al. 2010). This renewal proposal intends to continue and expand the very successful current MAP project, and our overall goal is to improve the understanding and parameterization of land-atmosphere-ocean interface processes by integrating the MERRA reanalysis with satellite, surface, and aircraft data. To further strengthen our expertise in aircraft and satellite measurements and data analysis in aerosol-cloud interactions, we have expanded our team by including Prof. Armin Sorooshian as a Co-PI. Four specific questions will be addressed: (a) how realistic is the terrestrial energy and hydrological cycle of the MERRA? (b) how do the near-surface atmospheric fields affect global energy and water cycle, dynamic vegetation and carbon cycle? (c) what is the diurnal cycle of summertime land-atmospheric boundary layer-convection coupling? and (d) what are the diurnal and seasonal cycles of the ocean surface-stratocumulus interactions? We emphasize the interdisciplinary studies of the global land-atmosphere-ocean interface processes (rather than just land processes or just land-atmosphere interactions) in this project for two reasons. First, such integrated studies are emphasized by the MAP program. Second, we have the experience in and track record for such studies. In fact, our model parameterizations of ocean-atmosphere interface processes (Zeng et al. 1998; Zeng and Beljaars 2005) and land-atmosphere interface processes (e.g., Zeng et al. 2002; Zeng and Decker 2009; Zeng et al. 2012a) as well as value-added datasets (e.g., Zeng et al. 2000; Zeng 2001; Barlage et al. 2005) have been widely used worldwide, including the implementation into the NCEP and ECMWF operational models as well as the NCAR Community Earth System Model (CESM; and Weather Research and Forecasting (WRF) model ( Furthermore, we have benefitted from the cross-fertilization of ideas in land-atmosphere versus ocean-atmosphere interactions. Section 3 discusses the relevance of our project to NASA earth science, and Section 4 describes the MERRA reanalysis system. Section 5 presents our proposed tasks including specific background for each task, and Section 6 provides our work plan and deliverables. 5

6 3 Relevance to NASA Earth Science The proposed work will directly address two NASA Earth Science Key Questions: How is the Earth system changing? and What are the sources of change in the Earth system and their magnitudes and trends? It will also help address two objectives in the NASA Strategic Goal 2.1 (Objective 2.1.5: Improve understanding of the roles of the ocean, atmosphere, land and ice in the climate system and improve predictive capability for its future evolution; and Objective 2.1.4: Quantify the key reservoirs and fluxes in the global water cycle and assess water cycle change and water quality). Our investigations using MERRA data represent one of the specific research themes of the current MAP solicitation. Our evaluation and improvement of the GEOS5 model (as used in MERRA) will directly contribute to the mission of GMAO as one of the two core projects of the MAP program. Our use of satellite, ground, and aircraft data is also emphasized by the MAP program. Furthermore, MERRA represents one of NASA s deliverables for the Climate Change Science Program (CCSP), and the proposed work will help accelerate its use by the broad community. The PI (Zeng) visited the GSFC several times before (including giving the GMAO seminar in November 2010). Zeng and two graduate students will visit GMAO during this project to accelerate not only our understanding of the GEOS5 and MERRA but also the transition of our research results (including model improvements) to MERRA and GEOS5. 4 Description of the MERRA reanalysis system To understand the MERRA data, it is necessary to understand the modeling and data assimilation systems as well as the in situ and satellite data used for assimilation, as documented in Rienecker et al. (2011). The modeling components of the MERRA system include the GEOS5 atmospheric general circulation model (AGCM) coupled with the Catchment Land Surface Model (LSM) (Koster et al. 2000), while the assimilation component includes the Gridpoint Statistical Interpolation (GSI) analysis (Derber et al. 2003). The GEOS5 AGCM uses a finite-volume dynamical core (Lin 2004). The moist physics parameterizations include the Relaxed Arakawa-Schubert (RAS) scheme for convection (Moorthi and Suarez 1992) and large-scale prognostic scheme for liquid versus ice phases of cloud condensate. The radiative transfer scheme (Chou and Suarez 1999) includes the absorption due to water vapor, O 3, O 2, CO 2, clouds, and aerosols. Interactions among the absorption and scattering by clouds, aerosols, molecules (Rayleigh scattering), and the surface are fully taken into account. Two atmospheric boundary layer (ABL) turbulent mixing schemes are used: the Louis et al. (1982) scheme for stable situations with no or weakly-cooling ABL cloud, and the Lock et al. (2000) scheme for unstable or cloud-topped ABLs. Two gravity wave drag schemes are also applied: one for the orographic gravity wave drag and the other for non-orographic waves. The Catchment LSM (Koster et al. 2000) considers a number of independent and irregularly shaped hydrological catchment elements in each atmospheric grid cell. Soil moisture variability is related to the topography and three bulk soil moisture variables (catchment deficit, root zone excess, and surface excess). The Catchment LSM includes the transpiration and other surface energy balance calculations from Koster and Suarez (1992). It also includes the three-layer snow submodel of Lynch-Stieglitz (1994) and Stieglitz et al. (2001). Radiative transfer through canopy is computed using a two-stream approximation with the 6

7 monthly mean snow-free albedos adjusted by the MODIS albedo data (Moody et al. 2005). The three-dimensional variational (3DVar) analysis is applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (Wu et al. 2002). It is coupled with the Community Radiative Transfer Model (CRTM) (Han et al. 2006; for satellite radiance assimilation. In particular, to address shocks introduced by imbalances in the mass-wind increments, the incremental analysis update (IAU) procedure (Bloom et al. 1996) is adopted. The MERRA system has a horizontal resolution of 1/2 latitude by 2/3 longitude. There are 72 vertical layers from surface to 0.01 hpa. Observed sea surface temperature and sea ice (Reynolds and Smith 1995) are used. Instantaneous three-dimensional (3-D) analysis fields and hourly 2-D diagnostic fields are available. Surface data, near-surface meteorology, selected upper-air levels, and vertically integrated fluxes and budgets are produced at hourly intervals. These products are particularly useful for some of our proposed tasks (to be discussed later). Recognizing the MERRA biases in the terrestrial hydrological cycle, a supplemental and improved set of land products (MERRA-Land) was generated recently (Reichle et al. 2011) and will be used in our project. MERRA-land uses the global raingauge-based precipitation and revised parameter values in the Catchment LSM rainfall interception treatment changes that effectively correct for known limitations in the MERRA surface meteorological forcings. Furthermore, as a NASA contribution to the National Climate Assessment, GMAO plans to do an updated 25-km reanalysis (including an aerosol analysis) for the EOS/Aura period (2004 onwards) (Bosilovich 2012), and this dataset will be used in some of our tasks as well. 5 Proposed work In order to reach our overall goal and address the four specific research questions raised in Section 2, we propose to carry out 6 tasks below. In the MERRA overview article, Rienecker et al. (2011) concluded that substantial differences between reanalyses and observations remain in poorly constrained quantities such as precipitation and surface fluxes. These differences, due to variations both in the models and in the analysis techniques, are an important measure of the uncertainty in reanalysis products. Partly for this reason, the first 4 tasks in Section 5.1 and 5.2 are devoted to land surface processes. Tasks 5 and 6 in Section 5.3 will focus on the land-atmosphere and ocean-atmosphere interactions because these issues are challenging and unresolved and can be better addressed using MERRA s unique capabilities (discussed in Section 5.3) along with surface, aircraft, and satellite data. 5.1 Terrestrial energy and hydrological cycle Task 1: Terrestrial energy and hydrological cycle In our current MAP project, we have evaluated the near-surface air temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes from five reanalysis products using in situ measurements from 33 flux tower sites over North America (Decker et al. 2012). We have also evaluated the temperature and precipitation from 63 weather stations and various near-surface atmospheric fields and surface fluxes using 9 stations from field campaigns over the Tibet Plateau (Wang and Zeng 7

8 2012). However, we didn t discuss how the performance of each reanalysis in surface fluxes was related to other hydrological components (e.g., soil moisture, snowmelt). In contrast, Reichle et al. (2011) evaluated precipitation, soil moisture, snow, and streamflow from MERRA and MERRA-land, but they didn t link their performances to surface fluxes. Therefore, there is a community consensus that all these quantities need to be evaluated together to better understand the full terrestrial energy and hydrological cycle. This will be addressed in Task 1 through several steps. Just as in Decker et al. (2012) and Wang and Zeng (2012), MERRA and other reanalysis products, such as the ERA-40 (Uppala et al. 2005), ERA-Interim (Dee et al. 2011), NCEP Climate Forecast System Reanalysis or CFSR (Saha et al. 2010), and NCEP reanalysis (Kalnay et al. 1996), will be evaluated together. Furthermore, the MERRA-Land product and the new Japanese 55-year reanalysis (JRA-55; to be available by early 2013; Ebita et al. 2011) will be evaluated. First, the comprehensive observations of near-surface atmospheric fields and surface fluxes from Decker et al. (2012) and Wang and Zeng (2012) will be combined with the soil moisture, streamflow, and snow observations from Reichle et al. (2011) to evaluate all these products. Furthermore, our team has been proactive in evaluating and improving the models at the NCAR CESM (e.g., Zeng et al. 2002, Dai et al. 2003), NCEP operational GFS model (e.g., Zeng et al. 1998; Wang et al. 2010), and ECMWF operational model (e.g., Zeng 2001; Zeng and Beljaars 2005). In fact, we are one of the very few groups whose model improvements over land and ocean and land surface value-added datasets have been implemented in all these models. Leveraging these current and prior efforts, additional datasets and analysis methods from these studies will be used in Task 1. We have also used snow and turbulent flux data from three forest sites and a grass site to evaluate and improve the snow submodel in the Noah LSM (i.e., the land component in the GFS) (Wang et al. 2010). Our snow improvements have been tested within NCEP for near-future implementation into GFS. Data from these sites will also be used in Task 1. Recognizing the spatial heterogeneity of point soil moisture measurements, we have worked with Russ Scott at the USDA Agricultural Research Service in Tucson to analyze the multi-decadal data from 88 raingauges (from 1956 to present) and from 19 soil moisture probes at 5-cm depth (from 2002 to present) over the Walnut Gulch Experimental Watershed (with an area of 150 km 2 ) in southern Arizona (Stillman et al. 2012). This provides an excellent dataset for reanalysis evaluation, and will be used in Task 1 as well. Under a NSF Instrument Grant, we have set up a prototype soil moisture measurement network over the U.S. ( The probe measures low-energy cosmic-ray neutrons above the ground, whose intensity is inversely correlated with soil water content and with water in any form above ground level (Zreda et al., 2008, 2011). In contrast to traditional point measurements, this novel, non-contact technique is capable of measuring average soil water content over a horizontal circle with a diameter of 700 m and depths up to 50 cm. This unique dataset will also be used in Task 1, representing a synergy of the proposed work with our current NSF instrumentation project. One of the improvement of MERRA-Land over MERRA is the reduction of the precipitation interception loss (Reichle et al. 2011). More generally, the evapotranspiration can be decomposed into three parts: dry leaf transpiration, wet leaf evaporation (from intercepted precipitation), and ground evaporation. We have addressed this decomposition and 8

9 the model evaporation improvement in the NCAR Community Land Model (CLM3.5; Sakaguchi and Zeng 2009) which has since been implemented in CLM4.0 as well as in WRF3.4. We will do a similar decomposition analysis of reanalysis products in Task 1 for both reanalysis evaluation and model improvement (especially in GEOS5 as used in MERRA). Task 2: Land surface skin temperature Land surface skin temperature (T s ) plays a significant role in the surface energy balance and is also widely available from remote sensing for model evaluation and data assimilation (e.g., Jin et al. 1997; Reichle et al. 2010). Over arid and semiarid regions that represent one-third of the global land, both offline land models and land-atmosphere coupled models (including CESM and GFS) still have difficulty in realistically simulating or predicting T s. Furthermore, these model biases negatively affect the assimilation of satellite radiance from surface-sensitive channels. A possible solution from prior studies (e.g., Zeng and Dickinson 1998; Mitchell et al. 2004; Chen and Zhang 2009; Chen et al. 2010) for improving the daytime T s simulation is to revise the formulation for computing roughness length for heat. The question is: how robust are such formulations with respect to different land models and different elevations? Such formulations, however, have a minimal effect on nighttime T s. We have recently addressed the T s diurnal cycle using both CLM (as used in CESM) and Noah (as used in GFS) (Zeng et al. 2012a). In situ data at Desert Rock (36.63 N, W; elevation: 1007 m), Nevada from 3-31 July 2007 and at Gaize (32.30 N, E; elevation: 4416 m), Tibet from 3-31 May 1998 were used. For instance, Noah significantly underestimates early afternoon T s by about 7 C at Desert Rock and 12 C at Gaize. It also underestimates the nighttime T s by about 1 C at Desert Rock and 3-6 C at Gaize. Our revisions in Zeng et al. (2012a) significantly improves the Noah results by decreasing the mean absolute deviation from 2.8 C to 0.5 C at Desert Rock, and from 5.8 C to 1.6 C at Gaize. By significantly reducing the T s bias, our new formulations also increase the utilization of satellite data (such as the NOAA-17 HIRS-3 and NOAA-18 AMSU-A) and reduce errors in brightness temperature (T b ) simulation at window channels (Zheng et al. 2012). For instance, NOAA-17 HIRS-3 channel 8 (centered at 11.11µm) is very sensitive to surface characteristics. Figures 1a,b show the spatial distribution of assimilated satellite data valid at 18:00 UTC, 1 July In the control run, most of the satellite data are excluded over the western continental U.S. (CONUS) due to relatively large T b biases and cloud conditions. With the improvement in T s, more satellite data are included in the data assimilation (Fig. 1b). Figures 1c,d give the histograms for land and various land surface categories within the red box. The frequency distributions for these surface types are strongly skewed to the left (negative bias) in the control run. These large errors are reduced in the sensitivity experiment (Fig. 1d) for all categories. The bias over land is reduced to 1.8K from 6.0K and the RMSE is reduced to 3.9K from 7.7K with respect to the control run. Since both CLM and Noah have this bias and both MERRA and GFS use the same data assimilation system, our hypotheses are: GEOS5 and MERRA also have this T s bias; very few satellite radiance data at surface-sensitive channels are assimilated in MERRA over arid regions; and our solutions in Zeng et al. (2012a) will improve the T s simulation in GEOS5, improve the T s results in MERRA, and increase the number of assimilated satellite data in MERRA. These hypotheses will be tested in Task 2 using the in situ data in Zeng et al. (2012a) and Zheng et al. (2012) as well as the GOES satellite-derived gridded fields 9

10 of hourly 0.5 T s data in cloud-free conditions (Pinker et al. 2009). Furthermore, because we focus on arid regions (i.e., with limited cloud cover most of the time), we will also use the MODIS global clear-sky T s data from Terra and Aqua that are available four times a day (e.g., Wan 2008; Jin and Dickinson 2012). Fig. 1: Spatial distribution of satellite pixels used in GSI: T b bias of channel 8, NOAA-17 HIRS-3 from (a) control run and (b) sensitivity run. Frequency distribution of T b bias for all clear-sky pixels in red boxes in Figs. 1a,b from (c) control run and (d) sensitivity run. T b biases and RMSE (1st and 2nd values in parentheses) for various land surface categories are also given (Figure 8 in Zheng et al. 2012). First, we will evaluate the MERRA T s data to document the bias over global arid regions. Then, we will run the GEOS5 with observed sea surface temperature and sea ice over the same period of MERRA with hourly and 3-hourly fields saved following MERRA. These model results will also be used in other tasks. We will then evaluate the simulated T s and surface energy balance over global arid regions and see if the T s bias exists. Assuming that the bias exists, we will then implement our revisions in Zeng et al. (2012a) in GEOS5 and repeat the simulations to assess their impacts. We will also evaluate the actual satellite radiance data in surface-sensitive channels that are assimilated in MERRA over arid regions. We will then choose one July and one January and repeat the MERRA processing based on the revised GEOS5. Its impact on the MERRA T s results and on the number of assimilated satellite data will be assessed. We will then pass these revisions (through our Co-PI, Bosilovich, at GMAO) for use in future data assimilation at GMAO and for implementation in the next version of GEOS. 5.2 Near-surface atmospheric fields and their impacts on energy and water cycle, dynamic vegetation and carbon cycle Task 3: Near-surface atmospheric fields Near-surface atmospheric fields include precipitation (rainfall versus snowfall), downward solar and longwave radiation, near-surface air temperature, humidity, and wind. Various reanalysis near-surface fields have been evaluated in the past (e.g., Decker et al. 2012; Wang and Zeng 2012). Recognizing the strengths and weaknesses of reanalysis data, different approaches have been developed for the adjustment of reanalysis data (Rodell et al. 2004; Qian et al. 2006; Sheffield et al. 2006) that have been widely used (e.g., in land modeling, hydrological modeling, drought monitoring, crop modeling, dynamic vegetation modeling). The usual approach is to use the reanalysis for diurnal (3-hourly or 6-hourly) cycle with the 10

11 mean (usually monthly, occasionally daily) values adjusted using in situ and satellite data. A major weakness of these data is the failure to consider diurnal amplitude adjustment (particularly of air temperature). One of the major progresses under our current MAP project is to develop the global 0.5 hourly surface air temperature data using reanalysis products with the monthly mean maximum and minimum temperatures adjusted based on the CRU in situ observations (see Section 1). The monthly maximum and minimum temperatures (and monthly mean temperature) from our final product are exactly the same as those from CRU at each grid cell, while our product also provides the consistent hourly data. This hourly product contains more than 1 Tb of data. In Task 3, we will expand our hourly temperature data development to the development of global 0.5 hourly data of all near-surface fields (precipitation, downward solar and longwave radiation, air temperature, humidity, and wind). Besides the temperature adjustment discussed above, the adjustment approach of other variables will initially follow prior and similar methods (Rodell et al. 2004; Qian et al. 2006; Sheffield et al. 2006). For instance, Qian et al. adjusted the NCEP reanalysis (Kalnay et al. 1996) precipitation data based on raingauge and satellite-raingauge merged data. They then adjusted specific humidity based on the revised air temperature and reanalysis relative humidity, and found good agreement of the adjusted specific humidity with the observed data (Dai 2006). For the downward solar flux, the anomaly was first adjusted for variations and trends using monthly station records of cloud cover anomaly and then for mean biases using satellite data (Zhang et al. 2004). Surface wind and pressure were not adjusted. We will then evaluate these methods using the flux tower data (that include all these fields) as used in Decker et al. (2012). If deficiencies in these methods are identified, we will develop the more appropriate methods based on these data analyses in Task 3. While it is challenging to develop an appropriate method that is applicable globally, we have demonstrated our capability and patience in temperature analysis. For instance, while it is quite easy to directly adjust daily maximum and minimum temperatures using CRU monthly data, we spent six months to realize that this direct approach does not always work over regions (e.g., high latitudes) with relatively small diurnal range, and then develop a new approach to ensure that the adjusted reanalysis data have exactly the same monthly mean maximum and mimimum (and monthly average) temperatures in CRU. In Task 3, we will also pay more attention to the precipitation partitioning into rainfall versus snowfall and into convective versus large-scale precipitation. Because of the latent heat of fusion, the separation between rainfall versus snowfall is very important when the precipitation data are used to drive land surface processes. The separation between convective and large-scale precipitation is also important for land modeling (e.g., canopy interception). In fact, one major difference between MERRA and MERRA-Land is the consideration of this separation (and hence reduction of precipitation interception) in the latter. Furthermore, we will pay attention to the partitioning of solar radiation into direct and diffuse radiation for the visible and near-infrared bands. In particular, this partitioning is affected by atmospheric processes and has a recognizable diurnal cycle. While comprehensive observational data are not available, we will at least compare various reanalysis products to provide the uncertainty range of these partitionings. 11

12 Task 4: Global offline modeling of land surface energy, water, and carbon cycle and dynamic vegetation The comprehensive data developed in Task 3 will be made widely available to the community for a variety of applications. In Task 4, we will evaluate the impact of these data on global offline land modeling. Just as in Tasks 1 and 2, the energy and water cycle will be emphasized. Furthermore, we will focus on the carbon cycle and dynamic vegetation because of their importance in the earth system and our lack of understanding of these processes. For instance, eight CMIP5 climate models clearly show large scattering among themselves and from observationally inferred values in the gross primary production (GPP) and leaf-area index (LAI) (Fig. 2). The NCAR CLM and the dynamic vegetation model (CNDV) will be used since we have been heavily involved in these models development (e.g., Zeng et al. 2008; Oleson et al. 2010; Lawrence et al. 2011). We will first do global offline CLM simulations using the new data and Qian et al. (2006) data. Comprehensive evaluations of the results (including the energy and water cycle) will then be done following those in Tasks 1 and 2 and in our prior efforts (e.g., Decker and Zeng 2009; Wang and Zeng 2011). Fig. 2: Terrestrial zonal average of gross primary production (GPP) and leaf-area index (LAI) from 60 S to 90 N from in the historical CMIP5 experiments from eight different climate models (color lines) and observations (solid black lines) (Figure 5 in Shao et al. 2012). In contrast to the energy and water cycle, the terrestrial carbon cycle takes much longer time to reach a quasi-equilibrium than the 55-year period of [i.e., the overlapped period of the new data and Qian et al. (2006) data]. We will do global offline CNDV simulations for 670 years by cycling 12 times the 55-year data in Task 4. This is the standard approach for offline dynamic vegetation-carbon cycle modeling. Based on our prior experience, 600 years are needed to reach a quasi-equilibrium. The analysis will follow our prior and current work (Zeng et al. 2008; Shao et al. 2012). We will analyze the different components in the carbon cycle, including the GPP, autotrophic respiration (Ra), net primary production (NPP = GPP Ra), heterotrophic respiration (Rh), and net ecosystem production (NEP = GPP Ra Rh). LAI will also be analyzed. Following our recent work (Shao et al. 2012), MODIS-based LAI data (Myneni et al. 2002) and 12

13 observationally inferred data for other quantities from different sources (e.g., Zhao et al. 2005; Jung et al. 2011) will be used. We will also evaluate the relationship between the energy and water cycle and the carbon cycle. Furthermore, we will evaluate the differences in energy and water cycle between the above CLM and CNDV simulations (caused by the dynamic vegetation versus observed vegetation data). 5.3 Land-atmosphere and ocean-atmosphere interactions Both land-atmosphere and ocean-atmosphere interactions are large and challenging issues and fully addressing each of them would require a full proposal. Our strategy here is to leverage the unique capability of MERRA and our prior work to address some of the relevant issues that are important for our understanding, data assimilation, and weather/climate modeling but yet remain to be resolved. One is the land surface-atmospheric boundary layer (ABL)-convection interaction over midlatitudes in summer. The other is the marine stratocumulus-aerosols-radiation-precipitation interaction. One of the unique opportunities offered by MERRA is the availability of 3-hourly atmospheric variables and their tendencies. For instance, for the liquid water content (LWC), these tendencies include contributions from all of the moist physics, turbulence, atmospheric dynamics, and analysis increment. The tendency from analysis increment is needed to constrain the evolution of the reanalysis steps to reconcile with all the various observations, both explicit observations as well as the effects in satellite channel radiances sensitive to moisture (Bosilovich et al. 2011). MERRA also provides the production rate and evaporation of precipitation. These will allow us to assess which processes are dominant in the production of LWC throughout the day in marine stratus/stratocumulus. Another unique capability of MERRA is the Incremental Analysis Update (IAU) that adds the analysis into a separate forecast cycle through the model budget equation. Therefore, MERRA is expected to have little spindown effect and provides a good opportunity to address spinup of the model fields and fluxes (specifically precipitation) that has been a difficult issue for reanalysis (Andersson et al. 2005). Furthermore, MERRA offers the Gridded Innovations and Observations (GIO) data, i.e., the observations assimilated and the associated analysis error and forecast error (Bosilovich, 2012). Task 5: Land-ABL-convection interaction over the Great Plains in summer Numerous studies have attempted to address the land-precipitation coupling, but our understanding remains limited and discrepancies still exist from different studies (e.g., Bosilovich et al. 2009). We recently proposed a new parameter Γ to estimate the landprecipitation coupling strength (Zeng et al. 2010; also see Section 1). The Γ value is easy to compute and insensitive to the horizontal scales used; however, it does not provide causality. A relatively high Γ is a necessary but not sufficient condition for a strong land-precipitation coupling. Kennedy et al. (2011) evaluated the atmospheric and surface quantities from MERRA using the in situ and satellite measurements over the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site. However, the actual land-precipitation coupling processes were not discussed in Kennedy et al. (2011) or Zeng et al. (2010). We have evaluated the MERRA precipitation in Decker et al. (2012) and Wang and Zeng (2012) using raingauge data. We have also addressed the precipitation spatial heterogeneity issue using 120 raingauges and 4 km hourly radar/raingauge merged Stage-IV data over a 13

14 200 km 200 km area within the state of Ohio (Kursinski and Zeng 2006). Furthermore, we have analyzed the hourly output of an earlier version of the NCAR climate model and found that the model contained a spurious cycle: too much drizzle was produced; the drizzle then evaporated in the ABL, never reaching the surface for soil moisture; this maintained a relatively moist ABL, producing drizzle again. In Task 5, we will focus on the land-abl-convection interactions over the Great Plains partly because of the ARM SGP data availability ( First, the hourly radar/gauge merged Stage-IV data over the Great Plains will be used to evaluate the precipitation diurnal cycle from MERRA and MERRA-Land. In particular, in addition to the afternoon convection, there is a well-known nocturnal precipitation peak over the Great Plains due to the low level jet (LLJ) (e.g., Balling 1985; Higgins et al. 2007). Then we will use the ARM SGP data (e.g., as used in Kennedy et al. 2011) to evaluate land surface, ABL, clouds, aerosols, and precipitation in MERRA-Land in Task 5. Note that while MERRA-Land is expected to produce more realistic land surface quantities than MERRA, they have the same atmospheric states. In particular, the coupling processes (e.g., Seneviratne et al. 2010) will be emphasized in these evaluations. For instance, soil moisture anomaly my produce a positive or a negative response in the precipitation, depending on the atmospheric profile (Findell and Eltahir 2003): wet soil moisture may increase surface evapotranspiration and hence moisten the ABL, while decreased sensible heat flux may slow down the daytime ABL growth. Furthermore, the ABL top entrainment is also important for ABL growth. How these processes compete to affect convection will be emphasized. In Task 5, a key idea is to analyze the 3-hourly budget terms of temperature, humidity, and cloud water and ice available at MERRA (and MERRA-Land). As a NASA contribution to the National Climate Assessment, GMAO plans to do an updated 25-km reanalysis (including an aerosol analysis) for the EOS/Aura period (2004 onwards) (Bosilovich 2012). This dataset is ideal for our budget analysis and will be available for our use. Furthermore, if needed (based on 3-hourly data evaluations), we will produce hourly budget terms through the help of Co-PI Bosilovich. These analyses would help us to better understand the coupling processes during the daytime and the role of LLJ in nocturnal precipitation peak. In Task 5, the results from GEOS5 will be analyzed and the differences between GEOS5 and MERRA-Land represent the impact of data assimilation. Furthermore, we will analyze the relationship between MERRA s performance and the use of observations in the assimilation (or lack thereof) using the MERRA Gridded Innovations and Observations (GIO) data mentioned earlier. From these analysis, we also expect to come up with potential ideas for GEOS5 model improvement related to the coupling processes. Then we will test these ideas using the replay capability of MERRA. Task 6: MERRA evaluation and budget analysis over marine stratocumulus regions Marine stratocumulus clouds are widely recognized to be important in the climate system (e.g., Bony and Dufresne 2005). Despite intensive efforts in the past few decades (including several field campaigns), these clouds remain difficult for current global climate models to represent correctly (Wyant et al. 2010). Our strategy here is to use the unique capabilities of MERRA in combination with surface, aircraft, and satellite measurements to shed new light on this challenging issue. 14

15 Cloud thickness (m) We have compared the liquid water path (LWP), cloud fraction, cloud top height, and cloud base height as retrieved by a suite of instruments on board several A-train satellites (the CPR aboard CloudSat, CALIOP aboard CALIPSO, and MODIS aboard Aqua) with ship observed quantities using a microwave radiometer (MWR) for LWP, a ceilometer for cloud fraction and base, and a millimeter-wave cloud radar (MMCR) for cloud top over the southeast Pacific (Brunke et al. 2010). For instance, CloudSat LWPs were found to be too high, while CloudSat/CALIPSO thicknesses were too high due to its cloud bases being too low, resulting in a very different relationship between LWP and cloud thickness (LWP h 9 ) than observed (LWP h 2 ) (Fig. 3a). The ship observations show a somewhat logarithmic increase in median LWP with increasing cloud fraction as in Zhou et al. (2006). However, MODIS cloud fraction is nearly independent of the median CloudSat LWP even when possible precipitation contamination is removed (Fig. 3b). Co-PI Sorooshian has conducted aircraft measurements of aerosol properties in stratus and stratocumulus off of the coast of California in a number of field campaigns (2005 MASE I, 2007 MASE II, 2011 E-PEACE) using the Center for Interdisciplinary Remotely Piloted Aircraft Studies (CIRPAS) Twin Otter. These field campaigns were focused on examining aerosol-cloud-precipitation-radiation interactions. In these studies, Sorooshian participated in detailed measurements of aerosol physicochemical properties (composition, size distribution, water-uptake properties, optical properties), and cloud and drizzle droplet size distributions, cloud droplet LWC, and other standard meteorological parameters. (a) CloudSat/CALIPSO or CloudSat/MODIS frequency LWP (g m -2 ) Cloud fraction (b) Fig. 3: Cloud thickness in (a) and fraction in (b) Ship versus LWP from cruises, ASTEX (sfc.) FIRE RACE TIWE (sfc.) ASTEX (air) CloudSat/ CALIPSO or CloudSat/ MODIS CloudSat/ CALIPSO or CloudSat/ MODIS (adjusted) CAM3.1 CAM LWP (g m -2 ) CloudSat/CALIPSO (with and without precipitation removed), NCAR models (CAM3.1 and CAM4), and other field experiments [AS- TEX, FIRE, RACE, and TIWE]. Shaded areas denote the frequency distribution of CloudSat LWP and CALIPSO thickness data. Thin line in (a) is the fit to LWP h 2 (Fig. 3 in Brunke et al. 2010). Owing to the diversity of measurements conducted in the California coastal zone during the summertime over a number of years, the data present a valuable opportunity for intercomparisons with satellite remote sensing data. As a result, these aircraft data have been combined with satellite retrievals of aerosol and cloud properties from NASA s A-Train constellation (Stephens et al. 2002, Sorooshian et al. 2009a, Lu et al. 2009). This series of five satellites in close formation provides a unique view of how aerosols influence cloud microphysics, precipitation, and radiative transfer. Properties retrieved by these satellites include LWP from CloudSat and MODIS, cloud optical depth from MODIS, cloud effective radius from MODIS and CloudSat, aerosol index, aerosol optical depth (AOD), and 15

16 Angstrom coefficient from MODIS. Sorooshian et al. (2009b) also used aircraft and A- Train satellite data in conjunction with process-based aerosol-cloud models to address the aerosol-cloud-precipitation interactions in the context of a series of metrics for aerosol-cloud interactions (McComiskey et al. 2009). In Task 6, we will evaluate MERRA results using observational data over the southeast Pacific, near the California coast, and over other stratocumulus regions as utilized in our prior studies. The newly available data from the VOCALS field campaign over the southeast Pacific (Wood et al. 2011) will also be used. The corresponding results from GEOS5 will also be analyzed, and the differences represent the effect of data assimilation on the relevant quantities. We will also evaluate the relationship between MERRA s performance and the use of observations in the assimilation (or lack thereof) using the MERRA Gridded Innovations and Observations (GIO) data mentioned earlier. We have done detailed budget analysis (e.g., of LWC) in the past. This has been particularly efficient in identifying model deficiencies. For instance, we found that earlier versions of the NCAR climate model could incorrectly have clouds without cloud water. In Task 6, we will do a detailed analysis of processes and parameters that contribute to the temporal variation of clouds (e.g., liquid water, cloud fraction) and precipitation using the hourly and 3-hourly MERRA output. This will also help us to identify the dominant processes (parameters) contributing to clouds and precipitation and hence point to the direction for model improvement. For instance, if precipitation does not reach the ocean surface in the model (e.g., due to evaporation below stratocumulus), what is the effect on the maintenance of the stratocumulus itself and what is the main feedback loop? We will also do a detailed analysis of processes and parameters that contribute to the averaged diurnal cycle of clouds and precipitation in each month. This will help us to better understand the diurnal cycle and the dominant mechanisms involved. For instance, if the model cloud diurnal cycle is too strong, what is the main mechanism and what is the impact on ocean surface shortwave and longwave radiation and the net heat flux? We will also do a detailed analysis of daily mean (rather than monthly) processes and parameters that contribute to the annual cycle of clouds and precipitation. In particular, the transition seasons will be focused to better understand what are the main mechanisms for the seasonal transition of the stratocumulus clouds and how the atmosphere is coupled with the ocean in this period. Furthermore, in Task 6, we will evaluate the spinup effects by looking at the fields at the same time but forecasted at different intervals (e.g., 6-hourly versus 12-hourly forecasts). Comparison of GEOS5 and MERRA results will allow us to evaluate the effect of data assimilation on the budget analysis. The model improvements from these analyses will also be tested using the replay capability of MERRA. Detailed discussion of extensive surfacebased, aircraft, and satellite data is omitted here as they have been presented in the above papers (and also due to page limitation). 6 Work plan and deliverables Milestones and the lead for each task (with Stud1 and Stud2 denoting the two new Ph.D. students to be hired) are 16