Michele Rienecker Ron Gelaro Siegfried Schubert Michael Bosilovich & Arthur Hou Arlindo da Silva

1. Michele Rienecker Ron Gelaro Siegfried Schubert Michael Bosilovich & Arthur Hou Arlindo da Silva Dick Dee & Ricardo Todling Steven Pawson & Ivanka Stajner Christian Keppenne Rolf Reichle & Randy Koster (Ricky Rood & Bob Atlas) GMAO Global Modeling and Assimilation Office NASA/GSFC Climate Analysis Workshop, Boulder, August 18-20, 2003

2. Global Modeling & Assimilation Office NSIPP & DAO merger - a new focus activity for NASA’s global modeling and data assimilation • The Office will be a core resource for NASA’s Earth Science Enterprise in the development and use of satellite observations. Our main thrust will be to maximize the impact of satellite observations in climate and weather prediction using comprehensive global models and data assimilation. More broadly we will support of ESE research priorities and decision support systems for national applications. • Science areas: • Subseasonal-to-Decadal Prediction • Weather prediction • Hydrological Cycle • Technical areas: satellite data assimilation: usage, new mission design, instrument team products • Agency Partnerships: NOAA/NCEP, JCSDA, ESMF, NCAR, GFDL, NOAA/CDEP

3. GMAO Assimilation Mission • Satellite Data usage: implementing and maximizing the impact of current operational and research satellite data on NWP, short-term climate prediction, chemistry assessments, etc • Observing system issues: Observing System Simulation Experiments (OSSEs) - which new instruments have the highest probability of adding useful information to the observation network. Observing System Experiments (OSEs) - identifying impact of existing instruments • Production of research-quality assimilated datasets - including trace gases, aerosol and climate products, with the aim of maximizing the return of NASA’s investment in Earth observations

4. GMAO Assimilation Efforts • Atmospheric Data Assimilation: • meteorology: PSAS  Grid-point statistical interpolation (NCEP) • 1º  1.25 º  36 levels (includes stratosphere ) using FvGCM • constituents: ozone, aerosols, carbon species • 2º  2.5 º  42 levels (includes stratosphere) • Ocean Data Assimilation: OI and EnKF with Poseidon OGCM • 1/3º  5/8 º near global • Land Surface Data Assimilation: • surface temperature: PSAS with CLM • soil moisture: EnKF with Catchment LSM

5. GMAO Historical Analyses • Atmosphere: • reprocessing for Instrument Teams • Jan 2000 to present for MODIS • TRMM-period (GEOS3) • ReSTS (Reanalysis for Studies of Trace Species) • 1991 to present • Support for Chemistry transport • stratospheric chemistry (GSFC: ozone) • tropospheric chemistry (Harvard: GEOS-Chem) • Ocean: • Initial conditions for S-I forecasts • 1987 to present for NSIPP forecasts • 1980 to present for ODASI intercomparisons • Land: • Initial soil moisture conditions for S-I forecasts • from GLDAS (Houser and GLDAS Team)

6. GMAO Future Analyses • Atmosphere: • MERRA: Modern Era Reanalysis for Research and Applications • Bosilovich: REASON CAN funded effort • reprocess satellite-era: 1979-2009 • 1º  1.25 º • transition Precipitation assimilation (GEOS3) to GEOS5 • include coupled skin temperature assimilation • focus on water and energy cycle • will be the GMAO analysis in support of atmospheric composition, etc • Aerosols • Carbon Cycle: • Atmosphere, Ocean, Land Surface

7. GEOS-1 GEOS-2 TRMM 1997/11 – 1999/12 GEOS-3 Terra 1999/11 – 2002/10 GEOS-4 FVDAS 2002/10 - present UARS 1991/01 - 1993/09 STRAT 1995/05 - 1998/05 MAESA 1994/03 - 1995/02 GEOS GCM FVCCM MODEL 2 2.5L70 1 1L48 1 1.25L55 45L46 2 2.5L46 ANALYSIS Physical-Space Statistical Analysis System Optimal Interpolation Incremental Analysis Update Intermittent Update Conventional Observations (radiosondes, aircraft, …) OBSERVATIONS NESDIS – Retrieved TOVS Temperature TOVS/AMSU Radiance Sea-Surface Wind from Scatterometer Total Precipitable Water

8. Common Analysis System NCEP developments • GMAO developments • GMAO run-time choices for NASA applications Next system (GEOS-5) • Unified AGCM (FvCore + tbd physics) • ESMF • Joint Analysis System with NCEP • JCSDA: Accelerate the operational implementation of new satellite data types • OSSE capabilities Common Observation Processing/QC NCEP AGCM Interchangeable Modules ESMF-based Analysis Interface GMAO AGCM

9. Xie-Arkin Xie-Arkin Xie-Arkin GPCP GPCP GPCP NCEP NCEP NCEP ERA12 ERA12 ERA12 GEOS GEOS GEOS Xie-Arkin GPCP NCEP ERA12 GEOS

10. January 1998 June 1998 Impact of TMI and SSM/I rainfall and TPW assimilation on Longwave Cloud Forcing in GEOS3 DAS Validated against CERES/TRMM Observations Control TMI, SSMI TPW assim A. Hou NASA/GMAO

11. Temperature: 100hPa, 20°S-20°N T [K] ReSTS: 100-hPa temperatures from GEOS-4 Daily temperatures are in good agreement with sondes, show annual progression with inter-seasonal, intra-seasonal and interannual variability S. Pawson NASA/GMAO

12. TOVS CO2 channels Limb sounders can have substantial impact Limited vertical information in TOVS GEOS-4: TOVS and SABER UKMO GEOS-4: TOVS • SABER = Sounding of the Atmosphere using • Broadband Emission Radiometry A. da Silva, NASA/GMAO SABER data courtesy Marty Mlynczak, LaRC

13. Ocean Data Assimilation: EnKF uses multivariate statistics to correct currents when only temperature data are assimilated 170W 140W TAO ADCP Poseidon model No assim OI assim T only EnKF T assim Correct T,S, u, v Keppenne & Rienecker NASA/GMAO

14. Theoretical estimates of prediction skill Predictability of JJA precipitation associated with SST 10 5 3 2 1 0.7 0.5 0.3 0.2 0.1 Predictability of JJA precipitation associated with SST & soil wetness 0.0 - 0.1 - 0.2 - 0.3 - 0.5 - 0.7 - 1 Test of land initialized by observed forcing Precipitation anomalies: the 1988 drought - 2 - 3 - 5 INITIALIZED NOT INITIALIZED OBSERVATIONS mm/day Land surface models and assimilation Large ensembles used to assess potential data impacts Forecast experiments with simple land initialization to test simulation results Improvements in areas consistent with theoretical results Koster & Suarez NASA/GMAO

15. # SMMR data/month 15 10 5 0 Soil moisture assimilation with the Ensemble Kalman filter A synthetic data experiment to test error modeling 3D-EnKF is better than 1D-EnKF where SMMR data are sparse. Prior (no assim.) 1D EnKF 3D EnKF Reichle & Koster NASA/GMAO

16. 1 = Water 2 = Evergreen Needleleaf Forest 3 = Evergreen Broadleaf Forest 4 = Deciduous Needleleaf Forest 5 = Deciduous Broadleaf Forest 6 = Mixed Cover 7 = Woodland 8 = Wooded Grassland 9 = Closed Shrubland 10 = Open Shrubland 11 = Grassland 12 = Cropland 13 = Bare Ground 14 = Urban and Build-Up 1 = Clay 2 = Silty Clay 3 = Sandy Clay 4 = Clay Loam 5 = Silty Clay Loam 6 = Sandy Clay Loam 7 = Loam 8 = Silty Loam 9 = Sandy Loam 10 = Loamy Sand 11 = Sand http://ldas.gsfc.nasa.gov/ Matthew.Rodell@nasa.gov Global Land Data Assimilation System (GLDAS) PI: P.R. Houser Co-I’s: M. Bosilovich,B. Cosgrove, J.K. Entin, J. Walker, K. Mitchell, and Hua-Lu Pan Science Team: M. Rodell, U. Jambor, J. Gottschalck, J. Radakovich, C.-J. Meng, K. Arsenault GOAL: Produce high resolution (1/4º-1/8º), optimal output fields of land surface states and fluxes in near real time. SIGNIFICANCE: Results are used for initialization of weather and climate forecast models, water resources applications, and hydrometeorological investigations. Observation-based forcing: mean downward shortwave radiation [W/m2] (top) and total precipitation [mm] (bottom), 1 March 2003. APPROACH: Parameterize, force, and constrain multiple, sophisticated land surface models with data from advanced ground and space-based observing systems. Sample output: daily total evapotranspiration [mm] (top) and mean root zone soil water content [%] (bottom), 1 March 2003. Sample parameter fields: USDA soil class (5’; top) and University of Maryland predominant vegetation type (1 km; bottom).

17. GMAO Analyses - Future Plans • Atmosphere: • MERRA: Modern Era Reanalysis for Research and Applications • Bosilovich: REASON CAN funded effort • reprocess satellite-era: 1979-2009 • 1º  1.25 º • GEOS-5 • begin reprocessing in 2005 • Carbon Cycle: • Atmosphere, Ocean, Land Surface