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Myong-In Lee, Siegfried Schubert, Max Suarez, Randy Koster, Michele Rienecker, and David Adamec

Current Subseasonal-to-Seasonal Prediction System and On-going Activities at NASA’s Global Modeling and Assimilation Office. Workshop on monthly-to-seasonal climate prediction Taipei, Taiwan 25-26 October 2003.

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Myong-In Lee, Siegfried Schubert, Max Suarez, Randy Koster, Michele Rienecker, and David Adamec

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  1. Current Subseasonal-to-Seasonal Prediction System and On-going Activities at NASA’s Global Modeling and Assimilation Office Workshop on monthly-to-seasonal climate prediction Taipei, Taiwan 25-26 October 2003 Myong-In Lee, Siegfried Schubert, Max Suarez, Randy Koster, Michele Rienecker, and David Adamec Global Modeling and Assimilation Office Earth Sciences Directorate

  2. GMAO Global Modeling and Assimilation Office NASA/GSFC Merger of NSIPP and the DAO • Science areas: • Subseasonal-to-Seasonal-to-Decadal Prediction • Weather prediction • Chemistry-climate connections • 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. The GMAO/NSIPP Forecast/Analysis System

  4. NSIPP CGCMv1 Forecast Ensembles AGCM(AMIP forced with Reynolds SST) 12 month Coupled Integrations: 19 ensemble members Atmospheric state perturbations: ’s randomly from previous integrations Ocean state estimate perturbations: ’s randomly from snapshots Ocean DAS(Surface wind analysis from R. Atlas, Reynolds SST, Temperature profiles by TAO) AGCM: NSIPP1 AGCM, 2 x 2.5 x L34 LSM: Mosaic (SVAT) OGCM: Poseidon v4, 1/3 x 5/8 x L27, with embedded mixed layer physics CGCM: Full coupling, once per day ODAS: Optimal Interpolation of in situ temperature profiles - daily, salinity adjustment (Troccoli & Haines), Jan1993-present, starting in every month

  5. Ensemble mean precipitation and ground temperature anomalies forecast for NDJ 2003 Nino3 SST forecast, initialized in September 2003 • Seasonal forecasts with NSIPP CGCMv1: • High resolution: 2° AGCM & 1/3° OGCM • Ocean initial states from ocean data assimilation • Ensembles used to indicate uncertainty Rienecker, Suarez, et al. GSFC/GMAO (NSIPP)

  6. Observations Ensemble member Ensemble mean Niño-3 Forecast SST anomalies up to 9-month lead April 1 starts September 1 starts NSIPP Coupled Model Hindcasts

  7. Impact of Ocean Assimilation

  8. Seasonally Varying Correlation Skill (1993 – 2002) June BSLN (bi-monthly) : (3 member ensemble) May PERS. Forecast (monthly) ASSIM (bi-monthly) : (6 member ensemble) July ~ August

  9. Anomaly correlation of forecast SSH with TOPEX data May starts Altimeter data not used in initialization Altimeter data used in initialization Lag 1 Lag 3 Lag 6 Lag 9 Kurkowski, Keppenne, Kovach

  10. Impact of Soil Moisture Initialization

  11. 1. Development of Model System 3. Develop Strategy for Producing Initial Conditions (ICs) for Forecasts -- Construct models -- Couple models; ensure proper behavior -- Continue model evolution -- Idealized predictability experiments -- TYPE 1: ICs based on met. forcing -- TYPE 2: ICs based on met. forcing and satellite data assimilation(MSR) 4. Establish Baseline of Forecast Skill Without Data Assimilation -- Forecast experiments using TYPE 1 ICs -- Optimize forecast skill; resolve key issues of forecast strategy 5. Determine Impacts of Satellite soil moisture Assimilation on Forecast Skill -- Forecast experiments using TYPE 2 ICs -- Compare forecasts with baseline established in #4 2. Establish Predictability in System NSIPP’s overall strategy for demonstrating the usefulness of satellite land data for seasonal forecasts completed work ongoing work future work

  12. Observations Predicted: AMIP 10 Predicted: Scaled LDAS 3. 1. 0.5 0.2 0 -0.2 -0.5 -1. -3. -10 1988 Midwestern U.S. Drought (JJA precipitation anomalies, in mm/day) Without soil moisture initialization With soil moisture initialization Koster et al 2003

  13. 1993 Midwestern U.S. Flood (JJA precipitation anomalies, in mm/day) Observations Predicted: AMIP Without soil moisture initialization Predicted: Scaled LDAS 10 3. With soil moisture initialization 1. 0.5 0.2 0 -0.2 -0.5 -1. -3. -10

  14. ENSO Response and Weather Extremes

  15. Skill of Z500mb: North America (NDJFM) 1.0 0.5 0.0 -0.5 NSIPP_AGCM ave corr = 0.46 Multi_AGCM ave corr = 0.44 CCA_OBSER ave corr = 0.24 -1.0 1980 1985 1990 1995 2000 M. Hoerling: CDC NSIPP Science Team

  16. 1983-1989 JFM Model (36 members) Observations The differences between the 1983 and 1989 January, February, March (JFM) mean fields (1983-1989) for the model simulations (top panels) and the observations (bottom panels). The left panels consist of the differences in the 200mb heights (color), and the differences in the 200mb variance in the daily meridional winds (contour intervals: 40 (m/s)2). The right panels are the differences in the precipitation. The model values are the averages of 36 ensemble members for each year.

  17. San Francisco Tampa Bay Histograms of the daily precipitation rates for January, February, March (JFM) for 1983 (red bars), and 1989 (blue bars). The left panel is for a grid point near San Francisco (38°N, 122.5°W), and the right panel is for a grid point near Tampa Bay (28°N, 82.5°W). Bins are every 4mm/day. The results are based on 36 JFM NSIPP model hindcasts.

  18. Probability Density Functions of Extreme Winter Storms that form in the Gulf of Mexico (DJF 1949-1998) Observations Red - El Nino winters Blue - La Nina winters Maximum value of the principal components associated with storms that form in the Gulf of Mexico. Thin curves are the NSIPP model results (9 ensemble members). Thick dashed curves are from the observations. Values are scaled so that the model and observed values have the same total variance. Units are arbitrary. The PDFs are the fits to a Gumbel Distribution. Schubert et al (2003)

  19. Subseasonal predictions-MJO

  20. 200 mb EEOF of velocity potential NSIPP-2.0 NSIPP-1 NCEP Rean. Julio Bacmeister (2003)

  21. Plans

  22. New approach: - weather capable climate model and climate-reliable weather model • Unified Goddard modeling system • AGCM: FVcore + evolving physics: combining GSFC developments with NCAR, GFDL collaborations • Working to include GISS under a common Goddard model “toolkit” (with Code 930) • LSM: Catchment LSM + features required for carbon, NWP, long-term climate • Development and validation in collaboration with other centers and general community • Next generation model • Modular, ESMF-based development of atmospheric model and subcomponents

  23. Forecast System Evolution • Analysis system (EKF, multi-variate OI) • Unified model • Higher Resolution (. 1°, 1/2° regional issues -e.g. NAME) • Observations (altimetry, soil moisture, snow, …) • Science • Link between weather and climate • Impact of other ocean basins • Subseasonal problem (MJO, soil moisture, etc.) • decadal focus on droughts and ENSO variability • evolution of full PDF

  24. “Snapshot” of water vapor (white) and precipitation (orange) from a simulation with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) AGCM run at 1/2 degree lat/lon resolution.

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