Current Subseasonal-to-Seasonal Prediction System and On-going Activities at NASA’s Global Modeling and Assimilation Office Myong-In Lee, Siegfried Schubert, Max Suarez, Randy Koster, Michele Rienecker, and David Adamec Global Modeling and Assimilation Office Earth Sciences Directorate 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
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. - PowerPoint PPT Presentation
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Current Subseasonal-to-Seasonal Prediction System and On-going Activities at NASA’s Global Modeling and Assimilation Office
Myong-In Lee, Siegfried Schubert, Max Suarez, Randy Koster, Michele Rienecker, and David Adamec
Global Modeling and Assimilation Office
Earth Sciences Directorate
Workshop on monthly-to-seasonal climate prediction
12 month Coupled Integrations: 19 ensemble members
AGCM (AMIP forced with Reynolds SST)
Ocean DAS (Surface wind analysis from R. Atlas, Reynolds SST, Temperature profiles by TAO)
Ocean state estimate perturbations:’s randomly from snapshots
Atmospheric state perturbations: ’s randomly from previous integrations
AGCM: NSIPP1 AGCM, 2 x 2.5 x L34LSM: Mosaic (SVAT)OGCM: Poseidon v4, 1/3 x 5/8 x L27, with embedded mixed layer physicsCGCM: Full coupling, once per day
ODAS: Optimal Interpolation of in situ temperature profiles - daily, salinity adjustment (Troccoli & Haines), Jan1993-present, starting in every month
Ensemble mean precipitation and ground temperature anomalies forecast for NDJ
2003
Rienecker, Suarez, et al.
GSFC/GMAO (NSIPP)
Seasonal forecasts with NSIPP CGCMv1:• High resolution: 2° AGCM & 1/3° OGCM• Ocean initial states from ocean data assimilation• Ensembles used to indicate uncertainty
Nino3 SST forecast, initialized in September 2003
Observations
Ensemble member
Ensemble mean
April 1 starts
September 1 starts
Niño-3 Forecast SST anomalies up to 9-month leadNiño-3 Forecast SST anomalies up to 9-month lead
3. Develop Strategy for Producing Initial Conditions (ICs) for Forecasts
-- 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
-- Idealized predictability experiments
2. Establish Predictability in System
NSIPP’s overallstrategy fordemonstratingthe usefulnessof satellite land datafor seasonalforecasts
completed workongoing workfuture work
Observations Predicted: AMIP
Predicted: Scaled LDAS
1988 Midwestern U.S. Drought(JJA precipitation anomalies, in mm/day)
10
-10
0
0.2
-0.2
0.5
-0.5
1.
-1.
3.
-3.
Withoutsoil moistureinitialization
With soil moisture
initialization
Koster et al 2003
-10
0
0.2
-0.2
0.5
-0.5
1.
-1.
3.
-3.
Observations Predicted: AMIP
1993 Midwestern U.S. Flood(JJA precipitation anomalies, in mm/day)
10
Withoutsoil moistureinitialization
Predicted: Scaled LDAS
With soil moisture
initialization
ENSO Response and Weather Extremes
Skill of Z500mb: North America (NDJFM)
NSIPP_AGCM ave corr = 0.46
Multi_AGCM ave corr = 0.44
CCA_OBSER ave corr = 0.24
1980 1985 1990 1995 2000
1.0
0.5
0.0
-0.5
-1.0
M. Hoerling: CDC
NSIPP Science Team
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.
JFM
odel
(36
mem
bers
)O
bser
vati
ons
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.
Probability Density Functions of Extreme Winter Storms that form in the Gulf of Mexico (DJF 1949-1998)
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)
Observations
Subseasonal predictions-MJO
200 mb EEOF of velocity potentialNSIPP-2.0 NSIPP-1 NCEP Rean.
Julio Bacmeister (2003)
Plans
New approach: - weather capable climate model and climate-reliable weather model
• 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
“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.