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

Global Modeling and Assimilation OfficeNASA/GSFC

GMAO

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

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

The GMAO/NSIPP Forecast/Analysis System

NSIPP CGCMv1 Forecast Ensembles NSIPP CGCMv1 Forecast Ensembles

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

NSIPP Coupled Model Hindcasts

Impact of Ocean Assimilation

Seasonally Varying Correlation Skill (1993 – 2002)

BSLN (bi-monthly) :

(3 member ensemble)

ASSIM (bi-monthly) :

(6 member ensemble)

May

July ~ August

June

PERS. Forecast (monthly)

Anomaly correlation of forecast SSH with TOPEX dataMay starts

Altimeter data not used in initialization Altimeter data used in initialization

Lag 1

Lag 3

Lag 6

Lag 9

Kurkowski, Keppenne, Kovach

Impact of Soil Moisture Initialization

1. Development of Model System

-- Construct models-- Couple models; ensure proper behavior-- Continue model evolution

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

– 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

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

• 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

“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.