Indian Institute of Tropical Meteorology (IITM) Suryachandra A. Rao Colloborators: Hemant, Subodh, Samir, Ashish & Kiran Dynamical Seasonal Prediction.

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Presentation transcript:

Indian Institute of Tropical Meteorology (IITM) Suryachandra A. Rao Colloborators: Hemant, Subodh, Samir, Ashish & Kiran Dynamical Seasonal Prediction of Indian Summer Monsoon-2010

NCEP Climate Forecast System (CFS)  An Operational climate forecast system (at NCEP) since August 2004  AGCM: NCEP Global Forecast System (T62L64/T126L64)  Model top 0.2 mb  Simplified Arakawa-Schubert convection (Pan)  Non-local PBL (Pan & Hong)  Ocean Model: MOM 3.0  40 levels  1 degree resolution, 1/3 degree on equator  Land Surface Model: Oregon State University 2 layer model

Design of Ensemble Experiments: Monsoon 2010 Atmospheric Initial States : from R-2 NCEP/DOE Reanalysis-II Global Data Assimilation System Ocean Initial States : from NCEP Global Ocean Data Assimilation (GODAS) forced by R-2 fluxes Fully coupled atmosphere-ocean (no flux correction)

Atmosphere 27-member forecast ensemble per month Forecasts for summer monsoon months alone 3-month lead forecasts Initial states 0000 GMT + or - 4 days from ocean state for each month Ocean NCEP Global Ocean Data Assimilation System (GODAS) Initial states 0000 GMT for 1st, 11th, 21st of each month Design of Ensemble Forecast

GPCP rainfall (cm) NCEP-CFS rainfall (cm) T62L64 T126L64 Overestimation of Rainfall over western Ghats/Eastern Arabian Sea Model comparison with GPCP 1Deg. Rainfall dataset In T126 run simulation of rainfall over Indian subcontinent is improved and OTCZ bias in reduced

TRMM rainfall (cm) NCEP-CFS rainfall (cm) Realistic simulation of rainfall over Western ghats. Spreading of rainfall Into eastern Arabian Sea still remains in T126 Model comparison with TRMM 0.25 deg. Rainfall dataset

ISO Variance in the model is reasonably well simulated, however, Its strength is almost double in the model Model comparison with GPCP 1 deg. Rainfall dataset

ISO Variance in the model is reasonably well simulated. Model comparison with TRMM 0.25 deg. Rainfall dataset

T62L64 T126L64 Northward propagating ISO signals are reasonably simulated in both the models Regression of rainfall anom. with rainfall anomalies averaged between o E and o N) at different lags

Space-time Spectra Simulated by T62/T126 Model GPCP T62 T126

CFS Prediction Skill of Dynamical Monsoon Indices Pattanaik and Arunkumar, (2009)

CFS Prediction Skill of Dynamical Monsoon Indices

CFS Prediction Skill of Rainfall

SSTA Predicted by CFS T62L64

Ensemble Spread of Predicted SST (March IC)

T62L64 T126L64 Dynamical Seasonal Prediction of Indian Monsoon Rainfall

Ensemble Spread of Predicted Rainfall (March IC)

NEW CFS 2.0 Model For a new CFS implementation 1. Analysis Systems : Operational DAS: Atmospheric (GSI) Ocean (GODAS) and Land (GLDAS) 2. Atmospheric Model : Operational GFS: New Noah Land Model 3. Ocean Model : New MOM4 Ocean Model New SEA ICE Model 1. An atmosphere at high horizontal resolution (spectral T382, ~35 km) and high vertical resolution (64 sigma pressure hybrid levels) 2. An interactive ocean with 40 levels in the vertical, to a depth of 4737 km, and high horizontal resolution of 0.25 degree at the tropics, tapering to a global resolution of 0.5 degree northwards and southwards of 10N and 10S respectively 3. An interactive sea-ice model 4. An interactive land model with 4 soil levels

Summary Seasonal outlook:  Near-Normal Conditions over Tropical Pacific.  Dynamical models also predict below normal rainfall over central India and above normal rainfall over Rajasthan and Southern India. Considering the similarities in predictions made by both dynamical models /Empirical Models, it is expected that central India may receive below normal rainfall and other parts of India may receive above normal rainfall.

Thank you