Seasonal Predictions for South Asia- Representation of Uncertainties in Global Climate Model Predictions A.K. Bohra & S. C. Kar National Centre for Medium Range Weather Forecasting Ministry of Earth Sciences Government of India ______________________________________________________________________________________________________________________________________________ Talk Delivered at the SASCOF-I, Pune held on April 2010

Weather & Climate Modeling at NCMRWF In NCMRWF Real-Time Global Data Assimilation and Forecast Systems are Run every day NCMRWF’S Forecasts are available in various spatial timescales

DYNAMICAL PREDICTION SYSTEM Medium Range Forecasting Global Model & Data Assimilation (GSI) System at T254/L64 Resolution (being upgraded to T382L64) Extended-Range and Seasonal prediction at T80L18 Resolution (Ensemble) Mesoscale Data Assimilation & Model over India – WRF It is run using Initial and Boundary Conditions from NCMRWF Global Model

NCMRWF & AGROMET ADVISORY SERVICE NCMRWF established Agromet-Advisory Service in India based on Location Specific Weather Forecasts. Agromet Field Units have been opened at all the Agro- Climate Zones. The Network of these Units are being managed by IMD now. The NCMRWF is an active partner in many projects in India related to Climate Risk Management (CRM) in Agriculture.

Real-time Global Analysis- useful for climate monitoring

Model Errors

Verification of NCMRWF operational global model Day 3 FCST 850hPa Wind against RS/RW over Indian Region (Jan Sep 2009) T254L64 T80L18

25º - 65º N & 60º -145º E Verification of major operational global model forecasts 850hPa Winds over Asian Region (Jun-Sep 2009)

Skill scores for Bias free rainfall for selected districts of India (monsoon-2009)

Experimental MME Rainfall Forecasting at Medium-Range Monsoon MME project was started by MoES during October 2007 NCMRWF, IMD and IITM to work jointly, Small Team Constituted During 2009 monsoon : MME forecast IMD, NCMRWF and IITM (off line)

Seasonal Prediction System

To improve the capacity in India’s Resource Management to cope with the impacts of Climate Variability A Platform for Policymakers & Resources Managers to have access to, and make use of, information generated by Climate Prediction Models. To provide the Planners with more Reliable Seasonal Climate Prediction Information and Guidance on who could be the Potential Beneficiaries of the Predictions. Idea is to develop a Multi-Model Ensemble Seasonal Prediction System. Associated Application Systems will also be developed for Energy Demand Water Resource Management Agriculture- Drought Prediction, Crop Yield. Work is in Progress towards this end. Seasonal Prediction & Application to Society (SeaPrAS)

Model Resolution: T80L18 (Kanamitsu et al, 1993, Kar, 2007) Seasonal Simulations using Global Model (In-GLM1) With Observed SST anomalies With March Persisted SST anomalies With Predicted SST anomalies Integration period: Total number of Ensemble: 18

SeaPrAS Model Climate compared to Observation ( )

SeaPrAS Current Skill Level Worldwide for Precip is also too Low Year-wise ACC for runs with observed SST and April persisted SST anomalies

SeaPrAS Probabilistic Seasonal Prediction Ensemble Mean is the Signal Ensemble Spread is the Noise having Normal Distribution Three-Category Probabilistic Prediction Scheme has been developed. Calibrated Seasonal Prediction will be produced. Reliability Diagram JJAS Rain Above Normal Tropics Red Indian Region Green Brier Score- JJAS Ab. Normal

SST-forced Variance Vs Internal variance SeaPrAS

Ensemble Spread of Rainfall Ensemble Spread of Rainfall increases as length of forecast increases SeaPrAS

Difference of Day-3 and Day-1 forecast rainfall & Day-6 and Day1 forecast rainfall SeaPrAS

Monsoon-2009 Seasonal Rainfall Predictions from NCMRWF Global Model

Predictions of SST with 6 months lead time do not have good skill Models indicate a range of possibilities of ENSO. Some predict presently weak La Nina conditions to continue Some other predict ENSO Neutral Conditions

Rainfall Anomalies (mm/day) obtained from ensemble mean Rainfall using Top: SST Scenario-1 Middle- SST Scenario-2 Bottom: SST Scenario-3

Ensemble Mean Rainfall Anomalies (mm/day) Predicted by NCMRWF INGLM1 Global Model using Predicted SST Anomalies

Probabilistic Prediction

Seasonal Predictions for South Asia (JJAS-2010) - Methodlogy

Monsoon Rainfall prediction Model Used- The NCMRWF Seasonal Prediction Model (InGLM1) at T80L18 resolution (1.5x1.5 degree lat-lon) The model has been integrated using the NCMRWF Initial Conditions (IC) From April 02, 03, 04, 05, 06 and 07, 2010 (6 different initial conditions) The model is forced with predicted Sea Surface Temperatures. Three different SST scenarios have been used for each IC. Total 18 member ensemble runs have been carried out. Seasonal mean rainfall anomalies have been computed with respect to Model climatology of 23 seasons ( ) with 18 member ensemble. Rainfall Anomalies for JJAS-2010 based on these runs were prepared on April 10, 2010

Predictions of SST with 6 months lead time do not have good skill Models indicate a range of possibilities of ENSO for JJAS Some predict present El Nino to continue but weaken; Some predict ENSO Neutral Conditions and some La Nina conditions SST Forecasts from March to December 2010 (from iri.ldeo.columbia.edu)

This Uncertainty in SST predictions has to be included in Seasonal Predictions of Monsoon. Therefore, in our methodology, different SST scenarios are created by adding and subtracting these uncertainties at each grid point of SST predictions. The control run is without any uncertainty. Seasonal Predictions based on this methodology shall be presented tomorrow at the forecast session

Uncertainties in the Seasonal Prediction of Monsoon Rainfall are due mainly to uncertainties in the Physical processes represented in the model Cumulus Convection is one such physical process important in the Tropics. Evaporation flux from the Ocean is very important to define growth of convective instability in the Indian monsoon region The NCMRWF seasonal prediction model is being improved to properly account for the evaporation flux so that causes of uncertainties are properly represented in the model.

New Project at NCMRWF on ‘Coupled Ocean-Atmosphere Modelling’ for Development of a Seamless Prediction System from Days to a Season This project will start during 2010 – 11 FY ‘Atmosphere-Ocean-Land Coupled Model’ with proper initialisation of the coupled system have to be used for improved model skill (particularly for Monsoon) NCMRWF/MoES has a MoU with UKMO for Unified Modelling System Currently Global and Regional UM (Atmosphere) with Data Assimilation being installed During this will be extended to include the Ocean and Sea-Ice also to form the fully coupled system NCMRWF/MoES and UKMO will work together to further improve the coupled model for Seamless prediction framework particularly for monsoon

Course Resolution UKMO Coupled Model Atmos: lon x lat, 38 layers Ocean: 1.0 lon x 1/3 lat, 42 layers (Upper Ocean 10) mts NCMRWF will implement higher resolution version: Atmos 60 km, 85L ; Ocean: 25 km 75L

Song Yang et al., 2008 Pattanaik & Arun Kumar, 2010 NCEP CFS T62L64 Atmos: 1.8 lon x 1.8 lat L64 Ocean: 1 lon x 1/3 lat L40 ( 10 mt upper ocean) All Plots T62L64 0 Lead Research: T126L64 Resolution is better

Current coarse resolution coupled models have to be further improved for realistic representation of Monsoon By , NCMRWF will have high resolution coupled model Hind cast runs will be made for 25 to 30 years to study model simulations and detailed model diagnostics, for model development A 25 member ensemble run for 6 to 9 months will be carried out every week at NCMRWF on experimental basis in real-time Similar ensemble data from UKMO and KMA will also be available for sharing with NCMRWF to prepare experimental probabilistic forecasts for weeks, month and a season

A Systematic Evaluation of the NCMRWF Seasonal Prediction System has been made. The System does a reasonably good job. In recent months, several other improvements have been made. Seasonal Monsoon Prediction using Dynamic Models is very Challenging and it is a purely Modeling Problem- We need to address Dynamics and Physics. At the same time, usefulness of Probabilistic Seasonal Predictions shall be demonstrated among planners/ scientists so that socio-economic sectors are identified for whom the forecasts with present low skill are beneficial. To conclude…

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