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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 SciencesGovernment of India_____________________________________________________________________________________________________________________________________________
_Talk Delivered at the SASCOF-I, Pune held on 13-15 April 2010
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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
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DYNAMICAL PREDICTION SYSTEM
Medium Range ForecastingGlobal 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
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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.
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Real-time Global Analysis- useful for climate monitoring
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Model Errors
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Verification of NCMRWF operational global model Day 3 FCST 850hPa Wind against RS/RW over Indian Region
(Jan 1999 - Sep 2009)
T254L64T80L18
0
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RM
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25º - 65º N & 60º -145º E
Verification of major operational global model forecasts850hPa Winds over Asian Region (Jun-Sep 2009)
850hPa Winds - Jun 2009
0
1
2
3
4
5
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1 2 3 4 5
Forecast Days
RM
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NCMRWF
NCEP
UKMO
ECMWF
850hPa Winds- Jul 2009
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Forecast Days
RM
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NCMRWF
NCEP
UKMO
ECMWF
850hPa Winds - Aug 2009
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1 2 3 4 5
Forecast Days
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NCMRWF
NCEP
UKMO
ECMWF
850hPa Winds - Sep 2009
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1 2 3 4 5
Forecast Days
RM
SE
VNCMRWF
NCEP
UKMO
ECMWF
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0
0.1
0.2
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H.K
.Sco
re fo
r ra
infa
ll(m
m)
Delhi J aipur Mumbai Lucknow Chennai Ranchi
24hr
48hr
72hr
96hr
120hr
Skill scores for Bias free rainfall for selected districts of India Skill scores for Bias free rainfall for selected districts of India (monsoon-2009)(monsoon-2009)
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Experimental MME Rainfall Forecasting at Medium-Range-
2009 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)
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Monsoon 2009: All India ETS Day5
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 2 3 4 5 6
Threshold (cm)
NCM
UKM
GFS
J MA
EMN
BCE
MME
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Seasonal Prediction System
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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)
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Model Resolution: T80L18 (Kanamitsu et al, 1993, Kar, 2007)
Seasonal Simulations using Global Model (In-GLM1)
With Observed SST anomaliesWith March Persisted SST anomaliesWith Predicted SST anomalies
Integration period: 1982-2004
Total number of Ensemble: 18
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SeaPrAS
Model Climate compared to Observation (1982-2004)
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SeaPrAS
Current Skill Level Worldwidefor Precipis also too Low
Anomaly Correlation Coefficients
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
YEAR
AC
C
OBS-SSTPERS-SST
Year-wise ACC for runs with observed SST and April persisted SST anomalies
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SeaPrASProbabilistic 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 NormalTropics RedIndian Region Green
Brier Score- JJAS Ab. Normal
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SST-forced Variance Vs Internal variance
SeaPrAS
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Ensemble Spread of Rainfall
Ensemble Spread of Rainfall increases as length of forecast increases
SeaPrAS
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Difference of Day-3 and Day-1 forecast rainfall & Day-6 and Day1 forecast rainfall
SeaPrAS
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Monsoon-2009Seasonal Rainfall Predictions from
NCMRWF Global Model
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Predictions of SST with 6 monthslead 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
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Rainfall Anomalies (mm/day) obtained from ensemble mean Rainfall using Top: SST Scenario-1 Middle- SST Scenario-2 Bottom: SST Scenario-3
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Ensemble Mean Rainfall Anomalies (mm/day) Predicted by NCMRWF INGLM1 Global Model using Predicted SST Anomalies
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Probabilistic Prediction
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Seasonal Predictions for South Asia(JJAS-2010) - Methodlogy
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Monsoon- 2010 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 (1982-2004) with 18 member ensemble.
Rainfall Anomalies for JJAS-2010 based on these runs were prepared on April 10, 2010
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Predictions of SST with 6 monthslead time do not have good skill
Models indicate a range of possibilities of ENSO for JJAS-2010.
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)
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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
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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.
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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 2010-11 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
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Course Resolution UKMO Coupled Model
Atmos: 1.875 lon x 1.125 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
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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
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Current coarse resolution coupled models have to be further improved for realistic representation of Monsoon
By 2011-12, 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
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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 veryChallenging 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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THANK YOU