Monsoons and Climate Change

Presentation made at WCRP Workshop, Lille, France16 June, 2010

K. Krishna KumarIndian Institute of Tropical Meteorology, Pune

(krishna@tropmet.res.in)

Motivation

Is Climate Change Impacting Agriculture and Food-grain

Production in water-limited tropical crop lands including monsoon dominated regions like India?

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Over the last decade, 31 out of 41 countries that hold 90% of the water-limited croplands show a decline in annual average growth rate of food grain production.

Food grain: cereals+coarse grains+pulsesData from FAOSTAT

Change (%) in rate of food grain production in 1996-2006 relative

to 1966-1996

Milesi C, A Samanta, H Hashimoto, K Krishna Kumar, S Ganguly, PS Thenkabail,

AN. Srivastava, RR Nemani, RB Myneni

Remote Sensing, 2010

% change in vegetation greenness during 1996-2006 compared to 1982-1992

as calculated from GIMMS-G NDVI

45% of the water-limited tropical croplands show a decline in relative growth of integrated NDVI over

the last decade

NDVI = NIR – RED / NIR+RED

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Change (%) in rate of

food grain production

in 1996-2006 relative

to 1966-1996

Change (%) in trend

of peak annual

precipitation in 1996-

2006 relative to 1966-

1996

Spatio-temporal deceleration in food grain production in India

Wet season Dry season

Kharif

Mean annual T =27.1 °C

Rabi

Mean annual T =22.5 °C

Impact of Growing Season Rainfall and Impact of Growing Season Rainfall and Night time Temps on Rice Yields in IndiaNight time Temps on Rice Yields in India

Tmin (°C)

SRES A2

Baseline

Krishna Kumar et al, 2010 Krishna Kumar et al, 2010

Observed Changes

Goswami et al., Dec., 2006

Changes in the Frequency Distribution of Extremes during 1951-1970 and 1980-2000

Error doubling Time for the last two quarters (1953-1978 and 1979-2004)

(18N-27N, 73E -85E)

Change in predictability of Indian summer monsoon on weather scales

Neena Mani et al, Geophys.Res. Letts (2009)

First Lyapunov exponent First Lyapunov exponent

Expected Future Changes Under Increased GHG Conditions

IPCC-AR4

Multi Model Ensemble vs. Best sub-set

Metrics for selection of best sub-set of models

•Mean Monsoon features•ENSO and its teleconnections•Intra-seasonal variability

Performance of GFDL as an examplePerformance of GFDL as an example

Obs

GFDL2.1

GFDL2.0

Mean Monsoon rainfall Monsoon-ENSO Tropical waves/MJO

Some Aspects of Asian Summer Monsoon Rainfall and Its ENSO Some Aspects of Asian Summer Monsoon Rainfall and Its ENSO Teleconnections as simulated by AR4 Models in 20Teleconnections as simulated by AR4 Models in 20 thth Century Century

Lin et al. 2006, J. Lin et al. 2006, J. ClimateClimate

Mean Monsoon RainfallMean Monsoon Rainfall Monsoon and ENSOMonsoon and ENSO Tropical Waves; MJOsTropical Waves; MJOs

Observed and IPCC-AR4 Ensemble mean Monsoon Rainfall and Annual Surface Temperature

Expected Future Changes in Rainfall and Temperature over India under IPCC SRES A1B GHG Scenarios

Krishna Kumar et al, 2010

Future (A1b-20C) Global Rainfall/SST

Precipitation

SSTa

Column integrated Moisture

Monsoon Circulation Strength

Krishna Kumar et al, 2010

Other Expected Changes in Monsoon Features

Annual Cycle

Length ofSeason

Monsoon &ENSO

MonsoonVariability

Krishna Kumar et al, 2010

Dynamical Downscaling at IITM(Resolution: 50km)

Evaluation experiment using LBCs derived from ERA-15 (1979-93)

LBCs from Hadley Centre Models

• Baseline (1961-90) – 3 members• A2 scenario (2071-2100) -3 members• B2 scenario (2071-2100)• 3 Members of QUMP (1961-2100) – A1b

LLBCs from ECHAMBaseline 1961-1990; A2 scenario :1991-2100; B2 scenario : 1991-2100

PRECIS

PRECIS captures important regional

information on summer monsoon

rainfall missing in its parent GCM simulations.

HadCM3HadCM3 PRECISPRECIS

Possible Climate Change impacts are examined in the:

• Extremes in rainfall and temperature

• Onset and advance of Monsoon

• Active/break cycles• Intensity and frequency

of Monsoon Depressions

Projections of Regional Tmax and Daily Rainfall ChangesProjections of Regional Tmax and Daily Rainfall Changes

Highest dailyHighest dailyTmax (C) inTmax (C) in

The BaselineThe BaselinePeriodPeriod

Expected Expected changechangein Tmax in in Tmax in FutureFutureunder A2under A2

Expected Expected changechangein Rainfallin RainfallIntensity inIntensity ina rainy daya rainy dayin futurein future

ExpectedExpectedchangechange

in No. ofin No. ofRainy DaysRainy Days

In futureIn futureunder A2under A2

Krishna Kumar et al, 2010Krishna Kumar et al, 2010

Impacts of Climate Change on Monsoon Depression Tracks and Intensity

Biases and problems in RCM simulationsStatistical Downscaling

Coldest night temperature

Frost days

Plans under CORDEX at IITMRun 3 RCMs (PRECIS, WRF and RegCM3) using LBC from at least 5 CMIP5 models

To accomplish the above taskit will take 1000 days of computer time with 64 processors and300TB of storage for each RCP

New HPC System at IITM • IBM P6 with 117 nodes and 3744

cores (4.7GHz) with a peak performance of 70.2TF

• 2 Peta bytes of Storage with a 3 Tier Architecture

West Asia

Issues on which a Scientific Consensus has not yet been arrived

• On the future projected strength of monsoon On the future projected strength of monsoon circulation and the quantum of rainfallcirculation and the quantum of rainfall

• Projected changes of sub-seasonal monsoon Projected changes of sub-seasonal monsoon behavior (eg. Onset, ISOs, Monsoon behavior (eg. Onset, ISOs, Monsoon Depressions, Extremes etc.) – Partly Limited by Depressions, Extremes etc.) – Partly Limited by Resolution of CMIP3 and limited RCM runsResolution of CMIP3 and limited RCM runs

• The response of ENSO to Global WarmingThe response of ENSO to Global Warming• The future strength of ENSO-Monsoon linkThe future strength of ENSO-Monsoon link

A lot more needs to be done…

• In improving the current generation of global and regional climate models in their ability to simulate the regional features of climate such as monsoon rainfall etc.

• In quantifying the uncertainties of projected climate and related impacts

• In enhancing the interaction between groups that are generating climate inputs and those using them for impact/adaptation assessments

Thank you!