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  • Kulkarni A, Sabade SS, 2008: Warming over India under Anthropogenic Climate Change. Clivar Exchanges, 13, 21-23.

The South Asian sub-continent (5-35o N, 65-95o E) which is a major part of NH is highly vulnerable to climate variability/change due to its dense human population. It lies in the torrid zone and hence has a very hot climate throughout the year. Also its economy and agriculture mainly depends on monsoon rainfall. Kripalani et al (2007a) have extensively analyzed the model outputs of 22 models from this WCRP Coupled Model Inter-comparison Project 3 (CMIP3) dataset for the twentieth century simulations to study the ability of models to simulate present climate over south Asian region. Ten out of 22 models could simulate the monsoon rainfall over South Asian domain reasonably well. These are bccr_bcm2_0 (bcr), ccma_cgcm3_1(ccm), cccma_cgcm3_1_t63(ccm2), cnrm_cm3(cnr), mpi_echam5(ech), miub_eco_g(eco), inmcm3_0(inm), miroc3_2_hires(mih), miroc3_2_medres(mim), ukmo_hadcm3(ukc). (for details of these models- resolution, data period etc please refer to Kripalani et al 2007a). Kripalani et al (2007b) have also examined these models for simulation of East Asian monsoon. The projections of South Asian monsoon precipitation in transient climate change experiment of 1% increase per year in CO2 till doubling has been discussed by Kripalani et al (2005, 2007a). As the surface-temperature plays major role in monsoon circulation, the ability of the same ten models to simulate surface temperature has been examined in this study. The future projections by these 10 models over Indian land region (land region in the area 5-35o N, 65-95o E) are examined in three twenty-first century climate change simulations from 2000 to 2100 (i) SRES B1 (low forcing ie. CO2 concentration about 550 ppm by 2100) ; (ii) SRES A1B (medium forcing ie. CO2 concentration of about 700 ppm by 2100) and (iii) SRES A2 (high forcing ie. CO2 concentration about 820 ppm by 2100) in two time slices 2031-2050 and 2081-2100. Scenario A2 has rapid increase of CO2. A1B exhibits much slow increase in CO2 as compared to A2.
In order to study the projections we apply the technique of multi-model ensemble (MME). MME is defined as average of simulation results from multiple models. This averaging will help to reduce the individual model biases and also the uncertainties arising due to different initial conditions.


Last Updated: 2008-08-22

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