The Once and Future Pulse of Indian Monsoonal Climate Introduction The climate of South Asia is dominated by the monsoon, which returns with remarkable regularity each summer and provides the rainfall needed to sustain over 60% of the world’s population. The vastness of the Asian continent and the unique configuration of the Tibetan Plateau means that the Asian summer monsoon is the most vigorous and influential of all the monsoon circulations (Pant and Rupa Kumar, 1997). The subsistence of India's burgeoning population and its rapidly surging economy is intricately tied to the monsoonal climate variability, more so in coming decades. Spreading throughout the Ganges region, Indians pursue a thriving agricultural lifestyle in the fertile alluvial plain that by many accounts is the nation's lifeblood. Variability in agriculture output, driven by the year-to-year strength of the summer (June to September) monsoon rains, tend to have an exaggerated economic and societal consequences. Temperature variability also has societal consequences, especially with a large poor population and infrastructure. Hot and cold spells routinely lead to several deaths every year ( ), in addition to exerting a strong impact on crop health and yield. Thus understanding the variability of the monsoonal climate is of great importance to devise sustainable policies, particularly in the wake of changing climate. The Indian monsoon has exhibited variability on inter-annual and inter-decadal time scales. The interannual variability is largely driven by the El Niño Southern Oscillation (ENSO) phenomenon in the tropical Pacific (5,6,7) which modulates the strength and location of the Walker circulation and consequently the monsoon winds and the convection over Indian subcontinent. Antecedent land conditions over Eurasia (xxx) and also tropospheric conditions (xxx) have also been shown to affect monsoon variability on a year to year basis. Interdecadal variability of the monsoon has been linked to the decadal variability of the ENSO phenomenon (xxx) and also to that of the land features (Xxx). Warming climate into the future due to increased green houses gases (GHG) is also shown (IPCC, AR4) to influence the monsoon climate variability – both precipitation and temperature. This could modulate the known monsoon variability mechanisms mentioned above, in unpredictable ways. Climate change will, in all likelihood, predispose India to enhanced threats from natural hazards linked to the atmosphere and oceans, besides stressing the availability of water and health of our key natural and managed ecosystems. There is therefore, an urgent need to undertake systematic research on changes that are happening, and likely to happen, over the Indian region along with their linkages to the global changes and also the impacts on diverse sectors of socio-economic activity. In this study we attempt to provide broad insights into these aspects from a suite of climate model simulations of present and future Indian monsoon climate. The paper is organized as follows. (i) A detailed description of the various data sets used in this study are first presented. (ii) Results describing the observed variability of the monsoon climate and the ability of the climate model simulations to capture them is next presented. (iii) This is followed by future projections and the impact on socio-economic activities, mainly crop yield and public health, the two key sectors of India’s growth. (iv) Summary and discussion of the findings in a broader policy context concludes the paper. Data Monthly data for several variables from a suite of 22 coupled atmosphere ocean general circulation models (CMIP) with a total of 48 ensemble members for the 20th Century Climate (20c3m) and for the future climate simulations based on SRES-A1B green house gas (GHG) emission scenarios (IPCC 2001) are used in this study1. Most of the 20c3m experiments were integrated according to observed anthropogenic forcing from the late 19th century and ending in 2000. A high resolution regional climate model of Hadley Centre for Climate Prediction of UK Meteorological Office, known as PRECIS, is run over the Indian subcontinent domain, with two sets of lateral and surface boundary forcing data generated from the Hadley Centre AOGCM corresponding to the present (1961-90) with observed GHG forcing, referred in the paper as ‘baseline’, and for the future (2071-2100) following SRES-A2 GHG emission scenarios, referred henceforth as A2. Details of the regional model domain, its simulated seasonal climatology and extremes are discussed in Kumar et al (2005). Observed precipitation CMAP (Xie and Arkin ..) and HadCRU3v land and sea surface temperature data set from University of East Anglia ( ) are used to verify coupled model simulated climatologies. Yearly all-India rice yield data during 19612006 is obtained from FAO ( ). All India monthly rainfall based on a network of 306 stations covering the entire country (Parthasarathy et al., 19xx), maximum and minimum temperatures for the period 1871 – 2006 are obtained from Indian Institute of Tropical Meteorology. 2 The all-Indian summer (June-Sep) monsoon rainfall index (AISMR) is computed from the monthly rainfall. This is probably the longest and most extensively used index for understanding the inter-annual fluctuations of the Indian monsoon rains and also as a proxy for several other climate studies (e.g., xxx). Results Observed Changes The percent anomalies of AISMR during 1871-2006 shown in Fig.1a reveal monsoon variability on inter-annual and inter-decadal time scales – but, interestingly there is no apparent long term trend. Occasional excursions of monsoon rainfall from the mean lead to large scale droughts and floods covering large parts of the country. These inter-annual positive(negative) fluctuations generally co-occur with the warm(cold) ENSO events in the Pacific Ocean as can be seen in this figure. This monsoon-ENSO relationship is fairly robust and is a continued source for monsoon forecasting (xxx). In recent years this relationship is seen to have weakened somewhat (Kumar et al., 1999a,b). They argue for the role of warming temperature trend over Eurasian region in this weakening. Large rainfall departures are also observed in the absence of ENSO events – which are attributed to the chaotic internal dynamics (xxx) and other surface boundary forcings (xxxx). . 1 2 http://www-pcmdi.llnl.gov/ipcc/about_ipcc.php http://www.tropmet.res.in Low frequency variability of the monsoon is quite clear from this figure – in that, during the middle part of the 20th century the monsoon shows a higher mean relative to the other periods. This epochal nature is believed to be driven by the corresponding epochal variations of ENSO (Kriplani, xxx) and North Atlantic forcings (Goswami xxx). The recent decades of below normal rainfall is interesting as it seems contrary to expectations from substantial warming trends in the Indian Ocean surrounding India (IPCC4). Though AISMR does not show any perceptible long-term trend during the past century, there is however, an evidence for a change in the rainfall characteristics in recent decades. Goswami et al (2007) show an increasing trend in the magnitude and frequency of extreme rainfall events during post 1950 period in a large region of Central India. They also show a corresponding decrease in the frequency of low rainfall events. This redistribution of rainfall character has serious implications for flood and water resources management. Surface air temperature is the most important meteorological variable for identifying global warming footprint. Fig. 1b shows the all-India mean annual surface air temperature anomalies with respect to 1961-90 climatology. The steep increasing trend in recent decades is quite dramatic and conforms to rapid trends in global surface temperatures (IPCC AR4). Future Projections of Monsoonal Climate Several studies in the past have assessed the performance of different climate models in an uncoupled observed SST forced (generally known as AMIP) mode (xxx Gadgil and Sajani, xxx, Kumar et al., 2005; Wang et al., 2005) and coupled (i.e., CMIP) mode (Annamalai et al., 2007; Kripalani et al., 2007) – in their ability to simulate the climatological features of the Indian monsoon and its ENSO teleconnections. These efforts also attempted to identify a unique subset of models that can be used with confidence for seasonal forecasting and to assess the future changes in the monsoonal climate over India. Unfortunately, these studies did not result in a unique model subset as the metrics used in the selection depends greatly on the choice of spatial and temporal scales of monsoon variability of interest – rendering such a model selection exercise futile. Thus, the multi-model ensemble seems to be the best approach to assess future changes. This is strongly advocated by IPCC4 (Chapters 8 and 10). They mention that – “the reason to focus on the multi-model mean is that averages across structurally different models empirically show better large-scale agreement with observations, because individual model biases tend to cancel (see Chapter 8). The expanded use of multi-model ensembles of projections of future climate change therefore provides higher quality and more quantitative climate change information compared to the TAR. Even though the ability to simulate present-day mean climate and variability, as well as observed trends, differs across models, no weighting of individual models is applied in calculating the mean.” The use of multi-model ensembles has been shown in other modelling applications to produce simulated climate features that are improved over single models alone (see Palmer et al., 2004; Krishnamurty et al., 2000; Rajagopalan et al., 2001). Consequently, in this paper we use the multi-model ensemble to assess the future monsoon climate change and refrain from selecting a model subset. The right panels on Fig. 2 show the simulated and observed annual cycles of rainfall and temperature averaged over India and the two box plots below depict the standard deviation of the simulated rainfall and the correlation with NINO4 SSTs (an ENSO index) - the observed values are shown as stars. It can be seen that almost all the models could simulate the annual cycle in rainfall and temperatures very well. The ensemble mean annual cycle of rainfall is slightly underestimated and the temperature slightly overestimated compared to observations. However, the median values of standard deviation of the monsoon seasonal rainfall and ENSO correlations (horizontal lines in the box plots) are very close to the observed values, albeit, there is a large spread among the models. The spatial patterns of the ensemble mean of simulated monsoon rainfall and annual temperatures are remarkably similar to the observed patterns (Fig.S1) despite substantial differences in the simulated rainfall patterns in the individual models (figures now shown). Thus, the multi-model ensemble mean is a good representation of the observed monsoonal climate features. This provides further credence to the multi-model ensemble approach advocated by IPCC4. The simulated annual temperature and monsoon rainfall, averaged over the Indian land grids from the ensembles for the 1901-2098 period are shown in Fig 2 a and b, respectively. The ensemble mean (black line) captures the observed variations (blue line) quite well. The ensemble mean suggest an increase of 2C by mid 21st century and over 3.5C by the end of the next century in the average annual temperature. Furthermore, the ensembles are tightly bunched, indicative of close agreements between the models. This substantial increase in average temperature can have a significant manifestation in the variability of extremes that could be detrimental to socio-economic activity. This will be examined in the following section. The ensemble member corresponding to the Hadley Center AOGCM (red line) appears to closely follow the multi-model ensemble mean. Unlike temperature, the simulated monsoon rainfall has a larger spread between the ensemble members and the ensemble mean rainfall shows a modest increase of about 8-10% by the end of the 21st century. Much of this increase seems to occur during the second half of the century. This increase can be seen to be within the limits of the variability exhibited by the observed monsoon rainfall in the 20th century. We examined the variability and seasonality of the projected rainfall (Fig S2a,b), an important aspect of rainfall. The ensemble mean annual cycle of rainfall for the present (1961-1990) and future (2070-2098) indicate nearly a 20% increase in the rainfall during May and October months in future. This is suggestive of an extended monsoon season in the future and consistent with recent studies (Meehl et al, 2006, Ramanathan et al 2007)) that show increased pre-monsoon rainfall under increased GHG conditions over India. The standard deviation of monsoon rainfall and the strength of the monsoonENSO correlation during the three periods (1961-90, 2041-60 and 2070-2098), shown as box plots in Fig S2b and c – indicate a slight increase (~10%) in the standard deviation in the future compared to the present period. The monsoon-ENSO correlation appears to be quite stable. This assessment, based on the entire suite of IPCC model runs, is in variance with earlier studies that have indicated considerable changes in monsoon rainfall variability and its ENSO teleconnection in future (Meehl and Arblaster, 2003; Kripalani et al , 2007, Annamalai et al, 2007 and Ashrit et al, 2003). This difference is due to the fact that these studies used a single or a small subset of models. Spatial patterns of expected ensemble mean change in rainfall and surface temperatures at the end of the 21st century relative to present are shown in Figure 3. It is clear that both oceans and land seem to show a substantial warming in the future – with land areas showing more warming than the oceans. Over the oceans, generally the warming is more in the tropical regions compared to extra tropics. In the Pacific ocean, an El Nino like warming pattern – with a warmer central and eastern tropical Pacific is quite conspicuous. There is a debate as to the a nature of SST pattern in the tropical Pacific (Vecchi et al., 2007) in a warmer futre climate, but the consensus from CMIP seems to be clearly towards an El Nino like warming. The associated positive rainfall change (Fig 3b) in the central and eastern Pacific corroborate this El Nino like SST pattern in the future. In addition, there is a projected rainfall increase over the Indian region in conjunction with the central and eastern tropical Pacific ocean warming – contrary to the reduction in Indian monsoon rainfall associated with El Nino (xxxx). We offer some insights into this paradox from examination of atmospheric circulation features related to the monsoon. The evolution of meridional wind index (Goswami ) over the Indian subcontinent domain (shown in Fig 4a) from CMIP ensembles and the ensemble mean for the 2000-2098 period is shown in Fig 4c. The meridional wind index is defined as the vertical meridional wind shear between 850 and 200 hPa and captures the strength of the monsoon rainfall and in particular, the reverse Hadley circulation – the key monsoon feature (Goswami xx). As can be seen, there is no trend in the strength of ensemble mean meridional wind index during the entire 21st century suggesting no perceptible change in the strength of dynamical monsoon circulation. Mid-tropospheric (500hPa) temperature gradient between the Indian land region and the Indian Ocean, a robust measure of the strength of the monsoon circulation than the surface gradient (Goswami et al., 2007), also indicate no trends in this century (figure not shown). These suggest that the strength of the monsoon circulation is likely to be steady into the future despite El Nino like conditions in the tropical Pacific. We computed the future change in area averaged total water content in the troposphere over the Indian monsoon region (shown in Fig 4d) – which shows a substantial increasing trend. The enhanced moisture availability over the Indian region, likely due to increased temperature and evaporation (IPCC4), seems to be main factor in the projected increase in the monsoon rainfall (Fig 2a). Sensitivity experiments from single climate models, of monsoon rainfall changes in the future under increased GHG forcing also suggest enhanced thermodynamical changes to monsoon rainfall (i.e., more moisture available in the atmosphere) than dynamical (Meehl and Arblaster, 2003, Sugi …, Dariaku and ..,). Societal Implications Indian society with modest infrastructure is highly vulnerable to even slight variations in weather extremes. In fact, climate change could have a major tipping in the socio-economic aspects. Therefore, it is important to understand the changes in weather extremes under future climate projections. This is further highlighted by an increasing occurrence of extreme events in recent periods such as - floods across many states during 2005-07, the prominently a 944 mm of rainfall in one day on 26 July, 2005 at Mumbai; high human mortality due to heat waves in one of the southern states in 2003, are to name a few. These events extracted a heavy toll on the economic and social health of India. Some recent studies have examined changes in extremes under future climate projections, using coarse resolution global climate models (e.g., May, , Sun et al 2007). High resolution models are ideal to investigate the variability in extreme weather conditions as they can capture the synoptic scale regional pecularities consistent with global conditions. As mentioned earlier, we used the Hadley Center’s regional climate model (PRECIS) with the lateral and surface boundary conditions from coarse resolution global model of Hadley Center (HadCM3). In particular, we focus on the extremes of maximum and minimum temperatures and the character of daily rainfall over India. The simulated rainfall and temperature values from HadCM3 are shown as red lines in Fig. 2. As can be seen this model closely captures the ensemble mean of CMIP simulations and thus, can be considered as its representative. The rainfall and temperature climotologies generated based on 30 year (1961-1990) baseline simulations reproduce the observed features very well (Fig. S1 c,d). Furthermore, the regional model is able to better resolve the orographic rainfall over India than the coarse resolution global coupled models (Fig S1xx). The regional model over estimated the rainfall in June and generally has a cold bias compared to observations (xxx Fig Sxxx) – also see Kumar et al, 2005 for details on the model set up and its performance over India. In Fig. 4a we show the highest daily temperature maximum (Tmax) during the 30 year baseline period. The values exceed 45C in many places in the northern and interior parts of southern India and even exceeding 50C in some pockets. The expected future changes in Tmax under A2 scenario during the later part of 21st century indicate an exceedance of 4C in many places in the northern India (Fig. 4b). This is a significant shift considering the base itself being over 45C. Similar increases are noted in the daily lowest Tmax, highest Tmin and lowest Tmin across the country in the future (figures not shown). The model underestimates the highest daily precipitation compared to observations though it indicates a general increase in the extreme precipitation amounts in the future. The number of rainy days during the monsoon season and the mean intensity of rainfall on a rainy day (Figs. 4 c,d) show a considerable decrease in the future in many parts of northern and central India. We should mention here that the model has a bias of simulating higher number of rainy days and lower intensity of rainfall on a rainy day compared to the observations but the simulated patterns match the observations remarkably well (Natcom, ). Reduction in the number of rainy days and increase in the intensity will have significant implications for water resources and agriculture management of India. With nearly 60% of cropped areas being primarily rainfed, Indian agricultural production and consequently, the country’s GDP, show a strong link with monsoon rainfall (Kumar et al 2004, Gadgil and Gadgil, 2006). Despite this strong link, temperature variations play an equally critical role in crop production, demonstrated in some recent studies. Auffhammer et al (2006) examined the direct and indirect effects of aerosols on the declining rice yields over India – mainly via temperature changes. Peng et al (2004) showed that an increase of night time temperature of 1C can result in the reduction of rice yields by about 10% in Phillipines. India’s first Initial National Communication (NATCOM) report provides some insights on the expected impacts of a future warming climate on various crops in India (Mall and Aggarwal, Aggarwal, Natcom, 20xx). Mainly they find that the positive impact of increasing CO2 levels (i.e., increased primary production and yield) will be greatly offset by increasing maximum and minimum temperatures which will tend to have a negative impact on crop yield. These studies bring to the fore the importance of changing temperatures on the major crops that are grown in this part of the world, which we investigate here. Relationship between all-India rice yields and the two important meteorological variables - growing season (i.e. monsoon) rainfall and night time temperatures during 1961-2006 is shown in Fig. 5a. The nonlinearity in the relationship is quite clear. The yields tend to decrease with higher night time temperatures even with high rainfall. The lower yields under low rainfall are much more aggravated with higher temperatures. Thus, higher temperatures seem to reduce the yields regardless of rainfall – consitent with the studies mentioned above, especially Peng et al., (2004). To assess the change we computed the PDF of minimum temperature at New Delhi for the baseline and future period, shown in Figure 5b. There is a significant shift of about 5C from the future simulations. We found similar shifts at almost all the locations, especially in the rice producing Indo-Gangetic plain. Similar shifts were seen for the day time temperatures as well. The increasing temperatures over India will start to play an important role in rice yields also that of other important crops such as wheat, which is a staple of majority of the population. Not withstanding the positive benefits that may occur in the croip yields under increasing CO2, the substantial change in temperatures could result in a net loss of productivity in the coming decades. Though we do not wish to extrapolate the production losses based on Peng et al (2004), who estimate a 10% loss to every degree C change in night time temps, we fear that in reality these losses could be even higher as the relationships would be nonlinear beyond a threshold. We do not rule out the possibility of adaptation and development of new cultivars which are more heat resistant, but the challenge to compensate for the temperature related losses may not be easy. With the population of India expected to reach 1.6 billion by the mid 21st Century and the fact that there is already a stagnation in the growth of area under cultivation and the yields of major crops like rice and wheat (xxx reference xxx), 50-100% growth in the yields of major crops of current levels is needed to achieve food security. Substantial projected increase in the day and night temperatures could be a major deterrent to this objective. Such a situation of decreased yields and increasing population could be a major socioeconomic tipping point. Temperature also has a huge impact on heat related mortality and morbidity. Each year thousands die in India due to heat waves during pre-monsoon season – highlighted by large number of deaths in recent heat waves across India (AP report, Choudhary et al; IMD publications etc). There is a general perception that people living in tropical climates have higher level of tolerance to heat and consequently, this impact of temperature is generally neglected. Hazat et al (2005) have examined heat related mortality in three major cities of the world, New Delhi, Sam Paulo and London, and found that the mortality risk due to heat exposure was highest for New Delhi. Fig. 5c shows the relation between daily mean temperature and mortality rate at New Delhi (figure courtesy Shakoor Hazat, personal communication). There is a very steep increase in mortality rate beyond a daily mean temperature of 40C, which will have serious consequences for public health. We also observed a significant shift towards higher temperature of the mean daily temperatures in future, much like that seen in the daily minimum temperatures (Figure 5b). The PDF of temperature spells (i.e., number of days) exceeding 40C at New Delhi in the baseline and future periods are shown in Figure 5d. In the base line the maximum spell is of the order of 12 days but in future it is as high as 45 days long. The PDF of the future clearly indicates increased probability of longer temperature spells detrimental for human mortality. Similar changes were observed at other locations around the country. Thus, the potential for longer temperature spells (i.e., more heat waves) will stress the already fragile public health system. From our analysis it seems clear that in the near term future (i.e., end of 21st century) there is a potential for a slight increase in monsoon rainfall but a substantial increase in the annual average temperature. No tipping of the monsoon climate to an extreme is state is envisioned. However, the projected rainfall and temperature changes seem to have a disproportionately large impact on key socio-economic sectors – agriculture and public health, which could have pervasive effect throughout the entire economy. We feel there could be a tipping in the socio-economic context which can be more quite devastating than a gradual tipping of the monsoon climate. Peng, S., et al (2004) Proc. Nat. Acad. Sci. USA 101, 9971-9975 Klein Tank, A.M.G., et al (2006) Journal of Geophys. Res. 111, doi:10.1029/2005JD006290 Annamalai, H., Hamilton, K. and Sperber, K.R. (2007) J Climate doi:10.1175/JCLI4035.1, 1071-1092 May, W. (2004) Clim. Dyn. 22, 183-204 Kothawale, D.R. and Kumar, K.R. (2005) GRL 32, doi:10.1029/2005GL023528 Sugi, M. and Yoshimura, J (2004) J. Clim. 17, 238-243 Kripalani, R.H., Oh, J.H., Kulkarni, A., Sabade, S.S. and Chaudhari, H.S. (2007) Theor. Appl. Climatol. 90, 133-159 Auffhammer, M., Ramanathan, V and Vincent, J.R. (2006) Proc. Nat. Acad. Sci. 52 19668-19672 Mall, R.K., Singh, R., Gupta, A., Srinivasan, G. and Rathore, L.S. (2006) Clim. Change 78, 445-478 Kumar, K.K., Rajagopalan, B. and Cane, M.A. (1999) Science, 284, 2156-2159 Ueda, H, Iwai, A., Kuwako, K and Hori ME (2006) Geophys. Res. Lett. 33: doi: 10.1029/2005GL025336 Dairaku, K and Emori, S (2006) Geophys. Res. Lett. 33 doi:10.1029/2005GL024754 Ashrit, R. G., H. Douville, and K. Rupa Kumar (2003) J. Meteorol. Soc. Jpn., 81, 779– 803 Goswami, B. N., V. Krishnamurthy, and H. Annamalai (1999 Q. J. R. Meteorol. Soc., 125, 611 –633 Webster, P. J., and S. Yang (1992) Q. J. R. Meteorol. Soc., 118, 877–926 Meehl, G. A., and J. M. Arblaster (2003) Clim. Dyn., 21, 659–675, doi:10.1007/s00382003-0343-3 Kitoh, A., S. Yukimoto, A. Noda, and T. Motoi (1997) J. Meteorol. Soc. Jpn., 75, 1019– 1031 Goswami B. N., M. S. Madhusoodanan, C. P. Neema, D. Sengupta (2006) Geophys. Res. Lett., 33, L02706, doi:10.1029/2005GL024803 B. N. Goswami, V. Venugopal, D. Sengupta, M. S. Madhusoodanan, and Prince K. Xavier (2006) Science 314: 1442-1445 [DOI: 10.1126/science.1132027] Kumar, K.K, Rajagopalan, B., Hoerling, M., Bates, G. and Cane, M. (2006) Science 314: 115-119; [DOI: 10.1126/science.1131152] Kumar, R.K., Sahai, A.K., Kumar, K.K, Patwardhan, S.K., Mishra, P.K., Revadekar, J.V., Kamala, K. and Pant, G.B. (2006) Curr. Sci. 90, 334-345 Jones, R. et al., (2004) Hadley Centre for Climate Prediction and Research, Met Office Hadley Centre, UK, 2004, pp. 40. Pant, G. B. and Kumar, R.K. (1997) Climates of South Asia, John Wiley & Sons, Chichester, pp. 320. 1999: Improved skills for weather and seasonal climate forecasts from multi-model superensemble. Science, September 3, 1999 (with C.M. Kishtawal, T. LaRow, D. Bachiochi, Z. Zhang, C.E. Williford, S. Gadgil and S. Surendran) 2000: Multi-model superensemble forecasts for weather and seasonal climate. Journal of Climate, Nov. 2000 (with C.M. Kishtawal, T. LaRow, D. Bachiochi, Z. Zhang, C.E. Williford, S. Gadgil and S. Surendran). Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, 2007: Cilmate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Palmer, T.N., et al., 2004: Development of a European multimodel ensemble system for seasonal to interannual prediction (DEMETER). Bull. Am. Meteorol. Soc., 85, 853–872. Climate Change 2007 - The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the IPCC (ISBN 978 0521 88009-1 Hardback; 978 0521 70596-7 Paperback) Climate Change 2007 - Impacts, Adaptation and Vulnerability Contribution of Working Group II to the Fourth Assessment Report of the IPCC (978 0521 88010-7 Hardback; 978 0521 70597-4 Paperback) Climate Change 2007 - Mitigation of Climate Change Contribution of Working Group III to the Fourth Assessment Report of the IPCC (978 0521 88011-4 Hardback; 978 0521 70598-1 Paperback) De, U.S. and R.K. Mukhopadhyay, 1998: Severe heat wave over the Indian subcontinent in 1998, in perspective of global climate. Curr. Sci., 75, 1308-1315. De, U.S., M. Khole and M. Dandekar, 2004: Natural hazards associated with meteorological extreme events. Nat. Hazards, 31, 487-497. Government ofAndhra Pradesh, 2004: Report of the state level committee on heat wave conditions inAndhra Pradesh State. Revenue (Disaster Management) Department. Hyderabad, India. 67pp. Aggarwal, P.K., 2003. Impact of climate change on Indian agriculture. J. Plant Biol., 30, 189-198. Aggarwal, P.K. and P.K. Mall, 2002: Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and cropmodels on impact assessment. Climatic Change, 52, 331-343. Aggarwal, P.K., P.K. Joshi, J.S. Ingramand R.K. Gupta, 2004:Adapting food systems of the Indo-Gangetic plains to global environmental change: key information needs to improve policy formulation. Environ. Sci. Policy, 7, 487-498. Mall, R.K. and P.K. Aggarwal, 2002: Climate change and rice yields in diverse agro-environments of India. I. Evaluation of impact assessment. Climatic Change, 52, 315-330. Kalra, N., P.K.Aggarwal, S. Chander, H. Pathak, R. Choudhary,A. Chaudhary,M. Sehgal, U.A. Soni,A. Sharma, M. Jolly, U.K. Singh, O.Ahmed and M.Z. Hussain, 2003: Impacts of climate change on agriculture. Climate Change and India: Vulnerability Assessment and Adaptation, P.R. Shukla, S.K. Sharma, N.H. Ravindranath, A. Garg and S. Bhattacharya, Eds., University Press, India, 193-226. Lal,M., K.K. Singh, L.S. Rathore, G. Srinivasan and S.A. Saseendran, 1998:Vulnerability of rice and wheat yields in N.W. India to future changes in climate. Agr. Forest Meteorol., 89, 101-114. Shukla, J., 1998: Predictability in the midst of chaos: A scientific basis for climate forecasting. Science, 282, 728–731. Palmer, T. N., 1993: Extended-range atmospheric prediction and the Lorenz model. Bull. Amer. Meteor. Fig. 1 a) All-India Summer (JJAS) Monsoon rainfall anomalies (% of 1961-90 mean) during 1871-2005. The 31-yr sliding mean of the anomalies is shown in green shaded curve. The red/blue bars indicate El Nino/La Nina years, respectively. b) All India annual surface temperature anomalies (respect to 1961-90 mean) during 1875-2005. 31year sliding mean is shown as thick black line. Fig. 2 CMIP simulations for the 1901-2098 period - a) Annual surface temperature over India and (b) Monsoon rainfall over India. The grey lines indicate the ensembles, the black line is the ensemble mean, the blue line is the observed. The red line is the ensemble member corresponding to the Hadley Center AOGCM. The right hand figures show the annual cycle of temperature and rainfall over India and, boxplots of standard deviation and monsoon-ENSO correlation, for the observational (1901-2000) period. The observed values are shown as red points in the boxplots. Fig. 3 a) Percent change of ensemble mean monsoon rainfall in future (2070-98) with respect to present (1961-90). b) Ensemble mean annual temperature change in the future. c) Standardized anomalies of total column precipitable water averaged over the domain shown in (a) - simulated from 19 models (one ensemble member per model) during 20012098. d) Standardized anomaly of meridional wind index (v-wind difference between 850hPa and 200hPa averaged over the domain shown in (b) - simulated from 21 models during 2001-2098. Standardization is done based on the mean and standard deviations of each model during the 2001-2020 period.Kaniki-fig5.jpeg Fig. 4 Projected changes in daily maximum (Tmax) temperature and number of rainy days over India from the high resolution (50km) regional climate model (PRECIS). (a) Pre-monsoon (March-June) Tmax for the baseline period (1961-90) and (b) projected future period (2071-2100) change. The PDFs in the inset of this figure show the probabilities of Tmax spells (in days) exceeding the 90th percentile (46.3 Deg C) value of the baseline period at New Delhi. The location of New Delhi is marked with a black dot in (b). (c) Projected future number of rainy days (>2.5 mm of rainfall) during monsoon season and (d) the projected change in the intensity (mm/day) of rainfall on a rainy day. Figure 5. (a) All India Rice yield change (in percentage) as a function of monsoon rainfall and average minimum temperature over India. (b) PDF of Tmin from PRECIS simulation for the baseline (blue) and future (red) period. (c) Human mortality in New Delhi as a function of mean temperature. (d) PDF of mean temperature spells (in days) exceeding 40 Deg. Fig. S1. CMIP Ensemble mean for the 1961-1990 period based on 20th century simulations - a) Monsoon Season Rainfall and c) Annual surface temperature. Observed means for the same period - (b) Monsoon Season Rainfall based on CMAP and (d) Annual Surface Temperatures based on HadCRU3v. Fig. S2. (a) Ensemble mean climatology of present (1961-1990) period (blue) and future (2070-2098) period (red). (b) Percent change in mean monthly rainfall in future relative to present. (c) Boxplots of standard deviation from the CMIP simulations for 1961-1990, 2041-2060 and 2070-2098 periods. (d) Same as (c) but for monsoon-ENSO correlation.