Interannual and long-term variability in the North Atlantic Oscillation and Indian Summer monsoon rainfall

Summary The interannual and decadal scale variability in the North Atlantic Oscillation (NAO) and its relationship with Indian Summer monsoon rainfall has been investigated using 108 years (1881–1988) of data. The analysis is carried out for two homogeneous regions in India, (Peninsular India and Northwest India) and the whole of India. The analysis reveals that the NAO of the preceding year in January has a statistically significant inverse relationship with the summer monsoon rainfall for the whole of India and Peninsular India, but not with the rainfall of Northwest India. The decadal scale analysis reveals that the NAO during winter (December–January–February) and spring (March–April–May) has a statistically significant inverse relationship with the summer monsoon rainfall of Northwest India, Peninsular India and the whole of India. The highest correlation is observed with the winter NAO. The NAO and Northwest India rainfall relationship is stronger than that for the Peninsular and whole of India rainfall on climatological and sub-climatological scales.Trend analysis of summer monsoon rainfall over the three regions has also been carried out. From the early 1930s the Peninsular India and whole of India rainfall show a significant decreasing trend (1% level) whereas the Northwest India rainfall shows an increasing trend from 1896 onwards.Interestingly, the NAO on both climatological and subclimatological scales during winter, reveals periods of trends very similar to that of Northwest Indian summer monsoon rainfall but with opposite phases.The decadal scale variability in ridge position at 500 hPa over India in April at 75° E (an important parameter used for the long-range forecast of monsoon) and NAO is also investigated.With 4 Figures     

Local onset and demise of the Indian summer monsoon

This paper introduces an objective definition of local onset and demise of the Indian summer monsoon (ISM) at the native grid of the Indian Meteorological Department’s rainfall analysis based on more than 100 years of rain gauge observations. The variability of the local onset/demise of the ISM is shown to be closely associated with the All India averaged rainfall onset/demise. This association is consistent with the corresponding evolution of the slow large-scale reversals of upper air and ocean variables that raise the hope of predictability of local onset and demise of the ISM. The local onset/demise of the ISM also show robust internannual variations associated with El Nino and the Southern Oscillation and Indian Ocean dipole mode. It is also shown that the early monsoon rains over northeast India has a predictive potential for the following seasonal anomalies of rainfall and seasonal length of the monsoon over rest of India.

     

An exploratory study on occurrence and impact of climate change on agriculture in Tamil Nadu,India

This study has been undertaken to examine the occurrence of climate change in Tamil Nadu, the southernmost state of India and its impact on rainfall pattern which is a primary constraint for agricultural production. Among the five sample stations examined across the state, the minimum temperature has increased significantly in Coimbatore while the same has decreased significantly in Vellore whereas both minimum and maximum temperatures have increased significantly in Madurai since 1969 with climate change occurring between late 1980s and early 1990s. As a result, the south-west monsoon has been disturbed with August rainfall increasing with more dispersion while September rainfall decreasing with less dispersion. Thus, September, the peak rainfall month of south-west monsoon before climate change, has become the monsoon receding month after climate change. Though there has been no change in the trend of the north-east monsoon, the quantity of October and November rainfall has considerably increased with increased dispersion after climate change. On the whole, south-west monsoon has decreased with decreased dispersion while north-east monsoon has increased with increased dispersion. Consequently, the season window for south-west monsoon crops has shortened while the north-east monsoon crops are left to fend against flood risk during their initial stages. Further, the incoherence in warming, climate change and rainfall impact seen across the state necessitates devising different indigenous and institutional adaptation strategies for different regions to overcome the adverse impacts of climate change on agriculture.

     

On the relationship between Indian monsoon withdrawal and Iran’s fall precipitation onset

Indian monsoon is the most prominent of the world’s monsoon systems which primarily affects synoptic patterns of India and adjacent countries such as Iran in interaction with large-scale weather systems. In this article, the relationship between the withdrawal date of the Indian monsoon and the onset of fall precipitation in Iran has been studied. Data included annual time series of withdrawal dates of the Indian monsoon prepared by the Indian Institute for Tropical Meteorology, and time series of the first date of 25 mm accumulated precipitation over Iran’s synoptic weather stations in a 10-day period which is the basis for the cultivation date. Both time series were considered in Julian calendar with the starting date on August 1. The studied period is 1960–2014 which covers 55 years of data from 36 meteorological stations in Iran. By classifying the withdrawal dates of the Indian monsoon in three stages of late, normal, and early withdrawals, its relation with the onset of fall precipitation in western, southwestern, southern, eastern, central, and northern regions of Iran was studied. Results demonstrated that in four out of the six mentioned regions, the late withdrawal of the Indian monsoon postpones the onset of fall precipitation over Iran. No significant relation was found between the onset of fall precipitation in central region of Iran and the monsoon’s withdrawal date. In the western, southwestern, southern, and eastern regions of Iran, the late monsoon delays the onset of fall’s precipitation; while in the south Caspian Sea coastal area, it causes the early onset of autumnal precipitation. The lag in onset of fall precipitation in Iran which is coordinated with the late withdrawal of monsoon is accompanied with prolonged subtropical high settling over Iran’s plateau that prevents the southward movement of polar jet frontal systems. Such conditions enhance northerly wind currents over the Caspian Sea which, in turn, increase the precipitation in Caspian coastal provinces, which has a different behavior from the overall response of Iran’s climate to the late withdrawal of monsoon. In the phase of early monsoon withdrawal, the subtropical jet is located at the 200 hPa level in 32.5° north latitude; compared with the late withdrawal date, it shows a 2° southward movement. Additionally, the 500 hPa trough is also located in the Eastern Mediterranean, and the MSL pressure anomaly is between ? 4 to ? 7 hPa. The Mediterranean trough in the late withdrawal phase is located in its central zones. It seems that the lack of significant correlation between late withdrawal date of Indian monsoon and late fall’s precipitation onset in the central region of Iran depends on three reasons:1. Lack of adequate weather stations in central region of Iran.2. Precipitation standard deviations over arid and warm regions are high.3. Central flat region of Iran without any source of humidity is located to the lee side of Zagros mountain range. So intensification or development of frontal systems is almost prohibited over there.     

Satellite evidence on the trade-offs of the food-water–air quality nexus over the breadbasket of India

Access to food, water, and good air quality is indispensable for human life, as reflected in various United Nations Sustainable Development Goals (SDGs); however, pursuing food security may pose threats to water security and/or air quality. An important case is northwest India including the Punjab and Haryana states, which is the ‘breadbasket’ of India with a significantly increasing paddy rice area. The rapid expansion of rice farming has stressed groundwater resources and impacted air quality. Satellite observations have the potential to provide data for better decisions on food security, water storage, and air pollution, which would be vital for regional sustainable development. Based on observations from multiple satellites from 2001 to 2018, we found that paddy rice expansion (+22%) increased groundwater depletion (−1.50 cm/yr), residue burning (+500%), and air pollution (+29%, PM_2.5) in the breadbasket of India. Moreover, satellite observations showed changes in these interactions after the enactment of a groundwater protection policy in 2009, which decelerated groundwater depletion (−1.20 cm/yr) due to delayed rice planting and harvest dates (∼15d); the latter elevated air pollution in November (+29%, PM_2.5). Our finding stresses the need to reconcile the trade-offs and consider the interactions among SDGs 2 (food), 3 (good health), 6 (clean water), and 11 (air quality in cities), in policy-making for sustainable development. An efficient crop residue ultilization and management system, bottom-up groundwater use regulations, and cropping system shift towards less water-consuming crops are critically required to resolve the trade-offs of the food-water–air quality nexus in the northern India. Our study also showcases remote sensing approaches and methods to support and aid the achievement of the SDGs and track their progreses to support regional sustainable development.     

Variations of Indian and African monsoons induced by insolation changes at 6 and 9.5 kyr BP

This study investigates the role of insolation in controlling the Indian and African monsoon evolutions during the Holocene using coupled ocean-atmosphere simulations of 0, 6, 9.5 kyr BP climates, for which only the variations of Earth’s orbital configuration are considered. The two monsoon systems are enhanced at 6 and 9.5 kyr BP, compared to 0 kyr BP, as a result of the intensified seasonal cycle of insolation in the Northern Hemisphere. The analysis of daily climatologies indicates that even though the length of the “celestial” summer season is shorter at 9.5 kyr BP, the rainy season is longer than at present. Emphasis is put on the impact of the precession on the seasonality, which partly explains why the relative amplification of the Indian and African monsoon varies between 9.5 and 6 kyr BP. Moreover, the changes in snow cover over the Tibetan Plateau play a critical role in reinforcing the 9.5 kyr BP monsoon in India during spring. The results suggest that the teleconnection between convection over India and subsidence over the Mediterranean regions, through the Rodwell and Hoskins mechanism, has an impact on the development of the African monsoon at 9.5 kyr BP.     

Association between extreme monsoons and the dipole mode over the Indian subcontinent

Summary The relationship of summer monsoon over India with the Indian Ocean Dipole Mode has been investigated applying simple statistical techniques. While a long time series of 132 years based on 1871–2002 for both summer monsoon rainfall as well as dipole mode index has been used in this study, the NCEP–NCAR reanalysis data (1948–2002) are used to examine the circulation features associated with the extreme dipole and monsoon phases. These flow patterns bring out the dynamics of the dipole – monsoon relationship. Lead/lag correlations between the dipole mode index and the Indian monsoon rainfall are computed. Results reveal that numerically the relationship is stronger following the monsoon. The lower troposphere flow patterns at 850 hPa associated with the extreme phases of the dipole and monsoon are consistent with the correlation analysis. Further a strong (weak) summer monsoon favours the development of the negative (positive) dipole event in autumn. The sliding correlations between Indian monsoon rainfall and the dipole mode index suggest that the impact of monsoon over dipole is weakening after 1960s. This weakening relationship has been evidenced by the composites of sea-surface temperature anomalies and circulation patterns. All the above analysis suggests that the summer monsoon has more influence on the dipole mode than vice-a-versa.     

Assessment of CORDEX-SA experiments in representing precipitation climatology of summer monsoon over India

The present work assesses the performance of 11 regional climate simulations in representing the precipitation patterns of summer monsoon over India for the period 1970–2005. These simulations have been carried out under Coordinated Regional Climate Downscaling Experiment–South Asia (CORDEX-SA) project. The regional climate models (RCMs) have been inter-compared as well as evaluated against the observation to identify the common weaknesses and differences between them. For this, a number of statistical analysis has been carried out to compare the model precipitation field with the corresponding observation. Model uncertainty has been also evaluated through bias studies and analysis of the spread in the ensemble mean (hereafter, ensemble). The models which perform better than the rest are identified and studied to look for any improvement in the ensemble performance. These better performing experiments (best RCM experiments) are further assessed over the monsoon core region (MCR) of India. This has been done to understand how well the models perform in a spatially homogeneous zone of precipitation which is considered to be a representative region of Indian summer monsoon characteristics. Finally, an additional analysis has been done to quantify the skill of models based on two different metrics—performance and convergence including a combination of the two. The experiment with regional model RegCM4 forced with the global model GFDL-ESM2M shows the highest combined mean skill in capturing the seasonal mean precipitation. In general, a significant dry bias is found over a larger part of India in all the experiments which seems most pronounced over the central Indian region. Ensemble on an average tends to outperform many of the individual experiments with bias of smaller magnitude and an improved spatial correlation compared with the observation. Experiments which perform better over India improve the results but only slightly in terms of agreement among experiments and bias.     

Smallholder adaptation to climate change: dynamics and limits in Northern Ghana

Climate change and land degradation result in decreasing yields and crop failures in Northern Ghana and have caused further impoverishment of Ghana’s poorest region. Farmers have diversified their livelihoods to adapt to uncertain environmental conditions in various ways. While traditionally a diversification of the production and migration were the prime means of adaptation, many farmers have started to intensify their production by adopting shallow groundwater irrigation for vegetable gardening for Ghana’s urban markets. This has helped to cope with a changing environment, ameliorated poverty and reversed rural–urban migration, while the local hydrology curbed an over-exploitation of groundwater resources, commonly associated with an uncontrolled farmer-driven expansion of groundwater irrigation. This research confirms that farmer-driven small-scale irrigation can play an important role in the process of climate change adaptation. However, while farmers tried to integrate in the larger economy, they have become subject to market failures that in their essence are caused by unfair and unpredictable patterns of global trade. It is this double exposure to global environmental change and economic globalization that need to be taken into consideration when local adaptive capacities are discussed. Many convincing arguments call for the revision of some of the most unfair and devastating economic practices; however, the need to enhance adaptive capacity towards global climate change for poor parts of the population in the south should be added to the discussion.     

All-India monthly and seasonal rainfall series: 1871–1993

Summary The Indian rainfall has often been used as a proxy data for the Asian monsoon as a whole for understanding the energy budget of the major circulation features and also used as an input parameter in estimating the other regional parameters. In view of this, a long homogeneous rainfall series of All-India (India taken as one unit) has been prepared based on a fixed and well distributed network of 306 raingauge stations over India by giving proper area-weightage. This paper contains a listing of All-India monthly, seasonal and annual homogeneous data series for the period 1871–1993. Some statistical details and long-term changes of the All-India monsoon rainfall have been discussed.With 4 Figures     

Modelling of Indian monsoon rainfall series by univariate box—Jenkins type of models

The time domain approach, i.e. Autoregressive (AR) processes, of time series analysis is applied to the monsoon rainfall series of India and its two major regions, viz. North-West India and Central India. Since the original time series shows no modelable structure due to the presence of high interannual variability, a 3-point running filter is ap-plied before exploring and fitting appropriate stochastic models. Out of several parsimonious models fitted, AR(3) is found to be most suitable. The usefulness of this fitted model is validated on an independent datum of 18 years and some skill has been noted. These models therefore can be used for low skill higher lead time forecasts of monsoon. Further the forecasts produced through such models can be combined with other forecasts to increase the skill of monsoon forecasts.     

On the link between extreme floods and excess monsoon epochs in South Asia

This paper provides a synoptic view of extreme monsoon floods on all the nine large rivers of South Asia and their association with the excess (above-normal) monsoon rainfall periods. Annual maximum flood series for 18 gauging stations spread over four countries (India, Pakistan, Bangladesh and Nepal) and long-term monsoon rainfall data were analyzed to ascertain whether the extreme floods were clustered in time and whether they coincided with multi-decade excess monsoon rainfall epochs at the basin level. Simple techniques, such as the Cramer’s t-test, regression and Mann–Kendall (MK) tests and Hurst method were used to evaluate the trends and patterns of the flood and rainfall series. MK test reveals absence of any long-term tendency in all the series. However, the Cramer’s t test and Hurst-Mandelbrot rescaled range statistic provide evidence that both rainfall and flood time series are persistent. Using the Cramer’s t-test the excess monsoon epochs for each basin were identified. The excess monsoon periods for different basins were found to be highly asynchronous with respect to duration as well as the beginning and end. Three main conclusions readily emerge from the analyses. Extreme floods (>90th percentile) in South Asia show a tendency to cluster in time. About three-fourth of the extreme floods have occurred during the excess monsoon periods between ~1840 and 2000 AD, implying a noteworthy link between the two. The frequency of large floods was higher during the post-1940 period in general and during three decades (1940s, 1950s and 1980s) in particular.     

Multivariate ENSO index and Indian monsoon rainfall: relationships on monthly and subdivisional scales

Summary El Ni?o/Southern Oscillation (ENSO) is known to cause world-wide weather anomalies. It influences the Indian Monsoon Rainfall (IMR) also. But due to large spatial and temporal variability of monsoon rains, it becomes difficult to state any single uniform relationship between the ENSO and IMR that holds good over different subdivisions of India, though the general type of relationship between all India monsoon rainfall and ENSO is known since long. The selection of the most suitable ENSO index to correlate with the IMR is another problem. The purpose of the present study is twofold, namely, to examine the relationship between the ENSO and IMR for entire monsoon season by using an ENSO index which represents the ENSO phenomenon in a comprehensive way, namely, the Multivariate ENSO Index (MEI) and to establish the relationships between MEI and IMR for every meteorological subdivision of India for each monsoon month; i.e. June, July, August and September. A comparison of MEI/IMR correlations has been made with Southern Oscillation Index (SOI)/IMR correlations. The result may find applications in the long range forecasting of IMR on monthly and subdivisional scales, especially over the high monsoon rainfall variability regions of Northwestern and the Peninsular India. Received October 27, 2000     

The influence of stochasticism on Indian summer monsoon rainfall and its impact on prediction

Output from a multi-millennial control simulation of the CSIRO Mark 2 coupled model has been used to investigate quantitatively the relation between the Indian summer monsoon rain and El Nino/Southern Oscillation events. A moving window correlation between these two features revealed marked interannual and multi-decadal variability with the correlation coefficient varying between ?0.8 and +0.2. This suggests that current observations showing a decline in this correlation are due to natural climatic variability. A scatter diagram of the anomalies of the Indian summer monsoon rainfall and NINO 3.4 surface temperature showed that in almost 40 % of the cases ENSO events were associated with rainfall anomalies opposite to those implied by the climatological correlation coefficient. Case studies and composites of global distributions of surface temperature and rainfall anomalies for El Nino (or La Nina) events highlight the opposite rainfall anomalies over India that can result from very similar ENSO surface temperature anomalies. Composite differences are used to demonstrate the unique sensitivity of Indian summer monsoon rainfall anomalies to ENSO events. The problem of predicting such anomalies is discussed in relation to the fact that time series of the monsoon rainfall, both observed and simulated, consist of white noise. Based on the scatter diagram it is concluded that in about 60 % of the cases seasonal or annual prediction of monsoon rainfall based on individual ENSO events will result in the correct outcome. Unfortunately, there is no way, a priori, of determining for a given ENSO event whether the correct or a rogue prediction will result. Analysis of the present model’s results suggest that this is an almost world-wide problem for seasonal predictions of rainfall.     

Seasonal persistence and propagation of intraseasonal patterns over the Indian monsoon region

The space-time evolution of convection over the monsoon region containing the Indian subcontinent, the Indian Ocean and the West Pacific has been studied. A multi-channel singular spectrum analysis of the daily outgoing longwave radiation has yielded two intraseasonal oscillatory patterns and two large-scale standing patterns as the most dominant modes of intraseasonal variability. The oscillatory modes vary on time scales of about 45 and 28 days and their average cycles of variability are shown to correspond to the life cycles of active and break periods of monsoon rainfall over India. During an active (break) cycle, a convection (dry) anomaly zone first appears in the equatorial Indian Ocean, subsequently expands to cover the Indian subcontinent and finally contracts to disappear in the northern part of India. Some eastward and northward movements are found to be associated with both oscillatory modes, while westward movement may also be associated with the 28-day mode. The oscillatory modes are shown to have a large spatial scale extending to the West Pacific. One of the standing modes has anomalies of uniform sign covering the entire region and is related to El Niño and southern oscillation (ENSO) pattern. The other standing mode has a dipole structure in the equatorial Indian Ocean associated with large-scale anomalies over India with the same sign as those over the western part of the dipole. These two standing modes persist throughout the monsoon season, each maintaining its respective pattern. The seasonal mean monsoon is mainly determined by the two standing patterns, without much contribution from the oscillatory modes. The relative role of the standing patterns (ENSO mode and dipole mode) seems to be important in determining the seasonal mean during certain years.     

Indian summer monsoon rainfall variability in global coupled ocean-atmospheric models

Performance of seven fully coupled models in simulating Indian summer monsoon climatology as well as the inter-annual variability was assessed using multi member 1 month lead hindcasts made by several European climate groups as part of the program called Development of a European multi-model ensemble system for seasonal-to-inter-annual prediction (DEMETER). Dependency of the model simulated Indian summer monsoon rainfall and global sea surface temperatures on model formulation and initial conditions have been studied in detail using the nine ensemble member simulations of the seven different coupled ocean–atmosphere models participated in the DEMETER program. It was found that the skills of the monsoon predictions in these hindcasts are generally positive though they are very modest. Model simulations of India summer monsoon rainfall for the earlier period (1959–1979) are closer to the ‘perfect model’ (attainable) score but, large differences are observed between ‘actual’ skill and ‘perfect model’ skill in the recent period (1980–2001). Spread among the ensemble members are found to be large in simulations of India summer monsoon rainfall (ISMR) and Indian ocean dipole mode (IODM), indicating strong dependency of model simulated Indian summer monsoon on initial conditions. Multi-model ensemble performs better than the individual models in simulating ENSO indices, but does not perform better than the individual models in simulating ISMR and IODM. Decreased skill of multi-model ensemble over the region indicates amplification of errors due to existence of similar errors in the individual models. It appears that large biases in predicted SSTs over Indian Ocean region and the not so perfect ENSO-monsoon (IODM-monsoon) tele-connections are some of the possible reasons for such lower than expected skills in the recent period. The low skill of multi-model ensemble, large spread among the ensemble members of individual models and the not so perfect monsoon tele-connection with global SSTs points towards the importance of improving individual models for better simulation of the Indian monsoon.     

Climate change,the monsoon,and rice yield in India

Recent research indicates that monsoon rainfall became less frequent but more intense in India during the latter half of the Twentieth Century, thus increasing the risk of drought and flood damage to the country’s wet-season (kharif) rice crop. Our statistical analysis of state-level Indian data confirms that drought and extreme rainfall negatively affected rice yield (harvest per hectare) in predominantly rainfed areas during 1966–2002, with drought having a much greater impact than extreme rainfall. Using Monte Carlo simulation, we find that yield would have been 1.7% higher on average if monsoon characteristics, especially drought frequency, had not changed since 1960. Yield would have received an additional boost of nearly 4% if two other meteorological changes (warmer nights and lower rainfall at the end of the growing season) had not occurred. In combination, these changes would have increased cumulative harvest during 1966–2002 by an amount equivalent to about a fifth of the increase caused by improvements in farming technology. Climate change has evidently already negatively affected India’s hundreds of millions of rice producers and consumers.     

Empirical Orthogonal Function (EOF) analysis of monsoon rainfall and satellite-observed outgoing long-wave radiation for Indian monsoon: a comparative study

Summary The present study involves the use of Empirical Orthogonal Function (EOF) analysis/Principal Component Analysis (PCA) to compare the dominant rainfall patterns from normal rainfall records over India, coupled with the major modes of the Outgoing Long-wave Radiation (OLR) data for the period (1979–1988) during the monsoon period (June–September). To understand the intraseasonal and interannual variability of the monsoon rainfall, daily and seasonal anomalies have been obtained by using the (EOF) analysis. Importantly, pattern characteristics of seasonal monsoon rainfall covering 68 stations in India are highlighted.The purpose is to ascertain the nature of rainfall distribution over the Indian continent. Based on this, the percentage of variance for both the rainfall and OLR data is examined. OLR has a higher spatial coherence than rainfall. The first principal component of rainfall data shows high positive values, which are concentrated over northeast as well as southeast, whereas for the OLR, the area of large positive values is concentrated over northwest and lower value over south India apart from the Indian ocean. The first five principal components explain 92.20% of the total variance for the rainfall and 99.50% of the total variance for the outgoing long-wave radiation. The relationship between monsoon rainfall and Southern Oscillations has also been examined and for the Southern Oscillations, it is 0.69 for the monsoon season. The El-Niño events mostly occurred during Southern Oscillations, i.e. Walker circulation. It has been found that the average number of low pressure system/low pressure system days play an important role during active (flood) or inactive (drought) monsoon year, but low pressure system days play more important role in comparison to low pressure systems and their ratio are (16:51) and (13:25) respectively. Significantly, the analysis identifies the spatial and temporal pattern characteristics of possible physical significance.     

Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982–2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982–2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson’s and Kendal’s correlation coefficient and Wilmort’s index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.     

Climate change adaptation implications for drought risk mitigation: a perspective for India

There is a growing evidence that the climate change do has implications for drought vulnerable India with studies projecting future possible reductions in monsoon related rainfall in the country. The existing drought risk mitigation and response mechanisms were looked into and gaps were identified by drawing lessons from previous disasters and response mechanisms. In absence of reliable climate predictions at the scales that make them useful for policy level planning, the emphasis was on identifying no-regret adaptation options those would reduce current vulnerabilities while mainstreaming the adaptation in the long run. The most notable climate change implications for the drought vulnerable India are the enhanced preparedness with due emphasis to the community based preparedness planning, reviewing the existing monsoon and drought prediction methodologies, and establishing drought monitoring and early warning systems in association with a matching preparedness at the input level.