The study of climate changes in India and search for robust evidences are issues of concern specially when it is known that poor people are very vulnerable to climate changes. Due to the vast size of India and its complex geography, climate in this part of the globe has large spatial and temporal variations. Important weather events affecting India are floods and droughts, monsoon depressions and cyclones, heat waves, cold waves, prolonged fog and snowfall. Results of this comprehensive study based on observed data and model reanalyzed fields indicate that in the last century, the atmospheric surface temperature in India has enhanced by about 1 and 1.1°C during winter and post-monsoon months respectively. Also decrease in the minimum temperature during summer monsoon and its increase during post-monsoon months have created a large difference of about 0.8°C in the seasonal temperature anomalies which may bring about seasonal asymmetry and hence changes in atmospheric circulation. Opposite phases of increase and decrease in the minimum temperatures in the southern and northern regions of India respectively have been noticed in the interannual variability. In north India, the minimum temperature shows sharp decrease of its magnitude between 1955 and 1972 and then sharp increase till date. But in south India, the minimum temperature has a steady increase. The sea surface temperatures (SST) of Arabian Sea and Bay of Bengal also show increasing trend. Observations indicate occurrence of more extreme temperature events in the east coast of India in the recent past. During summer monsoon months, there is a decreasing (increasing) trend in the frequency of depressions (low pressure areas). In the last century the frequency of occurrence of cyclonic storms shows increasing trend in the month of November. In addition there is increase in the number of severe cyclonic storms crossing Indian Coast. Analysis of rainfall Climatic Change (2007) 85: amount during different seasons indicate decreasing tendency in the summer monsoon rainfall over Indian landmass and increasing trend in the rainfall during pre-monsoon and post-monsoon months.

[1] Daily gridded (1°Â 1°) rainfall data prepared by the India Meteorological Department for the period have been used in this study to examine possible changes in the frequency of rain events in India in terms of their duration and intensity per day. So far as the duration is concerned, a rain event is classified as short, long, dry, or prolonged dry spell. Similarly in terms of intensity, a rainy day is considered as low, moderate, or heavy. Changes in the frequency of these events have great relevance from the point of view of climate change. Threshold and limiting values for defining the heavy and moderate rain days are calculated in accordance with the gamma probability distribution. Results show that the frequencies of moderate and low rain days considered over the entire country have significantly decreased in the last half century. On the basis of the duration of rain events it is inferred that long spells show a significant decreasing trend over India as a whole while short and dry spells indicate an increasing tendency with 5% significance. The characteristics of rain events are also examined over six homogenous rainfall zones separately since the spatial distribution of rainfall over India shows large variability. In this study, the changes in the frequencies of different categories of rain events suggest weakening of the summer monsoon circulation over India. This hypothesis of a weakening of monsoon circulation is supported by significant reduction in the 850 hPa wind fields in the National

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In the context of climate change and its impact on sectors like agriculture and health, it is important to examine the changes in the characteristics of temperature extremes of different intensities and duration. In this study, an India Meteorological Department gridded temperature dataset is used to examine the changes in the frequency of occurrence of extreme temperatures over India and its seven homogeneous regions during the period 1969–2005. Results indicate a significant decrease in the frequency of occurrence of cold nights in the winter months in India and in its homogeneous regions in the north except in the western Himalaya. Southern regions show a drastic decrease in the frequency of cold nights relative to the period 1969–75. A significant increasing trend in the number of warm days in summer is noticed only in the interior peninsula. In the entire country and on the east coast and west coast, the maximum number of warm days in summer has been noticed only during the last decade, 1996–2005. Further, in the whole country the maximum number of intense warm days and nights in summer has been observed in the last decade. A significant increase in the number of cold days in winter is observed in the north-central and northeast regions. Changes in the frequency of warm and cold exceedances indicate maximum warming in the west coast as compared with all other regions. In sum, such spatial and temporal changes in the characteristics of all categories of temperature extremes broadly suggest warming trends in large parts of India.

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Using data from 33 models from the CMIP5 historical and AMIP5 simulations, we have carried out a systematic analysis of biases in total precipitation and its convective and large-scale components over the south Asian region. We have used 23 years (1983–2005) of data, and have computed model biases with respect to the PERSIANN-CDR precipitation (with convective/large-scale ratio derived from TRMM 3A12). A clustering algorithm was applied on the total, convective, and large-scale precipitation biases seen in CMIP5 models to group them based on the degree of similarity in the global bias patterns. Subsequently, AMIP5 models were analyzed to conclude if the biases were primarily due to the atmospheric component or due to the oceanic component of individual models. Our analysis shows that the set of individual models falling in a given group is somewhat sensitive to the variable (total/convective/large-scale precipitation) used for clustering. Over the south Asian region, some of the convective and large-scale precipitation biases are common across groups, emphasizing that although on a global scale the bias patterns may be sufficiently different to cluster the models into different groups, regionally, it may not be true. In general, models tend to overestimate the convective component and underestimate the large-scale component over the south Asian region, although with spatially varying magnitudes depending on the model group. We find that the convective precipitation biases are largely governed by the closure and trigger assumptions used in the convection parameterization schemes used in these models, and to a lesser extent on details of the individual cloud models. Using two different methods: (i) clustering, (ii) comparing the bias patterns of models from CMIP5 with their AMIP5 counterparts, we find that, in general, the atmospheric component (and not the oceanic component through biases in SSTs and atmosphere-ocean feedbacks) plays a major role in deciding the convective and large-scale precipitation biases. However, the oceanic component has been found important for one of the convective groups in deciding the convective precipitation biases (over the maritime continent).

A general circulation model (GCM) is an alternative way for predicting Indian summer monsoon rainfall (ISMR) over the existing empirical/statistical models in recent time. However, the inherent biases present in the GCM affect its performance. Therefore, there is a high requirement for bias correction of the GCM. Few studies on bias correction of GCMs are available in the context of ISMR. A comparative study is reported in this paper on the six different bias correction methods by applying on the hindcast (May start, June-July-August-September) of the climate forecast system (CFS) model from the National Centers for Environmental Prediction (NCEP) for 27 years . Among the six methods discussed in this paper, three methods did not use any statistical transformation (Mean Bias-remove technique (U), Multiplicative shift technique (M) and Standardized-reconstruction technique (Z)) and the remaining three methods used statistical transformation (Regression technique (R), Quantile Mapping Method (Q), Principal Component Regression (PCR)). Finally, it was found that the Standardized-reconstruction technique (Z) and Quantile Mapping Method (Q) are more skilful than the others and both are equally skilful in simulating ISMR. Bias-corrected rainfall in four extreme years, out of which 1988 and 1994 are characterized by excess rainfall and 1987 and 2002 are characterized by deficit, are also examined here. Results indicate that both methods efficiently capture the extreme rainfall cases.

The accurate assessment of the potential impacts of climate change on societies and ecosystems requires regional and local‐scale climate change information. This assessment is critical for the development of local, national, and international policies to mitigate and adapt to the threat of climate change. Characterizing uncertainties in regional climate change projections (RCCPs) is therefore crucial for making informed decisions based on quantitative risk analysis. However, information about fine‐scale climate change and associated uncertainties is lacking due to the absence of a coordinating framework to improve the characterization of such uncertainties. Here we propose the inception of such a framework.