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The paper contributes to current knowledge of climate change impacts on Indian agriculture by accounting for spatial features that may influence the climate sensitivity of agriculture. Using panel data over a 20-year period and on 271 districts, this study estimates the impact of climate change on farm level net revenue in India. The key findings reveal that there is a significant positive spatial autocorrelation and that accounting for this can improve the accuracy of climate impact studies. Furthermore, the paper argues that better dissemination of knowledge among farmers through both market forces and local leadership will help popularize effective adaptation strategies to address climate change impacts. [ABSTRACT FROM AUTHOR]

Investigations into the climatic forcings that affect the long-term variability of the Indian summer monsoon are constrained by a lack of reliable rainfall data prior to the late nineteenth century. Extensive qualitative and quantitative meteorological information for the pre-instrumental period exists within historical documents, although these materials have been largely unexplored. This paper presents the first reconstruction of monsoon variability using documentary sources, focussing on western India for the period 1781-1860. Three separate reconstructions are generated, for (1) Mumbai, (2) Pune and (3) the area of Gujarat bordering the Gulf of Khambat. A composite chronology is then produced from the three reconstructions, termed the Western India Monsoon Rainfall reconstruction (WIMR). The WIMR exhibits four periods of generally deficient monsoon rainfall (1780-1785, 1799-1806, 1830-1838 and 1845-1857) and three of above-normal rainfall (1788-1794, 1813-1828 and 1839-1844). The WIMR shows good correspondence with a dendroclimatic drought reconstruction for Kerala, although agreement with the western Indian portion of the tree-ring derived Monsoon Asia Drought Atlas is less strong. The reconstruction is used to examine the long-term relationship between the El Nino-Southern Oscillation (ENSO) and monsoon rainfall over western India. This exhibits peaks and troughs in correlation over time, suggesting a regular long-term fluctuation. This may be an internal oscillation in the ENSO-monsoon system or may be related to volcanic aerosol forcings. Further reconstructions of monsoon rainfall are necessary to validate this. The study highlights uncertainties in existing published rainfall records for 1817-1846 for western India. [ABSTRACT FROM AUTHOR]

In a climate change scenario, the present work deals with the possibility of the changes in the rainfall pattern during the principal monsoon season (June 1-September 30) of the Indian summer monsoon. For this purpose three attributes are defined as DTMR, DHMR and DNMR representing the day when 10, 50 and 90 % of the accumulated summer monsoon rainfall is achieved respectively. Using a high resolution (1° × 1°) gridded rainfall data set for the last 50 years prepared by India Meteorological Department (Rajeevan et al. , in Curr Sci 91:296-306, ), the analysis has been carried out over the different parts of the Indian subcontinent. Using statistically robust significance tests, it is observed that the distribution of the three variables have changed significantly at 1 % (or 5 %) significance level in the last 50-year of period. The DTMR and DNMR arrive 2 days early over central India, whereas DHMR appears to arrive 6 days early over west India in the recent decades. The results presented in this paper are supported by the statistically robust significance tests; suggest an apparent change in terms of the arrival of the rainfall attributes during the last half century. [ABSTRACT FROM AUTHOR]

• We study weather effects on migration in rural India. • Positive weather shocks facilitate migration abroad and to cities in the same state. • Adverse weather shocks drive rural-urban migration to different states. • Urban climate migrants are less educated and are from agricultural households. • Approximately 8% of urbanization between 2005 and 2012 can be attributed to weather. In this paper, we move from the large strand of research that looks at evidence of climate migration to the questions: who are the climate migrants? and where do they go? These questions are crucial to design policies that mitigate welfare losses of migration choices due to climate change. We study the direct and heterogeneous associations between weather extremes and migration in rural India. We combine ERA5 reanalysis data with the India Human Development Survey household panel and conduct regression analyses by applying linear probability and multinomial logit models. This enables us to establish a causal relationship between temperature and precipitation anomalies and overall migration as well as migration by destination. We show that adverse weather shocks decrease rural-rural and international migration and push people into cities in different, presumably more prosperous states. A series of positive weather shocks, however, facilitates international migration and migration to cities within the same state. Further, our results indicate that in contrast to other migrants, climate migrants are likely to be from the lower end of the skill distribution and from households strongly dependent on agricultural production. We estimate that approximately 8% of all rural-urban moves between 2005 and 2012 can be attributed to weather. This figure might increase as a consequence of climate change. Thus, a key policy recommendation is to take steps to facilitate integration of less educated migrants into the urban labor market. [ABSTRACT FROM AUTHOR]

Interdependencies between the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Indian monsoon (IM) phenomena are investigated from data over the period of 1871-2013. Along with the bivariate analysis of directional couplings between ENSO, NAO, and IM, their trivariate analysis was carried out. To detect the seasonal features of directional couplings using Wiener-Granger causality, various temporal resolutions of the data ranging from a month to a half-year are used. Taking into account the seasonality of the processes, the influence of ENSO on NAO is detected which has different signs in winter and summer. The influence of NAO on ENSO is revealed only in the trivariate analysis. The strongest couplings are observed between ENSO and IM. All the detected couplings can be divided into two groups: the "fast" (with the characteristic time from a month to several months) and "slow" (with the characteristic time of a half-year or longer) ones. The fast couplings include bidirectional couplings between IM and ENSO in summer and autumn when the summer pattern of NAO influences the patterns of ENSO and IM in the next season. The slow couplings include the effects of ENSO on NAO and on the winter pattern of IM as well as the influence of IM on the summer pattern NAO. [ABSTRACT FROM AUTHOR]