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Industrial revolution and fast deforestation in 20th century pushes the earth to face climate change. Climate change became one of the most important global environmental challenges of the 21st century. Besides other weather abnormalities it is potential threat to world food security as agriculture production is directly dependent on various weather parameters. Even a small alteration in climate, adversely affects agriculture by decreasing the production. Global temperatures have already increased by 0.70°C over the past century and prediction of more rise of temperature is there. Possible changes in temperature, precipitation and CO2 concentration are expected to significantly impact crop growth. India could experience a 40% decline in agricultural productivity by the 2080s. Various food grain crops are going to affect badly under the temperature rise situation. It has been projected that under the scenario of a 2.50°C to 4.90°C temperature rise, rice yields will drop by 32% to 40% and wheat yields by 41% to 52%. This paper summarized study related to climate change projections and its impact on production of various crops. [ABSTRACT FROM AUTHOR]

Deployment of electric vehicles (EVs) is being widely used by governments across the world as a policy tool to mitigate the catastrophic effects of global warming and climate change. However, the demand for EVs has been slow in many large automotive markets, including India. To increase the uptake of EVs in India, the central government has introduced a new consumer incentive program and a special tax rate for EVs. In this paper, we estimate the cost-effectiveness of these EV promotion programs in achieving CO2 emission reduction for each State in India. We use emission data of the chosen vehicle and CO2 emission intensity data of each State's power grid for computation. The results are then compared to other benchmark investments in climate change mitigation. The findings indicate that subsidizing EVs may not be the most cost-efficient method to achieve CO2 reduction. [ABSTRACT FROM AUTHOR]

Global warming is the average temperature of the earth's surface which has increased over the past century by about 0.6 °C. This rising temperature may vary precipitation patterns, more frequent droughts, rise in sea level and intense storms and can be termed as climate change. To understand uneven precipitation pattern for monsoon dominated region like India, there is a need to study cloud processes at high resolution with the help of cloud microphysical properties. Ocean is the major and primary source of cloud whereas local water bodies and re-evaporated water over land could be secondary source. Paper focuses over ocean (Arabian Sea) as well as over land (central India) to know the effect of global warming on cloud microphysical properties such as cloud effective radius and cloud liquid water content. Warming signal in terms of rise in sea surface temperature (0.1 °C/decade) as well as rise in surface air temperature (0.05 °C/decade) are observed over Arabian Sea and central India respectively. Satellite data show an increasing (0.5 µm/decade) trend in cloud effective radius over Arabian Sea, whereas it decreases (− 0.1 µm/decade) over central India. Increasing trend in temperature and cloud properties is may be due to warming signal. Aerosol concentration over ocean and land further helped to understand cloud processes with cloud microphysical properties. Paper will focus on the effect of warming signal in cloud properties over Arabian Sea and central India. This type of high resolution study may help to understand cloud processes which in turn may help to understand precipitation patterns. [ABSTRACT FROM AUTHOR]

Global warming is a biggest issue around the world. In this research paper, the temporal and spatial trend analysis of seasonal and annual rainfall on Bhogavo River watersheds in Sabarmati lower basin of Gujarat state of India has been analysed using the data of 11 rain gauge stations installed in Bhogavo watershed. Linear regression, Mann–Kendall Test, Sen's slope test and innovative trend analysis methods are used to carry out monthly and annual rainfall trend analysis. In addition to the rainfall analysis, a number of rainy days change in magnitude as a percentage of mean rainfall have also been analysed using linear regression and Sen's slope method, respectively. The IDW method is used to develop a spatial distribution of annual and seasonal rainfall trend over the study area. From the results, it is concluded that annual rainfall shown increasing (positive) trend at nine stations out of 11 stations. The highest value of change in magnitude of trend as a percentage of mean monthly rainfall has been obtained in the month of July, attributing increasing trend at Sayla station and lowest value magnitude of trend as a percentage of mean rainfall in the monthly rainfall has been obtained in the month of August, attributing decreasing trend at Bavla station. [ABSTRACT FROM AUTHOR]

Impacts of global warming and local scale urbanization on precipitation are evident from observations; hence both must be considered in future projections of urban precipitation. Dynamic regional models at a fine spatial resolution can capture the signature of urbanization on precipitation, however simulations for multiple decades are computationally expensive. In contrast, statistical regional models are computationally inexpensive but incapable of assessing the impacts of urbanization due to the stationary relationship between predictors and predictand. This paper aims to develop a unique modelling framework with a demonstration for Mumbai, India, where future urbanization is projected using a Markov Chain Cellular Automata approach, long term projections with climate change impacts are performed using statistical downscaling and urban impacts are simulated with a dynamic regional model for limited number of years covering different precipitation characteristics. The evaluation of the statistical downscaling methodology over historical time period reveals large underestimation of the extreme rainfall, which is improved effectively by applying another regression model, for extreme days. The limited runs of dynamic downscaling models with different stages of urbanization for Mumbai, India, reveal spatially non uniform changes in precipitation, occurring primarily at the higher quantiles. The statistical and dynamical outputs are further integrated using quantile transformation for precipitation projection in Mumbai during 2050s. The projections show dominant impacts of urbanization compared to those from large scale changing patterns. The uniqueness of this computationally efficient framework lies in an integration of global and local factors for precipitation projections through a conjugal statistical–dynamical approach. [ABSTRACT FROM AUTHOR]

India has good reasons to be concerned about climate change as it could adversely affect the achievement of vital national development goals related to socio-economic development, human welfare, health, energy availability and use, and infrastructure. The paper attempts to develop a framework for integrated impact assessment and adaptation responses, using a recently built railroad coastal infrastructure asset in India as an example. The framework links climate change variables — temperature, rainfall, sea level rise, extreme events, and other secondary variables — and sustainable development variables — technology, institutions, economic, and other policies. The study indicates that sustainable development variables generally reduce the adverse impacts on the system due to climate change alone, except when they are inadequately applied. The paper concludes that development is a vital variable for integrated impact assessment. Well crafted developmental policies could result in a less-GHG intensive future, enhanced adaptive capacities of communities and systems, and lower impacts due to climate change. [ABSTRACT FROM AUTHOR]

Abstract: The increasing global warming is most likely to affect the magnitude and pattern of wind at a regional level and such an effect may or not follow the trend predicted at the global scale. Regional level exercises are therefore necessary while making decisions related to engineering infrastructure. In this paper an attempt is made to know the extent of change in design as well as operational wind conditions at two offshore locations along the west coast of India. The design wind speeds with return periods of 10, 50 and 100 years derived for two 30-year time slices in the past and future are compared. In two separate exercises the past and future wind at the local level is simulated by empirical downscaling as well as by interpolation of the general circulation model (GCM) output. Both sets of past and future data are fitted to the Generalized Pareto as well as Weibull distributions using the peak over threshold method to extract long term wind speeds with a specified return. It is noticed that at the given locations the operational and design wind may undergo an increase of around 11%–14% when no downscaling is adopted and 14%–17% when the GCM data are downscaled. Although these figures may suffer from a certain level of statistical uncertainty the study points out to take a relook into the safety margins kept in the design and operation of ocean structures in the light of global warming. [Copyright &y& Elsevier]

The Indian Ocean Dipole (IOD) can affect the El Niño-Southern Oscillation (ENSO) state of the following year, in addition to the well-known preconditioning by equatorial Pacific Warm Water Volume (WWV), as suggested by a study based on observations over the recent satellite era (1981-2009). The present paper explores the interdecadal robustness of this result over the 1872-2008 period. To this end, we develop a robust IOD index, which well exploits sparse historical observations in the tropical Indian Ocean, and an efficient proxy of WWV interannual variations based on the temporal integral of Pacific zonal wind stress (of a historical atmospheric reanalysis). A linear regression hindcast model based on these two indices in boreal fall explains 50 % of ENSO peak variance 14 months later, with significant contributions from both the IOD and WWV over most of the historical period and a similar skill for El Niño and La Niña events. Our results further reveal that, when combined with WWV, the IOD index provides a larger ENSO hindcast skill improvement than the Indian Ocean basin-wide mode, the Indian Monsoon or ENSO itself. Based on these results, we propose a revised scheme of Indo-Pacific interactions. In this scheme, the IOD-ENSO interactions favour a biennial timescale and interact with the slower recharge-discharge cycle intrinsic to the Pacific Ocean. [ABSTRACT FROM AUTHOR]

This article examines the structural aspects of climate vulnerabilities in the context of monsoon failures. The paper is based on a unique household survey, conducted in Tamil Nadu, India. The study uses a rural differentiation framework to interrogate unequal vulnerabilities to monsoon failures, based on measures such as Gini coefficients and Lorenz curves of monetary losses. Results show that negative consequences of climate change in general, and monsoon failures in particular, intensify pre-existing socio-economic disparities. When the rural differentiation theory is applied in a broader sense, the analysis shows that landed and farming households have greater exposure and losses. When we move beyond these aggregate categories, the revelation is that households with pre-existing disadvantages such as marginal landholders, subsistence farmers and agricultural workers are more vulnerable. • Global warming affects agriculture in South Asia in a major way through monsoon failures. • Vulnerability to monsoon failures can be measured by associated monetary losses. • These losses are highly unevenly distributed, pointing to significant inequality in impacts. • Landless households, marginal holders and subsistence farmers have higher losses. • Climate change intensifies existing structural disadvantages. [ABSTRACT FROM AUTHOR]

This paper seeks to evaluate and examine the validity of global warming at local or regional level. The phenomenon of global warming is of great importance and a very challenging one at present time. Thus, the need arises to study it at micro and meso-levels; as such a study will help in making plans to protect human and other living things from various impacts of climate change. After studying the variations in monthly temperatures in the State of Manipur of the Republic of India, and analyzing the warming trends, it has been found that the rise of mean temperature is mainly due to an increase in the minimum temperature in the state. [ABSTRACT FROM AUTHOR]

The paper surveys the energy problem in relation to global warming and the social criteria of India. India's failure to develop a policy on global warming is highlighted and her economic policy judged in relation to her energy options, including such notable work as jatropha development and nuclear energy. [ABSTRACT FROM AUTHOR]