An assessment of climate variability and climate change on heat wave characteristics in India

Heat wave is an important class of climate hazard due to its impact on human health. Changes in characteristics of heat waves such as intensity, duration, frequency and geographical exposure have the potential to have serious societal impacts. Observations suggest that in some parts of the world such changes are already happening, leading to impact on human health in terms of heat-stress and mortality. Heat waves are expected to intensify around the globe in the future, with potential increase in heat stress and heat-induced mortality in the absence of adaptation measures. India has a high current exposure to heat waves, and with limited adaptive capacity, impacts of increased heat waves might be quite severe. An understanding of heat wave characteristics is a pre-requisite to assess vulnerabilities due to heat waves.

The primary objective of this thesis was to understand and analyze the climatic mechanisms that are driving changes in heat waves in India. Daily observed maximum temperature data and a combination of re-analysis data for upper air circulation fields were used to identify key mechanisms responsible for occurrences of heat waves in India. Using observed data, this study explored whether changes in internal variability in the climate system are driving observed trends, or else the observed changes could be related to anthropogenic influence. Multiple research tools, methods and data were employed including interpolated station data, re-analyses data, general circulation model outputs and regional climate model outputs to address fundamental questions about the influence of climate variability and climate change on heat wave characteristics. Main results of the thesis are highlighted as:

1. This study showed that heat waves during El Ni~no years were longer and hotter, and argues that this is related to a delay in the onset of the Indian Summer Monsoon. Further, it was found that the shift in the circulation pattern due to El Ni~no resulted in (i) weakening of south-westerlies in the Arabian sea region, and (ii) the occurrence of a larger number of clear sky days over India. This explains heat waves during El Ni~no years were warmer and had a longer duration.

2. This study found that anthropogenic influence is detectable in the extreme temperatures over India. Further, using record breaking statistics the study showed that the number of local record breaking seasonal extremes, in climate with trends, were five times larger than the climate with no trends. Seasonal extremes are often associated with heat wave like conditions; this implies that the climate change may increase the chance of occurrence of extreme temperatures during heat waves.

3. Although the debate on the role of natural variability over climate change for the occurrence of individual events of extreme is still valid, results of this study indicate that the anthropogenic influence on general trends in extremes cannot be overruled for occurrence of individual record braking event. This is particularly important for India to understand the risk due to record breaking extremes, keeping its existing vulnerabilities in mind.

4. This study also presents the first projections of future heat waves in India based on multiple climate models and scenarios for CMIP5 data. Projections from climate models inform that heat waves are projected to be more intense, longer duration, frequent and has a tendency to occur earlier in the year. Southern India, currently not influenced by heat waves, is expected to be severely affected by the end of the twenty-first century. Projections indicate that a sizable part of India will experience heat stress conditions in the future. In northern India, the average number of days with extreme heat stress condition during pre-monsoon hot season will reach 30. The intensification of heat waves might lead to severe heat stress and increased mortality.

5. Further, the study suggests that CORDEX-RCMs (regional climate models for South Asia) are able to reproduce the observed inter-annual and seasonal variability of temperature better than their driving GCMs. This may be a reason why RCMs provide useful simulations of severe heat wave characteristics than their driving GCMs. However, for projection of heat wave characteristics CORDEX-RCMs show a higher inter-model uncertainty in comparison to their driving GCMs.