Comparative Study of Heat Indices in India Based on Observed and Model Simulated Data

Abstract

It has been reported that the occurrence of "Heat wave" conditions is increasing in different parts of the world. This increase in heat extremes is expected to lead to increasing levels of discomfort to the human beings. In addition, there are loss of working hours, adverse health impacts and deaths depending on the severity and duration of the heat extremes. It is well known that meteorologically it is not merely the rise in temperature which causes human discomfort. Temperature coupled with high humidity, low wind and impervious clothing form major causes of discomfort. Above all, the type of physical activities being undertaken in these adverse conditions is one of the important factors to decide the stress and strain encountered by a person. Studies on biometeorology have been carried out by several scientists for over a century and a number of heat indices have been developed which quantify the level of discomfort or heat stress in their working environments. Today, observed meteorological parameters as well as corresponding model outputs are freely available from several sources which can be used for estimating the values of heat indices at any location. Both dynamical and statistical downscaling methods are very useful in this regard. In this study, some of the commonly used heat indices have been calculated for the four major cities of Delhi, Mumbai, Chennai and Kolkata using daily observed parameters from the India Meteorological Department (IMD) for the period 1975 to 2005. Same indices are also calculated based on the simulated values from the Regional Climate Model (RegCM) of the Abdus Salam International Centre for Theoretical Physics (ICTP) over the same period. The RegCM simulated fields have been obtained from the COordinated Regional Downscaling EXperiments (CORDEX) over the South Asia domain. Comparison of characteristics of the four selected indices based on IMD observed data and RegCM simulations leads to the inference that there are several similarities between the two sets of data in terms of their annual cycles and inter-annual variations. This exercise conclusively shows the advantages of dynamical downscaling. Further, results of this study encourage for a comprehensive work in the future for the country wide mapping and projection of heat indices based on model simulations, development of suitable heat indices and classification of comfort classes for their use in warning system for human health related issues in India.