Kaustubh Salvi

Intensification of future severe heat waves in India and their effect on 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. This paper presents the first projections of future heat waves in India based on multiple climate models and scenarios for CMIP5 data. We find that heat waves are projected to be more intense, have longer durations and occur at a higher frequency and 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.

Credibility of statistical downscaling under nonstationary climate

Statistical downscaling (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity.

Uncertainty resulting from multiple data usage in statistical downscaling

Statistical downscaling (SD), used for regional climate projections with coarse resolution general circulation model (GCM) outputs, is characterized by uncertainties resulting from multiple models. Here we observe another source of uncertainty resulting from the use of multiple observed and reanalysis data products in model calibration. In the training of SD, for Indian Summer Monsoon Rainfall (ISMR), we use two reanalysis data as predictors and three gridded data products for ISMR from different sources. We observe that the uncertainty resulting from six possible training options is comparable to that resulting from multiple GCMs. Though the original GCM simulations project spatially uniform increasing change of ISMR, at the end of 21st century, the same is not obtained with SD, which projects spatially heterogeneous and mixed changes of ISMR. This is due to the differences in statistical relationship between rainfall and predictors in GCM simulations and observed/reanalysis data, and SD considers the latter.

Projections of Extreme Dry and Wet Spells in the 21st Century India Using Stationary and Non-stationary Standardized Precipitation Indices

The conventional approach to projecting meteorological extremes involves the application of indices such as the Standardized Precipitation Index (SPI) to rainfall simulated by General Circulation Models (GCMs). However, the sensitivity of SPI to the length of the records and the poor skills of GCMs in simulating rainfall are major drawbacks, leading to implausible projections. It is imperative to quantify and address these limitations before implementation of the approach. Here, we project the frequency of extreme dry and wet spells during the 21st century over India, incorporating special measures to alleviate the aforementioned limitations of the approach. We deploy kernel regression-based statistical downscaling to obtain improved 0.25-degree resolution monthly rainfall projections for India based on five GCMs and three emission scenarios (RCP2.6, RCP4.5, and RCP8.5) belonging to phase five of the Coupled Model Intercomparison Project. We also establish that the Standardized non-stationarity Precipitation Index (SnsPI), which incorporates changing climatic conditions considering linearly varying non-stationary scale parameter of the gamma distribution, is less sensitive to the length of the records as compared to SPI and we use both indices to obtain the frequency of future meteorological extremes. The results show an increase in the occurrences of extreme dry spells (EDS) over central, southeast coast, eastern region and some parts of northeast India. Differences between SPI and SnsPI based on the sensitivity are observed over, central India, where SPI overestimates EDS. Also, both the indices show diametrically opposite trends for areas under the influence of extreme wet spells in the future (2070-2099).