Spatio-temporal variation of extreme indices derived from observed and reanalysis products for detection of climate change across India
Authors: Kumar Sachidanand , Chanda Kironmala , Pasupuleti Srinivas 
Published: March 2020

&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;This article reports the research findings in a recent study (Kumar et al., 2020) that utilizes eight indices of climate change recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) for analyzing spatio-temporal trends in extreme precipitation and temperature at the daily scale across India. Observed gridded precipitation (1971-2017) and temperature (1971-2013) datasets from India Meteorological Department (IMD) are used along with reanalysis products from Climate Prediction Centre (CPC). The trends are estimated using non-parametric Mann-Kendall (MK) test and regression analysis. The trends in &amp;#8216;wet days&amp;#8217; (daily precipitation greater than 95&lt;sup&gt;th&lt;/sup&gt; percentile) and &amp;#8216;dry days&amp;#8217; (daily precipitation lower than 5&lt;sup&gt;th&lt;/sup&gt; percentile) are examined considering the entire year (annual) as well as monsoon months only (seasonal). At the annual scale, about 13% of the grid locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme &amp;#8216;wet days&amp;#8217;) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme &amp;#8216;dry days&amp;#8217;). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The spatio-temporal trends in &amp;#8216;warm days&amp;#8217; (daily maximum temperature greater than 95&lt;sup&gt;th&lt;/sup&gt; percentile), &amp;#8216;warm nights&amp;#8217; (daily minimum temperature greater than 95&lt;sup&gt;th&lt;/sup&gt; percentile), &amp;#8216;cold days&amp;#8217; (daily maximum temperature lower than 5&lt;sup&gt;th&lt;/sup&gt; percentile) and &amp;#8216;cold nights&amp;#8217; (daily minimum temperature lower than 5&lt;sup&gt;th&lt;/sup&gt; percentile) are also investigated for the aforementioned period. The number of &amp;#8216;warm days&amp;#8217; per year increased significantly at 14% of the locations, while the number of &amp;#8216;cold days&amp;#8217;, &amp;#8216;warm nights&amp;#8217; and &amp;#8216;cold nights&amp;#8217; per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices are also investigated for the future using CanESM2 projected data for RCP8.5 after suitable bias correction. Most of the locations (49% to 84%) indicate significant increasing (decreasing) trend in &amp;#8216;warm days&amp;#8217; (&amp;#8216;cold days&amp;#8217;) in the three epochs, 2006-2040, 2041-2070 and 2071-2100. Moreover, most locations (60% to 81%) show an increasing trend in &amp;#8216;warm nights&amp;#8217; and a decreasing trend in &amp;#8216;cold nights&amp;#8217; in all the epochs. A similar investigation for the historical and future periods using CPC data as the reference indicates that the trends, on comparison with IMD observations, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: extreme precipitation and temperature, climate change indices, spatio-temporal variation, India&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Kumar S., Chanda, K., Srinivas P., (2020), Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India, Theoretical and Applied Climatology, Springer, In press, DOI: 10.1007/s00704-020-03088-5.&lt;/p&gt;