Downscaling probability of long heatwaves based on seasonal mean daily maximum temperatures

Rasmus E Benestad,Frode Stordal,Abdelkader Mezghani,Milton E Pereira-Flores,Bob Van Oort,Flavio Justino,Kajsa M Parding,Helene B Erlandsen,Jana Sillmann

doi:10.5194/ascmo-4-37-2018

Abstract

Abstract. A methodology for estimating and downscaling the probability associated with the duration of heatwaves is presented and applied as a case study for Indian wheat crops. These probability estimates make use of empirical-statistical downscaling and statistical modelling of probability of occurrence and streak length statistics, and we present projections based on large multi-model ensembles of global climate models from the Coupled Model Intercomparison Project Phase 5 and three different emissions scenarios: Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. Our objective was to estimate the probabilities for heatwaves with more than 5 consecutive days with daily maximum temperature above 35 ∘C, which represent a condition that limits wheat yields. Such heatwaves are already quite frequent under current climate conditions, and downscaled estimates of the probability of occurrence in 2010 is in the range of 20 %–84 % depending on the location. For the year 2100, the high-emission scenario RCP8.5 suggests more frequent occurrences, with a probability in the range of 36 %–88 %. Our results also point to increased probabilities for a hot day to turn into a heatwave lasting more than 5 days, from roughly 8 %–20 % at present to 9 %–23 % in 2100 assuming future emissions according to the RCP8.5 scenario; however, these estimates were to a greater extent subject to systematic biases. We also demonstrate a downscaling methodology based on principal component analysis that can produce reasonable results even when the data are sparse with variable quality.

Highlights

1.1 Weather statistics and societyPeople have learnt to cope with climate variations and severe weather over historical times and have adapted to various weather-related risks
When the downscaled results for the principal component analysis (PCA) were used to recover the format of the original temperature records, an evaluation of the RCP4.5 ensemble indicated good skill for Tmax over the wheat growing Indo-Gangetic Plain (IGP) region, but low skills in the south (Supplement)
An evaluation of the ordinary linear regression (OLR) used to estimate the mean number of heatwaves for the different sites suggested a statistically significant dependency on the seasonal mean daily maximum temperature at the 1 % level, with an R2 of 0.2 (Supplement)

Summary

Introduction

People have learnt to cope with climate variations and severe weather over historical times and have adapted to various weather-related risks. In this respect, climate can be regarded as the statistical description of various weather variables (Benestad et al, 2017a), giving a picture of “typical” types of weather and what to expect. Climate can be regarded as the statistical description of various weather variables (Benestad et al, 2017a), giving a picture of “typical” types of weather and what to expect This statistical description includes the mean, variance, autocorrelation, periodicity, and duration of various climatological events. Local statistical temperature characteristics control the prospects for various aspects

Objectives

Methods

Results

Conclusion