Evaluation of Regional Climate Models (RCMs) Using Precipitation and Temperature-Based Climatic Indices: A Case Study of Florida, USA

Yared Bayissa,Mahadev Bhat,Assefa Melesse,Andualem Shiferaw,Tsegaye Tadesse

doi:10.3390/w13172411

Yared Bayissa, Mahadev Bhat + Show 3 more

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https://doi.org/10.3390/w13172411

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Abstract

The overarching objective of this study was to evaluate the performance of nine precipitation-based and twelve temperature-based climatic indices derived from four regional climate models (CRCM5-UQUAM, CanRCM4, RCA4 and HIRHAM5) driven by three global circulation models (CanESM2, EC-EARTH and MPI-ESM-LR) and their ensemble mean for the reference period of 31 years (1975–2005). The absolute biases, pattern correlation, the reduction of variance (RV) and the Standardized Precipitation Evapotranspiration Index (SPEI at 3-, 6- and 12-month aggregate periods) techniques were used to evaluate the climate model simulations. The result, in general, shows each climate model has a skill in reproducing at least one of the climatic indices considered in this study. Based on the pattern correlation result, however, EC-EARTH.HIRHAM5 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a relatively good skill in reproducing the observed climatic indices as compared to the other climate model simulations. EC-EARTH.RCA4, CanESM2.RCA4 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a good skill when evaluated using the reduction of variance. The ensemble mean of the RCMs showed relatively better skill in reproducing the observed temperature-based climatic indices as compared to the precipitation-based climatic indices. There were no exceptional differences observed among the performance of the climate models compared to the SPEI, but CanESM2.CRCM5-UQAM, EC-EARTH.RCA4 and the ensemble mean of the RCMs performed relatively good in comparison to the other climate models. The good performance of some of the RCMs has good implications for their potential application for climate change impact studies and future trend analysis of extreme events. They could help in developing an early warning system to mitigate and prepare for possible future impacts of climate extremes (e.g., drought) and vulnerability to climate change across Florida.

Highlights

Increase in the concentrations of the atmospheric greenhouse gases (i.e., carbon dioxide (CO2 ), methane [CH4 ], nitrous oxide [N2 O], etc.) trigger the rise in temperature that eventually changes the frequency of occurrence of extreme precipitation events in many regions across the globe [1]
A higher negative pattern correlation has been shown by CanESM2.CRCM5UQAM (r = −0.6) whereas a lower value has been observed by CanESM2.CanRCM4 (r = 0.1)
The ensemble mean, MPI-ESM-LR.CRCM5-UQAM, CanESM2.RCA4 and CanESM2.CRCM5-UQAM were shown to be capable of reproducing all the climatic indices except consecutive dry days (CDD) and TNx

Summary

Introduction

Increase in the concentrations of the atmospheric greenhouse gases (i.e., carbon dioxide (CO2 ), methane [CH4 ], nitrous oxide [N2 O], etc.) trigger the rise in temperature that eventually changes the frequency of occurrence of extreme precipitation events (e.g., flood, drought and hurricane) in many regions across the globe [1]. The frequency of occurrence of extreme events (i.e., floods, droughts, sea level rise, etc.) has increased in recent decades and caused an impact on the socio-economic and environmental sectors at large [3,4,5,6]. The trends of future climate extreme events can be projected and analyzed through the use of climate projection data from the global and regional climate models [9]

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