the evaluations of nex-gddp and marksim downscaled data sets over lali region, southwest iran

Downscaling of climatic variables is a difficult problem in the climate change impact studies. however, some climatic data sets exist that have been universally downscaled. these data sets introduce climatic data even in regions with scarce observations. in this study, nasa earth exchange global daily downscaled projections (nex-gddp) and markov simulation (marksim) downscaled data sets were evaluated over lali region, southwest iran by comparing the monthly rmse, average and variance differences between the observation data and general circulation models’ (gcms’) outputs during the time period 2010-2016. the nex-gddp data set contains 21 gcms under two representative concentration pathways (rcps), i.e. rcp4.5 and rcp8.5, from 1951 to 2099, and the marksim data set includes 17 gcms under all rcps from 2010 to 2095. results acknowledged the ability of both data sets in projecting the climatic variables in the study area. finally, noresm1m and gfdlcm3 depicted the best operation for precipitation and temperature, respectively.

The Evaluations of NEX-GDDP and Marksim Downscaled Data Sets Over Lali Region, Southwest Iran

Downscaling of climatic variables is a difficult problem in the climate change impact studies. However, some climatic data sets exist that have been universally downscaled. These data sets introduce climatic data even in regions with scarce observations. In this study, NASA Earth Exchange Global Daily Downscaled Projections (NEXGDDP) and Markov simulation (Marksim) downscaled data sets were evaluated over Lali region, southwest Iran by comparing the monthly RMSE, average and variance differences between the observation data and General Circulation Models’ (GCMs’) outputs during the time period 20102016. The NEXGDDP data set contains 21 GCMs under two Representative Concentration Pathways (RCPs), i.e. RCP4.5 and RCP8.5, from 1951 to 2099, and the Marksim data set includes 17 GCMs under all RCPs from 2010 to 2095. Results acknowledged the ability of both data sets in projecting the climatic variables in the study area. Finally, NorESM1M and GFDLCM3 depicted the best operation for precipitation and temperature, respectively.