Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods

• Published in: Theoretical and applied climatology 2020-08, Vol.141 (3-4), p.1135-1150
• Main Authors: Baghanam, Aida Hosseini, Eslahi, Mehdi, Sheikhbabaei, Ali, Seifi, Arshia Jedary
• Format: Article
• Language: English
• Subjects: Analysis; Aquatic Pollution; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Atmospheric Sciences; Climate change; Climate models; Climate science; Climatic data; Climatology; Computer simulation; Correlation coefficient; Correlation coefficients; Earth and Environmental Science; Earth Sciences; Environmental aspects; Environmental impact; Future precipitation; General circulation models; Global temperature changes; Intergovernmental Panel on Climate Change; Mann-Whitney U test; Mathematical models; Maximum temperatures; Meteorological research; Methods; Neural networks; Original Paper; Precipitation; Root-mean-square errors; Simulation; Statistical analysis; Statistical methods; Statistical models; Waste Water Technology; Water Management; Water Pollution Control; Weather
• ISSN: 0177-798X; 1434-4483
• DOI: 10.1007/s00704-020-03271-8

Due to the spatial-temporal inadequacy of large-scale general circulation models (GCMs), linking large-scale GCM data with small-scale local climatic data has found great interest. In this paper, in order to downscale minimum and maximum temperatures and precipitation predictands, the performance of three statistical downscaling techniques including Long Ashton Research Station-Weather Generator (LARS-WG), statistical downscaling model (SDSM), and artificial neural network (ANN) was compared based on Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5) in northwest Iran. For this purpose, a nonparametric test named Mann-Whitney test, Spearman correlation coefficient, and the root mean square error (RMSE) were utilized to assess the efficiency of downscaling models. To scrutinize the climate change impacts, periods of 1961–1990 and 1991–2005 were considered as the baseline and verification periods, respectively. The findings revealed the superior performance of the ANN model for minimum and maximum temperatures, while for precipitation predictand, the SDSM represented the best performance among the models. Simulation results for future temperature indicated an ascending trend as 0.1–1.3 °C, 0.3–1.7 °C, and 0.5–2.1 °C for LARS-WG, SDSM, and ANN techniques, respectively. On the other hand, simulation outputs for the precipitation indicated a descending trend of 10–30% in future precipitation of the region according to downscaling models under Representative Concentration Pathway 8.5 (RCP8.5) pessimistic scenario of Hadley Center Coupled Model version 3 (HadCM3) GCM model.