Document Type : Research Article
Authors
1
1- Assistant Professor, Soil and Watershed Conservation Research Department, Kermanshah Agricultural and Natural Resources Research and Education Center, Iran
2
2- Professor, Faculty of Natural Resources, University of Tehran
10.22067/geoeh.2024.86195.1450
Abstract
The use of different AOGCM models, which produce different outputs for climatic variables, is one of the most important sources of uncertainty in climate change discussions. It is generally accepted that the use of multiple AOGCM models or ensemble methods (EP) in order to emphasize uncertainty in weather forecasting is a strategy to mitigate the uncertainty associated with AOGCM models as a result of structural differences between global climate models and uncertainty in initial conditions. In this study, synoptic station data from the Kermanshah Meteorological Organization from January 1, 1993, to December 31, 2016, and downscaled data from 21 AOGCM models obtained from the NASA website (NEX-GDDP) for the past period (1976-2005) and future period (2020-2049) under the RCP4.5 scenarios were examined to assess the uncertainty of AOGCM models and reduce their uncertainty using different ensemble methods.
Results of the model comparison revealed that the MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models performed better in simulation. Furthermore, as expected, the models with the lowest errors received the highest weight, meaning that these models had the highest weight in simulating precipitation, maximum temperature, and minimum temperature in the past period and can be considered as the most suitable predictive models for the future with the least uncertainty in temperature and precipitation simulation. Examining the results of statistical coefficients of different ensemble methods showed that the ensemble method of some models (MEP) provided a better estimation with a coefficient of determination (R2) of 0.95 and efficiency coefficient (ME) of 0.92 compared to the baseline period data at the Kermanshah synoptic station. In the end, this method was considered to be the best ensemble method for AOGCM models.
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