Document Type : Research Paper

Authors

• Nazli Zenozi Alamdari ¹
• Behrouz Sobhani ²
• Mehdi Eshahi, ³
• Masihallah Mohammadi ³

¹ PhD Student of Hydrology and Meteorology, Department of Natural Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

² Professor, Department of Natural Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

³ PhD Student, Department of Natural Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

Introduction
Climate is a complex system that is changing primarily due to the increase in greenhouse gases. To study the effects of climate change on agricultural, hydrological, and environmental systems, general circulation models (GCMs) are used to simulate climate variables. These models, based on approved Intergovernmental Panel on Climate Change (IPCC) scenarios, enable the modeling of climate parameters over extended periods. Globally, various centers and models simulate future climatic conditions using different emission scenarios, physical structures, and computational approaches. The simulations from CMIP6 GCMs form the foundation for many IPCC conclusions regarding future climate changes. These data are utilized directly or after downscaling to evaluate local and regional climate changes (IPCC, 2021). This study analyzes and predicts trends in precipitation and minimum and maximum temperatures in East Azerbaijan Province under climate change conditions from 2021 to 2100.
 
Materials and methods
This study aims to investigate precipitation and minimum and maximum temperatures and their trends from 2021 to 2100 across stations in Tabriz, Ahar, Jolfa, Maragheh, and Miyaneh. Data from 12 CMIP6 models (ACCESS-CM2, BCC-CSM2-MR, CESM2, CNRM-CM6-1, CanESM5, MIROC6, MRI-ESM2-0, IPSL-CM6A-LR, GISS-E2-1-G, HadGEM3-GC31-LL, NESM3, and NorESM2-MM) were used under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The Kling-Gupta Efficiency (KGE) method was applied to identify the best models for simulating precipitation and temperature by comparing historical model data (1989–2018) with observed data from selected stations. Bias correction of model outputs was then used to forecast climate variables under the SSP scenarios. Finally, the mean time series of precipitation and minimum and maximum temperatures for the future period were compared with historical data to quantify changes over the 80-year horizon (2021–2100) for East Azerbaijan Province.
 
Results and discussion
The performance of 12 CMIP6 climate models was evaluated for generating past and present climate data (1989–2018). Based on uncertainty analysis, the BCC-CSM2-MR and MIROC6 models were identified as the best for simulating precipitation and temperature. These models were used, with bias correction, to predict precipitation and minimum and maximum temperatures for the future period (2021–2100) under optimistic, moderate, and pessimistic scenarios for East Azerbaijan Province. The results revealed that in all scenarios, annual temperatures are projected to increase while annual precipitation will decrease. Annual maximum temperatures across the selected stations are expected to increase by 0.57–6.41°C, while annual minimum temperatures will rise by 0.46–4.89°C. Precipitation is projected to decrease by 2.3% to 9.18%. The highest temperature increase and precipitation decrease are expected at Jolfa and Tabriz stations, respectively.
Conclusions
This study demonstrates that CMIP6 models effectively simulate future climate parameters and align well with historical climate data for East Azerbaijan Province. The high accuracy of these simulations makes them suitable for forecasting future climatic conditions and facilitating macro-level management strategies. Such strategies can enhance resource productivity, particularly in water resource management, to address the challenges posed by climate change.
 

Keywords

• BCC-CSM2-MR model
• Climate change
• MIROC6 model
• Rainfall
• SSP emission scenario