Journal of Rescue Relief
فصلنامه علمی پژوهشی امداد و نجات
jorar
Medical Sciences
http://jorar.ir
1
admin
2008-4544
2008-529x
8
10.61186/jorar
-
8888
-
en
jalali
1399
12
1
gregorian
2021
3
1
13
1
online
1
fulltext
fa
Climate change effects Management with the approach of the uncertainty of Atmosphere-Ocean General Circulation Models in Hamadan Province, Iran
Climate change effects Management with the approach of the uncertainty of Atmosphere-Ocean General Circulation Models in Hamadan Province, Iran
سایر موارد مرتبط با بحران، حوادث وامدادونجات
سایر موارد مرتبط با بحران، حوادث وامدادونجات
پژوهشي
Research Article
<div style="text-align: left;"><strong>INTRODUCTION:</strong> Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables.<br>
<strong>METHODS:</strong> In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software.<br>
<strong>FINDINGS:</strong> It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation.<br>
<strong>CONCLUSION:</strong> According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively.</div>
<div dir="ltr" style="margin-right: 1cm; text-align: justify;"><strong>INTRODUCTION:</strong> Since Iran is located in the semi-arid belt, it has faced such issues as drought, dust crisis, and intensified migration. The assessment of the effects of climate change includes identifying some key aspects of uncertainties used to estimate its impacts, such as uncertainties in the context of Atmosphere-Ocean General Circulation Models (AOGCMs): in regional-scale climatology, in statistical or dynamic downscaling methods, and parametric and structural uncertainties in different models. One of the most important sources of uncertainty in climate change is the use of different AOGCMs that produce different outputs for climate variables.<br>
<strong>METHODS:</strong> In this study, to investigate the uncertainty of AOGCM models, the downscaled data of the NASA Earth Exchange Global Daily Downscaled Projections dataset obtained from 21 AOGCMs with medium Representative Concentration Pathway4.5 scenario were downloaded from the NASA site for 81 cells in Hamadan Province, Iran. After the validation of the models, they were evaluated against the criteria of the coefficient of determination and model efficiency coefficient in comparison with the data of the Hamedan synoptic station in the statistical period of 1976-2005. To reduce the uncertainty of AOGCMs, the ensemble performance (EP) of models was used in Climate Data Operators software.<span dir="RTL"></span><br>
<strong>FINDINGS:</strong> It was revealed that MRI-CGCM3, MPI-ESM-LR, BNU-ESM, ACCESS1-0, MIROC-ESM, MIROC-ESM-CHEM, and MPI-ESM-MR models had better performance than similar models. It was also found that IPSL-CM5A-LR, CNRM-CM5, CSIRO-Mk3-6-0, CESM1-BGC, and GFDL-ESM2M had the lowest correlation between observational and simulation data of mean monthly precipitation.<span dir="RTL"></span><br>
<strong>CONCLUSION:</strong> According to the results, this method could provide a good estimate in the base period (1976-2005), compared to the data of the Hamedan synoptic station, and was more accurate compared to the single implementation method of each AOGCM model. The results of EP of models in the future period (2020-2049) showed that precipitation will not change considerably in the future and will increase by 0.23 mm. In addition, the average, maximum, and minimum annual temperatures will increase by 1.54°C, 1.7°C, and 1.40°C, respectively.</div>
Crisis Management, Climate Change, Hamadan Province, AOGCMs, Uncertainty
Crisis Management, Climate Change, Hamadan Province, AOGCMs, Uncertainty
49
60
http://jorar.ir/browse.php?a_code=A-10-605-1&slc_lang=fa&sid=1
Majid
Ahmadi
مجید
احمدی
ahmadimajid21@yahoo.com
10031947532846009486
10031947532846009486
No
Agricultural Meteorology, Kish International Campus, University of Tehran, Tehran, Iran
دانشجوی دکتری آب و هواشناسی کشاورزی پردیس بین المللی کیش، دانشگاه تهران
Ghasem
Azizi
قاسم
عزیزی
ghazizi@ut.ac.ir
10031947532846009487
10031947532846009487
Yes
Professor, Climatology, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran
عضو هیئت علمی دانشکده جغرافیا، دانشگاه تهران
Saeed
Bazgir
سعید
بازگیر
sbazgeer@ut.ac.ir
10031947532846009488
10031947532846009488
No
Agricultural Meteorology, Department of Physical Geography, Faculty of Geography, University of Tehran, Tehran, Iran
عضو هیئت علمی دانشکده جغرافیا، دانشگاه تهران
Mohammad
Hemmati
محمد
همتی
moh_hemmati2051@yahoo.com
10031947532846009489
10031947532846009489
No
Department of Physical Geography, Islamic Azad University, Imam Khomeini Memorial Branch, Shahr-e-Rey, Tehran, Iran
عضو هیئت علمی گروه جغرافیای طبیعی، دانشکده علوم انسانی، دانشگاه آزاد اسلامی واحد یادگار امام خمینی (ره)