An Introduction to Climate Change Models
Authors
1
Ph.D. Candidate, Department of Environmental Planning and Design, Environmental Sciences Research Institute, Shahid Beheshti University
2
Associate Professor, Department of Environmental Planning and Design, Environmental Sciences Research Institute, Shahid Beheshti University
3
Assistant Professor, Department of Environmental Technology, Environmental Sciences Research Institute, Shahid Beheshti University
4
Professor, Department of Agroecology, Environmental Sciences Research Institute,Shahid Beheshti University
10.22034/eiat.2025.217700
Abstract
Today, climate change, with its profound impact on the environment and human life, stands as one of the most significant global challenges. These changes have led to substantial economic cost damages and detrimental effects on the environment, human health, and well-being. Therefore, predicting climate change to manage and reduce the associated costs is of critical importance. A key step in climate change studies is selecting the most appropriate model to minimize the uncertainties related to predicting climatic parameters. This article aims to investigate the various climate change models to enable researchers to identify and select the most appropriate and efficient model based on their needs and goals. An accurate selection can facilitate better planning and more effective responses to crises resulting from future climate change. In this study, over 80 articles on climate change studies in Iran and worldwide were reviewed from scientific databases. The findings revealed that climate change prediction models are classified into five main categories: Energy Balance Models, Radiative-Convective Models or Single-Column Models, Dimensionally Constrained Models, Earth system models, and General Circulation Models. Furthermore, no single model is universally suitable for all objectives or temporal and spatial scales. However, all models, if appropriately selected and utilized, can provide valuable insights into the climate system. Considering the vast diversity of climate models and the importance of understanding them, this research introduces the various types of climate change prediction models.
Keywords
Subjects
احمدیان، م.، و منتصری، م. (2021). ارزیابی مدلهای گردش عمومی و رتبه بندی آنها به منظور شبیهسازی هیدرولوژیک. دانش آب و خاک، 31(4)،69-84.
اکبری، م. و صیاد، و. (1400). تحلیل مطالعات تغییر اقلیم در ایران. پژوهشهای جغرافیای طبیعی، 53(1).
باباییان، الف.، نجفی نیک، ز.، زابل عباسی، ف.، حبیبی نوخندان، م.، ادب، ح.، و ملبوسی، ش. (1388). ارزیابی تغییر اقلیم کشور در دوره 2039-2010 میلادی با استفاده از ریزمقیاس نمایی داده های مدل گردش عمومی جو ECHO-G.جغرافیا و توسعه، 16، 135-152.
باران پور، م.، خلیلی مقدم، ب.، ذرتی پور، الف. (1400). مطاللعه موردی: کانون گرد و غبار جنوب و جنوب شرق اهواز. هیدروژئومورفولوژی، 28، 107-126.
بندریخلفآبادی، و.، شکیبا، ع. و عظیمی، ف. (2013). پیش یابی رژیم بارش و دمای خوزستان با مدل های گردش عمومی جو. جغرافیای طبیعی، 19(6)، 59-70.
حق طلب،ن.، گودرزی، م.، حبیبی نوخندان، م.، یاوری، الف.ر.، و جعفری، ح،ر. (1392). مدل سازی اقلیم استان های تهران و مازندران با استفاده از مدل اقلیمی LARS-WG و مقایسه تغییرات آن در جبهه های شمالی و جنوبی البرز مرکزی. علوم و تکنولوژی محیطزیست، (56)، 37-49.
دوستان، ر. (1398). تحلیل بر تغییرات بارش در ایران. پژوهشهای اقلیم شناسی.25-13.
روشن، غ.ر.، خوش اخلاق، ف. و عزیزی، ق. (1391). آزمون مدل مناسب گردش عمومی جو برای پیش یابی مقادیر دما و بارش ایران، تحت شرایط گرمایش جهانی. جغرافیا و توسعه، (27)، 19-36.
زرین، الف.، داداشی رودباری، ع. (1399). پیشنگری چشمانداز بلندمدت دمای آینده ایران مبتنی بر برونداد پروژة مقایسة مدلهای جفتشدة فاز ششم (6CMIP). فیزیک زمین و فضا، دوره ،46 شماره ،3 پاییز ،1399 صفحة -583 602.
سیاری، ن.، علیزاده، الف.، بنایان اول، م.، فرید حسینی، ع.، حسامی کرمانی، م.ر. (1390). مقایسه دو مدل گردش عمومی جو (HadCM3،CGCM2) در پیش بینی پارامترهای اقلیمی و نیاز آبی گیاهان تحت تغییر اقلیم (مطالعه موردی: حوضه کشف رود). نشریه آب و خاک (علوم و صنایع کشاورزی)، 25 (4).
شرقی، الف، نورانی، و، آقایی لاهرودی، ف. (1396). ارزیابی اثرات تغییر اقلیم روی پارامترهای دما و بارش ایستگاه تبریز در دوره ۲۰۲۰-۲۰۴۹ با استفاده از مدل WG-LARS و روش عامل تغییر، شانزدهمین کنفرانس هیدرولیک ایران، دانشکده فنی و مهندسی، دانشگاه محقق اردبیلی، اردبیل 15 و 16 شهریور 1396.
شیخ ربیعی، م.ر.، پیروان، ح.ر. دانشکار آراسته، پ.، اکبری، م، معتمد وزیری، ب. (1400). مقایسهی کارایی مدل های SDSM و CCT در مطالعات تغییر اقلیم (مطالعه موردی حوزه آبخیز کرگانرود). نشریه هواشناسی و علوم جو، 4(2)، 128-146.
طاهری تیزرو، ع.، خدامرادپور، م. و مشهدیان، م.ج. (1396). ارزیابی شبیه سازی مولفه های بیلان آب سطحی توسط مدل اقلیمی منطقه ای RegCM4 (مطالعه موردی: حوضه تویسرکان، استان همدان). نشریه پژوهش های حفاظت آب و خاک، 24 (5).
کابوسی، ک. و کردجزی، م. (2019). بررسی اثر عدم قطعیت خروجی مدل های گردش عمومی در پیش بینی متغیرهای هواشناسی استان گلستان. علوم و مهندسی آبیاری، 42(2)، 197-213.
کاظمی راد، ل. و محمدی، ح. (1394). ارزیابی مدل مناسب گردش عمومی جو برای پیشبینی تغییرات اقلیمی استان گیلان. جغرافیا و مخاطرات محیطی، 4(4)، 55-74.
کریمی، م.، کاکی، س. و رفعتی، س. (1397). شرایط و مخاطرات اقلیمی آیندة ایران در تحقیقات اقلیمی، نشـر یة تحلیـل فضـایی مخاطرات محیطی، 5(3):1-22.
محمدی، ح.(1393). آب و هوا شناسی کاربردی. انتشارات دانشگاه تهران.
محمدی، ح.، امیری، الف.، ربانی، ف. (1395). تغییر اقلیم و مدل های اقلیمی. انتشارات دانشگاه تهران.
مدرسی، ف.، عراقی نژاد، ش.، ابراهیمی، ک.، خلقی، م. (1389). بررسی منطقهای پدیده تغییراقلیم با استفاده از آزمونهای آماری مطالعه موردی: حوضه آبریز گرگانرود-قرهسو. آب و خاک، 24(3).
Abbasian, M., Moghim, S., & Abrishamchi, A. (2019). Performance of the general circulation models in simulating temperature and precipitation over Iran. Theoretical and Applied Climatology, 135(3–4), 1465–1483. https://doi.org/10.1007/s00704-018-2456-y.
Ahmed, K., Sachindra, D. A., Shahid, S., Iqbal, Z., Nawaz, N., & Khan, N. (2020). Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms. Atmospheric Research, 236, 104806.
Arias Gómez, P. A., Ortega Villamizar, G. M., Villegas Villa, L. D., & Martínez Agudelo, J. A. (2021). Colombian climatology in CMIP5/CMIP6 models: Persistent biases and improvements.
Berger, A., Mesinger, F., & Sijacki, D. (Eds.). (2012). Climate change: inferences from paleoclimate and regional aspects. Springer Science & Business Media.
Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., ... & Eyring, V. (2020). Quantifying progress across different CMIP phases with the ESMValTool. Journal of Geophysical Research: Atmospheres, 125(21), e2019JD032321.
Brooks, W. R., & Newbold, S. C. (2014). An updated biodiversity nonuse value function for use in climate change integrated assessment models. Ecological Economics, 105, 342-349.
Chen, H., Sun, J., & Chen, X. (2014). Projection and uncertainty analysis of global precipitation‐related extremes using CMIP5 models. International journal of climatology, 34(8), 2730-2748.
Covey, C., AchutaRao, K. M., Cubasch, U., Jones, P., Lambert, S. J., Mann, M. E., ... & Taylor, K. E. (2003). An overview of results from the Coupled Model Intercomparison Project. Global and Planetary Change, 37(1-2), 103-133.
Dai, Z., Amatya, D. M., Sun, G., Trettin, C. C., Li, C., & Li, H. (2011). Climate variability and its impact on forest hydrology on South Carolina coastal plain, USA. Atmosphere, 2(3), 330-357.
Daneshvar, M. R. M.; Ebrahimi, M. and Nejadsoleymani, H. (2019). An overview of climate change in Iran: facts and statistics. Environmental Systems Research, 8(1): 1-10.
Das, J., Poonia, V., Jha, S., & Goyal, M. K. (2020). Understanding the climate change impact on crop yield over Eastern Himalayan Region: ascertaining GCM and scenario uncertainty. Theoretical and Applied Climatology, 142, 467-482.
Earth, B. (2019). Global temperature report for 2019. Berkeley Earth, Berkeley, California.
Edwards, P. N. (2000). A brief history of atmospheric general circulation modeling. In International Geophysics (Vol. 70, pp. 67-90). Academic Press.
Edwards, P. N. (2011). History of climate modeling. Wiley Interdisciplinary Reviews: Climate Change, 2(1), 128-139.
Emori, S., Taylor, K., Hewitson, B., Zermoglio, F., Juckes, M., Lautenschlager, M., & Stockhause, M. (2016). CMIP5 data provided at the IPCC Data Distribution Centre. Fact Sheet of the task group on data and scenario support for impact and climate analysis (TGICA) of the intergovernmental panel on climate change (IPCC), 8.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937-1958.
Fallah Kalaki, M., Shokri Kuchak, V., & Ramezani Etedali, H. (2021). Simulating the effects of climate change on runoff using the CMIP5 and CMIP6 climate models by SWAT hydrological model (Case study: Tashk-Bakhtegan Basin). Iran-Water Resources Research, 17(3), 345-359.
FAO, F. (2016). The state of food and agriculture: Climate change, agriculture and food security. Rome, Italy.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V. (2013). Climate change 2013: the physical science basis. Contribution of working group i to the fifth assessment report of the intergovernmental panel on climate change. In: Stock, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., et al. (Eds.), Eval. Clim. Model. Cambridge Cambridge Univ. Press.
Gao, J., Sheshukov, A. Y., Yen, H., Douglas-Mankin, K. R., White, M. J., & Arnold, J. G. (2019). Uncertainty of hydrologic processes caused by bias-corrected CMIP5 climate change projections with alternative historical data sources. Journal of Hydrology, 568, 551-561.
Gent, P. R. (2012). Coupled climate and earth system models. In Climate Change Modeling Methodology: Selected Entries from the Encyclopedia of Sustainability Science and Technology (pp. 5-30). New York, NY: Springer New York.
Ghazi, B., & Jeihouni, E. (2022). Projection of temperature and precipitation under climate change in Tabriz, Iran. Arabian Journal of Geosciences, 15(7), 1–11.
Goosse, H., BARRIAT, P. Y., LOUTRE, M. F., & ZUNZ, V. (2010). Introduction to climate dynamics and climate modeling. Centre de recherche sur la Terre et le climat Georges Lemaître-UCLouvain.
Hamed, M. M., Nashwan, M. S., & Shahid, S. (2022). Inter‐comparison of historical simulation and future projections of rainfall and temperature by CMIP5 and CMIP6 GCMs over Egypt. International Journal of Climatology, 42(8), 4316-4332.
Hamed, M. M., Nashwan, M. S., Shahid, S., bin Ismail, T., Wang, X. J., Dewan, A., & Asaduzzaman, M. (2022). Inconsistency in historical simulations and future projections of temperature and rainfall: A comparison of CMIP5 and CMIP6 models over Southeast Asia. Atmospheric Research, 265, 105927.
Hardy, J. T. (2003). Climate change: causes, effects, and solutions. John Wiley & Sons.
Harper, L. (2018). What Are Climate Models and How Accurate Are They. Blogs. ei. columbia. edu.
Hartmann, D.L.(2016). Global Climate Models, Second E. Elsevier, Boston, pp. 325–360.
Intergovernmental Panel on Climate Change. (1992). Climate change: the 1990 and 1992 IPCC assessments. WMO.
Iqbal, Z., Shahid, S., Ahmed, K., Ismail, T., Ziarh, G. F., Chung, E. S., & Wang, X. (2021). Evaluation of CMIP6 GCM rainfall in mainland Southeast Asia. Atmospheric Research, 254, 105525.
Kamruzzaman, M., Shahid, S., Islam, A. T., Hwang, S., Cho, J., Zaman, M. A. U., ... & Hossain, M. B. (2021). Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study. Theoretical and Applied Climatology, 145, 1385-1406.
Knutti, R., Allen, M. R., Friedlingstein, P., Gregory, J. M., Hegerl, G. C., Meehl, G. A., ... & Wigley, T. M. L. (2008). A review of uncertainties in global temperature projections over the twenty-first century. Journal of Climate, 21(11), 2651-2663.
Mall, RK., Gupta,.A., Sonkar, G. (2017) Effect of climate change on agricultural crops. In: Current Developments in Biotechnology and Bioengineering. Elsevier, pp 23–46.
Meehl, G. A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J. F., ... & Taylor, K. E. (2007). The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bulletin of the American meteorological society, 88(9), 1383-1394.
Moss, R. et al. (2008) Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies, Intergovernmental Panel on Climate Change, Geneva, pp. 132.
North, G. R. (2024). Energy balance models in climate science. Current Opinion in Chemical Engineering, 44.
North, G. R., & Kim, K. Y. (2017). Energy balance climate models. John Wiley & Sons.
O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., ... & Van Vuuren, D. P. (2014). A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic change, 122, 387-400.
O'Neill, B. C., Tebaldi, C., Van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., ... & Sanderson, B. M. (2016). The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development, 9(9), 3461-3482.
Pitman, A. J. (2003). The evolution of, and revolution in, land surface schemes designed for climate models. International Journal of Climatology: A Journal of the Royal Meteorological Society, 23(5), 479-510.
Pörtner, H. O., Roberts, D. C., Poloczanska, E. S., Mintenbeck, K., Tignor, M., Alegría, A., ... & Okem, A. (2022). IPCC, 2022: Summary for policymakers.
Rasch, P. J. (Ed.). (2012). Climate change modeling methodology: selected entries from the Encyclopedia of sustainability science and technology. Springer Science & Business Media.
Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PloS one, 8(6), e66428.
Readinger, C. (2007). Climate Models and Global Climate Change. Pro Quest.
Renno, N.O., & Huang, X. (2015). Radiative–Convective Equilibrium Climate Models. Elsevier.
Robinson, T. D., & Catling, D. C. (2012). An analytic radiative–convective model for planetary atmospheres. The Astrophysical Journal, 757(1), 104.
Romanello, M., McGushin, A., Di Napoli, C., Drummond, P., Hughes, N., Jamart, L., ... & Hamilton, I. (2021). The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. The Lancet, 398(10311), 1619-1662.
Rossby, C. G. (1946). A History of and Introduction to Climate Models.
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., & Eyring, V. (2020). Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?. Earth System Dynamics, 11(4), 1233-1258.
Semenov M.A. & Brooks R.J. (1999). Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research 11, 137-148.
Semenov, M. A., Barrow, E. M., & Lars-Wg, A. (2002). A stochastic weather generator for use in climate impact studies. User Man Herts UK, 1-27.
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K., ... & Miller, H. (2007). IPCC fourth assessment report (AR4). Climate change, 374.
Taylor, K. E., Balaji, V., Hankin, S., Juckes, M., Lawrence, B., & Pascoe, S. (2011). CMIP5 data reference syntax (DRS) and controlled vocabularies. Program for Climate Model Diagnosis and Intercomparison, http://pcmdi. github. io/mips/cmip5/docs/cmip5_data_reference_syntax. pdf (last access: 23 January 2023).
Taylor, K. E., Stouffer , R.J., Meehl , G.A.(2012). An overview of CMIP5 and the experiment design. Bulletin of the American meteorological society, 93(4) 485- 498.
Trenberth, K. E. (2001). Stronger evidence of human influences on climate: The 2001 IPCC Assessment. Environment: Science and Policy for Sustainable Development, 43(4), 8-19.North, G. R., & Kim, K. Y. (2017). Energy balance climate models. John Wiley & Sons.
Van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., ... & Rose, S. K. (2011). The representative concentration pathways: an overview. Climatic change, 109, 5-31.
Wang, B., Zheng, L., Liu, D. L., Ji, F., Clark, A., & Yu, Q. (2018). Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia. International Journal of Climatology, 38(13), 4891-4902.
Weigel, A. P., Knutti, R., Liniger, M. A., & Appenzeller, C. (2010). Risks of model weighting in multimodel climate projections. Journal of Climate, 23(15), 4175-4191.
Wyser, K., van Noije, T., Yang, S., von Hardenberg, J., O'Donnell, D., & Döscher, R. (2019). On the increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6.
Zhao, Q., Yu, P., Mahendran, R., Huang, W., Gao, Y., Yang, Z., ... & Guo, Y. (2022). Global climate change and human health: Pathways and possible solutions. Eco-Environment & Health, 1(2), 53-62.
