بررسی بی‌هنجاری و روند دمای ایران در پهنه‌های مختلف اقلیمی با استفاده از مدل‌های جفت شده پروژه مقایسه متقابل مرحله ششم (CMIP6)

نوع مقاله : مقاله پژوهشی‌

نویسندگان

1 استادیار آب و هواشناسی، گروه جغرافیا، دانشگاه فردوسی مشهد، مشهد، ایران

2 پژوهشگر پسادکتری آب و هواشناسی، گروه جغرافیا، دانشگاه فردوسی مشهد، مشهد، ایران

3 دانش آموخته کارشناسی ارشد آب و هواشناسی، گروه جغرافیا، دانشگاه فردوسی مشهد، مشهد، ایران

چکیده

دما یکی از عناصر شکل­گیری آب‌وهوا است و تغییرات آن می­تواند ساختار آب‌وهوای هر منطقه را تغییر دهد. برای بررسی چشم­انداز دمای آینده ایران از دو دسته داده شامل دمای 43 ایستگاه همدید و برونداد سه مدل BCC-CSM2-MR، CAMS-CSM1-0 و MRI-ESM2-0 از مجموعه مدل­های CMIP6 برای دو دوره تاریخی (2009-1990) و آینده (2100-2020) با تفکیک افقی ۱۰۰ کیلومتر استفاده شد. برونداد هر سه مدل برای دو سناریوی خوش­بینانه (SSP2-4.5) و بدبینانه (SSP5-8.5) بررسی شد. برای این منظور از سنجه­های آماری RMSE، NSE و KGE جهت درستی­سنجی مدل­ها استفاده شد. برای تصحیح اریبی برونداد مدل­ها از روش تغییر عامل دلتا (DCF) و برای مطالعه روند و شیب روند از آزمون­های من- کندال و سنس استفاده شد. نتایج بررسی مدل­ها در هفت پهنه اقلیمی ایران نشان داد که مدل BCC-CSM2-MR در دو پهنه BWh و Bsh عملکرد بهتری دارد و در پنج پهنه اقلیمی دیگر، مدل CAMS-CSM1-0 بهترین عملکرد را دارد. بی­هنجاری دما در دهه­های آتی در هر دو سناریو در ایران مثبت است و توزیع آن از توپوگرافی پیروی می­کند. همچنین روند دما در ایران افزایشی است. بیشینه روند افزایشی دما با نمره استاندارد 3.77 بر اساس سناریوی SSP5-8.5 به‌دست­آمده که در سطح 0.01 معنی­دار است. متوسط شیب روند دما در ایران طی دوره­های آتی به ازای هر سال به میزان 0.05 درجه سلسیوس افزایش خواهد داشت که رشد 0.01 را نسبت به دوره مشاهداتی نشان می­دهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Projected temperature anomalies and trends in different climate zones in Iran based on CMIP6

نویسندگان [English]

  • Azar Zarrin 1
  • abbasali dadashi-rodbari 2
  • Narges Salehabadi 3
1 Assistant Professor of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran
2 Postdoctoral Researcher of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran
3 MSc of Climatology, Ferdowsi University of Mashhad, Department of Geography, Mashhad, Iran
چکیده [English]

Climate change is a major challenge for human society and the natural environment. Evidence suggests that human activities play a role in increasing temperature at various temporal-spatial scales. The effects of climate change can be assessed by analyzing air temperature trends. According to the latest IPCC report, global average temperatures will increase by 1.5 degrees Celsius by the end of this century. The main purpose of this study is to assess CMIP6 projected temperature over different climate zones of Iran and its trend in the future. The result of this study can be useful for a wide range of management areas, especially the study of water resources, snow reserves, agriculture, and tourism.
   In this study, the mean annual temperature data of 43 synoptic stations of the Iran Meteorological Organization (IRIMO) were obtained from 1990 to 2009. To evaluate the anomaly and temperature trend in Iran until the end of the 21st century, the data of three models BCC-CSM2-MR, CAMS-CSM1-0, and MRI-ESM2-0 of CMIP6 models under two scenarios of SSP2.4-5 and SSP5.8-5 were used. We divided the period into four twenty-year periods, which are the first period (2020-2040), the second period (2041-2061), the third period (2061-2080), and the fourth period (2081-2100), respectively. To evaluate the air temperature which is the output of selected CMIP6 models, three statistical measures of RMSE, NSE and KGE were used. Using the Delta Change Factor (DCF) method, the bias of the models was corrected. Then, non-parametric Man-Kendall (MK) and Sen’s Slope Estimator tests were used to analyze trend analysis and trend slope in long-term data series.
    The maximum temperature is seen on the northern coast of the Persian Gulf and the Sea of Oman and the minimum temperature is seen in the northwest following the heights of the Zagros and also in the north of Iran (Alborz Mountains). The minimum annual temperature of 10.80°C was calculated based on observed data for the period 1990-2009, and the maximum temperature was 27.90°C on the coasts of the Oman Sea in southeastern Iran and Khuzestan province on the shores of the Persian Gulf in southwestern Iran. The intensity of the increase in temperature in Iran in mountainous areas is mainly due to the increase in the minimum temperature rather than to the maximum one.
   Generally, the intensity of the warming in Iran is mostly projected in cold and temperate regions. There is also a tendency for the temperature to rise further at higher latitudes.
   The projected temperature of CMIP6 models based on the SSP2.4-5 and SSP8.5-8 scenarios in Iran over four 20-year periods from 2020 to 2100 showed that the average slope of the temperature trend in Iran will reach 0.05°C per year, which shows an increase by a factor of 0.01 throughout Iran. As a general result, the annual trend of air temperature in Iran, based on observational data and projected output of CMIP6 models, shows warmer climate conditions for Iran. Temperature anomaly was not negative in any of the scenarios and periods; whereas positive anomaly is seen throughout the country. This increase in anomaly can be a major threat to the country's water resources.

کلیدواژه‌ها [English]

  • Temperature
  • CMIP6 models
  • SSP scenarios
  • DCF
  • Iran
رضیئی، ط.، ستوده، ف.، 1396، بررسی دقت مرکز اروپایی پیش‌بینی­های میان­مدت جوّی (ECMWF) در پیش­بینی بارش مناطق گوناگون اقلیمی ایران: فیزیک زمین و فضا، 43(1)، 133-147.
 
Abbasian, M., Moghim, S., and Abrishamchi, A., 2019, Performance of the general circulation models in simulating temperature and precipitation over Iran: Theoretical and Applied Climatology, 135, 1465-1483.
Allen, M., Antwi-Agyei, P., Aragon-Durand, F., Babiker, M., Bertoldi, P., Bind, M., and Cramer, W., 2019, Technical Summary: Global warming of 1.5°C: An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty.
Andrews, T., Forster, P. M., and Gregory, J. M., 2009, A surface energy perspective on climate change: Journal of Climate, 22, 2557-2570.
Angélil, O., Stone, D., Wehner, M., Paciorek, C. J., Krishnan, H., and Collins, W., 2017, An independent assessment of anthropogenic attribution statements for recent extreme temperature and rainfall events: Journal of Climate, 30, 5-16.
Araghi, A., Adamowski, J., Martinez, C. J., and Olesen, J. E., 2019, Projections of future soil temperature in northeast Iran: Geoderma, 349, 11-24.
Ayoubi, S., Karchegani, P. M., Mosaddeghi, M. R., and Honarjoo, N., 2012, Soil aggregation and organic carbon as affected by topography and land use change in western Iran: Soil and Tillage Research, 121, 18-26.
Childs, P. P., and Raman, S., 2005, Observations and numerical simulations of urban heat island and sea breeze circulations over New York City: Pure and Applied Geophysics, 162, 1955-1980.
Dai, A., and Bloecker, C. E., 2019, Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models: Climate dynamics, 52, 289-306.
Dai, A., Fyfe, J. C., Xie, S. P., and Dai, X., 2015, Decadal modulation of global surface temperature by internal climate variability: Nature Climate Change, 5, 555-559.
del Río, S., Anjum Iqbal, M., Cano-Ortiz, A., Herrero, L., Hassan, A., and Penas, A., 2013, Recent mean temperature trends in Pakistan and links with teleconnection patterns: International Journal of Climatology, 33, 277-290.
Dinpashoh, Y., Jahanbakhsh-Asl, S., Rasouli, A. A., Foroughi, M., and Singh, V. P., 2019, Impact of climate change on potential evapotranspiration (case study: west and NW of Iran): Theoretical and applied climatology, 136, 185-201.
Duhan, D., and Pandey, A., 2013, Statistical analysis of long term spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India: Atmospheric Research, 122, 136-149.
El-Nesr, M. N., Abu-Zreig, M. M., and Alazba, A. A., 2010, Temperature trends and distribution in the Arabian Peninsula: American Journal of Environmental Sciences, 6, 191-203.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E., 2016, Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization: Geoscientific Model Development (Online), 9(LLNL-JRNL-736881).
Fallah-Ghalhari, G., Shakeri, F., and Dadashi-Roudbari, A., 2019, Impacts of climate changes on the maximum and minimum temperature in Iran: Theoretical and Applied Climatology, 138, 1539-1562.
Florides, G. A., and Christodoulides, P., 2009, Global warming and carbon dioxide through sciences: Environment international, 35, 390-401.
García-García, A., Cuesta-Valero, F. J., Beltrami, H., and Smerdon, J. E., 2019, Characterization of air and ground temperature relationships within the CMIP5 historical and future climate simulations: Journal of Geophysical Research: Atmospheres, 124(7), 3903-3929.
Gidden, M., Riahi, K., Smith, S., Fujimori, S., Luderer, G., Kriegler, E., and Calvin, K. 2019, Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century: Geoscientific Model Development Discussions, 12, 1443-1475.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F., 2009, Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling: Journal of Hydrology, 377(1-2), 80-91.
Hardy, J. T., 2003, Climate Change: Causes, Effects, and Solutions: John Wiley & Sons.
IPCC, 2012: in Field, C. B., Barros, V., Stocker, T. F., Qin, D., Dokken, D. J., Ebi, K. L., Mastrandrea, M. D., Mach, K. J., Plattner, G. K., Allen, S. K., Tignor, M., Midgley, P. M. (eds.) WGI/WGII Special Report on Managing the Risks of ExtremeEvents and Disasters to Advance Climate Change Adaptation (SREX), Cambridge University Press, Cambridge, 582 pp.
IPCC, 2014, Climate Change, Synthesis Report, Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team, Pachauri, R. K., Meyer, L. A., (eds.), IPCC, Geneva, Switzerland.
IPCC, 2018, Summary for policymakers: in Masson-Delmotte, V., Zhai, P., Pörtner, H. O., Roberts, D., Skea, J., Shukla, P. R., ... , and Waterfield, T., (eds) Global warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related ..., World Meteorological Organization, Geneva, Switzerland.
Kalnay, E., and Cai, M., 2003, Impact of urbanization and land-use change on climate: Nature, 423, 528-531.
Kousari, M. R., Ahani, H., and Hendi-zadeh, R., 2013, Temporal and spatial trend detection of maximum air temperature in Iran during 1960–2005: Global and Planetary Change, 111, 97-110.
Lewkowicz, A. G., Bonnaventure, P. P., Smith, S. L., and Kuntz, Z., 2012, Spatial and thermal characteristics of mountain permafrost, northwest Canada: Geografiska Annaler: Series A, Physical Geography, 94, 195-213.
Linderholm, H. W., Folland, C. K., and Hurrell, J. W., 2008, Reconstructing Summer North Atlantic Oscillation (SNAO) variability over the last five centuries. Tree rings in archaeology: Climatology and Ecology, 6, 8-16.
Liu, H., Remer, L. A., Huang, J., Huang, H. C., Kondragunta, S., Laszlo, I., and Jackson, J. M., 2014, Preliminary evaluation of S-NPP VIIRS aerosol optical thickness: Journal of Geophysical Research: Atmospheres, 119, 3942-3962.
Lu, A., Pang, D., Ge, J., He, Y., Pang, H., and Yuan, L., 2006, Effect of landform on seasonal temperature structures across China in the past 52 years: Journal of Mountain Science, 3, 158.
Maghrabi, A. H., and Alotaibi, R. N., 2018, Long-term variations of AOD from an AERONET station in the central Arabian Peninsula: Theoretical and Applied Climatology, 134(3-4), 1015-1026.
Mendez, M., Maathuis, B., Hein-Griggs, D., & Alvarado-Gamboa, L. F. 2020, Performance evaluation of bias correction methods for climate change monthly precipitation projections over Costa Rica: Water, 12(2), 482.
Molavi-Arabshahi, M., Arpe, K., and Leroy, S. A. G., 2016, Precipitation and temperature of the southwest Caspian Sea region during the last 55 years: Their trends and teleconnections with large scale atmospheric phenomena: International Journal of Climatology, 36, 2156-2172.
Motagh, M., Walter, T. R., Sharifi, M. A., Fielding, E., Schenk, A., Anderssohn, J., and Zschau, J., 2008, Land subsidence in Iran caused by widespread water reservoir overexploitation: Geophysical Research Letters, 35, L16403.
Nengker, T., Choudhary, A., and Dimri, A. P., 2018, Assessment of the performance of CORDEX-SA experiments in simulating seasonal mean temperature over the Himalayan region for the present climate: part I: Climate dynamics, 50, 2411-2441.
Nerem, R. S., Beckley, B. D., Fasullo, J. T., Hamlington, B. D., Masters, D., and Mitchum, G. T., 2018, Climate-change–driven accelerated sea-level rise detected in the altimeter era: Proceedings of the National Academy of Sciences, 115, 2022-2025.
Nie, S., Fu, S., Cao, W., and Jia, X., 2020, Comparison of monthly air and land surface temperature extremes simulated using CMIP5 and CMIP6 versions of the Beijing Climate Center climate model: Theoretical and Applied Climatology, 1-16.
Rahimzadeh, F., Asgari, A., and Fattahi, E., 2009, Variability of extreme temperature and precipitation in Iran during recent decades: International Journal of Climatology: A Journal of the Royal Meteorological Society, 29, 329-343.
Rashid, I., Romshoo, S. A., Chaturvedi, R. K., Ravindranath, N. H., Sukumar, R., Jayaraman, M., ... & Sharma, J. 2015, Projected climate change impacts on vegetation distribution over Kashmir Himalayas: Climatic Change, 132(4), 601-613.
Räty, O., Räisänen, J., & Ylhäisi, J. S. 2014, Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations: Climate dynamics, 42(9-10), 2287-2303.
Salmi, T., Määttä, A., Anttila, P., Ruoho-Airola, T., Amnell, T., and Maatta, A., 2002, Detecting trends of annual values of atmospheric pollutants by the Mann-Kendall test and Sen’s slope estimates: The Excel template application MAKESENS.
Screen, J. A., and Simmonds, I., 2010, The central role of diminishing sea ice in recent Arctic temperature amplification: Nature, 464, 1334-1337.
Sellers, S., Ebi, K. L., and Hess, J., 2019, Climate change, human health, and social stability: addressing interlinkages: Environmental health perspectives, 127, 045002.
Shabalova, M. V., Van Deursen, W. P. A., & Buishand, T. A. 2003, Assessing future discharge of the river Rhine using regional climate model integrations and a hydrological model: Climate research, 23(3), 233-246.
Shi, Y., Gao, X., Zhang, D., and Giorgi, F., 2011, Climate change over the Yarlung Zangbo–Brahmaputra River Basin in the 21st century as simulated by a high-resolution regional climate model: Quaternary International, 244(2), 159-168.
Sigaroodi, S. K., and Ebrahimi, S., 2010, Effects of land use change on surface water regime (case study Orumieh Lake of Iran): Procedia Environmental Sciences, 2, 256-261.
Snyder, K. A., Evers, L., Chambers, J. C., Dunham, J., Bradford, J. B., and Loik, M. E., 2019, Effects of changing climate on the hydrological cycle in cold desert ecosystems of the Great Basin and Columbia Plateau: Rangeland Ecology and Management, 72, 1-12.
Soltani, M., Laux, P., Kunstmann, H., Stan, K., Sohrabi, M. M., Molanejad, M., and Zawar-Reza, P., 2016, Assessment of climate variations in temperature and precipitation extreme events over Iran: Theoretical and Applied Climatology, 126, 775-795.
Son, N. T., Chen, C. F., Chen, C. R., Chang, L. Y., and Minh, V. Q., 2012, Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data: International Journal of Applied Earth Observation and Geoinformation, 18, 417-427.
Sriver, R. L., Forest, C. E., and Keller, K., 2015, Effects of initial conditions uncertainty on regional climate variability: An analysis using a low-resolution CESM ensemble: Geophysical Research Letters, 42, 5468-5476.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M. M., Allen, S. K., Boschung, J., and Midgley, P. M., 2014, Climate change 2013: the physical science basis: Contribution of working group I to the fifth assessment report of IPCC the intergovernmental panel on climate change.
Stott, P. A., Christidis, N., Otto, F. E., Sun, Y., Vanderlinden, J. P., van Oldenborgh, G. J., and Zwiers, F. W., 2016. Attribution of extreme weather and climate-related events, Wiley Interdisciplinary Reviews: Climate Change, 7, 23-41.
Sun, J., Wang, H., and Yuan, W., 2009, Role of the tropical Atlantic sea surface temperature in the decadal change of the summer North Atlantic Oscillation: Journal of Geophysical Research: Atmospheres, 114(D20).
Tabari, H., and Marofi, S., 2011, Changes of pan evaporation in the west of Iran: Water Resources Management, 25(1), 97-111.
Tabari, H., Marofi, S., Aeini, A., Talaee, P. H., and Mohammadi, K., 2011, Trend analysis of reference evapotranspiration in the western half of Iran: Agricultural and Forest Meteorology, 151, 128-136.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A., 2012, An overview of CMIP5 and the experiment design: Bulletin of the American Meteorological Society, 93, 485-498.
Thayyen, R. J., and Dimri, A. P., 2014, Factors controlling Slope Environmental Lapse Rate (SELR) of temperature in the monsoon and cold-arid glacio-hydrological regimes of the Himalaya: The Cryosphere Discussions, 8, 5645-5686.
Trigo, R. M., Osborn, T. J., and Corte-Real, J. M, 2002, The North Atlantic Oscillation influence on Europe: climate impacts and associated physical mechanisms: Climate Research, 20, 9-17.
Wallace, J. M., Deser, C., Smoliak, B. V., and Phillips, A. S., 2016, Attribution of climate change in the presence of internal variability: in Climate change: multidecadal and beyond, 1-29.
Wheeler, T., and Von Braun, J., 2013, Climate change impacts on global food security: Science, 341, 508-513.
Wu, J., Zhang, P., Zha, J., Zhao, D., and Lu, W., 2019, Evaluating the long-term changes in temperature over the low-latitude plateau in China using a statistical downscaling method: Climate dynamics, 52, 4269-4292.
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., and Zhang, F., 2019, The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6: Geoscientific Model Development, 12, 1573-1600.
You, Q., Min, J., Fraedrich, K., Zhang, W., Kang, S., Zhang, L., and Meng, X., 2014, Projected trends in mean, maximum, and minimum surface temperature in China from simulations: Global and Planetary Change, 112, 53-63.
Yue, S., and Hashino, M., 2003, Temperature trends in Japan: 1900–1996: Theoretical and Applied Climatology, 75, 15-27.
Zhu, X., Dong, W., Wei, Z., Guo, Y., Gao, X., Wen, X., and Chen, J., 2018, Multi-decadal evolution characteristics of global surface temperature anomaly data shown by observation and CMIP5 models: International Journal of Climatology, 38, 1533-1542.