Abstract
Global warming in arid and semi-arid regions such as Iran is characterized by water scarcity and drought. In this paper, climate change impact on seasonal maximum and minimum temperature was done. To do so, three global climate models including CANESM2, GFDL-ESM2M, and HADGEM2-ES were used to simulate future climate change across Iran. SDSM model was used to downscale the CANESM2 model data. The data of GFDL-ESM2M and HADGEM2-ES models were downloaded from CORDEX database. In the present paper, the time period of 1976–2005 was considered as the base period and the time scales of 2011–2040, 2041–2070, and 2071–2099 as the future periods. The RCP2.6, RCP4.5, and RCP8.5 scenarios were chosen to future projection of minimum and maximum temperature. Taylor diagram was used to model evaluation. The results showed that the performance of SDSM model in minimum and maximum temperature downscaling in the base period is better than CORDEX database. One reason could be the smaller number of stations selected compared to CORDEX grid points. Results showed the highest positive anomalies of average maximum and minimum temperature compared to the base period studied (1976–2005), related to time period 2071–2099 and RCP8.5 by 6.69 and 6.61°C, respectively. The results showed that the maximum temperature will increase between 0.82 and 3.7°C on average depending on the season, time, and type of scenario. This value is in the range of 0.4–3.87°C for the minimum temperature. Results showed that hot days will increase. Results also showed that cold nights of winter in the coming decades will be warmer than the base period. The results also indicate that the frequency distributions of minimum and maximum temperatures in the future decades will shift to warmer temperatures in response to global warming.
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Notes
Coupled Model Intercomparison Project Phase 5
World Climate Research Programme
Coordinated Regional climate Downscaling Experiment
Representative concentration pathways (RCPs)
References
Adeyeria OE, Lawinb AE, Lauxc P, Isholad KA, Igee SO (2019) Analysis of climate extreme indices over the Komadugu-Yobe basin, Lake Chad region: past and future occurrences. Weather. Clim. Extremes 23:100194. https://doi.org/10.1016/j.wace.2019.100194
Alexandre MR, Ricardo MT, Fátima ES (2011) Evolution of extreme temperatures over Portugal: recent changes and future scenarios: Climate. Res. 48:177–192
Asare-Nuamaha P, Botchway E (2019) Understanding climate variability and change: analysis of temperature and rainfall across agro ecological zones in Ghana. Heliyon 5:e02654. https://doi.org/10.1016/j.heliyon.2019.e02654
Baez-Gonzalez AD, Torres-Meza MJ, Royo-Marquez MH, Kiniry JR (2018) Climate variability and trends in climate extremes in the priority conservation area El Tokio and adjacent areas in northeastern Mexico. Weather. Clim. Extremes 22:36–47. https://doi.org/10.1016/j.wace.2018.10.001
Bucchignani E, Cattaneo L, Panitz HJ, Mercogliano P (2016) Sensitivity analysis with the regional climate model COSMO-CLM over the CORDEX-MENA domain. Meteorol Atmospheric Phys 128:73–95. https://doi.org/10.1007/s00703-015-0403-3
Cabos W, Sein DV, Durán-Quesada A, Liguori G, Koldunov NV, Martínez-López B, Alvarez F, Sieck K, Limareva N, Pinto JG (2019) Dynamical downscaling of historical climate over CORDEX Central America domain with a regionally coupled atmosphere–ocean model. Clim Dyn 52(7-8):4305–4328. https://doi.org/10.1007/s00382-018-4381-2Chandra
Choi G, Collins D, Guoyu R et al (2009) Changes in means and extreme events of temperature and precipitation in the Asia-Pacific Network region, 1955-2007. Int J Climatol 29(13):1956–1975. https://doi.org/10.1002/joc.1979
Christidis N, Stott PA (2016) Attribution analyses of temperature extremes using a set of 16 indices. Weather and Climate Extremes 14:24–35. https://doi.org/10.1016/j.wace.2016.10.003
Chu W, Qiu S, Xu J (2016) Temperature change of Shanghai and its response to global warming and urbanization. Atmosphere 7:114. https://doi.org/10.3390/atmos7090114
Costa RL, de Mello Baptista GM, Gomes HB, dos Santos Silva FD, da Rocha Júnior RL, de Araújo Salvador M, Herdies DL (2020) Analysis of climate extremes indices over northeast Brazil from 1961 to 2014. Weather Clim Extremes 28:100254
Fallah Ghalhari GA, Dadashi Roudbari AA (2018) An investigation on thermal patterns in Iran based on spatial autocorrelation. Theor Appl Climatol 131:865–876. https://doi.org/10.1007/s00704-016-2015-3
Fallah Ghalhari GA, Khoshhal Dastjerdi J, Habibi Nokhandan M (2012) Using Mann Kendal and t-test methods in identifying trends of climatic elements: a case study of northern parts of Iran. Manag. Sci. Lett 2(3):911–920. https://doi.org/10.5267/j.msl.2011.10.015
Fallah-Ghalhari GA, Shakeri F, Dadashi-Roudbari AA (2019) Impacts of climate changes on the maximum and minimum temperature in Iran. Theor Appl Climatol 138:1539–1562. https://doi.org/10.1007/s00704-019-02906-9
Farjad B, Gupta A, Sartipizadeh H, Cannone AJ (2019) Novel approach for selecting extreme climate change scenarios for climate change impact studies. Sci. Total Environ 678:476–485. https://doi.org/10.1016/j.scitotenv.2019.04.218
Gasparrini A, Guo Y, Francesco S, Ana MV-C et al (2017) Projections of temperature-related excess mortality under climate change scenarios. Lancet Planet Health 1:e360–e367. https://doi.org/10.1016/S2542-5196(17)30156-0
Gibba P, Sylla MB, Okogbue EC, Gaye AT, Nikiema M, Kebe I (2019) State-of-the-art climate modeling of extreme precipitation over Africa: analysis of CORDEX added-value over CMIP5. Theor Appl Climatol 137:1041–1057. https://doi.org/10.1007/s00704-018-2650-y
Giorgi F, Jones C, Asrar GR (2009) Addressing climate information needs at the regional level: the CORDEX framework. World Meteorological Organization (WMO) Bulletin 58(3):175
Guo E, Zhang J, Wang YF, Quan L et al (2019) Spatiotemporal variations of extreme climate events in Northeast China during 1960–2014. Ecol. Indic 96(1:669–683. https://doi.org/10.1016/j.ecolind.2018.09.034
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science (80-.) 342:850–853. https://doi.org/10.1126/science.1244693
Hao Z, Wu M, Liu Y, Zhang X, Zheng J (2020) Multi-scale temperature variations and their regional differences in China during the Medieval Climate Anomaly. J Geogr Sci 30:119–130. https://doi.org/10.1007/s11442-020-1718-7
Houghton JT, Ding Y, Griggs DJ, Noguer M, Linden PJ, Dai X (2001) Climate Change 2001: the Scientific Basis. Cambridge Univ. Press, Cambridge
Ignacio VS, Ricardo IF, Pablo SZ, Pablo MF (2020) Risk of increasing temperature due to climate change on operation of the Spanish rail network, AIIT 2nd International Congress on Transport Infrastructure and Systems in a changing world (TIS ROMA 2019), 23rd-24th September 2019, Rome, Italy. Transportation Research Procedia 45(2020):5–12
Imada Y, Shiogama H, Takahashi C, Watanabe M, Mori M, Kamae Y, Maeda S (2018) Climate change increased the likelihood of the 2016 heat extremes in Asia. Bull. Amer Meteor Soc 99:97–101. https://doi.org/10.1175/BAMS-D-17-0109.1
IPCC, 2001: climate change 2001: the scientific basis. Contribution of Working Group 1 to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell and C. A. Johnson (eds). Cambridge University Press, Cambridge, UK, and New York, USA, p. 881.
IPCC (2013) Climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge/New York, p 1535. https://doi.org/10.1017/CBO9781107415324
IPCC (2014) Climate Change 2014: Impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by C. B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. . Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, et al. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,: Cambridge University Press
Kellermann P, Bubeck P, Kundela G, Dosio A, Thieken A (2016) Frequency analysis of critical meteorological conditions in a changing climate—assessing future implications for railway transportation in Austria. Climate 4(2):25–44. https://doi.org/10.3390/cli4020025
Klein Tank AMG, Können GP (2003) Trends in indices of daily temperature and precipitation extremes in Europe, 1946-99. J Climate 16:3665–3680
Kwak J, Kim S, Kim G (2016) Scrub typhus incidence modeling with meteorological factors in South Korea. International Journal of Environmental Research and Public Health. 12:7254–7273. https://doi.org/10.3390/ijerph120707254
Laprise R, Hernández-Díaz L, Tete K, Sushama L, Šeparović L, Martynov A, Winger K, Valin M (2013) Climate projections over CORDEX Africa domain using the fifth-generation Canadian Regional Climate Model (CRCM5). Clim Dyn 41(11-12):3219–3246
Masuda YJ, Castro B, Aggraeni I, Wolff NH, Ebi K, Garg T, Game ET, Krenz J, Spector J (2019) How are healthy, working populations affected by increasing temperatures? In the tropics? Implications for climate change adaptation policies. Global Environmental Change 56(2019):29–40. https://doi.org/10.1016/j.gloenvcha.2019.03.005
Mcmichael AJ, Woodruff RE, Hales S (2006) Climate change and human health: present and future risks. Lancet 367:859–869. https://doi.org/10.1016/S0140-6736(06)68079-3
Milanovic M, Gocic M, Trajkovic S (2015) Analysis of extreme climatic indices in the area of Nis and Belgrade for the period between 1974 and 2003. Agric. Agric. Sci. Procedia 4:408–415. https://doi.org/10.1016/j.aaspro.2015.03.046
Olsson T, Jakkila J, Veijalainen N, Backman L, Kaurola J, Vehviläinen B (2015) Impacts of climate change on temperature, precipitation and hydrology in Finland – studies using bias corrected Regional Climate Model data. Hydrol Earth Syst Sci 19(7):3217–3238
Pechlivanidis IG, Olsson J, Bosshard T, Sharma D, Sharma KC (2016) Multi-basin modelling of future hydrological fluxes in the Indian subcontinent. Water 8(5):177–198. https://doi.org/10.3390/w8050177
Rashid M, Mukand B (2013) Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin, Pakistan and India. Theor Appl Climatol 113:27–44. https://doi.org/10.1007/s00704-012-0765-0
Rashid M, Mukand B (2014) Future changes in extreme temperature events using the statistical downscaling model (SDSM) in the trans-boundary region of the Jhelum river basin. Weather Clim Extremes 5-6:56–66. https://doi.org/10.1016/j.wace.2014.09.001
Razavi T, Switzman H, Arain A, Coulibaly P (2016) Regional climate change trends and uncertainty analysis using extreme indices: a case study of Hamilton, Canada. Clim. Risk Manag 13:43–63. https://doi.org/10.1016/j.crm.2016.06.002
Rosenzweig, C., G. Casassa, D.J. Karoly, A. Imeson, C. Liu, A. Menzel, S. Rawlins, T.L. Root, B. Seguin, P. Tryjanowski (2007) Assessment of observed changes and responses in natural and managed systems. Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 79-131.
Saha G (2015) Climate change induced precipitation effects on water resources in the peace region of British Columbia, Canada. Climate 3:264–282. https://doi.org/10.3390/cli3020264
Savin NE, White Kenneth J (1977) The Durbin-Watson test for serial correlation with extreme sample sizes or many regressors. Econometrica 45(8):1989–1996. https://doi.org/10.2307/1914122
Shamir E, Halper E, Modrick ZT et al., 2019. Statistical and dynamical downscaling impact on projected hydrologic assessment in arid environment: a case study from Bill Williams River basin and Alamo Lake, Arizona. J Hydrol, 100019. https://doi.org/10.1016/j.hydroa.2019.100019
Solomon S, Qin D, Manning M., Chen Z, Marquis M, Averyt K.B, Tignor M, Miller H.L, 2007. Climate change: the physical science basis, contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, ISBN 978-0-521-88009-1 (pb: 978-0-521-70596-7).
Soltani M, Laux P, Kunstmann H, Stan K, Sohrabi MM, Molanejad M, Sabziparvar AA, Ranjbar SaadatAbadi A, Ranjbar F, Rousta I, Zawar-Reza P, Khoshakhlagh F, Soltanzadeh I, Babu CA, Azizi GH, Martin MV (2016) Assessment of climate variations in temperature and precipitation extreme events over Iran. Theor Appl Climatol 126:775–795. https://doi.org/10.1007/s00704-015-1609-5
Špička J (2011) Weather derivative design in agriculture – a case study of barley In the Southern Moravia Region. AGRIS on-line Pap Econ Inform 3(3):53–59
Steynor A, Leighton M, Kavonic J, Abrahams W, Magole L, Kaunda S, Mubaya CP (2020) Learning from climate change perceptions in southern African cities. Climate Risk Management 27(2020):100202. https://doi.org/10.1016/j.crm.2019.100202
Stott PA, Christidis N, Otto FEL, Sun Y, Vanderlinden J-P, van Oldenborgh GJ, Vautard R, von Storch H, Walton P, Yiou P, Zwiers FW (2016) Attribution of extreme weather and climate-related events. Wiley Interdiscip Rev Clim Chang 7(1):23–41
Stull RB (2000) Meteorology for scientists and engineers: a technical companion book to C. In: Donald Ahrens' Meteorology Today, 2nd Edition. University of British, Columbia, p 528
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., 106, 7183-7192 (also see PCMDI Report 55, http://wwwpcmdi.llnl.gov/publications/ab55.html).
Touré Halimatou A, Kalifa T, Kyei-Baffour N (2017) Assessment of changing trends of daily precipitation and temperature extremes in Bamako and Ségou in Mali from 1961- 2014. Weather Clim Extremes 18:8–16
Vicente-Serrano SM, Cabello D, Tomás-Burguera M et al (2015) Drought variability and land degradation in semiarid regions: assessment using remote sensing data and drought indices (1982–2011). Remote Sens 7:4391–4423. https://doi.org/10.3390/rs70404391
Warnatzsch EA, Reay DS (2019) Temperature and precipitation change in Malawi: Evaluation of CORDEX-Africa climate simulations for climate change impact assessments and adaptation planning. Sci Total Environ 654:378–392
Wilby RL, Dawson CW, Barrow EM (2002) SDSM— a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17(2):145–157. https://doi.org/10.1016/s1364-8152(01)00060-3
Wilby RL, Dawson CW, Murphy C, O’Connor P, Hawkins E (2014) The Statistical DownScaling Model - Decision Centric (SDSM-DC): conceptual basis and applications. Clim Res 61:259–276. https://doi.org/10.3354/cr01254
Wu X, Liu J, Li C, Yin J (2020) Impact of climate change on dysentery: Scientific evidences, uncertainty, modeling and projections. Sci Total Environ 714:136702
Zhang L, Zhao Y, Hein-Griggs D, Janes T, Tucker S, Ciborowski JJH (2020) Climate change projections of temperature and precipitation for the great lakes basin using the PRECIS regional climate model. J. Great Lakes Res 46(2):255–266. https://doi.org/10.1016/j.jglr.2020.01.013
Acknowledgements
The authors thank the Iranian Meteorological Organization for providing the required meteorological data. The authors also thank the CANESM2 and CORDEX modeling teams for providing the required data.
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This research was conducted with the financial support of Hakim Sabzevari University and the authors express their gratitude to the university officials.
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Fallah-Ghalhari, G., Shakeri, F. An assessment of Iran's seasonal temperature probability distribution variations in the future decades. Arab J Geosci 14, 319 (2021). https://doi.org/10.1007/s12517-021-06575-9
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DOI: https://doi.org/10.1007/s12517-021-06575-9