Abstract
Thermal bioclimate is a defining factor of agricultural production, ecological condition, public health, and species distribution. This study aimed at assessing the possible changes in the Middle East and North African (MENA) thermal bioclimate for two shared socioeconomic pathways (SSPs), SSP1-1.9 and SSP1-2.6, representing a temperature rise restricted to 1.5 and 2.0 °C above the pre-industrial level at the end of the century. Therefore, the study explains the probable least change in bioclimate due to climate change and what might happen for a 0.5 °C temperature rise above the 1.5 °C addressed by Paris Climate Agreement. A multimodel ensemble of eight global climate models was employed for this purpose. The results indicated a 0.5 °C further increase in temperature above the 1.5 °C temperature rise threshold would cause a nearly 0.8 to 1.0 °C increase in temperature in some parts of MENA, indicating a faster than global average increase in temperature in the region for higher temperature rise scenarios. Climate change would cause a decrease in thermal seasonality by 2–6% over nearly 90% of the study area. The diurnal temperature would decrease by 0.1 to 0.4 °C over the entire south, while the annual temperature range would decrease by 0.5 to 1.5 °C over a large area in the north. This would cause a decrease in isothermality nearly by 1% over most areas. The area with decreasing isothermality would expand by almost 150% for a further temperature rise by 0.5 °C. The results indicate a substantial change in bioclimate in MENA for a minor temperature change.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs00477-022-02275-2/MediaObjects/477_2022_2275_Fig12_HTML.png)
Similar content being viewed by others
Availability of data/code
All datasets of the thermal bioclimatic indicators (Historical, SSP1-1.9, and SSP1-2.6) are available at Figshare (https://doi.org/10.6084/m9.figshare.19310939.v1). The dataset (in.tif format) consists of 11 historical thermal bioclimatic and their projections for SSP1-1.9 and SSP1-2.6 for the near and far futures.
References
Abualnaja Y, Papadopoulos VP, Josey SA et al (2015) Impacts of climate modes on air-sea heat exchange in the Red Sea. J Clim 28:2665–2681. https://doi.org/10.1175/JCLI-D-14-00379.1
Abumoghli I, Goncalves A (2020) Environmental Challenges in the MENA Region. Faith Earth Updat
Adhikari P, Shin M-S, Jeon J-Y et al (2018) Potential impact of climate change on the species richness of subalpine plant species in the mountain national parks of South Korea. J Ecol Environ 42:36. https://doi.org/10.1186/s41610-018-0095-y
AFED (2017) Arab Environment in 10 Years. Annual Report of Arab Forum for Environment and Development. Annual Report of Arab Forum for Environment and Development, Beirut, Lebanon
Aihaiti A, Jiang Z, Zhu L, et al (2021) Risk changes of compound temperature and precipitation extremes in China under 1.5 °C and 2 °C global warming. Atmos Res 264:105838. https://doi.org/10.1016/j.atmosres.2021.105838
Almazroui M, Islam MN, Saeed S et al (2020a) Future Changes in Climate over the Arabian Peninsula based on CMIP6 Multimodel Simulations. Earth Syst Environ 4:611–630. https://doi.org/10.1007/s41748-020-00183-5
Almazroui M, Saeed F, Saeed S et al (2020b) Projected Change in Temperature and Precipitation Over Africa from CMIP6. Earth Syst Environ 4:455–475. https://doi.org/10.1007/s41748-020-00161-x
Barlow M, Zaitchik B, Paz S et al (2016) A review of drought in the Middle East and southwest Asia. J Clim 29:8547–8574. https://doi.org/10.1175/JCLI-D-13-00692.1
Bellard C, Bertelsmeier C, Leadley P et al (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377. https://doi.org/10.1111/j.1461-0248.2011.01736.x
Boucher O, Denvil S, Levavasseur G, et al (2018) IPSL IPSL-CM6A-LR model output prepared for CMIP6 CMIP
Braganza K, Karoly DJ, Arblaster JM (2004) Diurnal temperature range as an index of global climate change during the twentieth century. Geophys Res Lett. https://doi.org/10.1029/2004GL019998
Çaliskan O, Türkoglu N, Matzarakis A (2013) The effects of elevation on thermal bioclimatic conditions in Uludağ ( Turkey ). Atmósfera 26:45–57
Chen H, Sun J, Chen X (2014) Projection and uncertainty analysis of global precipitation-related extremes using CMIP5 models. Int J Climatol 34:2730–2748. https://doi.org/10.1002/joc.3871
Cheng J, Xu Z, Zhu R et al (2014) Impact of diurnal temperature range on human health: a systematic review. Int J Biometeorol 58:2011–2024. https://doi.org/10.1007/s00484-014-0797-5
Daham A, Han D, Matt Jolly W et al (2018) Predicting vegetation phenology in response to climate change using bioclimatic indices in Iraq. J Water Clim Chang 10:835–851. https://doi.org/10.2166/wcc.2018.142
Deng X, Perkins-Kirkpatrick SE, Lewis SC, Ritchie EA (2021) Evaluation of Extreme Temperatures Over Australia in the Historical Simulations of CMIP5 and CMIP6 Models. Earth’s Futur 9:e2020EF001902. https://doi.org/10.1029/2020EF001902
Dogar MM (2018) Impact of tropical volcanic eruptions on Hadley circulation using a high-resolution AGCM. Curr Sci 114:. https://doi.org/10.18520/cs/v114/i06/1284-1294
Dogar MM, Kucharski F, Azharuddin S (2017) Study of the global and regional climatic impacts of ENSO magnitude using SPEEDY AGCM. J Earth Syst Sci 126:30. https://doi.org/10.1007/s12040-017-0804-4
Dogar MM, Sato T (2018) Analysis of Climate Trends and Leading Modes of Climate Variability for MENA Region. J Geophys Res Atmos 123:13,13–74,91. https://doi.org/10.1029/2018JD029003
Döscher R, Acosta M, Alessandri A et al (2021) The EC-Earth3 Earth System Model for the Climate Model Intercomparison Project 6. Geosci Model Dev Discuss 2021:1–90. https://doi.org/10.5194/gmd-2020-446
Dosio A, Fischer EM (2018) Will Half a Degree Make a Difference? Robust Projections of Indices of Mean and Extreme Climate in Europe Under 1.5°C, 2°C, and 3°C Global Warming. Geophys Res Lett 45:935–944. https://doi.org/10.1002/2017GL076222
Ehbrecht M, Schall P, Ammer C et al (2019) Effects of structural heterogeneity on the diurnal temperature range in temperate forest ecosystems. For Ecol Manage 432:860–867. https://doi.org/10.1016/j.foreco.2018.10.008
Eyring V, Bony S, Meehl GA et al (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Farahat EA, Linderholm HW, Lechowicz MJ (2016) Influence of dust deposition and climate on the radial growth of Tsuga canadensis near its northern range limit. Eur J for Res 135:69–76. https://doi.org/10.1007/s10342-015-0917-8
Feng R, Yu R, Zheng H, Gan M (2018) Spatial and temporal variations in extreme temperature in Central Asia. Int J Climatol 38:e388–e400. https://doi.org/10.1002/joc.5379
Fouda MM, Salama A, Director NCS (2015) Climate Change and Biodiversity in Africa and MENA Region. Reg Action Clim Chang Alexandria, Egypt
Gaston KJ (2003) The structure and dynamics of geographic ranges. Oxford University Press on Demand
Ge F, Zhu S, Peng T, et al (2019) Risks of precipitation extremes over Southeast Asia: Does 1.5 °c or 2 °c global warming make a difference? Environ Res Lett 14:. https://doi.org/10.1088/1748-9326/aaff7e
Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J Hydrol 377:80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
Hamed MM, Nashwan MS, Shahid S (2022a) Inter-comparison of Historical Simulation and Future Projection of Rainfall and Temperature by CMIP5 and CMIP6 GCMs Over Egypt. Int J Climatol n/a:1–17. https://doi.org/10.1002/joc.7468
Hamed MM, Nashwan MS, Shahid S (2021) Performance Evaluation of Reanalysis Precipitation Products in Egypt using Fuzzy Entropy Time Series Similarity Analysis. Int J Climatol 41:5431–5446. https://doi.org/10.1002/joc.7286
Hamed MM, Nashwan MS, Shahid S et al (2022b) Inconsistency in historical simulations and future projections of temperature and rainfall: A comparison of CMIP5 and CMIP6 models over Southeast Asia. Atmos Res 265:105927. https://doi.org/10.1016/j.atmosres.2021.105927
Hamed MM, Nashwan MS, Shahid S (2022c) A novel selection method of CMIP6 GCMs for robust climate projection. Int J Climatol n/a: https://doi.org/10.1002/joc.7461
Hersbach H, Bell B, Berrisford P et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146:1999–2049. https://doi.org/10.1002/qj.3803
Hoegh-Guldberg O, Jacob D, Taylor M, et al (2019) The human imperative of stabilizing global climate change at 1.5 C. Science (80- ) 365:
Hu X-G, ** Y, Wang X-R et al (2015) Predicting Impacts of Future Climate Change on the Distribution of the Widespread Conifer Platycladus orientalis. PLoS ONE 10:e0132326
Hu Z, Li Q, Chen X et al (2016) Climate changes in temperature and precipitation extremes in an alpine grassland of Central Asia. Theor Appl Climatol 126:519–531. https://doi.org/10.1007/s00704-015-1568-x
Hulme M (2016) 1.5 °C and climate research after the Paris Agreement. Nat Clim Chang 6:222–224. https://doi.org/10.1038/nclimate2939
IPCC (2021) Summary for Policymakers. In: Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E., Lonnoy, J.B.R.M., T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou,. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
IPCC (2013) Climate change 2013: The physical science basis. United Kingdom and New York, NY, USA, Cambridge
Jeschke JM, Strayer DL (2008) Usefulness of bioclimatic models for studying climate change and invasive species. Ann N Y Acad Sci 1134:1–24
Jiang D, Hu D, Tian Z, Lang X (2020) Differences between CMIP6 and CMIP5 Models in Simulating Climate over China and the East Asian Monsoon. Adv Atmos Sci 37:1102–1118. https://doi.org/10.1007/s00376-020-2034-y
Joseph R, Zeng N (2011) Seasonally modulated tropical drought induced by volcanic aerosol. J Clim 24:2045–2060. https://doi.org/10.1175/2009JCLI3170.1
Ju J, Wu C, Yeh PJ-F, et al (2021) Global precipitation-related extremes at 1.5 °C and 2 °C of global warming targets: Projection and uncertainty assessment based on the CESM-LWR experiment. Atmos Res 264:105868. https://doi.org/10.1016/j.atmosres.2021.105868
Kamal ASMM, Hossain F, Shahid S (2021) Spatiotemporal changes in rainfall and droughts of Bangladesh for1.5 and 2 °C temperature rise scenarios of CMIP6 models. Theor Appl Climatol 146:527–542. https://doi.org/10.1007/s00704-021-03735-5
Karoly DJ, Karl B, Stott PA, et al (2003) Detection of a Human Influence on North American Climate. Science (80- ) 302:1200–1203. https://doi.org/10.1126/science.1089159
Kling H, Fuchs M, Paulin M (2012) Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. J Hydrol 424–425:264–277. https://doi.org/10.1016/j.jhydrol.2012.01.011
Knoben WJM, Freer JE, Woods RA (2019) Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrol Earth Syst Sci Discuss. https://doi.org/10.5194/hess-2019-327
Krasting JP, John JG, Blanton C, et al (2018) NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP
Kriticos DJ, Webber BL, Leriche A et al (2012) CliMond: Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol Evol 3:53–64. https://doi.org/10.1111/j.2041-210X.2011.00134.x
Lelieveld J, Proestos Y, Had**icolaou P et al (2016) Strongly increasing heat extremes in the Middle East and North Africa (MENA) in the 21st century. Clim Change 137:245–260. https://doi.org/10.1007/s10584-016-1665-6
Li J, Fan G, He Y (2020) Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci Total Environ 698:134141. https://doi.org/10.1016/j.scitotenv.2019.134141
Liu F, Chai J, Wang B et al (2016) Global monsoon precipitation responses to large volcanic eruptions. Sci Rep 6:1–11
Meinshausen M, Nicholls Z, Lewis J, et al (2019) The SSP greenhouse gas concentrations and their extensions to 2500. Geosci Model Dev Discuss 1–77
Molloy SW, Davis RA, Van Etten EJB (2014) Species distribution modelling using bioclimatic variables to determine the impacts of a changing climate on the western ringtail possum (Pseudocheirus occidentals; Pseudocheiridae). Environ Conserv 41:176–186. https://doi.org/10.1017/S0376892913000337
Morellet N, Bonenfant C, Börger L et al (2013) Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within Europe. J Anim Ecol 82:1326–1339. https://doi.org/10.1111/1365-2656.12105
Moss RH, Edmonds JA, Hibbard KA et al (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747–756. https://doi.org/10.1038/nature08823
Nashwan MS, Shahid S (2022) Future precipitation changes in Egypt under the 1.5 and 2.0°C global warming goals using CMIP6 multimodel ensemble. Atmos Res 265:105908. https://doi.org/10.1016/j.atmosres.2021.105908
Nashwan MS, Shahid S (2019) Spatial distribution of unidirectional trends in climate and weather extremes in Nile river basin. Theor Appl Climatol 137:1181–1199. https://doi.org/10.1007/s00704-018-2664-5
Nashwan MS, Shahid S, Dewan A et al (2020) Performance of five high resolution satellite-based precipitation products in arid region of Egypt: An evaluation. Atmos Res 236:104809. https://doi.org/10.1016/j.atmosres.2019.104809
Niranjan Kumar K, Ouarda TBMJ, Sandeep S, Ajayamohan RS (2016) Wintertime precipitation variability over the Arabian Peninsula and its relationship with ENSO in the CAM4 simulations. Clim Dyn 47:2443–2454. https://doi.org/10.1007/s00382-016-2973-2
O’Donnell MS, Ignizio DA (2012) Bioclimatic Predictors for Supporting Ecological Applications in the Conterminous United States. US Geol Surv Data Ser 691:10
O’Neill BC, Tebaldi C, Van Vuuren DP et al (2016) The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci Model Dev 9:3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
Ombadi M, Nguyen P, Sorooshian S, Hsua K (2020) Retrospective Analysis and Bayesian Model Averaging of CMIP6 Precipitation in the Nile River Basin. J Hydrometeorol. https://doi.org/10.1175/jhm-d-20-0157.1
Paltan H, Allen M, Haustein K, et al (2018) Global implications of 1.5 °C and 2 °C warmer worlds on extreme river flows. Environ Res Lett 13:94003. https://doi.org/10.1088/1748-9326/aad985
Pour SH, Wahab AKA, Shahid S, Wang X (2019) Spatial pattern of the unidirectional trends in thermal bioclimatic indicators in Iran. Sustain. https://doi.org/10.3390/su11082287
Pu Y, Liu H, Yan R et al (2020) CAS FGOALS-g3 Model Datasets for the CMIP6 Scenario Model Intercomparison Project (ScenarioMIP). Adv Atmos Sci 37:1081–1092. https://doi.org/10.1007/s00376-020-2032-0
Raes N, Cannon CH, Hijmans RJ, et al (2014) Historical distribution of Sundaland’s Dipterocarp rainforests at Quaternary glacial maxima. Proc Natl Acad Sci 111:16790 LP – 16795. https://doi.org/10.1073/pnas.1403053111
Ribeiro MM, Roque N, Ribeiro S, et al (2019) Bioclimatic modeling in the Last Glacial Maximum, Mid-Holocene and facing future climatic changes in the strawberry tree (Arbutus unedo L.). PLoS One 14:e0210062
Rogelj J, Popp A, Calvin KV et al (2018) Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat Clim Chang 8:325–332. https://doi.org/10.1038/s41558-018-0091-3
Sahour H, Vazifedan M, Alshehri F (2020) Aridity trends in the Middle East and adjacent areas. Theor Appl Climatol 142:1039–1054. https://doi.org/10.1007/s00704-020-03370-6
Salehie O, Hamed MM, Ismail T bin, Shahid S (2021a) Projection of Droughts in Amu Darya River Basin for Shared Socioeconomic Pathways. Prepr (Version 1) available Res Sq. https://doi.org/10.21203/rs.3.rs-1088081/v1
Salehie O, Hamed MM, Ismail T, et al (2021b) Selection of CMIP6 GCM With Projection of Climate Over The Amu Darya River Basin. Prepr (Version 1) available Res Sq 1–27. https://doi.org/10.21203/rs.3.rs-1031530/v1
Salehie O, Ismail T, Hamed MM, et al (2021c) Projection of Hot and Cold Extremes in the Amu River Basin of Central Asia using GCMs CMIP6. Prepr (Version 1) available Res Sq. https://doi.org/10.21203/rs.3.rs-1166107/v1
Salehie O, Ismail TB, Shahid S et al (2022) Assessment of Water Resources Availability in Amu Darya River Basin Using GRACE Data. Water 14:533. https://doi.org/10.3390/w14040533
Salman SA, Nashwan MS, Ismail T, Shahid S (2020) Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia. Hydrol Res 51:781–798. https://doi.org/10.2166/nh.2020.154
Salman SA, Shahid S, Ismail T et al (2017) Long-term trends in daily temperature extremes in Iraq. Atmos Res 198:97–107. https://doi.org/10.1016/j.atmosres.2017.08.011
Salman SA, Shahid S, Ismail T et al (2018) Unidirectional trends in daily rainfall extremes of Iraq. Theor Appl Climatol 134:1165–1177. https://doi.org/10.1007/s00704-017-2336-x
Salvacion AR (2020) Effect of climate on provincial-level banana yield in the Philippines. Inf Process Agric 7:50–57. https://doi.org/10.1016/j.inpa.2019.05.005
Shahid S (2010) Probable impacts of climate change on public health in Bangladesh. Asia Pacific J Public Heal 22:310–319. https://doi.org/10.1177/1010539509335499
Shahid S, Bin HS, Katimon A (2012) Changes in diurnal temperature range in Bangladesh during the time period 1961–2008. Atmos Res 118:260–270. https://doi.org/10.1016/j.atmosres.2012.07.008
Sheldon KS, Leaché AD, Cruz FB (2015) The influence of temperature seasonality on elevational range size across latitude: a test using Liolaemus lizards. Glob Ecol Biogeogr 24:632–641. https://doi.org/10.1111/geb.12284
Shi C, Jiang Z-H, Chen W-L, Li L (2018) Changes in temperature extremes over China under 1.5°C and 2°C global warming targets. Adv Clim Chang Res 9:120–129. https://doi.org/10.1016/j.accre.2017.11.003
Shiru MS, Chung ES, Shahid S, Wang X (2022) Comparison of precipitation projections of CMIP5 and CMIP6 global climate models over Yulin, China. Theor Appl Climatol 147:535–548. https://doi.org/10.1007/s00704-021-03823-6
Sintayehu DW (2018) Impact of climate change on biodiversity and associated key ecosystem services in Africa: a systematic review. Ecosyst Heal Sustain 4:225–239. https://doi.org/10.1080/20964129.2018.1530054
Sobh MT, Nashwan MS, Amer N (2022) High Resolution Reference Evapotranspiration for Arid Egypt: comparative analysis and evaluation of empirical and artificial intelligence models. Prepr (Version 1) available Res Sq. https://doi.org/10.21203/rs.3.rs-1366239/v1
Song YH, Nashwan MS, Chung ES, Shahid S (2021) Advances in CMIP6 INM-CM5 over CMIP5 INM-CM4 for precipitation simulation in South Korea. Atmos Res 247:105261. https://doi.org/10.1016/j.atmosres.2020.105261
Sun C, Jiang Z, Li W, et al (2019) Changes in extreme temperature over China when global warming stabilized at 1.5 °C and 2.0 °C. Sci Rep 9:14982. https://doi.org/10.1038/s41598-019-50036-z
Sun H, Wang A, Zhai J et al (2018) Impacts of global warming of 1.5 °C and 2.0 °C on precipitation patterns in China by regional climate model (COSMO-CLM). Atmos Res 203:83–94. https://doi.org/10.1016/j.atmosres.2017.10.024
Swart NC, Cole JNS, Kharin VV et al (2019) The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci Model Dev 12:4823–4873. https://doi.org/10.5194/gmd-12-4823-2019
Tatebe H, Ogura T, Nitta T et al (2019) Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geosci Model Dev 12:2727–2765. https://doi.org/10.5194/gmd-12-2727-2019
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
Tebaldi C, Debeire K, Eyring V et al (2021) Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6. Earth Syst Dyn 12:253–293. https://doi.org/10.5194/esd-12-253-2021
Terink W, Immerzeel WW, Droogers P (2013) Climate change projections of precipitation and reference evapotranspiration for the Middle East and Northern Africa until 2050. Int J Climatol 33:3055–3072. https://doi.org/10.1002/joc.3650
UNFCCC D (2015) 1/CP. 21, Adoption of the Paris Agreement. In: Paris Climate Change Conference
Varela R, Rodríguez-Díaz L, Barriopedro D et al (2021) Projected changes in the season of hot days in the Middle East and North Africa. Int J Climatol. https://doi.org/10.1002/joc.7360
Wasimi SA (2010) Climate change in the Middle East and North Africa (MENA) region and implications for water resources project planning and management. Int J Clim Chang Strateg Manag 2:297–320. https://doi.org/10.1108/17568691011063060
Yoon S, Lee W-H (2021) Methodological analysis of bioclimatic variable selection in species distribution modeling with application to agricultural pests (Metcalfa pruinosa and Spodoptera litura). Comput Electron Agric 190:106430. https://doi.org/10.1016/j.compag.2021.106430
Yu H, Zhang Y, Wang Z et al (2017) Diverse range dynamics and dispersal routes of plants on the Tibetan Plateau during the late Quaternary. PLoS ONE 12:e0177101
Yukimoto S, Kawai H, Koshiro T et al (2019) The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component. J Meteorol Soc Japan Ser II 97:931–965. https://doi.org/10.2151/jmsj.2019-051
Zohner CM, Mo L, Renner SS et al (2020) Late-spring frost risk between 1959 and 2017 decreased in North America but increased in Europe and Asia. Proc Natl Acad Sci U S A 117:12192–12200. https://doi.org/10.1073/pnas.1920816117
Funding
The authors did not receive support from any organization for the submitted work.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study's conception and design. Material preparation, data collection and analysis were performed by [Mohammed Magdy Hamed], [Mohammed Salem Nashwan] and [Shamsuddin Shahid]. All authors contributed to writing the first draft of the manuscript. [Mohammed Salem Nashwan] and [Shamsuddin Shahid] revised the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
We declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hamed, M.M., Nashwan, M.S. & Shahid, S. Projected changes in thermal bioclimatic indicators over the Middle East and North Africa under Paris climate agreement. Stoch Environ Res Risk Assess 37, 577–594 (2023). https://doi.org/10.1007/s00477-022-02275-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-022-02275-2