زرین، آذر، و داداشی رودباری، عباسعلی. (1401). بررسی مدلهای CMIP6 در برآورد دمای ایران با تأکید بر حساسیت اقلیم ترازمند (ECS) و پاسخ اقلیم گذرا (TCR). مجله ژئوفیزیک ایران، 17(1)، 39-56. https://doi.org/10.30499/ijg.2022.344862.1430
Abbass, K., Qasim, M. Z., Song, H., Murshed, M., Mahmood, H., & Younis, I. (2022). A review of the global climate change impacts, adaptation, and sustainable mitigation measures. Environmental Science and Pollution Research, 29(28), 42539-42559. https://doi.org/10.1007/s11356-022-19718-6
Aliyar, Q., Dhungana, S., & Shrestha, S. (2022). Spatio-temporal trend mapping of precipitation and its extremes across Afghanistan (1951–2010). Theoretical and Applied Climatology, 147, 605-626. https://doi.org/10.1007/s00704-021-03851-2
Asadi-RahimBeygi, N., Zarrin, A., Mofidi, A., & Dadashi-Roudbari, A. (2024). Near-term temperature extremes in Iran using the decadal climate prediction project (DCPP). Stochastic Environmental Research and Risk Assessment, 38, 447–466. https://doi.org/10.1007/s00477-023-02579-x
Babar, Z. A., Zhi, X., Ge, F., Riaz, M., Mahmood, A., Sultan, S., Shad, M.A., Aslam, C. M., & Ahmad, M. F. (2016). Assessment of Southwest Asia surface temperature changes: CMIP5 20th and 21st century simulations. Pakistan Journal of Meteorology, 13(25): 1-15. https://www.prdb.pk/article/assessment-of-southwest-asia-surface-temperature-changes-cm-190
Bai, H., Xiao, D., Wang, B., Liu, D. L., Feng, P., & Tang, J. (2021). Multi‐model ensemble of CMIP6 projections for future extreme climate stress on wheat in the North China Plain. International Journal of Climatology, 41, E171-E186. https://doi.org/10.1002/joc.6674
Beck, H. E., Van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., & Miralles, D. G. (2022). MSWX: Global 3-hourly 0.1 bias-corrected meteorological data including near-real-time updates and forecast ensembles. Bulletin of the American Meteorological Society, 103(3), E710-E732. https://doi.org/10.1175/BAMS-D-21-0145.1
Bevacqua, E., Zappa, G., Lehner, F., & Zscheischler, J. (2022). Precipitation trends determine future occurrences of compound hot–dry events. Nature Climate Change, 12(4), 350-355. https://doi.org/10.1038/s41558-022-01309-5
Dai, A., & Bloecker, C. E. (2019). Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models. Climate dynamics, 52(1-2), 289-306. https://doi.org/10.1007/s00382-018-4132-4
Daufresne, M., Lengfellner, K., & Sommer, U. (2009). Global warming benefits the small in aquatic ecosystems. Proceedings of the National Academy of Sciences, 106(31), 12788-12793. https://doi.org/10.1073/pnas.0902080106
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., ... & Williamson, M. S. (2019). Taking climate model evaluation to the next level. Nature Climate Change, 9(2), 102-110. https://doi.org/10.1038/s41558-018-0355-y
Farhat, F., Kashifi, M. T., Jamal, A., & Saba, I. (2022). Spatiotemporal projections of precipitation and temperature over Afghanistan based on CMIP6 global climate models. Modeling Earth Systems and Environment, 8(3), 4229-4242. https://doi.org/10.1007/s40808-022-01361-2
Fatima, E., Hassan, M., Hasson, S. U., Ahmad, B., & Ali, S. S. F. (2020). Future water availability from the western Karakoram under representative concentration pathways as simulated by CORDEX South Asia. Theoretical and Applied Climatology, 141, 1093-1108. https://doi.org/10.1007/s00704-020-03261-w
Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. https://doi.org/10.1016/S0022-1694(97)00125-X
Kang, Y., Khan, S., & Ma, X. (2009). Climate change impacts on crop yield, crop water productivity and food security–A review. Progress in natural Science, 19(12), 1665-1674. https://doi.org/10.1016/j.pnsc.2009.08.001
Kendall, M. G. (1948). Rank correlation methods, Griffin, No. 98. Harvard Book List (edited) 1955. https://psycnet.apa.org/record/1948-15040-000
Kumar, S., Chanda, K., & Pasupuleti, S. (2020). Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India. Theoretical and Applied Climatology, 140, 343-357. https://doi.org/10.1007/s00704-020-03088-5
Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). Geoscientific Model Development, 12(7), 3055-3070. https://doi.org/10.5194/gmd-12-3055-2019
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259. https://doi.org/10.2307/1907187
Men, B., Wu, Z., Liu, H., Tian, W., & Zhao, Y. (2020). Spatio-temporal analysis of precipitation and temperature: A case study over the Beijing–Tianjin–Hebei Region, China. Pure and Applied Geophysics, 177, 3527-3541. https://doi.org/10.1007/s00024-019-02400-3
Mishra, A. K., Özger, M., & Singh, V. P. (2011). Association between uncertainties in meteorological variables and water-resources planning for the state of Texas. Journal of Hydrologic Engineering, 16(12), 984-999. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000150
Politi, N., Vlachogiannis, D., Sfetsos, A., & Nastos, P. T. (2023). High resolution projections for extreme temperatures and precipitation over Greece. Climate Dynamics, 61(1-2), 633-667. https://doi.org/10.1007/s00382-022-06590-w
Rangwala, I., Miller, J. R., Russell, G. L., & Xu, M. (2010). Using a global climate model to evaluate the influences of water vapor, snow cover and atmospheric aerosol on warming in the Tibetan Plateau during the twenty-first century. Climate Dynamics, 34, 859-872. https://doi.org/10.1007/s00382-009-0564-1
Rehman, N., Adnan, M., & Ali, S. (2018). Assessment of CMIP5 climate models over South Asia and climate change projections over Pakistan under representative concentration pathways. International Journal of Global Warming, 16(4), 381-415. https://doi.org/10.1504/IJGW.2018.095994
Sachindra, D. A., Huang, F., Barton, A., & Perera, B. J. C. (2014). Statistical downscaling of general circulation model outputs to precipitation—part 2: bias‐correction and future projections. International Journal of Climatology, 34(11), 3282-3303. https://doi.org/10.1002/joc.3915
Scafetta, N. (2023). CMIP6 GCM ensemble members versus global surface temperatures. Climate Dynamics, 60(9-10), 3091-3120. https://doi.org/10.1007/s00382-022-06493-w
Sediqi, M. N., Hendrawan, V. S. A., & Komori, D. (2022). Climate projections over different climatic regions of Afghanistan under shared socioeconomic scenarios. Theoretical and Applied Climatology, 149(1-2), 511-524. https://doi.org/10.1007/s00704-022-04063-y
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American statistical association, 63(324), 1379-1389. https://doi.org/10.1080/01621459.1968.10480934
Suryavanshi, S., Joshi, N., Maurya, H. K., Gupta, D., & Sharma, K. K. (2022). Understanding precipitation characteristics of Afghanistan at provincial scale. Theoretical and Applied Climatology, 150(3-4), 1775-1791. https://doi.org/10.1007/s00704-022-04257-4
Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B. (2012). Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314. https://doi.org/10.5194/hess-16-3309-2012
Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E., Österle, H., ... & Best, M. (2011). Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. Journal of Hydrometeorology, 12(5), 823-848. https://doi.org/10.1175/2011JHM1369.1
Worku, G., Teferi, E., Bantider, A., & Dile, Y. T. (2020). Statistical bias correction of regional climate model simulations for climate change projection in the Jemma sub-basin, upper Blue Nile Basin of Ethiopia. Theoretical and Applied Climatology, 139, 1569-1588. https://doi.org/10.1007/s00704-019-03053-x
Xue, D., Lu, J., Leung, L. R., Teng, H., Song, F., Zhou, T., & Zhang, Y. (2023). Robust projection of East Asian summer monsoon rainfall based on dynamical modes of variability. Nature Communications, 14(1), 3856. https://doi.org/10.1038/s41467-023-39460-y
Yan, Y., You, Q., Wu, F., Pepin, N., & Kang, S. (2020). Surface mean temperature from the observational stations and multiple reanalyses over the Tibetan Plateau. Climate Dynamics, 55, 2405-2419. https://doi.org/10.1007/s00382-020-05386-0
Zarrin, A., & Dadashi-Roudbari, A. (2021). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 144, 643-660. https://doi.org/10.1007/s00704-021-03568-2
Zarrin, A., Dadashi-Roudbari, A., & Hassani, S. (2021). Historical variability and future changes in seasonal extreme temperature over Iran. Theoretical and Applied Climatology, 146, 1227-1248. https://doi.org/10.1007/s00704-021-03795-7
Zhai, J., Mondal, S. K., Fischer, T., Wang, Y., Su, B., Huang, J., ... & Uddin, M. J. (2020). Future drought characteristics through a multi-model ensemble from CMIP6 over South Asia. Atmospheric Research, 246, 105111. https://doi.org/10.1016/j.atmosres.2020.105111
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