نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناس ارشد اقلیم شناسی، دانشگاه فردوسی مشهد
2 دانشیار اقلیم شناسی، دانشگاه فردوسی مشهد
3 استادیار اقلیم شناسی، دانشگاه فردوسی مشهد
4 پژوهشگر پسادکتری اقلیم شناسی، دانشگاه فردوسی مشهد
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
In recent years, the importance of climate prediction has increased as a scientific source for understanding climate change and evaluating its consequences in political and economic decisions. Providing predictions with less uncertainty, especially for precipitation and temperature is of considerable importance for policymakers in time periods from several months to several decades. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP consists of three components (A, B, and C). Component A comprises the production and analysis of an extensive archive of retrospective forecasts. Component B undertakes ongoing production, analysis, and dissemination of experimental quasi-real-time multi-model forecasts, and Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (Boer et al. 2016). The aim of this study is to predict precipitation extremes using the decadal Climate Prediction Project contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 2021 to 2028 over Iran. For this purpose, two types of data including 77 synoptic stations and three DCPP models (BCC-CSM2-MR, MPI-ESM1-2-HR, and MRI-ESM2-0) with a horizontal resolution of 100 km were used. The precipitation output of DCPP models, each with nine variants (27 members) were used for two time periods, including Hindcast (1981-2018) and Forecast (2021-2028). To evaluate DCPP models, we used the Root Mean squared error (RMSE), the Pearson correlation coefficient (PCC), the Mean Bias Error (MBE), the Percent bias (PBIAS), and the Taylor diagram methods. In addition, Direct Model Output (DMO) was corrected by the Delta Change Factor (DCF) method, and the Independent Weighted Mean (IWM) was used to generate a multi-model ensemble from 27 members. In this study, the ETCCDI indices including days with Heavy precipitation (R10mm), days with Very heavy precipitation days (R20mm), Simple daily intensity (SDII), The maximum 1-day precipitation amounts (Rx1day), The maximum 3-day precipitation amounts (Rx3day), The maximum 5-day precipitation amounts (Rx5day) were calculated to analyze precipitation extremes for the regions of Iran. Furthermore, the evaluation of the DCPP models showed that the output of mentioned models is acceptable for the regions of Iran. Also, the performance of CMIP6-DCPP-MME is higher than the individual models. The result of the prediction of precipitation extremes showed that the six studied extreme precipitation indices will increase for the next decade. The Southwest and Northeast are the two hotspots of positive anomaly. In contrast, the southern coast of the Caspian Sea for the R10mm index will experience a negative anomaly for the next decade. The findings show that the southeastern region of Iran, from the eastern borders to the north of the Strait of Hormuz, will be the main area of negative precipition anomalies in the country in the next decade. So that the indices of days with heavy (R10mm) and very heavy (R20mm) precipition will decrease by 2.7 and 0.3 days, and daily precipition intensity (SDII) will decrease by 2.6 mm/day.
کلیدواژه‌ها [English]
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