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利用基于 TOPSIS 方法的 CMIP6 模型对伊朗锡斯坦和俾路支省未来降水和近地表气温的预测:案例研究。

Future precipitation and near surface air-temperature projection using CMIP6 models based on TOPSIS method: case study, Sistan-and-Baluchestan Province of Iran.

机构信息

Iranian National Institute for Oceanography and Atmospheric Science, Research Center of Atmospheric Sciences, Tehran, Iran.

出版信息

Environ Monit Assess. 2023 Nov 29;195(12):1548. doi: 10.1007/s10661-023-12084-x.

Abstract

Based on surface air temperature and precipitation, the current study examines the climate fluctuations over Sistan-and-Baluchestan Province, Iran's second-largest province. This area suffers from insufficient direct observations and a lack of climatic investigation. Three datasets were utilized including in situ data, gridded data (1984-2013), outputs of historical runs (during 1984-2013), and projections under the SSP5-8.5, SSP3-7.0, and SSP1-2.6 scenarios (in 2020-2049) of twenty-six Global Climate Models (GCMs) from the latest Coupled Model Intercomparison Project (CMIP6). The models' performance has been evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in Multi-Criteria Decision Making (MCDM) technique including eight metrics in both seasonal and annual scales. The surface air temperature showed an increasing trend in seasonal and annual scales during 1984-2013, while the monthly precipitation trend increased for September-October-November and decreased for the other seasons and annual scale during 1984-2013. The top-ranked models for simulating surface air temperature (precipitation) were CESM2 (GFDL-ESM4), IPSL-CM6A-LR (UKESM1-0-LL), ACCESS-CM2 (GFDL-ESM4), and MIROC-ES2L (MPI-ESM1-2-LR) models in DJF, MAM, JJA, and SON seasons, respectively, while ACCESS-CM2 (CNRM-CM6-1-HR) model outperformed others in annual scale. Bias-corrected outputs of the top-ranked CMIP6 GCMs showed an increasing trend for surface air temperature in all seasons (from a 0.7 K increase in December-January-February season under SSP3-7.0 scenario to a 2.5 K increase in June-July-August season under SSP5-8.5 scenario) for period 2020-2049, comparing with that in 1984-2013 period. Bias-corrected monthly precipitation projected by top-ranked CMIP6 GCMs indicated both increasing and decreasing trends depending on selected season and scenario. This varied from a 5 mm/month decrease within December-January-February season under SSP5-8.5 scenario to a 13 mm/month increase during the March-April-May season under SSP1-2.6 scenario in 2020-2050, comparing with that from previous years.

摘要

基于地面气温和降水资料,本研究考察了伊朗第二大省锡斯坦和俾路支省的气候波动情况。该地区的直接观测资料和气候研究都相对匮乏。本研究使用了三种数据集,包括实地观测数据、网格化数据(1984-2013 年)、历史时期(1984-2013 年)输出数据以及 26 个全球气候模式(GCM)在 SSP5-8.5、SSP3-7.0 和 SSP1-2.6 情景下(2020-2049 年)的预估数据,这些数据均来自最新的耦合模式比较计划(CMIP6)。使用多准则决策(MCDM)技术中的逼近理想解排序方法(TOPSIS)对模型的性能进行了评估和排名,其中包括在季节性和年度尺度上使用的八项指标。结果表明,1984-2013 年期间,地面气温在季节性和年度尺度上呈上升趋势,而月降水趋势在 9-11 月期间呈上升趋势,在其他季节和年度尺度上呈下降趋势。在 DJF、MAM、JJA 和 SON 季节中,表现最好的模拟地面气温(降水)的模型分别为 CESM2(GFDL-ESM4)、IPSL-CM6A-LR(UKESM1-0-LL)、ACCESS-CM2(GFDL-ESM4)和 MIROC-ES2L(MPI-ESM1-2-LR)模型,而在年度尺度上,ACCESS-CM2(CNRM-CM6-1-HR)模型的表现优于其他模型。在 2020-2049 年期间,表现最好的 CMIP6 GCMs 的经过偏差校正后的输出结果表明,与 1984-2013 年相比,所有季节的地面气温都呈上升趋势(从 SSP3-7.0 情景下 12 月-1 月-2 月季节的 0.7 K 上升到 SSP5-8.5 情景下 6 月-7 月-8 月季节的 2.5 K 上升)。经过偏差校正后的月度降水预测结果表明,CMIP6 GCMs 的表现各不相同,这取决于所选季节和情景。在 2020-2050 年期间,与前几年相比,SSP5-8.5 情景下 12 月-1 月-2 月季节的降水减少了 5 毫米/月,而 SSP1-2.6 情景下 3 月-4 月-5 月季节的降水增加了 13 毫米/月。

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