通过 CORDEX 非洲模型对瓦比谢贝莱盆地的温度和降水量进行历史模拟

Sisay Guta Alemu, C. H. Sime, T. A. Demissie
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引用次数: 0

摘要

如果不对全球气温上升和降水模式变化进行适当的研究和预测,将会产生重大的社会经济后果。区域气候模式(RCMs)被用来评估地方尺度的气候变化。然而,由于固有的局限性和方法上的限制,必须对单个模型的可靠性进行验证。本研究利用 1986 年至 2005 年的观测降雨量和气温数据,对 CORDEX 非洲区域气候模式的性能进行了评估。使用偏差、RMSE、r、MAE 等统计指标以及统计指标的简明图(即泰勒图)对模型性能进行了评估。在降雨模拟中,RACMO22T 在流域上部(降雨量大、气温低的地区)和流域下部(降雨量小、气温高的地区)表现出色,偏差分别为-8.64%和 6.19%。HIRHAM5 和 CCLM4-8 较好地模拟了流域上游地区的最高气温,偏差分别为(0.14 ℃ 和 -0.14 ℃),而 RCA4 则较好地模拟了流域下游地区的最高气温。CCLM4-8 对流域上游地区的最低气温模拟较好,但 HIRHAM5 和 RCA4 对流域下游地区的最低气温模拟较好。在降雨模拟方面,所有模式在干旱月份的模拟结果都略好于潮湿月份。与观测结果相比,所有模式都低估了研究区域的最高气温,高估了最低气温。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Historical simulations of temperature and precipitation from the CORDEX Africa model in the Wabi Shebele Basin
Rising global temperatures and shifting precipitation patterns have significant socio-economic consequences if not properly studied and predicted. Regional climate models (RCMs) are utilized to assess local-scale climate change. However, the reliability of individual models must be validated due to inherent limitations and methodological constraints. This study evaluates the performance of CORDEX Africa RCMs using observed rainfall and air temperature data from 1986 to 2005. Model performance was evaluated using statistical indicators such as bias, RMSE, r, MAE, and a concise plot of the statistical indicators which is Taylor’s diagram. In rainfall simulation, the RACMO22T performed admirably in the upper parts of the basin (region of high rainfall and cold temperature) and lower regions of the basin (region of low rainfall and hot temperature) with bias −8.64% and 6.19% respectively. HIRHAM5 and CCLM4-8 simulate well the maximum temperature in the upper parts with biases of (0.14 °C and −0.14 °C respectively), whereas RCA4 is well performed in the lower parts of the basin. CCLM4-8 is good for minimum temperature simulation in the upper parts, but HIRHAM5 and RCA4 are good in the lower parts of the basin. In rainfall simulation, all models are slightly good in dry months than in wet. All models underestimated the maximum temperature and overestimated the minimum temperature in the study area as compared to the observed.
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