Tesfaye Endalkachew, Abate Brook, Alemayehu Taye, Dile Yihun
Ethiopian Institute of Water Resources, Addis Ababa University, Addis Ababa, Ethiopia.
College of Architecture & Civil Engineering, Addis Ababa Science & Technology University, Addis Ababa, Ethiopia.
Heliyon. 2023 Sep 20;9(10):e20320. doi: 10.1016/j.heliyon.2023.e20320. eCollection 2023 Oct.
This study evaluates the skills of 30 CMIP5 GCMs and the Multimodel Ensemble (MME) in reproducing the characteristics of observed precipitation (Pr), minimum (T), and maximum (T) temperature over the Middle Awash sub-basin (MASB) in Ethiopia. The MME of the climate variables was generated using the simple arithmetic mean method. The entire analysis was performed on the raw historical GCM simulations (before bias correction) and observed data for the periods 1981-2005 based on monthly and annual time series data over the annual and seasonal temporal resolutions. This study considered two approaches. The first one was an evaluation of GCMs employing five statistical performance metrics (SPMs), i.e., mean, CV, PBIAS, RSR, and r. The second approach involves the application of multicriteria decision-making (MCDM) analysis, adopting three SPMs (PBIAS, RSR, and r). The relative weights of the three metrics were determined by the entropy method. Besides, the weighted average and compromise programming techniques were employed to rank and select the best-performing GCMs. The findings from the first approach using five SPMs demonstrate that, for a given variable of interest, a GCM that performs well for one SPM may fail to produce the same for another SPM on the same temporal scale. Likewise, for the same SPM at different resolutions, a GCM may perform well for a one-time scale but poorly for another. These suggested that the results of GCM skills relied mainly on the SPM, time scale, and data formats chosen for analysis. Hence, it is critical to comprehensively evaluate the skill of GCMs using multiple performance metrics over a range of spatial and temporal settings and data formats. In addition, results of the MCDM analysis proved that the ensemble of GCMs, which provide adequate performance in simulating the salient features of Pr, T, and T concomitantly across the MASB, encompass CMCC-CMS, BCC-CSM1.1(m), CMCC-CM, BNU-ESM, CanESM2, and MPI-ESM-MR. However, it was observed that different GCMs performed much differently in characterizing various variables over a range of temporal scales and data formats. The MME also proved its superior potential in duplicating the climate of the study area over several individual GCMs. The overall findings attested that instead of aggregating the ranks from the three variables into one, it is recommended to treat each variable independently while developing a subset of best-performing GCMs for ensembling since each GCM responds differently to each variable under a set of conditions. Finally, the approaches and findings from this study will be valuable input for subsequent climate and hydrologic studies in the study area and beyond.
本研究评估了30个耦合模式比较计划第五阶段（CMIP5）全球气候模式（GCMs）和多模式集合（MME）在再现埃塞俄比亚中阿瓦什次流域（MASB）观测降水（Pr）、最低温度（T）和最高温度（T）特征方面的技能。气候变量的多模式集合是使用简单算术平均法生成的。整个分析基于1981 - 2005年期间的原始历史GCM模拟（偏差校正前）以及年度和季节时间分辨率下的月度和年度时间序列数据的观测数据进行。本研究考虑了两种方法。第一种方法是使用五个统计性能指标（SPMs），即均值、变异系数（CV）、偏差百分比（PBIAS）、相对平方误差（RSR）和相关系数（r）来评估GCMs。第二种方法涉及多准则决策分析（MCDM）的应用，采用三个性能指标（PBIAS、RSR和r）。这三个指标的相对权重通过熵方法确定。此外，采用加权平均和折衷规划技术对表现最佳的GCMs进行排名和选择。使用五个性能指标的第一种方法的结果表明，对于给定的感兴趣变量，在同一时间尺度上，一个在一个性能指标上表现良好的GCM在另一个性能指标上可能表现不佳。同样，对于不同分辨率下的相同性能指标，一个GCM在一个时间尺度上可能表现良好，而在另一个时间尺度上可能表现不佳。这些结果表明，GCM技能的结果主要依赖于所选的性能指标、时间尺度和分析数据格式。因此，在一系列空间和时间设置以及数据格式上使用多个性能指标全面评估GCMs的技能至关重要。此外，多准则决策分析的结果证明，在模拟整个MASB的Pr、T和T的显著特征方面表现良好的GCMs集合包括CMCC - CMS、BCC - CSM1.1（m）、CMCC - CM、BNU - ESM、CanESM2和MPI - ESM - MR。然而，观察到不同的GCMs在一系列时间尺度和数据格式上表征各种变量时表现差异很大。多模式集合也证明了其在复制研究区域气候方面优于几个单独的GCMs的潜力。总体研究结果证明，在开发最佳表现GCMs子集进行集合时，建议不要将三个变量的排名汇总为一个，而是对每个变量独立处理，因为在一组条件下每个GCM对每个变量的响应不同。最后，本研究的方法和结果将为研究区域及其他地区后续的气候和水文研究提供有价值的输入。
