Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors
Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors
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DOI：
10.1007/s00382-013-1751-7
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发表时间：
2014-03-01
期刊：
影响因子：
4.6
通讯作者：
Favre, Alice
中科院分区：
文献类型：
--
作者：
Kim, J.;Waliser, Duane E.;Favre, Alice
评估 CORDEX-非洲区域气候模型 (RCM) 后报实验的月平均降水量、平均 (T-AVG)、最高 (T-MAX) 和最低 (T-MIN) 表面气温以及云量，以了解模型技能和系统偏差。所有 RCM 都合理地模拟了这些变量的基本气候特征，但这些模型也存在系统偏差。所有 RCM 在模拟非洲西部降水方面均表现出比东部地区更高的保真度，对热带地区降水的模拟比撒哈拉北部地区的保真度更高。萨赫勒西部地区雨季降雨量的年际变化比埃塞俄比亚高地的模拟效果更好。 T-AVG 和 T-MAX 的 RCM 技能高于 T-MIN，并且从区域来看，亚热带地区的 RCM 技能高于热带地区。 RCM 模拟云量的技能通常低于模拟降水或温度的技能。对于所有变量，多模型集成 (ENS) 通常优于 ENS 中包含的单个模型。本研究的一个总体结论是，一些模型偏差因区域、变量和指标而系统性地变化，这给定义单个代表性指标来衡量模型保真度带来了困难，尤其是构建 ENS 时。这是气候变化影响评估研究中的一个重要问题，因为大多数评估模型都是针对特定区域/部门运行的，并从模型输出中得出强制数据。因此，模型评估和 ENS 构建必须根据具体分析和/或评估的要求分别针对区域、变量和指标进行。使用多个参考数据集的评估表明，参考数据的交叉检验、质量控制和不确定性估计在模型评估中至关重要。
Monthly-mean precipitation, mean (T-AVG), maximum (T-MAX) and minimum (T-MIN) surface air temperatures, and cloudiness from the CORDEX-Africa regional climate model (RCM) hindcast experiment are evaluated for model skill and systematic biases. All RCMs simulate basic climatological features of these variables reasonably, but systematic biases also occur across these models. All RCMs show higher fidelity in simulating precipitation for the west part of Africa than for the east part, and for the tropics than for northern Sahara. Interannual variation in the wet season rainfall is better simulated for the western Sahel than for the Ethiopian Highlands. RCM skill is higher for T-AVG and T-MAX than for T-MIN, and regionally, for the subtropics than for the tropics. RCM skill in simulating cloudiness is generally lower than for precipitation or temperatures. For all variables, multi-model ensemble (ENS) generally outperforms individual models included in ENS. An overarching conclusion in this study is that some model biases vary systematically for regions, variables, and metrics, posing difficulties in defining a single representative index to measure model fidelity, especially for constructing ENS. This is an important concern in climate change impact assessment studies because most assessment models are run for specific regions/sectors with forcing data derived from model outputs. Thus, model evaluation and ENS construction must be performed separately for regions, variables, and metrics as required by specific analysis and/or assessments. Evaluations using multiple reference datasets reveal that cross-examination, quality control, and uncertainty estimates of reference data are crucial in model evaluations.