This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.

中文翻译：

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气候变化模型多模型集成的新方法：自然变率以及历史和未来气候表示的观点


本研究开发了一种将气候模式选择和多模式集合（MME）构建相结合的新方法，以有效地表示模式不确定性，从而提高不同情景下极端降雨变化评估的一致性。我们的重点是在两个全球气候模型 (GCM) 的推动下，结合 10 个区域气候模型 (RCM) 模拟，特别是在气候变化的情况下估算设计降雨量。我们假设 RCM 的极端降雨模拟中的自然变化和统计上较高的矩属性没有完全保留。因此，MME 方法在气候变化研究中可能更有效，这主要归功于多种气候模型的使用。首先，提出了一项实验研究来验证所提出的建模框架方法的有效性，该方法采用 L 矩来量化气候模型之间的相对重要性及其在 MME 构建中表示自然变化的用途。然后，将所提出的方法应用于历史（1981-2005）和未来（2006-2100）时期从汉江流域多个 RCM 收集的气候变化情景。结果表明，根据自然变率选择的气候模型表现出更好的性能，与汉江流域观测到的年最大降雨量（AMR）的分布几乎相同。所选情景的范围比所有情景的范围相对更窄，并且变化率与有限过零更加一致，分别反映了模型性能和一致性在历史和未来时期的改善。 RCP8下MME的变化率。5 近期（2011-2040）和远期（2071-2100）的增长率约为 20%，中期（2041-2070）的增长率略低于其他时期增幅约为10%。