The Key Role of Temporal Stratification for GCM Bias Correction in Climate Impact Assessments

IF 7.3 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Earths Future Pub Date : 2024-08-19 DOI:10.1029/2023EF004242
Nicolás A. Vásquez, Pablo A. Mendoza, Wouter J. M. Knoben, Louise Arnal, Miguel Lagos-Zúñiga, Martyn Clark, Ximena Vargas
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Abstract

Characterizing climate change impacts on water resources typically relies on Global Climate Model (GCM) outputs that are bias-corrected using observational data sets. In this process, two pivotal decisions are (a) the Bias Correction Method (BCM) and (b) how to handle the historically observed time series, which can be used as a continuous whole (i.e., without dividing it into sub-periods), or partitioned into monthly, seasonal (e.g., 3 months), or any other temporal stratification (TS). Here, we examine how the interplay between the choice of BCM, TS, and the raw GCM seasonality may affect historical portrayals and projected changes. To this end, we use outputs from 29 GCMs belonging to the CMIP6 under the Shared Socioeconomic Pathway 5–8.5 scenario, using seven BCMs and three TSs (entire period, seasonal, and monthly). The results show that the effectiveness of BCMs in removing biases can vary depending on the TS and climate indices analyzed. Further, the choice of BCM and TS may yield different projected change signals and seasonality (especially for precipitation), even for climate models with low bias and a reasonable representation of precipitation seasonality during a reference period. Because some BCMs may be computationally expensive, we recommend using the linear scaling method as a diagnostics tool to assess how the choice of TS may affect the projected precipitation seasonality of a specific GCM. More generally, the results presented here unveil trade-offs in how BCMs are applied, regardless of the climate regime, urging the hydroclimate community to carefully implement these techniques.

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气候影响评估中时间分层对 GCM 偏差校正的关键作用
确定气候变化对水资源的影响通常依赖于利用观测数据集进行偏差校正的全球气候模式(GCM)输出结果。在此过程中,有两个关键决定:(a) 偏差校正方法 (BCM);(b) 如何处理历史上观测到的时间序列,可以将其作为一个连续的整体(即不划分为子时期),或划分为月度、季节(如 3 个月)或任何其他时间分层 (TS)。在此,我们将研究 BCM、TS 和原始 GCM 季节性之间的相互作用如何影响历史描述和预测变化。为此,我们使用了属于 CMIP6 的 29 个 GCM 在共享社会经济路径 5-8.5 情景下的输出结果,并使用了 7 种 BCM 和 3 种 TS(全周期、季节和月度)。结果表明,根据所分析的 TS 和气候指数的不同,BCM 在消除偏差方面的效果也会不同。此外,即使对于偏差较小且合理反映了参考时段降水季节性的气候模式,选择 BCM 和 TS 也可能产生不同的预测变化信号和季节性(尤其是降水)。由于某些 BCM 的计算成本可能很高,我们建议使用线性缩放方法作为诊断工具,以评估 TS 的选择如何影响特定 GCM 的降水季节性预测。总体而言,本文介绍的结果揭示了在应用生物累积模型时的权衡取舍,无论气候制度如何,敦促水文气候界谨慎应用这些技术。
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来源期刊
Earths Future
Earths Future ENVIRONMENTAL SCIENCESGEOSCIENCES, MULTIDI-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
11.00
自引率
7.30%
发文量
260
审稿时长
16 weeks
期刊介绍: Earth’s Future: A transdisciplinary open access journal, Earth’s Future focuses on the state of the Earth and the prediction of the planet’s future. By publishing peer-reviewed articles as well as editorials, essays, reviews, and commentaries, this journal will be the preeminent scholarly resource on the Anthropocene. It will also help assess the risks and opportunities associated with environmental changes and challenges.
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