两套经过偏差校正的 2018 年英国区域气候预测(UKCP18),包括英国的气温、降水和潜在蒸散量。

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Earth System Science Data Pub Date : 2024-05-16 DOI:10.5194/essd-2024-132
Nele Reyniers, Qianyu Zha, Nans Addor, Timothy J. Osborn, Nicole Forstenhäusler, Yi He
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引用次数: 0

摘要

摘要英国气候预测 2018(UKCP18)区域气候模式(RCM)12 公里区域扰动物理集合(UKCP18-RCM-PPE)是英国气象局制作的最新一套英国国家气候预测的三个部分之一。它已被广泛用于气候影响评估。在本研究中,我们报告了原始 UKCP18-RCM 模拟中存在的严重偏差,如果不对这些偏差进行调整,影响评估可能会恶化。我们使用了两种方法对 UKCP18-RCM 进行偏差校正:一种是使用经验量值的非参数量值映射,另一种是为部门间影响模式相互比较项目(ISIMIP)第三阶段开发的旨在保留气候变化信号的变体。具体而言,对 1981 年至 2080 年的日气温和降水模拟进行了调整。在同一时期,还使用彭曼-蒙蒂斯公式估算潜在蒸散量,然后使用后一种方法进行偏差校正。这两种方法都成功地纠正了一系列日气温、降水和潜在蒸散量指标的偏差,并在较小程度上减少了多日降水指标的偏差。对未来变化预测的探索性分析证实了冬季更潮湿、更温暖,夏季更炎热、更干燥的预期,并显示温度和降水分布的不同部分变化不均。这两种偏差校正方法几乎同样很好地保留了气候变化信号以及预测变化的分布。我们将变化因子法作为降水量的基准,结果表明该方法无法捕捉到一系列变量的变化,因此不适用于大多数影响评估。通过比较两种偏差校正方法之间以及 12 个集合成员内部的差异,我们表明,气候模式参数化带来的未来降水和气温变化的不确定性远远大于从这两种偏差校正方法中选择一种带来的不确定性。最后,我们将为偏差校正数据集的使用提供指导。使用 ISIMIP3BA 进行偏差校正的数据集可在以下资料库中公开获取:降水和温度资料库 https://doi.org/10.5281/zenodo.6337381(Reyniers 等,2022a)和潜在蒸散量资料库 https://doi.org/10.5281/zenodo.6320707(Reyniers 等,2022b)。使用量子绘图法进行偏差校正的数据集可在 https://doi.org/10.5281/zenodo.8223024 网站上查阅(Zha 等人,2023 年)。
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Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain
Abstract. The United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km regional perturbed physics ensemble (UKCP18-RCM-PPE) is one of the three strands of the latest set of UK national climate projections produced by the UK Met Office. It has been widely adopted in climate impact assessment. In this study, we report biases in the raw UKCP18-RCM simulations that are significant and are likely to deteriorate impact assessments if they are not adjusted. Two methods were used to bias-correct UKCP18-RCM: non-parametric quantile mapping using empirical quantiles and a variant developed for the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) designed to preserve the climate change signal. Specifically, daily temperature and precipitation simulations for 1981 to 2080 were adjusted for the 12 ensemble members. Potential evapotranspiration was also estimated over the same period using the Penman-Monteith formulation and then bias-corrected using the latter method. Both methods successfully corrected biases in a range of daily temperature, precipitation and potential evapotranspiration metrics, and reduced biases in multi-day precipitation metrics to a lesser degree. An exploratory analysis of the projected future changes confirms the expectation of wetter, warmer winters and hotter, drier summers, and shows uneven changes in different parts of the distributions of both temperature and precipitation. Both bias-correction methods preserved the climate change signal almost equally well, as well as the spread among the projected changes. The change factor method was used as a benchmark for precipitation, and we show that it fails to capture changes in a range of variables, making it inadequate for most impact assessments. By comparing the differences between the two bias-correction methods and within the 12 ensemble members, we show that the uncertainty in future precipitation and temperature changes stemming from the climate model parameterisation far outweighs the uncertainty introduced by selecting one of these two bias-correction methods. We conclude by providing guidance on the use of the bias-corrected data sets. The data sets bias adjusted with ISIMIP3BA are publicly available in the following repositories: https://doi.org/10.5281/zenodo.6337381 for precipitation and temperature (Reyniers et al., 2022a) and https://doi.org/10.5281/zenodo.6320707 for potential evapotranspiration (Reyniers et al., 2022b) . The datasets bias-corrected using the quantile mapping method are available at https://doi.org/10.5281/zenodo.8223024 (Zha et al., 2023) .
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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