来自HAPPI区域气候集合模拟的全球变暖1.5°C和2.0°C下欧洲的极端天气

K. Sieck, C. Nam, L. Bouwer, D. Rechid, D. Jacob
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引用次数: 3

摘要

摘要本文提出了一套新的欧洲区域气候模式模拟数据集,它大大提高了我们在全球变暖低和中等水平下检测极端天气变化的能力。该数据集提供了一个独特的和物理上一致的数据集,因为它来自一个大的区域气候模式模拟集合。这些模拟是由国际HAPPI联盟的两个全球气候模式驱动的。该集合分别由100 × 10年模拟和25 × 10年模拟组成。与以前的气候模拟相比,这些大型集合可以用改进的信噪比来研究区域气候变化和极端天气。量化了全球变暖1.5°C和2.0°C温度目标的4个气候指标的变化:年平均近地表视温> 28°C的日数(ATG28);年最大5天降水量(RX5day);50年回归期日降水强度(RI50yr);以及每年的连续干旱天数。这项工作表明,即使对预估全球平均温度中的一个小信号,极端温度和降水指数的变化也可以得到稳健的估计。对于与温度有关的指数,百分位数的变化也可以高置信度地估计出来。这些数据可以构成量身定制的气候信息的基础,有助于在与政策相关的尺度上采取适应性措施,指出0.5°C的低水平全球变暖的潜在影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weather extremes over Europe under 1.5 °C and 2.0 °C global warming from HAPPI regional climate ensemble simulations
Abstract. This paper presents a novel data set of regional climate model simulations over Europe that significantly improves our ability to detect changes in weather extremes under low and moderate levels of global warming. The data set provides a unique and physically consistent data set, as it is derived from a large ensemble of regional climate model simulations. These simulations were driven by two global climate models from the international HAPPI consortium. The set consists of 100 × 10-year simulations and 25 × 10-year simulations, respectively. These large ensembles allow for regional climate change and weather extremes to be investigated with an improved signal-to-noise ratio compared to previous climate simulations. The changes in four climate indices for temperature targets of 1.5 °C and 2.0 °C global warming are quantified: number of days per year with daily mean near-surface apparent temperature of > 28 °C (ATG28); the yearly maximum 5-day sum of precipitation (RX5day); the daily precipitation intensity of the 50-yr return period (RI50yr); and the annual Consecutive Dry Days (CDD). This work shows that even for a small signal in projected global mean temperature, changes of extreme temperature and precipitation indices can be robustly estimated. For temperature related indices changes in percentiles can also be estimated with high confidence. Such data can form the basis for tailor-made climate information that can aid adaptive measures at a policy-relevant scales, indicating potential impacts at low levels of global warming at steps of 0.5 °C.
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