Bias-adjusting for underestimated large-scale European warming in regional climate model simulations and implications for future extremes

crossref(2024)

引用 0|浏览4
暂无评分
摘要
Our warming climate enhances both the frequency and intensity of weather extremes. To enable adequate mitigation and adaptation decision-making and planning,  accurate long-term climate projections across scales are essential. Europe has warmed faster than any other World Meteorological Organization region in the last decades, yet a vast majority of RCM simulations does not capture the strong observed temperature rise. This discrepancy is in part related to the widespread use of constant aerosol representations in RCMs, and emerges most clearly during summer, i.e. the period of strong insolation. Thereby it also affects (changes in) heat extremes even more strongly than the mean warming. This warming mismatch is, crucially, not restricted to the past but also affects climate projections. Several European national climate services of several European countries still rely on these simulations and solutions are required. Here, we present a novel method to adjust the large-scale warming in RCM simulations based on a reference such as observations or other model simulations. In particular, we re-assemble RCM simulations to match the long-term annual mean temperature evolution over Western Europe in state-of-the-art GCMs, which show less (or no) underestimation of the observed summer warming than the RCMs. We demonstrate that our approach preserves the high-resolution information provided by the RCMs, but ensures consistency with respect to both historic as well as projected large-scale warming. It employs existing regional climate information without the use of interpolation methods, and, as the re-assembling is performed based solely on yearly average temperatures, ensures consistency among different climate variables. We show how correcting European warming affects projections of both the mean state as well as weather extremes at the national level, and illustrate results for Switzerland, a small country characterized by complex orography. 
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要