Have precipitation extremes and annual totals been increasing in the world's dry regions over the last 60 years?
Sebastian Sippel, Jakob Zscheischler, Martin Heimann, Holger Lange, Miguel D. Mahecha, Geert Jan van Oldenborgh, Friederike E. L. Otto, Markus Reichstein
在过去几十年中，全球陆地大部分地区的日极端降雨量和年总降雨量都有所增加。这些观测结果与气候变暖的理论考虑是一致的。然而，直到最近，这些全球趋势还没有显示出对水分有限的陆地地区的持续影响。Donat et al.（2016，Nature气候变化，doi：10.1038/nclimate 2941）最近发表的一项研究发现，全球干旱地区日极端降雨量和年降雨总量迅速增加。在这里，我们重新评估了各自的分析，发现a）在数据标准化之前选择参考期所引入的统计伪影导致对报告趋势的高估高达40%，并且b）“全球干旱地区”的定义影响了报告的全球极端降雨汇总趋势。使用相同的观测数据集，但考虑到统计人为因素，并使用替代的、公认的干燥定义，我们发现世界干旱地区的强降水没有显著增加。适当的数据预处理方法和考虑到干旱定义的不确定性，对于量化世界干旱地区的空间汇总趋势至关重要。鉴于这一问题与许多可能受影响的利害关系方高度相关，我们呼吁谨慎考虑具体的数据处理方法，包括与干旱地区定义有关的问题，以保证所通报的气候变化相关调查结果的可靠性。
英文摘要：
Daily rainfall extremes and annual totals have increased in large parts of the global land area over the last decades. These observations are consistent with theoretical considerations of a warming climate. However, until recently these global tendencies have not been shown to consistently affect land regions with limited moisture availability. A recent study, published by Donat et al. (2016, Nature Climate Change, doi:10.1038/nclimate2941), now identified rapid increases in globally aggregated dry region daily extreme rainfall and annual rainfall totals. Here, we reassess the respective analysis and find that a) statistical artifacts introduced by the choice of the reference period prior to data standardization lead to an overestimation of the reported trends by up to 40%, and also that b) the definition of `dry regions of the globe' affect the reported globally aggregated trends in extreme rainfall. Using the same observational dataset, but accounting for the statistical artifacts and using alternative, well-established dryness definitions, we find no significant increases in heavy precipitation in the world's dry regions. Adequate data pre-processing approaches and accounting for uncertainties regarding the definition of dryness are crucial to the quantification of spatially aggregated trends in the world's dry regions. In view of the high relevance of the question to many potentially affected stakeholders, we call for a cautionary consideration of specific data processing methods, including issues related to the definition of dry areas, to guarantee robustness of communicated climate change relevant findings.
Downscaling with AI reveals the large role of internal variability in fine-scale projections of climate extremes
National Institute of Water and Atmospheric Research (NIWA)（国家水气和大气研究所）；ARC Centre of Excellence for Weather of the 21st Century（21世纪天气卓越研究中心）；University of New South Wales（新南威尔士大学）；Climate Change Research Centre, University of New South Wales（新南威尔士大学气候变化研究中心）；Te Aka Mātuatua School of Science, University of Waikato（怀卡托大学理学院）
Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models
Western Washington University（西华盛顿大学）；Utah State University（犹他州立大学）；Joint Global Change Research Institute（全球变化联合研究所）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；AI and Data Analytics Division, Pacific Northwest National Laboratory（太平洋西北国家实验室数据分析与人工智能分部）
El Ciclo de las Manchas Solares y la Precipitación en la Región del Eje Cafetero - Colombia
为了探讨太阳活动周期对区域气候的可能影响，本文通过相关分析，建立了太阳黑子指数与年降水量的反相关关系：Cenicafé（1942-2014），Naranjal（1951-2014），La Bella（1951-2014），Miguel Valencia（1953-2014），located in the Region of the Eje Cafefeet.这种关系表明，前三个站的太阳黑子指数序列延迟一年和两年时，前者较高，后者较高，具有很高的统计意义。对塞尼卡菲、纳兰哈尔和拉贝拉台站系列计算的自相关和半变异函数直到24个延迟，清楚地表明前两个台站的周期为10年或11年。对于La Bella站，延迟6和延迟16中的周期具有明显的反比关系。Cenicafé、Naranjal和La Bella三个站降水的快速傅立叶变换频谱显示出相互矛盾的结果，Cenicafé的频谱仅表示11年的周期，Naranjal的频谱倾向于11年的周期，La Bella的频谱显示出11年和22年周期的强烈存在。一个可能的解释，这种明显的矛盾是暴露。最后，为了证明区域降水与太阳活动11年周期的耦合，对太阳黑子周期最小值的重合年的平均降水量与该周期峰值的重合年的平均降水量进行了统计比较。这一比较表明，塞尼卡菲、纳兰哈尔和拉贝拉台站的系列具有很高的统计意义。论证太阳黑子周期对区域降水的影响。这一结果对区域氢气可利用率的…
DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models
Utah State University（犹他州立大学）；Joint Global Change Research Institute（全球变化联合研究所）；Pacific Northwest National Laboratory（西北太平洋国家实验室）；Western Washington University（西华盛顿大学）；Indiana University（印第安纳大学）
Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks
Western Washington University（西华盛顿大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；Indiana University（印第安纳大学）；Joint Global Change Research Institute（联合全球变化研究所）；Computer Science Department, Western Washington University（西华盛顿大学计算机科学系）；Computing & Analytics Division, Pacific Northwest National Laboratory（太平洋西北国家实验室计算与分析部）；Earth and Atmospheric Sciences Department, Indiana University（印第安纳大学地球与大气科学系）
