The intrinsic difficulties in building realistic climate models and in providing complete, reliable and meaningful observational datasets, and the conceptual impossibility of testing theories against data imply that the usual Galilean scientific validation criteria do not apply to climate science. The different epistemology pertaining to climate science implies that its answers cannot be singular and deterministic; they must be plural and stated in probabilistic terms. Therefore, in order to extract meaningful estimates of future climate change from a model, it is necessary to explore the model' uncertainties. In terms of societal impacts of scientific knowledge, it is necessary to accept that any political choice in a matter involving complex systems is made under unavoidable conditions of uncertainty. Nevertheless, detailed probabilistic results in science can provide a baseline for a sensible process of decision making.
Quantum Artificial Intelligence for the Science of Climate Change
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Tropical Meteorology, Ministry of Earth Sciences（印度热带气象研究所）；Krea University（克雷亚大学）；University of Petroleum and Energy Studies（石油与能源研究大学）；Queen Mary University of London（伦敦玛丽女王大学）
A Self-Evolving AI Agent System for Climate Science
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Seoul National University（首尔大学）；Ocean University of China（中国海洋大学）；Laoshan Laboratory（崂山实验室）；Columbia University（哥伦比亚大学）；Japan Agency for Marine-Earth Science and Technology（日本海洋研究开发机构）；The Chinese University of Hong Kong（香港中文大学）；East China Normal University（华东师范大学）；Chinese Academy of Meteorological Sciences（中国气象科学研究院）；Tsinghua University（清华大学）
Have precipitation extremes and annual totals been increasing in the world's dry regions over the last 60 years?
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。
