Collaborative Research: Arctic extreme temperature and precipitation - Detection and projection of their climatic change and physical causes

北极的极端气温和降水量变化可能特别大，预计北极将出现一些最大的人为变暖。 在过去和未来情景的观测和模拟中，极端温度和降水的物理原因的一致性可以表明预测变化的稳健性。通过几个模型对极端情况进行一致的模拟，可以提供更多的极端事件样本，从而更有信心地评估极端事件不断变化的风险。 PI将利用CORDEX（区域）和CMIP5（全球）气候模拟程序的观测记录和输出，调查北极极端温度和降水事件的可能变化。 该项目旨在通过一个侧重于极端温度或降水事件以及支持它们的物理过程的分析框架来检测、归因和理解极端温度或降水的变化。 PI将使用模式识别工具自组织地图（SOM）评估极端降水过程中观察到的和模拟的变化，该工具允许构建极端事件集合的一致性，多变量视图。 所开发的方法也将应用于地球其他区域的极端现象研究，扩大评估极端现象变化及其基本物理原因的能力。 该项目将培训新一代科学家，使他们能够对极端情况的变化及其预测的不确定性进行综合分析。

Changes in temperature and precipitation extremes could be especially large in the Arctic, which is projected to have some of the greatest anthropogenic warming. Consistency of physical causes of extreme temperature and precipitation in observations and in simulations of past and future scenarios can indicate the robustness of projected changes. Consistent simulations of extremes by several models can provide much greater sampling of extreme events, leading to more confident assessment of the changing risk of extreme events. The PI's will investigate possible changes in extreme temperature and precipitation events in the Arctic using observational records and output from the CORDEX (regional) and CMIP5 (global) climate modeling programs. The project aims to detect, attribute, and understand changes in extreme temperature or precipitation through an analytical framework that focuses on both the extreme temperature or precipitation events and the physical processes which support them. The PI's will assess observed and simulated changes in extreme precipitation processes using a pattern-recognition tool, Self-Organizing Maps (SOMs), that allows construction of a coherent, multivariate view of collections of extreme events. The methods developed will also have application to studies of extremes in other regions of the planet, extending capacity for assessing changes in extremes and their underlying physical causes. The project will train a new generation of scientists equipped to perform ensemble analysis of the changes in extremes and the uncertainties in their projection.