Understanding Dynamical Linkages between the Arctic and Midlatitudes

了解北极和中纬度地区之间的动态联系

• 批准号: RGPIN-2019-05406
• 负责人: Kushner, Paul
• 金额: $ 4.44万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738254
• 关键词: Understanding; Dynamical; Linkages; between; Arctic

Over thirty years ago some of the earliest computer models of climate change successfully predicted that warming in the Arctic caused by greenhouse gas pollution would rapidly outpace warming in the rest of the world. This "Arctic amplification" (AA) of global warming is linked to melting of sea-ice, land-ice, snow, and permafrost. AA profoundly affects ecosystems, economic systems, and populations of Canada and other Northern nations. Despite this successful prediction, our understanding of the drivers, characteristics, rate, and implications of AA is unsettled. This limits our confidence in predicting future Arctic change, its causes, and its consequences. Initially, AA was thought to be caused by a positive feedback in which melting, brightly reflective sea ice and snow uncovers the darker land and sea surface, which can absorb more sunlight and drive more warming. But recent research on AA, including contributions from my research group, suggests that we need to consider a broader view on the two-way atmospheric linkages between the Arctic and the rest of the globe. In particular, we need to focus on the long-range transport of heat and moisture in the atmosphere, as well as long-range atmospheric teleconnections. We have also learned that the influence of Arctic change extends well outside the Arctic, which has practical implications for climate prediction. For example, it seems that current models are too insensitive to the influence of sea ice, snow, and the Arctic stratosphere, which limits predictability of climate on seasonal to multi-year timescales. To advance our understanding of AA and its implications, my atmospheric physics research group will analyze the latest successors to the earliest climate models: realistic Earth System Models (ESMs) that include atmosphere, land, sea ice, snow, and ocean processes and all their interactions. We will proceed on two tracks. For decadal and longer timescales, we will artificially trigger AA in ESMs by perturbing the Arctic, midlatitudes, and tropics, and from the surface to the stratosphere. Our physical analysis of these controlled experiments will help us untangle the many processes at play in linkages between the Arctic and more southern regions. To help improve climate prediction, we will design model experiments to tune the coupling between the surface, stratosphere, and the rest of the atmosphere, to help understand the simulation problems of current models. This approach promises to improve our understanding of physical drivers of predictability and potentially improve forecasts. These projects follow from my leadership of the Canadian Sea Ice and Snow Evolution Network (CanSISE, 2013-2019). The current proposal builds on observational, ESM, and forecast system work that took place within CanSISE. As we did for CanSISE, I will emphasize the education and professional promotion of the research personnel (undergraduate, graduate, and postdoctoral) supported by the requested funds.

三十多年前，一些最早的气候变化计算机模型成功预测，由温室气体污染引起的北极变暖将迅速超过世界其他地区的变暖。全球变暖的这种“北极放大”（AA）与海冰、陆地冰、雪和永久冻土的融化有关。 AA 深刻影响着加拿大和其他北方国家的生态系统、经济系统和人口。尽管预测成功，但我们对 AA 的驱动因素、特征、速率和影响的理解尚未确定。这限制了我们预测未来北极变化、其原因及其后果的信心。最初，AA被认为是由正反馈引起的，其中融化的、反光的海冰和雪暴露了较暗的陆地和海洋表面，这可以吸收更多的阳光并导致更多的变暖。但最近对 AA 的研究，包括我的研究小组的贡献，表明我们需要考虑更广泛的视角来看待北极与全球其他地区之间的双向大气联系。我们特别需要关注大气中热量和湿气的远程传输，以及远程大气遥相关。我们还了解到，北极变化的影响远远超出了北极，这对气候预测具有实际意义。例如，当前的模型似乎对海冰、雪和平流层的影响过于不敏感，这限制了季节性到多年时间尺度上气候的可预测性。为了加深我们对 AA 及其影响的理解，我的大气物理研究小组将分析最早的气候模型的最新继承者：现实的地球系统模型（ESM），包括大气、陆地、海冰、雪和海洋过程及其所有互动。我们将沿着两条轨道前进。对于十年和更长的时间尺度，我们将通过扰动北极、中纬度和热带地区，从地表到平流层，人为地触发 ESM 中的 AA。我们对这些受控实验的物理分析将帮助我们理清北极与南部地区之间联系中发挥作用的许多过程。为了帮助改进气候预测，我们将设计模型实验来调整地表、平流层和大气其他部分之间的耦合，以帮助理解当前模型的模拟问题。这种方法有望提高我们对可预测性的物理驱动因素的理解，并有可能改善预测。这些项目是我领导加拿大海冰雪演化网络（CanSISE，2013-2019）的成果。当前的提案建立在 CanSISE 内进行的观测、ESM 和预测系统工作的基础上。正如我们对 CanSISE 所做的那样，我将强调由所要求的资金支持的研究人员（本科生、研究生和博士后）的教育和专业晋升。