Collaborative Research: Fingerprinting Energy and Water Cycle Extremes through a Scale-Interaction Lens

极端天气事件，如热浪、冷空气爆发(CAO)、干旱和强降水，通常会在中西部北部或南部大平原大小的地区造成破坏。这些极端通常嵌入到大规模气象模式(LMP)中。例如，干旱可能发生在高压中心内，这是起源于北太平洋并延伸到整个美国大陆的高潮和低谷模式的一部分。LMPs又可能由厄尔尼诺/南方涛动(ENSO)事件和北极涛动(AO)等行星尺度气候模式(PCM)产生或影响。因此，理解和预测极端天气事件，或预测气候变化引起的极端天气事件的频率和强度的变化，必须从多尺度的角度来考虑这些事件，认识到导致它们的区域、大陆和行星尺度的相互作用。这里所做的工作试图开发一套全面的多尺度度量标准，或“指纹”，以表征导致区域极端天气事件的大气过程。一旦指纹被开发出来，它们就被用来确定更大规模的过程导致或强烈影响区域极值的动力机制。这项研究还使用指纹和随后的动态分析来评估区域极端气候模型模拟的真实性及其与LMP和PCM的关联。一种分析技术是利用两层准地转模式中的位势高度趋势方程来确定“最佳模式”，也就是经过优化的波型，以便从平均气流中提取能量，从而在几天内实现快速增长。主要研究人员(PI)及其合作者先前的工作使用了这种方法来表明，行星尺度的环流异常可以以一种促进CAO的最佳模式的增长的方式改变平均状态。之前的其他工作将夏季赤道至极地温度的长期下降与北美地区锋面天气系统的减少联系在一起，这反过来又促进了美国夏季的干燥和温暖。鉴于极端天气造成的干扰，这项工作具有社会和科学意义。该项目支持一名研究生和一名博士后研究员，从而在这一研究领域培养未来的劳动力。佐治亚理工学院的PI与整合科学、数学和计算的教育中心(CEISMC)合作，CEISMC在亚特兰大地区的学校开展K-12外联活动，并组织夏季研讨会，重点关注代表性不足的学生。佛罗里达州立大学的PI与本科生一起制作大陆范围CAO的长期预测，农业、能源和水利部门的利益相关者已经使用了这些预测。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Extreme weather events such as heat waves, cold air outbreaks (CAOs), droughts, and heavy precipitation commonly cause disruptions over regions the size of the northern midwest or the southern great plains. These extremes are generally embedded in large-scale meterological patterns (LMPs). For instance a drought might occur within a high-pressure center which is part of a pattern of highs and lows originating over the North Pacific and extending across the continental US. The LMPs may in turn be generated or influenced by planetary-scale climate modes (PCMs) such as El Nino/Southern Oscillation (ENSO) events and the Arctc Oscillation (AO). Thus efforts to understand and predict extreme weather events, or to anticipate changes in their frequency and intensity due to climate change, must consider the events from a multi-scale perspective that recognizes the interplay of regional, continental, and planetary scales that contribute to them.Work performed here seeks to develop a comprehensive suite of multi-scale metrics, or "fingerprints", for characterizing the atmospheric processes responsible for regional extreme weather events. Once the fingerprints are developed they are used to determine the dynamical mechanisms through which larger-scale processes lead to, or strongly influence, the regional extremes. The research also uses the fingerprints and subsequent dynamical analysis to assess the realism of climate model simulations of regional extremes and their associations with LMPs and PCMs. One analysis technique uses the the tendency equation for geopotential height in a two-layer quasi-geostrophic model to identify "optimal modes", meaning wave patterns which are optimally shaped to extract energy from the mean flow to achieve rapid growth over a few days. Prior work by the Principle Investigators (PIs) and their collaborators used this approach to show that planetary-scale circulation anomalies can alter the mean state in a way that promotes the growth of an optimal mode that promotes CAOs. Additional prior work links the long-term reduction in the summertime equator-to-pole temperature contrast to a reduction in frontal weather systems over North America, which in turn promotes dryer and warmer summers in the US.The work is of societal as well as scientific interest given the disruptions caused by extreme weather. The project supports a graduate student and a postdoctoral research fellow, thereby developing the future workforce in this research area. The PI at Georgia Tech works with the Center for Education Integrating Science, Mathematics and Computing (CEISMC), which conducts K-12 outreach in Atlanta-area schools and organizes summer workshops focused on underrepresented students. The PI at Florida State University works with undergraduate students to produce long-range forecasts of continental-scale CAOs, which have been used by stakeholders from the agriculture, energy, and water sectors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.