CAREER: Understanding Changes in Summertime Continental Temperature Extremes

职业：了解夏季大陆极端温度的变化

• 批准号: 2338237
• 负责人: Karen McKinnon
• 金额: $ 94.45万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2029-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338237&HistoricalAwards=false
• 关键词: CAREER; Understanding; Changes; Summertime; Continental

Heat waves of unprecedented intensity and duration are expected in a warming world, and the record-breaking heat waves of recent years confirm this expectation. The increasing frequency of such events is alarming and naturally leads to concerns that the severity of heat waves is outpacing the rise in mean temperature. But the extent to which the hottest days are warming faster than the average temperature increase is hard to quantify, and certainly heat waves would become more severe even if the hottest days warmed at the same rate as all the other days. One challenge is that heat extremes are rare by definition, thus there is limited sample size to make statistically robust conclusions. The sample size issue is particularly challenging as the extent to which the hottest days warm faster is likely to vary from one region to another. A further challenge is that we do not have a satisfactory understanding of the physical mechanisms that might lead to stronger warming trends for hot days than for average days.Work under this award addresses both the extent to which temperature increases are greater for the hottest days and the physical mechanisms the might be responsible. The analysis of temperature trends compares trends for the 50th and 95th percentiles of the temperature distribution on a regional basis and uses various statistical methods to overcome sample size limitations and account for natural variability. The analysis also considers output from the simulations available from the Climate Model Intercomparison Project (CMIP) and the Large Ensembles created with the Community Earth System Model (CESM). Work addressing physical mechanisms focuses the extent to which drying of the soil contributes to temperature extremes, in particular that drying accompanies heat waves and drying limits the extent to which the surface can cool through evaporation and transpiration, thus leading to greater warming. The dependence of temperature extremes on soil moisture and evapotranspiration is explored by applying a surface energy budget equation to observations and model output, and through idealized experiments with CESM.The educational component of this CAREER proposal seeks to build a bridge between climate and data science to foster more effective collaboration between the two fields. One activity is the development of an educational module for a freshman class in environmental science intended to teach statistical thinking through hands-on data analysis. The module uses Juypiter notebooks to give students access to observational data for their own hometowns, and guides them through the calculation of means and extremes and their variation over time. Another activity is a workshop for 30-40 graduate students working at the interface between data science and climate science, to be held at the National Center for Atmospheric Research (NCAR). The workshop is mostly devoted to a 'science hackathon' in which teams of students with a mix of disciplinary backgrounds will work together to solve specific problems related to climate extremes. The award provides support for 20 attendees at the workshop who are chosen through an open competitive process.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.

在全球变暖的背景下，预计热浪的强度和持续时间将是前所未有的，而近年来破纪录的热浪证实了这一预期。此类事件发生频率的增加令人震惊，自然会引起人们的担忧，即热浪的严重程度超过了平均气温的上升速度。 但最热的日子升温速度快于平均气温上升的程度很难量化，而且即使最热的日子升温速度与其他日子相同，热浪肯定会变得更加严重。 一项挑战是，根据定义，极端高温是罕见的，因此样本量有限，无法得出统计上可靠的结论。 样本量问题尤其具有挑战性，因为最热的日子变暖更快的程度可能因地区而异。进一步的挑战是，我们对可能导致炎热天气比普通天气更强烈的变暖趋势的物理机制还没有令人满意的理解。该奖项的工作既解决了最热天气温度升高的程度，也解决了最热天气温度升高的程度。可能负责的物理机制。 温度趋势分析比较了区域内温度分布的 50% 和 95% 的趋势，并使用各种统计方法来克服样本量限制并解释自然变化。 该分析还考虑了气候模型比对项目 (CMIP) 和使用社区地球系统模型 (CESM) 创建的大型集合的模拟输出。 解决物理机制的工作重点关注土壤干燥对极端温度的影响程度，特别是干燥伴随着热浪，并且干燥限制了表面通过蒸发和蒸腾而冷却的程度，从而导致更严重的变暖。 通过将地表能量收支方程应用于观测和模型输出，并通过 CESM 进行理想化实验，探索极端温度对土壤湿度和蒸散的依赖性。该职业提案的教育部分旨在在气候和数据科学之间建立一座桥梁，促进两个领域之间更有效的合作。 其中一项活动是为环境科学新生班开发一个教育模块，旨在通过动手数据分析教授统计思维。 该模块使用 Juypiter 笔记本让学生访问自己家乡的观测数据，并指导他们计算平均值和极值及其随时间的变化。 另一项活动是在国家大气研究中心 (NCAR) 举办为 30-40 名从事数据​​科学和气候科学交叉领域工作的研究生举办的研讨会。 该研讨会主要致力于“科学黑客马拉松”，不同学科背景的学生团队将共同努力解决与极端气候相关的具体问题。该奖项为研讨会上通过公开竞争过程选出的 20 名与会者提供支持。该奖项反映了 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。