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.

在一个变暖的世界里，预计会出现强度和持续时间都前所未有的热浪，近年来破纪录的热浪证实了这一预期。这类事件发生的频率越来越高，令人担忧，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。 但是，最热的日子比平均气温上升更快的程度很难量化，当然，即使最热的日子以与其他日子相同的速度变暖，热浪也会变得更加严重。 一个挑战是，根据定义，极端高温是罕见的，因此样本量有限，无法得出统计学上可靠的结论。 样本量问题尤其具有挑战性，因为最热的日子升温更快的程度可能因地区而异。另一个挑战是，我们对可能导致炎热天气比平均天气更强的变暖趋势的物理机制没有令人满意的理解。该奖项的工作涉及最热天气温度升高的程度以及可能负责的物理机制。 对温度趋势的分析比较了区域基础上温度分布的第50和第95季度的趋势，并使用各种统计方法来克服样本大小的限制，并说明自然变异性。 分析还考虑了气候模式相互比较项目（CMIP）和社区地球系统模式（CESM）创建的大型集合的模拟输出。 涉及物理机制的工作侧重于土壤干燥在多大程度上促成极端温度，特别是干燥伴随着热浪，干燥限制了地表通过蒸发和蒸腾作用冷却的程度，从而导致更大程度的升温。 极端温度对土壤水分和蒸散的依赖性是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的理想化实验来探索的。该CAREER提案的教育部分旨在建立气候和数据科学之间的桥梁，以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）为30-40名从事数据科学和气候科学之间接口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”，其中具有学科背景的学生团队将共同努力解决与气候极端有关的具体问题。该奖项为通过公开竞争程序选出的20名研讨会与会者提供支持。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。