Dynamic and thermodynamic mechanisms of desert amplification in a warming climate

自1979年以来，观测到的陆地气温增幅最大的地区是覆盖地球表面三分之一的沙漠地区。作为世界上最大和最干燥的沙漠，撒哈拉沙漠-阿拉伯半岛（SDAP）合并地区经历了最大和最持久的变暖，由于温室气体浓度升高而增加的加热。这种加速变暖被称为沙漠放大（DA），可以改变SDAP及其周边地区的区域和大尺度气候，影响非洲雨林、水资源供应、生物多样性、农业和人类健康。在SDAP以北，这些变化可能导致地中海生态系统的退缩和沙漠化的加剧。在南方，它们可能影响撒哈拉以南国家与西非季风相关的降雨模式。萨赫勒地区的降雨和天气也可能被改变，从而以飓风和沙尘云的形式直接影响美国。因此，揭示DA的性质和成因对于理解大尺度大气过程和气候变化的影响至关重要。然而，DA的主要过程和机制在很大程度上仍然未知。本项目旨在研究20世纪和21世纪气候变化的热力学和动力学原因。最重要的假设是，日晒是气候变化的一个几乎普遍的特征，主要是由于干燥地表的温度递减率更陡以及干湿地区的大尺度大气耦合。研究人员将通过将观测分析与气候模拟相结合的方法来探索数据同化的机制和反馈。他们将分析各种观测数据集，量化控制DA的一阶因素。他们将采用不同复杂程度的模式，包括单柱辐射对流模式、区域气候模式和综合全球气候模式。这种分层建模方法对于解决这一复杂问题、隔离关键的相互作用以及对气候变化的因果机制进行更深入的了解至关重要。理解数据分析将引起公众、决策者、政府和各个学科的科学家的极大兴趣。本项目将训练研究生进行遥感、数据分析、数值模拟和气候理论的交叉研究。它还将有助于进一步开发和记录开源的clilab软件，该软件已经被世界各地的研究人员和教师使用。该奖项反映了美国国家科学基金会的法定使命，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。

Since 1979, the largest increase in the observed air temperature over land has occurred over the deserts which cover a third of the Earth’s surface. As the world’s largest and driest deserts, the combined Sahara Desert-Arabian Peninsula (SDAP) region has experienced the greatest and most persistent warming with the increased heating due to the elevated greenhouse gas concentrations. Known as desert amplification (DA), this accelerated warming can alter regional and large-scale climate over SDAP and surrounding regions, impacting the African rainforests, water availability, biodiversity, agriculture, and human health. To the north of SDAP, these changes may lead to the retraction of the Mediterranean ecosystem and increased desertification. To the south, they may influence the rainfall patterns associated with the West African Monsoon in Sub-Saharan countries. The Sahelian rainfall and weather could also be modified, thereby directly affecting the United States in the form of hurricanes and dust clouds. Hence, uncovering the nature and cause of DA is essential for understanding large-scale atmospheric processes and the impacts of climate change. However, the major processes and mechanisms of DA are still largely unknown.This project aims to study the thermodynamic and dynamic causes of DA in the 20th and 21st century. The overarching hypothesis is that DA is a near-universal feature of climate change primarily due to steeper temperature lapse rates over drier surfaces and the large-scale atmospheric coupling of wet and dry regions. The investigators will explore the mechanisms and feedbacks of DA by combining observational analyses with climate modeling. They will analyze various observational data sets to quantify first-order factors controlling DA. They will employ models of varying complexity including a single column radiative-convective model, a regional climate model, and comprehensive global climate models. Such hierarchical modeling approach is essential for tackling this complex problem, isolating key interactions, and achieving a deeper insight on causal mechanisms of climate change. Understanding DA will be of great interest to the public, policy makers, governments and scientists across various disciplines. This project will provide training for graduate students in research at the interface of remote sensing, data analysis, numerical modeling, and climate theory. It will also contribute to the further development and documentation of the open-source CLIMLAB software, already in use by researchers and instructors worldwide.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.