Dynamic and thermodynamic mechanisms of desert amplification in a warming climate

点击翻译按钮获取中文摘要

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.