PREEVENTS Track 2: Collaborative Research: Ocean Salinity as a predictor of US hydroclimate extremes

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项目编号	1663138
	PREEVENTS Track 2: Collaborative Research: Ocean Salinity as a predictor of US hydroclimate extremes
	Laifang Li
主持机构	Duke University
项目开始年	2017
	2017-08-01
项目结束日期	2021-07-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	130018(USD)
国家	美国
语种	英语
英文摘要	Water availability is a fundamental necessity for society. As the largest moisture reservoir and ultimate moisture source, water from the oceans sustains terrestrial precipitation and is thus key to understanding variability in the water cycle on land. Floods and droughts represent extremes of the water cycle that have enormous consequences for society. In recent years Western drought has led to billions of dollars of agricultural losses and extensive wildfires, while floods produced similar losses in the South, Midwest and East of the US. They are caused by an excess or deficit of moisture exported from ocean to land. Moisture evaporating from the ocean surface is the ultimate source for terrestrial precipitation. Thus, the availability of the oceanic moisture supply modulates the severity of hydroclimate extremes on land. As moisture exits the ocean, it leaves a signature in sea surface salinity. Recent studies have provided remarkable new evidence that salinity can be utilized as a skillful predictor of precipitation in the US Midwest, Southwest and other regions. The salinity precursors significantly outperform temperature-based predictors, especially in the years with heavy precipitation or exceptional drought. Thus, sea surface salinity has great potential to provide a transformative improvement to seasonal forecasts of US hydroclimate extremes. This project will develop the scientific basis for a drought and flood early warning system for the US based on these new insights into the predictive potential of ocean salinity and the expanding salinity monitoring system that uses both in-situ measurements and satellites. This will lead to a number of societal benefits: lives saved and property preserved from wildfires and floods; improved crop yields resulting from more accurate seasonal rainfall forecasts; national security advances realized by better anticipation of destabilized regions affected by drought or flood crises; and more accurate forecasting of energy demand and the impact of water shortages on power plants. Several undergraduate students will have the opportunity to gain valuable research experience, and thus the project will help to train the next generation of climate scientists. Project findings will also be incorporated into graduate courses taught through the MIT/WHOI joint program and at Duke University, and the knowledge will be disseminated to the general public. The processes that produce the newly identified relationships between extreme precipitation and sea surface salinity will be explored. Daily precipitation data and a Bayesian statistical framework will be used to sample the extreme events in the US. Based on the Bayesian inference, the pre-season salinity precursors will be explored and mechanisms by which the water cycle generates the salinity signatures determined by calculating atmospheric moisture fluxes and the terms in the surface salinity budget. In addition, the oceanic moisture flux onto land will be tracked, and the processes assessed by which extremes develop through the moisture supply and/or energy redistribution in the atmospheric column. Machine-learning algorithms to predict extremes using the sea surface salinity precursors will be developed and applied. Novel approaches will be used in this project, including the use of Bayesian statistics to identify the optimal sea surface salinity and temperature predictors for rainfall extremes, analysis of the oceanic salinity budget to identify the driving atmospheric variables, analysis of the atmospheric circulations that transport water from ocean to land, and the development of machine learning algorithms to provide optimal seasonal predictions of extreme drought or floods.
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文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/71375
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Laifang Li.PREEVENTS Track 2: Collaborative Research: Ocean Salinity as a predictor of US hydroclimate extremes.2017.