| 项目编号: | 1663704
|
| 项目名称: | PREEVENTS Track 2: Collaborative Research: Ocean Salinity as a predictor of US hydroclimate extremes |
| 作者: | Raymond Schmitt
|
| 承担单位: | Woods Hole Oceanographic Institution
|
| 批准年: | 2017
|
| 开始日期: | 2017-08-01
|
| 结束日期: | 2021-07-31
|
| 资助金额: | 339259
|
| 资助来源: | US-NSF
|
| 项目类别: | Continuing grant
|
| 国家: | US
|
| 语种: | 英语
|
| 特色学科分类: | Geosciences - Integrative and Collaborative Education and Research
|
| 英文关键词: | sea surface salinity
; ocean
; extreme
; us
; land
; extreme event
; extreme precipitation
; us midwest
; us hydroclimate extreme
; valuable research experience
; salinity signature
; skillful predictor
; temperature predictor
; oceanic salinity budget
; ocean surface
; rainfall extreme
; oceanic moisture flux
; surface salinity budget
; enormous consequence
; flood
; oceanic moisture supply
; temperature-based predictor
; ocean salinity
; pre-season salinity precursor
; extreme drought
; hydroclimate extreme
; salinity precursor
; salinity monitoring system
|
| 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. |
| 资源类型: | 项目
|
| 标识符: | http://119.78.100.158/handle/2HF3EXSE/89653
|
| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
|
|
There are no files associated with this item.
|
| Recommended Citation: | |
Raymond Schmitt. PREEVENTS Track 2: Collaborative Research: Ocean Salinity as a predictor of US hydroclimate extremes. 2017-01-01.
|
|
|