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

水的供应是社会的基本需要。作为最大的水分储存库和最终的水分来源，来自海洋的水维持陆地降水，因此是了解陆地水循环变化的关键。洪水和干旱是水循环的极端情况，对社会造成巨大影响。近年来，西部干旱导致了数十亿美元的农业损失和大范围的野火，而洪水在美国南部，中西部和东部造成了类似的损失。它们是由从海洋输出到陆地的水分过多或不足引起的。 从海洋表面蒸发的水分是陆地降水的最终来源。因此，海洋水分供应的可用性调节了陆地上极端水文气候的严重性。当水分离开海洋时，它会在海洋表面的盐度中留下一个签名。最近的研究提供了引人注目的新证据，表明盐度可以作为美国中西部、西南部和其他地区降水的熟练预测者。盐度的前体显着优于基于温度的预测，特别是在强降水或异常干旱的年份。 因此，海面盐度有很大的潜力，提供一个变革性的改善，美国水文气候极端的季节性预测。该项目将根据对海洋盐度预测潜力的新认识以及利用现场测量和卫星的不断扩大的盐度监测系统，为美国的干旱和洪水预警系统奠定科学基础。这将带来一系列社会效益：拯救生命，保护财产免遭野火和洪水之害;通过更准确的季节性降雨预报提高作物产量;通过更好地预测受干旱或洪水危机影响的不稳定地区，实现国家安全进步;更准确地预测能源需求和缺水对发电厂的影响。一些本科生将有机会获得宝贵的研究经验，因此该项目将有助于培养下一代气候科学家。项目研究结果还将纳入通过麻省理工学院/世界卫生组织联合方案和杜克大学教授的研究生课程，并将向公众传播知识。将探讨产生最近确定的极端降水与海面盐度之间关系的过程。每日降水量数据和贝叶斯统计框架将用于对美国的极端事件进行采样。基于贝叶斯推断，季前盐度前体将探讨和机制，水循环产生的盐度签名确定通过计算大气水分通量和表面盐度预算的条款。此外，还将跟踪海洋中流向陆地的水分，并评估通过大气柱中的水分供应和/或能量重新分布而形成极端情况的过程。将开发和应用机器学习算法，利用海面盐度前兆预测极端情况。该项目将采用新的方法，包括利用贝叶斯统计数据确定最佳的海面盐度和极端降雨温度预测因子，分析海洋盐度收支以确定驱动大气变量，分析将水从海洋输送到陆地的大气环流，以及开发机器学习算法，以提供极端干旱或洪水的最佳季节性预测。

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.