Collaborative Research: Coupled Ocean Mixed Layer Processes Driving Sea Surface Temperature

本项目旨在研究上层海洋混合层深度变化对海表温度的影响。假设是快速的时间尺度海气相互作用，如日太阳变暖、阵风和阵雨，可以修正为更长的时间尺度的季节性到亚季节性海温变率。该项目将首先制定一个概念性框架来确定这些贡献，然后根据整个热带和亚热带海洋收集的数据分析该框架的指标。这些统计数据将确定特定的海洋-大气耦合过程，这对区域和全球环流模式的海温预测至关重要。如果成功，这项工作将改善天气预报，从而挽救生命和财产。该提案将支持一名女性早期职业研究员。约翰逊将继续作为导师和贡献者参与指导物理海洋学女性以增加保留率（MPOWIR），并参加由海洋领导联盟（COL）领导的促进海洋科学现场安全的研讨会。提出了一种评估高频海洋混合层变率对海温季节和亚季节异常作用的新方法。利用观测数据，该项目将开发和测试一个随机模型，该模型集成了大气强迫和混合层深度之间的高频共变率，以预测海温演变。将利用来自系泊平台和剖面仪器的现有观测资料，测量同步的海气相互作用和上层海洋过程。由观测指标强迫的单柱混合模式也将被用来提供更多关于导致上层海洋变率的过程的信息。这项工作将为非常简单的耦合海洋混合层模式提供可能性，这种模式比恒深海洋混合层模式更准确地模拟海温异常及其与大气的耦合。结果将为理解耦合模式如何解决海洋混合层变率以准确预测海温异常提供一个框架。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。

This project would investigate the effects of upper ocean mixed layer depth variability on sea surface temperature (SST). The hypothesis is that fast timescale air-sea interactions such as diurnal solar warming, wind gusts and rain showers rectify onto longer timescale seasonal to sub-seasonal SST variability. The project would first develop a conceptual framework to identify these contributions, then metrics from this framework would be analyzed from data collected throughout the tropical and subtropical oceans. These statistics will identify specific ocean-atmosphere coupled processes essential for SST prediction in regional and global circulation models. If successful, this work would improve weather predictions that save lives and property. This proposal will support a female early-career researcher. Johnson will continue her involvement with Mentoring Physical Oceanography Woman to Increase Retention (MPOWIR) as a mentor and contributor, and with workshops to promote field safety for ocean sciences lead by the Consortium for Ocean Leadership (COL). A new method is presented that evaluates the role of high frequency ocean mixed layer variability on seasonal to subseasonal SST anomalies. Using statistics from observations, the project will develop and test a stochastic model that integrates the high frequency co-variability between atmospheric forcing and mixed layer depth to predict SST evolution. Existing observations from moored platforms and profiling instruments that measure coincident air-sea interaction and upper ocean processes will be utilized. Single column mixing models forced by the observational metrics would also be utilized to provide more information about processes that contribute to upper ocean variability. This work will open the possibility for very simple coupled ocean mixed layer models that simulate SST anomalies and their coupling to the atmosphere more accurately than constant-depth ocean mixed layer models. Results will provide a framework for understanding how well coupled models need to resolve ocean mixed layer variability to accurately predict SST anomalies.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.