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

该项目将调查海洋上层混合层深度变化对海表温度的影响。假设是，快速的时间尺度的海气相互作用，如日太阳变暖，阵风和阵雨纠正到更长的时间尺度的季节到亚季节SST变化。该项目将首先制定一个概念框架，以确定这些贡献，然后根据在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区域和全球环流模式中对SST预测至关重要的特定海洋-大气耦合过程。如果成功，这项工作将改善天气预报，挽救生命和财产。该提案将支持一名女性早期职业研究人员。约翰逊将继续参与指导物理海洋学女性以提高保留率（MPOWIR）作为导师和贡献者，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。提出了一种新的方法，评估高频海洋混合层变率的季节性和亚季节性SST异常的作用。该项目将利用观测的统计数据开发和测试一个随机模型，该模型将大气强迫和混合层深度之间的高频协变综合起来，以预测SST演变。将利用系泊平台和测量同时发生的海气相互作用和上层海洋过程的剖面测量仪器的现有观测结果。还将利用由观测指标强制建立的单柱混合模型，提供更多关于造成上层海洋变化的过程的信息。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，这种模式比恒定深度海洋混合层模式更准确地模拟SST异常及其与大气的耦合。结果将提供一个框架，了解如何耦合模型需要解决海洋混合层的变化，以准确地预测SST异常。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.