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

该项目将研究上层海洋混合层深度变化对海表面温度(SST)的影响。假设是，日太阳变暖、阵风和阵雨等快速时间尺度的海-气相互作用会修正为更长时间尺度的季节性到次季节性SST变率。该项目将首先制定一个概念框架，以确定这些贡献，然后将从整个热带和亚热带海洋收集的数据中分析这一框架中的指标。这些统计数据将确定区域和全球环流模式中对海温预报至关重要的具体海洋-大气耦合过程。如果成功，这项工作将改善天气预报，拯救生命和财产。这项提议将支持一位女性早期职业研究人员。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(MPOWIR)，并参与由海洋领导联盟(COL)领导的促进海洋科学实地安全的研讨会。提出了一种新的方法来评估高频海洋混合层变率对季节到亚季节海温异常的影响。利用观测的统计数据，该项目将开发和测试一个随机模式，该模式整合了大气强迫和混合层深度之间的高频协变性，以预测SST的演变。将利用系泊平台和测量重合海气相互作用和上层海洋过程的剖面仪进行的现有观测。还将利用观测指标强制的单柱混合模式，提供更多关于造成上层海洋可变性的过程的信息。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，以了解耦合模式需要如何很好地解决海洋混合层变异性，以准确预测SST异常。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.