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

合作研究：耦合海洋混合层过程驱动海面温度

• 批准号: 2219999
• 负责人: Aneesh Subramanian
• 金额: $ 3.73万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219999&HistoricalAwards=false
• 关键词: Collaborative; Research; Coupled; Ocean; Mixed

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.

该项目将研究上海流层深度变异性对海面温度（SST）的影响。假设是，快速的时间尺度空气相互作用，例如昼夜太阳变暖，阵风和雨水阵雨将纠正到较长的时间尺度季节性到季节性的SST变异性。该项目将首先开发一个概念框架来确定这些贡献，然后将从整个热带和亚热带海洋中收集的数据中分析该框架的指标。这些统计数据将确定特定的海洋 - 大气耦合过程对于区域和全球循环模型中SST必不可少的过程。如果成功，这项工作将改善挽救生命和财产的天气预测。该建议将支持女性早期研究员。约翰逊将继续参与指导物理海洋学女性，以增加保留率（MPOWIR）作为导师和贡献者，并通过讲习班来促进由海洋领导财团（COL）领导的海洋科学的现场安全。提出了一种新方法，该方法评估了高频海洋混合层变异性在季节性SST异常中的作用。使用来自观测值的统计数据，该项目将开发和测试一个随机模型，该模型在大气强迫和混合层深度之间集成了高频可相互变化，以预测SST的演变。将利用系泊平台和分析工具的现有观察结果，这些观察将使用一致的空气相互作用和高海洋工艺。观察指标强制强制的单列混合模型也将用于提供有关有助于上海变异性的过程的更多信息。这项工作将为非常简单的耦合海洋混合层模型打开可能性，该模型比恒定的深度海洋混合层模型更准确地模拟SST异常及其与大气的耦合。结果将提供一个框架，以了解耦合模型如何解决海洋混合层可变性以准确预测SST异常。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估标准来通过评估来获得支持的。