Collaborative Research: Determining the Role of Ocean Dynamics in Atlantic Sea Surface Temperature Variations Using a Hierarchy of Coupled Models

观测和模型表明，大西洋海面温度（SST）表现出显着的低频（年际至年代际）变化，并且这些变化的很大一部分与气候系统的内部变化有关。然而，这些大西洋内部海温变化的起源尚未完全了解。该项目将开发一个耦合模型层次结构，其中各种海洋过程被禁用；比较模型对将使一维和三维海洋动力学的作用能够稳健地分离，以及风和浮力在产生海洋动力学变化中的单独作用。许多关于大西洋海温年代际变化的文献相当狭隘地关注大西洋多年代际变化（AMV）以及大气和海洋在其中的作用。这项研究不是关注一种特定模式（例如 AMV），而是在多个时间尺度上隔离整个大西洋盆地与特定海洋过程相关的大西洋海温变化模式。这种变化会影响区域和全球气候，包括北美和欧洲的气温、萨赫勒地区的降雨量以及大西洋飓风的频率和强度。提高对低频海温变化的了解对于季节性到十年时间尺度的气候预测至关重要。了解大气强迫和海洋动力学在设置海表温度异常中各自的作用对于海表温度的可预测性具有影响，因为如果海洋动力学发挥主导作用，则预期可预测性会更高，并且了解有关监测洋流作为十年预测系统一部分的重要性的实际问题。该项目还将通过向更广泛的社区提供协方差判别分析代码来推进气候数据分析的统计方法。该提案将为 WHOI 的一名研究生和北卡罗来纳州的一名博士后研究员各提供一年的支持。该提案还将支持 2 名女性早期职业 PI。首席 PI Buckley 是指导物理海洋学女性以提高保留率 (MPOWIR) 指导小组的联合领导者。 PI 还将担任当地公立学校年度科学博览会的导师和评委，参与 K-12 科学技术工程数学 (STEM) 教育。该项目的目标是理清大气强迫和各种海洋动力过程在大西洋海温变化和年际至十年时间尺度的可预测性中的作用。为了实现这一目标，将使用社区地球系统模型版本 2 (CESM2) 开发耦合模型的层次结构。模型层次结构将包括不同复杂性的海洋模型组件，比较模型对将使团队能够定量确定海洋动力学特定方面的作用，包括一维过程（垂直混合、年际混合层深度变化、夹带）和三维海洋动力学（包括风和浮力驱动过程）在驱动海表温度变化方面的作用。一种称为协方差判别分析的严格统计技术将用于诊断模型对之间大西洋海温方差的主要差异，从而阐明特定海洋过程对大西洋海温变化的影响。此外，对层次结构中模型之间大西洋海温的可预测性进行比较，将有助于阐明海洋过程在可预测性中的作用。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

Observations and models indicate that Atlantic sea surface temperatures (SSTs) exhibit significant low- frequency (interannual to decadal) variability, and a significant portion of these variations is related to internal variations of the climate system. However, the origin of these internal Atlantic SST variations is yet to be fully understood. This project will develop a hierarchy of coupled models in which various oceanic processes are disabled; comparing model pairs will enable the role of one-dimensional and three-dimensional ocean dynamics to be separated robustly, as well the separate roles of wind and buoyancy forcing in creating dynamical ocean variations. Much of the literature on decadal Atlantic SST variations is focused rather narrowly on the Atlantic Multidecadal Variability (AMV) and the role of the atmosphere and the ocean therein. Instead of focusing on one specific mode (e,g., the AMV), this research will isolate the modes of Atlantic SST variability related to specific oceanic processes, throughout the Atlantic basin on multiple time scales. This variability impacts regional and global climate, including temperatures across North America and Europe, rainfall in the Sahel region, and frequency and intensity of Atlantic hurricanes. Improved knowledge of low-frequency SST variability is essential for efforts aimed at climate predictions on seasonal to decadal time scales. Understanding the respective roles of atmospheric forcing and ocean dynamics in setting SST anomalies has implications for predictability of SSTs, as higher predictability is expected if ocean dynamics play a dominant role, and practical questions regarding the importance of monitoring ocean currents as part of a decadal prediction system. This project will also advance statistical methods for analysis of climate data by providing codes for covariance discriminant analysis to the broader community. This proposal will support a graduate student at WHOI and a postdoctoral researcher at NC State for a year each. The proposal will also support 2 female early career PIs. Lead PI Buckley is a Co-Leader of a mentoring group for Mentoring Physical Oceanography Women to Increase Retention (MPOWIR). The PIs will also engage in K-12 Science-Technology- Engineering-Math (STEM) education by serving as mentors and judges for the local public school annual science fairs.The goal of this project is to disentangle the roles of atmospheric forcing and various ocean dynamical processes in Atlantic SST variability and predictability on interannual-to-decadal time scales. In order to achieve this goal, a hierarchy of coupled models will be developed using the Community Earth System Model version 2 (CESM2). The model hierarchy will include ocean model components of varying complexity and comparing model pairs will enable the team to quantitatively determine the roles of specific aspects of ocean dynamics, including one-dimensional processes (vertical mixing, interannual mixed layer depth variations, entrainment) and three-dimensional ocean dynamics (including wind and buoyancy-driven processes), on driving SST variability. A rigorous statistical technique, called covariance discriminant analysis, will be applied to diagnose the leading differences in Atlantic SST variance between model pairs, thus elucidating the impact of specific ocean processes on Atlantic SST variability. Additionally, a comparison of the predictability of Atlantic SST between models in the hierarchy, will help elucidate the role of oceanic processes in predictability.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.