A Stochastic Approach to Representing Unresolved Mesoscales in Ocean Circulation Models

表示海洋环流模型中未解决的中尺度的随机方法

• 批准号: 1736708
• 负责人: Ian Grooms
• 金额: $ 57.77万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1736708&HistoricalAwards=false
• 关键词: Stochastic; Approach; Representing; Unresolved; Mesoscales

Ocean models are indispensable tools for understanding, estimating, and predicting ocean dynamics. A key challenge in the construction of accurate ocean models is the fact that computational resources preclude direct resolution of all physical scales; depending on their intended use, the next generation of global ocean models will have horizontal spatial resolutions from 1 degree to 1/10 degree. Models at the coarser end of this range, which support longer-term ensemble forecasting (e.g. climate prediction) and reanalysis applications, are unable to represent the scale of the most energetic dynamics, the mesoscale, over large regions of the globe. Mesoscale eddies (on order 100 km) play a leading role in the transport of heat and biogeochemical tracers, and the parameterization of their effects in global ocean models has a long history. High-resolution models support shorter-term forecasting and studies of ocean dynamical processes, but they are too expensive for long-term or large-ensemble applications. An emerging paradigm in modeling the effects of unresolved scales in ocean (and atmosphere) models is based on the observation that unresolved small-scale dynamics interact with resolvable dynamics in a non-deterministic way; the net tracer transports associated with these unresolved scales are stochastic, and require stochastic models. The construction of accurate, stochastic models of real physical fields is a young field of research, as is the field of stochastic parameterization into computational ocean models. This project will significantly extend both these fields by bringing to bear sophisticated statistical models and systematic Bayesian optimization to ocean modeling. The project will also provide training for a post-doctoral fellow at the intersection of physical oceanography and applied mathematics. It will strengthen collaborations between university-based researchers and scientists at the National Center for Atmospheric Research (NCAR) through the Community Earth System Model (CESM) Ocean Model Working Group (OMWG). A stochastic model of the global mesoscale eddy field will be developed. A stochastic parameterization of mesoscale eddies will be implemented and tested in the 1-degree version of the ocean component of the CESM version 3.This project aims to develop a framework for stochastic parameterization of tracer transports in the coarse- resolution ocean model setting used for long-term or large ensemble applications. Unlike other approaches that model the transport directly, the stochastic approach models the eddy velocity and tracer anomaly fields directly, and uses them to construct realistic tracer fluxes. This idea has recently begun to be developed in the context of eddy-permitting ocean models to model 'backscatter' that energizes the partially resolved mesoscale eddies, but only in the past year or two has it begun to be developed in the coarse-model case, which is significantly different than the eddy-permitting case. It is particularly important to develop realistic stochastic parameterizations for coarse models though, because the ensemble-simulation scenarios for which these coarse models are increasingly used require realistic patterns of eddy-driven large-scale variability. The uncertainty associated with mesoscale eddy transports absolutely must be included in the models via realistic stochastic parameterizations for the conclusions of these studies to be accurate. A benefit of this approach is that the eddy fields are easier to model and observe than the transport; the approach also results in realistic non-Gaussian tracer flux distributions. The approach works within the Gent-McWilliams (GM) framework and results in a stochastic parameterization of the eddy bolus velocity. The project has two main components: (i) the development of a stochastic model of the eddy field, and (ii) the implementation, thorough testing, and validation of the associated stochastic parameterization in an IPCC-class global climate model. Both components will involve close collaboration with scientists in the Community Earth System Model (CESM) Ocean Model Working Group (OMWG) at NCAR.

海洋模型是理解、估计和预测海洋动力学不可或缺的工具。构建精确海洋模型的一个关键挑战是计算资源无法直接解析所有物理尺度；根据其预期用途，下一代全球海洋模型将具有 1 度到 1/10 度的水平空间分辨率。该范围较粗端的模型支持长期集合预测（例如气候预测）和再分析应用，但无法代表全球大部分地区最具活力的动力学规模，即中尺度。中尺度涡流（100公里量级）在热量和生物地球化学示踪剂的传输中发挥着主导作用，其影响在全球海洋模型中的参数化有着悠久的历史。高分辨率模型支持短期预测和海洋动力学过程研究，但对于长期或大型集合应用来说成本太高。对海洋（和大气）模型中未解决的尺度的影响进行建模的新兴范式是基于对未解决的小尺度动力学与可解决的动力学以非确定性方式相互作用的观察；与这些未解决的尺度相关的净示踪剂传输是随机的，并且需要随机模型。真实物理场的精确随机模型的构建是一个年轻的研究领域，计算海洋模型的随机参数化领域也是如此。该项目将通过将复杂的统计模型和系统贝叶斯优化应用于海洋建模来显着扩展这两个领域。该项目还将为物理海洋学和应用数学交叉领域的博士后研究员提供培训。它将通过社区地球系统模型（CESM）海洋模型工作组（OMWG）加强大学研究人员和国家大气研究中心（NCAR）科学家之间的合作。将开发全球中尺度涡流场的随机模型。中尺度涡流的随机参数化将在 CESM 第 3 版的海洋部分的 1 度版本中实施和测试。该项目旨在开发一个用于粗分辨率海洋模型设置中示踪剂输运随机参数化的框架长期或大型集成应用。与直接模拟输运的其他方法不同，随机方法直接模拟涡流速度和示踪异常场，并使用它们构建真实的示踪通量。这个想法最近开始在允许涡流的海洋模型的背景下发展，以模拟“反向散射”，为部分解析的中尺度涡流提供能量，但只是在过去一两年才开始在粗模型情况下发展，这与允许涡流的情况显着不同。然而，为粗略模型开发现实的随机参数化尤为重要，因为越来越多地使用这些粗略模型的集合模拟场景需要涡流驱动的大规模变化的现实模式。为了使这些研究的结论准确，必须通过现实的随机参数化将与中尺度涡流传输相关的不确定性绝对包含在模型中。这种方法的一个好处是涡流场比传输更容易建模和观察。该方法还产生真实的非高斯示踪剂通量分布。该方法在 Gent-McWilliams (GM) 框架内工作，并产生涡流速度的随机参数化。该项目有两个主要组成部分：(i) 涡流场随机模型的开发，以及 (ii) 在 IPCC 级全球气候模型中实施、彻底测试和验证相关随机参数化。这两个部分都将涉及与 NCAR 社区地球系统模型 (CESM) 海洋模型工作组 (OMWG) 科学家的密切合作。

项目成果

Parameterizing the Impact of Unresolved Temperature Variability on the Large‐Scale Density Field: Part 1. Theory.

参数化未解决的温度变化对大尺度密度场的影响：第 1 部分：理论。

• DOI: 10.1029/2020ms002185
• 发表时间: 2020-11-04
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Z. Stanley;I. Grooms;William Kleiber;Scott Bachman;Frédéric Castruccio;Alistair Adcroft
• 通讯作者: Alistair Adcroft

Diagnosing, modeling, and testing a multiplicative stochastic Gent-McWilliams parameterization

乘法随机 Gent-McWilliams 参数化的诊断、建模和测试

• DOI: 10.1016/j.ocemod.2018.10.009
• 发表时间: 2024-09-14
• 期刊: Ocean Modelling
• 影响因子: 3.2
• 作者: I. Grooms;W. Kleiber
• 通讯作者: W. Kleiber

Parameterizing the Impact of Unresolved Temperature Variability on the Large‐Scale Density Field: 2. Modeling

参数化未解决的温度变化对大尺度密度场的影响：2. 建模

• DOI: 10.1029/2021ms002844
• 发表时间: 2022-03
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Kenigson, J. S.;Adcroft, A.;Bachman, S. D.;Castruccio, F.;Grooms, I.;Pegion, P.;Stanley, Z.
• 通讯作者: Stanley, Z.

An eddifying Stommel model: fast eddy effects in a two-box ocean

令人陶醉的斯托梅尔模型：两箱海洋中的快速涡流效应

• DOI: 10.1080/03091929.2018.1464566
• 发表时间: 2018-09
• 期刊: Geophysical & Astrophysical Fluid Dynamics
• 影响因子: 1.3
• 作者: Barham, William;Grooms, Ian
• 通讯作者: Grooms, Ian

On energy exchanges between eddies and the mean flow in quasigeostrophic turbulence

准地转湍流中涡流与平均流之间的能量交换

• DOI: 10.1017/jfm.2019.969
• 发表时间: 2020-02
• 期刊: Journal of Fluid Mechanics
• 影响因子: 3.7
• 作者: Barham, William;Grooms, Ian
• 通讯作者: Grooms, Ian