Collaborative Research: From Adjoints for the Few to Adjoints for the Many: Integrating the Use of Adjoint Methods in Earth System Modeling

协作研究：从少数人的伴随到多人的伴随：在地球系统建模中整合伴随方法的使用

• 批准号: 1751120
• 负责人: Patrick Heimbach
• 金额: $ 13.75万
• 依托单位: University of Texas at Austin
• 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: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751120&HistoricalAwards=false
• 关键词: Collaborative; Research; Adjoints; Few; Many

Adjoint models are useful mathematical tools for studies that require state estimates (e.g. forecasts or projections), and sensitivities of model output with respect to model input. Adjoint methods allow efficient and comprehensive computation of model sensitivity to very high dimensional spaces of inputs. An adjoint model can be used to efficiently quantify the effects of changes in the spatio-temporal distribution of surface winds, buoyancy fluxes, or mixing distribution in the ocean, on quantities such as carbon uptake in the Southern Ocean. Developing adjoint models for given state-of-the-art geophysical models can be as complex as developing the geophysical models themselves, which is one reason why adjoints are not frequently used. This project will advance the tools needed to implement adjoint models.The project will pursue three inter-related work packages to overcome these limitations:(1) bringing the open-source AD tool OpenAD to full maturity on four open-source Earth system modeling frameworks used in climate research and education(2) developing configurations around several science applications where the sensitivity of quantities of interest (cost functions) to inputs (control variables) can be configured for realistic runtimes,(3) developing a generic Message Passing Interface (MPI)-based framework to enable the coupling between high-resolution forward and low-resolution adjoint models. The proposed geoscience studies will be of interest to computational and climate scientists. A broader impact of this research will be to enable adjoint methods to be more easily used across the geosciences, with potential to be transformative for data assimilation and sensitivity analysis in a wide range of geoscience and other disciplines.

伴随模型是需要状态估计（例如预测或投影）的研究的有用数学工具，并且对模型输入的敏感性。伴随方法可以有效且全面地计算模型灵敏度对非常高的输入空间的敏感性。伴随模型可用于有效地量化地表风，浮力通量或海洋中混合分布的变化对南方海洋中碳吸收等数量的影响。为给定最先进的地球物理模型开发伴随模型可能与开发地球物理模型本身一样复杂，这就是为什么不经常使用伴随的原因之一。 该项目将推动实施伴随模型所需的工具。该项目将追求三个相关的工作包来克服这些局限性：（1）将开源的AD AD工具开放到在四个开源地球系统建模框架中完全成熟，以在气候研究和教育中使用的四个开源地球系统建模框架（2）在几个科学应用程序中开发配置的配置（2），以配置量的数量（成本量），以控制功能（成本）（控制功能）（控制功能）（控制功能）（控制功能）（控制功能）（控制功能）（控制功能）（控制量） Runtimes，（3）开发一个通用消息传递接口（MPI）的框架，以实现高分辨率向前和低分辨率伴随模型之间的耦合。拟议的地球科学研究将吸引计算和气候科学家。 这项研究的更广泛的影响将是使伴随方法更容易在整个地球科学中使用，并且有可能在广泛的地球科学和其他学科中进行数据同化和灵敏度分析。

项目成果

Sicopolis-AD: Quick-Start Manual

Sicopolis-AD：快速入门手册

• DOI: 10.2172/1499025
• 发表时间: 2019
• 期刊: ANL Technical Report
• 作者: Logan, L.C.

Quantifying Dynamical Proxy Potential Through Shared Adjustment Physics in the North Atlantic

通过北大西洋共享调整物理学来量化动力代理潜力

• DOI: 10.1029/2020jc016112
• 发表时间: 2020
• 期刊: Journal of Geophysical Research: Oceans
• 作者: Loose, N.;Heimbach, P.;Pillar, H. R.;Nisancioglu, K. H.
• 通讯作者: Nisancioglu, K. H.

SICOPOLIS-AD v1: an open-source adjoint modeling framework for ice sheet simulation enabled by the algorithmic differentiation tool OpenAD

• DOI: 10.5194/gmd-13-1845-2020
• 发表时间: 2020-04
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: Liz C. Logan;S. Narayanan;R. Greve;P. Heimbach

Using a regional ocean model to understand the structure and variability of acoustic arrivals in Fram Strait

• DOI: 10.1121/10.0000513
• 发表时间: 2020-02-01
• 期刊: JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA
• 影响因子: 2.4
• 作者: Geyer, Florian;Sagen, Hanne;Vazquez, Heriberto J.
• 通讯作者: Vazquez, Heriberto J.

The Importance of Remote Forcing for Regional Modeling of Internal Waves

远程强迫对于内波区域模拟的重要性

• DOI: 10.1029/2019jc015623
• 发表时间: 2020
• 期刊: Journal of Geophysical Research: Oceans
• 作者: Mazloff, Matthew R.;Cornuelle, Bruce;Gille, Sarah T.;Wang, Jinbo
• 通讯作者: Wang, Jinbo