In model comparisons with observational data, not all data contain information that is useful for answering a specific science question. If non-relevant or highly uncertain data are included in a comparison metric, they can reduce the significance of other observations that matter for the scientific process of interest. Sources of noise and correlations among summed quantities within a comparison metric affect the significance of a signal that is useful for testing model skill. In the setting of the tropical Pacific, we introduce an "inquiry dependent" (ID) metric of model-data comparison that determines the relative importance of the TOGA-TAO buoy observations of the ocean temperature, salinity, and horizontal currents for influencing upper-ocean vertical turbulent mixing as represented by the K-Profile Parameterization (KPP) embedded in the MIT general circulation model (MITgcm) for the 2004-2007 time period. The ID metric addresses a challenge that the wind forcing is likely a more significant source of uncertainty for the ocean state than the turbulence itself, and that the observations are correlated in time, space, and across ocean state variables. In this approach the MITgcm is used to infer variability and relationships in and among the data, and to determine the response structures that are most relevant for constraining uncertain parameters. We demonstrate that the ID metric is able to distinguish the effects due to parameter perturbations from those due to uncertain winds and that it is important to include multiple kinds of data in the comparison, suggesting that the ID metric is appropriate for use in a calibration of the KPP model parameters using mooring observations of the ocean state.
A Framework for Hybrid Physics-AI Coupled Ocean Models
New York University（纽约大学）；Princeton University Atmospheric and Oceanic Sciences Program（普林斯顿大学大气与海洋科学项目）；Lamont-Doherty Earth Observatory at Columbia University（哥伦比亚大学拉蒙特-多尔蒂地球观测站）；NOAA Geophysical Fluid Dynamics Laboratory（美国国家海洋和大气管理局地球物理流体动力学实验室）；NSF-National Center for Atmospheric Research（美国国家科学基金会国家大气研究中心）；Columbia University（哥伦比亚大学）；Department of Civil and Environmental Engineering, Rowan University（罗文大学土木与环境工程系）；Center for Data Science, New York University（纽约大学数据科学中心）
Impact of Data-Driven Eddy Parameterization on Climate State in an Idealized Coupled CESM Model
Courant Institute School of Mathematics, Computing, and Data Science, New York University（纽约大学柯朗数学科学研究所）；Atmospheric and Oceanic Sciences Program, Princeton University（普林斯顿大学大气与海洋科学项目）
Data Driven Air Entrainment Velocity Parameterization by Breaking Waves
NASA/GSFC（美国国家航空航天局戈达德太空飞行中心）；Department of Mechanical Engineering, University of Texas at Dallas（德克萨斯大学达拉斯分校机械工程系）；Earth System Science Interdisciplinary Center, University of Maryland（马里兰大学地球系统科学跨学科中心）
Calibration of a neural network ocean closure for improved mean state and variability
Courant Institute School of Mathematics, Computing, and Data Science, New York University, New York, NY, USA（科朗学院数学、计算与数据科学学院，纽约大学，纽约，纽约州，美国）；Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA（大气与海洋科学项目，普林斯顿大学，普林斯顿，新泽西州，美国）
NORi: An ML-Augmented Ocean Boundary Layer Parameterization
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology（麻省理工学院地球、大气与行星科学系）；Center for Computational Science and Engineering, Massachusetts Institute of Technology（麻省理工学院计算科学与工程中心）；Department of Physics, Imperial College London（伦敦帝国学院物理系）；atdepth；Aeolus Labs（Aeolus实验室）；Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino（托里诺理工学院环境、土地与基础设施工程系）
Adjoint-based online learning of two-layer quasi-geostrophic baroclinic turbulence
Hong Kong University of Science and Technology（香港科技大学）；Institut de Physique du Globe de Paris（巴黎地球物理研究所）；Université Grenoble-Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；National Oceanography Centre, Southampton（南安普顿国家海洋中心）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）
Reduced Cloud Cover Errors in a Hybrid AI-Climate Model Through Equation Discovery And Automatic Tuning
Faculty of Geosciences and Environment, University of Lausanne（洛桑大学地球与环境科学学院）；Expertise Center for Climate Extremes, University of Lausanne（洛桑大学气候极端研究中心）；University of Bremen, Institute of Environmental Physics (IUP)（不来梅大学环境物理研究所）
On the sensitivity of coastal quasigeostrophic edge wave interaction to bottom boundary characteristics: possible implications for eddy parameterizations
Generative data assimilation highlights fronts as key regulators of ocean energy cascade
University of Washington（华盛顿大学）；California Institute of Technology（加州理工学院）；ENS, PSL Université, Ecole Polytechnique, Sorbonne Université, CNRS（法国高等师范学院、巴黎文理研究大学、巴黎综合理工学院、索邦大学、法国国家科学研究中心）
Bayesian Optimization for reanalysis and calibration of highly energetic sea state events simulated with a spectral third-generation wave model
EDF R&D, National Laboratory for Hydraulics and Environment (LNHE)（法国电力集团研发部，国家水力学与环境实验室（LNHE））；Saint-Venant Hydraulics Laboratory, LHSV, ENPC, Institut Polytechnique de Paris（圣维南水力学实验室，ENPC，巴黎理工学院）
