Pavel Perezhogin

• PhD
• PostDoc at Courant Institute of Mathematical Sciences

Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data‐driven methods, with uncertainty quantification. For example, Guillaumin and Zanna (2021, https://doi.org/10.1029/2021ms002534) proposed a Machine Learning (ML) model th...

We propose a data-driven framework to describe spatiotemporal climate variability in terms of a few entities and their causal linkages. Given a high-dimensional climate field, the methodology first reduces its dimension- ality into a set of regionally constrained patterns. Causal relations among such patterns are then inferred in the interventional...

Ocean mesoscale eddies are often poorly represented in climate models, and therefore, their effects on the large scale circulation must be parameterized. Traditional parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020), https://doi.or...

Mesoscale eddies produce lateral (2D) fluxes that need to be parameterized in eddy-permitting (1/4-degree) global ocean models due to insufficient horizontal resolution. Here, we systematically apply methods from the 3D LES community to parameterize lateral vorticity fluxes produced by mesoscale eddies leveraging an explicit filtering approach toge...

Ocean models at intermediate resolution (1/4°), which partially resolve mesoscale eddies, can be seen as Large eddy simulations of the primitive equations, in which the effect of unresolved eddies must be parameterized. In this work, we propose new subgrid models that are consistent with the physics of two‐dimensional flows. We analyze subgrid flux...

Accurately quantifying air-sea fluxes is important for understanding air-sea interactions and improving coupled weather and climate systems. This study introduces a probabilistic framework to represent the highly variable nature of air-sea fluxes, which is missing in deterministic bulk algorithms. Assuming Gaussian distributions conditioned on the...

Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the climate system. Parameterization is an approach to capture the effect of these processes, without resolving them explicitly. In recent years, data-driven parameterization...

This study addresses the boundary artifacts in machine-learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023). We focus on the boundary condition (BC) treatment within the existing convolutional neural network (CNN) models and aim to mitig...

• A data-driven mesoscale eddy parameterization is implemented and evaluated in the GFDL MOM6 ocean model • Filtering schemes are proposed to improve the numerical and physical properties of the parameterization • The subgrid parameterization improves the representation of the energy distributions and the climatological mean flow

We propose a data-driven framework to simplify the description of spatiotemporal climate variability into few entities and their causal linkages. Given a high-dimensional climate field, the methodology first reduces its dimensionality into a set of regionally constrained patterns. Time-dependent causal links are then inferred in the interventional...

We address the question of how to use a machine learned (ML) parameterization in a general circulation model (GCM), and assess its performance both computationally and physically. We take one particular ML parameterization (Guillaumin & Zanna, 2021, https://doi.org/10.1002/essoar.10506419.1) and evaluate the online performance in a different model...

Integration of machine learning (ML) models of unresolved dynamics into numerical simulations of fluid dynamics has been demonstrated to improve the accuracy of coarse resolution simulations. However, when trained in a purely offline mode, integrating ML models into the numerical scheme can lead to instabilities. In the context of a 2D, quasi-geost...

Ocean models at intermediate resolution (1/4 degree), which partially resolve mesoscale eddies, can be seen as Large eddy simulations (LES) of the primitive equations, in which the effect of unresolved eddies must be parameterized. In this work, we propose new subgrid models that are consistent with the physics of two-dimensional (2D) flows. We ana...

We address the question of how to use a machine learned parameterization in a general circulation model, and assess its performance both computationally and physically. We take one particular machine learned parameterization \cite{Guillaumin1&Zanna-JAMES21} and evaluate the online performance in a different model from which it was previously tested...

Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data-driven methods, with uncertainty quantification. For example, Guillaumin and Zanna (2021) proposed a Machine Learning (ML) model that predicts subgrid forcing and its lo...

Plain Language Summary Accurately predicting climate change requires running intensive computer simulations called climate models. Climate models divide the world into grid cells, solving an approximation of continuous equations that model the true dynamics. For accurate predictions, these cells must be small, or equivalently models must be high‐re...

Optimal disturbances of a turbulent stably stratified plane Couette flow in a wide range of Reynolds and Richardson numbers are studied. These disturbances are computed based on a simplified system of equations in which turbulent Reynolds stresses and heat fluxes are approximated by isotropic viscosity and diffusivity with the coefficients obtained...

Large-scale inclined organized structures in stably stratified turbulent shear flows were revealed in the numerical simulation and indirectly confirmed by the field measurements in the stable atmospheric boundary layer. Spatial scales and forms of these structures coincide with those of the optimal disturbances of a simplified linear model. In this...

In this paper, we present a parallel version of the finite-element model of the Arctic Ocean (FEMAO) configured for the White Sea and based on MPI technology. This model consists of two main parts: an ocean dynamics model and a surface ice dynamics model. These parts are very different in terms of the number of computations because the complexity o...

In this paper, we present a parallel version of the finite element model of the Arctic Ocean (FEMAO) configured for the White sea and based on the MPI technology. This model consists of two main parts: an ocean dynamics model and a surface ice dynamics model. These parts are very different in terms of the amount of computations because the complexi...

Eddy-permitting numerical ocean models resolve mesoscale turbulence only partly, that leads to underestimation of eddy kinetic energy (EKE). Mesoscale dynamics can be amplified by using kinetic energy backscatter (KEB) parameterizations returning energy from the unresolved scales. We consider two types of KEB: stochastic and negative viscosity ones...

In the present work the possibility of turbulence closure applying to improve barotropic jet instability simulation at coarse grid resolutions is considered. This problem is analogous to situations occurring in eddy-permitting ocean models when Rossby radius of deformation is partly resolved on a computational grid. We show that the instability is...

Eddy-permitting numerical ocean models often resolve mesoscale turbulence only partly, which leads to underestimation of eddy kinetic energy (EKE). Mesoscale dynamics can be amplified by using kinetic energy backscatter (KEB) parameterizations returning energy from the unresolved scales. We consider two types of KEB: stochastic and negative viscosi...

The problem of modelling 2D isotropic turbulence in a periodic rectangular domain excited by the forcing pattern of prescribed spatial scale is considered. This setting could be viewed as the simplest analogue of the large scale quasi-2D circulation of the ocean and the atmosphere. Since the direct numerical simulation (DNS) of this problem is usua...

Statistical properties of different finite-dimensional approximations of two-dimensional ideal fluid equations are studied. A special class of approximations introduced by A.M. Obukhov (systems of hydrodynamic type) is considered. Vorticity distributions over area and quasi-equilibrium coherent structures are studied. These coherent structures are...

In this work we are considering the problem of modeling 2D isotropic turbulence in a periodic rectangular domain excited by the forcing pattern of prescribed spatial scale. This setting could be viewed as the simplest analog of the large scale quasi 2D circulation of the ocean and the atmosphere. Since the direct numerical simulation (DNS) of this...

Contrary to a viscous fluid at high Reynolds numbers, the equations of a two-dimensional ideal fluid have an infinite number of invariants, the presence of which complicates both its statistical description and the numerical modeling. In this study, the numerical modeling of quasi-equilibrium states of an ideal fluid is carried out at a high resolu...

The influence of numerical approximations on statistical characteristics of modelled two-dimensional turbulence sustained by a stochastic external forcing is studied. The ability of various finite-difference and semi-Lagrangian schemes to reproduce reliably the dual energy and enstrophy cascades for coarse spatial resolution is tested. It is also s...

Equilibrium states of Arakawa approximations of a two-dimensional incompressible inviscid fluid are investigated in the case of high resolution 81922. Comparison of these states with quasiequilibrium states of a viscid fluid is made. Special attention is paid to the stepped shape of large coherent structures and to the presence of small vortices in...