A deformation-based parametrization of ocean mesoscale eddy reynolds stresses

Ocean mesoscale eddies strongly affect the strength and variability of large-scale ocean jets such as the Gulf Stream and Kuroshio Extension. Their spatial scales are too small to be fully resolved in many current climate models and hence their effects on the large-scale circulation need to be parametrized. Here we propose a parametrization of mesoscale eddy momentum fluxes based on large-scale flow deformation. The parametrization is argued to be suitable for use in eddy-permitting ocean general circulation models, and is motivated by an analogy between turbulence in Newtonian fluids (such as water) and laminar flow in non-Newtonian fluids. A primitive-equations model in an idealised double-gyre configuration at eddy-resolving horizontal resolution is used to diagnose the relationship between the proposed closure and the eddy fluxes resolved by the model. Favourable correlations suggest the closure could provide an appropriate deterministic parametrization of mesoscale eddies. The relationship between the closure and different representations of the Reynolds stress tensor is also described. The parametrized forcing possesses the key quasi-geostrophic turbulence properties of energy conservation and enstrophy dissipation, and allows for upgradient fluxes leading to the sharpening of vorticity gradients. The implementation of the closure for eddy-permitting ocean models requires only velocity derivatives and a single parameter that scales with model resolution

Applications of deep learning to ocean data inference and subgrid parameterization

Oceanographic observations are limited by sampling rates, while ocean models are limited by finite resolution and high viscosity and diffusion coefficients. Therefore, both data from observations and ocean models lack information at small and fast scales. Methods are needed to either extract information, extrapolate, or upscale existing oceanographic data sets, to account for or represent unresolved physical processes. Here we use machine learning to leverage observations and model data by predicting unresolved turbulent processes and subsurface flow fields. As a proof of concept, we train convolutional neural networks on degraded data from a high-resolution quasi-geostrophic ocean model. We demonstrate that convolutional neural networks successfully replicate the spatiotemporal variability of the subgrid eddy momentum forcing, are capable of generalizing to a range of dynamical behaviors, and can be forced to respect global momentum conservation. The training data of our convolutional neural networks can be subsampled to 10–20% of the original size without a significant decrease in accuracy. We also show that the subsurface flow field can be predicted using only information at the surface (e.g., using only satellite altimetry data). Our results indicate that data-driven approaches can be exploited to predict both subgrid and large-scale processes, while respecting physical principles, even when data are limited to a particular region or external forcing. Our in-depth study presents evidence for the successful design of ocean eddy parameterizations for implementation in coarse-resolution climate models

Building Ocean Climate Emulators

The current explosion in machine learning for climate has led to skilled, computationally cheap emulators for the atmosphere. However, the research for ocean emulators remains nascent despite the large potential for accelerating coupled climate simulations and improving ocean forecasts on all timescales. There are several fundamental questions to address that can facilitate the creation of ocean emulators. Here we focus on two questions: 1) the role of the atmosphere in improving the extended skill of the emulator and 2) the representation of variables with distinct timescales (e.g., velocity and temperature) in the design of any emulator. In tackling these questions, we show stable prediction of surface fields for over 8 years, training and testing on data from a high-resolution coupled climate model, using results from four regions of the globe. Our work lays out a set of physically motivated guidelines for building ocean climate emulators

A conceptual model of ocean heat uptake under climate change

© 2014 American Meteorological Society. Aconceptual model of ocean heat uptake is developed as a multilayer generalization of Gnanadesikan. The roles of Southern Ocean Ekman and eddy transports, North Atlantic Deep Water (NADW) formation, and diapycnal mixing in controlling ocean stratification and transient heat uptake are investigated under climate change scenarios, including imposed surface warming, increased Southern Ocean wind forcing, with or without eddy compensation, and weakened meridional overturning circulation (MOC) induced by reduced NADW formation. With realistic profiles of diapycnal mixing, ocean heat uptake is dominated by Southern Ocean Ekman transport and its long-term adjustment controlled by the Southern Ocean eddy transport. The time scale of adjustment setting the rate of ocean heat uptake increases with depth. For scenarios with increased Southern Ocean wind forcing or weakened MOC, deepened stratification results in enhanced ocean heat uptake. In each of these experiments, the role of diapycnal mixing in setting ocean stratification and heat uptake is secondary. Conversely, in experiments with enhanced diapycnal mixing as employed in ''upwelling diffusion'' slab models, the contributions of diapycnal mixing and Southern Ocean Ekman transport to the net heat uptake are comparable, but the stratification extends unrealistically to the sea floor. The simple model is applied to interpret the output of an Earth system model, the Second Generation Canadian Earth System Model (CanESM2), in which the atmospheric CO2 concentration is increased by 1%yr-1 until quadrupling, where it is found that Southern Ocean Ekman transport is essential to reproduce the magnitude and vertical profile of ocean heat uptake

Optimal Excitation of Interannual Atlantic Meridional Overturning Circulation Variability

The optimal excitation of Atlantic meridional overturning circulation (MOC) anomalies is investigated in an ocean general circulation model with an idealized configuration. The optimal three-dimensional spatial structure of temperature and salinity perturbations, defined as the leading singular vector and generating the maximum amplification of MOC anomalies, is evaluated by solving a generalized eigenvalue problem using tangent linear and adjoint models. Despite the stable linearized dynamics, a large amplification of MOC anomalies, mostly due to the interference of nonnormal modes, is initiated by the optimal perturbations. The largest amplification of MOC anomalies, found to be excited by high-latitude deep density perturbations in the northern part of the basin, is achieved after about 7.5 years. The anomalies grow as a result of a conversion of mean available potential energy into potential and kinetic energy of the perturbations, reminiscent of baroclinic instability. The time scale of growth of MOC anomalies can be understood by examining the time evolution of deep zonal density gradients, which are related to the MOC via the thermal wind relation. The velocity of propagation of the density anomalies, found to depend on the horizontal component of the mean flow velocity and the mean density gradient, determines the growth time scale of the MOC anomalies and therefore provides an upper bound on the MOC predictability time. The results suggest that the nonnormal linearized ocean dynamics can give rise to enhanced MOC variability if, for instance, overflows, eddies, and/or deep convection can excite high-latitude density anomalies in the ocean interior with a structure resembling that of the optimal perturbations found in this study. The findings also indicate that errors in ocean initial conditions or in model parameterizations or processes, particularly at depth, may significantly reduce the Atlantic MOC predictability time to less than a decade.Earth and Planetary Science

Radiative effects of clouds and water vapor on an axisymmetric monsoon

Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement 794063 and the UK Natural Environment Research Council’s Grant NE/R000727/1.Monsoons are summertime circulations shaping climates and societies across the tropics and subtropics. Here the radiative effects controlling an axisymmetric monsoon and its response to climate change are investigated using aquaplanet simulations. The influences of clouds, water vapor, and CO2 on the axisymmetric monsoon are decomposed using the radiation-locking technique. Seasonal variations in clouds and water vapor strongly modulate the axisymmetric monsoon, reducing net precipitation by approximately half. Warming and moistening of the axisymmetric monsoon by seasonal longwave cloud and water vapor effects are counteracted by a strong shortwave cloud effect. The shortwave cloud effect also expedites onset of the axisymmetric monsoon by approximately two weeks, whereas longwave cloud and water vapor effects delay onset. A conceptual model relates the timing of monsoon onset to the efficiency of surface cooling. In climate change simulations CO2 forcing and the water vapor feedback have similar influences on the axisymmetric monsoon, warming the surface and moistening the region. In contrast, clouds have a negligible effect on surface temperature yet dominate the monsoon circulation response. A new perspective for understanding how cloud radiative effects shape the monsoon circulation response to climate change is introduced. The radiation-locking simulations and analyses advance understanding of how radiative processes influence an axisymmetric monsoon, and establish a framework for interpreting monsoon–radiation coupling in observations, in state-of-the-art models, and in different climate states.Publisher PDFPeer reviewe

A data-driven framework for dimensionality reduction and causal inference in climate fields

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 sense through the fluctuation-response formalism, as shown in Baldovin et al. (2020). These two steps allow to explore how regional climate variability can influence remote locations. To distinguish between true and spurious responses, we propose a novel analytical null model for the fluctuation-dissipation relation, therefore allowing for uncertainty estimation at a given confidence level. Finally, we select a set of metrics to summarize the results, offering a useful and simplified approach to explore climate dynamics. We showcase the methodology on the monthly sea surface temperature field at global scale. We demonstrate the usefulness of the proposed framework by studying few individual links as well as "link maps", visualizing the cumulative degree of causation between a given region and the whole system. Finally, each pattern is ranked in terms of its "causal strength", quantifying its relative ability to influence the system's dynamics. We argue that the methodology allows to explore and characterize causal relationships in high-dimensional spatiotemporal fields in a rigorous and interpretable way

A Data-Driven Approach for Parameterizing Submesoscale Vertical Buoyancy Fluxes in the Ocean Mixed Layer

The parameterizations of submesoscale ($<10$km) ocean surface flows are critical in capturing the subgrid effects of vertical fluxes in the ocean mixed layer, yet they struggle to infer the full-complexity of these fluxes in relation to the large scale variables that help set them. In this work, we present a data-driven approach for the submesoscale parameterization, utilizing information from the high-resolution submesoscale-permitting MITgcm-LLC4320 simulation (LLC4320). The new parameterization is given by a Convolutional Neural Network (CNN) trained to infer the subgrid mixed layer vertical buoyancy fluxes as a function of relevant large scale variables. In contrast to previous physics-based approaches, such as the Mixed Layer Eddy (MLE) parameterization, here the CNN infers vertical fluxes that are directly computed from the LLC4320 data, where the submesoscales are resolved down to a resolution of approximately 2km. The CNN has significantly high skill compared with the MLE parameterization, which we demonstrate over a wide range of dynamical regimes and resolutions. We find that the improved skill can be attributed to learned physical relationships between submesoscale fluxes and the large scale strain field, currently missing from submesoscale parameterizations in General Circulation Models

Eddy-mixing entropy and its maximization in forced-dissipative geostrophic turbulence

An equilibrium, or maximum entropy, statistical mechanics theory can be derived for ideal, unforced and inviscid, geophysical flows. However, for all geophysical flows which occur in nature,forcing and dissipation play a major role. Here, a study of eddy-mixing entropy in a forced dissipative barotropic ocean model is presented. We heuristically investigate the temporal evolution of eddy-mixing entropy, as defined for the equilibrium theory, in a strongly forced and dissipative system. It is shown that the eddy-mixing entropy provides a descriptive tool for understanding three stages of the turbulence life cycle: growth of instability; formation of large scale structures; and steady state fluctuations. The fact that the eddy-mixing entropy behaves in a dynamically balanced way is not a priori clear and provides a novel means of quantifying turbulent disorder in geophysical flows. Further, by determining the relationship between the time evolution of entropy and the maximum entropy principle, evidence is found for the action of this principle in a forced dissipative flow. The maximum entropy potential vorticity statistics are calculated for the flow and are compared with numerical simulations. Deficiencies of the maximum entropy statistics are discussed in the context of the mean-field approximation for energy. This study highlights the importance of entropy and statistical mechanics in the study of geostrophic turbulence