Impact of geothermal heating on the global ocean circulation

The response of a global circulation model to a uniform geothermal heat flux of 50 mW m-2 through the sea floor is examined. If the geothermal heat input were transported upward purely by diffusion, the deep ocean would warm by 1.2°C. However, geothermal heating induces a substantial change in the deep circulation which is larger than previously assumed and subsequently the warming of the deep ocean is only a quarter of that suggested by the diffusive limit. The numerical ocean model responds most strongly in the Indo-Pacific with an increase in meridional overturning of 1.8 Sv, enhancing the existing overturning by approximately 25%

Parameterizing Vertical Mixing Coefficients in the Ocean Surface Boundary Layer using Neural Networks

Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data. One such component is the eddy diffusivity model, where vertical turbulent fluxes of a quantity are parameterized from a variable eddy diffusion coefficient and the mean vertical gradient of the quantity. In this work, we improve a parameterization of vertical mixing in the ocean surface boundary layer by enhancing its eddy diffusivity model using data-driven methods, specifically neural networks. The neural networks are designed to take extrinsic and intrinsic forcing parameters as input to predict the eddy diffusivity profile and are trained using output data from a second moment closure turbulent mixing scheme. The modified vertical mixing scheme predicts the eddy diffusivity profile through online inference of neural networks and maintains the conservation principles of the standard ocean model equations, which is particularly important for its targeted use in climate simulations. We describe the development and stable implementation of neural networks in an ocean general circulation model and demonstrate that the enhanced scheme outperforms its predecessor by reducing biases in the mixed-layer depth and upper ocean stratification. Our results demonstrate the potential for data-driven physics-aware parameterizations to improve global climate models

Deep learning of systematic sea ice model errors from data assimilation increments

Data assimilation is often viewed as a framework for correcting short-term error growth in dynamical climate model forecasts. When viewed on the time scales of climate however, these short-term corrections, or analysis increments, can closely mirror the systematic bias patterns of the dynamical model. In this study, we use convolutional neural networks (CNNs) to learn a mapping from model state variables to analysis increments, in order to showcase the feasibility of a data-driven model parameterization which can predict state-dependent model errors. We undertake this problem using an ice-ocean data assimilation system within the Seamless system for Prediction and EArth system Research (SPEAR) model, developed at the Geophysical Fluid Dynamics Laboratory, which assimilates satellite observations of sea ice concentration every 5 days between 1982--2017. The CNN then takes inputs of data assimilation forecast states and tendencies, and makes predictions of the corresponding sea ice concentration increments. Specifically, the inputs are states and tendencies of sea ice concentration, sea-surface temperature, ice velocities, ice thickness, net shortwave radiation, ice-surface skin temperature, sea-surface salinity, as well as a land-sea mask. We find the CNN is able to make skillful predictions of the increments in both the Arctic and Antarctic and across all seasons, with skill that consistently exceeds that of a climatological increment prediction. This suggests that the CNN could be used to reduce sea ice biases in free-running SPEAR simulations, either as a sea ice parameterization or an online bias correction tool for numerical sea ice forecasts.Comment: 38 pages, 8 figures, 10 supplementary figure

Geothermal Heating and its Influence on the Meridional Overturning Circulation

The effect of geothermal heating on the meridional overturning circulation is examined using an idealized, coarse-resolution ocean general circulation model. This heating is parameterized as a spatially uniform heat flux of 50 mW m-2 through the (flat) ocean floor, in contrast with previous studies that have considered an isolated hotspot or a series of plumes along the mid-Atlantic ridge. The equilibrated response is largely advective: a deep perturbation of the meridional overturning cell on the order of several Sv is produced, connecting with an upper-level circulation at high latitudes, allowing the additional heat to be released to the atmosphere. Risingmotion in the perturbation deep cell is concentrated near the equator. The upward penetration of this cell is limited by the thermocline, analogous to the role of the stratosphere in limiting the upward penetration of convective plumes in the atmosphere. The magnitude of the advective response is inversely proportional to the deep stratification; with a weaker background meridional overturning circulation and a less stratified abyss, the overturning maximum of the perturbation deep cell is increased. This advective response also cools the low-latitude thermocline. The qualitative behavior is similar in both a single hemisphere and double hemisphere configuration.The anomalous circulation driven by geothermal fluxes is more substantial than previously thought. We are able to understand the structure and strength of the response in the idealized geometry and further extend these ideas to explain the results of Adcroft et al. [2001], where the impact of geothermal heating was examined using a global configuration

Ocean currents break up a tabular iceberg

In December 2020, giant tabular iceberg A68a (surface area 3900 km 2 ) broke up in open ocean much deeper than its keel, indicating that the breakage was not immediately caused by collision with the seafloor. Giant icebergs with lengths exceeding 18.5 km account for most of the calved ice mass from the Antarctic Ice Sheet. Upon calv - ing, they drift away and transport freshwater into the Southern Ocean, modifying ocean circulation, disrupting sea ice and the marine biosphere, and potentially triggering changes in climate. Here, we demonstrate that the A68a breakup event may have been triggered by ocean-current shear, a new breakup mechanism not previously reported. We also introduce methods to represent giant icebergs within climate models that currently do not have any representation of them. These methods open opportunities to explore the interactions between icebergs and other components of the climate system and will improve the fidelity of global climate simulation

Comparing ocean surface boundary vertical mixing schemes including langmuir turbulence

Six recent Langmuir turbulence parameterization schemes and five traditional schemes are implemented in a common single‐column modeling framework and consistently compared. These schemes are tested in scenarios versus matched large eddy simulations, across the globe with realistic forcing (JRA55‐do, WAVEWATCH‐III simulated waves) and initial conditions (Argo), and under realistic conditions as observed at ocean moorings. Traditional non‐Langmuir schemes systematically underpredict large eddy simulation vertical mixing under weak convective forcing, while Langmuir schemes vary in accuracy. Under global, realistic forcing Langmuir schemes produce 6% (−1% to 14% for 90% confidence) or 5.2 m (−0.2 m to 17.4 m for 90% confidence) deeper monthly mean mixed layer depths than their non‐Langmuir counterparts, with the greatest differences in extratropical regions, especially the Southern Ocean in austral summer. Discrepancies among Langmuir schemes are large (15% in mixed layer depth standard deviation over the mean): largest under wave‐driven turbulence with stabilizing buoyancy forcing, next largest under strongly wave‐driven conditions with weak buoyancy forcing, and agreeing during strong convective forcing. Non‐Langmuir schemes disagree with each other to a lesser extent, with a similar ordering. Langmuir discrepancies obscure a cross‐scheme estimate of the Langmuir effect magnitude under realistic forcing, highlighting limited understanding and numerical deficiencies. Maps of the regions and seasons where the greatest discrepancies occur are provided to guide further studies and observations

Challenges and Prospects in Ocean Circulation Models

We revisit the challenges and prospects for ocean circulation models following Griffies et al. (2010). Over the past decade, ocean circulation models evolved through improved understanding, numerics, spatial discretization, grid configurations, parameterizations, data assimilation, environmental monitoring, and process-level observations and modeling. Important large scale applications over the last decade are simulations of the Southern Ocean, the Meridional Overturning Circulation and its variability, and regional sea level change. Submesoscale variability is now routinely resolved in process models and permitted in a few global models, and submesoscale effects are parameterized in most global models. The scales where nonhydrostatic effects become important are beginning to be resolved in regional and process models. Coupling to sea ice, ice shelves, and high-resolution atmospheric models has stimulated new ideas and driven improvements in numerics. Observations have provided insight into turbulence and mixing around the globe and its consequences are assessed through perturbed physics models. Relatedly, parameterizations of the mixing and overturning processes in boundary layers and the ocean interior have improved. New diagnostics being used for evaluating models alongside present and novel observations are briefly referenced. The overall goal is summarizing new developments in ocean modeling, including: how new and existing observations can be used, what modeling challenges remain, and how simulations can be used to support observations.Peer reviewe

Challenges and Prospects in Ocean Circulation Models

We revisit the challenges and prospects for ocean circulation models following Griffies et al. (2010). Over the past decade, ocean circulation models evolved through improved understanding, numerics, spatial discretization, grid configurations, parameterizations, data assimilation, environmental monitoring, and process-level observations and modeling. Important large scale applications over the last decade are simulations of the Southern Ocean, the Meridional Overturning Circulation and its variability, and regional sea level change. Submesoscale variability is now routinely resolved in process models and permitted in a few global models, and submesoscale effects are parameterized in most global models. The scales where nonhydrostatic effects become important are beginning to be resolved in regional and process models. Coupling to sea ice, ice shelves, and high-resolution atmospheric models has stimulated new ideas and driven improvements in numerics. Observations have provided insight into turbulence and mixing around the globe and its consequences are assessed through perturbed physics models. Relatedly, parameterizations of the mixing and overturning processes in boundary layers and the ocean interior have improved. New diagnostics being used for evaluating models alongside present and novel observations are briefly referenced. The overall goal is summarizing new developments in ocean modeling, including: how new and existing observations can be used, what modeling challenges remain, and how simulations can be used to support observations