Mechanical coupling of the atmosphere to the ocean surface in general circulation models is represented using bulk wind stress formulations. The stress is often based on either absolute wind velocity, τa, or the more correct wind velocity relative to the ocean surface currents, τr. Here, we use coarse-graining to disentangle wind work by these formulations at different length-scales. We show that both can be reasonably accurate in forcing the ocean at length-scales larger than the mesoscales, with τa overestimating wind work by 10%. However, τa and τr show stark and opposing systematic biases in how they drive the mesoscales; τa does negligible (albeit positive) work on the mesoscales, while τr yields eddy-killing (negative work) that is artificially exaggerated by a factor of ≈4. We derive an analytical criterion for eddy-killing to occur, which shows that exaggerated eddy killing is due to resolution mismatch between the atmosphere and ocean. Our criterion highlights the disproportionate effect small-scale winds Ο(100)km can have on the dynamics of mesoscale ocean eddies, despite the dominant atmospheric motions being at length-scales larger than Ο(103) km. The eddy-killing criterion shows that large-scale winds do not necessarily cause eddy-killing but are merely an amplification factor for wind work on the mesoscales, which can be either positive or negative depending on the local alignment of small-scale winds with the ocean eddies. We propose a simple reformulation of τr, without introducing tuning parameters, to remove spurious eddy-killing from air-sea resolution mismatch that is often present in climate models.

This work documents version two of the Department of Energy’s Energy Exascale Earth System Model (E3SM). E3SM version 2 (E3SMv2) is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid-latitudes and 30 km at the equator and poles. The model performance is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima (DECK) simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate is generally realistic, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Programme (WCRP) assessment. However, E3SMv2 significantly underestimates the global mean surface temperature in the second half of the historical record. An analysis of single-forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol-related forcing.

The physical circulation of the Southern Ocean sets the surface concentration and thus air-sea exchange of CO2. However, we have a limited understanding of the three-dimensional circulation that brings deep carbon-rich waters to the surface. Here, we introduce and analyze a novel high-resolution ocean model simulation with active biogeochemistry and online Lagrangian particle tracking. We focus our attention on a subset of particles with high dissolved inorganic carbon (DIC) that originate below 1000 m and eventually upwell into the surface mixed layer. We find that 71% of the DIC-enriched water upwelling across 1000 m is concentrated near topographic features, which occupy just 33% of the Antarctic Circumpolar Current. Once particles upwell to the surface mixed layer, their DIC decorrelates on timescales of ~1.5 months—an order of magnitude longer than their residence time. Our results show that Southern Ocean bathymetry plays a key role in delivering carbon-rich waters to the surface.

The objective of this study is to understand relations between multiple physical and environmental factors and red  tide, which is a common name for harmful algal blooms occurring along coastal regions worldwide. Large concentrations of Karenia brevis, a toxic mixotrophic dinoflagellate, make up the red tide along the West Florida Shelf (WFS) in the Gulf of Mexico. Besides being toxic, red tide causes unpleasant odor and scenery, which result in multiple environmental and socioeconomic impacts and public health issues.  Understanding the physical and biogeochemical processes that control the occurrence of red tide is important for studying the impact of climate change on red tide frequency, and accordingly assessing the future environmental and socioeconomic impacts of red tide under different mitigation techniques and climate scenarios. We use observation and reanalysis data in the WFS to train machine learning (ML) models to predict red tide, as a classification problem of large bloom or no bloom. We develop the ML model using seasonal input data of Peace River and Caloosahatchee River outflow, alongshore and offshore wind speed, and Loop Current position. The Loop Current, which is a warm ocean current that enters and loops through the Gulf of Mexico before exiting to join the Gulf Stream, can be detected from sea surface height. In addition to the observation and reanalysis data, these variables can be simulated by the Earth system models (ESMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), especially by the high-resolution models of the High Resolution Model Intercomparison Project (HighResMIP) of CMIP6. This is needed to understand the frequency and future trends of red tide under different Shared Socioeconomic Pathways (SSPs) of CMIP6. In this preliminary study, we investigate the impact of different choices regarding ML model selection and training dataset on the accuracy of red tide prediction, and the physical interpretation of the results. We also discuss the validation of ESMs data for predictive modeling, and ensemble methods for improving predictive performance. The study provides several insights that can be useful for predicting the future trends of red tide under SSPs using CMIP6 data.