Description: The Weddell Sea polynya is a large opening in the open-ocean sea ice cover associated with intense deep convection in the ocean. A necessary condition to form and maintain a polynya is the presence of a strong subsurface heat reservoir. This study investigates the processes that control the stratification and hence the buildup of the subsurface heat reservoir in the Weddell Sea. To do so, a climate model run for 200 years under preindustrial forcing with two eddying resolutions in the ocean (0.25° CM2.5 and 0.10° CM2.6) is investigated. Over the course of the simulation, CM2.6 develops two polynyas in the Weddell Sea, while CM2.5 exhibits quasi-continuous deep convection but no polynyas, exemplifying that deep convection is not a sufficient condition for a polynya to occur. CM2.5 features a weaker subsurface heat reservoir than CM2.6 owing to weak stratification associated with episodes of gravitational instability and enhanced vertical mixing of heat, resulting in an erosion of the reservoir. In contrast, in CM2.6, the water column is more stably stratified, allowing the subsurface heat reservoir to build up. The enhanced stratification in CM2.6 arises from its refined horizontal grid spacing and resolution of topography, which allows, in particular, a better representation of the restratifying effect by transient mesoscale eddies and of the overflows of dense waters along the continental slope.

Description: This study examines the role of processes transporting tracers across the Polar Front (PF) in the depth interval between the surface and major topographic sills, which this study refers to as the “PF core.” A preindustrial control simulation of an eddying climate model coupled to a biogeochemical model [GFDL Climate Model, version 2.6 (CM2.6)– simplified version of the Biogeochemistry with Light Iron Nutrients and Gas (miniBLING) 0.1° ocean model] is used to investigate the transport of heat, carbon, oxygen, and phosphate across the PF core, with a particular focus on the role of mesoscale eddies. The authors find that the total transport across the PF core results from a ubiquitous Ekman transport that drives the upwelled tracers to the north and a localized opposing eddy transport that induces tracer leakages to the south at major topographic obstacles. In the Ekman layer, the southward eddy transport only partially compensates the northward Ekman transport, while below the Ekman layer, the southward eddy transport dominates the total transport but remains much smaller in magnitude than the near-surface northward transport. Most of the southward branch of the total transport is achieved below the PF core, mainly through geostrophic currents. This study finds that the eddy-diffusive transport reinforces the southward eddy-advective transport for carbon and heat, and opposes it for oxygen and phosphate. Eddy-advective transport is likely to be the leading-order component of eddy-induced transport for all four tracers. However, eddy-diffusive transport may provide a significant contribution to the southward eddy heat transport due to strong along-isopycnal temperature gradients.

Description: We present a new framework for global ocean–sea-ice model simulations based on phase 2 of the Ocean Model Intercomparison Project (OMIP-2), making use of the surface dataset based on the Japanese 55-year atmospheric reanalysis for driving ocean–sea-ice models (JRA55-do). We motivate the use of OMIP-2 over the framework for the first phase of OMIP (OMIP-1), previously referred to as the Coordinated Ocean–ice Reference Experiments (COREs), via the evaluation of OMIP-1 and OMIP-2 simulations from 11 state-of-the-science global ocean–sea-ice models. In the present evaluation, multi-model ensemble means and spreads are calculated separately for the OMIP-1 and OMIP-2 simulations and overall performance is assessed considering metrics commonly used by ocean modelers. Both OMIP-1 and OMIP-2 multi-model ensemble ranges capture observations in more than 80 % of the time and region for most metrics, with the multi-model ensemble spread greatly exceeding the difference between the means of the two datasets. Many features, including some climatologically relevant ocean circulation indices, are very similar between OMIP-1 and OMIP-2 simulations, and yet we could also identify key qualitative improvements in transitioning from OMIP-1 to OMIP-2. For example, the sea surface temperatures of the OMIP-2 simulations reproduce the observed global warming during the 1980s and 1990s, as well as the warming slowdown in the 2000s and the more recent accelerated warming, which were absent in OMIP-1, noting that the last feature is part of the design of OMIP-2 because OMIP-1 forcing stopped in 2009. A negative bias in the sea-ice concentration in summer of both hemispheres in OMIP-1 is significantly reduced in OMIP-2. The overall reproducibility of both seasonal and interannual variations in sea surface temperature and sea surface height (dynamic sea level) is improved in OMIP-2. These improvements represent a new capability of the OMIP-2 framework for evaluating process-level responses using simulation results. Regarding the sensitivity of individual models to the change in forcing, the models show well-ordered responses for the metrics that are directly forced, while they show less organized responses for those that require complex model adjustments. Many of the remaining common model biases may be attributed either to errors in representing important processes in ocean–sea-ice models, some of which are expected to be reduced by using finer horizontal and/or vertical resolutions, or to shared biases and limitations in the atmospheric forcing. In particular, further efforts are warranted to resolve remaining issues in OMIP-2 such as the warm bias in the upper layer, the mismatch between the observed and simulated variability of heat content and thermosteric sea level before 1990s, and the erroneous representation of deep and bottom water formations and circulations. We suggest that such problems can be resolved through collaboration between those developing models (including parameterizations) and forcing datasets. Overall, the present assessment justifies our recommendation that future model development and analysis studies use the OMIP-2 framework.