Description: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958–2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (∼ 1∘) to eddy-resolving (∼ 0.1∘) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar, and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and the Antarctic Circumpolar Current – are significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve” most of these features, the improvements in temperature and salinity are inconsistent among the different model families, and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.

Description: The evaluation and model element description of the second version of the unstructured-mesh Finite-volumE Sea ice-Ocean Model (FESOM2.0) are presented. The new version of the model takes advantage of the finite-volume approach, whereas its predecessor version, FESOM1.4 was based on the finite-element approach. The model sensitivity to arbitrary Lagrangian–Eulerian (ALE) linear and nonlinear free-surface formulation, Gent–McWilliams eddy parameterization, isoneutral Redi diffusion and different vertical mixing schemes is documented. The hydrographic biases, large-scale circulation, numerical performance and scalability of FESOM2.0 are compared with its predecessor, FESOM1.4. FESOM2.0 shows biases with a magnitude comparable to FESOM1.4 and simulates a more realistic Atlantic meridional overturning circulation (AMOC). Compared to its predecessor, FESOM2.0 provides clearly defined fluxes and a 3 times higher throughput in terms of simulated years per day (SYPD). It is thus the first mature global unstructured-mesh ocean model with computational efficiency comparable to state-of-the-art structured-mesh ocean models. Other key elements of the model and new development will be described in follow-up papers.

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.

Description: Ocean model biases such as the North West corner cold bias connected to the location of the Gulf Stream path, the warm bias in upwelling zones, the warm bias in the Southern Ocean, and model drift like the deep ocean warm bias which tends to peak in around 800 to 1000 m depth in the Atlantic Ocean are issues common among state-of-the-art ocean models. These issues are often amplified when the ocean model is coupled to an atmosphere model to perform climate simulations. Furthermore, unrealistic freezing of the Labrador Sea is an issue in various climate models. With the unstructured mesh approach in our Finite Element Sea ice Ocean Model (FESOM) we are able to systematically investigate the benefits of local refinement of the ocean model grid both in an uncoupled set-up (sea-ice ocean only) as well as in a fully coupled climate model (atmosphere- land-sea ice-ocean). While the horizontal ocean model resolution is 25 km on average in the finer grids, we refine the grids in some key areas to up to 5 km. Therefore we can explicitly resolve ocean eddies and simulate eddy-mean flow interactions in these key areas. The atmosphere-land component of our AWI-CM (Alfred Wegener Institute Climate Model) is ECHAM6-JSBACH developed at the Max-Planck-Institute for Meteorology in Hamburg, Germany. Here we present results of century-long uncoupled and coupled simulations on ocean model grids with different local refinements while keeping the atmosphere resolution constant in the coupled simulations. Results indicate that high horizontal resolutions in key regions such as the Gulf Stream / North Atlantic Current area or the Agulhas Stream can reduce biases such as the North West corner cold bias and the deep ocean model drift.

Description: The polar atmospheric surface layer is often stably stratified, which strongly influences turbulent transport processes between the atmosphere and sea ice/ocean. Transport is usually parametrized applying Monin Obukhov Similarity Theory (MOST) which delivers transfer coefficients as a function of stability parameters (see below). In a series of papers (Gryanik and Lüpkes, 2018; Gryanik et al., 2020,2021; Gryanik and Lüpkes, 2022) it has been shown that differences between existing parametrizations are large, especially for strong stability. One reason is that they are based on different data sets, for which the origin of differences is still unclear. In this situation Gryanik et al. (2021) as well as Gryanik and Lüpkes (2022) proposed a numerically efficient method, which can be used for most of the existing data sets and their specific stability dependences. A package of parametrization resulted that is suitable for its application in weather prediction and climate models. Especially, calculation of fluxes over sea ice were improved. Combined with latest parametrizations of surface roughness it has a large impact on large scale fields as shown recently by Schneider et al. (2021) who applied some members of the package.