Scale analysis based on coarse-graining has been proposed recently as an alternative to Fourier analysis. It requires interpolation to a regular mesh for data from unstructured-mesh models. We propose an alternative coarse-graining method which relies on implicit filters using powers of discrete Laplacians. This method can work on arbitrary (structured or unstructured) meshes and is applicable to the direct output of unstructured-mesh models. Illustrations and details are provided for discrete fields placed at vertices of triangular meshes. The placement on triangles is also briefly discussed.

The Alfred Wegener Institute Climate Model (AWI-CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice-ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max-Planck-Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI-CM performs better than the average of CMIP5 models. AWI-CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea ice extent in September over the past 30 years is 20-30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present-day climate under the strong emission scenario SSP585 are similar to the multi-model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2 to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical and Southern Ocean. Furthermore, in the northern mid-latitudes in boreal summer and autumn as well as in the southern mid-latitudes a more zonal atmospheric flow is projected throughout the year.

We propose to make the damping time scale, which governs the decay of pseudo-elastic waves in the Elastic Viscous Plastic (EVP) sea ice solvers, independent of the external time step and large enough to warrant numerical stability for a moderate number of internal time steps. In this case, EVP becomes very close to the recently proposed modified EVP (mEVP) method in terms of stability. With the proposed damping time scale, the numerical stability of EVP is independent of mesh resolution in setups where the sea ice model component is called every time step of the ocean model. In a simple test case dealing with sea ice breaking under the action of a moving cyclone, EVP with specified damping time scales can produce linear kinematic features very similar to those from the mEVP method. There is more difference in simulated Arctic sea ice thickness and linear kinematic features in realistic configurations, but the difference is minor considering model uncertainties associated with parameter choices in sea ice models.

Substantial changes have occurred in the Arctic Ocean in the last decades. Not only sea ice has retreated significantly, but also the ocean at mid-depth showed a warming tendency. By using simulations we identified a mechanism that intensifies the upward trend in ocean heat supply to the Arctic Ocean through Fram Strait. The reduction in sea ice export through Fram Strait induced by Arctic sea ice decline increases the salinity in the Greenland Sea, which lowers the sea surface height and strengthens the cyclonic gyre circulation in the Nordic Seas. The Atlantic Water (AW) volume transport to the Nordics Seas and Arctic Ocean is consequently strengthened. This enhances the warming trend of the Arctic AW layer, potentially contributing to the “Atlantification” of the Eurasian Basin. Therefore, the Nordic Seas can play the role of a switchyard for the Arctic sea ice decline to influence the Arctic heat budget at mid-depth.

We examine the historical and projected hydrography in the deep basin of the Arctic Ocean in 23 climate models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The comparison between historical simulations and observational climatology shows that the simulated Atlantic Water (AW) layer is too deep and too thick among the majority of the models and in the multi-model mean (MMM). Moreover, the halocline is too fresh in the MMM. These issues indicate that there is no visible improvement in the representation of Arctic hydrography in the CMIP6 compared to the CMIP5. The climate projections reveal that the sub-Arctic seas are outstanding warming hotspots, supplying a strong warming trend in the Arctic AW layer. The MMM temperature increase averaged in the upper 700 m till the end of the 21st century in the Arctic Ocean is about 40% and 60% higher than the global mean in the SSP245 and SSP585 scenarios, respectively. Comparing the AW temperature in the present day with its future change among the models shows that the temperature climate change signals are not sensitive to the model biases in the present-day simulations. The upper-ocean salinity is projected to become fresher in the Arctic deep basin in the MMM. However, the salinity spread is rather large and the tendency toward stronger upper ocean stratification in the MMM is not shared among all the models. The identified hydrography biases and spread call for a collective effort for systematic improvements of coupled model simulations.

In this paper we assessed the representation of Arctic sea surface salinity (SSS) and liquid freshwater content (FWC) in the historical simulation of 31 CMIP6 models with comparison to 39 CMIP5 models, and investigated the projected changes in Arctic liquid FWC and freshwater budget in two scenarios (SSP245 and SSP585) of the CMIP6 models. While CMIP6 multi-model mean (MMM) shows an amelioration in representing Arctic SSS compared to CMIP5, no significant reduction is found in the overestimation of FWC and overall model spreads of future changes of Arctic freshwater budget. CMIP6 MMM projects a SSS decrease in most parts of the Arctic Ocean, a slight SSS increase in the Eurasian Basin, and the strongest increase in FWC along the periphery of the Arctic Basin. In the historical simulation, the MMM river runoff, net precipitation, Bering Strait and Barents Sea Opening freshwater transports are 93±34 mSv, 58±109 mSv, 80±32 mSv, and -20±17 mSv, respectively. In the last decade of the 21st century, these budget terms will increase to 138±47 mSv, 123±93 mSv, 83±35 mSv, and 33±47 mSv in the SSP585 scenario. Sea ice meltwater flux will decrease to about zero in the mid-21st century in both SSP245 and SSP585. Freshwater exports through Fram and Davis straits will be higher in the future, and the Fram Strait export will remain larger. The Arctic Ocean is projected to hold a total of 160,300±62,330 km3 freshwater in the SSP585 scenario by 2100, about 60% more than its historical climatology.

Using the depth (z) and density (ϱ) frameworks, we analyze local contributions to AMOC variability in a 900-year simulation with the AWI climate model. Both frameworks reveal a consistent interdecadal variability, however the correlation between their maxima deteriorates on year-to-year scales. We demonstrate the utility of analyzing the spatial patterns of sinking and diapycnal transformations through depth levels and isopycnals. The success of this analysis relies on the spatial binning of these maps which is especially crucial for the maps of vertical velocities which appear to be too noisy in the main regions of up- and downwelling because of stepwise bottom topography. Furthermore, we show that the AMOC responds to fast (annual or faster) fluctuations in atmospheric forcing associated with the NAO. This response is more obvious in the ϱ than in the z framework. In contrast, the link between AMOC deep water production south of Greenland is found for slower fluctuations and is consistent between the frameworks.

Many state-of-the-art climate models do not simulate the Atlantic Water (AW) layer in the Arctic Ocean realistically enough to address the question of future Arctic Atlantification and its associated feedback. Biases concerning the AW layer are commonly related to insufficient resolution and exaggerated mixing in the ocean component as well as unrealistic Atlantic-Arctic Ocean exchange. Based on sensitivity experiments with FESOM1.4, the ocean-sea ice component of the global climate model AWI-CM1, we show that even if all impediments for simulating AW realistically are addressed in the ocean model, new biases in the AW layer develop after coupling to an atmosphere model. By replacing the wind forcing over the Arctic with winds from a coupled simulation we show that a common bias in the atmospheric sea level pressure (SLP) gradient and its associated wind bias lead to differences in surface stress and Ekman transport. Fresh surface water gets redistributed leading to changes in steric height distribution. Those changes lead to a strengthening of the anticyclonic surface circulation in the Canadian Basin, so that the deep counterflow carrying warm AW gets reversed and a warm bias in the Canadian Basin develops. An underestimation of sea ice concentration can significantly amplify the induced ocean biases. The SLP and anticyclonic wind bias in the Nordic Seas weaken the cyclonic circulation leading to reduced AW transport into the Arctic Ocean through Fram Strait but increased AW transport through the Barents Sea Opening. These effects together lead to a cold bias in the Eurasian Basin.

As the sea-ice modeling community is shifting to advanced numerical frameworks, developing new sea-ice rheologies, and increasing model spatial resolution, ubiquitous deformation features in the Arctic sea ice are now being resolved by sea-ice models. Initiated at the Forum for Arctic Modelling and Observational Synthesis (FAMOS), the Sea Ice Rheology Experiment (SIREx) aims at evaluating current state-of-the-art sea-ice models using existing and new metrics to understand how the simulated deformation fields are affected by different representations of sea-ice physics (rheology) and by model configuration. Part I of the SIREx analysis is concerned with evaluation of the statistical distribution and scaling properties of sea-ice deformation fields from 35 different simulations against those from the RADARSAT Geophysical Processor System (RGPS). For the first time, the Viscous-Plastic (and the Elastic-Viscous-Plastic variant), Elastic-Anisotropic-Plastic, and Maxwell-Elasto-Brittle rheologies are compared in a single study. We find that both plastic and brittle sea-ice rheologies have the potential to reproduce the observed RGPS deformation statistics, including multi-fractality. Model configuration (e.g. numerical convergence, atmospheric forcing, spatial resolution) and physical parameterizations (e.g. ice strength parameters and ice thickness distribution) both have effects as important as the choice of sea-ice rheology on the deformation statistics. It is therefore not straightforward to attribute model performance to a specific rheological framework using current deformation metrics. In light of these results, we further evaluate the statistical properties of simulated Linear Kinematic Features (LKFs) in a SIREx Part II companion paper.

Simulating sea-ice drift and deformation in the Arctic Ocean is still a challenge because of the multi-scale interaction of sea-ice floes that compose the Arctic sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project formed within the Forum of Arctic Modeling and Observational Synthesis (FAMOS) to collect and design skill metrics to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) and provide guidance for modeling small-scale deformation. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state-of-the-art sea ice models and compared to deformation features derived from RADARSAT Geophysical Processor System (RGPS). All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric forcing affect simulated LKFs as much as the model’s sea ice rheology and numerics. Only in very high resolution simulations (≤2\,km) the concentration and thickness anomalies along LKFs are large enough to affect air-ice-ocean interaction processes.

We examine the historical evolution and projected changes in the hydrography of the deep basin of the Arctic Ocean in 23 climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The comparison between historical simulations and an observational climatology shows that the simulated Atlantic Water (AW) layer is too deep and thick in the majority of models, including the multi-model mean (MMM). Moreover, the halocline is too fresh in the MMM. Overall our findings indicate that there is no obvious improvement in the representation of the Arctic hydrography in CMIP6 compared to CMIP5. The climate change projections reveal that the sub-Arctic seas are outstanding warming hotspots, causing a strong warming trend in the Arctic AW layer. The MMM temperature increase averaged over the upper 700 m at the end of the 21st century is about 40\% and 60\% higher in the Arctic Ocean than the global mean in the SSP245 and SSP585 scenarios, respectively. Salinity in the upper few hundred meters is projected to decrease in the Arctic deep basin in the MMM. However, the spread in projected salinity changes is rather large and the tendency toward stronger upper 400 m ocean stratification in the MMM is not simulated by all the models. The identified biases and projection uncertainties call for a concerted effort for major improvements of coupled climate models.