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We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice–Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25–125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model.

Widespread mismatches between proxy-based and modelling studies of the Last Glacial Maximum (LGM) has limited better understanding about interglacial-glacial climate change. In this study, we incorporate non-breaking surface waves (NBW) induced mixing into an ocean model to assess the potential role of waves in changing a simulation of LGM upper oceans. Our results show a substantial 40 m subsurface warming introduced by surface waves in LGM summer, with larger magnitudes relative to the present-day ocean. At the ocean surface, according to the comparison between the proxy data and our simulations, the incorporation of the surface wave process into models can potentially decrease the model-data discrepancy for the LGM ocean. Therefore, our findings suggest that the inclusion of NBW is helpful in simulating glacial oceans.

The impact of Arctic sea ice decline on the weather and climate in midlatitudes is still much debated, with observations suggesting a strong link and models a much weaker link. In this study, we use the atmospheric model OpenIFS in a set of model experiments following the protocol outlined in the Polar Amplification Model Intercomparison Project (PAMIP) to investigate whether the simulated atmospheric response to future changes in Arctic sea ice fundamentally depends on model resolution. More specifically, we increase the horizontal resolution of the model from 125 to 39 km with 91 vertical levels; in a second step, resolution is further increased to 16 km with 137 levels in the vertical. The model does produce a response to sea ice decline with a weaker midlatitude Atlantic jet and increased blocking in the high-latitude Atlantic, but no sensitivity to resolution can be detected with 100 members. Furthermore, we find that the ensemble convergence toward the mean is not impacted by the model resolutions considered here.

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models. The degree to which Arctic sea ice decline influences the mid-latitude atmospheric circulation is widely debated. Here, the authors use a coordinated multi-model experiment to show that Arctic sea ice loss causes a weakening of the mid-latitude westerly winds, but the effect is overall small.

Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.

In this study, we evaluate the performance of the latest version of the Alfred Wegener Institute Climate Model, AWI-CM3, in two configurations at different resolutions, demonstrating that higher spatial resolution substantially enhances the model’s ability to reproduce key climate variables and processes. The medium-resolution configuration consistently reduces climatological biases compared to both the low-resolution setup and the CMIP6 (Coupled Model Intercomparison Project, phase 6) multi-model mean, particularly in polar regions and areas characterized by strong mesoscale dynamics. Improvements are especially notable in the simulation of sea ice variability, ocean circulation, and ocean-atmosphere interactions. The medium-resolution simulation also exhibits greater interannual variability, which may reflect a more realistic representation of underlying processes, but whose implications will need to be fully assessed with multiple ensemble members. We conclude that long-term, eddy-permitting climate projections offer promising avenues for reducing structural uncertainties in future climate projections. As global modeling efforts move toward CMIP7 and beyond, our results highlight the importance of pursuing medium-resolution strategies in parallel with improved physical parameterizations and ensemble-based evaluation to more robustly capture the nonlinearities of the Earth system.

A new global climate model setup using FESOM2.0 for the sea ice‐ocean component and ECHAM6.3 for the atmosphere and land surface has been developed. Replacing FESOM1.4 by FESOM2.0 promises a higher efficiency of the new climate setup compared to its predecessor. The new setup allows for long‐term climate integrations using a locally eddy‐resolving ocean. Here it is evaluated in terms of (1) the mean state and long‐term drift under preindustrial climate conditions, (2) the fidelity in simulating the historical warming, and (3) differences between coarse and eddy‐resolving ocean configurations. The results show that the realism of the new climate setup is overall within the range of existing models. In terms of oceanic temperatures, the historical warming signal is of smaller amplitude than the model drift in case of a relatively short spin‐up. However, it is argued that the strategy of “de‐drifting” climate runs after the short spin‐up, proposed by the HighResMIP protocol, allows one to isolate the warming signal. Moreover, the eddy‐permitting/resolving ocean setup shows notable improvements regarding the simulation of oceanic surface temperatures, in particular in the Southern Ocean. Key Points A new climate model setup using an unstructured‐mesh ocean‐sea ice component has been developed Mean state and long‐term drift under preindustrial climate conditions are evaluated Modeled climates using coarse and eddy‐resolving ocean configurations are compared

We report on the first multi-year kilometre-scale global coupled simulations using ECMWF's Integrated Forecasting System (IFS) coupled to both the NEMO and FESOM ocean–sea ice models, as part of the H2020 Next Generation Earth Modelling Systems (nextGEMS) project. We focus mainly on an unprecedented IFS-FESOM coupled setup, with an atmospheric resolution of 4.4 km and a spatially varying ocean resolution that reaches locally below 5 km grid spacing. A shorter coupled IFS-FESOM simulation with an atmospheric resolution of 2.8 km has also been performed. A number of shortcomings in the original numerical weather prediction (NWP)-focused model configurations were identified and mitigated over several cycles collaboratively by the modelling centres, academia, and the wider nextGEMS community. The main improvements are (i) better conservation properties of the coupled model system in terms of water and energy budgets, which also benefit ECMWF's operational 9 km IFS-NEMO model; (ii) a realistic top-of-the-atmosphere (TOA) radiation balance throughout the year; (iii) improved intense precipitation characteristics; and (iv) eddy-resolving features in large parts of the mid- and high-latitude oceans (finer than 5 km grid spacing) to resolve mesoscale eddies and sea ice leads. New developments at ECMWF for a better representation of snow and land use, including a dedicated scheme for urban areas, were also tested on multi-year timescales. We provide first examples of significant advances in the realism and thus opportunities of these kilometre-scale simulations, such as a clear imprint of resolved Arctic sea ice leads on atmospheric temperature, impacts of kilometre-scale urban areas on the diurnal temperature cycle in cities, and better propagation and symmetry characteristics of the Madden–Julian Oscillation.

Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snowpack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

A new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea‐surface salinity and sea‐level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea‐ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere‐ocean‐ice interaction occurs. One‐year‐long sea‐ice forecasts initialized on 1 January, 1 April, 1 July and 1 October from 2003 to 2019 are described. To correct systematic forecast errors, sea‐ice concentration from 2011 to 2019 is calibrated by trend‐adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea‐ice edge raw forecast skill is within the range of operational global subseasonal‐to‐seasonal forecast systems, outperforming a climatological benchmark for about 2 weeks in the Arctic and about 3 weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea‐ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations. Plain Language Summary Ocean data sparseness and systematic model errors pose problems for the initialization of coupled seasonal forecasts, especially in polar regions. Our global forecast system follows a seamless approach with refined ocean resolution in the Arctic. The new version presented here features higher computational efficiency and utilizes more ocean and sea‐ice observations. Ice‐edge forecasts outperform a climatological benchmark for about 1 month, comparable to established systems. Key Points We describe an upgrade of the AWI Coupled Prediction System with new ocean and atmosphere models and more observations assimilated Independent evaluations show advances in the new version on the analysis of the sea‐ice and ocean states against the old one Calibrated sea‐ice edge forecasts outperform a climatological benchmark for around 1 month in both hemispheres