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The Coupled Model Intercomparison Project phase 6 (CMIP6) HighResMIP is a new experimental design for global climate model simulations that aims to assess the impact of model horizontal resolution on climate simulation fidelity. We describe a hierarchy of global coupled model resolutions based on the Hadley Centre Global Environment Model 3 – Global Coupled vn 3.1 (HadGEM3-GC3.1) model that ranges from an atmosphere–ocean resolution of 130 km–1∘ to 25 km–1/12∘, all using the same forcings and initial conditions. In order to make such high-resolution simulations possible, the experiments have a short 30-year spinup, followed by at least century-long simulations with constant forcing to assess drift.We assess the change in model biases as a function of both atmosphere and ocean resolution, together with the effectiveness and robustness of this new experimental design. We find reductions in the biases in top-of-atmosphere radiation components and cloud forcing. There are significant reductions in some common surface climate model biases as resolution is increased, particularly in the Atlantic for sea surface temperature and precipitation, primarily driven by increased ocean resolution. There is also a reduction in drift from the initial conditions both at the surface and in the deeper ocean at higher resolution. Using an eddy-present and eddy-rich ocean resolution enhances the strength of the North Atlantic ocean circulation (boundary currents, overturning circulation and heat transport), while an eddy-present ocean resolution has a considerably reduced Antarctic Circumpolar Current strength. All models have a reasonable representation of El Niño–Southern Oscillation. In general, the biases present after 30 years of simulations do not change character markedly over longer timescales, justifying the experimental design.

A new climate model, HadGEM3 N96ORCA1, is presented that is part of the GC3.1 configuration of HadGEM3. N96ORCA1 has a horizontal resolution of ~135 km in the atmosphere and 1° in the ocean and requires an order of magnitude less computing power than its medium‐resolution counterpart, N216ORCA025, while retaining a high degree of performance traceability. Scientific performance is compared to both observations and the N216ORCA025 model. N96ORCA1 reproduces observed climate mean and variability almost as well as N216ORCA025. Patterns of biases are similar across the two models. In the northwest Atlantic, N96ORCA1 shows a cold surface bias of up to 6 K, typical of ocean models of this resolution. The strength of the Atlantic meridional overturning circulation (16 to 17 Sv) matches observations. In the Southern Ocean, a warm surface bias (up to 2 K) is smaller than in N216ORCA025 and linked to improved ocean circulation. Model El Niño/Southern Oscillation and Atlantic Multidecadal Variability are close to observations. Both the cold bias in the Northern Hemisphere (N96ORCA1) and the warm bias in the Southern Hemisphere (N216ORCA025) develop in the first few decades of the simulations. As in many comparable climate models, simulated interhemispheric gradients of top‐of‐atmosphere radiation are larger than observations suggest, with contributions from both hemispheres. HadGEM3 GC3.1 N96ORCA1 constitutes the physical core of the UK Earth System Model (UKESM1) and will be used extensively in the Coupled Model Intercomparison Project 6 (CMIP6), both as part of the UK Earth System Model and as a stand‐alone coupled climate model. Plain Language Summary In this article, a new version of the climate model currently used in the United Kingdom (HadGEM3) is presented and analyzed. The circulation of the atmosphere and the oceans is simulated on a relatively coarse spatial grid with a grid cell size of about 120 km. The advantage of using a coarse spatial grid is that less computing power (on a supercomputer) is needed compared to using a finer grid. This gives an opportunity to do many more simulations of the ways in which Earth's climate may evolve in the decades and centuries ahead. We have carefully compared a simulation of the climate around the year 2000 with climate observations from that time and with a simulation from the same model with a finer spatial grid. Our results show that our new, coarse‐grid version is representing the current climate reasonably well, for instance, with regards to climate variability in the tropics and major ocean currents. However, there are clear differences between the two models. In the coarse‐grid model, the ocean surface is too cold in the northwest Atlantic, while in the fine‐grid version it is too warm in the Southern Ocean around Antarctica. We look into explanations for these inaccuracies. Key Points A low‐resolution, traceable version of the current Met Office Hadley Centre climate model HadGEM3 GC3.1 is presented The scientific performance is comparable to the medium‐resolution version, while requiring much less computational resources In the low‐resolution version the Southern Ocean warm bias is reduced, linked with a more realistic ocean circulation

Purpose of Review Assessment of the impact of ocean resolution in Earth System models on the mean state, variability, and future projections and discussion of prospects for improved parameterisations to represent the ocean mesoscale. Recent Findings The majority of centres participating in CMIP6 employ ocean components with resolutions of about 1 degree in their full Earth System models (eddy-parameterising models). In contrast, there are also models submitted to CMIP6 (both DECK and HighResMIP) that employ ocean components of approximately 1/4 degree and 1/10 degree (eddy-present and eddy-rich models). Evidence to date suggests that whether the ocean mesoscale is explicitly represented or parameterised affects not only the mean state of the ocean but also the climate variability and the future climate response, particularly in terms of the Atlantic meridional overturning circulation (AMOC) and the Southern Ocean. Recent developments in scale-aware parameterisations of the mesoscale are being developed and will be included in future Earth System models. Summary Although the choice of ocean resolution in Earth System models will always be limited by computational considerations, for the foreseeable future, this choice is likely to affect projections of climate variability and change as well as other aspects of the Earth System. Future Earth System models will be able to choose increased ocean resolution and/or improved parameterisation of processes to capture physical processes with greater fidelity.

Coupled Numerical Weather Prediction (NWP) models have only recently been implemented for short‐term environmental prediction and both challenges and benefits are evident in polar regions. Their simulation of surface exchange over sea ice depends on the model's sea‐ice characteristics, however these are hard to constrain due to a lack of in situ and accurate remotely sensed observations. We focus on the Fram Strait region during peak melt conditions and during the passage of an Arctic cyclone: very challenging conditions for coupled NWP. We use in situ aircraft observations from the Arctic Summertime Cyclones field campaign in July‐August 2022, plus satellite products, to evaluate a set of 5‐day forecasts from the Met Office Unified Model. Our model set ups are based on operational GC4 (Global Coupled 4) and developmental GC5 (Global Coupled 5) configurations, which use the CICE5.1 and SI3 sea‐ice models respectively. We find a combination of deficiencies in the simulated sea‐ice field, due to initialization and modeling problems. An initially low concentration of sea ice results in excessive absorption of shortwave radiation by the ocean, leading to excessive basal melting of the sea ice, and further sea‐ice loss; leading to relatively poorly simulated sea‐ice fields in general. In contrast, the passage of an Arctic cyclone and its impact on sea‐ice velocities are captured well. Although we demonstrate several deficiencies in the short‐term forecasts of two state‐of‐the‐art coupled NWP models, we also find promising aspects of model performance and some clear benefits from a fully coupled atmosphere‐ice‐ocean system. Plain Language Summary Weather prediction in the Arctic requires an accurate representation of sea ice as it plays a key role in the exchange of momentum, heat and moisture between the surface and the atmosphere. We investigate a challenging set of conditions for weather forecasting: the passage of an Arctic cyclone over Fram Strait in the European Arctic during peak summertime sea ice melting. We use observations made during the Arctic Summertime Cyclones field campaign in July‐August 2022 to evaluate forecasts from the Met Office Unified Model that feature ocean and sea ice model components that interact (are “coupled”) with the atmosphere. We find discrepancies in the simulated sea ice field that result from issues in the satellite observations fed into the models and model biases. A lack of sea ice results in increased heat absorption by the ocean, after which the warmer water melts the sea ice faster, forming a feedback loop known as the ice‐albedo feedback. The passing cyclone also drives changes in the sea ice cover, but we find that these effects are generally simulated well in the forecasts. Overall, despite the issues discussed, we find that using such coupled models are advancing weather prediction in the Arctic. Key Points Sea ice melts too fast in 5‐day coupled forecasts from August 2022, verified using in situ aircraft observations and satellite‐based products The assimilated sea‐ice fraction product is biased low, leading to accelerated sea ice melt through the ice‐albedo feedback mechanism The passage of an Arctic cyclone provides a short‐term dynamical forcing of the sea ice that is reasonably well represented in the forecasts

The UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments. Plain Language Summary We describe the UK submission to the Detection and Attribution Model Intercomparison Project (DAMIP), using the HadGEM3‐GC3.1 climate model. The model's near‐surface temperature responses to different human and natural climate drivers are compared with other climate models and observed temperature changes. The experiments help to understand the evolution of the model's simulated historical global temperatures. One of the more interesting model features is the variability in the Southern Ocean which manifests itself as occasional surface warming due to deep ocean heat coming to the surface. This behavior, which occurs more often in simulations that cool than in simulations that warm, appears to be unusual compared to other models. The investigation of this model behavior demonstrates that DAMIP model experiments are not just useful for climate change detection and attribution, but also for understanding how a model responds to different climate drivers. Climate model participation in DAMIP is encouraged. Key Points The UK's contribution to the Detection and Attribution Model Intercomparison project (DAMIP) is described The climate model's global temperature response to different anthropogenic and natural drivers is examined and compared to other models Southern Ocean temperature variability is unusual and sensitive to climate driver

Antarctic sea ice prediction has garnered increasing attention in recent years, particularly in the context of the recent record lows of February 2022 and 2023. As Antarctica becomes a climate change hotspot, as polar tourism booms, and as scientific expeditions continue to explore this remote continent, the capacity to anticipate sea ice conditions weeks to months in advance is in increasing demand. Spurred by recent studies that uncovered physical mechanisms of Antarctic sea ice predictability and by the intriguing large variations of the observed sea ice extent in recent years, the Sea Ice Prediction Network South (SIPN South) project was initiated in 2017, building upon the Arctic Sea Ice Prediction Network. The SIPN South project annually coordinates spring-to-summer predictions of Antarctic sea ice conditions, to allow robust evaluation and intercomparison, and to guide future development in polar prediction systems. In this paper, we present and discuss the initial SIPN South results collected over six summer seasons (December-February 2017-2018 to 2022-2023). We use data from 22 unique contributors spanning five continents that have together delivered more than 3000 individual forecasts of sea ice area and concentration. The SIPN South median forecast of the circumpolar sea ice area captures the sign of the recent negative anomalies, and the verifying observations are systematically included in the 10-90% range of the forecast distribution. These statements also hold at the regional level except in the Ross Sea where the systematic biases and the ensemble spread are the largest. A notable finding is that the group forecast, constructed by aggregating the data provided by each contributor, outperforms most of the individual forecasts, both at the circumpolar and regional levels. This indicates the value of combining predictions to average out model-specific errors. Finally, we find that dynamical model predictions (i.e., based on process-based general circulation models) generally perform worse than statistical model predictions (i.e., data-driven empirical models including machine learning) in representing the regional variability of sea ice concentration in summer. SIPN South is a collaborative community project that is hosted on a shared public repository. The forecast and verification data used in SIPN South are publicly available in near-real time for further use by the polar research community, and eventually, policymakers.

We compare the mass budget of the Arctic sea ice for 15 models submitted to the latest Coupled Model Intercomparison Project (CMIP6), using new diagnostics that have not been available for previous model inter-comparisons. These diagnostics allow us to look beyond the standard metrics of ice cover and thickness to compare the processes of sea ice growth and loss in climate models in a more detailed way than has previously been possible. For the 1960–1989 multi-model mean, the dominant processes causing annual ice growth are basal growth and frazil ice formation, which both occur during the winter. The main processes by which ice is lost are basal melting, top melting and advection of ice out of the Arctic. The first two processes occur in summer, while the latter process is present all year. The sea ice budgets for individual models are strikingly similar overall in terms of the major processes causing ice growth and loss and in terms of the time of year during which each process is important. However, there are also some key differences between the models, and we have found a number of relationships between model formulation and components of the ice budget that hold for all or most of the CMIP6 models considered here. The relative amounts of frazil and basal ice formation vary between the models, and the amount of frazil ice formation is strongly dependent on the value chosen for the minimum frazil ice thickness. There are also differences in the relative amounts of top and basal melting, potentially dependent on how much shortwave radiation can penetrate through the sea ice into the ocean. For models with prognostic melt ponds, the choice of scheme may affect the amount of basal growth, basal melt and top melt, and the choice of thermodynamic scheme is important in determining the amount of basal growth and top melt. As the ice cover and mass decline during the 21st century, we see a shift in the timing of the top and basal melting in the multi-model mean, with more melt occurring earlier in the year and less melt later in the summer. The amount of basal growth reduces in the autumn, but it increases in the winter due to thinner sea ice over the course of the 21st century. Overall, extra ice loss in May–June and reduced ice growth in October–November are partially offset by reduced ice melt in August and increased ice growth in January–February. For the individual models, changes in the budget components vary considerably in terms of magnitude and timing of change. However, when the evolving budget terms are considered as a function of the changing ice state itself, behaviours common to all the models emerge, suggesting that the sea ice components of the models are fundamentally responding in a broadly consistent way to the warming climate. It is possible that this similarity in the model budgets may represent a lack of diversity in the model physics of the CMIP6 models considered here. The development of new observational datasets for validating the budget terms would help to clarify this.

We present a method for analysing changes in the modelled volume budget of the Arctic sea ice as the ice declines during the 21st century. We apply the method to the CMIP5 global coupled model HadGEM2-ES to evaluate how the budget components evolve under a range of different forcing scenarios. As the climate warms and the ice cover declines, the sea ice processes that change the most in HadGEM2-ES are summer melting at the top surface of the ice due to increased net downward radiation and basal melting due to extra heat from the warming ocean. There is also extra basal ice formation due to the thinning ice. However, the impact of these changes on the volume budget is affected by the declining ice cover. For example, as the autumn ice cover declines the volume of ice formed by basal growth declines as there is a reduced area over which this ice growth can occur. As a result, the biggest contribution to Arctic ice decline in HadGEM2-ES is the reduction in the total amount of basal ice growth during the autumn and early winter. Changes in the volume budget during the 21st century have a distinctive seasonal cycle, with processes contributing to ice decline occurring in May–June and September to November. During July and August the total amount of sea ice melt decreases, again due to the reducing ice cover. The choice of forcing scenario affects the rate of ice decline and the timing and magnitude of changes in the volume budget components. For the HadGEM2-ES model and for the range of scenarios considered for CMIP5, the mean changes in the volume budget depend strongly on the evolving ice area and are independent of the speed at which the ice cover declines.

A new method of sea ice model evaluation is demonstrated. Data from the network of Arctic ice mass balance buoys (IMBs) are used to estimate distributions of vertical energy fluxes over sea ice in two densely sampled regions – the North Pole and Beaufort Sea. The resulting dataset captures seasonal variability in sea ice energy fluxes well, and it captures spatial variability to a lesser extent. The dataset is used to evaluate a coupled climate model, HadGEM2-ES (Hadley Centre Global Environment Model, version 2, Earth System), in the two regions. The evaluation shows HadGEM2-ES to simulate too much top melting in summer and too much basal conduction in winter. These results are consistent with a previous study of sea ice state and surface radiation in this model, increasing confidence in the IMB-based evaluation. In addition, the IMB-based evaluation suggests an additional important cause for excessive winter ice growth in HadGEM2-ES, a lack of sea ice heat capacity, which was not detectable in the earlier study.Uncertainty in the IMB fluxes caused by imperfect knowledge of ice salinity, snow density and other physical constants is quantified (as is inaccuracy due to imperfect sampling of ice thickness) and in most cases is found to be small relative to the model biases discussed. Hence the IMB-based evaluation is shown to be a valuable tool with which to analyse sea ice models and, by extension, better understand the large spread in coupled model simulations of the present-day ice state. Reducing this spread is a key task both in understanding the current rapid decline in Arctic sea ice and in constraining projections of future Arctic sea ice change.