Abstract. The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.

During the past decades, the Arctic has experienced significant tropospheric warming, with varying decadal warming rates. However, the relative contributions from potential drivers and modulators of the warming are yet to be further quantified. Here, we utilize a unique set of multi‐model large‐ensemble atmospheric simulations to isolate the respective contributions from the combined external radiative forcing (ERF‐AL), interdecadal Pacific variability (IPV), Atlantic multidecadal variability (AMV), and Arctic sea‐ice concentration changes (ASIC) to the warming during 1979–2013. In this study, the ERF‐AL impacts are the ERF impacts directly on the atmosphere and land surface, excluding the indirect effects through SST and SIC feedback. The ERF‐AL is the primary driver of the April–September tropospheric warming during 1979–2013, and its warming effects vary at decadal time scales. The IPV and AMV intensify the warming during their transitioning periods to positive phases and dampen the warming during their transitioning periods to negative phases. The IPV impacts are prominent in winter and spring and are stronger than AMV impacts on 1979–2013 temperature trends. The warming impacts of ASIC are generally restricted to below 700 hPa and are strongest in autumn and winter. The combined effects of these factors reproduce the observed accelerated and step‐down Arctic warming in different decades, but the intensities of the reproduced decadal variations are generally weaker than in the observed.

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Abstract. A substantial part of Arctic climate predictability at interannual time scales stems from the knowledge of the initial sea ice conditions. Among all the variables characterizing sea ice, sea ice volume, being a product of sea ice area/concentration (SIC) and thickness (SIT), is the most sensitive parameter for climate change. However, the majority of climate prediction systems are only assimilating the observed SIC due to lack of long-term reliable global observation of SIT. In this study the EC-Earth3 Climate Prediction System with anomaly initialization to ocean, SIC and SIT states is developed. In order to evaluate the benefits of specific initialized variables at regional scales, three sets of retrospective ensemble prediction experiments are performed with different initialization strategies: ocean-only; ocean plus SIC; and ocean plus SIC and SIT initialization. The increased skill from ocean plus SIC initialization is small in most regions, compared to ocean-only initialization. In the marginal ice zone covered by seasonal ice, skills regarding winter SIC are mainly gained from the initial ocean temperature anomalies. Consistent with previous studies, the Arctic sea ice volume anomalies are found to play a dominant role for the prediction skill of September Arctic sea ice extent. Winter preconditioning of SIT for the perennial ice in the central Arctic Ocean results in increased skill of SIC in the adjacent Arctic coastal waters (e.g. the Laptev/East Siberian/Chukchi Seas) for lead time up to a decade. This highlights the importance of initializing SIT for predictions of decadal time scale in regional Arctic sea ice. Our results suggest that as the climate warming continues and the central Arctic Ocean might become seasonal ice free in the future, the controlling mechanism for decadal predictability may thus shift from being the sea ice volume playing the major role to a more ocean-related processes.

Abstract. The role of surface ocean anomalies for the continental Northern Hemisphere snow cover is investigated, together with the interactions between snow cover and atmosphere. Four observational datasets and two large multi-model ensembles of atmosphere-only simulations are used, with prescribed sea surface temperature (SST) and sea ice concentration (SIC). A first ensemble uses observed interannually varying SST and SIC conditions for 1979–2014, while a second ensemble is identical except for SIC where a repeated climatological cycle is used. SST and external forcing typically explain 10 to 25 % of the snow cover variance in model simulations, with a dominant forcing from the tropical and North Pacific SST, while no robust influence of the SIC is found. In observations, the Ural blocking is the main driver of the November and April snow cover over Eastern Eurasia, while the North Atlantic Oscillation (NAO) dominates the snow cover forcing in January. In November and more robustly in January, dipolar anomalies of snow cover over Eurasia, with positive anomalies over Europe and negative anomalies over Southern Siberia, also precede the Arctic Oscillation (AO) by one month. In models, snow cover over western Eurasia in January also precedes by one or two months a negative AO phase. The detailed outputs from one of the models suggest that both the western Eurasia snow cover and polar vortex are generated by Ural blocking, and that both snow cover and polar vortex anomalies act to generate the AO one or two months later.