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.

Severe precipitation events occur rarely and are often localised in space and of short duration, but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of the occurrence of these rare events. These are often projected using data from regional climate model (RCM) simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterisations in the RCMs, the simulated present-day climate usually has biases relative to observations; these biases can be in the mean and/or in the higher moments. Therefore, the RCM results are adjusted to account for these deficiencies. However, this does not guarantee that the adjusted projected results will match the future reality better, since the bias may not be stationary in a changing climate. In the present work, we evaluate different adjustment techniques in a changing climate. This is done in an inter-model cross-validation set-up in which each model simulation, in turn, performs pseudo-observations against which the remaining model simulations are adjusted and validated. The study uses hourly data from historical and RCP8.5 scenario runs from 19 model simulations from the EURO-CORDEX ensemble at a 0.11∘ resolution. Fields of return levels for selected return periods are calculated for hourly and daily timescales based on 25-year-long time slices representing the present-day (1981–2005) and end-21st-century (2075–2099). The adjustment techniques applied to the return levels are based on extreme value analysis and include climate factor and quantile-mapping approaches. Generally, we find that future return levels can be improved by adjustment, compared to obtaining them from raw scenario model data. The performance of the different methods depends on the timescale considered. On hourly timescales, the climate factor approach performs better than the quantile-mapping approaches. On daily timescales, the superior approach is to simply deduce future return levels from pseudo-observations, and the second-best choice is using the quantile-mapping approaches. These results are found in all European subregions considered. Applying the inter-model cross-validation against model ensemble medians instead of individual models does not change the overall conclusions much.

Severe precipitation events occur rarely and are often localized in space and of short duration, but are important for societal managing of infrastructure such as sewage systems, metros etc. Therefore, there is a demand for estimating expected future changes in the statistics of these rare events. These are usually projected using RCM scenario runs combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to RCM imperfections, the modelled climate for the present-day usually has errors relative to observations. Therefore, the RCM results are ‘error corrected‘ to match observations more closely in order to increase reliability of results. In the present work we evaluate different error correction techniques and compare with non-corrected projections. This is done in an inter-model cross-validation setup, in which each model in turn plays the role of observations, against which the remaining error-corrected models are validated. The study uses hourly data (historical & RCP8.5 late 21st century) from 13 models covering the EURO-CORDEX ensemble at 0.11 degree resolution (about 12.5 km), from which fields of selected return levels are extracted for 1 h and 24 h duration. The error correction techniques applied to the return levels are based on extreme value analysis and include analytical quantile-quantile matching together with a simpler climate factor approach. The study identifies regions where the error correction techniques perform differently, and therefore contributes to guidelines on how and where to apply calibration techniques when projecting extreme return levels.

Wine production is climate dependent and highly sensitive to weather variability, which makes the sector a good indicator of ongoing and future climate change impacts. Under high-end climate change (HECC), temperatures in Scotland are projected to increase significantly by the end of the twenty-first century. This raises the possibility of future temperatures becoming sufficiently high to support the growth of wine grapes. In this paper, we explore to what extent Scotland might become suitable for wine production under HECC using a climate analogue approach. Specifically, we address the following questions. What are the projected late twenty-first century temperature changes in Scotland? Where in Europe are current climates (based on summer and annual temperatures) similar to those projected for Scotland by the end of the twenty-first century under HECC? Are any of these locations currently wine grape growing regions? The temperature analogues towards the end of the twenty-first century occurred at more southerly latitudes in Europe, with some variability from west to east arising from the influence of continental climates. Temperature analogues alone match with several current wine grape growing regions of Europe, suggesting that future climates in Scotland could support wine production. However, when precipitation and/or lithology and topography are also taken into account, no matches were found with existing European wine grape growing regions. This study demonstrates how the use of climate analogues in combination with other environmental datasets can be useful in understanding future climate change impacts, especially under HECC.

The Paris Agreement established the 1.5 and 2 °C targets based on the recognition “that this would significantly reduce the risks and impacts of climate change”. We tested this assertion by comparing impacts at the regional scale between low-end (< 2 °C; RCP2.6) and high-end (> 4 °C; RCP8.5) climate change scenarios accounting for interactions across six sectors (agriculture, forestry, biodiversity, water, coasts and urban) using an integrated assessment model. Results show that there are only minor differences in most impact indicators for the 2020s time slice, but impacts are considerably greater under high-end than low-end climate change in the 2050s and 2080s. For example, for the 2080s, mitigation consistent with the Paris Agreement would reduce aggregate Europe-wide impacts on the area of intensive agriculture by 21% (on average across climate models), on the area of managed forests by 34%, on water stress by 14%, on people flooded by 10% and on biodiversity vulnerability by 16%. Including socio-economic scenarios (SSPs 1, 3, 4, 5) results in considerably greater variation in the magnitude, range and direction of change of the majority of impact indicators than climate change alone. In particular, socio-economic factors much more strongly drive changes in land use and food production than changes in climate, sometimes overriding the differences due to low-end and high-end climate change. Such impacts pose significant challenges for adaptation and highlight the importance of searching for synergies between adaptation and mitigation and linking them to sustainable development goals.

Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere.

Surface mass balance (SMB) is the builder of the Greenland ice sheet and the driver of ice dynamics. Quantifying the past, present and future state of SMB is important to understand the drivers and climatic processes that control SMB, and to both initialize and run ice sheet models which will help clarify sea level rise, and how likely changes in ice sheet extent feedback within the climate system. Regional climate models (RCMs) and climate reanalysis are used to quantify SMB estimates. Although different models have different spatial and temporal biases and may include different processes giving significant uncertainty in both SMB and the ice sheet dynamic response to it, all RCMs show a recent declining trend in SMB from the Greenland ice sheet, driven primarily by enhanced melt rates. Here, we present new simulations of the Greenland ice sheet SMB at 5 km resolution from the RCM HIRHAM5. The RCM is driven by the ERA-Interim reanalysis and the global climate model (GCM) EC-Earth v2.3 to make future projections for climate scenarios RCP8.5 and RCP4.5. Future estimates of SMB are affected by biases in driving global climate models, and feedbacks between the ice sheet surface and the global and regional climate system are neglected, likely resulting in significant underestimates of melt and precipitation over the ice sheet. These challenges will need to be met to better estimate the role climate change will have in modulating the surface mass balance of the Greenland ice sheet.

Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5 models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere.

The albedo of the surface of ice sheets changes as a function of time due to the effects of deposition of new snow, ageing of dry snow, bare ice exposure, melting and run-off. Currently, the calculation of the albedo of ice sheets is highly parameterized within the earth system model EC-Earth by taking a constant value for areas with thick perennial snow cover. This is an important reason why the surface mass balance (SMB) of the Greenland ice sheet (GrIS) is poorly resolved in the model. The purpose of this study is to improve the SMB forcing of the GrIS by evaluating different parameter settings within a snow albedo scheme. By allowing ice-sheet albedo to vary as a function of wet and dry conditions, the spatial distribution of albedo and melt rate improves. Nevertheless, the spatial distribution of SMB in EC-Earth is not significantly improved. As a reason for this, we identify omissions in the current snow albedo scheme, such as separate treatment of snow and ice and the effect of refreezing. The resulting SMB is downscaled from the lower-resolution global climate model topography to the higher-resolution ice-sheet topography of the GrIS, such that the influence of these different SMB climatologies on the long-term evolution of the GrIS is tested by ice-sheet model simulations. From these ice-sheet simulations we conclude that an albedo scheme with a short response time of decaying albedo during wet conditions performs best with respect to long-term simulated ice-sheet volume. This results in an optimized albedo parameterization that can be used in future EC-Earth simulations with an interactive ice-sheet component.

The albedo of the surface of ice sheets changes as a function of time, due to the effects of deposition of new snow, ageing of dry snow, melting and runoff. Currently, the calculation of the albedo of ice sheets is highly parameterized within the Earth System Model EC-Earth, by taking a constant value for areas with thick perennial snow cover. This is one of the reasons that the surface mass balance (SMB) of the Greenland ice sheet (GrIS) is poorly resolved in the model. To improve this, eight snow albedo schemes are evaluated here. The resulting SMB is downscaled from the lower resolution global climate model topography to the higher resolution ice sheet topography of the GrIS, such that the influence of these different SMB climatologies on the long-term evolution of the GrIS is tested by ice sheet model simulations. This results in an optimised albedo parameterization that can be used in future EC-Earth simulations with an interactive ice sheet component.

Although there in general are no significant long‐term correlations between the quasi‐biennial oscillation (QBO) and the El Niño–Southern Oscillation (ENSO) in observations, we find that the QBO and the ENSO were aligned in the 3 to 4 years after the three warm ENSO events in 1982, 1997, and 2015. We investigate this indicated relationship with a version of the EC‐Earth climate model which includes nonorographic gravity waves. We analyze the modeled QBO in ensembles forced with climatological sea surface temperatures (SSTs) and observed SSTs. In the ensemble with observed SSTs we find a strong and significant alignment of the ensemble members in the equatorial stratospheric winds in the 2 to 4 years after the strong ENSO event in 1997. This alignment also includes the observed QBO. No such alignment is found in the ensemble with climatological SSTs. These results indicate that strong warm ENSO events can lock the phase of the QBO.