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The occurrence of extreme climate events in the coming years is modulated by both global warming and internal climate variability. Anticipating changes in frequency and intensity of such events in advance may help minimize the impact on climate-vulnerable sectors and society. Decadal climate predictions have been developed as a source of climate information relevant for decision-making at multi-annual timescales. We evaluate the multi-model forecast quality of the CMIP6 decadal hindcasts in predicting a set of indices measuring different characteristics of temperature and precipitation extremes for the forecast years 1-5. The multi-model ensemble skillfully predicts the temperature extremes over most land regions, while the skill is more limited for precipitation extremes. We further compare the prediction skill for these extreme indices to the skill for mean temperature and precipitation, finding that the extreme indices are predicted with lower skill, particularly those representing the most extreme days. We find only small and region-dependent improvements from model initialization in comparison to historical forcing simulations. This systematic evaluation of decadal hindcasts is essential when providing a climate service based on decadal predictions so that the user is informed on the trustworthiness of the forecasts for each specific region and extreme event.

Decadal climate predictions are a relatively new source of climate information for interannual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and forecast periods) with skill that can be used to develop climate services to inform users in several sectors and define benchmarks for improvements in forecast systems. This work evaluates the quality of multi-model forecasts of near-surface air temperature, precipitation, Atlantic multidecadal variability index (AMV), and global near-surface air temperature (GSAT) anomalies generated from all the available retrospective decadal predictions contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6). The predictions generally show high skill in predicting temperature, AMV, and GSAT, while the skill is more limited for precipitation. Different approaches for generating a multi-model forecast are compared, finding small differences between them. The multi-model ensemble is also compared to the individual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period, and region. Furthermore, the decadal predictions are compared to the historical simulations to estimate the impact of initialization. An added value is found for several ocean and land regions for temperature, AMV, and GSAT, while it is more reduced for precipitation. Moreover, the full ensemble is compared to a subensemble to measure the impact of the ensemble size. Finally, the implications of these results in a climate services context, which requires predictions issued in near–real time, are discussed.

In recent decades, three major volcanic eruptions of different intensity have occurred (Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991), with reported climate impacts on seasonal to decadal timescales that could have been potentially predicted with accurate and timely estimates of the associated stratospheric aerosol loads. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of volcanic aerosols on the climate experienced during the years that followed those eruptions through the use of decadal predictions. The interest of conducting this exercise with climate predictions is that, thanks to the initialisation, they start from the observed climate conditions at the time of the eruptions, which helps to disentangle the climatic changes due to the initial conditions and internal variability from the volcanic forcing. The protocol consists of repeating the retrospective predictions that are initialised just before the last three major volcanic eruptions but without the inclusion of their volcanic forcing, which are then compared with the baseline predictions to disentangle the simulated volcanic effects upon climate. We present the results from six Coupled Model Intercomparison Project Phase 6 (CMIP6) decadal prediction systems. These systems show strong agreement in predicting the well-known post-volcanic radiative effects following the three eruptions, which induce a long-lasting cooling in the ocean. Furthermore, the multi-model multi-eruption composite is consistent with previous work reporting an acceleration of the Northern Hemisphere polar vortex and the development of El Niño conditions the first year after the eruption, followed by a strengthening of the Atlantic Meridional Overturning Circulation the subsequent years. Our analysis reveals that all these dynamical responses are both model- and eruption-dependent. A novel aspect of this study is that we also assess whether the volcanic forcing improves the realism of the predictions. Comparing the predicted surface temperature anomalies in the two sets of hindcasts (with and without volcanic forcing) with observations we show that, overall, including the volcanic forcing results in better predictions. The volcanic forcing is found to be particularly relevant for reproducing the observed sea surface temperature (SST) variability in the North Atlantic Ocean following the 1991 eruption of Pinatubo.

The enhanced warming trend and precipitation decline in the Mediterranean region make it a climate change hotspot. We compare projections of multiple Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) historical and future scenario simulations to quantify the impacts of the already changing climate in the region. In particular, we investigate changes in temperature and precipitation during the 21st century following scenarios RCP2.6, RCP4.5 and RCP8.5 for CMIP5 and SSP1-2.6, SSP2-4.5 and SSP5-8.5 from CMIP6, as well as for the HighResMIP high-resolution experiments. A model weighting scheme is applied to obtain constrained estimates of projected changes, which accounts for historical model performance and inter-independence in the multi-model ensembles, using an observational ensemble as reference. Results indicate a robust and significant warming over the Mediterranean region during the 21st century over all seasons, ensembles and experiments. The temperature changes vary between CMIPs, CMIP6 being the ensemble that projects a stronger warming. The Mediterranean amplified warming with respect to the global mean is mainly found during summer. The projected Mediterranean warming during the summer season can span from 1.83 to 8.49 ∘C in CMIP6 and 1.22 to 6.63 ∘C in CMIP5 considering three different scenarios and the 50 % of inter-model spread by the end of the century. Contrarily to temperature projections, precipitation changes show greater uncertainties and spatial heterogeneity. However, a robust and significant precipitation decline is projected over large parts of the region during summer by the end of the century and for the high emission scenario (−49 % to −16 % in CMIP6 and −47 % to −22 % in CMIP5). While there is less disagreement in projected precipitation than in temperature between CMIP5 and CMIP6, the latter shows larger precipitation declines in some regions. Results obtained from the model weighting scheme indicate larger warming trends in CMIP5 and a weaker warming trend in CMIP6, thereby reducing the difference between the multi-model ensemble means from 1.32 ∘C before weighting to 0.68 ∘C after weighting.

Seasonal mean atmospheric circulation in Europe can vary substantially from year to year. This diversity of conditions impacts many socioeconomic sectors. Teleconnection indices can be used to characterize this seasonal variability, while seasonal forecasts of those indices offer the opportunity to take adaptation actions a few months in advance. For instance, the North Atlantic Oscillation has proven useful as a proxy for atmospheric effects in several sectors, and dynamical forecasts of its evolution in winter have been shown skillful. However the NAO only characterizes part of this seasonal circulation anomalies, and other teleconnections such as the East Atlantic, the East Atlantic Western Russia or the Scandinavian Pattern also play an important role in shaping atmospheric conditions in the continent throughout the year. This paper explores the quality of seasonal forecasts of these four teleconnection indices for the four seasons of the year, derived from five different seasonal prediction systems. We find that several teleconnection indices can be skillfully predicted in advance in winter, spring and summer. We also show that there is no single prediction system that performs better than the others for all seasons and teleconnections, and that a multi-system approach produces results that are as good as the best of the systems.

This work provides a new methodology based on a statistical downscaling with a perfect prognosis approach to produce seasonal predictions of near-surface wind speeds at the local scale. Hybrid predictions combine a dynamical prediction of the four main Euro-Atlantic Teleconnections (EATC) and a multilinear statistical regression, which is fitted with observations and includes the EATC as predictors. Once generated, the skill of the hybrid predictions is assessed at 17 tall tower locations in Europe targeting the winter season. For comparative purposes, hybrid predictions have also been produced and assessed at a pan-European scale, using the ERA5 100 m wind speed as the observational reference. Overall, results indicate that hybrid predictions outperform the dynamical predictions of near-surface wind speeds, obtained from five prediction systems available through the Climate Data Store of the Copernicus Climate Change Service. The performance of a multi-system ensemble prediction has also been assessed. In all cases, the enhancement is particularly noted in northern Europe. By being more capable of anticipating local wind speed conditions in higher quality, hybrid predictions will boost the application of seasonal predictions outside the field of pure climate research.

We investigate the impact of increasing horizontal model resolution on the oceanic mixing processes in the North Atlantic, their drivers, their link with the Atlantic Meridional Overturning Circulation (AMOC), and the propagation of newly generated dense waters through the deep western boundary current (DWBC). We use three versions of the EC-Earth Earth system model, one of standard resolution (SR, ∼1° in the ocean), one of high resolution (HR, ∼0.25° in the ocean), and one of very high resolution (VHR, ∼1/12° in the ocean). The higher resolutions allow for the explicit simulation of mesoscale processes that are parameterised at the coarse resolution, with additional improvements in ocean topography, boundary currents, and air–sea interactions. We find that the North Atlantic Oscillation plays a critical role in driving the mixed layer depth (MLD) in the Labrador Sea at HR and VHR. The three model configurations also show the influence of surface salinity signals in the mixing, with the VHR configuration showing a distinct slow propagation of these signals from the eastern subpolar gyre into the Labrador Sea. Furthermore, March MLD shows a strong positive bias in HR, which is reduced in VHR. In terms of the AMOC, resolution plays a pivotal role in shaping its response to the mixing. At the highest resolutions, the signal of the newly formed dense waters propagates faster along the better-resolved boundary current, indicating a shift from advective propagation to wave propagation of the signals. Additionally, the persistence of the AMOC responses to MLD is much shorter in VHR (less than 2 years) than for SR and HR, which exhibit longer-lived changes. These differences highlight how resolution affects both the timing and spatial reach of the AMOC changes. Our study underscores the importance of model resolution in accurately simulating the North Atlantic's oceanic processes and their implications for the AMOC. While the VHR configuration offers a more realistic climatology of the Labrador Sea MLD, the results also demonstrate significant differences in variability and persistence across resolutions. These findings stress the need for high-resolution simulations to improve the understanding of deep ocean processes and their connection to larger climate systems, although they also highlight challenges in comparing simulated and observed data, particularly given the sparse historical observations and the lack of decadal variability in the model simulations.

Ocean mesoscale structures, which are parameterized in models with standard resolutions on the order of 1° or coarser, have an impact at larger scales, affecting the ocean mean state and circulation. Here we study the effects of increasing model ocean resolution to mesoscale eddy-resolving scales on the representation of the North Atlantic mean state, by comparing an ensemble of four HighResMIP coupled historical simulations with nominal ocean resolutions of at least 1/10° – corresponding to the models CESM1-CAM5-SE-HR, EC-Earth3P-VHR, HadGEM3-GC31-HH, and MPI-ESM1-2-ER – to a baseline of 39 Coupled Model Intercomparison Project phase 6 (CMIP6) simulations at coarser resolution. We find an improved representation of the Gulf Stream (GS) structure and position in the mesoscale-resolving ensemble, which leads to significantly reduced surface temperature and salinity biases north of Cape Hatteras (NCH). While higher resolution lessens the mean cold–fresh surface biases in the Central North Atlantic (CNA), the improvement is not statistically significant, as some mesoscale-resolving models still present an overly weak North Atlantic Current (NAC). Important differences also occur in the Labrador (LS) and western Irminger Seas (IS). Although the mesoscale-resolving ensemble exhibits larger warm and salty local biases at the surface compared to the low-resolution one, its full-depth profile reveals significantly weaker vertical stratification in the area, closer to observations. This reduced stratification in the high-resolution ensemble is consistent with the presence of stronger (although not significantly stronger) deep water convection in the region. While in the LS the wide range of MLD observational estimates makes model assessment challenging, in the Nordic Seas and along the East Greenland Current, convection in the high-resolution model ensemble is in better agreement with observational records, compared to the low-resolution ensemble. Another clear improvement in the mesoscale-resolving ensemble is found for the representation of the Atlantic overturning in depth-space, which is significantly closer to RAPID observations at 26.5° N than in the low-resolution counterpart; however, it still remains too shallow compared to observations and reanalyses. The subpolar gyre (SPG), as characterized by the barotropic streamfunction, is not significantly stronger in the higher resolution ensemble, although it presents a narrower and locally stronger boundary current.

Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.

Numerical Earth System Models (ESMs) are our best tool to predict the evolution of atmospheric CO2 concentration and its effect on Global temperature. However, large uncertainties exist among ESMs in the variance of the year-to-year changes of atmospheric CO2 concentration. This prevents us from precisely understanding its past evolution and from accurately estimating its future evolution. Here we analyze various ESMs simulations from the 6th Coupled Model Intercomparison Projects (CMIP6) to understand the origins of the inter-model uncertainty in the interannual variability of the atmospheric CO2 concentration. Considering the observed period 1986-2013, we show that most of this uncertainty is coming from the simulation of the land CO2 flux internal variability. Although models agree that those variations are driven by El Niño Southern Oscillation (ENSO), similar ENSO-related surface temperature and precipitation teleconnections across models drive different land CO2 fluxes, pointing to the land vegetation models as the dominant source of the inter-model uncertainty.