We aim at reducing the uncertainties in the climate predictions of the Arctic region which is going under rapid changes with global repercussions. We analyse the spread in the Atlantic water core temperature across multi member CMIP6 historical simulations, focusing on different regions of the Arctic Ocean. While the redistribution of heat plays a critical role in the dynamics of the Arctic Ocean basins, it is usually not well represented in climate models, leading to divergent projections of future changes in the Arctic. To address this limitation, we compare CMIP6 model outputs with available reanalysis and observational products, in order to identify the biases within the model simulations and develop new metrics to constrain the model ensemble spread. Such metrics can be used to select the multi model ensemble members and construct a subsample with improved representation of the core temperature evolution over the historical period resulting in a reduced uncertainty in near-term future projections of the Arctic climate.

Changes in surface freshwater fluxes are a main factor governing the response of the ocean circulation to future climate change. However, they are not well-represented in the most recent generation of Earth System Models (CMIP6), as most CMIP6 models do not include an interactive ice sheet component. Instead, most of them use a very idealized representation of ice sheets. While this approach may yield the correct order of magnitude for present-day meltwater runoff, it might not accurately extrapolate the increasing ice melt under future global warming. Here, we address this deficiency by prescribing physically plausible meltwater fluxes from the Greenland ice sheet in a CMIP6 model, EC-Earth3, under a strong global warming scenario (SSP5-8.5) until the 23rd century. The meltwater fields were obtained from a CESM2-CISM simulation in which the Greenland ice sheet was fully coupled. The corresponding meltwater flux reaches about 0.4 Sv by the year 2300, comparable to what is often used in water hosing experiments. Using two EC-Earth ensembles of four members each (with and without Greenland meltwater flux), we compare the impact of this previously underestimated runoff on long-term projections of deep-water formation in the North Atlantic and on the evolution of the Atlantic Meridional Overturning Circulation. Our results allow us to quantify the importance of Greenland meltwater on AMOC weakening under strong global warming.

We investigate the role of the subpolar North Atlantic (SPNA) for downstream predictability, using two decadal climate prediction systems. We use the subpolar extreme cold and fresh anomaly event developing in winter 2013/2014 as initial conditions and evaluate ensemble predictions of the two systems in the following decade. In addition, we perform ensemble pacemaker experiments where the models are forced toward observed ocean temperature and salinity anomalies in the SPNA from November 2014 through December 2019. The pacemaker experiments show improved skill along the Atlantic Water pathway, compared with the standard decadal predictions, and we therefore conclude that the correct description of the ocean in the SPNA is the key. The enhanced skill is most prominent in subsurface salinity in the form of propagating anomalies. Observations show that ocean anomalies propagate across the North Atlantic and further north along the Norwegian coast. Anomalous ocean temperatures can modify heat exchange between ocean and atmosphere and influence the circulation of the atmosphere. Since such anomalies can persist for months, it potentially opens a door to seasonal prediction of the atmosphere. However, climate models used for seasonal and decadal prediction fail to predict these propagating anomalies. In our study we aim to find causes for this lack of predictability. We use the record low temperatures observed in the North Atlantic in 2015 as a test case. We employ two climate prediction systems and make two types of predictions: one type is a standard prediction initialized with winter 2014/2015 ocean conditions; the other type is initialized in the same way and in addition, the model is kept close to observations in the subpolar gyre during the whole prediction, in the region where the cold blob was formed. Comparing the two types of predictions, we see that keeping the model close to observations in the subpolar gyre increases the predictability along the Norwegian coast. This points to the subpolar gyre as a key area where models need to be improved. Knowing temperature and salinity in the subpolar gyre increases skill downstream along the Atlantic Water pathway Salinity anomalies propagate northward, while temperature anomalies are more difficult to trace A pacemaker experiment has been used for decadal predictions for the first time

The acceleration of Greenland ice sheet melting over the past decades is raising concern regarding the impacts on ocean circulation in the North Atlantic and Arctic regions. Global climate models struggle to assess these impacts as they do not include a realistic amount of meltwater from the Greenland ice sheet. Using an extended observation-based dataset of runoff and solid ice discharge for the recent historical period (1920–2019), we force the EC-Earth3 climate model following the ensemble approach and protocol of a previous study using a different model. We observe a slight increase of the ensemble mean AMOC with a large spread in the response: 0.20± 0.81 Sv for the maximum AMOC at 45 ^∘ N. We notice that members with a strong initial AMOC state (18 Sv) show a strengthening of the AMOC, while members with intermediate AMOC strength between 16 and 18 Sv are not affected by the freshwater forcing. Weaker initial AMOC members respond with a mean weakening of - 0.21± 0.58 Sv of the AMOC. The AMOC ensemble spread is reduced by half in the freshwater forced ensemble at the end of the experiment and the negative trend is stronger compared to historical simulations without the forcing. This suggests the possible ability of the freshwater to constrain AMOC variability on multi-decadal time-scales. Comparison of the two ten member ensembles with ORAS5 reanalysis shows a reduction of the surface temperature and salinity biases in the freshwater forced ensemble in key regions such as the North Atlantic subpolar gyre and the Beaufort Gyre relative to the historical simulations. Recent trends are also more aligned with reanalysis in those regions. Arctic Ocean Atlantic Water subsurface core temperature is also closer to reanalysis but still strongly biassed. Members with a high AMOC initial state display an unrealistic Atlantic water layer core depth.

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.

This study summarizes the findings of several realistic freshwater forcing experiments around Greenland and surrounding regions, which were conducted using climate and ocean models over the historical period. The results of the experiments are discussed in terms of their impact on the Atlantic Meridional Overturning Circulation (AMOC) as well as on temperature and salinity changes in the North Atlantic and in the Arctic. It was determined that the addition of freshwater led to a decrease of the AMOC and a reduction of the temperature and salinity biases in the North Atlantic. These results will be of particular interest to researchers interested in the effects of current and future Greenland melting on local and global ocean.

Global climate models (CMIP6 models) are the basis for future predictions and projections, but these models typically have large biases in their mean state of the Arctic Ocean. Considering a transect across the Arctic Ocean, with a focus on the depths between 100-700m, we show that the model spread for temperature and salinity anomalies increases significantly during the period 2025-2045. The maximum model spread is reached in the period 2045-2055 with a standard deviation 10 times higher than in 1993-2010. The CMIP6 models agree that there will be warming, but do not agree on the degree of warming. This aspect is important for long-term management of societal and ecological perspectives in the Arctic region. We therefore test a new approach to find models with good performance. We assess how CMIP6 models represent the horizontal patterns of temperature and salinity in the period 1993-2010. Based on this, we find four models with relatively good performance (MPI-ESM1-2-HR, IPSL-CM6A-LR, CESM2-WACCM, MRI-ESM2-0). For a more robust model evaluation, we consider additional metrics (e.g., climate sensitivity, ocean heat transport) and also compare our results with other recent CMIP6 studies in the Arctic Ocean. Based on this, we find that two of the models have an overall better performance (MPI-ESM1-2-HR, IPSL-CM6A-LR). Considering projected changes for temperature for the period 2045-2055 in the high end ssp585 scenario, these two models show a similar warming in the Mid Layer (300-700m; 1.1-1.5°C). However, in the low end ssp126 scenario, IPSL-CM6A-LR shows a considerably higher warming than MPI-ESM1-2-HR. In contrast to the projected warming by both models, the projected salinity changes for the period 2045-2055 are very different; MPI-ESM1-2-HR shows a freshening in the Upper Layer (100-300m), whereas IPSL-CM6A-LR shows a salinification in this layer. This is the case for both scenarios. The source of the model spread appears to be in the Eurasian Basin, where warm waters enter the Arctic. Finally, we recommend being cautious when using the CMIP6 ensemble to assess the future Arctic Ocean, because of the large spread both in performance and the extent of future changes.

Abstract A supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non‐interactive multi‐model ensembles (NI), which combines model outputs a‐posteriori. Supermodels with Earth system models (ESMs) has not been developed because it is technically challenging to combine models with different state space. Here, we formulate the first supermodel framework for ESMs and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated by them on a common grid to handle discrepancies in grid and resolution. We compare the performance of two supermodel approaches to that of the NI. In the first (EW), the models are connected to the equal‐weight multi‐model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in the ocean and in the atmosphere, where the ocean drives the variability. The time variability of the supermodel multi‐model mean SST is reduced compared to observations, most where synchronisation is not achieved and is lower‐bounded by NI. The damping is larger in EW, for which variability in the individual models is also damped. Hence, under partial synchronisation, the unsynchronized variability gets damped in the multi‐model average pseudo‐observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi‐model mean in the synchronised regions. A trained supermodel remains to be tested.

The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill and reduced biases. But are we exploiting the full capacity of state-of-the-art models in making forecasts and projections? Supermodeling is a recent step forward in the multimodel ensemble approach. Instead of combining model output after the simulations are completed, in a supermodel individual models exchange state information as they run, influencing each other’s behavior. By learning the optimal parameters that determine how models influence each other based on past observations, model errors are reduced at an early stage before they propagate into larger scales and affect other regions and variables. The models synchronize on a common solution that through learning remains closer to the observed evolution. Effectively a new dynamical system has been created, a supermodel, that optimally combines the strengths of the constituent models. The supermodel approach has the potential to rapidly improve current state-of-the-art weather forecasts and climate predictions. In this paper we introduce supermodeling, demonstrate its potential in examples of various complexity, and discuss learning strategies. We conclude with a discussion of remaining challenges for a successful application of supermodeling in the context of state-of-the-art models. The supermodeling approach is not limited to the modeling of weather and climate, but can be applied to improve the prediction capabilities of any complex system, for which a set of different models exists.

Recent studies have suggested that the Atlantic water pathway connecting the subpolar North Atlantic (SPNA) with the Nordic Seas and Arctic Ocean may lead to skillful predictions of sea surface temperature and salinity anomalies in the eastern Nordic Seas. To investigate the role of the SPNA for such anomalies downstream, we designed a pacemaker experiment, using two decadal climate prediction systems based on EC-Earth3 and NorCPM. We focus on the subpolar extreme cold anomaly in 2015 and its subsequent development, a feature not well captured and predicted. The pacemaker experiment follows the protocol of the CMIP6 DCPP-A retrospective forecasts or hindcasts initialized November 1, 2014, but the models are forced to follow the observed ocean temperature and salinity anomalies in the SPNA from ocean reanalysis from November 2014 through to December 2019. Two sets of 10-year hindcasts are performed with 10 members for EC-Earth3 and 30 members for NorCPM. We here detail and discuss the design of this pacemaker experiment and present results, comparing with the initialized CMIP6 DCPP-A experiment assessing differences in decadal prediction skill outside the SPNA. We conclude that the pacemaker experiments show improved skill compared to the standard decadal predictions for the eastern Norwegian Sea, and therefore the SPNA is key for successful decadal predictions in the region.

Climate models usually can not afford to include an interactive ice sheet component for Greenland, which leads to a wrong representation of the variability of the freshwater fluxes released from the Greenland ice melt into the North Atlantic. We propose here to force externally a climate model (EC-Earth3) over several decades (1920-2014) with an observational dataset of runoff and solid ice discharge values for Greenland and surrounding glaciers and ice caps. It has been shown in a similar study with the IPSL-CM6-LR model that an enhancement of freshwater can modify the circulation and the convection in this region. The simulated mixed layer depths in the Nordic seas and the strength of the Atlantic Meridional Overturning Circulation will be investigated to assess the impact of these increasing freshwater fluxes on the oceanic circulation over the period. The response in salinity and stratification in the Arctic will also be analysed as well as the ability for the system to capture abrupt changes like the 1995 warming in the subpolar gyre.

The Atlantic Meridional Overturning Circulation (AMOC) is a crucial element of the Earth climate. It is a complex circulation system difficult to monitor and to model. There is considerable debate regarding its evolution over the last century as well as large uncertainty about its fate at the end of this century. We depict here the progress since the IPCC SROCC report, offering an update of its chapter 6.7. We also show new results from a high-resolution ocean model and a CMIP6 model to investigate the impact of Greenland Ice Sheet (GrIS) melting, a key uncertainty for past and future AMOC changes. The ocean-only simulation at 1/24° resolution in the Arctic-North Atlantic Ocean performed over the period 2004–2016 indicates that the spread of the Greenland freshwater runoff toward the center of the Labrador Sea, where oceanic convection occurs, seems larger in this model than in a CMIP6 model. Potential explanations are related to the model spatial resolution and the representation of mesoscale processes, which more realistically transport the freshwater released around the shelves and, through eddies, provides strong lateral exchanges between the fine-scale boundary current and the convective basin in the Labrador Sea. The larger freshening of the Labrador Sea in the high-resolution model then strongly affects deep convection activity. In the simulation including GrIS melting, the AMOC weakens by about 2 Sv after only 13 years, far more strongly than what is found in the CMIP6 model. This difference raises serious concerns on the ability of CMIP6 models to correctly assess the potential impact of GrIS melting on the AMOC changes over the last few decades as well as on its future fate. To gain confidence in the GrIS freshwater impacts on climate simulations and therefore in AMOC projections, urgent progress should be made on the parameterization of mesoscale processes in ocean models.

The Greenland and Antarctic ice sheets are losing mass to the ocean. This additional freshwater flux to the ocean is only expected to increase in the future, but it is usually not included in current climate model simulations as ice sheets are not modelled interactively. However, this freshwater flux will influence multiple aspects of the climate response. We develop a plausible, future freshwater forcing scenarios for both ice sheets and use a high-resolution, eddy-permitting version of EC-Earth3 to simulate the response to a high emission scenario. We investigate the effect of this additional freshwater on sea ice, ocean circulation, surface temperatures, and sea level by comparing the simulations to the HighResMIP EC-Earth3 simulations without ice sheet mass loss.

Greenland ice sheet experienced an intensive melting in the last century, especially in the 1920s and over the last decades. The supplementary input into the ocean could disrupt the freshwater budget of the North Atlantic. Simultaneously, some signs of a recent weakening of the Atlantic Meridional Overturning Circulation (AMOC) have been reported. In order to better understand the possible impact of the increasing melting on the North Atlantic circulation, salinity and temperature trends, we construct an observation-based estimate of the freshwater fluxes spanning from 1840 to 2014. The estimate is based on runoff fluxes coming from Greenland ice sheet and surrounding glaciers and ice caps. Input from iceberg melting is also included and spatially distributed over the North Atlantic following an observed climatology. We force a set of historical simulations of the IPSL-CM6A-LR coupled climate model with this reconstruction from 1920 to 2014. The ten-member ensemble mean displays freshened and cooled waters around Greenland, which spread in the subpolar gyre, and then towards the subtropical gyre and the Nordic Seas. Over the whole period, the convection is reduced in the Labrador and Nordic Seas, while it is slightly enhanced in the Irminger Sea, and the AMOC is weakened by $$0.32 \pm 0.35$$  Sv at $$26 ^\circ $$  N. The multi-decadal trend of the North Atlantic surface temperature obtained with the additional freshwater forcing is slightly closer to observations than in standard historical simulations, although the two trends are only different at the 90% confidence level. Slight improvement of the Root Mean Square Error with respect to observations in the subpolar gyre region suggests that part of the surface temperature variability over the recent decades may have been forced by the release of freshwater from Greenland and surrounding regions since the 1920s. Finally, we highlight that the AMOC decrease due to Greenland melting remains modest in these simulations and can only explain a very small amount of the $$3\pm 1$$  Sv weakening suggested in a recent study.

Abstract This study presents the global climate model IPSL‐CM6A‐LR developed at Institut Pierre‐Simon Laplace (IPSL) to study natural climate variability and climate response to natural and anthropogenic forcings as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This article describes the different model components, their coupling, and the simulated climate in comparison to previous model versions. We focus here on the representation of the physical climate along with the main characteristics of the global carbon cycle. The model's climatology, as assessed from a range of metrics (related in particular to radiation, temperature, precipitation, and wind), is strongly improved in comparison to previous model versions. Although they are reduced, a number of known biases and shortcomings (e.g., double Intertropical Convergence Zone [ITCZ], frequency of midlatitude wintertime blockings, and El Niño–Southern Oscillation [ENSO] dynamics) persist. The equilibrium climate sensitivity and transient climate response have both increased from the previous climate model IPSL‐CM5A‐LR used in CMIP5. A large ensemble of more than 30 members for the historical period (1850–2018) and a smaller ensemble for a range of emissions scenarios (until 2100 and 2300) are also presented and discussed.

We present the first results from a supermodel constructed using three state-of-the-art earth system models: NorESM, CESM, MPIESM. A supermodel is an interactive ensemble in which models are optimally combined so that the systematic errors of the individual models compensate to achieve a model with superior performance. In the supermodel, the individual models are synchronized every month using data assimilation to handle the discrepancies of grid, resolution and variable representativity between the models. In particular, we assimilate a pseudo sea surface temperature (SST) that is computed as a weighted combination of the SST of the individual models. The synchronization of the models distinguishes this approach from the standard multi-model ensemble approach in which model outputs are combined a-posteriori. The data assimilation method used is the Ensemble Optimal Interpolation (EnOI) scheme, for which the covariance matrices are constructed from preindustrial control simulations of the individual models. The performances of a first version of the supermodel based on equal weights is compared to the individual models performances for the period 1980 to 2010. Synchronisation of the surface ocean is achieved in most places and dynamical regimes such as ENSO are occurring in phase. The biases of each model are reduced and the pathway of the Gulf Stream improved. The variability of the supermodel is not larger than in the super ensemble mean, but it is shown with an idealized model that the deflation is cause by a misconstruction of the pseudo observation and can be counteracted by perturbing them. The Perspectives for performing predictions and climate change experiments with the supermodel method are presented and discussed.

Greenland experienced intensive melting over the last century, especially in the 1920s and over the last decades. The supplementary input into the ocean is influencing the freshwater budget of the North Atlantic. Simultaneously, some signs of a recent weakening of the Atlantic meridional overturning circulation (AMOC) have been reported. In order to better understand the possible impact of the melting on the North Atlantic circulation, salinity and temperature trends, we construct an observation-based estimate of the freshwater fluxes from 1840 to 2014 associated to the runoff fluxes from Greenland ice sheet and surrounding glaciers and ice caps. Input from iceberg melting is also included and spatially distributed over the North Atlantic following an observed climatology. We force historical simulations of the IPSL-CM6A-LR coupled climate model with this reconstruction from 1920 to 2014. The 10-member ensemble mean displays freshened and cooled waters around Greenland, which spread in the subpolar gyre, and then towards the subtropical gyre and the Nordic Seas. Over the whole period, the convection is reduced in the Labrador and Nordic Seas, while it is slightly enhanced in the Irminger Sea, and the AMOC is reduced by 0.32±0.35 Sv at 26°N. This highlights that the AMOC decrease due to Greenland melting remains modest in these simulations and can only explain a very moderate amount of the 3±1 Sv weakening suggested in a recent study. The multi-decadal trend of the North Atlantic surface temperature obtained with the additional freshwater forcing is more in line with observations than in standard historical simulations. We also show a clear improvement of the representation of the 1995 abrupt warming in the subpolar gyre in the melting ensemble, which may thus be partly forced by Greenland ice sheet melting. Mechanisms at play imply changes in the variability of the AMOC in the melting ensemble as compared to the historical one. Such an impact on forced decadal variability has crucial consequences for decadal prediction systems that may gain skill by including observed Greenland ice sheet melting.

Il est probable qu'a terme les emissions de nanoparticules soient reglementees et ce sont donc les concentrations en nombre qui seront considerees. Il convient donc d'adapter les modeles afin de pouvoir simuler correctement les concentrations en nombre, dans les ambiances confinees comme dans l'atmosphere. Un modele de dynamique des particules capable de suivre avec autant de precision la concentration en nombre que la concentration en masse, avec un temps de calcul optimal, a ete developpe. La dynamique des particules depend de divers processus, les plus importants etant la condensation/evaporation, suivie par la nucleation, la coagulation, et les phenomenes de depots. Ces processus sont bien connus pour les particules fines et grossieres, mais dans le cas des nanoparticules, certains phenomenes additionnels doivent etre pris en compte, notamment l'effet Kelvin pour la condensation/ evaporation et les forces de van der Waals pour la coagulation. Le travail a tout d'abord porte sur le processus de condensation/evaporation, qui s'avere etre le plus complique numeriquement. Les particules sont presumees spheriques. L'effet Kelvin est pris en compte car il devient considerable pour les particules de diametre inferieur a 50 nm. Les schemas numeriques utilises reposent sur une approche sectionnelle : l'echelle granulometrique des particules est discretisee en sections, caracterisees par un diametre representatif. Un algorithme de repartition des particules est utilise, apres condensation/evaporation, afin de conserver les diametres representatifs a l'interieur de leurs sections respectives. Cette redistribution peut se faire en terme de masse ou de nombre. Un des points cle de l'algorithme est de savoir quelle quantite, de la masse ou du nombre, doit etre redistribuee. Une approche hybride consistant a repartir la quantite dominante dans la section de taille consideree (le nombre pour les nanoparticules et la masse pour les particules fines et grossieres) a ete mise en place et a permis d'obtenir une amelioration de la precision du modele par rapport aux algorithmes existants, pour un large choix de conditions. Le processus de coagulation pour les nanoparticules a aussi ete resolu avec une approche sectionnelle. La coagulation est regie par le mouvement brownien des nanoparticules. Pour cette approche, il a ete constate qu'il est plus efficace de calculer le noyau de coagulation en utilisant le diametre representatif de la section plutot que de l'integrer sur la section entiere. Les simulations ont aussi pu montrer que les interactions de van der Waals amplifient fortement le taux de coagulation pour les nanoparticules. La nucleation a ete integree au modele nouvellement developpe en incorporant un terme source de nanoparticules dans la premiere section, commencant a un nanometre. La formulation de ce taux de nucleation correspond a celle de l'acide sulfurique mais le traitement des interactions numeriques entre nucleation, coagulation et condensation/evaporation est generique. Differentes strategies de couplage visant a resoudre separement ou en meme temps les trois processus sont discutees. Afin de pouvoir proposer des recommandations, differentes methodes numeriques de couplage ont ete developpees puis evaluees par rapport au temps de calcul et a la precision obtenue en terme de concentration massique et numerique