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The Sahel semiarid region was marked during the twentieth century by significant modulations of its rainfall regime at the decadal time scale. Part of these modulations have been associated with the internal variability of the climate system, linked to changes in oceanic sea surface temperature. More recently, several studies have highlighted the influence of external forcings during the dry period in the 1980s and the recovery around the 2000s. In this work we evaluate the internally and externally driven decadal modulations of Sahel rainfall during the entire twentieth century using a set of 12 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We begin by proposing a physically based definition of Sahel rainfall that takes into account the southward bias in the location of the Sahelian ITCZ simulated by all the models. Our results show that the amplitude of the decadal variability, which is underestimated by most models, is mainly produced by the internally driven component. Conversely, the external forcing tends to enhance the synchrony of the simulated and observed decadal modulations in most models, providing statistically significant correlations of the historical ensemble mean with observations in 1/3 of the models, namely IPSL-CM6A-LR, INM-CM5-0, MRI-ESM2-0, and GISS-E2-1-G. Further analysis of the detection and attribution runs of the IPSL-CM6A-LR shows that anthropogenic aerosol dominate the decadal modulations of Sahel rainfall simulated by this model, suggesting that at least a part of the impact is ocean-mediated and operated through shifts in the ITCZ and the Saharan heat low.

Previous studies agree on an impact of the Atlantic Multidecadal Variability (AMV) on total seasonal rainfall amounts over the Sahel. However, whether and how AMV affects the distribution of rainfall or the timing of the West African Monsoon is not well known. Here we analyze daily rainfall outputs from atmosphere-ocean coupled models. Models show dry biases over the Sahel, where the mean intensity is consistently smaller than observations, and wet biases over the Guinea Coast, where they simulate too many rainy days. In addition, most models underestimate the average length of the rainy season over the Sahel, some due to a too late monsoon onset and others due to a too early cessation. In response to a persistent positive AMV pattern imposed in the Atlantic, following a protocol largely consistent with the one proposed by the Component C of the Decadal Climate Prediction Project (DCPP-C), models show an enhancement in total summer rainfall over West African land mass, including the Sahel. Both the number of wet days and the intensity of daily rainfall events are enhanced over the Sahel. The former explains most of the changes in seasonal rainfall in the northern fringe, while the latter is more relevant in the southern region, where higher rainfall anomalies occur. This dominance is connected to the changes in the number of days per type of event: the frequency of both moderate and heavy events increases over the Sahel’s northern fringe. Conversely, over the southern limit, it is mostly the frequency of heavy events which is enhanced, affecting the mean rainfall intensity there. Extreme rainfall events are also enhanced over the whole Sahel in response to a positive phase of the AMV. Models with stronger negative biases in rainfall amounts tend to show weaker changes in response to AMV, suggesting systematic biases could affect the simulated responses. The monsoon onset over the Sahel shows no clear response to AMV, while the demise tends to be delayed and the overall length of the monsoon season enhanced between 2 and 5 days with the positive AMV pattern. The effect of AMV on the seasonality of the monsoon is more consistent to the West of 10º W, with all models showing a statistically significant earlier onset, later demise and enhanced monsoon season with the positive phase of the AMV. Our results suggest a potential for the decadal prediction of changes in the intraseasonal characteristics of rainfall over the Sahel, including the occurrence of extreme events.

A potential future slowdown or acceleration of the Atlantic Meridional Overturning Circulation (AMOC) would have profound impacts on global and regional climate. Recent studies have shown that AMOC responds, among many other processes, to anthropogenic changes in tropical Indian ocean (TIO) temperature. However, internal unforced co-variations between these two basins are largely unexplored as of yet. Here, we use the ERSST v5, HadISST v1, and COBE v2 gridded observational products for the period 1870-2014, as well as dedicated simulations with coupled climate models, and show that internal changes in sea surface temperature gradients between the Indian and Atlantic Ocean (SSTgrad) can drive teleconnections that influence internal variations of North Atlantic climate and AMOC.We separate the unforced observed component (i.e., internal signal) from the forced signal following the residuals method presented by Smith et al. (2019). In the absence of direct AMOC observation we estimate AMOC variability from an SST index (SSTAMOC; Caesar et al., 2018). We find a robust observed relationship between the unforced tropical SSTgrad and SSTAMOC when TIO leads by ~25 years. This time-lag is in line with a recently described mechanism of anomalous tropical Atlantic rainfall patterns that originate from TIO warming and cause anomalously saline tropical Atlantic surface water which slowly propagate northward into the subpolar North Atlantic, ultimately altering oceanic deep convection and AMOC (Hu and Fedorov, 2019; Ferster et al. 2021). Our study now suggests that it is the tropical SSTgrad that drives those AMOC changes, with a limited role for the western tropical Pacific. Pre-industrial control simulations with the IPSL-CM6A-LR model confirm this relationship, indicating a time lag of ~25 years between SSTgrad and SSTAMOC variations. These simulations also confirm that the SSTAMOC is representative of unforced AMOC variations when SSTAMOC leads by 5 years. This work therefore indicates that an unforced pathway between tropical ocean temperature and AMOC exists with a ~20 year lag, which opens the potential for using SSTgrad as precursor to predict future AMOC changes. Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G., & Saba, V. (2018). Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), 191-196.Ferster, B. S., Fedorov, A. V., Mignot, J., & Guilyardi, E. (2021). Sensitivity of the Atlantic meridional overturning circulation and climate to tropical Indian Ocean warming. Climate Dynamics, 1-19.Hu, S., & Fedorov, A. V. (2019). Indian Ocean warming can strengthen the Atlantic meridional overturning circulation. Nature climate change, 9(10), 747-751.Smith, D. M., Eade, R., Scaife, A. A., Caron, L. P., Danabasoglu, G., DelSole, T. M., ... & Yang, X. (2019). Robust skill of decadal climate predictions. Npj Climate and Atmospheric Science, 2(1), 1-10.

Future changes in Sahel precipitation are uncertain because of large differences between projections of various climate models. We assess the effect of climate change on Sahel precipitation in summer and for the end of the 21st century. We show that uncertainty in Sahel precipitation is associated with uncertainty at simulating future changes in surface air temperature over the northern Hemisphere. We point out the Atlantic Ocean and Euro-Mediterranean surface air temperature as drivers of the Sahel precipitation change uncertainty. We use a storyline approach, a statistical method, to construct scenarios of changes in Sahel precipitation, whose differences only depend on future changes in Atlantic Ocean and Euro-Mediterranean surface air temperature. We show that uncertainty in changes in Atlantic Ocean and Euro-Mediterranean surface air temperature explains up to 50% of Sahel precipitation change uncertainty. The approach also allows selecting models to better understand uncertainty in Sahel precipitation change, focusing on the mechanisms at play. We suggest that reducing uncertainty in the future warming of the North Atlantic and the Euro-Mediterranean areas would then allow reducing uncertainty in future changes in Sahel precipitation.

Economically based on rain-fed agriculture and grazing, the Sahel region is extremely vulnerable to possible shifts in its rainfall pattern associated with the West African monsoon. Short-term climate prediction, i.e. in the horizon of 5 to 30 years, the so-called "decadal" time scale, is highly relevant for economic and structural planning.In this study we evaluate the impact of model initialization in the skill and reliability with which Sahel rainfall amounts are predicted by different models at decadal timescales. Using initialized and uninitialized (historical simulations) hindcasts from height (8) CMIP6 models and their ensemble mean, our results show that the impact of initialisation depends on the model. Half of the models (MPI-ESM1-2-LR, MRI-ESM2-0, IPSL-CM6A-LR and EC-Earth3) show skillful MSSS scores in their uninitialized experiments and the initialization does not provide further improvements. This situation is very different from the previous coordinated exercise performed within CMIP5 suggesting an enhanced role of external forcing on the interannual to decadal modulation of the Sahel rainfall in CMIP6. However, for NorCMP1 while initialized predictions show skillful MSSS scores, uninitialized ones show very weak scores, suggesting the initialization improves the forced response and the internal variability in this model. Our results also show that the MRI-ESM2-0 model is more skillful than the multi-model ensemble. In addition to MSSS scores, the reliability of these prediction systems is also tested. Preliminary results show positive impacts of the initialization in five out of height models. In general, our results suggest that the initialization does not greatly improve MSSS scores but it does enhance reliability.

Coastal countries in West Africa heavily rely on the ocean, which is a major source of food and employment. This is mainly due to the presence of coastal upwelling, upward motion of sea water bringing the nutrient-rich deeper waters into the illuminated surface layers in the coastal zone, where they become available for photosynthesis. The resulting phytoplankton production, the base of the food chain, render coastal upwelling the most productive of large marine ecosystems in the world’s oceans. Recently, decadal variability and predictability of coastal upwelling systems has received a lot of attention, since near-term changes of upwelling systems could have a strong impact of living marine resources and hence surrounding countries economy. On this aspect, recent progress has been made in generating near-term (“decadal”) predictions of physical using Earth system models (ESMs). Initialized forecasts have shown significant predictability from 1 to 10 years in advance for climate events showing substantial decadal variability.Our objective here is two-fold: first we investigate the decadal variability of the Senegalo-mauritanian upwelling system (SMUS) in the reanalysis and historical simulations from eleven climate models using indices based on the SST and wind stress and also identify the processes controlling this variability. Second, we exploit the decadal prediction experiments of CMIP6 (DCPP-A), to investigate this upwelling predictability. Our results show that the SMUS is characterized by a strong decadal variability, in part linked to the Atlantic Multidecadal Variability Consequently, the DCPP- A experiment shows strong and generally significant correlation prediction scores at various lead times for the dynamical indices (Ekman transport and Ekman pumping). However, even though coastal SST are also significantly predictable, non-significant ACC scores are found for the thermal upwelling indices. The analysis concludes on trying to qualify and quantify the predictable components of the SMUS and possible applications.

Variable fluxes of anthropogenic CO2 emissions into the land and the ocean and the remaining proportion in the atmosphere reflect on the global carbon budget variations and further modulate global climate change. A more accurate reconstruction of the global carbon budget in the past decades and a more reliable prediction of the variations in the next years are crucial for assessing the effectiveness of climate change mitigation policies and supporting global carbon stocktaking and monitoring in compliance with the goals of the Paris Agreement.In this study, we investigate reconstructions and predictions of the CO2 fluxes and atmospheric CO2 growth from ensemble prediction simulations using 5 Earth System Model (ESM) - based decadal prediction systems. These novel prediction systems driven by CO2 emissions with an interactive carbon cycle enable prognostic atmospheric CO2 and represent atmospheric CO2 growth variations in response to the strength of CO2 fluxes into the ocean and the land, which are missing in the conventional concentration-driven decadal prediction systems with prescribed atmospheric CO2 concentration.The reconstructions generated by assimilating physical ocean and atmosphere data products into the prediction systems are able to reproduce the annual mean historical variations of the CO2 fluxes and atmospheric CO2 growth. Multi-model ensemble means best match the assessments of CO2 fluxes and atmospheric CO2 growth rate from the Global Carbon Project with correlations of 0.79, 0.82, and 0.98 for atmospheric CO2 growth rate, air-land CO2 fluxes, and air-sea CO2 fluxes, respectively. The CO2 emission-driven prediction systems with an interactive carbon cycle still maintain the predictive skill of CO2 fluxes and atmospheric CO2 growth as found in conventional concentration-driven prediction systems, i.e., about 2 years for the air-land CO2 fluxes and atmospheric CO2 growth, the air-sea CO2 fluxes have higher skill up to 5 years. The ESM-based prediction systems are capable to reconstruct and predict the variations in the global carbon cycle and hence are powerful tools for supporting carbon budgeting and monitoring, especially in the decarbonization processes. Furthermore, we investigate the contribution of uncertainty in the predictions of CO2 fluxes and atmospheric CO2 growth rate from internal climate variability, different model responses, and emission-forcing reductions to identify the prominent challenge in limiting the skill of CO2 predictions.

Future changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analysing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro-Mediterranean temperatures explain up to 50% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro-Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro-Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in another storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that reducing uncertainty in future changes in Sahel precipitation could be achieved by reducing uncertainty in the future warming of the North Atlantic and the Euro-Mediterranean areas.

International audience; Evaluating the potential climatic suitability for premium wine production is crucial for adaptation planning in Europe. While new wine regions may emerge out of the traditional boundaries, most of the present-day renowned winemaking regions may be threatened by climate change. Here, we analyse the future evolution of the geography of wine production over Europe, through the definition of a novel climatic suitability indicator, which is calculated over the projected grapevine phenological phases to account for their possible contractions under global warming. Our approach consists in coupling six different de-biased downscaled climate projections under two different scenarios of global warming with four phenological models for different grapevine varieties. The resulting suitability indicator is based on fuzzy logic and is calculated over three main components measuring (i) the timing of the fruit physiological maturity, (ii) the risk of water stress and (iii) the risk of pests and diseases. The results demonstrate that the level of global warming largely determines the distribution of future wine regions. For a global temperature increase limited to 2°C above the pre-industrial level, the suitable areas over the traditional regions are reduced by about 4%/°C rise, while for higher levels of global warming, the rate of this loss increases up to 17%/°C. This is compensated by a gradual emergence of new wine regions out of the traditional boundaries. Moreover, we show that reallocating better-suited grapevine varieties to warmer conditions may be a viable adaptation measure to cope with the projected suitability loss over the traditional regions. However, the effectiveness of this strategy appears to decrease as the level of global warming increases. Overall, these findings suggest the existence of a safe limit below 2°C of global warming for the European winemaking sector, while adaptation might become far more challenging beyond this threshold.

In response to increasing human emissions, the global ocean is continually warming. The spatial distribution of this warming can result from several mechanisms, difficult to disentangle in observations. Idealized modeling studies have successfully separated the contribution of additional heat passively entering the ocean from the contribution of the changing circulation redistributing the pre-existing heat in response to perturbations in air–sea fluxes. However, the time scales of these different contributions have been largely unexplored so far. Here, we revisit this decomposition with a novel numerical framework to investigate the mechanisms driving regional ocean warming and its emergence from internal variability. Based on the IPSL-CM6A-LR coupled model and its large ensemble of transient climate change simulations, we extract both the internal fluctuations and the externally forced signal in each component of the surface fluxes. With a stand-alone configuration of the ocean, we then test the response to perturbations applied on all surface fluxes together or individually. We find that the contribution of the different processes can largely vary in time, reinforcing or counteracting each other, causing the time of emergence of subsurface temperature changes to be advanced or delayed. Anthropogenic warming in the upper ocean water masses is generally driven by the uptake of excess heat passively stored by the ocean circulation. Circulation changes have a minor role at the time when these signals emerge. On the contrary, in the deeper ocean, circulation changes are much more sensitive to surface forcings and play an important role in setting the time scales of ocean warming, through redistributive warming or cooling.

The ocean responds to climate change through modifications of heat, freshwater and momentum fluxes at its boundaries. Disentangling the specific role of each of these contributors in shaping the changes of the thermohaline structure of the ocean is central for our process understanding of climate change and requires the design of specific numerical experiments. While it has been partly addressed by modeling studies using idealized CO2 forcings, the time evolution of these individual contributions during historical and projected climate change is however lacking. Here, we propose a novel modeling framework to isolate these contributions in coupled climate models for which large ensembles of historical and scenario simulations are available. The first step consists in reproducing a coupled pre-industrial control simulation with an ocean-only configuration, forced by prescribed fluxes at its interface, diagnosed from the coupled model. In a second step, we extract the external forcing perturbations from the historical+scenario ensemble of coupled simulations, and we add them to the prescribed fluxes of the ocean-only configuration. We then successfully replicate the ocean's response to historical and projected climate change in the coupled model during 1850–2100. In a third step, this full response is decomposed in sensitivity experiments in which the forcing perturbations are applied individually to the heat, freshwater and momentum fluxes. Passive tracers of temperature and salinity are implemented to discriminate the addition of heat and freshwater flux anomalies from the redistribution of pre-industrial heat and salt content in response to ocean circulation changes. Here, we first present this general framework and then apply it to the IPSL-CM6A-LR model and its ocean component NEMO3.6. This framework brings new opportunities to precisely explore the mechanisms driving historical and projected ocean changes within single climate models.

In recent decades three major volcanic eruptions of different intensity have occurred: Mount Agung (1963), El Chichón (1982) and Mount Pinatubo (1991), with reported climate impacts on seasonal-to-decadal timescales and providing a high prediction potential. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of such volcanic eruptions on decadal prediction, which consists in performing initialised sets of predictions just before the three historical volcanic eruptions, but in which the volcanic aerosol forcing is excluded. The impact of the volcanic eruptions is therefore determined by comparing these new forecasts with those included in the corresponding retrospective prediction experiment DCPP-A, which include historical volcanic aerosol forcing. Here we present the results from six CMIP6 decadal prediction systems (CanESM5, CESM1, EC-Earth3, HadGEM3, IPSL-CM6A and CMCC-CM2-SR5). The global mean temperature cooling is comparable among models and consistent with previous studies. The surface temperature response pattern in the first years is similar across all the models and for the individual volcanic eruptions. At later forecast times (years 6-9), differences among the models and eruptions emerge. Preliminary results show that the volcanic eruptions impact the atmospheric and oceanic dynamics, as shown in previous studies, although some differences across models emerge, specifically on the ocean overturning and gyre circulation changes.

We investigate the representation of the Canary upwelling system (CUS) in six global coupled climate models operating at high and standard resolution as part of the High Resolution Model Intercomparison Project (HighResMIP). For this project the resolution of the ocean and/or atmosphere components was increased. The models performance in reproducing the observed CUS is assessed in terms of various upwelling indices based on SST, wind stress and sea surface height, focussing on the effect of increasing model spatial resolution. Our analysis shows that an increase of spatial resolution depends on the sub-domain of the CUS considered. Strikingly, along the Iberian Peninsula region, which is the northernmost part of the CUS, the models show lower skill at higher resolution compared to their corresponding lower resolution version in both components for all the indices analyzed in this study. On the contrary, over the southernmost part of the CUS, from the north of Morocco to the Senegalese coast, the high ocean and atmosphere resolution models simulate a more realistic upwelling than the standard resolution models, which largely differ from the range of observational estimates. These results suggest that increasing resolution is not a sufficient condition to obtain a systematic improvement in the simulation of the upwelling phenomena as represented by the indices considered here, and other model improvements notably in terms of the physical parameterizations may also play a role.

In order to improve our ability to predict the near-term evolution of climate, it may be important to accurately predict the evolution of atmospheric CO2, and thus carbon sinks. Following on from process-driven improvements of decadal predictions in physical oceanography, we focus on improving our understanding of the internal processes and variables driving CO2 uptake by the North Atlantic ocean. Specifically, we use the CMIP6 model IPSLCM6A to investigate the drivers of ocean-atmosphere CO2 flux variability in the North Atlantic subpolar gyre (NA SPG) on seasonal to decadal timescales. We find that DpCO2 (CO2 partial pressure difference between atmosphere and ocean) variability dominates over sea surface temperature (SST) and sea surface salinity (SSS) variability on all timescales within the NA SPG. Meanwhile, at the ice-edge, there are significant roles for both ice concentration and surface winds in driving the overall CO2 flux changes. Investigating the interannual DpCO2 variability further, we find that this variability is itself driven largely by variability in simulated mixed layer depths in the northern SPG. On the other hand, SSTs show an important contribution to DpCO2 variability in the southern SPG and on longer (decadal) timescales. Initial extensions into a multi-model context show similar results. By determining the key regions and processes important for skilful decadal predictions of ocean-atmosphere CO2 fluxes, we aim to both improve confidence in these predictions as well as highlight key targets for climate model improvement.

Predicting carbon fluxes and atmospheric CO2 can constrain the expected next-year atmospheric CO2 growth rate and thereby allow to independently monitor total anthropogenic CO2 emission rates. Several studies have established predictive skill in retrospective forecasts of carbon fluxes. These studies are usually backed by perfect-model simulations of single models showing the origins of predictive skill in carbon fluxes and atmospheric CO2 concentration. Yet, a comprehensive multi-model comparison of perfect-model predictions, which can be valuable in explaining differences in retrospective predictions, is still lacking. Moreover, as of now, we don't have sufficient understanding of how well do the models predict their own integrated carbon cycles and how congruent this predictability is across models.Here, we show the predictive skill of land and ocean carbon fluxes as well as atmospheric CO2 concentration in seven Earth-System-Models. Our first results indicate predictive skill of globally aggregated carbon fluxes of 2±1 years and atmospheric CO2 of 3±2 years. However, the regional patterns, hotspots and origins of predictive skill diverge among models. This heterogeneity explains the regional differences found in existing retrospective forecasts and backs the overall consistent predictability time-scales at global scale.

The intraseasonal characteristics of rainfall are crucial in monsoon regions, in particular foragriculture. Crop yields indeed depend on the rainfall seasonal amount, but also on otherintraseasonal characteristics such as the onset of the rainy season or the distribution of rainy days.In the Sahel region, while the average amount of seasonal rainfall has been shown to be marked bystrong decadal variability over the 20 th century, the modulations of intraseasonal rainfallcharacteristics has received less attention in the literature so far. In this study, we show that thefrequency and intensity of intraseasonal rainfall events in Senegal exhibit a marked decadalvariability over the 1918-2000 period, similar to that of mean seasonal rainfall in the Sahel duringthe 20 th century, and in phase with the Atlantic Multidecadal Variability (AMV). The representationof the decadal modulation of these rainfall indices is further investigated using the atmosphericcomponent of the IPSL-CM6 model forced by the historically observed SST. Preliminary resultsshow that the model represents fairly well the observed decadal modulation of these extreme events.Thus, the model simulations allow an in-depth understanding of the associated mechanism.

During the 20th Century, Sahel rainfall has experienced strong variability at decadal timescales, partly attributed to the internal variability of the climate system, mediated by changes in oceanic sea surface temperature (SST). However, a stronger emphasis has been more recently given to the role of external forcing. Thus, the attribution of past decadal modulations of Sahel rainfall is still under debate.In this study, we propose a diagnostic of the contribution of external forcing to observed Sahel rainfall decadal modulations based on a correlation analysis. We apply it to six models of the sixth coupled model intercomparison project (CMIP6) in the whole 20th Century. Our results show that external forcings induce a weak amplitude of Sahel rainfall modulations in the models and these modulations are in general insignificantly correlated with the observed modulations. There are two notable exceptions: IPSL-CM6A-LR and INM-CM5-0 models.With the IPSL-CM6-LR model, our results show that this correlation primarily arises from the role of anthropogenic aerosols. This effect is partly explained via the ocean mediated mechanism in particular the North Atlantic ocean and Mediterranean sea. In CanESM5 and CNRM-CM6-1 models, the Sahel rainfall decadal forced modulations are also dominantly due to anthropogenic aerosols, although not significantly correlated with the observations. In addition, the greenhouse gases (GHG) contribute significantly to the forced response of these models, which could explain partly the insignificant correlation of these CMIP6 models.

A potential future slowdown or acceleration of the Atlantic Meridional Overturning Circulation (AMOC) would have profound impacts on global and regional climate. Recent studies have shown that AMOC responds, among many other processes, to anthropogenic changes in tropical Indian ocean (TIO) temperature. However, internal unforced co-variations between these two basins are largely unexplored as of yet. Here, we use the ERSST5 and HadISST4 gridded observational products for the period 1870-2014, as well as dedicated simulations with coupled climate models, to illustrate how unforced changes in TIO sea surface temperature can drive teleconnections that influence internal variations of North Atlantic climate and AMOC. We separate the unforced observed component from the forced signal following the residuals method presented by Smith et al. (2019): the forced response is estimated from the CMIP6 multi-model ensemble mean and then subtracted from observed variability, leaving the unforced residual. In the absence of direct AMOC observation we estimate AMOC variability from a SST index first proposed by Caesar et al. (2018), the Caesar Index (CI). We find a robust observed relationship between unforced TIO and unforced CI when TIO leads by ~30 years. This time-lag is in line with a recently described mechanism of anomalous tropical Atlantic rainfall patterns that originate from TIO warming and cause anomalously saline tropical Atlantic surface water which slowly propagate northward into the subpolar North Atlantic, ultimately altering oceanic deep convection and AMOC (Ferster et al. 2021). Pre-industrial control simulations with the IPSL-CM6A-LR model confirm this relationship, indicating a time lag of ~30 years between TIO and CI variations. These simulations also confirm that the CI is representative of unforced AMOC variations when CI leads by 10 years. This work therefore indicates that an unforced pathway between TIO temperature and AMOC exists with a ~20 year lag, which opens the potential for using TIO temperature as precursor to predict future AMOC changes. Caesar, L., Rahmstorf, S., Robinson, A., Feulner, G., & Saba, V. (2018). Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), 191-196.Ferster, B. S., Fedorov, A. V., Mignot, J., & Guilyardi, E. (2021). Sensitivity of the Atlantic meridional overturning circulation and climate to tropical Indian Ocean warming. Climate Dynamics, 1-19.Smith, D. M., Eade, R., Scaife, A. A., Caron, L. P., Danabasoglu, G., DelSole, T. M., ... & Yang, X. (2019). Robust skill of decadal climate predictions. Npj Climate and Atmospheric Science, 2(1), 1-10.

Decadal climate prediction is a scientific endeavour of potentially large societal impacts. Yet such predictions remain challenging, as they predict climate skilfully only under certain circumstances or in specific regions. Moreover, decadal climate prediction simulations rely on dedicated coupled climate model simulations that are particularly expensive. In this study, we build upon earlier research by Menary et al. (2021) in search of a method to make skilful and cheap decadal climate predictions by constructing predictions from existing climate model simulations using the so-called analogue method.The analogue method draws on the idea that there is decadal memory in the climatic state at the start of a prediction. This method identifies the observed state of the climate system at the start of a prediction and then screens the archive of available model simulations for comparable climatic states. It then selects a number of modelled climate states that are similar to the observed situation, and uses the years after the selected simulated climate states as prediction. Using a simple analogue method based on temperature trends in the North Atlantic basin, Menary et al. (2021) demonstrated skilful prediction of North Atlantic SST on par with dynamical decadal prediction simulations. In this study, we refine the original method by using more sophisticated algorithms to select the analogues, and choosing decadal prediction of seasonal European climate as our target. These new selection algorithms include multivariate regression at different time lags as well as non-linear methods. Menary, MB, J Mignot, J Robson (2021) Skilful decadal predictions of subpolar North Atlantic SSTs using CMIP model-analogues. Environ. Res. Lett. 16 064090. https://doi.org/10.1088/1748-9326/ac06fb

Robust projections of the Indian Summer Monsoon Rainfall (ISMR) are critical as it provides 80 % of the annual precipitation to more than one billion people who are very vulnerable to changes. However, even over the historical period, CMIP (Coupled Model Intercomparison Project) coupled models have difficulties to reproduce the observed ISMR trends and are affected by a large inter-model spread, which question the reliability of the ISMR projections. When studying climate response, three main sources of uncertainties exist : scenario uncertainties, internal variability and models bias. We study the impact of the latter on the historical response of ISMR of 34 models from CMIP6. First we show that model local biases over India do not impact significantly how they simulate the response of ISMR over the recent period. However, when we enlarge the analysis to the whole tropics and study the impact of regional and remote rainfall and SST biases on ISMR historical response by using a Maximum Covariance Analysis (MCA), we do find statistical significant relationships, which may provide observational constraints on future ISMR projections. Our results highlight the key-role of the temperature gradient errors between the arid regions surrounding India and the Arabian Sea on one hand, and of Pacific rainfall and SST biases on the other hand, as an important source of inter-model spread in the ISMR response. The physical mechanisms underlying these statistical relationships between ISMR response and the inter-model spread are finally explored.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015.

International audience; We investigate the impact of Arctic sea ice loss on the Atlantic meridional overturning circulation (AMOC) and North Atlantic climate in a coupled general circulation model (IPSL-CM5A2) perturbation experiment, wherein Arctic sea ice is reduced until reaching an equilibrium of an ice-free summer. After several decades we observe AMOC weakening caused by reduced dense water formation in the Iceland basin due to the warming of surface waters, and later compensated by intensification of dense water formation in the Western Subpolar North Atlantic. Consequently, AMOC slightly weakens in deep, dense waters but recovers through shallower, less dense waters overturning. In parallel, wind-driven intensification and southeastward expansion of the subpolar gyre cause a depth-extended cold anomaly ∼2°C around 50°N that resembles the North Atlantic "warming hole." We conclude that compensating dense water formations drive AMOC changes following sea ice retreat and that a warming hole can develop independently of the AMOC modulation.

28 pages, 9 figures, 6 tables, supporting information https://doi.org/10.1029/2021JC017676.-- Data Availability Statement: SSS CCI datasets are freely available at https://catalogue.ceda.ac.uk/uuid/4ce685bff631459fb2a30faa699f3fc5. The PI-MEP MDB are freely available as NetCDF files at https://www.salinity-pimep.org/data/mdb.html; corresponding validation reports are available on https://www.salinity-pimep.org/reports/mdb.html. SMAP salinity data are produced by Remote Sensing Systems and sponsored by the NASA Ocean Salinity Science Team. Data are available at www.remss.com. Aquarius data are available at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L3_SSS_SMID_ANNUAL_V5. CATDS SSS are available at the CATDS Production Data Center (CPDC), www.catds.fr. ISAS and Glorys fields are taken from (https://catalogue.marine.copernicus.eu/; INSITU_GLO_TS_OA_REP_OBSERVATIONS_013_002 and GLOBAL-REANALYSIS-PHY-001-030 products, respectively). The GOSUD RV data set is available at https://doi.org/10.17882/39475, the ships of opportunity data set is available at http://www.legos.obs-mip.fr/observations/sss/datadelivery/dmdata. Argo data are taken from http://www.coriolis.eu.org/, Sea Surface Salinity (SSS) is an increasingly used Essential Ocean and Climate Variable. The Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI + SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale. They display large year-to-year seasonal variations over the 2010–2019 decade, sometimes by more than ±0.4 over large regions. The robust standard deviation of the monthly CCI SSS minus in situ Argo salinities is 0.15 globally, while it is at least 0.20 with individual satellite SSS fields. r2 is 0.97, similar or better than with original datasets. The correlation with independent ship thermosalinographs SSS further highlights the CCI data set excellent performance, especially near land areas. During the SMOS-Aquarius period, when the representativity uncertainties are the largest, r2 is 0.84 with CCI while it is 0.48 with the Aquarius original data set. SSS CCI data are freely available and will be updated and extended as more satellite data become available, This study was funded by ESA CCI contract 4000123663/18/I-NB, With the institutional support of the ‘Severo OchoaCentre of Excellence’ accreditation (CEX2019-000928-S)

International audience The Institut Pierre-Simon Laplace Climate Modeling Center has produced an ensemble of extended historical simulations using the IPSL-CM6A-LR climate model. This ensemble (referred to as IPSL-EHS) is composed of 32 members over the 1850–2059 period that share the same external forcings but differ in their initial conditions. In this study, we assess the simulated decadal to multidecadal climate variability in the IPSL-EHS. In particular, we examine the global temperature evolution and recent warming trends, and their consistency with ocean heat content and sea ice cover. The model exhibits a large low-frequency internal climate variability. In particular, a quasi-bicentennial mode of internal climate variability is present in the model and is associated with the Atlantic Meridional Overturning Circulation. Such variability modulates the global mean surface air temperature changes over the historical period by about $\sim$0.1K. This modulation is found to be linked to the phase present in the initial condition state of each member. This variability appears to decrease during the 1850–2018 period in response to external forcings. The analysis of the ocean heat content reveals furthermore an overestimation of the ocean stratification, which likely leads to an overestimation of the recent warming rate on average

Decadal Climate Predictions (DCP) have gained considerable attention for their potential utility in promoting optimised plans of adaptation to climate change and variability. Their effective applicability to a targeted problem is nevertheless conditional on a detailed evaluation of their ability to simulate the near-term climate evolution under specific conditions. Here we explore the performance of the IPSL-CM5A-LR DCP system in predicting air temperature over Europe, by proposing a systematic assessessment of the prediction skill for different time windows (periods of the calendar time, forecast years and months/seasons). In this framework, we also compare raw and de-biased hindcasts, in which the temperature outputs have been corrected using a quantile matching method. The systematic analysis allows to discern certain conditions conferring larger predictability, which we find to be intermittent in time. The predictions appear more skilful around the 1960s and after the 1980s, in coincidence with large shifts of the Atlantic Multidecadal Variability, which are well reproduced in the hindcasts. Averages on longer forecast periods also generally imply better prediction skill, while the best predicted months appear to be mainly those between late spring and early autumn. Moreover, we find an overall added value due to initialisation, while de-biased predictions significantly outperform raw predictions only for a few specific time windows. Finally, we discuss the potential implications of the proposed systematic exploration of skill opportunities in DCPs for integrated applications in climate sensitive sectors.

Sea Surface Salinity (SSS) is an Essential Ocean and Climate Variable, which is increasingly used as part of climate studies. SSS measurements are available from three satellite missions, SMOS, Aqu...

The Atlantic Multidecadal Variability (AMV) is a large-scale climate phenomenon with crucial impacts on human societies and ecosystems. Its periodicity and drivers are controversial due to the short temporal extent of instrumental observations and competing impacts of external forcing and internal variability. Here, we use a well-verified set of paleoclimate proxy records and compare four regression methods to perform different reconstructions of the AMV since 850 C.E., built to only reflect internal variability in the Atlantic. The best performing reconstruction, when verified both against climate model outputs and independent proxy records is obtained using the non-linear random forest method. It exhibits large multi-decadal variations in the range of 20-90 years, a broader range than the 50-70 years identified in instrumental records. The reconstruction shows that AMV autocorrelation properties have experienced a significant change in the recent decades, suggesting an early warning signal for the proximity of a tipping point in the Atlantic.

Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution. All authors were supported by the EUCP project funded by the European Commission's Horizon 2020 programme, Grant Agreement number 776613. JM was also supported by the french ANR MOPGA project ARCHANGE and by the EU-H2020 Blue Action (GA 727852) and 4C projects (GA 821003). MGD also received funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964. Peer Reviewed "Article signat per 14 autors/es: Gabriele C. Hegerl, Andrew P. Ballinger, Ben B. B. Booth, Leonard F. Borchert, Lukas Brunner, Markus G. Donat, Francisco J. Doblas-Reyes, Glen R. Harris, Jason Lowe, Rashed Mahmood, Juliette Mignot, James M. Murphy, Didier Swingedouw and Antje Weisheimer"

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.

International audience; A salient feature of anthropogenic climate change is the enhanced warming of the tropical Indian Ocean (TIO) relative to the tropics. Recent studies show that this warming can remotely modulate the Atlantic meridional overturning circulation (AMOC). Motivated by these results, we systematically study the sensitivity of the AMOC and Atlantic climate to changes in TIO temperature using the latest coupled climate model from the Institut Pierre Simon Laplace (IPSL-CM6A-LR). Ensemble experiments nudging the TIO surface temperatures by-2°C,-1°C, +1°C, and +2°C are conducted. Within a few years after the forcing is imposed, different atmospheric teleconnections begin to drive the AMOC "fast" and "slow" responses, yielding after 150 years an AMOC equilibrium sensitivity of about +9.4 Sv per 1°C of relative TIO warming. A water mass transformation analysis shows that the fast response to TIO warming (on decadal timescales) is largely driven by surface cooling in the Labrador Sea caused by an induced positive North Atlantic Oscillation (NAO)-like mean pattern. By contrast, the slow response (on multi-decadal to centennial timescales) is driven by the gradual advection of positive salinity anomalies from the tropical Atlantic, which predominantly affect the Nordic Seas. The response is non-linear in that a TIO warming strengthens the AMOC through increase in Labrador Sea deep water formation, while a TIO cooling slows down the AMOC via sea ice expansion over the Nordic Seas deep-water formation region, ultimately leading to the AMOC shutdown in the-2ºC-TIO experiment. These results help understand the role of interbasin connections and AMOC drivers in a warming climate.

Skillful, reliable and seamless climate information for the next 1-40 years is crucial for policy- and other decision makers to develop suitable planning strategies. This poses a challenge for the scientific community, which is split up into the prediction community (developing initialized predictions up to multi-annual time scales, e.g. 10 years), and the climate projection community (providing long-term projections). As predictions are initialized with the observed climate state at the start of the integration, they are often more skillful for lead times of a few years (depending on variable and region) compared to uninitialized climate projections, which can provide information beyond 10 years. Thus, most useful climate information for the next 1-40 years would likely need to draw upon information from both sources. However, temporal merging from different sources is challenging, e.g., it can lead to discontinuities in the central estimates at the respective transition points, which pose problems for interpretation and communication alike.The aim of this study is to explore if skillful and seamless climate information can be provided by applying a model weighting scheme to initialized decadal predictions and projections. The model specific weights are based on the respective past model performance compared to observations. Whereas for climate projections each model is assigned a single weight, for initialized decadal predictions these weights are calculated for each forecast year separately. Here, we apply the weighting technique to CMIP6 decadal predictions and climate projections from 8 different models.

The intraseasonal characteristics of rainfall have important implications for agriculture in the Sahel. For example, the development and yield of millet, sorghum and maize depend not only on the rainfall seasonal total amounts, but also on the onset of the rainy season and the seasonal distribution of rainy days as well as the occurrence of dry spells. However, the decadal variability of intraseasonal rainfall characteristics in the Sahel and in particular in Senegal has received little attention in the literature so far. In this study, we analyze the decadal modulations of the intraseasonal characteristics of the monsoon season in Senegal over the period 1918-2000. From daily rainfall data measured at different stations in Senegal, we have defined indices characterizing, among others, the number of rainy days, the average intensity of rainy days, the starting day and ending day of the rainy season. The spatial patterns of the mean indices generally show a north/south gradient and their temporal modulations show a clear decadal signal. Application of EOF (Empirical Orthogonal Function) analysis provides a main mode of variability showing same-signed loads throughout the territory. The associated PCs show strong decadal variability for most indices with a strong link to the Atlantic Multidecadal Variability. The exception are the indices related to the duration of the monsoon season, which show a weaker decadal variability with a clear trend.

The oceanic region located off the of the Iberian Peninsula at 43°N to south of Senegal at about 10°N, coasts is one of the most productive in the world in terms of marine ecosystems. This is due to the presence of the Canary Upwelling System (CUS). This upwelling region is separated into two distinct areas: the Iberian coast and the Northwest African coast. Improving our knowledge of the functioning and long term changes in the CUS is of crucial importance, since the much of the food resources and economy of neighboring countries greatly depends on its characteristics. Most of research efforts aimed at the understanding of the functioning of the CUS and its seasonal to long term variations, are based on observations and regional models operating at very high resolution. However, observational datasets based on satellite products, which are suitable to study upwelling systems, cover short periods of time, which does not allow for a robust estimate of long-term variations (i.e. climate change) of the upwellings and the associated mechanisms. The use of very high-resolution regional ocean models leads to a correct representation of the physical mechanisms associated to the upwellings, but the numerical experiments entail an important computational cost, which also limits the study of long-term changes. Standard coupled ocean-atmosphere models, such as those used in the international exercises like Coupled Model Experiment Phase (CMIP), provide an interesting alternative to study decadal to long-term changes in the upwellings. Recently, studies based on coupled models, focusing on the response of the upwellings to climate change, have received increasing attention. However, these studies show contradictory results on the question whether coastal upwelling will be more intense or weak in the next decades. One of the reasons for this uncertainty is the low resolution of climate models, making it difficult to properly resolve coastal zone processes. The main goal of this study is to evaluate the ability of an ensemble of global coupled models in simulating the properties of the CUS (seasonal cycle, intensity and thermal signatures). The numerical experiments used here were performed within the H2020 PRIMAVERA European project, which is part of the HighResMIP initiative at European level. We will use pairs of models operating at diverse nominal resolutions under present-day climate conditions. Our objective will be to study the impact of model resolution in the representation of the CUS.