Christophe Cassou’s research while affiliated with Université Paris Dauphine-PSL and other places

Plain Language Summary Day‐to‐day variations in European temperatures are strongly linked to fluctuations in the large‐scale atmospheric circulation over the North Atlantic basin. Europe is one of the fastest warming regions in the world, and it is essential to understand the contribution of atmospheric circulation to these recent temperature trends. This requires a proper quantification of the relationship between a given atmospheric circulation (e.g., a SLP map) and the associated temperature anomaly over Europe. Here we present a novel approach to address this issue, based on artificial intelligence techniques. In an idealized framework (i.e., numerical simulations of a climate model), we show that this approach has excellent results and outperforms the traditional analogs circulation method. We show that the neural network used in this study is able to learn a lot of information from a single pressure map, such as the season, and even the day of the year with only a small error. Our results are very promising for further research on the contribution of atmospheric variability to temperature variations.

In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets (published here https://doi.org/10.5281/zenodo.15327155 Smith et al., 2025a) to produce updated estimates for key indicators of the state of the climate system: net emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. This year, we additionally include indicators for sea-level rise and land precipitation change. We follow methods as closely as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. The indicators show that human activities are increasing the Earth’s energy imbalance and driving faster sea-level rise compared to the AR6 assessment. For the 2015–2024 decade average, observed warming relative to 1850–1900 was 1.24 [1.11 to 1.35] °C, of which 1.23 [1.0 to 1.5] °C was human-induced. The 2024 observed record in global surface temperature (1.52°C best estimate) is well above the best estimate of human-caused warming (1.36°C). However, the 2024 observed warming can still be regarded as a typical year, considering the human induced warming level and the state of internal variability associated with the phase of El Niño and Atlantic variability. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.27 [0.2–0.4] °C per decade over 2015–2024. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 53.6 ± 5.2 GtCO2e per year over the last decade (2014–2023), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track decreases or increases in the rate of the climatic changes presented here.

Internal variability arising from the inherently chaotic nature of the climate system has amplified or obscured human-caused changes, especially at regional scales in the extratropics, where its contribution to climate variability is the largest. It is virtually certain that this will continue in the near-term. We here focus on the Northern Europe region, whose variability is largely controlled by the North Atlantic Oscillation (NAO) and the Atlantic Meridional Overturning Circulation (AMOC) through remote dynamical and thermodynamic processes, and introduce the concept of internal variability storylines (IVS) to explore, understand, and quantify the role of the two combined drivers of internal variability in the modulation of the anthropogenic warming by 2040 in winter. Based on a large ensemble of historical-scenario simulations, we show that the high-impact IVS, characterised by weak AMOC decline and a decadal shift of the NAO toward dominant positive phase, leads faster to warmer-wetter conditions independently of actual and future greenhouse gases emissions. By contrast, amplified AMOC reduction and more recurrent negative NAO can considerably damp both warming and wettening at near-term. In the latter IVS, we provide evidence that winter-severe conditions similar to those in 2010, that had been responsible for widespread socio-economic disruptions, remain almost as likely to occur by 2040. Reframing the uncertain climate outcomes into the physical science space in a conditional form through the prism of IVS makes climate information relevant for accurate risk assessments and adaptation planning.

The year 2023 shattered numerous heat records globally and regionally. We here focus on the origins of the unprecedented and persistent warm sea surface temperature (SST) anomalies in the North Atlantic Ocean. Using a combination of multiple observational datasets and climate model large ensemble outputs, we show that the 2023 North Atlantic SST event is a manifestation of anthropogenically-forced warming, exacerbated by an extreme phase of internal climate variability and associated regional surface flux anomalies. The long-term stratification increase due to anthropogenically-driven ocean warming amplified the impacts of internal variability. At current global warming level, the 2023 event is assessed as a decadal-type occurrence for SST anomalies averaged across the entire North Atlantic basin, but it is estimated as a centennial-type event for the subtropics and the eastern basin. The distinctive horseshoe-shaped regional distribution of these anomalies is crucial to consider for correct communication and risk assessment in a warming climate.

The CNRM-Cerfacs Climate Prediction System (C3PS) is a new research modeling tool for performing climate reanalyses and seasonal-to-multiannual predictions for a wide array of earth system variables. C3PS is based on the CNRM-ESM2-1 model including interactive aerosols and stratospheric chemistry schemes as well as terrestrial and marine biogeochemistry enabling a comprehensive representation of the global carbon cycle. C3PS operates through a seamless coupled initialization for the atmosphere, land, ocean, sea ice and biogeochemistry components that allows a continuum of predictions across seasonal to interannual time-scales. C3PS has also contributed to the Decadal Climate Prediction Project (DCPP-A) as part of the sixth Coupled Model Intercomparison Project (CMIP6). Here we describe the main characteristics of this novel earth system-based prediction platform, including the methodological steps for obtaining initial states to produce forecasts. We evaluate the entire C3PS initialisation procedure with the most up-to-date observations and reanalysis over 1960-2021, and we discuss the overall performance of the system in the light of the lessons learnt from previous and actual prediction platforms. Regarding the forecast skill, C3PS exhibits comparable seasonal predictive skill to other systems. At the decadal scale, C3PS shows significant predictive skill in surface temperature during the first two years after initialisation in several regions of the world. C3PS also exhibits potential predictive skill in net primary production and carbon fluxes several years in advance. This expands the possibility of applications of forecasting systems, such as the possibility of performing multi-annual predictions of marine ecosystems and carbon cycle.

Plain Language Summary Extreme temperatures events in Western Europe have been rising fast, and current global climate models are not able to reproduce this excess. There are different hypotheses to explain this discrepancy. One is that the large‐scale atmospheric dynamics, responsible for the local weather, is not correctly represented by the models: indeed, the frequency and amplitude of some specific weather phenomena have been shown to be insufficiently reproduced, especially in summer. Here, we study one such phenomenon, namely the transient deep depressions, or extratropical cyclones, that travel across the Atlantic basin. A significant large increase of the number of these events is found in summer in the region of the North Atlantic off the western European coast. Depressions in that region are accompanied by high temperatures in continental western Europe. An ensemble of state of the art climate models are also analyzed and none of them is able to correctly reproduce the frequency of deep depressions nor their large trend, which suggests a common origin with the insufficient prediction of western European extreme heat events. Great caution should be used when analyzing climate change predictions in the region, and even more so when studying changes in complex dynamical phenomena.

Over the last 70 years, extreme heat has been increasing at a disproportionate rate in Western Europe, compared to climate model simulations. This mismatch is not well understood. Here, we show that a substantial fraction (0.8 °C [0.2°−1.4 °C] of 3.4 °C per global warming degree) of the heat extremes trend is induced by atmospheric circulation changes, through more frequent southerly flows over Western Europe. In the 170 available simulations from 32 different models that we analyzed, including 3 large model ensembles, none have a circulation-induced heat trend as large as observed. This can be due to underestimated circulation response to external forcing, or to a systematic underestimation of low-frequency variability, or both. The former implies that future projections are too conservative, the latter that we are left with deep uncertainty regarding the pace of future summer heat in Europe. This calls for caution when interpreting climate projections of heat extremes over Western Europe, in view of adaptation to heat waves.

Interconnections between ocean basins are recognized as an important driver of climate variability. Recent modeling evidence suggests that the North Atlantic climate can respond to persistent warming of the tropical Indian Ocean sea surface temperature (SST) relative to the rest of the tropics (rTIO). Here, we use observational data to demonstrate that multi-decadal changes in pantropical ocean temperature gradients lead to variations of an SST-based proxy of the Atlantic Meridional Overturning Circulation (AMOC). The largest contribution to this temperature gradient-AMOC connection comes from gradients between the Indian and Atlantic Oceans. The rTIO index yields the strongest connection of this tropical temperature gradient to the AMOC. Focusing on the internally generated signal in three observational products reveals that an SST-based AMOC proxy index has closely followed low-frequency changes of rTIO temperature with about 26-year lag since 1870. Analyzing the pre-industrial control simulations of 44 CMIP6 climate models shows that the AMOC proxy index lags simulated mid-latitude AMOC variations by 4 ± 4 years. These model simulations reveal the mechanism connecting AMOC variations to pantropical ocean temperature gradients at a 27 ± 2 years lag, matching the observed time lag in 28 out of the 44 analyzed models. rTIO temperature changes affect the North Atlantic climate through atmospheric planetary waves, impacting temperature and salinity in the subpolar North Atlantic, which modifies deep convection and ultimately the AMOC. Through this mechanism, observed internal rTIO variations can serve as a multi-decadal precursor of AMOC changes with important implications for AMOC dynamics and predictability.