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Climate models can produce accurate representations of the most important modes of climate variability, but they cannot be expected to follow their observed time evolution. This makes direct comparison of simulated and observed variability difficult and creates uncertainty in estimates of forced change. We investigate the role of three modes of climate variability, the North Atlantic Oscillation, El Niño–Southern Oscillation and the Southern Annular Mode, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. We use particle filter data assimilation to constrain the observed variability in a global climate model without nudging, producing a near-free-running model simulation with the time evolution of these modes similar to those observed. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated and evaluate the extent to which these are confounded with the forced response. The assimilated model is significantly closer than the “forcing only” simulations to annual temperature and precipitation observations over many regions, in particular the tropics, the North Atlantic and Europe. The results indicate where initialised simulations that track these modes could be expected to show additional skill. Assimilating the three modes cannot explain the large discrepancy previously found between observed and modelled variability in the southern extra-tropics but constraining the El Niño–Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions.

The occurrence of climate extremes could have dramatic impacts on various sectors such as agriculture, water supply, and energy production. This study aims to understand part of the variability in the extreme rainfall indices over Guinea coast that can be related to the Atlantic equatorial mode (AEM), whose positive phases are associated with an increase in the intensity and frequency of rainfall events. We use six extreme indices computed from six observed rainfall databases and historical and SSP5-8.5 simulations from 24 general circulation models (GCMs) that participate in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to study changes in extreme rainfall events over Guinea coast during July–September. Under present-day conditions, we found that current GCMs clearly overestimate the frequency of wet events and the maximum number of consecutive wet days. The magnitude of the other extreme indices simulated is within the range of the observations which, moreover, present a large spread. Our results confirm the existing studies. However, less attention has been paid to the evaluation of the modelled rainfall extremes associated with the AEM under different climate conditions, while the variability of the AEM is expected to decrease in the future, with a potentially significant impact on the extreme events. Here, we use six (one) observed rainfall (sea surface temperature) data and 24 GCM outputs to investigate the present-day, near-term, mid-term, and long-term future links between the AEM and the extreme rainfall events over the Guinea coast. The biases in the extreme rainfall responses to the AEM are subject to a large spread across the different models and observations. For the long-term future (2080–2099), less frequent and more intense rainfall events are projected. As an illustration, the multimodel ensemble median (EnsMedian) maximum rainfall during 5 consecutive wet days (RX5day) would be 21 % higher than under present-day conditions. Moreover, the variability of the majority of the extreme indices over the Guinea coast is projected to increase (48 % for RX5day in the long-term future). By contrast, the decreased variability of the AEM in a warmer climate leads to a reduced magnitude of the rainfall extreme responses associated with AEM over the Guinea coast. While under present-day conditions the AEM explains 18 % of the RX5day variance in the EnsMedian, this value is reduced to 8 % at the end of 21st century. As a consequence, in absolute, there is a projected increase in the total variability of most of the extreme rainfall indices, but the contribution of the AEM to this variability weakens in a warmer future climate.

Surface mass balance (SMB) over the Antarctic Ice Sheet must be better understood to document current Antarctic contribution to sea-level rise. Field point data using snow stakes and ice cores are often used to evaluate the state of the ice sheet's mass balance as well as to validate SMB derived from regional climate models, which are then used to produce future climate projections. However, spatial representativeness of individual point data remains largely unknown, particularly in the coastal regions of Antarctica with highly variable terrains. Here, we compare ice core data collected at the summit of eight ice rises along the coast of Dronning Maud Land, as well as at the Dome Fuji site, and shallow ice-penetrating radar data over these regions. Shallow radar data has the advantage of being spatially extensive with a temporal resolution that varies between annual and sub-decadal resolution from which we can derive a SMB record over the entire radar survey. This comparison allows us therefore to evaluate the spatial variability of SMB and the spatial representativeness of ice-core derived SMB. We found that ice core mean SMB is very local and the difference with radar-derived SMB increases in a logarithmic-fashion as the surface covered by the radar data increases, with for most ice rises a plateau ~1–2 km away from the ice crest where there are strong wind-topography interactions, and ~10 km where the ice shelves begin. The relative uncertainty in measuring SMB also increases rapidly as we move away from the ice core sites. Five of our ice rise sites show a strong spatial representativeness in terms of temporal variability, while the other three sites show it is limited to a surface areas between 20–120 km2. The Dome Fuji site on the other hand shows a small difference between pointwise and area mean SMB, as well as a strong spatial representativeness in terms of temporal variability. We found no simple parameterization that could represent the spatial variability observed at all the sites. However, these data clearly indicate that local spatial SMB variability must be considered when assessing mass balance as well as comparing modeled SMB values to point field data.

The future evolution of the West Antarctic Ice Sheet (WAIS) will strongly influence the global sea-level rise in the coming decades. Ice shelf melting in that sector is partly controlled by the low-pressure system located off the West Antarctic coast, namely the Amundsen Sea Low (ASL). When the ASL is deep, an overall increase in ice shelf melting is noticed. Because of the sparse observational network and the strong internal variability, our understanding of the long-term climate changes in the atmospheric circulation is limited, and therefore its impact on ice melting as well. Among all the processes involved in the West Antarctic climate variability, an increasing number of studies have pointed out the strong impact of the climate in the tropical Pacific. However, most of those studies focus on the past decades, which prevents the analysis of the role of the multi-decadal tropical variability on the West Antarctic climate. Here, we combine annually-resolved paleoclimate records, in particular ice core and coral records, and the physics of climate models through paleoclimate data assimilation to provide a complete spatial multi-field reconstruction of climate variability in the tropics and Antarctic. This allows for studying both the year-to-year and multi-decadal variability of the tropical-Antarctic teleconnections. As data assimilation provides a climate reconstruction that is dynamically constrained, the contribution of the tropical variability on the West Antarctic climate changes can be directly assessed. Our results indicate that climate variability in the tropical Pacific is the main driver of ASL variability at the multi-decadal time scale, with a strong link to the Interdecadal Pacific Oscillation (IPO). However, the deepening of the Amundsen Sea Low over the 20th century cannot be explained by tropical climate variability. By using large ensembles of climate model simulations, our analysis suggests anthropogenic forcing as the primary driver of this 20th century ASL deepening. In summary, the 20th century ASL deepening is explained by the forcing, but the multi-decadal variability related to the IPO is superimposed on this long-term trend.

The observed evolution of Antarctic sea ice extent is marked by an abrupt decrease in 2016/2017. After several years of gradual increase culminated in an all-time record high in 2014/2015, a rapid decline in 2016 led to an unprecedented minimum, and unusual low extents have been observed since then. Even though this record has now been beaten, the sudden drop from extreme high values to a minimum in less than two years is unique to this event, whose dynamics are still uncertain. While it was likely triggered by anomalous atmospheric conditions in the prior months, the contribution of the ocean conditions, as a preconditioning which amplified the response of the sea ice or helped to maintain the anomalies for a longer period, still needs to be quantified. To evaluate the respective influences of the atmosphere and ocean on this 2016 event, we have performed sensitivity experiments using the circum-Antarctic fully coupled model (ice-sheet–ocean–sea-ice–atmosphere) PARASO. First, a control experiment with the model forced by lateral boundary conditions derived from observations (ERA5 in the atmosphere, ORAS5 in the ocean) is performed over the period 1985-2018. In such a set-up, the model correlates well with the observations and is able to capture the 2016 drop. Then, the model is integrated again between 2016 and 2018 with the same atmospheric boundary forcing, but with different initial conditions in the ocean: namely, ocean conditions from previous years in the control run are used as initial state in 2016 in the sensitivity experiments, producing an ensemble of 5 members. Preliminary results indicate that the 2016 drop is captured by all members, suggesting the atmospheric boundary forcing as the dominant driver and confirming that the event is induced by large-scale atmospheric dynamics. However, some variability is present in the amplitude and timing of the drop, as well as in the evolution and recovery of the sea ice in the following months, which may be influenced by the different states of the ocean. Related processes are further investigated by examining different oceanic and atmospheric fields, focussing on the role of ocean preconditioning by identifying the differences between the members and their impact.

In this study, we assess the ability of the ocean-sea ice general circulation models that participated in the Ocean Model Intercomparison Project (OMIP) to simulate the seasonal cycle of the ocean mixed layer depth in pan-Arctic seas. We focus on the central Arctic Ocean, Beaufort, Chukchi, East Siberian, Laptev, Kara, and Barents Seas. All models underestimate the mixed layer depth by about 15m on average during summertime compared to the MIMOC (Monthly Isopycnal/Mixed layer Ocean Climatology) observational data. In fall and winter, differences of several tens of meters are noticed between the models themselves, and between the models and the observational data. Some models generate too deep mixed layers, while others produce too shallow mixed layers. The magnitude of these inter-model variations differs depending on the sea under consideration.In almost all the seas, OMIP models with similar ocean stratification compared to MIMOC observational data display the best mixed layer depth at the end of the winter. Furthermore, all models simulate more or less the same sea ice mass balance and thus salt flux into the ocean during sea ice freezing. We argue that the discrepancies between models are not so much linked to the surface salt balance but rather to the accuracy with which those models reproduce the ocean stratification. To substantiate this behavior, we apply a simple conceptual model, which simulates the fall/winter month-to-month evolution of the mixed layer depth in ice-covered regions. In almost fully sea ice-covered regions such as the central Arctic Ocean, Beaufort, and Chukchi Seas, this simplified dynamics captures very well the behavior of the general circulation models, and this highlights that the main difference between the models is the ocean stratification. At the same time, in the East Siberian, Laptev, and Kara Seas, inter-model variations are not explained by the differences in ocean stratification, even though they contain a significant concentration of sea ice. In not fully sea ice-covered regions, such as the Barents Sea, the mixed layer depth dynamics is different: the retreat of the ice cover during summer is more significant than in fully covered regions, hence favoring exchanges with the atmosphere.

The Atlantic equatorial mode (AEM) is an interannual oceanic internal mode of variability which impacts the tropical circulation during its active phases in the boreal summer. A positive phase of the AEM is characterized by above-normal sea surface temperature anomalies in the eastern equatorial Atlantic which lead to positive rainfall anomalies over the Guinea Coast, a region located in the southern part of West Africa. The AEM appears as the leading oceanic driver of the Guinea Coast rainfall (GCR) during the monsoon season, and the AEM-GCR relation during the last century is stationary. Moreover, extreme rainfall events over the Guinea Coast are also enhanced by the AEM-positive phases. Therefore, there is a need to study how the relationship between the AEM and extreme rainfall indices would change under future global warming. The present work assesses this relationship between the AEM and the Guinea Coast extreme rainfall indices in the historical simulations performed by 24 General Circulation Models (GCMs) participating in the sixth phase of the Coupled Models Intercomparison Project (CMIP6). Results indicate that the extreme rainfall responses to the AEM under present-day climate conditions are qualitatively well reproduced by the GCMs in the 1995-2014 period, although there are substantial biases in their magnitudes. For the future changes, we consider the CMIP6 Shared Socio-economic pathway 5-8.5 (SSP5-8.5) simulations and three different periods: the near-term (2021-2040), the mid-term (2041-2060) and the long-term (2080-2099). Relative to the present-day period, our results indicate an overall gradual increase with time in the mean and variability of the different extreme indices for the Guinea Coast. However, the future influence of the AEM on the extreme rainfall indices decreases with time, which is in line with the projected decrease in the future AEM variability.

Ice cores remain the highest resolution proxy for measuring past surface mass balance (SMB) that can be used for model-data comparison. However, there is a clear difference in the spatial resolution of the ice cores, with a surface sample on the order of cm2, and the spatial resolution of models, with at best a surface footprint on the order of a few km2. Comparing ice core SMB records and model SMB outputs directly is therefore not a one-to-one comparison. In addition, it is well known that ice cores, as point measurements, sample very local SMB conditions which can be affected by local wind redistribution of the SMB at the surface.We set out to answer the question: how representative are ice-cores of regional SMB? For this, we use several ground-penetrating radar (GPR) surveys in East Antarctica, which have co-located ice core drill sites. Most of our sites share a relatively similar climatology, as they are all coastal ice promontories/rises along the Dronning Maud Land coast, with the exception of the Dome Fuji survey on the high plateau in the interior of the continent.We will show that the comparison of the SMB signals of the GPR and the ice core records allows us to estimate the spatial footprint of the ice cores, and that this spatial footprint varies widely from site to site. We will provide a summary of the spatial and temporal characteristics for each location.

Observations and model results provide two complementary sources of information on past climate variations. The paleo -or proxy - data characterize the changes that occurred at a particular time while models can be used to infer the mechanisms responsible for those changes. Observations and model results are thus ideally used jointly and the first step before applying models to study past and future climate is to evaluate their ability to reproduce the signal recorded in the data. Paleodata and model results can also be combined more profoundly using data assimilation. While classical model-data comparison can only assess the skills of the model, data assimilation can guide the model results to have a better agreement with the real observed changes and thus to reproduce more accurately the processes at their origin.The main challenges in paleo data assimilation will be reviewed here. Issues related to the generation of the model results, the model data comparison and the data assimilation technique itself will be addressed. Some recent achievements and perspectives will then be presented, first for spatial reconstructions based on data assimilation and secondly for the quantification of internal and forced climate variability as well as the role of atmospheric and oceanic circulation in past climate changes.

Understanding the variability of Antarctic sea ice is still a challenge. After decades of modest growth, an unprecedented minimum in the sea ice extent (SIE) was registered in summer 2017, and, following years of anomalously low SIE, a new record was established in early 2022. These two memorable minima have received great attention as single cases, but a comprehensive analysis of summer SIE minima is currently lacking. Indeed, other similar events are present in the observational record, although minor compared to the most recent ones, and a full analysis of all summer SIE minima is essential to separate potential common drivers from event-specific dynamics, in order to ultimately improve our understanding of the Antarctic sea ice and climate variability. In this work, we examine sea ice and atmospheric conditions during and before all summer SIE minima over the satellite period up to 2022. We use observations and reanalysis data and compare our main findings with results from an ocean-sea ice model (NEMO-LIM) driven by prescribed atmospheric fields from ERA5. Examining SIE and sea ice concentration (SIC) anomalies, we find that the main contributors to the summer minima are the Ross and Weddell sectors. However, the two regions play different roles and the variability of the Ross Sea seems to explain most of the minima, with typical negative SIE anomalies about twice the ones in the Weddell Sea. Furthermore, the distribution of SIC anomalies is also different: in the Weddell Sea, they exhibit a dipolar structure, with increased SIC next to the continent and decreased SIC at the sea ice margin, while the Ross Sea displays a more homogenous decrease. We also examine the role of wintertime sea ice conditions before the summer SIE minima and find mixed results depending on the period: the winter conditions seem relevant in the most recent events, after 2017, but marginal for previous years. Next, we consider the influence of the atmosphere on the SIE minima, which appears to play a major role: after analyzing the anomalous atmospheric circulation during the preceding spring, we find that different large-scale anomalies can lead to similar regional prevailing winds that drive the summer minima. Specifically, the SIE minima seem to be associated with dominant north-westerly anomalous winds in the Weddell Sea, while a south-westerly anomalous flow prevails in the Ross Sea. Finally, we investigate the relative contribution of dynamic (e.g. ice transport) and thermodynamic (e.g. local melting) processes to the summer minima. Our results suggest that the exceptional sea ice loss in both the Ross and Weddell sectors is dominated by thermodynamic processes, while dynamics are also present but with a minor role.

The fluctuations of atmospheric CO2 concentrations over the preindustrial Common Era are generally attributed to changes in land carbon storage, caused primarily by changes in surface air temperature but also by changes in land use. This dominant influence of the land carbon cycle is consistent with the negative correlation between atmospheric CO2 concentrations and δ13CO2 variations recorded in ice cores. By performing an ensemble of sensitivity experiments with the LOVECLIM model, we confirm the potentially large role that temperature changes have on the land carbon cycle. However, this process alone cannot explain the magnitude of the reconstructed atmospheric CO2 and δ13CO2 variations. In particular, even when the model is constrained to follow reconstructed temperature changes by data assimilation, and when applying relatively large values of the climate-carbon feedback parameter, it can only explain about 50% of the atmospheric CO2 decrease between the 12th and the seventeenth century. We find that land use changes are likely responsible for most of the observed long term atmospheric CO2 trend over the first millennium of the Common Era, and for up to 30% of the decrease observed after 1600 CE. In addition, in our experiments, changes in southern hemisphere westerly winds induce slightly smaller changes in atmospheric CO2 concentrations than those associated with land use change, and variations in δ13CO2 of the same order of magnitude as the observed ones. Combining the effects of changes in temperature, land use and winds over the Southern Ocean provides a reasonable agreement with reconstructions for atmospheric CO2 concentrations and δ13CO2, especially for the low CO2 values observed during the seventeenth century. This underlines the important contribution of both land and ocean carbon processes. Nevertheless, some uncertainties remain on the origin of the relatively high CO2 concentrations reconstructed during the eleventh and sixteenth centuries.

The Guinea Coast is the southern part of the West African continent. Its summer rainfall variability mostly occurs on interannual timescales and is highly influenced by the sea surface temperature (SST) variability in the eastern equatorial Atlantic, which is the centre of action of the Atlantic Niño mode. Using both historical and scenario (SSP5–8.5) simulations from 31 general circulation models (GCMs) participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we first show that these models present a wet bias during boreal summer. This bias is associated with overly high mean boreal summer SSTs in the eastern equatorial and south Atlantic regions. Next, we analyse the near-term, mid-term and long-term changes of the Atlantic Niño relative to the present-day situation, in a climate with a high anthropogenic emission of greenhouse gases. We find a gradual decrease in the equatorial Atlantic SST anomalies associated with the Atlantic Niño in the future. This result reflects a possible reduction of the Atlantic Niño variability in the future due to a weakening of the Bjerknes feedback over the equatorial Atlantic. In a warmer climate, an anomalous higher sea level pressure in the western equatorial Atlantic relative to the eastern equatorial Atlantic weakens the climatological trade winds over the equatorial Atlantic. As a result, the eastern equatorial Atlantic thermocline is deeper and responds less to the Atlantic Niño events. Among the models that simulate a realistic rainfall pattern associated with the Atlantic Niño in the present-day climate, there are 12 GCMs which project a long-term decrease in the Guinea Coast rainfall response related to the Atlantic Niño. In these models, the zonal 850 hPa wind response to the Atlantic Niño over the equatorial Atlantic is strongly attenuated in the future climate. We also find that 12 other GCMs show no robust change in the patterns associated with the Atlantic Niño. There is a higher confidence in the mid-term and long-term reduction of the rainfall associated with the Atlantic Niño over the Atlantic Ocean than over the Guinea Coast. We also found a projected decrease in the convection associated with the Atlantic Niño in the majority of the models.

During the last deglaciation, the climate evolves from a cold state at the Last Glacial Maximum (LGM) at 21 ka (thousand years ago) with large ice sheets to the warm Holocene at ∼9 ka with reduced ice sheets. The deglacial ice sheet melt can impact the climate through multiple ways: changes of topography and albedo, bathymetry and coastlines, and freshwater fluxes (FWFs). In the PMIP4 (Paleoclimate Modelling Intercomparison Project – Phase 4) protocol for deglacial simulations, these changes can be accounted for or not depending on the modelling group choices. In addition, two ice sheet reconstructions are available (ICE-6G_C and GLAC-1D). In this study, we evaluate all these effects related to ice sheet changes on the climate using the iLOVECLIM model of intermediate complexity. We show that the two reconstructions yield the same warming to a first order but with a different amplitude (global mean temperature of 3.9 ∘C with ICE-6G_C and 3.8 ∘C with GLAC-1D) and evolution. We obtain a stalling of temperature rise during the Antarctic Cold Reversal (ACR, from ∼14 to ∼12 ka) similar to proxy data only with the GLAC-1D ice sheet reconstruction. Accounting for changes in bathymetry in the simulations results in a cooling due to a larger sea ice extent and higher surface albedo. Finally, freshwater fluxes result in Atlantic meridional overturning circulation (AMOC) drawdown, but the timing in the simulations disagrees with proxy data of ocean circulation changes. This questions the causal link between reconstructed freshwater fluxes from ice sheet melt and recorded AMOC weakening.

Changes in sea ice conditions and atmospheric circulation over the Southern Ocean play an important role in modulating Antarctic climate. However, observations of both sea ice and wind conditions are limited in Antarctica and the Southern Ocean, both temporally and spatially. Ice core chemistry data can be used to reconstruct changes over annual, decadal, and millennial timescales. To facilitate sea ice and wind reconstructions, the CLIVASH2k working group has compiled a database of two species, sodium [Na+] and sulphate [SO42-], commonly measured ionic species. The database contains records from 105 Antarctic ice cores, containing records with a maximum age duration of 2000 years. An initial filter has been applied, based on evaluation against climate observations, to identify sites suitable for reconstructing past sea ice conditions, wind strength, or atmospheric circulation.

The spatio-temporal structure of natural climate variability has to be taken into account when unraveling observed climatic changes and simulate future climate change. However, based on the comparison of temperature reconstructions and climate model simulations covering the past two millenia, it has been argued that climate models are biased. They would simulate too little temporal temperature variability and too high correlations between temperature time series from different continents. One of the proposed causes is the lack of internal climate variability in climate models on centennial time scales, for instance variability related to the Atlantic Meridional Overturning Circulation (AMOC). We present a perturbed-parameter ensemble with the iLOVECLIM earth system model containing various levels of AMOC-related internal climate variability to investigate the effect on the spatio-temporal temperature variability structure. The model ensemble shows that indeed enhanced AMOC variability leads to more continental-scale temperature variability, but it also increases the spatio-temporal temperature correlations between different continents. However, combining the iLOVECLIM results with CMIP5 model results and various PAGES-2K temperature field reconstructions, we find that neither model results or reconstructions are robust. We show overall agreement for the magnitude of continental temperature variability in models and reconstructions, but both the simulated and the reconstructed ranges are large. This is even more true when considering higher order metrices like inter-continental temperature correlations or temperature variability land-sea contrasts. For such metrices, uncertainties in both model results and temperature reconstructions are so large that they hamper our ability to constrain simulated spatio-temporal structure of centennial temperature variability. As a result, we cannot determine the importance of AMOC variability for the climatic evolution over the past two millenia.

During the Last Deglaciation, the climate evolves from a cold state at the Last Glacial Maximum at 21 ka with large ice sheets, to the warm Holocene at ~9 ka with reduced ice sheets. The deglacial ice sheet melt can impact the climate through multiple ways: changes of topography and albedo, bathymetry and coastlines, as well as fresh water fluxes. In the PMIP4 protocol for deglacial simulations, these changes can be accounted or not depending on the modelling group choices. In addition, two ice sheet reconstructions are available (ICE-6G_C and GLAC-1D). In this study, we evaluate all these effects related to ice sheet changes on the climate using the iLOVECLIM model of intermediate complexity. We show that the two reconstructions yield the same warming to a first order, but with a different amplitude (3.9 °C with ICE-6G_C and 3.8 °C with GLAC-1D) and evolution. We obtain a stalling of temperature rise during the Antarctic Cold Reversal (from ~14 ka to ~12 ka) similar to proxy data only with the GLAC-1D ice sheet reconstruction. Accounting for changes in bathymetry in the simulations results in a cooling due to a larger sea ice extent and higher surface albedo. Finally, fresh water fluxes result in AMOC drawdown, but the timing in the simulations disagrees with proxy data of ocean circulation changes. This questions the links between reconstructed fresh water fluxes from ice sheet melt and recorded AMOC weakening and their representation in models.

The East Asian summer monsoon (EASM) plays a crucial role for ecosystems and societies in East Asia past, present, and future. However, substantial uncertainties remain regarding EASM variability on interdecadal to multidecadal timescales because of the short length of instrumental data in East Asia. This study extended the EASM circulation index in the modern meteorological studies to the paleoclimate over the past half-millennium (1470–1998 CE) to reconcile the understanding of the EASM variability in paleoclimate and modern meteorological studies. The EASM index is reconstructed based on the common signal from the three main types of the proxy records (the tree rings, speleothems, and historical documentary data) related to EASM. The reconstructed EASM index captures the simultaneous changes of the “Meiyu precipitation” and the southwesterly anomalies in South China on interdecadal to multidecadal timescales, which is a dynamic pattern visible and well-documented in the modern meteorology. Analysis of the reconstructed EASM index suggests that the interdecadal to multidecadal EASM variability is closely associated with the Pacific-Japan teleconnection pattern, which acts as a bridge between the negative phase of the Pacific Decadal Oscillation and the anomalous anticyclonic circulation over the western North Pacific. It also indicates that the EASM variability over the recent 30 years (1992–2021 CE) falls within the range of natural variability over the past half-millennium.

The seasonal cycle of the Antarctic sea ice extent is strongly asymmetric, with a relatively slow increase after the summer minimum followed by a more rapid decrease after the winter maximum. This cycle is intimately linked to the seasonal cycle of the insolation received at the top of the atmosphere, but sea ice processes as well as the exchanges with the atmosphere and ocean may also play a role. To quantify these contributions, a series of idealized sensitivity experiments have been performed with an eddy-permitting (1/4∘) NEMO-LIM3 (Nucleus for European Modelling of the Ocean–Louvain-la-Neuve sea ice model version 3) Southern Ocean configuration, including a representation of ice shelf cavities, in which the model was either driven by an atmospheric reanalysis or coupled to the COSMO-CLM2 regional atmospheric model. In those experiments, sea ice thermodynamics and dynamics as well as the exchanges with the ocean and atmosphere are strongly perturbed. This perturbation is achieved by modifying snow and ice thermal conductivities, the vertical mixing in the ocean top layers, the effect of freshwater uptake and release upon sea ice growth and melt, ice dynamics, and surface albedo. We find that the evolution of sea ice extent during the ice advance season is largely independent of the direct effect of the perturbation and appears thus mainly controlled by initial state in summer and subsequent insolation changes. In contrast, the melting rate varies strongly between the experiments during the retreat, in particular if the surface albedo or sea ice transport are modified, demonstrating a strong contribution of those elements to the evolution of ice coverage through spring and summer. As with the advance phase, the retreat is also influenced by conditions at the beginning of the melt season in September. Atmospheric feedbacks enhance the model winter ice extent response to any of the perturbed processes, and the enhancement is strongest when the albedo is modified. The response of sea ice volume and extent to changes in entrainment of subsurface warm waters to the ocean surface is also greatly amplified by the coupling with the atmosphere.

Author(s): Li, X; Cai, W; Meehl, GA; Chen, D; Yuan, X; Raphael, M; Holland, DM; Ding, Q; Fogt, RL; Markle, BR; Wang, G; Bromwich, DH; Turner, J; Xie, SP; Steig, EJ; Gille, ST; Xiao, C; Wu, B; Lazzara, MA; Chen, X; Stammerjohn, S; Holland, PR; Holland, MM; Cheng, X; Price, SF; Wang, Z; Bitz, CM; Shi, J; Gerber, EP; Liang, X; Goosse, H; Yoo, C; Ding, M; Geng, L; Xin, M; Li, C; Dou, T; Liu, C; Sun, W; Wang, X; Song, C | Abstract: Over the modern satellite era, substantial climatic changes have been observed in the Antarctic, including atmospheric and oceanic warming, ice sheet thinning and a general Antarctic-wide expansion of sea ice, followed by a more recent rapid loss. Although these changes, featuring strong zonal asymmetry, are partially influenced by increasing greenhouse gas emissions and stratospheric ozone depletion, tropical–polar teleconnections are believed to have a role through Rossby wave dynamics. In this Review, we synthesize understanding of tropical teleconnections to the Southern Hemisphere extratropics arising from the El Nino–Southern Oscillation, Interdecadal Pacific Oscillation and Atlantic Multidecadal Oscillation, focusing on the mechanisms and long-term climatic impacts. These teleconnections have contributed to observed Antarctic and Southern Ocean changes, including regional rapid surface warming, pre-2015 sea ice expansion and its sudden reduction thereafter, changes in ocean heat content and accelerated thinning of most of the Antarctic ice sheet. However, due to limited observations and inherent model biases, uncertainties remain in understanding and assessing the importance of these teleconnections versus those arising from greenhouse gases, ozone recovery and internal variability. Sustained pan-Antarctic efforts towards long-term observations, and more realistic dynamics and parameterizations in high-resolution climate models, offer opportunities to reduce these uncertainties.

Although the West Antarctic surface climate has experienced large changes over the past decades with widespread surface warming, an overall increase in snow accumulation and a deepening of the Amundsen Sea Low, the exact role of human activities in these changes has not yet been fully investigated, which limits confidence in future projections. Here, we perform a detection and attribution analysis using instrumental and proxy-based reconstructions, and two large climate model simulation ensembles to quantify the forced response in these observed changes. We show that surface climate changes since the 1950s were driven by anthropogenic forcing, in particular the greenhouse gas forcing and stratospheric ozone depletion. Therefore, our results indicate that the 21st century changes will depend on both the greenhouse gas emissions and the ozone layer recovery.

Climate models can produce accurate representations of the most important modes of climate variability, but they cannot be expected to follow their observed time-evolution. This makes direct comparison of simulated and observed variability difficult, and creates uncertainty in estimates of forced change. We investigate the role of three modes of climate variability, the North Atlantic Oscillation, El-Niño Southern Oscillation and the Southern Annular Mode, as pacemakers of climate variability since 1781, evaluating where their evolution masks or enhances forced climate trends. We use particle filter data assimilation to constrain the observed variability in a global climate model without nudging, producing a near free running model simulation with the time-evolution of these modes similar to those observed. Since the climate model also contains external forcings, these simulations, in combination with model experiments with identical forcing but no assimilation, can be used to compare the forced response to the effect of the three modes assimilated, and evaluate to what extent these are confounded with the forced response. The assimilated model is significantly closer than the “forcing only” simulations to annual temperature and precipitation observations over many regions, in particular the tropics, the north Atlantic and Europe. The results indicate where initialized simulations that track these modes could be expected to show additional skill. Assimilating the three modes cannot explain the large discrepancy previously found between observed and modelled variability in the southern extra-tropics but constraining the El-Niño Southern Oscillation reconciles simulated global cooling with that observed after volcanic eruptions.

Previous studies have shown the existence of internal multidecadal variability in the Southern Ocean using multiple climate models. This variability, associated with deep ocean convection, can have significant climate impacts. In this work, we use sensitivity studies based on Geophysical Fluid Dynamics Laboratory (GFDL) models to investigate the linkage of this internal variability with the background ocean mean state. We find that mean ocean stratification in the subpolar region that is dominated by mean salinity influences whether this variability occurs, as well as its time scale. The weakening of background stratification favors the occurrence of deep convection. For background stratification states in which the low-frequency variability occurs, weaker ocean stratification corresponds to shorter periods of variability and vice versa. The amplitude of convection variability is largely determined by the amount of heat that can accumulate in the subsurface ocean during periods of the oscillation without deep convection. A larger accumulation of heat in the subsurface reservoir corresponds to a larger amplitude of variability. The subsurface heat buildup is a balance between advection that supplies heat to the reservoir and vertical mixing/convection that depletes it. Subsurface heat accumulation can be intensified both by an enhanced horizontal temperature advection by the Weddell Gyre and by an enhanced ocean stratification leading to reduced vertical mixing and surface heat loss. The paleoclimate records over Antarctica indicate that this multidecadal variability has very likely happened in past climates and that the period of this variability may shift with different climate background mean state.

The Antarctic sea ice variability and its underlying drivers remain overall unsettled, particularly since the sea ice extent (SIE) during the last two decades has first exhibited a slight increase, somewhat in contrast with the global warming trend, followed by a rapid reduction in the more recent years. The unprecedented SIE minimum registered in February 2022 has received great attention and already constitutes an important case study, as the prior record low in 2017. However, other extreme anomalous events are present in the observational record, and a comprehensive analysis of both minima and maxima in the summer SIE is essential to identify and separate potential common drivers from event-specific dynamics, ultimately advancing our general understanding of the Antarctic sea ice and climate variability.In this work, we aim at assessing the relative roles of atmospheric and oceanic processes in the summer SIE extremes and at disentangling the dynamic contributions to sea ice changes - such as wind-driven transport and divergence - from the thermodynamic part (freezing and melting). Furthermore, we identify the key regions at play during such events, the local dominant mechanisms, and the mutual interactions that result in a total maximum or minimum. The timing and persistence of the sea ice, atmosphere and ocean anomalies in the prior months are also examined to clarify the time scales of the processes during the melting season that lead to the summer extremes.We use observations and reanalysis data over the satellite period (1979-2022) and compare our main findings with results obtained from an ocean-sea ice model (NEMO-LIM) driven by prescribed atmospheric fields from ERA5 on the same period. While the model may not be able to capture all the extremes in the observational record, examining its own variability provides valuable insights on the dynamics of the Antarctic sea ice extremes.

A large majority of the direct observational record for glacier changes falls within the industrial period, from the 19th century onward, associated with global glacier retreat. Given this availability of data and the significant focus in contemporary glacier modelling falling on recent retreat, glacier models are typically calibrated using – and validated with – only observations of glaciers that are considerably out of equilibrium. In order to develop a broader picture of the skill of one glacier model – the Open Global Glacier Model (OGGM) – we model glaciers for extended historical timescales of 850–2004 CE using a selection of six general circulation model (GCM) outputs. We select glaciers for which long-term length observations are available in order to compare these observations with the model results, and we find glaciers with such observations in almost all glacierised regions globally. In many regions, the mean modelled glacier changes are consistent with observations, with recent observed retreat in these regions typically at the steeper end of the range of modelled retreats. However, on the scale of individual glaciers, performance of the model is worse, with overall correlation between observed and modelled retreat weak for all of the GCM datasets used to force the model. We also model the same set of glaciers using modified climate time series from each of the six GCMs that keep temperature or precipitation constant, testing the impact of each individually. Temperature typically explains considerably more variance in glacier lengths than precipitation, but results suggest that the interaction between the two is also significant within OGGM and neither can be seen as a simple proxy for glacier length changes. OGGM proves capable of reproducing recent observational trends on at least a qualitative level in many regions, with a modelling period over a considerably larger timescale than it is calibrated for. Prospects are good for more widespread use of OGGM for timescales extending to the pre-industrial period, where glaciers were typically larger and experience less rapid (and less globally consistent) geometry changes, but additional calibration will be required in order to have confidence in the magnitude of modelled changes, particularly on the scale of individual glaciers.

The summer Guinean Coast (GC) rainfall (GCR) displays a strong variability on different timescales that are driven by Sea Surface Temperature (SST) variations and amplified by land–atmosphere processes. However, the relationships between the GCR and SST modes of variability in the pre-monsoon (March–May, MAM), post-monsoon (October–November, ON) and Harmattan (December–February, DJF) seasons are not well known nor understood. Using observational dataset covering the twentieth century, we extend the conclusion obtained in previous studies that mainly analyzed the summer period (June–September, JJAS) by considering changes in SST-rainfall linkages throughout the year. We show that, in boreal winter, SST interannual variability in the tropical basins are anticorrelated with the GCR. The South Atlantic Ocean Dipole (SAOD) and the Atlantic Niño (ATL3) appear, however, as major drivers of the pre-monsoon and monsoon GCR. In MAM, both modes are in opposite phases with the GCR. Below normal SST in the tropical South Atlantic in MAM leads to a surface divergence south of the equator, and the resulting southerlies bring moist air into coastal Guinea, increasing rainfall. During JJAS, ATL3 and SAOD are in phase with the GCR. During ON, the eastern Mediterranean Sea anomalous warming strengthens the Saharan Heat Low, whose extension in the tropical North Atlantic enhances the low-level westerly Jet. This jet transports moisture into GC. The stationarity of the correlations between the GCR and SST indices has also been assessed, and the strongest and most stationary links are obtained during the monsoon season.

Climate change affects forest growth in numerous and sometimes opposite ways, and the resulting trend is often difficult to predict for a given site. Integrating and structuring the knowledge gained from the monitoring and experimental studies into process-based models is an interesting approach to predict the response of forest ecosystems to climate change. While the first generation of models operates at stand level, one now needs spatially explicit individual-based approaches in order to account for individual variability, local environment modification and tree adaptive behaviour in mixed and uneven-aged forests that are supposed to be more resilient under stressful conditions. The local environment of a tree is strongly influenced by the neighbouring trees, which modify the resource level through positive and negative interactions with the target tree. Among other things, drought stress and vegetation period length vary with tree size and crown position within the canopy. In this paper, we describe the phenology and water balance modules integrated in the tree growth model HETEROFOR (HETEROgenous FORest) and evaluate them on six heterogeneous sessile oak and European beech stands with different levels of mixing and development stages and installed on various soil types. More precisely, we assess the ability of the model to reproduce key phenological processes (budburst, leaf development, yellowing and fall) as well as water fluxes. Two two-phase models differing regarding their response function to temperature during the chilling period (optimum and sigmoid functions) and a simplified one-phase model are used to predict budburst date. The two-phase model with the optimum function is the least biased (overestimation of 2.46 d), while the one-phase model best accounts for the interannual variability (Pearson's r=0.68). For the leaf development, yellowing and fall, predictions and observations are in accordance. Regarding the water balance module, the predicted throughfall is also in close agreement with the measurements (Pearson's r=0.856; bias =-1.3 %), and the soil water dynamics across the year are well reproduced for all the study sites (Pearson's r was between 0.893 and 0.950, and bias was between −1.81 and −9.33 %). The model also reproduced well the individual transpiration for sessile oak and European beech, with similar performances at the tree and stand scale (Pearson's r of 0.84–0.85 for sessile oak and 0.88–0.89 for European beech). The good results of the model assessment will allow us to use it reliably in projection studies to evaluate the impact of climate change on tree growth in structurally complex stands and test various management strategies to improve forest resilience.

The Totten Glacier in East Antarctica is of major climate interest because of the large fluctuation of its grounding line and of its potential vulnerability to climate change. The Totten ice shelf melt rate is predicted to increase under future climate conditions, but this increase may differ on whether the landfast ice is represented in the model or not. Using a series of high-resolution, regional NEMO-LIM-based experiments, including an explicit treatment of ocean – ice shelf interactions and a landfast ice representation, we simulate the ocean – ice interactions in the Totten Glacier area for both historical (1995-2014) and future (end of the 21 st following RCP 4.5) periods. We show major changes between historical and projection runs as increased ice shelf melt rate, loss in sea ice production or intensified ocean circulation. Moreover, the representation of landfast ice dampens the ice shelf melt rate increase. The Totten ice shelf melt rate is increased between the two periods by either +41% when landfast ice is taken into account, or by 58% when it is not taken into account. This highlights the importance of including a landfast ice representation in our ocean models in order to predict realistic ice shelf melt rate increase in East Antarctica.