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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 they are 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 explains most of the minima, with typical negative SIE anomalies about twice as large as 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 are relevant in the most recent events, after 2017, but they are marginal for previous years. Next, we consider the influence of the atmosphere on the SIE minima, which is shown 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 are generally associated with dominant northwesterly anomalous winds in the Weddell Sea, while a southwesterly 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 indicate that the exceptional sea ice loss in both the Ross and Weddell sectors is dominated at the large scale by thermodynamic processes, while dynamics are also present but with a minor role.

Surface mass balance (SMB) of the Antarctic Ice Sheet must be better understood to document the current Antarctic contribution to sea-level rise. In situ 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 assess 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 terrain. 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 have the advantage of being spatially extensive, with a temporal resolution that varies between a yearly and multi-year resolution, from which we can derive a SMB record over the entire radar survey. This comparison therefore allows us 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 a plateau ∼ 1–2 km away from the ice crest for most ice rises, 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 that it is limited to a surface area 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, and therefore must be included in the estimate of the uncertainty of the observations.

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, prior to the satellite era (1970 onwards). 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 (CLimate Variability in Antarctica and the Southern Hemisphere over the past 2000 years) working group has compiled a database of two species, sodium [Na+] and sulfate [SO42-], commonly measured ionic species. The database (https://doi.org/10.5285/9E0ED16E-F2AB-4372-8DF3-FDE7E388C9A7; Thomas et al., 2022) comprises 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 sea ice concentration, geopotential height (500 hPa), and surface wind fields to identify sites suitable for reconstructing past sea ice conditions, wind strength, or atmospheric circulation.

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.

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.

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.

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.

The spatio-temporal structure of natural climate variability has to be taken into account when unravelling observed climatic changes and simulating future climate change. However, based on the comparison of temperature reconstructions and climate model simulations covering the past 2 millennia, 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 timescales, 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 enhanced AMOC variability indeed 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 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 metrics like inter-continental temperature correlations or temperature variability land–sea contrasts. For such metrics, 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 2 millennia.

The Totten Glacier in East Antarctica is of major climatic interest because of the large fluctuations in its grounding line and potential vulnerability to climate change. Here, we use a series of high-resolution, regional NEMO-LIM-based (Nucleus for European Modelling of the Ocean coupled with the Louvain-la-Neuve sea ice model) experiments, which include an explicit treatment of ocean–ice shelf interactions, as well as a representation of grounded icebergs and fast ice, to investigate the changes in ocean–ice interactions in the Totten Glacier area between the recent past (1995–2014) and the end of the 21st century (2081–2100) under SSP4–4.5 climate change conditions. By the end of the 21st century, the wide areas of multiyear fast ice simulated in the recent past are replaced by small patches of first year fast ice along the coast, which decreases the total summer sea ice extent. The Antarctic Slope Current is accelerated by about 116 %, which decreases the heat exchange across the shelf and tends to reduce the ice shelf basal melt rate, but this effect is counterbalanced by the effect of the oceanic warming. As a consequence, despite the accelerated Antarctic Slope Current, the Totten ice shelf melt rate is increased by 91 % due to the intrusion of warmer water into its cavity. The representation of fast ice dampens the ice shelf melt rate increase throughout the 21st century, as the Totten ice shelf melt rate increase reaches 136 % when fast ice is not taken into account. The Moscow University ice shelf melt rate increase is even more impacted by the representation of fast ice, with a 36 % melt rate increase with fast ice, compared to a 75 % increase without a fast ice representation. This influence of the representation of fast ice in our simulations on the basal melting rate trend over the 21st century is explained by the large impact of the fast ice for present-day conditions (∼25 % difference in m yr−1), while the impact decreases significantly at the end of the 21st century (∼4 % difference in m yr−1). As a consequence, the reduction in the fast ice extent in the future induces a decrease in the fast ice effect on the ice shelf melt rate that partly compensates for the increase due to warming of the ocean. This highlights the importance of including a representation of fast ice to simulate realistic ice shelf melt rate increase in East Antarctica under warming conditions.

Although the global-mean sea level (GMSL) rose over the twentieth century with a positive contribution from thermosteric and barystatic (ice sheets and glaciers) sources, the driving processes of GMSL changes during the pre-industrial Common Era (PCE; 1–1850 CE) are largely unknown. Here, the contributions of glacier and ice sheet mass variations and ocean thermal expansion to GMSL in the Common Era (1–2000 CE) are estimated based on simulations with different physical models. Although the twentieth century global-mean thermosteric sea level (GMTSL) is mainly associated with temperature variations in the upper 700 m (86 % in reconstruction and 74 ± 8 % in model), GMTSL in the PCE is equally controlled by temperature changes below 700 m. The GMTSL does not vary more than ±2 cm during the PCE. GMSL contributions from the Antarctic and Greenland ice sheets tend to cancel each other out during the PCE owing to the differing response of the two ice sheets to atmospheric conditions. The uncertainties of sea-level contribution from land-ice mass variations are large, especially over the first millennium. Despite underestimating the twentieth century model GMSL, there is a general agreement between the model and proxy-based GMSL reconstructions in the CE. Although the uncertainties remain large over the first millennium, model simulations point to glaciers as the dominant source of GMSL changes during the PCE.

Currently available data-assimilation-based reconstructions of past climate variations have only used statistical proxy system models to make the link between climate model outputs and indirect observations from tree rings. However, the linearity and stationarity assumptions of the statistical approach may have limitations. In this study, we incorporate the process-based dendroclimatic model MAIDEN into a data assimilation procedure using the reconstruction of near-surface air temperature, precipitation and winds in the midlatitudes of the Southern Hemisphere over the past 400 years as a test case. We compare our results with a data assimilation approach including a linear regression as a proxy system model for tree-ring width proxies. Overall, when compared to instrumental data, the reconstructions using MAIDEN as a proxy system model offer a skill equivalent to the experiment using the regression model. However, knowing the advantages that a process-based model can bring and the improvements that can still be made with MAIDEN, those results are promising.

We explore a methodology to statistically downscale snowfall – the primary driver of surface mass balance in Antarctica – from an ensemble of historical (1850–present day) simulations performed with an earth system model over the coastal region of Dronning Maud Land (East Antarctica). This approach consists of associating daily snowfall simulations from a polar-oriented regional atmospheric climate model at 5.5 km spatial resolution with specific weather patterns observed over 1979–2010 CE with the atmospheric reanalyses ERA-Interim and ERA5. This association is then used to generate the spatial distribution of snowfall for the period from 1850 to present day for an ensemble of 10 members from the Community Earth System Model (CESM2). The new dataset of daily and yearly snowfall accumulation based on this methodology is presented in this paper (MASS2ANT dataset; https://doi.org/10.5281/zenodo.4287517; Ghilain et al., 2021). Based on a comparison with available ice cores and spatial reconstructions, our results show that the spatio-temporal distribution of snowfall is improved in the downscaled dataset compared with the CESM2 simulations. This dataset thus provides information that may be useful in identifying the large-scale patterns associated with the local precipitation conditions and their changes over the past century.

We introduce PARASO, a novel five-component fully coupled regional climate model over an Antarctic circumpolar domain covering the full Southern Ocean. The state-of-the-art models used are the fast Elementary Thermomechanical Ice Sheet model (f.ETISh) v1.7 (ice sheet), the Nucleus for European Modelling of the Ocean (NEMO) v3.6 (ocean), the Louvain-la-Neuve sea-ice model (LIM) v3.6 (sea ice), the COnsortium for Small-scale MOdeling (COSMO) model v5.0 (atmosphere) and its CLimate Mode (CLM) v4.5 (land), which are here run at a horizontal resolution close to 1/4∘. One key feature of this tool resides in a novel two-way coupling interface for representing ocean–ice-sheet interactions, through explicitly resolved ice-shelf cavities. The impact of atmospheric processes on the Antarctic ice sheet is also conveyed through computed COSMO-CLM–f.ETISh surface mass exchange. In this technical paper, we briefly introduce each model's configuration and document the developments that were carried out in order to establish PARASO. The new offline-based NEMO–f.ETISh coupling interface is thoroughly described. Our developments also include a new surface tiling approach to combine open-ocean and sea-ice-covered cells within COSMO, which was required to make this model relevant in the context of coupled simulations in polar regions. We present results from a 2000–2001 coupled 2-year experiment. PARASO is numerically stable and fully operational. The 2-year simulation conducted without fine tuning of the model reproduced the main expected features, although remaining systematic biases provide perspectives for further adjustment and development.

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.

Large open-ocean polynyas, defined as ice-free areas within the sea ice pack, have only rarely been observed in the Southern Ocean over the past decades. In addition to smaller recent events, an impressive sequence occurred in the Weddell Sea in 1974, 1975 and 1976 with openings of more than 300 000 km2 that lasted the full winter. These big events have a huge impact on the sea ice cover, deep-water formation, and, more generally, on the Southern Ocean and the Antarctic climate. However, we have no estimate of the frequency of the occurrence of such large open-ocean polynyas before the 1970s. Our goal here is to test if polynya activity could be reconstructed using continental records and, specifically, observations derived from ice cores. The fingerprint of big open-ocean polynyas is first described in reconstructions based on data from weather stations, in ice cores for the 1970s and in climate models. It shows a signal characterized by a surface air warming and increased precipitation in coastal regions adjacent to the eastern part of the Weddell Sea, where several high-resolution ice cores have been collected. The signal of the isotopic composition of precipitation is more ambiguous; thus, we base our reconstructions on surface mass balance records alone. A first reconstruction is obtained by performing a simple average of standardized records. Given the similarity between the observed signal and the one simulated in models, we also use data assimilation to reconstruct past polynya activity. The impact of open-ocean polynyas on the continent is not large enough, compared with the changes due to factors such as atmospheric variability, to detect the polynya signal without ambiguity, and additional observations would be required to clearly discriminate the years with and without open-ocean polynya. Thus, it is reasonable to consider that, in these preliminary reconstructions, some high snow accumulation events may be wrongly interpreted as the consequence of polynya formation and some years with polynya formation may be missed. Nevertheless, our reconstructions suggest that big open-ocean polynyas, such as those observed in the 1970s, are rare events, occurring at most a few times per century. Century-scale changes in polynya activity are also likely, but our reconstructions are unable to precisely assess this aspect at this stage.

Ice cores are an important record of the past surface mass balance (SMB) of ice sheets, with SMB mitigating the ice sheets' sea level impact over the recent decades. For the Antarctic Ice Sheet (AIS), SMB is dominated by large-scale atmospheric circulation, which collects warm moist air from further north and releases it in the form of snow as widespread accumulation or focused atmospheric rivers on the continent. This suggests that the snow deposited at the surface of the AIS should record strongly coupled SMB and surface air temperature (SAT) variations. Ice cores use δ18O as a proxy for SAT as they do not record SAT directly. Here, using isotope-enabled global climate models and the RACMO2.3 regional climate model, we calculate positive SMB–SAT and SMB–δ18O annual correlations over ∼90 % of the AIS. The high spatial resolution of the RACMO2.3 model allows us to highlight a number of areas where SMB and SAT are not correlated, and we show that wind-driven processes acting locally, such as foehn and katabatic effects, can overwhelm the large-scale atmospheric contribution in SMB and SAT responsible for the positive SMB–SAT annual correlations. We focus in particular on Dronning Maud Land, East Antarctica, where the ice promontories clearly show these wind-induced effects. However, using the PAGES2k ice core compilations of SMB and δ18O of Thomas et al. (2017) and Stenni et al. (2017), we obtain a weak annual correlation, on the order of 0.1, between SMB and δ18O over the past ∼150 years. We obtain an equivalently weak annual correlation between ice core SMB and the SAT reconstruction of Nicolas and Bromwich (2014) over the past ∼50 years, although the ice core sites are not spatially co-located with the areas displaying a low SMB–SAT annual correlation in the models. To resolve the discrepancy between the measured and modeled signals, we show that averaging the ice core records in close spatial proximity increases their SMB–SAT annual correlation. This increase shows that the weak measured annual correlation partly results from random noise present in the ice core records, but the change is not large enough to match the annual correlation calculated in the models. Our results thus indicate a positive correlation between SAT and SMB in models and ice core reconstructions but with a weaker value in observations that may be due to missing processes in models or some systematic biases in ice core data that are not removed by a simple average.

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 reconstructed surface-temperature time series from boreholes in Antarctica have significantly contributed to our understanding of multidecadal and centennial temperature changes and thus provide a good way to evaluate the ability of climate models to reproduce low-frequency climate variability. However, up to now, there has not been any systematic model–data comparison based on temperature from boreholes at a regional or local scale in Antarctica. Here, we discuss two different ways to perform such a comparison using borehole measurements and the corresponding reconstructions of surface temperature at the West Antarctic Ice Sheet (WAIS) Divide, Larissa, Mill Island, and Styx Glacier in Antarctica. The standard approach is to compare the surface temperature simulated by the climate model at the grid cell closest to each site with the reconstructions in the time domain derived from the borehole temperature observations. Although some characteristics of the reconstructions, for instance the nonuniform smoothing, limit to some extent the model–data comparison, several robust features can be evaluated. In addition, a more direct model–data comparison based on the temperature measured in the boreholes is conducted using a forward model that simulates explicitly the subsurface temperature profiles when driven with climate model outputs. This comparison in the depth domain is not only generally consistent with observations made in the time domain but also provides information that cannot easily be inferred from the comparison in the time domain. The major results from these comparisons are used to derive metrics that can be applied for future model–data comparison. We also describe the spatial representativity of the sites chosen for the metrics. The long-term cooling trend in West Antarctica from 1000 to 1600 CE (−1.0 ∘C) is generally reproduced by the models but often with a weaker amplitude. The 19th century cooling in the Antarctic Peninsula (−0.94 ∘C) is not reproduced by any of the models, which tend to show warming instead. The trend over the last 50 years is generally well reproduced in West Antarctica and at Larissa (Antarctic Peninsula) but overestimated at other sites. The wide range of simulated trends indicates the importance of internal variability in the observed trends and shows the value of model–data comparison to investigate the response to forcings.

Tree-ring archives are one of the main sources of information to reconstruct climate variations over the last millennium with annual resolution. The links between tree-ring proxies and climate have usually been estimated using statistical approaches, assuming linear and stationary relationships. Both assumptions may be inadequate, but this issue can be overcome by ecophysiological modelling based on mechanistic understanding. In this respect, the model MAIDEN (Modeling and Analysis In DENdroecology) simulating tree-ring growth from daily temperature and precipitation, considering carbon assimilation and allocation in forest stands, may constitute a valuable tool. However, the lack of local meteorological data and the limited characterization of tree species traits can complicate the calibration and validation of such a complex model, which may hamper palaeoclimate applications. The goal of this study is to test the applicability of the MAIDEN model in a palaeoclimate context using as a test case tree-ring observations covering the 20th century from 21 Eastern Canadian taiga sites and 3 European sites. More specifically, we investigate the model sensitivity to parameter calibration and to the quality of climatic inputs, and we evaluate the model performance using a validation procedure. We also examine the added value of using MAIDEN in palaeoclimate applications compared to a simpler tree-growth model, i.e. VS-Lite. A Bayesian calibration of the most sensitive model parameters provides good results at most of the selected sites with high correlations between simulated and observed tree growth. Although MAIDEN is found to be sensitive to the quality of the climatic inputs, simple bias correction and downscaling techniques of these data improve significantly the performance of the model. The split-sample validation of MAIDEN gives encouraging results but requires long tree ring and meteorological series to give robust results. We also highlight a risk of overfitting in the calibration of model parameters that increases with short series. Finally, MAIDEN has shown higher calibration and validation correlations in most cases compared to VS-Lite. Nevertheless, this latter model turns out to be more stable over calibration and validation periods. Our results provide a protocol for the application of MAIDEN to potentially any site with tree-ring width data in the extratropical region.

Improving our knowledge of the temporal and spatial variability of the Antarctic Ice Sheet (AIS) surface mass balance (SMB) is crucial to reduce the uncertainties of past, present, and future Antarctic contributions to sea level rise. An examination of the surface air temperature–SMB relationship in model simulations demonstrates a strong link between the two. Reconstructions based on ice cores display a weaker relationship, indicating a model–data discrepancy that may be due to model biases or to the non-climatic noise present in the records. We find that, on the regional scale, the modeled relationship between surface air temperature and SMB is often stronger than between temperature and δ18O. This suggests that SMB data can be used to reconstruct past surface air temperature. Using this finding, we assimilate isotope-enabled SMB and δ18O model output with ice core observations to generate a new surface air temperature reconstruction. Although an independent evaluation of the skill is difficult because of the short observational time series, this new reconstruction outperforms the previous reconstructions for the continental-mean temperature that were based on δ18O alone. The improvement is most significant for the East Antarctic region, where the uncertainties are particularly large. Finally, using the same data assimilation method as for the surface air temperature reconstruction, we provide a spatial SMB reconstruction for the AIS over the last 2 centuries, showing large variability in SMB trends at a regional scale, with an increase (0.82 Gt yr−2) in West Antarctica over 1957–2000 and a decrease in East Antarctica during the same period (−0.13 Gt yr−2). As expected, this is consistent with the recent reconstruction used as a constraint in the data assimilation.

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 Antarctic temperature changes over the past millennia remain more uncertain than in many other continental regions. This has several origins: (1) the number of high-resolution ice cores is small, in particular on the East Antarctic plateau and in some coastal areas in East Antarctica; (2) the short and spatially sparse instrumental records limit the calibration period for reconstructions and the assessment of the methodologies; (3) the link between isotope records from ice cores and local climate is usually complex and dependent on the spatial scales and timescales investigated. Here, we use climate model results, pseudoproxy experiments and data assimilation experiments to assess the potential for reconstructing the Antarctic temperature over the last 2 millennia based on a new database of stable oxygen isotopes in ice cores compiled in the framework of Antarctica2k (Stenni et al., 2017). The well-known covariance between δ18O and temperature is reproduced in the two isotope-enabled models used (ECHAM5/MPI-OM and ECHAM5-wiso), but is generally weak over the different Antarctic regions, limiting the skill of the reconstructions. Furthermore, the strength of the link displays large variations over the past millennium, further affecting the potential skill of temperature reconstructions based on statistical methods which rely on the assumption that the last decades are a good estimate for longer temperature reconstructions. Using a data assimilation technique allows, in theory, for changes in the δ18O–temperature link through time and space to be taken into account. Pseudoproxy experiments confirm the benefits of using data assimilation methods instead of statistical methods that provide reconstructions with unrealistic variances in some Antarctic subregions. They also confirm that the relatively weak link between both variables leads to a limited potential for reconstructing temperature based on δ18O. However, the reconstruction skill is higher and more uniform among reconstruction methods when the reconstruction target is the Antarctic as a whole rather than smaller Antarctic subregions. This consistency between the methods at the large scale is also observed when reconstructing temperature based on the real δ18O regional composites of Stenni et al. (2017). In this case, temperature reconstructions based on data assimilation confirm the long-term cooling over Antarctica during the last millennium, and the later onset of anthropogenic warming compared with the simulations without data assimilation, which is especially visible in West Antarctica. Data assimilation also allows for models and direct observations to be reconciled by reproducing the east–west contrast in the recent temperature trends. This recent warming pattern is likely mostly driven by internal variability given the large spread of individual Paleoclimate Modelling Intercomparison Project (PMIP)/Coupled Model Intercomparison Project (CMIP) model realizations in simulating it. As in the pseudoproxy framework, the reconstruction methods perform differently at the subregional scale, especially in terms of the variance of the time series produced. While the potential benefits of using a data assimilation method instead of a statistical method have been highlighted in a pseudoproxy framework, the instrumental series are too short to confirm this in a realistic setup.

This PAGES (Past Global Changes) 2k (climate of the past 2000 years working group) special issue of Climate of the Past brings together the latest understanding of regional change and impacts from PAGES 2k groups across a range of proxies and regions. The special issue has emerged from a need to determine the magnitude and rate of change of regional and global climate beyond the timescales accessible within the observational record. This knowledge also plays an important role in attribution studies and is fundamental to understanding the mechanisms and environmental and societal impacts of recent climate change. The scientific studies in the special issue reflect the urgent need to better understand regional differences from a truly global view around the PAGES themes of “Climate Variability, Modes and Mechanisms”, “Methods and Uncertainties”, and “Proxy and Model Understanding”.

It is standard to compare climate model results covering the past millennium and reconstructions based on various archives in order to test the ability of models to reproduce the observed climate variability. Up to now, glacier length fluctuations have not been used systematically in this framework even though they offer information on multi-decadal to centennial variations complementary to other records. One reason is that glacier length depends on several complex factors and so cannot be directly linked to the simulated climate. However, climate model skill can be measured by comparing the glacier length computed by a glacier model driven by simulated temperature and precipitation to observed glacier length variations. This is done here using the version 1.0 of the Open Global Glacier Model (OGGM) forced by fields derived from a range of simulations performed with global climate models over the past millennium. The glacier model is applied to a set of Alpine glaciers for which observations cover at least the 20th century. The observed glacier length fluctuations are generally well within the range of the simulations driven by the various climate model results, showing a general consistency with this ensemble of simulations. Sensitivity experiments indicate that the results are much more sensitive to the simulated climate than to OGGM parameters. This confirms that the simulations of glacier length can be used to evaluate the climate model performance, in particular the simulated summer temperatures that largely control the glacier changes in our region of interest. Simulated glacier length is strongly influenced by the internal variability in the system, putting limitations on the model–data comparison for some variables like the trends over the 20th century in the Alps. Nevertheless, comparison of glacier length fluctuations on longer timescales, for instance between the 18th century and the late 20th century, appear less influenced by the natural variability and indicate clear differences in the behaviour of the various climate models.

The dominant modes of variability of precipitation for the whole of China over the past millennium and the mechanism governing their spatial structure remain unclear. This is mainly due to insufficient high-resolution proxy records of precipitation in western China. Numerous tree-ring chronologies have recently been archived in publicly available databases through PAGES2k activities, and these provide an opportunity to refine precipitation field reconstructions for China. Based on 479 proxy records, including 371 tree-ring width chronologies, a tree-ring isotope chronology, and 107 drought/flood indices, we reconstruct the precipitation field for China for the past half millennium using the optimal information extraction method. A total of 3631 of 4189 grid points in the reconstruction field passed the cross-validation process, accounting for 86.68 % of the total number of grid points. The first leading mode of variability of the reconstruction shows coherent variations over most of China. The second mode is a north–south dipole in eastern China characterized by variations of the same sign in western China and northern China (except for Xinjiang province). It is likely controlled by the El Niño–Southern Oscillation (ENSO) variability. The third mode is a sandwich triple mode in eastern China including variations of the same sign in western China and central China. The last two modes are reproduced by most of the six coupled climate models' last millennium simulations performed in the framework of the Paleoclimate Modelling Intercomparison Project Phase III (PMIP3). In particular, the link of the second mode with ENSO is confirmed by the models. However, there is a mismatch between models and proxy reconstructions in the time development of different modes. This mismatch suggests the important role of internal variability in the reconstructed precipitation mode variations of the past 500 years.

The integration of climate proxy information with general circulation model (GCM) results offers considerable potential for deriving greater understanding of the mechanisms underlying climate variability, as well as unique opportunities for out-of-sample evaluations of model performance. In this study, we combine insights from a new tree-ring hydroclimate reconstruction from Scandinavia with projections from a suite of forced transient simulations of the last millennium and historical intervals from the CMIP5 and PMIP3 archives. Model simulations and proxy reconstruction data are found to broadly agree on the modes of atmospheric variability that produce droughts–pluvials in the region. Despite these dynamical similarities, large differences between simulated and reconstructed hydroclimate time series remain. We find that the GCM-simulated multi-decadal and/or longer hydroclimate variability is systematically smaller than the proxy-based estimates, whereas the dominance of GCM-simulated high-frequency components of variability is not reflected in the proxy record. Furthermore, the paleoclimate evidence indicates in-phase coherencies between regional hydroclimate and temperature on decadal timescales, i.e., sustained wet periods have often been concurrent with warm periods and vice versa. The CMIP5–PMIP3 archive suggests, however, out-of-phase coherencies between the two variables in the last millennium. The lack of adequate understanding of mechanisms linking temperature and moisture supply on longer timescales has serious implications for attribution and prediction of regional hydroclimate changes. Our findings stress the need for further paleoclimate data–model intercomparison efforts to expand our understanding of the dynamics of hydroclimate variability and change, to enhance our ability to evaluate climate models, and to provide a more comprehensive view of future drought and pluvial risks.

Climate trends in the Antarctic region remain poorly characterized, owing to the brevity and scarcity of direct climate observations and the large magnitude of interannual to decadal-scale climate variability. Here, within the framework of the PAGES Antarctica2k working group, we build an enlarged database of ice core water stable isotope records from Antarctica, consisting of 112 records. We produce both unweighted and weighted isotopic (δ18O) composites and temperature reconstructions since 0 CE, binned at 5- and 10-year resolution, for seven climatically distinct regions covering the Antarctic continent. Following earlier work of the Antarctica2k working group, we also produce composites and reconstructions for the broader regions of East Antarctica, West Antarctica and the whole continent. We use three methods for our temperature reconstructions: (i) a temperature scaling based on the δ18O–temperature relationship output from an ECHAM5-wiso model simulation nudged to ERA-Interim atmospheric reanalyses from 1979 to 2013, and adjusted for the West Antarctic Ice Sheet region to borehole temperature data, (ii) a temperature scaling of the isotopic normalized anomalies to the variance of the regional reanalysis temperature and (iii) a composite-plus-scaling approach used in a previous continent-scale reconstruction of Antarctic temperature since 1 CE but applied to the new Antarctic ice core database. Our new reconstructions confirm a significant cooling trend from 0 to 1900 CE across all Antarctic regions where records extend back into the 1st millennium, with the exception of the Wilkes Land coast and Weddell Sea coast regions. Within this long-term cooling trend from 0 to 1900 CE, we find that the warmest period occurs between 300 and 1000 CE, and the coldest interval occurs from 1200 to 1900 CE. Since 1900 CE, significant warming trends are identified for the West Antarctic Ice Sheet, the Dronning Maud Land coast and the Antarctic Peninsula regions, and these trends are robust across the distribution of records that contribute to the unweighted isotopic composites and also significant in the weighted temperature reconstructions. Only for the Antarctic Peninsula is this most recent century-scale trend unusual in the context of natural variability over the last 2000 years. However, projected warming of the Antarctic continent during the 21st century may soon see significant and unusual warming develop across other parts of the Antarctic continent. The extended Antarctica2k ice core isotope database developed by this working group opens up many avenues for developing a deeper understanding of the response of Antarctic climate to natural and anthropogenic climate forcings. The first long-term quantification of regional climate in Antarctica presented herein is a basis for data–model comparison and assessments of past, present and future driving factors of Antarctic climate.

The pre-industrial millennium is among the periods selected by the Paleoclimate Model Intercomparison Project (PMIP) for experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and the fourth phase of the PMIP (PMIP4). The past1000 transient simulations serve to investigate the response to (mainly) natural forcing under background conditions not too different from today, and to discriminate between forced and internally generated variability on interannual to centennial timescales. This paper describes the motivation and the experimental set-ups for the PMIP4-CMIP6 past1000 simulations, and discusses the forcing agents orbital, solar, volcanic, and land use/land cover changes, and variations in greenhouse gas concentrations. The past1000 simulations covering the pre-industrial millennium from 850 Common Era (CE) to 1849 CE have to be complemented by historical simulations (1850 to 2014 CE) following the CMIP6 protocol. The external forcings for the past1000 experiments have been adapted to provide a seamless transition across these time periods. Protocols for the past1000 simulations have been divided into three tiers. A default forcing data set has been defined for the Tier 1 (the CMIP6 past1000) experiment. However, the PMIP community has maintained the flexibility to conduct coordinated sensitivity experiments to explore uncertainty in forcing reconstructions as well as parameter uncertainty in dedicated Tier 2 simulations. Additional experiments (Tier 3) are defined to foster collaborative model experiments focusing on the early instrumental period and to extend the temporal range and the scope of the simulations. This paper outlines current and future research foci and common analyses for collaborative work between the PMIP and the observational communities (reconstructions, instrumental data).

Occupying about 14 % of the world's surface, the Southern Ocean plays a fundamental role in ocean and atmosphere circulation, carbon cycling and Antarctic ice-sheet dynamics. Unfortunately, high interannual variability and a dearth of instrumental observations before the 1950s limits our understanding of how marine–atmosphere–ice domains interact on multi-decadal timescales and the impact of anthropogenic forcing. Here we integrate climate-sensitive tree growth with ocean and atmospheric observations on southwest Pacific subantarctic islands that lie at the boundary of polar and subtropical climates (52–54° S). Our annually resolved temperature reconstruction captures regional change since the 1870s and demonstrates a significant increase in variability from the 1940s, a phenomenon predating the observational record. Climate reanalysis and modelling show a parallel change in tropical Pacific sea surface temperatures that generate an atmospheric Rossby wave train which propagates across a large part of the Southern Hemisphere during the austral spring and summer. Our results suggest that modern observed high interannual variability was established across the mid-twentieth century, and that the influence of contemporary equatorial Pacific temperatures may now be a permanent feature across the mid- to high latitudes.

The multi-decadal to centennial hydroclimate changes in East Africa over the last millennium are studied by comparing the results of forced transient simulations by six general circulation models (GCMs) with published hydroclimate reconstructions from four lakes: Challa and Naivasha in equatorial East Africa, and Masoko and Malawi in southeastern inter-tropical Africa. All GCMs simulate fairly well the unimodal seasonal cycle of precipitation in the Masoko–Malawi region, while the bimodal seasonal cycle characterizing the Challa–Naivasha region is generally less well captured by most models. Model results and lake-based hydroclimate reconstructions display very different temporal patterns over the last millennium. Additionally, there is no common signal among the model time series, at least until 1850. This suggests that simulated hydroclimate fluctuations are mostly driven by internal variability rather than by common external forcing. After 1850, half of the models simulate a relatively clear response to forcing, but this response is different between the models. Overall, the link between precipitation and tropical sea surface temperatures (SSTs) over the pre-industrial portion of the last millennium is stronger and more robust for the Challa–Naivasha region than for the Masoko–Malawi region. At the inter-annual timescale, last-millennium Challa–Naivasha precipitation is positively (negatively) correlated with western (eastern) Indian Ocean SST, while the influence of the Pacific Ocean appears weak and unclear. Although most often not significant, the same pattern of correlations between East African rainfall and the Indian Ocean SST is still visible when using the last-millennium time series smoothed to highlight centennial variability, but only in fixed-forcing simulations. This means that, at the centennial timescale, the effect of (natural) climate forcing can mask the imprint of internal climate variability in large-scale teleconnections.

Recent studies have investigated the potential link between the freshwater input derived from the melting of the Antarctic ice sheet and the observed recent increase in sea ice extent in the Southern Ocean. In this study, we assess the impact of an additional freshwater flux on the trend in sea ice extent and concentration in simulations with data assimilation, spanning the period 1850–2009, as well as in retrospective forecasts (hindcasts) initialised in 1980. In the simulations with data assimilation, the inclusion of an additional freshwater flux that follows an autoregressive process improves the reconstruction of the trend in ice extent and concentration between 1980 and 2009. This is linked to a better efficiency of the data assimilation procedure but can also be due to a better representation of the freshwater cycle in the Southern Ocean. The results of the hindcast simulations show that an adequate initial state, reconstructed thanks to the data assimilation procedure including an additional freshwater flux, can lead to an increase in the sea ice extent spanning several decades that is in agreement with satellite observations. In our hindcast simulations, an increase in sea ice extent is obtained even in the absence of any major change in the freshwater input over the last decades. Therefore, while the additional freshwater flux appears to play a key role in the reconstruction of the evolution of the sea ice in the simulation with data assimilation, it does not seem to be required in the hindcast simulations. The present work thus provides encouraging results for sea ice predictions in the Southern Ocean, as in our simulation the positive trend in ice extent over the last 30 years is largely determined by the state of the system in the late 1970s.

The last interglacial (LIG), also identified to the Eemian in Europe, began at approximately 130 kyr BP and ended at about 115 kyr BP (before present). More and more proxy-based reconstructions of the LIG climate are becoming more available even though they remain sparse. The major climate forcings during the LIG are rather well known and therefore models can be tested against paleoclimatic data sets and then used to better understand the climate of the LIG. However, models are displaying a large range of responses, being sometimes contradictory between them or with the reconstructed data. Here we would like to investigate causes of these differences. We focus on a single climate model, LOVECLIM, and we perform transient simulations over the LIG, starting at 135 kyr BP and run until 115 kyr BP. With these simulations, we test the role of the surface boundary conditions (the time-evolution of the Northern Hemisphere (NH) ice sheets) on the simulated LIG climate and the importance of the parameter sets (internal to the model, such as the albedos of the ocean and sea ice), which affect the sensitivity of the model. The magnitude of the simulated climate variations through the LIG remains too low compared to reconstructions for climate variables such as surface air temperature. Moreover, in the North Atlantic, the large increase in summer sea surface temperature towards the peak of the interglacial occurs too early (at ∼128 kyr BP) compared to the reconstructions. This feature as well as the climate simulated during the optimum of the LIG, between 131 and 121 kyr BP, does not depend on changes in surface boundary conditions and parameter sets. The additional freshwater flux (FWF) from the melting NH ice sheets is responsible for a temporary abrupt weakening of the North Atlantic meridional overturning circulation, which causes a strong global cooling in annual mean. However, the changes in the configuration (extent and albedo) of the NH ice sheets during the LIG only slightly impact the simulated climate. Together, configuration of and FWF from the NH ice sheets greatly increase the magnitude of the temperature variations over continents as well as over the ocean at the beginning of the simulation and reduce the difference between the simulated climate and the reconstructions. Lastly, we show that the contribution from the parameter sets to the climate response is actually very modest.

The consistency between new quantitative reconstructions of Arctic sea ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The response of the models mainly follows the increase in summer insolation, modulated to a limited extent by changes in atmospheric circulation. This leads to differences between regions in the models that are smaller than in the reconstruction. It is, however, impossible to precisely assess the models' skills because the sea ice concentration changes at the mid-Holocene are small in both the reconstructions and the models and of the same order of magnitude as the reconstruction uncertainty. Performing simulations with data assimilation using the model LOVECLIM amplifies the regional differences and improves the model–data agreement as expected. This is mainly achieved through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin, inducing an increase in the ice concentration in the Barents and Chukchi seas. This underlines the potential role of atmospheric circulation in explaining the reconstructed changes during the Holocene.

The large natural variability of the Antarctic sea ice is a key characteristic of the system that might be responsible for the small positive trend in sea ice extent observed since 1979. In order to gain insight of the processes responsible for this variability, we have analysed in a control simulation performed with a coupled climate model a positive ice–ocean feedback that amplifies sea ice variations. When sea ice concentration increases in a region, in particular close to the ice edge, the mixed layer depth tends to decrease. This can be caused by a net inflow of ice, and thus of freshwater, that stabilizes the water column. A second stabilizing mechanism at interannual timescales is associated with the downward salt transport due to the seasonal cycle of ice formation: brine is released in winter and mixed over a deep layer while the freshwater flux caused by ice melting is included in a shallow layer, resulting in a net vertical transport of salt. Because of this stronger stratification due to the presence of sea ice, more heat is stored at depth in the ocean and the vertical oceanic heat flux is reduced, which contributes to maintaining a higher ice extent. This positive feedback is not associated with a particular spatial pattern. Consequently, the spatial distribution of the trend in ice concentration is largely imposed by the wind changes that can provide the initial perturbation. A positive freshwater flux could alternatively be the initial trigger but the amplitude of the final response of the sea ice extent is finally set up by the amplification related to the ice–ocean feedback. Initial conditions also have an influence as the chance to have a large increase in ice extent is higher if starting from a state characterized by a low value.

The mid-Holocene (6 kyr BP; thousand years before present) is a key period to study the consistency between model results and proxy-based reconstruction data as it corresponds to a standard test for models and a reasonable number of proxy-based records is available. Taking advantage of this relatively large amount of information, we have compared a compilation of 50 air and sea surface temperature reconstructions with the results of three simulations performed with general circulation models and one carried out with LOVECLIM, a model of intermediate complexity. The conclusions derived from this analysis confirm that models and data agree on the large-scale spatial pattern but the models underestimate the magnitude of some observed changes and that large discrepancies are observed at the local scale. To further investigate the origin of those inconsistencies, we have constrained LOVECLIM to follow the signal recorded by the proxies selected in the compilation using a data-assimilation method based on a particle filter. In one simulation, all the 50 proxy-based records are used while in the other two only the continental or oceanic proxy-based records constrain the model results. As expected, data assimilation leads to improving the consistency between model results and the reconstructions. In particular, this is achieved in a robust way in all the experiments through a strengthening of the westerlies at midlatitude that warms up northern Europe. Furthermore, the comparison of the LOVECLIM simulations with and without data assimilation has also objectively identified 16 proxy-based paleoclimate records whose reconstructed signal is either incompatible with the signal recorded by some other proxy-based records or with model physics.

Both historical and idealized climate model experiments are performed with a variety of Earth system models of intermediate complexity (EMICs) as part of a community contribution to the Intergovernmental Panel on Climate Change Fifth Assessment Report. Historical simulations start at 850 CE and continue through to 2005. The standard simulations include changes in forcing from solar luminosity, Earth's orbital configuration, CO2, additional greenhouse gases, land use, and sulphate and volcanic aerosols. In spite of very different modelled pre-industrial global surface air temperatures, overall 20th century trends in surface air temperature and carbon uptake are reasonably well simulated when compared to observed trends. Land carbon fluxes show much more variation between models than ocean carbon fluxes, and recent land fluxes appear to be slightly underestimated. It is possible that recent modelled climate trends or climate–carbon feedbacks are overestimated resulting in too much land carbon loss or that carbon uptake due to CO2 and/or nitrogen fertilization is underestimated. Several one thousand year long, idealized, 2 × and 4 × CO2 experiments are used to quantify standard model characteristics, including transient and equilibrium climate sensitivities, and climate–carbon feedbacks. The values from EMICs generally fall within the range given by general circulation models. Seven additional historical simulations, each including a single specified forcing, are used to assess the contributions of different climate forcings to the overall climate and carbon cycle response. The response of surface air temperature is the linear sum of the individual forcings, while the carbon cycle response shows a non-linear interaction between land-use change and CO2 forcings for some models. Finally, the preindustrial portions of the last millennium simulations are used to assess historical model carbon-climate feedbacks. Given the specified forcing, there is a tendency for the EMICs to underestimate the drop in surface air temperature and CO2 between the Medieval Climate Anomaly and the Little Ice Age estimated from palaeoclimate reconstructions. This in turn could be a result of unforced variability within the climate system, uncertainty in the reconstructions of temperature and CO2, errors in the reconstructions of forcing used to drive the models, or the incomplete representation of certain processes within the models. Given the forcing datasets used in this study, the models calculate significant land-use emissions over the pre-industrial period. This implies that land-use emissions might need to be taken into account, when making estimates of climate–carbon feedbacks from palaeoclimate reconstructions.

Using the climate model of intermediate complexity LOVECLIM in an idealised framework, we assess three data-assimilation methods for reconstructing the climate state. The methods are a nudging, a particle filter with sequential importance resampling, and a nudging proposal particle filter and the test case corresponds to the climate of the high latitudes of the Southern Hemisphere during the past 150 yr. The data-assimilation methods constrain the model by pseudo-observations of surface air temperature anomalies obtained from the same model, but different initial conditions. All three data-assimilation methods provide with good estimations of surface air temperature and of sea ice concentration, with the nudging proposal particle filter obtaining the highest correlations with the pseudo-observations. When reconstructing variables that are not directly linked to the pseudo-observations such as atmospheric circulation and sea surface salinity, the particle filters have equivalent performance and their correlations are smaller than for surface air temperature reconstructions but still satisfactory for many applications. The nudging, on the contrary, obtains sea surface salinity patterns that are opposite to the pseudo-observations, which is due to a spurious impact of the nudging on vertical exchanges in the ocean.

From 10 to 8 ka BP (thousand years before present), paleoclimate records show an atmospheric and oceanic cooling in the high latitudes of the Southern Hemisphere. During this interval, temperatures estimated from proxy data decrease by 0.8 °C over Antarctica and 1.2 °C over the Southern Ocean. In order to study the causes of this cooling, simulations covering the early Holocene have been performed with the climate model of intermediate complexity LOVECLIM constrained to follow the signal recorded in climate proxies using a data assimilation method based on a particle filtering approach. The selected proxies represent oceanic and atmospheric surface temperature in the Southern Hemisphere derived from terrestrial, marine and glaciological records. Two mechanisms previously suggested to explain the 10–8 ka BP cooling pattern are investigated using the data assimilation approach in our model. The first hypothesis is a change in atmospheric circulation, and the second one is a cooling of the sea surface temperature in the Southern Ocean, driven in our experimental setup by the impact of an increased West Antarctic melting rate on ocean circulation. For the atmosphere hypothesis, the climate state obtained by data assimilation produces a modification of the meridional atmospheric circulation leading to a 0.5 °C Antarctic cooling from 10 to 8 ka BP compared to the simulation without data assimilation, without congruent cooling of the atmospheric and sea surface temperature in the Southern Ocean. For the ocean hypothesis, the increased West Antarctic freshwater flux constrainted by data assimilation (+100 mSv from 10 to 8 ka BP) leads to an oceanic cooling of 0.7 °C and a strengthening of Southern Hemisphere westerlies (+6%). Thus, according to our experiments, the observed cooling in Antarctic and the Southern Ocean proxy records can only be reconciled with the reconstructions by the combination of a modified atmospheric circulation and an enhanced freshwater flux.

Observations over the last 30 yr have shown that the sea ice extent in the Southern Ocean has slightly increased since 1979. Mechanisms responsible for this positive trend have not been well established yet. In this study we tackle two related issues: is the observed positive trend compatible with the internal variability of the system, and do the models agree with what we know about the observed internal variability? For that purpose, we analyse the evolution of sea ice around the Antarctic simulated by 24 different general circulation models involved in the 5th Coupled Model Intercomparison Project (CMIP5), using both historical and hindcast experiments. Our analyses show that CMIP5 models respond to the forcing, including the one induced by stratospheric ozone depletion, by reducing the sea ice cover in the Southern Ocean. Some simulations display an increase in sea ice extent similar to the observed one. According to models, the observed positive trend is compatible with internal variability. However, models strongly overestimate the variance of sea ice extent and the initialization methods currently used in models do not improve systematically the simulated trends in sea ice extent. On the basis of those results, a critical role of the internal variability in the observed increase of sea ice extent in the Southern Ocean could not be ruled out, but current models results appear inadequate to test more precisely this hypothesis.

Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice–ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an ensemble Kalman filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the use of synthetic data (i.e. model-generated data), and secondly, through the assimilation of real satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

We examine the recent (1979–2010) and future (2011–2100) characteristics of the summer Arctic sea ice cover as simulated by 29 Earth system and general circulation models from the Coupled Model Intercomparison Project, phase 5 (CMIP5). As was the case with CMIP3, a large intermodel spread persists in the simulated summer sea ice losses over the 21st century for a given forcing scenario. The 1979–2010 sea ice extent, thickness distribution and volume characteristics of each CMIP5 model are discussed as potential constraints on the September sea ice extent (SSIE) projections. Our results suggest first that the future changes in SSIE with respect to the 1979–2010 model SSIE are related in a complicated manner to the initial 1979–2010 sea ice model characteristics, due to the large diversity of the CMIP5 population: at a given time, some models are in an ice-free state while others are still on the track of ice loss. However, in phase plane plots (that do not consider the time as an independent variable), we show that the transition towards ice-free conditions is actually occurring in a very similar manner for all models. We also find that the year at which SSIE drops below a certain threshold is likely to be constrained by the present-day sea ice properties. In a second step, using several adequate 1979–2010 sea ice metrics, we effectively reduce the uncertainty as to when the Arctic could become nearly ice-free in summertime, the interval [2041, 2060] being our best estimate for a high climate forcing scenario.

The investigation of observed borehole temperatures has proved to be a valuable tool for the reconstruction of ground surface temperature histories. However, there are still many open questions concerning the significance and accuracy of the reconstructions from these data. In particular, the temperature signal of the warming after the Last Glacial Maximum is still present in borehole temperature profiles. It is shown here that this signal also influences the relatively shallow boreholes used in current paleoclimate inversions to estimate temperature changes in the last centuries by producing errors in the determination of the steady state geothermal gradient. However, the impact on estimates of past temperature changes is weaker. For deeper boreholes, the curvature of the long-term signal is significant. A correction based on simple assumptions about glacial–interglacial temperature changes shows promising results, improving the extraction of millennial scale signals. The same procedure may help when comparing observed borehole temperature profiles with the results from numerical climate models.

Two hindcast (1983–2007) simulations are performed with the global, ocean-sea ice models NEMO-LIM2 and NEMO-LIM3 driven by atmospheric reanalyses and climatologies. The two simulations differ only in their sea ice component, while all other elements of experimental design (resolution, initial conditions, atmospheric forcing) are kept identical. The main differences in the sea ice models lie in the formulation of the subgrid-scale ice thickness distribution, of the thermodynamic processes, of the sea ice salinity and of the sea ice rheology. To assess the differences in model skill over the period of investigation, we develop a set of metrics for both hemispheres, comparing the main sea ice variables (concentration, thickness and drift) to available observations and focusing on both mean state and seasonal to interannual variability. Based upon these metrics, we discuss the physical processes potentially responsible for the differences in model skill. In particular, we suggest that (i) a detailed representation of the ice thickness distribution increases the seasonal to interannual variability of ice extent, with spectacular improvement for the simulation of the recent observed summer Arctic sea ice retreats, (ii) the elastic-viscous-plastic rheology enhances the response of ice to wind stress, compared to the classical viscous-plastic approach, (iii) the grid formulation and the air-sea ice drag coefficient affect the simulated ice export through Fram Strait and the ice accumulation along the Canadian Archipelago, and (iv) both models show less skill in the Southern Ocean, probably due to the low quality of the reanalyses in this region and to the absence of important small-scale oceanic processes at the models' resolution (~1°).

One of the main features of the oceanic circulation along Antarctica is the Antarctic Slope Current (ASC). This circumpolar current flows westwards and contributes to communication between the three major oceanic basins around Antarctica. The ASC is not very well known due to remote location and the presence of sea ice during several months, allowing in situ studies only during summertime. Moreover, only few modelling studies of this current have been carried out. Here, we investigate the sensitivity of this simulated current to four different resolutions in a coupled ocean-sea ice model and to two different atmospheric forcing sets. Two series of simulations are conducted. For the first series, global model configurations are run at coarse (2°) to eddy-permitting (0.25°) resolutions with the same atmospheric forcing. For the second series, simulations with two different atmospheric forcings are performed using a regional circumpolar configuration (south of 30° S) at 0.5° resolution. The first atmospheric forcing is based on a global atmospheric reanalysis and satellite data, while the second is based on a downscaling of the global atmospheric reanalysis by a regional atmospheric model calibrated to Antarctic meteorological conditions. Sensitivity experiments to resolution indicate that a minimum model resolution of 0.5° is needed to capture the dynamics of the ASC in terms of water mass transport and recirculation. Sensitivity experiments to atmospheric forcing fields shows that the wind speed along the Antarctic coast strongly controls the water mass transport and the seasonal cycle of the ASC. An increase in annual mean of easterlies by about 30 % leads to an increase in the mean ASC transport by about 40 %. Similar effects are obtained on the seasonal cycle: using a wind forcing field with a larger seasonal cycle (+30 %) increases by more than 30 % the amplitude of the seasonal cycle of the ASC. To confirm the importance of wind seasonal cycle, a simulation without wind speed seasonal cycle is carried out. This simulation shows a decrease by more than 50 % of the amplitude of the ASC transport seasonal cycle without changing the mean value of ASC transport.

Many sources of uncertainty limit the accuracy of climate projections. Among them, we focus here on the parameter uncertainty, i.e. the imperfect knowledge of the values of many physical parameters in a climate model. Therefore, we use LOVECLIM, a global three-dimensional Earth system model of intermediate complexity and vary several parameters within a range based on the expert judgement of model developers. Nine climatic parameter sets and three carbon cycle parameter sets are selected because they yield present-day climate simulations coherent with observations and they cover a wide range of climate responses to doubled atmospheric CO2 concentration and freshwater flux perturbation in the North Atlantic. Moreover, they also lead to a large range of atmospheric CO2 concentrations in response to prescribed emissions. Consequently, we have at our disposal 27 alternative versions of LOVECLIM (each corresponding to one parameter set) that provide very different responses to some climate forcings. The 27 model versions are then used to illustrate the range of responses provided over the recent past, to compare the time evolution of climate variables over the time interval for which they are available (the last few decades up to more than one century) and to identify the outliers and the "best" versions over that particular time span. For example, between 1979 and 2005, the simulated global annual mean surface temperature increase ranges from 0.24 °C to 0.64 °C, while the simulated increase in atmospheric CO2 concentration varies between 40 and 50 ppmv. Measurements over the same period indicate an increase in global annual mean surface temperature of 0.45 °C (Brohan et al., 2006) and an increase in atmospheric CO2 concentration of 44 ppmv (Enting et al., 1994; GLOBALVIEW-CO2, 2006). Only a few parameter sets yield simulations that reproduce the observed key variables of the climate system over the last decades. Furthermore, our results show that the model response, including its ocean component, is strongly influenced by the model sensitivity to an increase in atmospheric CO2 concentration but much less by its sensitivity to freshwater flux in the North Atlantic. They also highlight weaknesses of the model, in particular its large ocean heat uptake.

The main characteristics of the new version 1.2 of the three-dimensional Earth system model of intermediate complexity LOVECLIM are briefly described. LOVECLIM 1.2 includes representations of the atmosphere, the ocean and sea ice, the land surface (including vegetation), the ice sheets, the icebergs and the carbon cycle. The atmospheric component is ECBilt2, a T21, 3-level quasi-geostrophic model. The ocean component is CLIO3, which consists of an ocean general circulation model coupled to a comprehensive thermodynamic-dynamic sea-ice model. Its horizontal resolution is of 3° by 3°, and there are 20 levels in the ocean. ECBilt-CLIO is coupled to VECODE, a vegetation model that simulates the dynamics of two main terrestrial plant functional types, trees and grasses, as well as desert. VECODE also simulates the evolution of the carbon cycle over land while the ocean carbon cycle is represented by LOCH, a comprehensive model that takes into account both the solubility and biological pumps. The ice sheet component AGISM is made up of a three-dimensional thermomechanical model of the ice sheet flow, a visco-elastic bedrock model and a model of the mass balance at the ice-atmosphere and ice-ocean interfaces. For both the Greenland and Antarctic ice sheets, calculations are made on a 10 km by 10 km resolution grid with 31 sigma levels. LOVECLIM1.2 reproduces well the major characteristics of the observed climate both for present-day conditions and for key past periods such as the last millennium, the mid-Holocene and the Last Glacial Maximum. However, despite some improvements compared to earlier versions, some biases are still present in the model. The most serious ones are mainly located at low latitudes with an overestimation of the temperature there, a too symmetric distribution of precipitation between the two hemispheres, and an overestimation of precipitation and vegetation cover in the subtropics. In addition, the atmospheric circulation is too weak. The model also tends to underestimate the surface temperature changes (mainly at low latitudes) and to overestimate the ocean heat uptake observed over the last decades.

Climate proxy data provide noisy, and spatially incomplete information on some aspects of past climate states, whereas palaeosimulations with climate models provide global, multi-variable states, which may however differ from the true states due to unpredictable internal variability not related to climate forcings, as well as due to model deficiencies. Using data assimilation for combining the empirical information from proxy data with the physical understanding of the climate system represented by the equations in a climate model is in principle a promising way to obtain better estimates for the climate of the past. Data assimilation has been used for a long time in weather forecasting and atmospheric analyses to control the states in atmospheric General Circulation Models such that they are in agreement with observation from surface, upper air, and satellite measurements. Here we discuss the similarities and the differences between the data assimilation problem in palaeoclimatology and in weather forecasting, and present and conceptually compare three data assimilation methods that have been developed in recent years for applications in palaeoclimatology. All three methods (selection of ensemble members, Forcing Singular Vectors, and Pattern Nudging) are illustrated by examples that are related to climate variability over the extratropical Northern Hemisphere during the last millennium. In particular it is shown that all three methods suggest that the cold period over Scandinavia during 1790–1820 is linked to anomalous northerly or easterly atmospheric flow, which in turn is related to a pressure anomaly that resembles a negative state of the Northern Annular Mode.

An upgraded version of the biogeochemical model SWAMCO is coupled to the ocean-sea-ice model NEMO-LIM to explore processes governing the spatial distribution of the iron supply to phytoplankton in the Southern Ocean. The 3-D NEMO-LIM-SWAMCO model is implemented in the ocean domain south of latitude 30° S and runs are performed over September 1989–December 2000. Model scenarios include potential iron sources (atmospheric deposition, iceberg calving/melting and continental sediments) as well as iron storage within sea ice, all formulated based on a literature review. When all these processes are included, the simulated iron profiles and phytoplankton bloom distributions show satisfactory agreement with observations. Analyses of simulations and sensitivity tests point to the key role played by continental sediments as a primary source for iron. Iceberg calving and melting contribute by up to 25% of Chl-a simulated in areas influenced by icebergs while atmospheric deposition has little effect at high latitudes. Activating sea ice-ocean iron exchanges redistribute iron geographically. Stored in the ice during winter formation, iron is then transported due to ice motion and is released and made available to phytoplankton during summer melt, in the vicinity of the marginal ice zones. Transient iron storage and transport associated with sea ice dynamics stimulate summer phytoplankton blooming (up to 3 mg Chl-a m-3 in the Weddell Sea and off East Antarctica but not in the Ross, Bellingshausen and Amundsen Seas. This contrasted feature results from the simulated variable content of iron in sea ice and release of melting ice showing higher ice-ocean iron fluxes in the continental shelves of the Weddell and Ross Seas than in the Eastern Weddell Sea and the Bellingshausen-Amundsen Seas. This study confirms that iron sources and transport in the Southern Ocean likely provide important mechanisms in the geographical development of phytoplankton blooms and associated ecosystems.

An ensemble of simulations of the climate of the past millennium conducted with a three-dimensional climate model of intermediate complexity are constrained to follow temperature histories obtained from a recent compilation of well-calibrated surface temperature proxies using a simple data assimilation technique. Those simulations provide a reconstruction of the climate of the Arctic that is compatible with the model physics, the forcing applied and the proxy records. Available observational data, proxy-based reconstructions and our model results suggest that the Arctic climate is characterized by substantial variations in surface temperature over the past millennium. Though the most recent decades are likely to be the warmest of the past millennium, we find evidence for substantial past warming episodes in the Arctic. In particular, our model reconstructions show a prominent warm event during the period 1470–1520. This warm period is likely related to the internal variability of the climate system, that is the variability present in the absence of any change in external forcing. We examine the roles of competing mechanisms that could potentially produce this anomaly. This study leads us to conclude that changes in atmospheric circulation, through enhanced southwesterly winds towards northern Europe, Siberia and Canada, are likely the main cause of the late 15th/early 16th century Arctic warming.