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Satellite observations show a small overall increase in Antarctic sea ice extent (SIE) over the period 1979–2015. However, this upward trend needs to be balanced against recent pronounced SIE fluctuations occurring there. In the space of 3 years, the SIE sank from its highest value ever reached in September 2014 to record low in February 2017. In this work, a set of six state-of-the-art global climate models is used to evaluate the potential predictability of the Antarctic sea ice at such timescales. This first multi-model study of Antarctic sea ice predictability reveals that the ice edge location can potentially be predicted up to 3 years in advance. However, the ice edge location predictability shows contrasted seasonal performances, with high predictability in winter and no predictability in summer. The reemergence of the predictability from one winter to next is provided by the ocean through its large thermal inertia. Sea surface heat anomalies are stored at depth at the end of the winter and influences the sea ice advance the following year as they resurface. The effectiveness of this mechanism across models is found to depend upon the depth of the mixed layer. One should be very cautious about these potential predictability estimates as there is evidence that the Antarctic sea ice predictability is promoted by deep Southern Ocean convection. We therefore suspect models with excessive convection to show higher sea ice potential predictability results due to an incorrect representation of the Southern Ocean.

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.

Abstract. 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.

Several processes have been hypothesized to explain the slight overall expansion of Antarcticsea ice over the satellite observation era, including externally forced changes in local winds orin the Southern Ocean’s hydrological cycle, as well as internal climate variability. Here, weshow the critical influence of an ocean–sea-ice feedback. Once initiated by an externalperturbation, it may be sufficient to sustain the observed sea-ice expansion in the Ross Sea,the region with the largest and most significant expansion. We quantify the heat trapped atthe base of the ocean mixed layer and demonstrate that it is of the same order of magnitudeas the latent heat storage due to the long-term changes in sea-ice volume. The evidence thussuggests that the recent ice coverage increase in the Ross Sea could have been achievedthrough a reorganization of energy within the near-surface ice-ocean system.

Occupying 14% of the world’s surface, the Southern Ocean plays a fundamental role in global climate, ocean circulation, carbon cycling and Antarctic ice-sheet stability. 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 mid-twentieth century, a phenomenon predating the observational record. Climate reanalysis and modelling shows 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.

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.

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice concentration and sea ice drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea ice drift estimated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measurements of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to identify model errors in the Antarctic sea ice area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements.

The Antarctic sea ice widely impacts the Earth climate through its interactions with the ocean and the atmosphere. Unlike its Arctic counterpart, the sea ice surrounding Antarctica has been slightly expanding since 1979. A possible explanation for this observed increase relies on the internal variability of the Antarctic sea ice, particularly large at multi-decadal timescales. Unfortunately, reliable observations of sea ice do not span a long enough time period to properly examine how the sea ice varies between decades. The goal of this doctoral thesis is first to investigate the mean state and the internal variability of the Antarctic sea ice simulated by general circulation models (GCMs). This allows checking if these models reproduce reasonably well the observations and if the observed positive trend in sea ice extent is compatible with the internal variability of the models. Second, we assess the impact of the initialisation of a simulation using various data assimilation methods on the skill of the predictions of the Antarctic sea ice at decadal timescales. According to our results, the observed increase in Antarctic sea ice extent is compatible with the internal variability simulated by current GCMs, although it is a relatively rare event. Unfortunately, these models generally display systematic biases in the way they reproduce the mean state and the internal variability of the Antarctic sea ice. In an idealised framework, we show that the interannual variations of the Antarctic sea ice are not predictable beyond three years. Besides, an adequate initialisation clearly improves the skill of the prediction of the multi-decadal trends in sea ice extent, in both idealised and realistic conditions., (SC - Sciences) -- UCL, 2014

In this study, we assess systematically the impact of different initialisation procedures on the predictability of the sea ice in the Southern Ocean. These initialisation strategies are based on three data assimilation methods:the nudging, the particle filter with sequential importance resampling and the nudging proposal particle filter. An Earth system model of intermediate complexity is used to perform hindcast simulations in a perfect model approach. The predictability of the Antarctic sea ice at interannual to multi-decadal timescales is estimated through two aspects: the spread of the hindcast ensemble, indicating the uncertainty of the ensemble, and the correlation between the ensemble mean and the pseudo-observations, used to assess the accuracy of the prediction. Our results show that at decadal timescales more sophisticated data assimilation methods as well as denser pseudo-observations used to initialise the hindcasts decrease the spread of the ensemble. However, our experiments did not clearly demonstrate that one of the initialisation methods systematically provides with a more accurate prediction of the sea ice in the Southern Ocean than the others. Overall, the predictability at interannual timescales is limited to 3 years ahead at most. At multi-decadal timescales, the trends in sea ice extent computed over the time period just after the initialisation are clearly better correlated between the hindcasts and the pseudo-observations if the initialisation takes into account the pseudo-observations. The correlation reaches values larger than 0.5 in winter. This high correlation has likely its origin in the slow evolution of the ocean ensured by its strong thermal inertia, showing the importance of the quality of the initialisation below the sea ice.

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 observedsince 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 largelyimposed 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 influenceas the chance to have a large increase in ice extent is higher if starting from a state characterized by a low value.

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.

In contrast to meteorology, data assimilation in palaeoclimatology is relatively new, but the interest in it is growing as it gives a more reliable state estimation of the past climate changes. We conduct experiments with the three-dimensional Earth system model of intermediate complexity LOVECLIM using particle filtering to reconstruct past climate states over the last 150 years. We assimilate surface temperature from a twin experiment over southern hemisphere with assimilation period of three months. We consider two particle filtering methods: sequential importance re sampling and an extremely efficient particle filter (P.J. van Leeuwen "Non linear data assimilation in geosciences: an extremely efficient particle filter", Q.J.R. Meteorol. Soc. 2010). In sequential importance re sampling, a set of particles, where a particle is a realization of the model with random perturbation of initial conditions, is integrated over a season. Then, when observations of sea surface temperature become available, the set is re sampled according to an importance weight of each particle. This importance weight comes from computation of the likelihood of the state obtained by each particle. The extremely efficient particle filter used here is based on sequential importance re sampling and nudging. In addition to the assimilation of surface temperature each three months, sea surface temperature is nudged every day through the fluxes coming from the atmosphere to the ocean such that it is slightly pulled towards the seasonal/monthly mean calculated from observations. We compare results of the simulations obtained using LOVECLIM only, LOVECLIM constrained by sequential importance re sampling, and LOVECLIM constrained by the extremely efficient particle filter.