Insights on past and future sea-ice evolution from combining observations and models

We discuss the current understanding of past and future sea-ice evolution as inferred from combining model simulations and observations. In such combined analysis, the models allow us to enhance our understanding behind the observed evolution of sea ice, while the observations allow us to assess how realistically the models represent the processes that govern sea-ice evolution in the real world. Combined, observations and models thus provide robust insights into the functioning of sea ice in the Earth's climate system, and can inform policy decisions related to the future evolution of the ice cover. We find that models and observations agree well on the sensitivity of Arctic sea ice to global warming and on the main drivers for the observed retreat. In contrast, a robust reduction of the uncertainty range of future sea-ice evolution remains difficult, in particular since the observational record is often too short to robustly examine the impact of internal variability on model biases. Process-based model evaluation and model evaluation based on seasonal-prediction systems provide promising ways to overcome these limitations

Assimilation of sea-ice concentration in a global climate model — physical and statistical aspects

We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways of specifying the analysis updates for mean thickness. Because the conservation of mean ice thickness or actual ice thickness in the analysis updates leads to poor assimilation performance, we introduce a proportional dependence between concentration and mean thickness analysis updates. Assimilation with these proportional mean-thickness analysis updates leads to good assimilation performance for sea-ice concentration and thickness, both in identical-twin experiments and when assimilating sea-ice observations. The simulation of other Arctic surface fields in the coupled model is, however, not significantly improved by the assimilation. To understand the physical aspects of assimilation errors, we construct a simple prognostic model of the sea-ice thermodynamics, and analyse its response to the assimilation. We find that an adjustment of mean ice thickness in the analysis update is essential to arrive at plausible state estimates. To understand the statistical aspects of assimilation errors, we study the model background error covariance between ice concentration and ice thickness. We find that the spatial structure of covariances is best represented by the proportional mean-thickness analysis updates. Both physical and statistical evidence supports the experimental finding that assimilation with proportional mean-thickness updates outperforms the other two methods considered. The method described here is very simple to implement, and gives results that are sufficiently good to be used for initialising sea ice in a global climate model for seasonal to decadal predictions

Statistical indicators of Arctic sea-ice stability-prospects and limitations

This is the final version of the article. Available from the European Geosciences Union via the DOI in this record.We examine the relationship between the mean and the variability of Arctic sea-ice coverage and volume in a large range of climates from globally ice-covered to globally ice-free conditions. Using a hierarchy of two column models and several comprehensive Earth system models, we consolidate the results of earlier studies and show that mechanisms found in simple models also dominate the interannual variability of Arctic sea ice in complex models. In contrast to predictions based on very idealised dynamical systems, we find a consistent and robust decrease of variance and autocorrelation of sea-ice volume before summer sea ice is lost. We attribute this to the fact that thinner ice can adjust more quickly to perturbations. Thereafter, the autocorrelation increases, mainly because it becomes dominated by the ocean water's large heat capacity when the ice-free season becomes longer. We show that these changes are robust to the nature and origin of climate variability in the models and do not depend on whether Arctic sea-ice loss occurs abruptly or irreversibly. We also show that our climate is changing too rapidly to detect reliable changes in autocorrelation of annual time series. Based on these results, the prospects of detecting statistical early warning signals before an abrupt sea-ice loss at a "tipping point" seem very limited. However, the robust relation between state and variability can be useful to build simple stochastic climate models and to make inferences about past and future sea-ice variability from only short observations or reconstructions.This work was carried out under the programme of the Netherlands Earth System Science Centre (NESSC), financially supported by the Ministry of Education, Culture and Science (OCW). We also acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. We thank Vasilis Dakos for helping to apply his early warnings R package and Chao Li for making available the MPI-ESM model output. S. B. gratefully acknowledges Arie Staal for his fruitful and revealing approaches to savour scientific achievements. We are also indebted to Till Wagner and Ian Eisenman for their valuable comments and their very amiable and cooperative spirit. Finally, we acknowledge two anonymous reviewers who helped us to improve the manuscript

Sea-ice extent and its trend provide limited metrics of model performance

We examine how the evaluation of modelled sea-ice coverage against reality is affected by uncertainties in the retrieval of sea-ice coverage from satellite, by the usage of sea-ice extent to overcome these uncertainties, and by internal variability. We find that for Arctic summer sea ice, model biases in sea-ice extent can be qualitatively different from biases in sea-ice area. This is because about half of the CMIP5 models and satellite retrievals based on the Bootstrap and the ASI algorithm show a compact ice cover in summer with large areas of high-concentration sea ice, while the other half of the CMIP5 models and satellite retrievals based on the NASA Team algorithm show a loose ice cover. For the Arctic winter sea-ice cover, differences in grid geometry can cause synthetic biases in sea-ice extent that are larger than the observational uncertainty. Comparing the uncertainty arising directly from the satellite retrievals with those that arise from internal variability, we find that the latter by far dominates the uncertainty estimate for trends in sea-ice extent and area: most of the differences between modelled and observed trends can simply be explained by internal variability. For absolute sea-ice area and sea-ice extent, however, internal variability cannot explain the difference between model and observations for about half the CMIP5 models that we analyse here. All models that we examined have regional biases, as expressed by the root-mean-square error in concentration, that are larger than the differences between individual satellite algorithms

Ice-free at 1.5°C?

Rapid CommunicationThis is the author accepted manuscript. The final version is available from Nature Publishing Group via the DOI in this record

Changing state of Arctic sea ice across all seasons

The decline in the floating sea ice cover in the Arctic is one of the most striking manifestations of climate change. In this review, we examine this ongoing loss of Arctic sea ice across all seasons. Our analysis is based on satellite retrievals, atmospheric reanalysis, climate-model simulations and a literature review. We find that relative to the 1981-2010 reference period, recent anomalies in spring and winter sea ice coverage have been more significant than any observed drop in summer sea ice extent (SIE) throughout the satellite period. For example, the SIE in May and November 2016 was almost four standard deviations below the reference SIE in these months. Decadal ice loss during winter months has accelerated from -2.4%/decade from 1979 to 1999 to-3.4%/decade from 2000 onwards. We also examine regional ice loss and find that for any given region, the seasonal ice loss is larger the closer that region is to the seasonal outer edge of the ice cover. Finally, across all months, we identify a robust linear relationship between pan-Arctic SIE and total anthropogenic CO2 emissions. The annual cycle of Arctic sea ice loss per ton of CO2 emissions ranges from slightly above 1 m(2) throughout winter to more than 3 m(2) throughout summer. Based on a linear extrapolation of these trends, we find the Arctic Ocean will become sea-ice free throughout August and September for an additional 800 +/- 300 Gt of CO2 emissions, while it becomes ice free from July to October for an additional 1400 +/- 300Gt of CO2 emissions

Observations reveal external driver for Arctic sea-ice retreat

The very low summer extent of Arctic sea ice that has been observed in recent years is often casually interpreted as an early-warning sign of anthropogenic global warming. For examining the validity of this claim, previously IPCC model simulations have been used. Here, we focus on the available observational record to examine if this record allows us to identify either internal variability, self-acceleration, or a specific external forcing as the main driver for the observed sea-ice retreat. We find that the available observations are sufficient to virtually exclude internal variability and self-acceleration as an explanation for the observed long-term trend, clustering, and magnitude of recent sea-ice minima. Instead, the recent retreat is well described by the superposition of an externally forced linear trend and internal variability. For the externally forced trend, we find a physically plausible strong correlation only with increasing atmospheric CO2 concentration. Our results hence show that the observed evolution of Arctic sea-ice extent is consistent with the claim that virtually certainly the impact of an anthropogenic climate change is observable in Arctic sea ice already today

A 1-D modelling study of Arctic sea-ice salinity

We use a 1-D model to study how salinity evolves in Arctic sea ice. To do so, we first explore how sea-ice surface melt and flooding can be incorporated into the 1-D thermodynamic Semi-Adaptive Multi-phase Sea-Ice Model (SAMSIM) presented by Griewank and Notz (2013). We introduce flooding and a flushing parametrization which treats sea ice as a hydraulic network of horizontal and vertical fluxes. Forcing SAMSIM with 36 years of ERA-interim atmospheric reanalysis data, we obtain a modelled Arctic sea-ice salinity that agrees well with ice-core measurements. The simulations thus allow us to identify the main drivers of the observed mean salinity profile in Arctic sea ice. Our results show a 1.5–4 g kg−1 decrease of bulk salinity via gravity drainage after ice growth has ceased and before flushing sets in, which hinders approximating bulk salinity from ice thickness beyond the first growth season. In our simulations, salinity interannual variability of first-year ice is mostly restricted to the top 20 cm. We find that ice thickness, thermal resistivity, freshwater column, and stored energy change by less than 5% on average when the full salinity parametrization is replaced with a prescribed salinity profile