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

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

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

A non-destructive method for measuring the salinity and solid fraction of growing sea ice in situ

We describe an instrument developed to make in situ measurements of salinity and solid-fraction profiles in growing sea ice. The vertical resolution of the measurements is up to a few millimeters, with a temporal resolution of up to fractions of a second. The technique is based on impedance measurements between platinum wires around which sea ice grows. Data obtained using this instrument in laboratory experiments are in good agreement with theoretical predictions. In a field test in the Arctic, the bulk salinity of growing sea ice has been measured in situ throughout the whole depth of the ice layer. The data are compared with bulk salinities obtained from ice cores, and confirm the general understanding that the bulk salinity in ice-core studies is significantly underestimated in the lower parts of the cores. The approach can also be used in other glaciological applications and for general studies of two-phase, two-component porous media

On the origin of discrepancies between observed and simulated memory of Arctic Sea ice

To investigate the inherent predictability of sea ice and its representation in climate models, we compare the seasonal-to-interannual memory of Arctic sea ice as given by lagged correlations of sea-ice area anomalies in large model ensembles (Max Planck Institute Grand Ensemble and Coupled Model Intercomparison Project phase 6) and multiple observational products. We find that state-of-the-art climate models significantly overestimate the memory of pan-Arctic sea-ice area from the summer months into the following year. This cannot be explained by internal variability. We further show that the observed summer memory can be disentangled regionally into a reemergence of positive correlations in the perennial ice zone and negative correlations in the seasonal ice zone; the latter giving rise to the discrepancy between observations and model simulations. These findings could explain some of the predictability gap between potential and operational forecast skill of Arctic sea-ice area identified in previous studies

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

Arctic sea-ice evolution as modeled by Max Planck Institute for Meteorology's Earth system model

We describe the evolution of Arctic sea ice as modeled by the Max Planck Institute for Meteorology's Earth System Model (MPI-ESM). The modeled spatial distribution and interannual variability of the sea-ice cover agree well with satellite observations and are improved relative to the model's predecessor ECHAM5/MPIOM. An evaluation of modeled sea-ice coverage based on sea-ice area gives, however, conflicting results compared to an evaluation based on sea-ice extent and is additionally hindered by uncertainties in the observational record. Simulated trends in sea-ice coverage for the satellite period range from more strongly negative than observed to positive. The observed evolution of Arctic sea ice is incompatible with modeled internal variability and probably caused by external forcing. Simulated drift patterns agree well with observations, but simulated drift speed is generally too high. Simulated sea-ice volume agrees well with volume estimates of the PIOMAS reanalysis for the past few years. However, a preceding Arctic wide decrease in sea-ice volume starts much earlier in MPI-ESM than in PIOMAS. Analyzing this behavior in MPI-ESM's ocean model MPIOM, we find that the modeled volume trend depends crucially on the specific choice of atmospheric reanalysis forcing, which casts some doubt on the reliability of estimates of volume trends. In our CMIP5 scenario simulations, we find a substantial delay in sea-ice response to increasing CO2 concentration; a seasonally ice-free Arctic can result for a CO2 concentration of around 500 ppm. Simulated winter sea-ice coverage drops rapidly to near ice-free conditions once the mean Arctic winter temperature exceeds −5°C

Photoassociation of Ultracold NaLi

We perform photoassociation spectroscopy in an ultracold $^{23}$Na-$^6$Li mixture to study the $c^3\Sigma^+$ excited triplet molecular potential. We observe 50 vibrational states and their substructure to an accuracy of 20 MHz, and provide line strength data from photoassociation loss measurements. An analysis of the vibrational line positions using near-dissociation expansions and a full potential fit is presented. This is the first observation of the $c^3\Sigma^+$ potential, as well as photoassociation in the NaLi system.Comment: 6 pages, 3 figure

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 m² throughout winter to more than 3 m² 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 ± 300 Gt of CO₂ emissions

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