Classifying the Ice Seasons 1982-2016 Using the Weighted Ice Days Number as a New Winter Severity Characteristic

Sea ice is a key climate factor and it restricts considerably the winter navigation in severe seasons on the Baltic Sea. So determining ice conditions severity and describing ice cover behavior at severe seasons are necessary. The ice seasons severity degree is studied at the years 1982 to 2016. A new integrative characteristic named the weighted ice days number of the season is introduced to determine the ice season severity. The ice concentration data on the Baltic Sea published in the European Copernicus Programme are used to calculate the maximal ice extent and the weighted ice days number of the seasons. Both the ice season severity characteristics are used to classify the winters with respect of severity. The ice seasons 1981/82, 1984/85, 1985/86, 1986/87, 1995/96 and 2002/03 are classified as severe by the weighted ice days number. Only three seasons of this list are severe by both the criteria. We interpret this coincidence as the evidence of enough-during extensive ice cover in these three seasons. In the winter 2010/11 ice cover extended widely for some time, but did not last longer. At 2002/03 and a few other ice seasons the Baltic Sea was ice-covered in moderate extent, but the ice cover stayed long time. For 11 winters (32 % of the period) the relational weighted ice days number differs considerably (> 10 %) from the relational maximal ice extent. These winters yield one third of the studied ice seasons. Statistically every 6th winter is severe by the weighted ice days number whereas only statistically every 8th winter is severe by the maximal ice extent on the Baltic. Hence there are more intrinsically severe seasons than the maximal ice extent gives. The maximal ice extent fails to account with the ice cover durability. The weighted ice days number enables to describe the ice cover behavior more representatively. Using the weighted ice days number adds the temporal dimension to the ice season severity study

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

Sea ice inertial oscillations in the Arctic Basin

International audienceAn original method to quantify the amplitude of inertial motion of oceanic and ice drifters, through the introduction of a non-dimensional parameter M defined from a spectral analysis, is presented. A strong seasonal dependence of the magnitude of sea ice inertial oscillations is revealed, in agreement with the corresponding annual cycles of sea ice extent, concentration, thickness, advection velocity, and deformation rates. The spatial pattern of the magnitude of the sea ice inertial oscillations over the Arctic Basin is also in agreement with the sea ice thickness and concentration patterns. This argues for a strong interaction between the magnitude of inertial motion on one hand, the dissipation of energy through mechanical processes, and the cohesiveness of the cover on the other hand. Finally, a significant multi-annual evolution towards greater magnitudes of inertial oscillations in recent years, in both summer and winter, is reported, thus concomitant with reduced sea ice thickness, concentration and spatial extent

The early twentieth century warming and winter Arctic sea ice

The Arctic featured the strongest surface warming over the globe during the recent decades, and the temperature increase was accompanied by a rapid decline in sea ice extent. However, little is known about Arctic sea ice change during the Early Twentieth Century Warming (ETCW) during 1920–1940, also a period of a strong surface warming, both globally and in the Arctic. Here, we investigate the sensitivity of Arctic winter surface air temperature (SAT) to sea ice during 1875–2008 by means of simulations with an atmospheric general circulation model (AGCM) forced by estimates of the observed sea surface temperature (SST) and sea ice concentration. The Arctic warming trend since the 1960s is very well reproduced by the model. In contrast, ETCW in the Arctic is hardly captured. This is consistent with the fact that the sea ice extent in the forcing data does not strongly vary during ETCW. AGCM simulations with observed SST but fixed sea ice reveal a strong dependence of winter SAT on sea ice extent. In particular, the warming during the recent decades is strongly underestimated by the model, if the sea ice extent does not decline and varies only seasonally. This suggests that a significant reduction of Arctic sea ice extent may have also accompanied the Early Twentieth Century Warming, pointing toward an important link between anomalous sea ice extent and Arctic surface temperature variability

Determining the Regime Shift of the Baltic Sea Ice Seasons during 1982–2016

The distributions of ice season characteristics in the Baltic Sea during 1982–2016 were studied. A shift in the ice season regime was observed in 2006–2007. Ice season characteristics before and after the shift were determined in the northern and southern regions of the Baltic Sea, and the contributions of these areas to the shift regime were evaluated. To study changes in ice conditions, satellite data on the daily ice concentration over the Baltic Sea provided by the Copernicus Marine Environment Monitoring Service were used. The ice cover extent and number of ice days were calculated. The maximal ice extent event of the ice season shifted from the beginning of March to the end of January in 2007. The average ice concentration over the Baltic Sea was 18% and 10% before and after the shift, respectively. On average, 32 and 19 ice days occurred before and after the shift, respectively. The average ice concentration in the northern Baltic was 54% before and 34% after the shift, and the concentration in the southern Baltic was 8% before and 7% after the shift. The determined shift of the ice season characteristics indicated that extensive ice cover does not last long during the after-shift seasons

Predictability of the Arctic sea ice edge

Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the “truth” disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts

Antarctic Sea Ice variations 1973 - 1975

Variations in the extent and concentration of sea ice cover on the Southern Ocean are described for the three-year period 1973-75 using information derived from the Nimbus-5 passive microwave imager

Will Arctic sea ice thickness initialization improve seasonal forecast skill?

Arctic sea ice thickness is thought to be an important predictor of Arctic sea ice extent. However, coupled seasonal forecast systems do not generally use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. To investigate how large this source is, a set of ensemble potential predictability experiments with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent forecasts up to 8 months ahead, especially in summer. Perturbing sea ice thickness also has a significant impact on the forecast error in Arctic 2 m temperature a few months ahead. These results suggest that advancing capabilities to observe and assimilate sea ice thickness into coupled forecast systems could significantly increase skill

Seasonal Arctic sea ice forecasting with probabilistic deep learning

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss

Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss