An Updated Assessment of the Changing Arctic Sea Ice Cover

Sea ice is an essential component of the Arctic climate system. The Arctic sea ice cover has undergone substantial changes in the past 40+ years, including decline in areal extent in all months (strongest during summer), thinning, loss of multiyear ice cover, earlier melt onset and ice retreat, and later freeze-up and ice advance. In the past 10 years, these trends have been further reinforced, though the trends (not statistically significant at p <0.05) in some parameters (e.g., extent) over the past decade are more moderate. Since 2011, observing capabilities have improved significantly, including collection of the first basin-wide routine observations of sea ice freeboard and thickness by radar and laser altimeters (except during summer). In addition, data from a year-long field campaign during 2019–2020 promises to yield a bounty of in situ data that will vastly improve understanding of small-scale processes and the interactions between sea ice, the ocean, and the atmosphere, as well as provide valuable validation data for satellite missions. Sea ice impacts within the Arctic are clear and are already affecting humans as well as flora and fauna. Impacts outside of the Arctic, while garner-ing much attention, remain unclear. The future of Arctic sea ice is dependent on future CO2 emissions, but a seasonally ice-free Arctic Ocean is likely in the coming decades. However, year-to-year variability causes considerable uncertainty on exactly when this will happen. The variability is also a challenge for seasonal prediction

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

400 predictions: the SEARCH Sea Ice Outlook 2008–2015

Each Arctic summer since 2008, the Sea Ice Outlook (SIO) has invited researchers and the engaged public to contribute predictions regarding the September extent of Arctic sea ice. The public character of SIO, focused on a number whose true value soon becomes known, brings elements of constructive gamification and transparency to the science process. We analyze the performance of more than 400 predictions from SIO’s first eight years, testing for differences in ensemble skill across years, months and five types of method: heuristic, statistical, mixed, and ice-ocean or ice-ocean-atmosphere modeling. Results highlight a pattern of easy and difficult years, corresponding roughly to the distinction between climate and weather. Difficult years, in which most predictions are far from the observed extent, tend to have large positive or negative excursions from the overall downward trends. In contrast to these large interannual effects, ensemble improvement from June to July and August is modest. Among method types, predictions based on statistics and ice-ocean-atmosphere modeling perform better. Thinning ice that is sensitive to summer weather, complicating prediction, reflects our transitional era between a past Arctic cool enough to retain much thick, resistant multiyear ice; and a warmed future Arctic where little ice remains at summer’s end

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 linear mixed effects model for seasonal forecasts of Arctic sea ice retreat

With sea ice cover declining in recent years, access to open Arctic waters has become a growing interest to numerous stakeholders. Access requires time for planning and preparation, which creates the need for accurate seasonal forecasts of summer sea ice characteristics. One important attribute is the timing of sea ice retreat, of which current statistical and dynamic sea ice models struggle to make accurate seasonal forecasts. We develop a linear mixed effects model to provide forecast of sea ice retreat over five major regions of the Arctic – Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas. In this, the fixed effect – i.e. the mean influence of the atmosphere on sea ice retreat – is modeled using predictors that directly influence the dynamics or thermodynamics of sea ice, and random effects are grouped regionally to capture the local-scale effects on sea ice. The model exhibits very good skill in forecast of sea ice retreat at lead times of up to half a year over these regions

Causes and evolution of winter polynyas north of Greenland

During the 42-year period (1979–2020) of satellite measurements, four major winter (December–March) polynyas have been observed north of Greenland: one in December 1986 and three in the last decade, i.e., February of 2011, 2017, and 2018. The 2018 polynya was unparalleled in its magnitude and duration compared to the three previous events. Given the apparent recent increase in the occurrence of these extreme events, this study aims to examine their evolution and causality, in terms of forced versus natural variability. The limited weather station and remotely sensed sea ice data are analyzed combining with output from the fully coupled Regional Arctic System Model (RASM), including one hindcast and two ensemble simulations. We found that neither the accompanying anomalous warm surface air intrusion nor the ocean below had an impact (i.e., no significant ice melting) on the evolution of the observed winter open-water episodes in the region. Instead, the extreme atmospheric wind forcing resulted in greater sea ice deformation and transport offshore, accounting for the majority of sea ice loss in all four polynyas. Our analysis suggests that strong southerly winds (i.e., northward wind with speeds greater than 10 m s−1) blowing persistently over the study region for at least 2 d or more were required over the study region to mechanically redistribute some of the thickest Arctic sea ice out of the region and thus to create open-water areas (i.e., a latent heat polynya). To assess the role of internal variability versus external forcing of such events, we carried out and examined results from the two RASM ensembles dynamically downscaled with output from the Community Earth System Model (CESM) Decadal Prediction Large Ensemble (DPLE) simulations. Out of 100 winters in each of the two ensembles (initialized 30 years apart: one in December 1985 and another in December 2015), 17 and 16 winter polynyas were produced north of Greenland, respectively. The frequency of polynya occurrence had no apparent sensitivity to the initial sea ice thickness in the study area pointing to internal variability of atmospheric forcing as a dominant cause of winter polynyas north of Greenland. We assert that dynamical downscaling using a high-resolution regional climate model offers a robust tool for process-level examination in space and time, synthesis with limited observations, and probabilistic forecasts of Arctic events, such as the ones being investigated here and elsewhere.</p

Investigating the local-scale influence of sea ice on Greenland surface melt

Rapid decline in Arctic sea ice cover in the 21st century may have wide-reaching effects on the Arctic climate system, including the Greenland ice sheet mass balance. Here, we investigate whether local changes in sea ice around the Greenland ice sheet have had an impact on Greenland surface melt. Specifically, we investigate the relationship between sea ice concentration, the timing of melt onset and open-water fraction surrounding Greenland with ice sheet surface melt using a combination of remote sensing observations, and outputs from a reanalysis model and a regional climate model for the period of 1979–2015. Statistical analysis points to covariability between Greenland ice sheet surface melt and sea ice within Baffin Bay and Davis Strait. While some of this covariance can be explained by simultaneous influence of atmospheric circulation anomalies on both the sea ice cover and Greenland melt, within Baffin Bay we find a modest correlation between detrended melt onset over sea ice and the adjacent ice sheet melt onset. This correlation appears to be related to increased transfer of sensible and latent heat fluxes from the ocean to the atmosphere in early sea ice melt years, increasing temperatures and humidity over the ice sheet that in turn initiate ice sheet melt

Climate Sensitivity Studies of the Greenland Ice Sheet Using Satellite AVHRR, SMMR, SSM/I and in Situ Data

The feasibility of using satellite data for climate research over the Greenland ice sheet is discussed. In particular, we demonstrate the usefulness of Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) and Global Area Coverage (GAC) data for narrow-band albedo retrieval. Our study supports the use of lower resolution AVHRR (GAC) data for process studies over most of the Greenland ice sheet. Based on LAC data time series analysis, we can resolve relative albedo changes on the order of 2-5%. In addition, we examine Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) passive microwave data for snow typing and other signals of climatological significance. Based on relationships between in situ measurements and horizontally polarized 19 and 37 GHz observations, wet snow regions are identified. The wet snow regions increase in aerial percentage from 9% of the total ice surface in June to a maximum of 26% in August 1990. Furthermore, the relationship between brightness temperatures and accumulation rates in the northeastern part of Greenland is described. We found a consistent increase in accumulation rate for the northeastern part of the ice sheet from 1981 to 1986

Arctic sea ice melt onset favored by an atmospheric pressure pattern reminiscent of the North American-Eurasian Arctic pattern

The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively

Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time