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Sea ice drift in the Arctic Ocean impacts ice mass balance, ocean currents, ice deformation, and freshwater output into lower latitudes. Satellite observation reveals that the Arctic sea ice drift has accelerated under global warming. Meanwhile, previous studies also found that local atmospheric intraseasonal oscillation modulates the Arctic sea ice drift. However, the mechanisms linking the Arctic sea ice drift change to the general warming and intraseasonal oscillation in the local atmosphere are not clearly addressed. Based on a sea ice‒ocean coupled model, this study finds that: (1) The atmospheric intraseasonal oscillation leads to a higher climatological sea ice drift speed despite it produces thicker ice in the Arctic marginal seas, since the elevating effect of increased wind speed yields the suppressing effect of increased ice thickness on sea ice drift speed. (2) The warming of local atmosphere results in substantial elevation of the Arctic sea ice drift speed through generating basin-scale reduction of sea ice thickness. Developing a more sophisticated sea ice dynamical equation may be an essential way to reduce the wide-existing positive bias in sea ice drift modeling. [ABSTRACT FROM AUTHOR]

An operational synoptic-scale sea ice forecasting system for the Southern Ocean, namely the Southern Ocean Ice Prediction System (SOIPS), has been developed to support ship navigation in the Antarctic sea ice zone. Practical application of the SOIPS forecasts had been implemented for the 38th Chinese National Antarctic Research Expedition for the first time. The SOIPS is configured on an Antarctic regional sea ice–ocean–ice shelf coupled model and an ensemble-based localized error subspace transform Kalman filter data assimilation model. Daily near-real-time satellite sea ice concentration observations are assimilated into the SOIPS to update sea ice concentration and thickness in the 12 ensemble members of the model state. By evaluating the SOIPS performance in forecasting sea ice metrics in a complete melt–freeze cycle from 1 October 2021 to 30 September 2022, this study shows that the SOIPS can provide reliable Antarctic sea ice forecasts. In comparison with non-assimilated EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) data, annual mean root mean square errors in the sea ice concentration forecasts at a lead time of up to 168 h are lower than 0.19, and the integrated ice edge errors in the sea ice forecasts in most freezing months at lead times of 24 and 72 h maintain around 0.5×106 km 2 and below 1.0×106 km 2 , respectively. With respect to the scarce Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, the mean absolute errors in the sea ice thickness forecasts at a lead time of 24 h are lower than 0.3 m, which is in the range of the ICESat-2 uncertainties. Specifically, the SOIPS has the ability to forecast sea ice drift, in both magnitude and direction. The derived sea ice convergence rate forecasts have great potential for supporting ship navigation on a fine local scale. The comparison between the persistence forecasts and the SOIPS forecasts with and without data assimilation further shows that both model physics and the data assimilation scheme play important roles in producing reliable sea ice forecasts in the Southern Ocean. [ABSTRACT FROM AUTHOR]

Due to global warming, the Arctic sea ice extent (SIE) is rapidly decreasing each year. According to the Intergovernmental Panel on Climate Change (IPCC) climate model projections, the summer Arctic will be nearly sea-ice-free in the 2050s of the 21st century, which will have a great impact on global climate change. As a result, accurate predictions of Arctic sea ice are of significant interest. In most current studies, the majority of deep-learning-based SIE prediction models focus on one-step prediction, and they not only have short lead times but also limited prediction skill. Moreover, these models often lack interpretability. In this study, we construct the Ice temporal fusion transformer (IceTFT) model, which mainly consists of the variable selection network (VSN), the long short-term memory (LSTM) encoder, and a multi-headed attention mechanism. We select 11 predictors for the IceTFT model, including SIE, atmospheric variables, and oceanic variables, according to the physical mechanisms affecting sea ice development. The IceTFT model can provide 12-month SIE directly, according to the inputs of the last 12 months. We evaluate the IceTFT model from the hindcasting experiments for 2019–2021 and prediction for 2022. For the hindcasting of 2019–2021, the average monthly prediction errors are less than 0.21 ×106 km 2 , and the September prediction errors are less than 0.1 ×106 km 2 , which is superior to the models from Sea Ice Outlook (SIO). For the prediction of September 2022, we submitted the prediction to the SIO in June 2022, and IceTFT still has higher prediction skill. Furthermore, the VSN in IceTFT can automatically adjust the weights of predictors and filter spuriously correlated variables. Based on this, we analyze the sensitivity of the selected predictors for the prediction of SIE. This confirms that the IceTFT model has a physical interpretability. [ABSTRACT FROM AUTHOR]

This study investigates the thermodynamical and dynamical influences of the intense cyclone in the Arctic Ocean in August 2012 on the synoptic‐scale sea ice evolution, using the Arctic Ice Ocean Prediction System (ArcIOPS). As it is hard to fully isolate sea ice loss owing to extreme cyclone from that owing to background atmospheric state in most previous studies on this topic, this study introduces a newly developed algorithm to remove the cyclone component in atmospheric forcing, and conducts sea ice and heat budget analyses in two simulations driven by atmospheric forcing with and without the cyclone. Strong impact of the intense cyclone on sea ice locates on the east side of the cyclone's path, that is, Pacific Arctic for this case. The cyclone affects sea ice in two ways. First, cyclone‐induced enhancement in ice‐ocean interaction leads to increased sea ice basal melt in the Pacific Arctic and part of the Atlantic Arctic, which induces strong sea ice area and volume loss when the cyclone's intensity peaks. Second, as the cyclone strengthens, the increases in air temperature, humidity and wind speed accelerate turbulent heat exchange at the air‐ocean and air‐ice interfaces, leading to enhanced local sea ice surface melt in the Chukchi Sea and northern Beaufort Sea. The cyclone‐induced strong winds stir sea ice leading to enhanced gradients in sea ice velocity field and thus increased sea ice deformation, which further induces strong sea ice area loss. This study also demonstrates a precise atmospheric forcing field is essential for sea ice modeling. Plain Language Summary: We use a numerical model to study the effects of a strong Arctic cyclone on sea ice. First, we use a newly developed "cyclone removal algorithm" to remove the cyclone from the atmospheric data that is used to force the model. We can then compare how the sea ice changes between the model runs with and without the cyclone. We find that the cyclone has a strong local impact on sea ice, and sea ice losses are different on the two sides of the cyclone's path. The cyclone results in warm, moist air and strong wind, which leads to strong sea ice surface melting. At the same time, the cyclone increases the transfer of heat from the ocean to the sea ice, causing strong sea ice bottom melting. The strong wind also leads to more sea ice ridging and a reduction in sea ice area. Our study indicates that the effects of cyclones on sea ice are complex and that atmospheric data must accurately represent cyclones if we want to correctly model changes in the Arctic sea ice. Key Points: Removing a cyclone from the atmospheric forcing in an Arctic model reveals its contributions to sea ice loss along its trajectoryOceanic and turbulent heat fluxes are the key factors driving enhanced ice basal and surface melt during an intense cyclone in the ArcticStrong sea ice loss locates on the east side of the trajectory of an intense cyclone [ABSTRACT FROM AUTHOR]

• Energetic sea ice drift occurs under negative phase of Arctic Oscillation with positive phase of Arctic Dipole. • Wind-ice stress dominates ice floes away from islands and continents. • Balance exists between internal ice stress and sea surface height gradient in thick multiyear ice zone. By analyzing an Arctic ice-ocean coupled simulation, we study the linkage between wintertime sea ice drift and atmospheric circulation, and interpret the driving force terms in the sea ice dynamic equation. Sea ice drift anomaly is featured by an anticyclonic (cyclonic) gyre when regulated by negative (positive) phase of Arctic Oscillation with positive (negative) phase of Arctic Dipole, and a quasi-meridional stream from Chukchi-Beaufort (Barents-Kara) Seas to Barents-Kara (Chukchi-Beaufort) Seas when regulated by positive (negative) phase of Arctic Oscillation with positive (negative) phase of Arctic Dipole. Sea ice drift anomaly, when regulated by the mode alone, resembles spatial pattern of leading atmospheric mode. Decomposing sea ice dynamical equation shows that wind-ice stress dominates sea ice drift in areas away from islands and continental coastlines, ocean-ice stress acts as a resistant power to partly cancel the wind-ice stress in these areas, while in the coastal areas such as the thick multiyear ice zone north of the Canadian Arctic Archipelago, the wind-ice and ocean-ice stresses are small, the balance exists between sea surface height potential gradient and internal ice stress divergence. Developing more sophisticated internal ice stress expression in ice model is of great important to correctly project future sea ice change for the ice modeling community. [ABSTRACT FROM AUTHOR]

The satellite observations unveiled that the July sea ice extent of the Arctic shrank to the lowest value, since 1979, in 2020 with a major ice retreat in the Eurasian shelf seas including Kara, Laptev, and East Siberian seas. Based on the ERA-5 reanalysis products, we explored the impacts of warm and moist air-mass transport on this extreme event. The results revealed that anomalously high energy and moisture converged into these regions in the spring months (April to June) of 2020, leading to a burst of high moisture content and warming within the atmospheric column. The convergence is accompanied by local enhanced downward longwave surface radiation and turbulent fluxes, which is favorable for initiating an early melt onset in the region with severe ice loss. Once the melt begins, solar radiation plays a decisive role in leading to further sea ice depletion due to ice–albedo positive feedback. The typical trajectories of the synoptic cyclones that occurred on the Eurasian side in spring 2020 agree well with the path of atmospheric flow. Assessments suggest that variations in characteristics of the spring cyclones are conducive to the severe melt of sea ice. We argue that large-scale atmospheric circulation and synoptic cyclones acted in concert to trigger the exceptional poleward transport of total energy and moisture from April to June to cause this record minimum of sea ice extent in the following July. [ABSTRACT FROM AUTHOR]

The extreme Arctic sea ice minima in the twenty-first century have been attributed to multiple factors, such as anomalous atmospheric circulation, excess solar radiation absorbed by open ocean, and thinning sea ice in a warming world. Most likely it is the combination of these factors that drives the extreme sea ice minima, but how the factors rank in setting the conditions for these events has not been quantified. To address this question, the sea ice budget of an Arctic regional sea ice–ocean model forced by atmospheric reanalysis data is analyzed to assess the development of the observed sea ice minima. Results show that the ice area difference in the years 2012, 2019, and 2007 is driven to over 60% by the difference in summertime sea ice area loss due to air–ocean heat flux over open water. Other contributions are small. For the years 2012 and 2020 the situation is different and more complex. The air–ice heat flux causes more sea ice area loss in summer 2020 than in 2012 due to warmer air temperatures, but this difference in sea ice area loss is compensated by reduced advective sea ice loss out of the Arctic Ocean mainly caused by the relaxation of the Arctic dipole. The difference in open water area in early August leads to different air–ocean heat fluxes, which distinguishes the sea ice minima in 2012 and 2020. Further, sensitivity experiments indicate that both the atmospheric circulation associated with the Arctic dipole and extreme storms are essential conditions for a new low record of sea ice extent. [ABSTRACT FROM AUTHOR]

In this study, we retrieve an Arctic sea ice lead fraction from AMSR2 passive microwave data in winter from 2012 to 2020 based on an algorithm developed for AMSR-E data. The derived AMSR2 sea ice lead fraction is validated against MODIS images. The results show that the derived AMSR2 sea ice lead detects approximately 50% of the ice leads shown in the MODIS images, which is close to the amount of sea ice lead detected from the AMSR-E data from 2002 to 2011. Utilizing the retrievals from both the AMSR-E and AMSR2, our analysis shows no significant trend, but moderate interannual variation exists for the ice lead fraction in the Arctic basin scale over the past two decades. The maximum width and total length of sea ice lead show a significant decreasing trend for the whole Arctic, but the mean width does not exhibit a significant change over the studied period. In the Beaufort Sea the lead fraction varies from 2.06% to 12.35%, with a mean value of 5.72%. In the Greenland Sea the mean lead fraction over the studied period is 5.77%, and there is a significant increase in the lead fraction, with a rate of 0.13% per year. The maximum width in the Greenland Sea is substantially higher than that of other regions, and the mean width increases significantly. [ABSTRACT FROM AUTHOR]