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An operational synoptic-scale sea ice forecasting system for the Southern Ocean, namely 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 model state. By evaluating the SOIPS performance on forecasting sea ice metrics in a complete melt-freeze cycle from October 1, 2021 to September 30, 2022, this study shows that the SOIPS can provide reliable Antarctic sea ice forecasts. In comparison with the OSISAF data, annual mean root mean square errors of the sea ice concentration forecasts at leading time of up to 168-hour are lower than 0.19, and the integrated ice-edge errors of sea ice forecasts in most freezing months at leading times of 24-hour and 72-hour maintain around 0.5 × 106 km2 and below 1.0 × 106 km2, respectively. With respect to the scarce ICESat-2 observations, the mean absolute errors of the sea ice thickness forecasts at leading time of 24-hour are lower than 0.3 m, which is in range of the ICESat-2 uncertainties. Specifically, the SOIPS has a promised capacity in forecasting sea ice drift, both in magnitude and direction. The derived sea ice convergence rate forecasts have a high potential in supporting ship navigation on local fine scale. [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]

Atmospheric circulation associated with the Arctic dipole (AD) pattern plays a crucial role in modulating the variations of summertime sea ice concentration (SIC) within the Pacific Arctic sector (PAS). Based on reanalysis data and satellite observations, we found that the impacts of atmospheric circulation associated with a positive AD (AD+) on SIC change over different regions of the PAS [including the East Siberian Sea (ESS), Beaufort and Chukchi Seas (BCS), and Canadian Arctic Archipelago (CAA)] are dependent on the phase shifts of Pacific decadal oscillation (PDO). Satellite observations reveal that SIC anomalies, influenced by AD+ during PDO− relative to that during PDO+, varies significantly in summer by 4.9%, −7.3%, and −6.4% over ESS, BCS, and CAA, respectively. Overall, the atmospheric anomalies over CAA and BCS in terms of specific humidity, air temperature, and thereby downward longwave radiation (DLR), are enhanced (weakened) in the atmospheric conditions associated with AD+ during PDO− (PDO+). In these two regions, the larger (smaller) increases in specific humidity and air temperature, associated with AD+ during PDO− (PDO+), are connected to the increased (decreased) poleward moisture flux, strengthened (weakened) convergence of moisture and heat flux, and in part to adiabatic heating. As a consequence, the DLR and surface net energy flux anomalies over the two regions are reinforced in the atmospheric scenarios associated with AD+ during PDO− compared with that during PDO+. Therefore, smaller SIC anomalies are identified over CAA and BCS in the cases related to AD+ during PDO− than during PDO+. Essentially, the changes of the DLR anomaly in CAA and BCS are in alignment with geopotential height anomalies, which are modulated by the anticyclonic circulation pattern in association with AD+ during varying PDO phases. In contrast, the SIC changes over ESS is primarily attributed to the variations in mechanical wind forcing and sea surface temperature (SST) anomalies. The cloud fraction anomalies associated with AD+ during different PDO phases are found not to be a significant contributor to the variations of sea ice anomaly in the studied regions. Given the oscillatory nature of PDO, we speculate that the recent shift to the PDO+ phase may temporarily slow the observed significant decline trend of the summertime SIC within PAS of the Arctic. [ABSTRACT FROM AUTHOR]

The Arctic regional coupled sea-ice–ocean–atmosphere model (ArcIOAM) has been developed to provide reliable Arctic sea ice prediction on seasonal timescales. The description and implementation of ArcIOAM and its preliminary results for the year of 2012 are presented in this paper. In the ArcIOAM configuration, the Community Coupler 2 (C-Coupler2) is used to couple the Arctic sea-ice–oceanic configuration of the MITgcm (Massachusetts Institute of Technology general circulation model) with the Arctic atmospheric configuration of the Polar WRF (Weather Research and Forecasting) model. A scalability test is performed to investigate the parallelization of the coupled model. As the first step toward reliable Arctic seasonal sea ice prediction, ArcIOAM, implemented with two-way coupling strategy along with one-way coupling strategy, is evaluated with respect to available observational data and reanalysis products for the year of 2012. A stand-alone MITgcm run with prescribed atmospheric forcing is performed for reference. From the comparison, all the experiments simulate reasonable evolution of sea ice and ocean states in the Arctic region over a 1-year simulation period. The two-way coupling has better performance in terms of sea ice extent, concentration, thickness and sea surface temperature (SST), especially in summer. This result indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution. [ABSTRACT FROM AUTHOR]

In comparison with seasonal sea ice (first-year ice, FY ice), multiyear (MY) sea ice is thicker and has more opportunity to survive through the summer melting seasons. Therefore, the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer. As a response, the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover. Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice. During the recent decades, many active and passive sensors onboard a variety of satellites (QuikSCAT, ASCAT, SSMIS, ICESat, CryoSat-2, etc.) have been used to monitor the dramatic loss of Arctic MY ice. The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations. We summarize the primary satellite data sources that are used to identify MY ice. The methodology to classify MY ice and derive MY ice concentration is reviewed. The interannual variability and trends in the MY ice time series in terms of coverage, thickness, volume, and age composition are evaluated. The potential causes associated with the observed Arctic MY ice loss are outlined, which are primarily related to the export and melting mechanisms. In addition, the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion, as well as the roles of synoptic weather, are analyzed. The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed. Moreover, some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed. [ABSTRACT FROM AUTHOR]

The relative importance of the factors contributing to Arctic spring sea ice decline remains an open question. Here we suggest a new major factor. In spring the North Pacific Gyre Oscillation (NPGO) shows a significantly increasing trend during 1998–2016, in contrast to the insignificant trend of the Pacific Decadal Oscillation (PDO) index. Nearly 40% of Arctic springtime sea ice concentration trend is statistically related to the increase of the NPGO index but only 4% to the PDO trend. Through the destructive linear interference with climatological Aleutian low, the increasing NPGO index tends to weaken the upward propagation of planetary waves, which enhances the strength of the stratospheric Arctic vortex and the Arctic Oscillation (AO). The positive AO anomalies influence the surface wind field and temperature pattern anomalies, contributing to negative sea ice anomalies in the Arctic. Key Points: An increase in the NPGO index for the 1998–2016 period statistically explains nearly 40% of Arctic springtime sea ice lossThe increasing NPGO index produces an anomalous high, weakening climatological Aleutian low and the upward propagation of planetary wavesThe weakened upward propagated waves strengthen the stratospheric vortex and the Arctic Oscillation, which lead to reduced Arctic sea ice [ABSTRACT FROM AUTHOR]

In an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather forecasts from the Global Forecast System of the National Centers for Environmental Prediction drive the sea ice–ocean coupled model. The ensemble mean sea-ice forecasts were used to facilitate the Chinese National Arctic Research Expedition in summer 2017. The forecasted sea-ice concentration is evaluated against AMSR2 and Special Sensor Microwave Imager/Sounder sea-ice concentration data. The forecasted sea-ice thickness is compared to the in-situ observations and the Pan-Arctic Ice-Ocean Modeling and Assimilation System. These comparisons show the promising potential of ArcIOPS for operational Arctic sea-ice forecasts. Nevertheless, the forecast bias in the Beaufort Sea calls for a delicate parameter calibration and a better design of the assimilation system. [ABSTRACT FROM AUTHOR]

Sea ice data assimilation can greatly improve forecasts of Arctic sea ice evolution. Many previous sea ice data assimilation studies were conducted without assimilating ocean state variables, even though the sea ice evolution is closely linked to the oceanic conditions, both dynamically and thermodynamically. Based on the method of a localized ensemble error subspace transform Kalman filter, satellite‐retrieved sea ice concentration and sea ice thickness are assimilated into an Arctic sea ice‐ocean model. As a new addition, sea surface temperature (SST) data are also assimilated. The additional assimilation of SST improves not only the simulated ocean temperature in the mixed layer of the ocean substantially but also the accuracy of sea ice edge position, sea ice extent, and sea ice thickness in the marginal sea ice zone. The improvement in the simulated potential temperature in the upper 1,000 m can be attributed to the enhanced vertical convection processes in the regions where the assimilated observational SST is colder than the simulated SST without assimilation. The improvements in the sea ice edge position and sea ice thickness simulations are primarily caused by the SST data assimilation reducing biases in the simulated SST and the associated coupled ocean‐sea ice processes. Our investigation suggests that, due to the complex interaction between the sea ice and ocean, assimilating ocean data should be an indispensable component of numerical polar sea ice forecasting systems. Key Points: Sea surface temperature assimilation improves upper ocean temperature, sea ice edge, and marginal sea ice thickness simulationsSimulated upper ocean temperatures improve more where vertical convection processes are more importantSea ice edge and thickness simulations are improved due to the correction of the SST bias [ABSTRACT FROM AUTHOR]

The Pacific sector of the Arctic Ocean (PA, hereafter) is a region sensitive to climate change. Given the alarming changes in sea ice cover during recent years, knowledge of sea ice loss with respect to ice advection and melting processes has become critical. With satellite-derived products from the National Snow and Ice Center (NSIDC), a 38-year record (1979–2016) of the loss in sea ice area in summer within the Pacific-Arctic (PA) sector due to the two processes is obtained. The average sea ice outflow from the PA to the Atlantic-Arctic (AA) Ocean during the summer season (June–September) reaches 0.173×106 km 2 , which corresponds to approximately 34 % of the mean annual export (October to September). Over the investigated period, a positive trend of 0.004×106 km 2 yr -1 is also observed for the outflow field in summer. The mean estimate of sea ice retreat within the PA associated with summer melting is 1.66×106 km 2 , with a positive trend of 0.053×106 km 2 yr -1. As a result, the increasing trends of ice retreat caused by outflow and melting together contribute to a stronger decrease in sea ice coverage within the PA (0.057×106 km 2 yr -1) in summer. In percentage terms, the melting process accounts for 90.4 % of the sea ice retreat in the PA in summer, whereas the remaining 9.6 % is explained by the outflow process, on average. Moreover, our analysis suggests that the connections are relatively strong (R=0.63), moderate (R=-0.46), and weak (R=-0.24) between retreat of sea ice and the winds associated with the dipole anomaly (DA), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO), respectively. The DA participates by impacting both the advection (R=0.74) and melting (R=0.55) processes, whereas the NAO affects the melting process (R=-0.46). [ABSTRACT FROM AUTHOR]

Against the backdrop of Arctic sea ice decline, vertical mixing in the interior Arctic Ocean will most likely change, but it is still unclear how the Arctic Ocean and sea ice will respond. In this paper, a sea ice‐ocean model with a simple parameterization for interior background mixing is used to investigate the Arctic Ocean and sea ice response to a scenario of increased vertical mixing. It is found that more vertical mixing reduces sea ice thickness all year round and decreases summertime sea ice concentration. More vertical mixing leads to a cooling of the Arctic halocline layer and Atlantic Water layer below. The increased vertical mixing speeds up vertical heat and salinity exchange, brings the underlying warm and saline water into the surface layer, and contributes to the sea ice decline. Vertical salinity gradient of the Arctic halocline layer reduces together with a much fresher Atlantic Water layer, and more volume of saline water enters the deep ocean below the Atlantic Water layer. As a result, the reduced Arctic Ocean stratification leads to an adjustment of the circulation pattern. Cyclonic circulation anomalies occur in the surface layer shallower than 20‐m depth and in the interior ocean deeper than 700‐m depth, while anticyclonic circulation anomalies occur between these depths. Our study suggests that the extra heat and salinity exchange induced by more vertical mixing will have a noticeable impact on the upper ocean structure, ocean circulation, and sea ice in a changing Arctic Ocean. Plain Language Summary: In the Arctic, sea ice can isolate the ocean from wind forcing. So now, the Arctic Ocean is in a quasi‐motionless status. When global warming continues, sea ice in the Arctic Ocean will decrease, more wind energy will input into the ocean, then the Arctic Ocean will become active. More frequently, vertical heat and salt exchanges will happen. The heat in the deep ocean will be transported to the surface, then sea ice will further melt. Meanwhile, vertical distribution changes of temperature and salinity in the Arctic Ocean will induce changes of ocean currents. Key Points: Increased vertical mixing leads to a cooling of the Arctic halocline layer and Atlantic Water layerThe reduced Arctic Ocean stratification induces an adjustment of the circulation patternMore vertical mixing reduces sea ice thickness all year round and decreases summertime sea ice concentration [ABSTRACT FROM AUTHOR]

The impact of assimilating weekly CryoSat‐2 sea ice thickness data together with daily SMOS sea ice thickness and daily SSMIS sea ice concentration data on the sea ice fields of a coupled sea ice–ocean model of the Arctic Ocean is investigated. The sea‐ice model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter coded in the Parallel Data Assimilation Framework (PDAF). A period of three months from 1 November 2011 to 30 January 2012 is selected to assess the skill of the assimilation system in the cold season. Compared to the unassimilated solution and a solution where only sea ice concentration is assimilated, the model–data misfits are substantially reduced in areas of both thick and thin ice. The sea ice thickness estimates agree significantly better with in situ observations in the central Arctic Ocean than the sea ice thickness obtained from assimilating SMOS data alone, while the sea ice concentration shows very small improvements. The sea ice fields obtained by the joint assimilation of SMOS and CryoSat‐2 data also have lower errors in thickness and concentration than those obtained from directly assimilating a statistically merged SMOS and CryoSat‐2 sea ice thickness product. These lower errors suggest that model dynamics play a significant role in data blending. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice-ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.2°C at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation. A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice-ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season. [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]

The affiliations of the authors of this article unfortunately contained a mistake. [ABSTRACT FROM AUTHOR]

Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong 's first trans-Arctic Passage in summer 2017 - CORRIGENDUM. [Extracted from the article]