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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]

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]

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]

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]