Sea Ice Concentration Research Articles

Seasonal evolution modes of tropical sea surface temperature anomalies and their links to Antarctic sea ice anomalies

Abstract Previous studies have explored the teleconnections between variability of Antarctic sea ice cover and tropical sea surface temperature (SST) across the Pacific, Atlantic, and Indian Ocean basins, typically focusing on each basin individually. However, there has been limited investigation into the impact of tropical SSTs—particularly from a seasonal evolution perspective—on Antarctic sea ice cover. In this study, we employ the self-organizing map (SOM) method to identify and analyze the primary modes of seasonal SST evolution in the tropical oceans from 1854 to 2022. We also project changes in the frequency of these modes through the 21st century. Moreover, we examine the seasonal variability of Antarctic sea ice concentration in relation to these tropical SST modes over the past four decades. Our results reveal that tropical SST anomalies display both uniform and shifted seasonal evolution patterns. Notably, the frequency of switched modes—namely, transitions from La Niña to El Niño (node 8) and from El Niño to La Niña (node 3)—is expected to increase in future climate. Interestingly, nearly mirrored SST seasonal evolution patterns do not lead to entirely opposite atmospheric circulation anomalies in the southern mid-high latitudes, nor do they result in completely inverse Antarctic sea ice cover anomalies.

Tropical cyclone activity over western North Pacific favors Arctic sea ice increase.

Teleconnections between the tropics and the Arctic have attracted a lot of scientific interest. However, the mechanisms by which tropical synoptic-scale systems influence the variability of Arctic sea ice remain unknown. In this study, we highlight the impacts of tropical cyclone (TC) activity over the western North Pacific (WNP) on Arctic Sea Ice Concentration (SIC) using observational evidence and climate model simulation experiments. Our findings demonstrate significant positive correlations between Accumulated Cyclone Energy (ACE) in the WNP and SIC in the Arctic-Pacific Sector (APS), particularly when considering a 30-day lag. The TC activity over the WNP induces Rossby wave train propagation towards the Arctic, leading to anomalous cyclonic circulation over the upper troposphere of the APS. The anomalous cyclone over the Arctic, on one hand, signifies the deepening of the Arctic polar vortex and diminishes adiabatic warming over the APS, subsequently inducing cooling and drying of the lower Arctic air. This process reduces downward longwave radiation, promoting an increase in September APS SIC. On the other hand, the anomalous cyclone over the Arctic hinders the export of sea ice and local melting processes throughout the Fram Strait. These findings contribute to a deeper comprehension of tropics-Arctic teleconnections.

Regulatory factors and climatic impacts of marine heatwaves over the Arctic Ocean from 1982 to 2020

AbstractArctic warming has been substantially greater than that in the rest of the world and has had an important influence on the global climate. This study first explores the temporal and spatial evolutionary characteristics of marine heatwaves (MHWs) over the Arctic Ocean in multiyear ice (MYI), first‐year ice (FYI), and open‐water (OPW) regions from 1982 to 2020. MHWs in the Arctic Ocean show obvious spatial and seasonal variations, mainly occurring over the FYI region in the JAS (July–August–September, JAS), and their occurrences have a significant increasing trend in recent decades, accompanied by an abrupt increase since 2010. Furthermore, a multivariable network‐based method is adopted to delineate the relationship between different climatic factors and MHWs in the Arctic Ocean and the climatic impacts of MHWs. The results show that the correlations between different climatic factors and MHWs in JAS in 2010–2020 are generally stronger than those in 1982–2009, and the main influencing factors of MHWs in different ice covers are different. MHWs in the MYI region are mainly affected by freshwater dilution processes, such as sea‐ice concentrations (SIC), precipitation, and mixed‐layer salinity. For the FYI region, the 2‐m air temperature and total heat flux mainly affect MHWs by thermodynamic processes, and the 500‐hPa geopotential height affects MHWs mainly by large‐scale atmospheric circulation. The MHWs in the OPW region are mainly related to the SIC, 850‐hPa geopotential height, and 10‐m v‐wind, indicating that they are correlated with atmospheric processes and wind fields. MHWs in JAS are also revealed to reduce or delay the formation of sea ice in OND (October–November–December, OND) by storing more abnormal heat, indicating that unfrozen ocean surfaces may lead to enhanced Arctic amplification in the following seasons.

Atmospheric warming during rapid sea ice loss over the Barents–Kara seas in winter

AbstractThe atmospheric temperature change associated with rapid sea ice loss events over the Barents–Kara Seas (BKS) is explored. Rapid sea ice loss events are defined as periods with a tendency for sea ice concentration averaged over BKS to be below the fifth percentile of the probability density function distribution during the winters of 1979–2020 for at least three consecutive days. Composite analysis shows that there is a significantly positive bottom‐amplified temperature anomaly ahead of the rapid sea ice loss, which is closely associated with a wave train composed of a Ural blocking and an upstream positive phase of the North Atlantic Oscillation. This structure is favorable for the transport of warm and moist air into the BKS, and therefore, the tropospheric temperature, including the surface air temperature, increases through horizontal warm‐temperature advection, specifically through warm advection of the climatological temperature by the anomalous wind. The cooling over the BKS due to the adiabatic effect and vertical mixing opposes the horizontal warm‐temperature advection above and below about 900 hPa, respectively. However, an increase in skin temperature prominently results from enhanced downward long‐wave radiation, which is also the main contributor to the rapid sea ice loss.

Interdecadal Variation in the Impact of Arctic Sea Ice on El Niño–Southern Oscillation: The Role of Atmospheric Mean Flow

Abstract This study reveals the remarkable interdecadal changes in the influence of boreal winter Arctic sea ice concentration (SIC) anomalies (ASICAs) in the Greenland–Barents Seas on the subsequent El Niño–Southern Oscillation (ENSO) development. Winter ASICA is strongly associated with the subsequent winter ENSO before the late 1980s and after the late 2000s, but their connection is weak during the 1990s and the 2000s. The interdecadal variations in the influence of ASICA on ENSO are associated with changes in the spatial structure of the ASICA-induced North Pacific atmospheric anomalies. During high-correlation periods, winter SIC increases in the Greenland–Barents Seas lead to tropospheric cooling via the suppression of upward surface heat fluxes, which further trigger an atmospheric teleconnection from the Arctic to the North Pacific. The accompanying North Pacific Oscillation–like atmospheric anomalies result in sea surface temperature (SST) warming in the subtropical North Pacific, which extends southward into the tropical Pacific via wind–evaporation–SST feedback and leads to surface westerly anomalies over the tropical western Pacific in the following summer. The tropical western Pacific westerly wind anomalies impact the subsequent ENSO development via triggering positive Bjerknes air–sea interaction. During low-correlation periods, atmospheric anomalies over the North Pacific generated by the winter ASICA are located more northward and cannot induce marked subtropical North Pacific SST anomalies and thus have a weak impact on the following ENSO development. Numerical experiments suggest that the interdecadal variation in the spatial structure of the North Pacific atmospheric anomalies induced by the winter ASICA is partly attributed to change in the atmospheric mean flow. Significance Statement Arctic sea ice is an important component of the global climate system. Studies have shown that Arctic sea ice has decreased significantly in recent decades, which is considered to be one of the most important responses of Earth’s climate system to global climate change. A number of studies have shown that Arctic sea ice anomalies have significant impacts on weather and climate over mid–high latitudes through tropospheric and stratospheric processes. Recent studies have suggested that the effects of Arctic sea ice anomalies could extend to the tropics via air–sea interactions. El Niño–Southern Oscillation (ENSO) is the strongest air–sea coupled system in the tropics and can have a significant impact on climate over the globe. ENSO is also considered to be one of the most important sources of subseasonal–seasonal climate predictability over many parts of the globe. It is therefore important to study the factors for the ENSO occurrence. A recent study showed that Arctic sea ice anomalies during boreal winter in the Greenland–Barents Seas have a significant impact on the following winter ENSO. In this study, we further reveal that the influence of winter Arctic sea ice anomalies in the Greenland–Barents Seas on the subsequent ENSO is unstable and has undergone significant interdecadal variation. We then investigate the mechanisms underlying the interdecadal variation via observational analysis and numerical simulations. The results of this study not only have implications for improving the prediction of ENSO but also could improve our understanding of the physical link between high-latitude climate systems and tropical air–sea coupling systems.

Predictability and applicability evaluation of winter temperatures in China based on Eurasian Arctic sea ice concentrations in autumn

Predictability and applicability evaluation of winter temperatures in China based on Eurasian Arctic sea ice concentrations in autumn

Modeling the contribution of leads to sea spray aerosol in the high Arctic

Abstract. Elongated open-water areas in sea ice (leads) release sea spray particles to the atmosphere. However, there is limited knowledge on the amount, properties and drivers of sea spray emitted from leads, and no existing parameterization of this process is available for use in models. In this work, we use measurements of aerosol fluxes from Nilsson et al. (2001) to produce an estimate of the location, timing and amount of sea spray emissions from leads at the scale of the Arctic Ocean for 1 year. Lead fractions are derived using sea ice data sets from numerical models and satellite detection. The proposed parameterization estimates that leads account for 0.3 %–9.8 % of the annual sea salt aerosol number emissions in the Arctic Ocean regions where sea ice concentration is greater than 80 %. Assuming similar size distributions to those from emissions from the open ocean, leads account for 30 %–85 % of mass emissions in sea ice regions. The total annual mass of sea salt emitted from leads, 0.1–2.1 Tg yr−1, is comparable to the mass of sea salt aerosol transported above sea ice from the open ocean, according to the MERRA-2 reanalysis. In addition to providing the first estimates of possible upper and lower bounds of sea spray emissions from leads, the conceptual model developed in this work is implemented and tested in the regional atmospheric chemistry model WRF-Chem. Given the estimates obtained in this work, the impact of sea spray from leads on Arctic clouds and radiative budget needs to be further explored.

Arctic climate feedback response to local sea-ice concentration and remote sea surface temperature changes in PAMIP simulations

Arctic climate feedback response to local sea-ice concentration and remote sea surface temperature changes in PAMIP simulations

Moisture Transformation in Warm Air Intrusions Into the Arctic: Process Attribution With Stable Water Isotopes

AbstractWarm Airmass Intrusions (WAIs) from the mid‐latitudes significantly impact the Arctic water budget. Here, we combine water vapor isotope measurements from the MOSAiC expedition, with a Lagrangian‐based process attribution diagnostic to track moisture transformation in the central Arctic Ocean during two WAIs, under contrasting sea‐ice concentrations (SIC). During winter with high SIC, two moisture supplies are identified. The first is Arctic moisture, locally‐sourced over the sea ice, with isotopic composition influenced by kinetic fractionation during ice‐cloud formation and vapor deposition. This moisture is rapidly overprinted by low‐latitude moisture advected poleward during WAI. In summer under low SIC, moisture is supplied through evaporation from land and ocean, with moisture removal via liquid‐cloud and dew formation. The isotopic composition reflects the influence of higher relative humidity at the evaporation sites. Given the projected increase of frequency and duration of WAIs, our study contributes to assessing process changes in the Arctic water cycle.

A Model that Explains the Contrasting SST Trends in the Southern Pacific Ocean

Sea surface temperature (SST) of the southern Pacific Ocean plays an important role in ocean-atmospheric interactions, influencing both regional and global climate and ecosystems. The contrasting SST trends in the southern Pacific Ocean during 1993-2021 are noted by analyzing satellite and in-situ datasets, consisting of SST warming trend (0.05℃·yr−1) in the region of 80°W-180,30°S-50°S and cooling trend (−0.04℃·yr−1) in the area of 60°W-150°W, 55°S-70°S. A detailed trend analysis for the wind and sea ice concentration suggest that Ekman transport and sea ice radiative positive feedback are the two key contributors to the contrasting SST trends. The intensified downwelling (upwelling), induced by the Ekman transport and strengthened westerlies, gives rise to warm (cold) water swarming in (upturning) from lower latitudes (deeper ocean), which causes SST warming (cooling). Moreover, the sea ice increase at higher latitudes, due to both the cold water transport from deeper ocean layers and broken ice transport from polar regions, strengthens the cooling trend through sea ice positive radiative feedback. In conclusion, these findings underscore the complexity of SST trends in the southern Pacific Ocean, highlighting the critical roles of Ekman transport and sea ice radiative feedback in shaping regional climate dynamics.Plain Language Summary: The southern Pacific Ocean is an indispensable part in the climate system while it still remained poorly observed. The SST trend is regarded as a critical indicator of global climate change. Using the satellite and in-situ datasets, we find that the contrasting SST trends from 1993 to 2021, one area in this region is warmed by 0.3℃ per decade, while another area is cooled by -0.1℃ per decade. Considering impacts of various factors such as wind, sea ice and radiation, the primarily explanation is that wind-driven warm (cold) water from the subtropics (deeper ocean) flows in and goes downward (goes upward and flow away) in the warm (cold) area through oceanic circulation and leads to the SST warming (cooling). Additionally, the cooling trend is also related to the cold water that flows to higher latitudes and forms ice there and at the same time, trash ice from polar regions driven by enhanced south wind speed will be transported there. As a result, ice increases, reflects more shortwave radiation to space, and surface downward shortwave radiation decreases which contributes to SST decreases.

Cold Season Cloud Response to Sea Ice Loss in the Arctic

Abstract Based on satellite observations, we investigate the cloud responses during the cold season, from October to the following March, to interannual fluctuations in Arctic sea ice concentrations during September and October. It is found that the cloud responses differ between the periods 2000-2018 and 1982-1999. From 2000-2018, increased Arctic Ocean cloud cover in October and November transitioned to significantly less cloud cover and cloud radiative forcing (CRF) from January to March, in response to lower sea ice concentrations in September and October. The lower CRF in January and March preconditions the sea ice and likely contributes to higher sea ice concentrations the following September. This suggests a negative cloud feedback on sea ice concentration, which may contribute to sea ice recovery after a dramatic decrease. In contrast, from 1982 to 1999, generally increased cloud cover and cloud radiative forcing persisted from October to March following lower sea ice concentrations in the preceding September and October. The cause of the different cloud responses to sea ice changes in these two time periods remains unclear and requires further investigation.

Population changes in a Southern Ocean krill predator point towards regional Antarctic sea ice declines

While foraging, marine predators integrate information about the environment often across wide-ranging oceanic foraging grounds and reflect these in population parameters. One such species, the southern right whale (Eubalaena australis; SRW) has shown alterations to foraging behaviour, declines in body condition, and reduced reproductive rates after 2009 in the South African population. As capital breeders, these changes suggest decreased availability of their main prey at high-latitudes, Antarctic krill (Euphausia superba). This study analysed environmental factors affecting prey availability for this population over the past 40 years, finding a notable southward contraction in sea ice, a 15–30% decline in sea ice concentration, and a more than two-fold increase in primary production metrics after 2008. These environmental conditions are less supportive of Antarctic krill recruitment in known SRW foraging grounds. Additionally, marginal ice zone, sea ice concentration and two primary production metrics were determined to be either regionally significant or marginally significant predictors of calving interval length when analysed using a linear model. Findings highlight the vulnerability of recovering baleen whale populations to climate change and show how capital breeders serve as sentinels of ecosystem changes in regions that are difficult or costly to study.

The IPWP as a capacitor for autumn sea ice loss in Northeastern Canada

The Indo-Pacific Warm Pool (IPWP) has been warming due largely to increasing greenhouse gas emissions, but its impact on Arctic sea ice remains unclear. Our study finds a significant negative correlation between the IPWP index and sea ice concentration in northeastern Canada during boreal autumn (October-December). Our results suggest that IPWP warming statistically accounts for 45% of sea ice loss observed in this region. We introduce the “Arctic capacitor effect of the IPWP”, a novel concept that expounds upon the distant connection between greenhouse gas emissions and Arctic sea ice loss. Specifically, as greenhouse gases elevate temperatures in the IPWP, increasing temperature gradient and tropical convection, a planetary wavetrain is initiated. This wavetrain, along with transit eddy feedback, traverses towards the Arctic and thereby influences the strength of the Arctic vortex and its associated effects on Arctic sea ice. Our findings highlight the crucial role of tropical oceans in the broader context of global climate change, emphasizing the necessity of accounting for their impact on polar climate.

Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model

Abstract. We evaluate the vertical turbulent-kinetic-energy (TKE) mixing scheme of the NEMO-SI3 ocean–sea-ice model in sea-ice-covered regions of the Arctic Ocean. Specifically, we assess the parameters involved in TKE mixed-layer-penetration (MLP) parameterization. This ad hoc parameterization aims to capture processes that impact the ocean surface boundary layer, such as near-inertial oscillations, ocean swells, and waves, which are often not well represented in the default TKE scheme. We evaluate this parameterization for the first time in three regions of the Arctic Ocean: the Makarov, Eurasian, and Canada basins. We demonstrate the strong effect of the scaling parameter that accounts for the presence of sea ice. Our results confirm that TKE MLP must be scaled down below sea ice to avoid unrealistically deep mixed layers. The other parameters evaluated are the percentage of energy penetrating below the mixed layer and the length scale of its decay with depth. All these parameters affect mixed-layer depth and its seasonal cycle, surface temperature, and salinity, as well as underlying stratification. Shallow mixed layers are associated with stronger stratification and fresh surface anomalies, and deeper mixed layers correspond to weaker stratification and salty surface anomalies. Notably, we observe significant impacts on sea-ice thickness across the Arctic Ocean in two scenarios: when the scaling parameter due to sea ice is absent and when the TKE mixed-layer-penetration process vanishes. In the former case, we observe an increase of several meters in mixed-layer depth, along with a reduction in sea-ice thickness ranging from 30 to 40 cm, reflecting the impact of stronger mixing. Conversely, in the latter case, we notice that a shallower mixed layer is accompanied by a moderate increase in sea-ice thickness, ranging from 10 to 20 cm, as expected from weaker mixing. Additionally, interannual variability suggests that experiments incorporating a scaling parameter based on sea-ice concentration display an increased mixed-layer depth during periods of reduced sea ice, which is consistent with observed trends. These findings underscore the influence of enhanced ocean mixing, through specific parameterizations, on the physical properties of the upper ocean and sea ice.

How Does the East Siberian Sea Ice Affect the June Drought Over Northwest China After 2000?

AbstractChanges in Arctic sea ice have exerted remarkably effects on the Eurasian climates, but it is unclear whether Arctic sea ice also contributes to Northwest China's ongoing summer drought. This study investigates the influence of the interannual variability of Arctic sea ice on the June drought in Northwest China from 1979 to 2021. It reveals that the early‐autumn sea ice in the East Siberian Sea is correlated with drought conditions in June in Northwest China, with a more pronounced connection during the period of 2000/2001–2020/2021 (P2) compared to 1979/1980–1999/2000 (P1). Mitigated drought in Northwest China is associated with anomalously high sea ice concentration (SIC) in the East Siberian Sea. Further analysis suggests that the strengthened link may be due to greater SIC variability in the East Siberian Sea during P2 than P1. In P2, positive early‐autumn SIC anomaly is linked to anomalous northeasterly winds, promoting drier soil and widespread cooling in the East European Plain. This dry soil signal may persist into the ensuing spring and early summer, inducing an anticyclonic circulation anomaly over Siberia, which could facilitate the water vapor convergence in Northwest China, thereby enhancing humidity conditions in the region. The insights from this study could offer valuable information for improved prediction of droughts in Northwest China.

Statistical seasonal prediction of Arctic sea ice concentration based on spatiotemporal anomaly persistent method

Abstract Accurate prediction of Arctic sea ice is crucial for high-latitude and even mid-latitude climate prediction. It significantly affects atmospheric circulation, the environment, ecology, and maritime transport. This study developed a statistical prediction model to predict monthly Arctic sea ice concentration (SIC) for up to one year based on the season-reliant empirical orthogonal functions (SEOFs) technique. Its prediction skill was compared with that of a dynamical prediction model. The spatiotemporal pattern of sea ice anomalies, which exhibit strong seasonality and are maintained for a significant period above the seasonal time scale by atmosphere-ocean interactions, was extracted using SEOFs. A prediction model was constructed by extrapolating from the recent anomalous state of sea ice to predict the future. Experimental retrospective predictions with monthly time resolution for 1982–2021 were performed to validate the prediction skill of Arctic SIC and areal extent. Statistically significant prediction skills were achieved over several months, even up to six months, exceeding the skill of the dynamical model.

Evaluation of the ArcIOPS sea ice forecasts during 2021–2023

The operational sea ice forecasts from the Arctic Ice Ocean Prediction System (ArcIOPS) during 2021–2023 are validated against satellite-retrieved sea ice concentration and drift data, in situ and reanalyzed sea ice thickness data. The results indicate that the ArcIOPS has a reliable capacity on the Arctic sea ice forecasts for the future 7 days. Over the validation period, the root mean square error (RMSE) of the ArcIOPS sea ice concentration forecasts at a lead time of up to 168 h ranges between 8% and 20%, and the integrated ice edge error (IIEE) is lower than 1.6 × 106 km2 with respect to the Hai Yang 2B (HY-2B) sea ice concentration data. Compared to the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS), sea ice volume evolution from the ArcIOPS forecasts is closer to that derived from the CS2SMOS sea ice thickness observations, which have been assimilated into the ArcIOPS. Sea ice thickness comparisons at three locations in the Beaufort Sea between the ArcIOPS forecasts and in situ mooring observations also prove that the sea ice thickness forecasts are credible, which sets a solid basis for supporting ice-breaker navigation in the Arctic thick ice zone. The sea ice drift deviations between the ArcIOPS forecasts and the National Snow and Ice Data Center (NSIDC) data are lower than 4 cm/s in most of the months. Future work will emphasize on developing multi-variable data assimilation scheme and fully coupled air‒ice‒ocean forecasting system for the Arctic sea ice forecasts.

Assessing the representation of Arctic sea ice and the marginal ice zone in ocean–sea ice reanalyses

Abstract. The recent development of data-assimilating reanalyses of the global ocean and sea ice enables a better understanding of the polar region dynamics and provides gridded descriptions of sea ice variables without temporal and spatial gaps. Here, we study the spatiotemporal variability of the Arctic sea ice area and thickness using the Global ocean Reanalysis Ensemble Product (GREP) produced and disseminated by the Copernicus Marine Service (CMS). GREP is compared and validated against the state-of-the-art regional reanalyses PIOMAS and TOPAZ, as well as observational datasets of sea ice concentration and thickness for the period 1993–2020. Our analysis presents pan-Arctic metrics but also emphasizes the different responses of ice classes, the marginal ice zone (MIZ), and pack ice to climate changes. This aspect is of primary importance since the MIZ accounts for an increasing percentage of the summer sea ice as a consequence of the Arctic warming and sea ice extent retreat, among other processes. Our results show that GREP provides reliable estimates of present-day and recent-past Arctic sea ice states and that the seasonal to interannual variability and linear trends in the MIZ area are properly reproduced, with the ensemble spread often being as broad as the uncertainty of the observational dataset. The analysis is complemented by an assessment of the average MIZ latitude and its northward migration in recent years, a further indicator of the Arctic sea ice decline. There is substantial agreement between GREP and reference datasets in the summer. Overall, GREP is an adequate tool for gaining an improved understanding of the Arctic sea ice, also in light of the expected warming and the Arctic transition to ice-free summers.

Physics-informed deep convolutional network for combined sea ice concentration and velocity prediction

Accurate prediction of short-term fluctuations in Arctic sea ice is important for safe use of Arctic shipping routes. While deep-learning models can improve the accuracy of sea-ice predictions, the accuracy and predictability of short-term sea-ice conditions are limited in most purely data-driven deep-learning models that consider a single aspect of sea ice, without considering the physical variation law between several sea ice factors. We propose dual-task prediction models for sea ice concentration (SIC) and sea ice velocity (SIV), and incorporate a loss function that simultaneously addresses dynamic constraints of SIC and SIV into each model, based on dynamics terms in the sea ice control equation. Comparative experiments are performed to determine which model structure best performs at predicting SIC and SIV. We report a dual-task branching structure to be more suitable for predicting SIC and SIV, and a post-decoder branch network structure to best predict SIC and SIV. Adding a sea ice dynamics equation to the loss function improves the model fit with dynamics law, improving the predictability and prediction accuracy of SIC and SIV.

Analysis of Dynamic Changes in Sea Ice Concentration in Northeast Passage during Navigation Period

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage.