Arctic Sea Ice Extent Research Articles

Indian summer monsoon’s role in shaping variability in Arctic sea ice

The impacts of Arctic sea ice loss on summertime weather in the Northern Hemisphere have garnered considerable attention. Despite the extensive focus on this relationship, the influence of tropical systems on Arctic regions has been relatively underexplored, with only a limited number of existing studies concentrating exclusively on either dynamic or thermodynamic effects. This study aims to address this gap by examining a barotropic anomalous circulation over the Arctic region associated with Indian Summer Monsoon (ISM) rainfall. The observed anomalous circulation exhibits a distinct zonally dipole pattern, characterized by anomalous high pressure over Northern Canada and Asia, coupled with anomalous low pressure located east of Greenland. Verification through model experiments demonstrates that the diabatic heating of ISM rainfall contribute to the formation of the observed ISM-related circulation. The modulation of surface clear sky downwelling longwave radiation (DLRclear sky\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{DLR}}_{{clear\\; sky}}$$\\end{document}) by the circulation anomalies over the Arctic modified surface thermal conditions, thereby influencing subsequent variations in sea ice thickness and concentration. Under anomalous high pressure, DLRclear sky\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{DLR}}_{{clear\\; sky}}$$\\end{document} increases, leading to a decline in sea ice thickness, and vice versa. Additionally, from a dynamic standpoint, low-level wind-driven sea ice drift helps shape the spatial distribution and extent of sea ice cover. Besides, the impacts of ISM on Arctic sea ice are largely independent of contemporary ENSO. These findings present fresh perspectives on the role of extrapolar phenomena, such as the ISM, in driving variability in Arctic sea ice during the summer months. This enhanced comprehension holds promise for enhancing predictions of changes in summertime Arctic sea ice extent.

Drivers of summer Arctic sea-ice extent at interannual time scale in CMIP6 large ensembles revealed by information flow

Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in the late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply the Liang-Kleeman information flow method to five different large ensembles from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the 1970-2060 period to investigate the extent to which fluctuations in winter sea-ice volume, air temperature and ocean heat transport drive changes in subsequent summer Arctic sea-ice extent. This allows us to go beyond classical correlation analyses. Results show that air temperature is the most important controlling factor of summer sea-ice extent at interannual time scale, and that winter sea-ice volume and Atlantic Ocean heat transport play a secondary role. If we replace air temperature by net shortwave and downward longwave radiations, we find that the sum of influences from both radiations is almost similar to the air temperature influence, with the longwave radiation being dominant in driving changes in summer sea-ice extent. Finally, we find that the influence of air temperature is more prominent during periods of large sea-ice reduction and that this temperature influence has overall increased since 1970.

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.

Four- to Six-Year Periodic Variation of Arctic Sea-Ice Extent and Its Three Main Driving Factors

Four- to Six-Year Periodic Variation of Arctic Sea-Ice Extent and Its Three Main Driving Factors

A regional analysis of climate change effects on global snow crab fishery

AbstractThe snow crab fishery faces increasing vulnerability to environmental factors, yet the literature on the relationship between climate change and snow crab harvest remains limited. This study estimates snow crab harvest functions using climate change indicators with unbalanced panel data of snow crab production from the eastern Bering Sea (Alaska), the southern Gulf of St. Lawrence (Canada), the Sea of Japan, and the Barents Sea (Norway‐Russia). The relationship between snow crab biomass, stock, and catch is analyzed and the endogeneity of stock in the harvest function is also addressed using climate change indicators as instrumental variables (IVs). The results show that the extent of Arctic sea ice is effective in addressing the endogeneity, and the random effects IV model with error components two stage least squares estimator performs the best to control heterogeneity. A 1% increase in snow crab fishing effort is associated with a 0.42% increase in snow crab harvest, and a 1% increase in snow crab stock causes a 0.98% increase in snow crab harvest. The reported estimates indicate a large stock‐harvest elasticity and provide supporting evidence to prioritize stock enhancement in snow crab fishery policy designs to maintain stocks at sustainable levels and minimize government expenditures on subsidies.Recommendations This study explores how snow crab harvests are influenced by snow crab populations and fishing efforts in the context of global warming across various global regions, including the Bering Sea, the Gulf of St. Lawrence, the Sea of Japan, and the Barents Sea. A 1% increase in fishing effort is associated with a 0.42% increase in harvest, while a 1% increase in snow crab population leads to a 0.98% increase in harvest, showing a high dependency on snow crab biomass. Arctic sea ice extent is identified as a crucial climate factor affecting snow crab biomass and harvests, making it a valuable variable for understanding and managing snow crab populations. The study supports the prioritization of stock enhancement policies by fishery agencies and suggests standardizing how fishing effort is measured across different regions to improve snow crab fishery management and future research.

Arctic sea-ice extent: No record minimum in 2023 or recent years

Arctic sea-ice extent reaches its minimum each year in September. On 11 September 2023 the minimum was 4.969 million square kilometers (mill.km2). This was not a record low, which occurred in 2012, when the minimum was 4.175 mill.km2, 0.794 mill.km2 less than the minimum in 2023. However, the ice extent had decreased by 0.432 mill.km2 compared with 2022. Nevertheless, the summer melting in 2023 was remarkably less than expected when considering the strong heat waves in the atmosphere and ocean, with record temperatures set around the world. In general, there is a high correlation between the long-term decrease in sea-ice extent and the increasing CO2 in the atmosphere, where the increase of CO2 in recent decades explains about 80% of the decrease in sea ice in September, while the remainder is caused by natural variability.

Spatio-temporal dynamics in microalgal communities in Arctic land-fast sea ice

Sea ice microalgae are an important source of energy for the polar marine food web, representing the primary carbon source prior to pelagic phytoplankton blooms. Here we investigate community dynamics of sea ice microalgal communities in land-fast sea ice across six different fjords in high-Arctic Svalbard, Norway, during Spring (April – May). We found that light (0.1 – 23% incoming PAR / 0.1 – 193 μmol photons m-2s-1) played a central role in determining community composition, with more diverse assemblages observed in sites with more light transmitted to the bottom ice community. In April, microalgal assemblages were similar when under-ice light transmittance was similar, independent of geographical location, however this light-derived separation of community structure was not evident in May. At all sites, assemblages were dominated by pennate diatoms, with the most abundant taxon being Nitzschia frigida. However, with increasing under-ice light transmittance, we saw an increase in the relative abundance of Dinophyceae, Navicula spp. and Thalassiosira spp.. A positive relationship between light and δ13C enrichment and C:N ratios in the ice algal biomass demonstrated the effect of light on the biochemical composition of ice algae. Light did not correlate with cell abundance or chlorophyll a concentration. With anticipated changes to Arctic sea ice extent and snow cover as a result of climate change, we will see shifts in the light transmitted to the bottom ice community. These shifts, whether caused by reduced light transmittance from increased snow cover or increased light transmittance from thinning ice, snow depth or increased rainfall, will likely alter sea ice microalgal community composition, which in turn, may influence the success of secondary production and biogeochemical cycling in polar waters.

Advancements in Multi-Year Ice Concentration Estimation from SSM/I 91.6GHz Observations

To enhance the LOMAX algorithm for sea ice concentration analysis in the polar regions, SSM/I 91.6GHz data was utilized, addressing the underuse of higher frequency data. The refinement process involved redefining PCT values for one-year and multi-year ice regions through both interpolation and least squares methods. Moreover, band operations were conducted to facilitate Arctic multi-year ice concentration retrieval. Comparative analyses with the NT algorithm indicated that the Arctic sea ice extents determined by both algorithms were similar, affirming the credibility of the modified LOMAX algorithm. When examining the results for March and September, the updated LOMAX algorithm demonstrated improved accuracy over the NT algorithm, especially under summer ice melt conditions, highlighting the enhanced performance and reliability of the refined algorithm in various seasonal contexts.

Comparison of Arctic and Antarctic sea ice spatial–temporal changes during 1979–2018

In recent years, the Arctic and Antarctic sea ice changes fluctuated significantly, and the overall trend of sea ice changes in the Arctic and Antarctic is opposite. However, studying the change characteristics and trends of the Arctic and Antarctic sea ice on more sensitive sea ice parameters and on smaller time scales still has high research value and significance. The Antarctic and Arctic sea ice spatial–temporal changes are studied and compared from the aspects of sea ice concentration, sea ice extent, first-year ice extent, multi-year ice extent, melting period and freezing period based on the daily sea ice concentration data during three periods of 1979–2018, 2009–2018, and 2014–2018, in order to more comprehensively reflect the Arctic and Antarctic sea ice changes. The results are as follows: Sea ice concentration showed a decreasing trend with the maximum negative change rate of −1.771 %/year in the Arctic and an increasing trend with the maximum positive change rate of 1.007 %/year in the Antarctic, and sea ice concentration in the Arctic and Antarctic showed a decreasing trend accompanied by the decrease of latitude. The Antarctic sea ice extent increased slowly, however, there was a more obvious decrease in the Arctic sea ice extent. The Arctic sea ice melting period increased 0.1564 days/year and the freezing period decreased 0.1646 days/year, and the Antarctic melting period increased 0.0402 days/year and the freezing period decreased −0.037 days/year. First-year ice extent in the Arctic and Antarctic showed a increasing trend, and multi-year ice extent showed a decreasing trend.

Seasonal Prediction of Regional Arctic Sea Ice Using the High‐Resolution Climate Prediction System CMA‐CPSv3

AbstractSea ice is a central part of the Arctic climate system, and its changes have a significant impact on the Earth's climate. Yet, its state, especially in summer, is not fully understood and correctly predicted in dynamical forecast systems. In this study, the seasonal prediction skill of Arctic sea ice is investigated in a high‐resolution dynamical forecast system, the China Meteorological Administration Climate Prediction System (CMA‐CPSv3). A 7‐month‐long retrospective forecast is made every other month from 2001 to 2021. Employing the anomaly correlation coefficient as the metric of the prediction skill, we show that CMA‐CPSv3 can predict regional Arctic sea ice extent and sea ice thickness up to 7 lead months. The Bering Sea exhibits the highest prediction skill among the 14 Arctic subregions. CMA‐CPSv3 outperforms the anomaly persistence forecast in the Bering Sea, Sea of Okhotsk, Laptev Sea, and East Siberian Sea. The sources of the sea ice prediction skill partly come from the good performance of upper ocean temperature in CMA‐CPSv3. This holds true not only for winter sea ice in the Arctic marginal seas but also for summer sea ice in several Arctic central seas. Furthermore, CMA‐CPSv3 exhibits a strong relationship between the variability of sea ice and surface heat fluxes. This underscores the importance of enhancing the representation of air‐sea heat exchanges in dynamical forecast systems to improve the prediction skill of sea ice.

Quantifying zoobenthic blue carbon storage across habitats within the Arctic’s Barents Sea

IntroductionThe Arctic sea ice extent in September (when it is at its lowest) has declined 13% Q10 per decade, and the Arctic Ocean is becoming a more Atlantic-influenced system. Rapid climate-forced changes are taking place in many high-latitude marine ecosystems. The Barents Sea is one such high-latitude shelf ecosystem, between approximately 70° and 80°N in the Norwegian Arctic. The purpose of the current study was to estimate zoobenthic blue carbon across multiple habitats within the Barents Sea (trough, basin, shelf, and shallows), potentially providing values to aid ecosystem-based management of these areas under future climate change scenarios.MethodWe tested this by capture and analysis of 947 high-resolution (each 405.7 × 340.6 mm, 12 MB, 5 megapixels) seabed images at 17 sites with latitudinal cline, linked to a collection of corresponding oceanographic data. Biotas within these images were identified to one of the 14 functional groups and the density was calculated. Mean stored carbon per individual was assigned by ash mass (AM) and ash-free dry mass (AFDM) of individuals caught within Agassiz trawl deployments at the same sites.ResultsTrough sites, except for one site (B16), have a low quantity of zoobenthic blue carbon compared with the shallow, shelf, and basin habitats.DiscussionThe results of a previous study focused entirely on trough habitats and are therefore difficult to scale up as the basis for a meaningful estimate of across-habitat zoobenthic blue carbon in the Barents Sea. Compared with the trough and the basin, the shelf and shallow habitats of the Barents Sea are also subjected to more trawling events through demersal fisheries and showed higher zoobenthic blue carbon stock values.

Impact of atmospheric rivers on Arctic sea ice variations

Abstract. Arctic sea ice has been declining rapidly in recent decades. We investigate how the poleward transport of moisture and heat from lower latitudes through atmospheric rivers (ARs) influences Arctic sea ice variations. We use hourly ERA5 (fifth-generation European Reanalysis) data for 1981–2020 at 0.25∘ × 0.25∘ resolution to examine the meteorological conditions and sea ice changes associated with ARs in the Arctic. In the years 2012 and 2020, which had an extremely low summer Arctic sea ice extent, we show that the individual AR events associated with large cyclones initiate a rapid sea ice decrease through turbulent heat fluxes and winds. We carry out further statistical analysis of the meteorological conditions and sea ice variations for 1981–2020 over the entire Arctic Ocean. We find that on weather timescales the atmospheric moisture content anticorrelates significantly with the sea ice concentration tendency almost everywhere in the Arctic Ocean, while the dynamic sea ice motion driven by northward winds further reduces the sea ice concentration.

Description of FIO-ESM version 2.1 and evaluation of its sea ice simulations

To improve Arctic sea ice simulations by the First Institute of Oceanography–Earth System Model (FIO–ESM), the model version has been updated from FIO–ESM v2.0 to FIO–ESM v2.1 by upgrading its sea ice component from Los Alamos Sea–Ice Model (CICE) version 4.0 (CICE4.0) to CICE6.0, and improving the ice–ocean heat exchange process from a two–equation boundary condition parameterization to a more realistic three–equation boundary condition parameterization. Numerical experiments show that the underestimation of Arctic summer sea ice extent (SIE) in FIO–ESM v2.0 is significantly improved by the model enhancements. The root mean square error of the simulated Arctic September SIE during 1979–2014 is reduced from 2.9 million to 0.7 million km2. Nevertheless, the biases of Antarctic SIE increase following the model version update. FIO–ESM v2.1 performs well for the simulations of surface air temperature, sea surface temperature, Atlantic Meridional Overturning Circulation, and Arctic SIE; however, it overestimates summer SIE in the Antarctic. Furthermore, future projections based on FIO–ESM v2.1 indicate that the first ice–free Arctic summer will occur in the 2050s and the 2040s under SSP2–4.5 and SSP5–8.5, respectively.

Impact of the Nares Strait sea ice arches on the long-term stability of the Petermann Glacier ice shelf

Abstract. One of the last remaining floating tongues of the Greenland ice sheet (GrIS), the Petermann Glacier ice shelf (PGIS), is seasonally shielded from warm Atlantic water (AW) by the formation of sea ice arches in the Nares Strait. However, continued decline of the Arctic sea ice extent and thickness suggests that arch formation is likely to become anomalous, necessitating an investigation into the response of PGIS to a year-round mobile and thin sea ice cover. We use a high-resolution unstructured grid 3-D ocean–sea ice–ice shelf setup, featuring an improved sub-ice-shelf bathymetry and a realistic PGIS geometry, to investigate in unprecedented detail the implications of transitions in the Nares Strait sea ice regime, that is, from a thick and landfast sea ice regime to a mobile, and further, a thin and mobile sea ice regime, with regard to PGIS basal melt. In all three sea ice regimes, basal melt near the grounding line (GL) presents a seasonal increase during summer, driven by a higher thermal driving. The stronger melt overturning increases the friction velocity slightly downstream, where enhanced friction-driven turbulent mixing further increases the thermal driving, substantially increasing the local melt. As the sea ice cover becomes mobile and thin, wind and (additionally in winter) convectively upwelled AW from the Nares Strait enter the PGIS cavity. While its effect on basal melting is largely limited to the shallower (<200 m) drafts during winter, in summer it extends to the GL (ca. 600 m) depth. In the absence of an increase in thermal driving, increased melting under the deeper (>200 m) drafts in winter is solely driven by the increased vertical shear of a more energetic boundary layer current. A similar behaviour is noted when transitioning from a mobile to a thin mobile sea ice cover in summer, when increases in thermal driving are negligible and increases in melt are congruent with increases in friction velocity. These results suggest that the projected continuation of the warming of the Arctic Ocean until the end of the 21st century and the accompanying decline in the Arctic sea ice extent and thickness will amplify the basal melt of PGIS, impacting the long-term stability of the Petermann Glacier and its contribution to the future GrIS mass loss and sea level rise.

Analysis of Spatiotemporal Variations and Influencing Factors of Sea Ice Extent in the Arctic and Antarctic

The 44 years (1979–2022) of satellite-derived sea ice extent in the Arctic and Antarctic reveals the details and new trends in the process of polar sea ice coverage changes. The speed of Arctic sea ice extent reduction and the interannual difference significantly increased after 2004. Trend analysis suggests that the Arctic Ocean may experience an ice-free period around 2060. The maximum anomaly of Arctic sea ice extent has gradually transitioned from September to October, indicating a trend of prolonged melting period. The center of gravity of sea ice in the Arctic Ocean is biased towards the Pacific side, and the spatial distribution pattern of sea ice is greatly influenced by the Atlantic warm current. The dynamism of the sea ice extent on the Atlantic side is significantly greater than in other regions. Since 2014, the Antarctic sea ice extent has shifted from slow growth to a rapid decreasing trend; the sea ice extent reached a historical minimum in 2022, decreasing by 2.02 × 106 km2 compared to 2014. The Antarctic experiences seven months of ice growth each year and five months of ice melting period, the annual change patterns of sea ice extent in the Arctic and Antarctic are slightly different.

Short- and Mid-Term Forecasting of Pan-Arctic Sea Ice Volume Using Variational Mode Decomposition and Bidirectional Long Short-Term Memory

The well-documented decrease in the annual minimum Arctic sea ice extent over the past few decades is an alarming indicator of current climate change. However, much less is known about the thickness of the Arctic sea ice. Developing accurate forecasting models is critical to better predict its changes and monitor the impacts of global warming on the total Arctic sea ice volume (SIV). Significant improvements in forecasting performance are possible with the advances in signal processing and deep learning. Accordingly, here, we set out to utilize the recent advances in machine learning to develop non-physics-based techniques for forecasting the sea ice volume with low computational costs. In particular, this paper aims to provide a step-wise decision process required to develop a more accurate forecasting model over short- and mid-term horizons. This work integrates variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) for multi-input multi-output pan-Arctic SIV forecasting. Different experiments are conducted to identify the impact of several aspects, including multivariate inputs, signal decomposition, and deep learning, on forecasting performance. The empirical results indicate that (i) the proposed hybrid model is consistently effective in time-series processing and forecasting, with average improvements of up to 60% compared with the case of no decomposition and over 40% compared with other deep learning models in both forecasting horizons and seasons; (ii) the optimization of the VMD level is essential for optimal performance; and (iii) the use of the proposed technique with a divide-and-conquer strategy demonstrates superior forecasting performance.

Applications of Deep Learning-Based Super-Resolution Networks for AMSR2 Arctic Sea Ice Images

Studies have indicated that the decrease in the extent of Arctic sea ice in recent years has had a significant impact on the Arctic ecosystem and global climate. In order to understand the evolution of sea ice, it is becoming increasingly imperative to have continuous observations of Arctic-wide sea ice with high spatial resolution. Passive microwave sensors have the benefit of being less susceptible to weather, wider coverage, and higher temporal resolution. However, it is challenging to retrieve accurate parameters of sea ice due to the low spatial resolution of passive microwave images. Therefore, improving the spatial resolution of passive microwave images is beneficial for reducing the uncertainty of sea ice parameters. In this paper, four competitive multi-image super-resolution (MISR) networks are selected to explore the applicability of the networks on multi-frequency Advanced Microwave Scanning Radiometer 2 (AMSR2) images of Arctic sea ice. The upsampling factor is set to 4 in the experiment. Firstly, the optimal input lengths of the image sequence for the four MISR networks are found, and then the best network on different frequency band images is further identified. Furthermore, some factors, including seasons, sea ice motion, and polarization mode of images, that may affect the super-resolution (SR) results are analyzed. The experimental results indicate that utilizing images from winter yields superior SR results. Conversely, SR results are the worst during summer across all four MISR networks, exhibiting the largest difference in PSNR of 4.48 dB. Additionally, the SR performance is observed to be better for images with smaller magnitudes of sea ice motion compared to those with larger motions, with the maximum PSNR difference of 2.04 dB. Finally, the SR results for vertically polarized images surpass those for horizontally polarized images, showcasing an average advantage of 4.02 dB in PSNR and 0.0061 in SSIM. In summary, valuable suggestions for selecting MISR models for passive microwave images of Arctic sea ice at different frequency bands are offered in this paper. Additionally, the quantification of the various impact factors on SR performance is also discussed in this paper, which provides insights into optimizing MISR algorithms for passive microwave sea ice imagery.

Disentangling Dynamic from Thermodynamic Summer Ice Area Loss from Observations (1979–2021): A Potential Mechanism for a “First-Time” Ice-Free Arctic

Abstract In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging, and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that nonlinear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012)—one with a semipermanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer—shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 whereas, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback—together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first-time” ice-free Arctic.

Arctic marginal ice zone interannual variability and change point detection using two definitions (1983–2022)

The ongoing decline in Arctic sea ice extent and thickness underscores the scientific significance of monitoring the marginal ice zone (MIZ), a transitional region between the open ocean and pack ice. In this study, we used Bootstrap sea ice concentration (SIC) to detect the trend and change point of the Arctic MIZ over 40 years (1983–2022) using two different MIZ definitions: SIC threshold-based (MIZ t ) and SIC anomaly-based (MIZ). This study marks the exploration of a SIC anomaly-based definition of the MIZ over the Arctic. While the two MIZ definitions yield comparable seasonal trends in marginal ice zone fraction (MIZF), the MIZ fraction values peak during the transition periods (e.g. freeze-up and break-up), while the MIZ t fraction values peak in August. The analysis also uncovers consistently higher MIZF values for the MIZ than for MIZ t across all seasons. Moreover, October and August show the fastest rate of increase in MIZ t fraction and MIZ fraction, reflecting the coinciding rapid decrease in sea ice extent during those particular months. Employing the pruned exact linear time, a multiple change point detection method, highlights a significant increase in the MIZ fraction in October (after 2005) and MIZ fraction in August (after 2007). This can be indicative of the recent climate change impacts in the Arctic region that may be linked with shifts in SIC and sea ice mobility for MIZ t and MIZ, respectively.

Unprecedented sea-ice minima enhances algal production deposited at the Arctic seafloor

Sea-ice in the Arctic is declining, with 2018 a particularly low year for ice extent, driven by anomalously warm atmospheric circulation in winter 2017/18. This is consistent with a multi-decadal trend to an earlier ice-free Barents Sea as climate change rapidly warms the Arctic. Here we investigate a N–S transect in the Barents Sea, crossing the Polar Front from Atlantic waters in the south to Arctic waters in the north, focusing on the organic geochemical signature (pigments and lipids) in surface sediments sampled in summer, between the years of 2017–19. Early ice-out in summer 2018 was confirmed by satellite imagery, tracking the evolution of Arctic sea-ice extent between years. Consistent with less extensive sea-ice cover in 2018 we found increases in multiple chlorophyll and carotenoid pigments as well as fatty acids (reflecting recent phytoplankton delivery) in the northern part of our transect at the seafloor. We attribute this to nutrient and organic matter release from earlier 2018 ice-out leading to stratification, post-melt phytoplankton blooms and the deposition of organic matter to the seafloor, evidenced by pigments and lipids. Organic matter delivered to the seafloor in 2018 was reactive and highly labile, confirming its deposition in the most recent season, pointing to rapid deposition. Correlations were found during ice-free periods between satellite-derived chlorophyll a and multiple indicators of water column productivity deposited at the seafloor. We also found convincing evidence of multi-year biogeochemical change across the Polar Front, where sedimentary change is marked by chlorophyll degradation products providing evidence of grazing, indicative of a tightly coupled ecosystem close to the marginal ice zone. Overall, our results show the tight coupling of Arctic productivity with the delivery and quality of organic matter to the seafloor and how this varies across the Barents Sea. More frequent early summer sea-ice loss driven by climate warming in the Barents Sea will have consequences for the delivery of organic matter to the seafloor with impacts for benthic organisms, microbiology and the sequestration of carbon.