Ice Variability Research Articles

Case studies on the parameterization schemes of sea ice fragmentation for ocean waves

Sea ice on the Southern Ocean has large seasonal variations. Floe size distribution has an important influence on the dynamic and thermodynamic processes of sea ice in the region with large seasonal variation and the Marginal Ice Zone. In the work, we introduced a prognostic floe size distribution (FSD) into a sea ice model and improved the calculation of lateral melt of sea ice. On this basis, we implemented two schemes of sea ice fragmentation for ocean waves and performed case studies on the effects of swell fracture on Antarctic sea ice variations. From the studies, we show it that the two schemes of sea ice fragmentation have unique characteristics in the mass transfer of sea ice among the floe size categories; if the break-up of ice floe is neglected, the effect of the improvement in lateral melt rate calculation on sea ice simulation is not significant; the simulated patterns of reduced sea ice concentration in March because of the effects of sea ice fragmentation and modification in calculation of lateral melt rate are similar since the two schemes of sea ice fragmentation both have close connections with sea ice thickness; the simulated sea ice area fraction for individual floe size categories varies with sea ice fragmentation schemes; this is due to their difference in characteristics of sea ice mass transfer among the floe size categories.

Sea ice variability and maritime activity around Svalbard in the period 2012\u20132019

Climate change is strongly impacting the Arctic environment, leading to rapid sea ice loss. In some sectors, the retreating ice edge is perceived as an opportunity to expand and develop economic activities. Previous studies show this development in the Canadian and Russian Arctic. This paper examines mobility patterns of cruise ships and fishing vessels around Svalbard, a major hotspot of maritime activity and retreating sea ice cover, in relation to sea ice variability between August 2012 and September 2019. The results show a slight overall increase in fisheries and cruise activity, as well as remarkable trends of stretching operational seasons and expanding navigational areas in these sectors. Overall increasing activity and changing mobility patterns provoke a discussion about the implications for safe navigation and sustainable management, thus raising issues of high pan-Arctic relevance.

Natural variability of the Arctic Ocean sea ice during the present interglacial

The impact of the ongoing anthropogenic warming on the Arctic Ocean sea ice is ascertained and closely monitored. However, its long-term fate remains an open question as its natural variability on centennial to millennial timescales is not well documented. Here, we use marine sedimentary records to reconstruct Arctic sea-ice fluctuations. Cores collected along the Lomonosov Ridge that extends across the Arctic Ocean from northern Greenland to the Laptev Sea were radiocarbon dated and analyzed for their micropaleontological and palynological contents, both bearing information on the past sea-ice cover. Results demonstrate that multiyear pack ice remained a robust feature of the western and central Lomonosov Ridge and that perennial sea ice remained present throughout the present interglacial, even during the climate optimum of the middle Holocene that globally peaked ∼6,500 y ago. In contradistinction, the southeastern Lomonosov Ridge area experienced seasonally sea-ice-free conditions, at least, sporadically, until about 4,000 y ago. They were marked by relatively high phytoplanktonic productivity and organic carbon fluxes at the seafloor resulting in low biogenic carbonate preservation. These results point to contrasted west-east surface ocean conditions in the Arctic Ocean, not unlike those of the Arctic dipole linked to the recent loss of Arctic sea ice. Hence, our data suggest that seasonally ice-free conditions in the southeastern Arctic Ocean with a dominant Arctic dipolar pattern, may be a recurrent feature under "warm world" climate.

Trends, abrupt shifts and interannual variability of the Arctic Wintertime Seasonal Sea Ice from 1979 to 2019

AbstractThe Arctic winter seasonal sea ice (WSSI) concentration from 1979 to 2019 is derived from passive microwave data. Based on Empirical Orthogonal Function (EOF) analysis, the WSSI time series includes regionally different trends, abrupt shifts and interannual variations. The time series of the first EOF mode (PC1) mainly represents the WSSI trend, which is characterized by an increase, particularly in the Pacific sector. PC1 confirms two abrupt shifts in WSSI in 1989 and 2007, with a variance of 31%. After 2007, the large-scale atmospheric circulation anomaly shows a strengthened wavenumber 3 structure at high latitudes associated with a mid-tropospheric low-pressure anomaly in central and western Siberia and a high-pressure anomaly in eastern Siberia in summer and autumn. These patterns have promoted the increased transport of moist static energy to the central Arctic and contributed to increased near-surface air temperatures that may enhance ice melting in summer and reduce ice growth in autumn and winter. The changes in ice melt and growth have had opposite effects in the Pacific and Atlantic sectors: WSSI has increased in the Pacific sector due to the replacement of multi-year ice by WSSI, and decreased in the Atlantic sector due to the replacement of WSSI by open water.

Spatiotemporal Variability in Modeled Bottom Ice and Sea Surface Dimethylsulfide Concentrations and Fluxes in the Arctic During 1979–2015

Field observations suggest that oceanic emissions of dimethylsulfide (DMS) may play a dominant role in the production of Arctic aerosols and clouds and therefore modulate the surface irradiance, during spring and summer. DMS is produced not only in the water column but also in various sea ice habitats. The ongoing recession of Arctic sea ice is expected to enhance DMS emissions, but the magnitude of this increase is highly uncertain. Here we investigate the spatiotemporal variability in bottom ice and sea surface DMS concentrations and fluxes using a regional sea ice‐ocean physical‐biogeochemical model. Model results indicate that the observed accelerated decline of Arctic sea ice extent since the beginning of the 21st century is associated with upward trends in May–August pan‐Arctic‐averaged sea surface DMS concentration and sea‐to‐air DMS flux. On the other hand, strong interannual variability and statistically insignificant trends are found for bottom ice DMS concentration and ice‐to‐sea DMS flux, owing to the counteracting effects of the shrinking horizontal extent and the vertical thinning of sea ice on ice algal production. The pan‐Arctic DMS climatology products based on model simulation and satellite algorithms provide dynamically based spatial details that are absent in the in situ measurement‐based climatology due to limited spatiotemporal data coverage and inevitable extrapolation bias. Lastly, model results indicate that the bottom ice DMS and its precursor dimethylsulfoniopropionate production can be the only local source of oceanic DMS emissions into the atmosphere during May prior to pelagic blooms, suggesting that it may be a key component of the biological control on Arctic climate at that time.

Examining tidal impacts on seasonal circulation and hydrography variability over the eastern Canadian shelf using a coupled circulation-ice regional model

A coupled circulation and sea ice model is used to examine the tidal impacts on the seasonal variability of circulation, hydrography and sea ice over the eastern Canadian shelf (ECS). The model performance is assessed using in-situ and satellite remote sensing observations. The tidal impacts have significant spatial variability, which are relatively small over the Labrador and Newfoundland Shelves, moderate over the Scotian Shelf (ScS), and significant over the St. Lawrence River Estuary (SLRE), northwestern Gulf of St. Lawrence (GSL), southwestern ScS, Gulf of Maine (GoM), Bay of Fundy (BoF), and the northern flank of Georges Bank (GeB). The tidal impacts on the seasonal mean circulation are greater in winter than in summer in the SLRE, while greater in summer than in winter over several other areas in the GSL, the GoM-BoF and the southwest ScS. The tidal impacts on temperature and salinity are the most significant near fronts, where both tidal mixing and frontal circulation play important roles. The tidal residual circulation, especially that due to tidal rectification in the GSL and GoM-BoF, spreads the large tidal impacts generated near fronts into broader areas. The changes in circulation and stratification also account for the reduced sea ice concentrations in the GSL.

Biomarker Distributions in (Sub)‐Arctic Surface Sediments and Their Potential for Sea Ice Reconstructions

AbstractTo evaluate the present sea ice changes in a longer‐term perspective, the knowledge of sea ice variability on preindustrial and geological time scales is essential. For the interpretation of proxy reconstructions it is necessary to understand the recent signals of different sea ice proxies from various regions. We present 260 new sediment surface samples collected in the (sub‐)Arctic Oceans that were analyzed for specific sea ice (IP25) and open‐water phytoplankton biomarkers (brassicasterol, dinosterol, and highly branched isoprenoid [HBI] III). This new biomarker data set was combined with 615 previously published biomarker surface samples into a pan‐Arctic database. The resulting pan‐Arctic biomarker and sea ice index (PIP25) database shows a spatial distribution correlating well with the diverse modern sea ice concentrations. We find correlations of PBIP25, PDIP25, and PIIIIP25 with spring and autumn sea ice concentrations. Similar correlations with modern sea ice concentrations are observed in Baffin Bay. However, the correlations of the PIP25 indices with modern sea ice concentrations differ in Fram Strait from those of the (sub‐)Arctic data set, which is likely caused by region‐specific differences in sea ice variability, nutrient availability, and other environmental conditions. The extended (sea ice) biomarker database strengthens the validity of biomarker sea ice reconstructions in different Arctic regions and shows how different sea ice proxies combined may resolve specific seasonal sea ice conditions.

Extremes become routine in an emerging new Arctic

The Arctic is rapidly warming and experiencing tremendous changes in sea ice, ocean and terrestrial regions. Lack of long-term scientific observations makes it difficult to assess whether Arctic changes statistically represent a ‘new Arctic’ climate. Here we use five Coupled Model Intercomparison Project 5 class Earth system model large ensembles to show how the Arctic is transitioning from a dominantly frozen state and to quantify the nature and timing of an emerging new Arctic climate in sea ice, air temperatures and precipitation phase (rain versus snow). Our results suggest that Arctic climate has already emerged in sea ice. Air temperatures will emerge under the representative concentration pathway 8.5 scenario in the early- to mid-twenty-first century, followed by precipitation-phase changes. Despite differences in mean state and forced response, these models show striking similarities in their anthropogenically forced emergence from internal variability in Arctic sea ice, surface temperatures and precipitation-phase changes. The short observational record makes it difficult to gauge how unprecedented recent Arctic warming is. A multi-model large ensemble estimates a new Arctic climate has emerged for sea-ice extent. As the Arctic shifts from a primarily frozen state, temperature and precipitation follow within decades.

Space–time variations of sea ice in Bohai Sea in the winter of 2009–2010 simulated with a coupled ocean and ice model

Sea ice variations in the severe winter of 2009–2010 in the Bohai Sea are simulated with a coupled ocean and ice model. The modelled ice coverage shows good agreement with satellite observations. Integrated over three sub-regions [the extended Liaodong Bay (LDB), Bohai Bay, and Laizhou Bay], the temporal variations of ice volume and surface air temperature are well correlated at time scales of 5–15 days and longer than 15 days. In LDB, at time scales of 5–15 days, the maximum ice volume lags the minimum surface air temperature by 42 h on average. In LDB, a significant amount of ice formed in the northern part is advected southward and then melts, and the trajectories of the positions of “ice centroids” further demonstrate the distinct trend of southward ice drift in this area. The impacts of winds are obvious during strong wind events. The results of multiple linear regression suggest that the hourly ice drift variations are mainly due to surface currents with significant tidal signals, and the daily ice drift variations can be mostly attributed to surface winds and secondly to surface ocean currents.

Characteristics of Atmospheric Circulation Associated with Variability of Sea Ice in the Arctic

The paper investigates the role of atmospheric circulation in the surface layer in forming the Arctic ice structure. For the analysis, the empirical orthogonal function (EOF) method of decomposition of the surface wind field is used, and the reaction of ice to changes in the principal components of leading EOF modes is investigated using statistical methods. Analyzing the rate of ice change in the Arctic associated with the Arctic ocean oscillation mode, we concluded that this mode’s variability leads to the formation of a seesaw in the ice field between two regions. From the one side, it is the region of the central deep-water part of the Arctic, including the East Siberian Sea, and from the other side, it is all other marginal seas. The second (“dipole”) mode is most associated with an increase/decrease in the ice thickness at the Arctic exit through the Fram Strait, as well as the formation of the so-called “ice factory” in the coastal region of the Beaufort Sea in the positive phase of this mode. There is also a significant relationship between the variability of third mode and the arrival of Atlantic waters with a high heat content into the Arctic through the Barents opening, which creates preconditions for ice formation in this region.

What Came First: The Helix or the H2O?

Jiang, Tian, and colleagues observe long-range interactions between two complementary DNA strands, resulting in an ordered chain of helical water. This hidden conformation of water has been observed, but the helix is commonly associated with biomolecules. Perhaps water—necessary for biological processes—is the key driver of the predominance of helices.

Spatial and Temporal Variations of Arctic Sea Ice From 2002 to 2017

AbstractThe variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. In this paper, the latest sea ice data sets from the European Space Agency Climate Change Initiative were firstly combined to comprehensively analyze the spatial and temporal variation of Arctic sea ice concentration (SIC) and sea ice thickness (SIT) from 2002 to 2017. The results show that during this period, the SIC of the Kara Sea and the Barents Sea decreases the most significantly, reaching −1.11%·year−1. The Arctic annual average sea ice extent (SIE) and sea ice area (SIA) exhibit noticeable decreasing trends, reaching −0.0592 × 106 km·year−1 and −0.0628 × 106 km·year−1, respectively. Both of Arctic SIE and SIA vary seasonally, and they reach the minimum values of 4.97 × 106 km2 and 3.96 × 106 km2 in September and the maximum values of 14.58 × 106 km2 and 13.35 × 106 km2 in March, respectively. Moreover, the Arctic SIE and SIA anomaly series from 2002 to 2017 were analyzed based on the linear regression method. It is found the most noticeable decreasing trends of the Arctic SIE and SIA are from July to October. The maximum variation of the SIE is −1.06 × 106 km2(10a)−1, and the maximum variation of the SIA is −1.09 × 106 km2(10a)−1. The Arctic SIE and SIA present the smallest decreasing trends from April to May, which are −0.19 × 106 km2(10a)−1 and −0.2 × 106 km2(10a)−1, respectively. The Arctic SIT also displays a pronounced decreasing trend with −0.015 m·year−1 in autumn and −0.018 m·year−1 in winter. The annual average SIT reaches a minimum of 1.28 m in 2013 and a maximum of 1.66 m in 2005. Particularly, the SIT of the Hudson Bay decreases the most significantly, reaching −0.051 m·year−1. Finally, the Arctic average SIT was tested by the Mann‐Kendall method. It is found that the SIT has a sudden change in 2007, from a slight increase in 2003–2007 to a significant decrease in 2007–2017. Overall, the decreasing trend of the Arctic sea ice (both SIC and SIT) is significant in the past decade. These findings can contribute to future research on polar regions and global climate change.

Role of atmospheric factors in forcing Arctic sea ice variability

The spatial structure of the Arctic sea ice concentration (SIC) variability and the connection to atmospheric as well as radiative forcing during winter and summer for the 1979–2017 period are investigated. The interannual variability with different spatial characteristics of SIC in summer and winter is extracted using the empirical orthogonal function (EOF) analysis. The present study confirms that the atmospheric circulation has a strong influence on the SIC through both dynamic and thermodynamic processes, as the heat flux anomalies in summer are radiatively forced while those in winter contain both radiative and “circulation-induced” components. Thus, atmospheric fluctuations have an explicit and extensive influence to the SIC through complex mechanisms during both seasons. Moreover, analysis of a variety of atmospheric variables indicates that the primary mechanism about specific regional SIC patterns in Arctic marginal seas are different with special characteristics.

Nonstationary relationship between sea ice overKara–Laptevseas duringAugust–Septemberand Ural blocking in the following winter

AbstractIn the current manuscript, we investigate the relationship between sea‐ice extent (August–September) over Kara–Laptev Seas (SIEKL) and the variability of Ural blocking in the following winter months (December–February, DJF). It is found that the linkage between SIEKL and Ural blocking in the following winter arises partly due to the influence of barotropic teleconnection pattern, emanated from the east coast of North America. The teleconnection can influence the mean Kara–Laptev sea ice variability during August–September. The memory of this teleconnection in the sea surface temperature (SST) anomalies over the North Atlantic Ocean further influences the Ural blocking in the following winter. The relationship between the SIEKL and Ural blocking has experienced a significant interdecadal change around mid‐1990s. The SIEKL‐related positive geopotential height anomalies over the Ural Mountains in the following winter are stronger during 1979–1996 period compare to the 1997–2017 periods. The differences in geopotential height anomalies between the two periods are related to the differences in the strength of synoptic‐scale eddy feedback. The feedback is stronger in the earlier period, which is conductive to the maintenance and amplification of positive geopotential height anomalies over the Ural Mountains, whereas the feedback is weaker in the later period. Further analysis suggests that the weakening of synoptic‐scale eddy feedback is due to weakening in the intensity of wintertime synoptic‐scale eddy activity on an interdecadal scale over the Barents Sea and northern Siberian sector.

Seasonal and interannual variability of landfast sea ice in Atka Bay, Weddell Sea, Antarctica

Abstract. Landfast sea ice (fast ice) attached to Antarctic (near-)coastal elements is a critical component of the local physical and ecological systems. Through its direct coupling with the atmosphere and ocean, fast-ice properties are also a potential indicator of processes related to a changing climate. However, in situ fast-ice observations in Antarctica are extremely sparse because of logistical challenges and harsh environmental conditions. Since 2010, a monitoring program observing the seasonal evolution of fast ice in Atka Bay has been conducted as part of the Antarctic Fast Ice Network (AFIN). The bay is located on the northeastern edge of Ekström Ice Shelf in the eastern Weddell Sea, close to the German wintering station Neumayer III. A number of sampling sites have been regularly revisited each year between annual ice formation and breakup to obtain a continuous record of sea-ice and sub-ice platelet-layer thickness, as well as snow depth and freeboard across the bay. Here, we present the time series of these measurements over the last 9 years. Combining them with observations from the nearby Neumayer III meteorological observatory as well as auxiliary satellite images enables us to relate the seasonal and interannual fast-ice cycle to the factors that influence their evolution. On average, the annual consolidated fast-ice thickness at the end of the growth season is about 2 m, with a loose platelet layer of 4 m thickness beneath and 0.70 m thick snow on top. Results highlight the predominately seasonal character of the fast-ice regime in Atka Bay without a significant interannual trend in any of the observed variables over the 9-year observation period. Also, no changes are evident when comparing with sporadic measurements in the 1980s and 1990s. It is shown that strong easterly winds in the area govern the year-round snow distribution and also trigger the breakup of fast ice in the bay during summer months. Due to the substantial snow accumulation on the fast ice, a characteristic feature is frequent negative freeboard, associated flooding of the snow–ice interface, and a likely subsequent snow ice formation. The buoyant platelet layer beneath negates the snow weight to some extent, but snow thermodynamics is identified as the main driver of the energy and mass budgets for the fast-ice cover in Atka Bay. The new knowledge of the seasonal and interannual variability of fast-ice properties from the present study helps to improve our understanding of interactions between atmosphere, fast ice, ocean, and ice shelves in one of the key regions of Antarctica and calls for intensified multidisciplinary studies in this region.

Massive Ice Control on Permafrost Coast Erosion and Sensitivity

AbstractHigh overall rates of permafrost cliff retreat, coupled with spatial variability, have been accompanied by increased uncertainty over future landscape dynamics. We map long‐term (>80 years) retreat of the shoreline and photogrammetrically analyze historic aerial imagery to quantify the processes at a permafrost coast site with massive ground ice. Retreat rates have been relatively constant, but topographic changes show that subsidence is a potentially critical but often ignored component of coastal sensitivity, exceeding landward recession by over three times during the last 24 years. We calibrate novel passive seismic surveys along clear and variable exposures of massive ground ice and then spatially map key subsurface layers. Combining decadal patterns of volumetric change with new ground ice variation maps enables past trends to be interpreted, future volumetric geomorphic behavior to be better constrained, and improves the assessment of permafrost coast sensitivity and the release of carbon‐bearing material.

Spatio-temporal change and variability of Barents-Kara sea ice, in the Arctic: Ocean and atmospheric implications

The reduction in Arctic sea-ice in recent decades has been a significant indicator of climate change and is related to weather pattern changes across the Arctic regions. In this study, for the period 1979–2018, we addressed the processes controlling the sea-ice cover in the Barents-Kara Sea (BKS). The inter-annual variability of the sea-ice extent (SIE) in BKS was analyzed using passive microwave satellite observations. The ocean-atmospheric forcing variables which including air temperature (AT), sea surface temperature (SST) and outgoing long-wave radiation (OLR) were derived from ERA-Interim reanalysis data. The spatial correlation analysis was performed for the year 2016 and 1998, where sea ice concentration (SIC) in BKS is recorded as a minimum and maximum respectively. The long-term analysis (1979–2018) shows negative trends of the Arctic SIE (−4.7 ± 0.3% decade−1) with the largest decrease in the Barents Sea (−23 ± 2.5% decade−1) and Kara Sea (−7.3 ± 0.9% decade−1). However, the sea-ice decline in the Barents Sea was recorded very high during the winter (−17.6 ± 2.2% decade−1) compared to the Kara Sea (−0.8 ± 0.2% decade−1). Sea-ice cover in the Barents Sea is more likely to recede during the summers −4.1 ± 0.7 x 103 km2yr−1 due to warm inflow of Atlantic waters. Correlation analysis using statistically significant trend values with p-values ≤ 0.01 was performed from 1982 to 2018, the SIC of BKS showing significant negative correlation analyses with SST (−0.75; p-value = 0.01), SAT (−0.84; p-value = 0.00) and OLR (−0.76; p-value = 0.00). In recent years, Atlantic Multi-decadal Oscillation (AMO) index has become positive due to an increase in SST anomalies, while, AMO does not reflect the cooling events in the BKS. During recent Arctic climate change, caused by atmospheric heat transport, the loss of sea-ice at BKS is becoming a major factor. In this study, new perspectives of the complex processes associated with Arctic warming and the declining sea-ice in the BKS region are demonstrated.

Achievements in ArCS theme 5: Study on Arctic climate predictability

Scientific achievements of ArCS Theme 5 (AT5), whose goal is to assess the predictability of Arctic-related climate variations, are reviewed. AT5 clarified some important roles played by the responses of the Arctic stratosphere and land surface to the Arctic sea ice variations in the predictability of the Arctic climate and its remote influence to lower latitudes. AT5 demonstrated that some climatic phenomena occurring in the tropics and even in the southern hemisphere remotely influence the Arctic climate, wherein the influence from the tropics is found to lead to higher predictability of the Arctic climate while that from the southern hemisphere is a limiting factor for the predictability. AT5 also investigated how long the Arctic sea ice is predictable using a state-of-the-art climate model.

Clouds damp the radiative impacts of polar sea ice loss

Abstract. Clouds play an important role in the climate system: (1) cooling Earth by reflecting incoming sunlight to space and (2) warming Earth by reducing thermal energy loss to space. Cloud radiative effects are especially important in polar regions and have the potential to significantly alter the impact of sea ice decline on the surface radiation budget. Using CERES (Clouds and the Earth's Radiant Energy System) data and 32 CMIP5 (Coupled Model Intercomparison Project) climate models, we quantify the influence of polar clouds on the radiative impact of polar sea ice variability. Our results show that the cloud short-wave cooling effect strongly influences the impact of sea ice variability on the surface radiation budget and does so in a counter-intuitive manner over the polar seas: years with less sea ice and a larger net surface radiative flux show a more negative cloud radiative effect. Our results indicate that 66±2% of this change in the net cloud radiative effect is due to the reduction in surface albedo and that the remaining 34±1 % is due to an increase in cloud cover and optical thickness. The overall cloud radiative damping effect is 56±2 % over the Antarctic and 47±3 % over the Arctic. Thus, present-day cloud properties significantly reduce the net radiative impact of sea ice loss on the Arctic and Antarctic surface radiation budgets. As a result, climate models must accurately represent present-day polar cloud properties in order to capture the surface radiation budget impact of polar sea ice loss and thus the surface albedo feedback.

Reconstruction of Sea Ice Concentration in Northern Baffin Bay Using Deuterium Excess in a Coastal Ice Core From the Northwestern Greenland Ice Sheet

AbstractVariations in the conditions of sea ice in the northern part of Baffin Bay and North Open Water polynya influence human activity in northwestern Greenland through oceanic circulation and heat balance between air and sea. To evaluate the impact of variations in sea ice conditions on the surrounding environment, it is important to understand the mechanism of sea ice variations over long periods. In this study, we estimated the age of the SIGMA‐A ice core collected northwestern Greenland Ice Sheet and researched the relationship between annual or seasonal deuterium excess (d‐excess) and seasonal sea ice concentration. We found that a temporal variation in the spring d‐excess in the ice core negatively correlated significantly with that of sea ice concentration in February–April in northern Baffin Bay from 1979–2005 (r = −0.61,p < 0.001). Using this relationship, we reconstructed the temporal variations in sea ice concentrations for 100 years from the ice core drilled in the northwestern Greenland Ice Sheet. The sea ice concentration in the early twentieth century was lower than that in the present. The decrease in sea ice concentration was consistent with analytical results for marine sediments obtained from Baffin Bay. We also suggested that the sea ice concentration was controlled by atmospheric conditions from the 1920s to 1940s based on examinations of correlations with the North Atlantic Oscillation index and air temperature in Ilulissat and by oceanographic conditions from 1945–1955, 1959–1969, and 1982–1992 based on the Atlantic Multidecadal Oscillation index and meridional heat transport to western Greenland.