Monthly Sea Ice Research Articles

Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977

Abstract. The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km2, respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at https://doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).

Impact of the climate regime shift around 2000 on recruitment of Antarctic krill at the Antarctic Peninsula and South Georgia

We examined how the climate regime shift around 2000 affected Antarctic krill recruitment variability at the North Antarctic Peninsula (NAP) and South Georgia (SG) during the period of 1990s through to 2000s. We used sea surface temperature (SST) as a proxy of the monthly sea ice extent and related it to the recruitment index (proportion of 1-year-old krill) of each succeeding year for NAP and SG by examining the correlation of the time series before and after 2000. For NAP, recruitment index was negatively correlated with winter SST at NAP in the 1990s but with autumn SST in the 2000s, suggesting that good recruitment was supported by extensive winter sea-ice in the 1990s but by extensive autumn sea-ice in the 2000s. This was attributed to the regime shift featured by winter warming and autumn cooling after 2000. For SG, recruitment was negatively correlated with winter SST at NAP in the 1990s but positively correlated with autumn SST in the southern Scotia Sea (SSS) in the 2000s. According to particle tracking experiment from NAP to SG in relation to the sea-ice extent and position of SACCF (Southern Antarctic Circumpolar Current Front), less sea-ice extent in 2000s meant less larvae distributing as north as SACCF, resulting in much fewer larval being transported to SG. Hence, we hypothesized that the regime shift weakened the connection between NAP and SG, but instead SSS became the main driver of recruitment at SG. We presume the krill population demonstrated adaptability to the regime shift by offsetting the unfavorable winter condition with the favorable autumn condition at NAP and for SG, replacing the source of recruits with those closer to it.

Synchronous Variation Patterns of Monthly Sea Ice Anomalies at the Arctic and Antarctic

Abstract Sea ice variability in the opposite polar regions is examined holistically by applying the self-organizing map (SOM) method to global monthly sea ice concentration data over two periods. The results show that the variability modes of sea ice decrease in the Arctic correspond to an overall sea ice increase in the Antarctic, and vice versa. In particular, the monthly sea ice anomaly patterns are dominated by in-phase variability across the Arctic that is stronger in the marginal seas particularly the Barents Sea than the central Arctic Ocean. The corresponding Antarctic sea ice variability is characterized by a zonal wavenumber-3 structure or a dipole pattern of out-of-phase variability between the Bellingshausen/Amundsen Seas and the rest of the Southern Ocean. The frequency of occurrence of these dominant patterns exhibits pronounced seasonal as well as decadal variability and the latter is closely related to the Pacific decadal oscillation and Atlantic multidecadal oscillation. Other less frequent patterns seem to be associated with the central Pacific El Niño and spatially heterogeneous interannual variability of sea surface temperature (SST) in the Indian and the Atlantic Oceans. The dominant modes explain 57% of the four-decade domain-averaged trends in the annual polar sea ice concentration, with more explained in the eastern than western Arctic Ocean and in the Weddell Sea and the Amundsen Sea in the Antarctic. The spatial patterns of the leading modes can be largely explained by the dynamic (sea ice drift) and thermodynamic (sea ice melt) effects of the anomalous atmospheric circulations associated with SST and sea level pressure anomalies. Significance Statement The purpose of this study is to extract the main modes of monthly global sea ice concentration variability in the past four decades, explain the mechanisms behind the occurrences of these modes, and examine the contributions of these modes to the trend in annual global sea ice concentration. Sea ice extent in the past four decades has shown a significant declining trend in the Arctic and a slight, but significant increasing trend in the Antarctic. By jointly analyzing the sea ice variability and trends in the two polar regions, the results here provide a reference for what might have contributed to the opposite sea ice trends in Arctic and Antarctic and highlight the important influence of large-scale sea surface temperature anomalies on the trends in the two polar regions.

Deriving Antarctic Sea‐Ice Thickness From Satellite Altimetry and Estimating Consistency for NASA's ICESat/ICESat‐2 Missions

AbstractWith the launch of the Ice, Cloud and Land Elevation Satellite‐2 (ICESat‐2), densely measuring the Antarctic ocean all year long, monthly sea ice thickness changes can be inspected. However, the consistency between ICESat‐2 and its predecessor ICESat remains unknown if a long‐term change of the Antarctic sea ice thickness is to be examined. Due to the complex physical structure of Antarctic sea ice, an improved One‐Layer Method (OLMi) for sea‐ice thickness retrieval, with an ∼0.3 m uncertainty, is proposed in this study. Using this method, the monthly sea ice variation in each sea sector of Antarctica from ICESat‐2 is inspected, revealing detailed characteristics of Antarctic sea ice over sector scale such as bi‐modal distributions. The different retrieving methods for the total freeboard from ICESat are found to be ∼0.036 m lower than that from ICESat‐2. The circum‐Antarctic sea ice thickness shows a decreasing tendency (2003–2020), especially in summer with −0.014 m/yr.

Probabilistic forecasting of the Arctic sea ice edge with contour modeling

Sea ice, or frozen ocean water, freezes and melts every year in the Arctic. Forecasts of where sea ice will be located weeks to months in advance have become more important as the amount of sea ice declines due to climate change, for maritime planning and other uses. Typical sea ice forecasts are made with ensemble models, physics-based models of sea ice and the surrounding ocean and atmosphere. This paper introduces Mixture Contour Forecasting, a method to forecast sea ice probabilistically using a mixture of two distributions, one based on postprocessed output from ensembles and the other on observed sea ice patterns in recent years. At short lead times, these forecasts are better calibrated than unadjusted dynamic ensemble forecasts and other statistical reference forecasts. To produce these forecasts, a statistical technique is introduced that directly models the sea ice edge contour, the boundary around the region that is ice-covered. Mixture Contour Forecasting and reference methods are evaluated for monthly sea ice forecasts for 2008–2016 at lead times ranging from 0.5–6.5 months using one of the European Centre for Medium-Range Weather Forecasts ensembles.

Assessment of decadal variability in sea ice in the Community Earth System Model against a long-term regional observational record: implications for the predictability of an ice-free Arctic

AbstractUncertainties in the timing of a seasonal ice cover in the Arctic Ocean depend on model physics and parameterizations, natural variability at decadal timescales and uncertainties in climate scenarios and forcings. We use the Gridded Monthly Sea-Ice Extent and Concentration, 1850 Onward product to assess the simulated decadal variability from the Community Earth System Model – Large Ensemble (CESM-LE) in the Pacific, Eurasian and Atlantic sector of the Arctic where a longer observational record exists. Results show that sea-ice decadal (8-16 years) variability in CESM-LE is in agreement with the observational record in the Pacific sector of the Arctic, underestimated in the Eurasian sector of the Arctic, specifically in the East-Siberian Sea, and slightly overestimated in the Atlantic sector of the Arctic, specifically in the Greenland Sea. Results also show an increase in variability at decadal timescales in the Eurasian and Pacific sectors during the transition to a seasonally ice-free Arctic, in agreement with the observational record although this increase is delayed by 10-20 years. If the current sea-ice retreat in the Arctic continues to be Pacific-centric, results from the CESM-LE suggest that uncertainty in the timing of an ice-free Arctic associated with natural variability is realistic, but that a seasonal ice cover may occur earlier than projected.

Changes at the edge: trends in sea ice, ocean temperature and ocean color at the Northwest Atlantic/Southern Arctic interface

AbstractSpatial and temporal trends of remotely sensed sea-ice cover, sea surface temperatures, chlorophyll-aconcentration and primary production in the Baffin Bay, Davis Strait and Labrador Sea were analyzed for the 1998–2017 period. We found spatial variability in the trends of these cryospheric, biologic and oceanographic phenomena. For example, in the northern Baffin Bay, we observed decreases in annual sea-ice persistence, yet increases along the Labrador Sea-ice edge during winter, with the latter having significant correlations with broader atmospheric patterns. In general, we observed increases in summer sea surface temperatures across the study region, except a small area of cooling along the southern Greenlandic coast. We also found significant negative trends in April chlorophyll-aand primary production followed by significant positive trends for both biological phenomena in May, owing to anomalously high values in 2014 and 2015. Notably, we found a significant positive correlation between days of monthly sea ice presence in April with May primary production quantities. Finally, we found a significant positive trend in total annual primary production over the study period. This novel finding suggests an important relationship between the timing of breakup along the sea-ice edge and peaks in biological production.

Winter Arctic Sea Ice Thickness From ICESat‐2 Freeboards

AbstractNational Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System, is now providing routine, very high‐resolution, surface elevation data across the globe, including the Arctic and Southern oceans. In this study, we demonstrate our new processing chain for converting the along‐track ICESat‐2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion, we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model. The coarse resolution (~100 km) snow data are redistributed onto the high‐resolution (approximately 30–100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA's Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from European Space Agency's CryoSat‐2 satellite mission, with ICESat‐2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19 February to 21 March) 2008 ice thickness estimates using the same input assumptions, which show an ~0.37 m or ~20% thinning across an inner Arctic Ocean domain in this 11‐year time period.

Multidecadal Arctic sea ice thickness and volume derived from ice age

Abstract. Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.

Sea ice export through the Fram Strait derived from a combined model and satellite data set

Abstract. Sea ice volume export through the Fram Strait plays an important role in the Arctic freshwater and energy redistribution. The combined model and satellite sea ice thickness (CMST) data set assimilates CryoSat-2 and soil moisture and ocean salinity (SMOS) thickness products together with satellite sea ice concentration. The CMST data set closes the gap of stand-alone satellite-derived sea ice thickness in summer and therefore allows us to estimate sea ice volume export during the melt season. In this study, we first validate the CMST data set using field observations, and then we estimate the continuous seasonal and interannual variations in Arctic sea ice volume flux through the Fram Strait from September 2010 to December 2016. The results show that seasonal and interannual sea ice volume export vary from about -240(±40) to -970(±60) km3 and -1970(±290) to -2490(±280) km3, respectively. The sea ice volume export reaches its maximum in spring and about one-third of the yearly total volume export occurs in the melt season. The minimum monthly sea ice export is −11 km3 in August 2015, and the maximum (−442 km3) appears in March 2011. The seasonal relative frequencies of sea ice thickness and drift suggest that the Fram Strait outlet in summer is dominated by sea ice that is thicker than 2 m with relatively slow seasonal mean drift of about 3 km d−1.

Variability and Predictability of the Bering Strait Ocean Heat Transport and Arctic Ocean Sea Ice Extent

Background: This study examines the monthly, seasonal, and interannual variations in Pacific Ocean heat transport entering the Arctic Ocean through the Bering Strait, and its influence on sea ice extent in the Arctic Ocean. Methods: Monthly ocean heat transport is calculated using temperature and volumetric transport data from moorings deployed in the Bering Strait. Pearson correlations are calculated between the observed detrended monthly cumulative Bering Strait ocean heat transport and the detrended monthly sea ice extent time series from May through September. Results: An increase in the spring variability of the Bering Strait ocean heat transport is found since 2010, associated with both increased volume flux and water temperatures in May and June. A significant negative correlation between the Bering Strait ocean heat transport and Arctic sea ice extent in the Pacific sector is observed for May, June, and July, both within and outside the marginal ice zone, with a sharp decline in predictability for August and September. Conclusion: The Bering Strait ocean heat transport is a skillful predictor for early melt season sea ice extent in the Pacific sector but loses predictive skills later in the summer in August and September due to changes in ice dynamics, in accordance with the loss of predictive skill in Global Climate Models.

The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows

Abstract. The Arctic sea ice cover of 2016 was highly noteworthy, as it featured record low monthly sea ice extents at the start of the year but a summer (September) extent that was higher than expected by most seasonal forecasts. Here we explore the 2016 Arctic sea ice state in terms of its monthly sea ice cover, placing this in the context of the sea ice conditions observed since 2000. We demonstrate the sensitivity of monthly Arctic sea ice extent and area estimates, in terms of their magnitude and annual rankings, to the ice concentration input data (using two widely used datasets) and to the averaging methodology used to convert concentration to extent (daily or monthly extent calculations). We use estimates of sea ice area over sea ice extent to analyse the relative “compactness” of the Arctic sea ice cover, highlighting anomalously low compactness in the summer of 2016 which contributed to the higher-than-expected September ice extent. Two cyclones that entered the Arctic Ocean during August appear to have driven this low-concentration/compactness ice cover but were not sufficient to cause more widespread melt-out and a new record-low September ice extent. We use concentration budgets to explore the regions and processes (thermodynamics/dynamics) contributing to the monthly 2016 extent/area estimates highlighting, amongst other things, rapid ice intensification across the central eastern Arctic through September. Two different products show significant early melt onset across the Arctic Ocean in 2016, including record-early melt onset in the North Atlantic sector of the Arctic. Our results also show record-late 2016 freeze-up in the central Arctic, North Atlantic and the Alaskan Arctic sector in particular, associated with strong sea surface temperature anomalies that appeared shortly after the 2016 minimum (October onwards). We explore the implications of this low summer ice compactness for seasonal forecasting, suggesting that sea ice area could be a more reliable metric to forecast in this more seasonal, “New Arctic”, sea ice regime.

Responsiveness of Polar Sea Ice Extent to Air Temperature 1979-2016

Detrended correlation analysis of mean monthly sea ice extent with air temperature at an annual time scale in both Polar Oceans shows the expected negative correlation in 14 out of 36 cases studied. The other 22 cases, including the high profile case of September sea ice extent in the Arctic, show no evidence that temperature alone explains sea ice extent. We conclude that other factors such as wind, clouds, solar irradiance, and ocean circulation may be relevant in the study of differences in mean monthly sea ice extent for the same calendar month from year to year.

The impact of averaging methods on the trend analysis of the Antarctic sea ice extent and perimeter

To estimate the long-term trends in changes for the Antarctic sea ice, daily extents of sea ice were integrated to monthly averages. Different integration methods, however, result in monthly ice extents with inconsistent boundaries that may have further impact on the estimated trend rates. We used random sets to model the sea ice extent and we compared five averaging methods to derive the monthly expectation from a set of daily ice extents and examined their differences in the trend analysis. The Antarctic sea ice extent exhibited consistent statistically significant upward trends for the period November 1978–December 2014 at a rate of approximately 24±2.3×103km2yr−1. The trends in the sea ice perimeter, however, do not agree and its estimation is more sensitive to the averaging methods. Large gaps among monthly sea ice boundaries occurred in the regions where sea ice retreated or expanded dramatically in a single month, especially in the Weddell Sea and the Indian Ocean during the month of December. The study showed that more attention should be given to these regions during periods that the daily sea ice experiences more notable dynamics. We finally commented that the median set can serve as a widely applicable method for both ice extent and perimeter and that the Vorob’ev expectation is appropriate only for the extent estimation.

전지구 해양·해빙예측시스템 NEMO-CICE/NEMOVAR의 북극 영역 해빙초기조건 특성 분석

s: In this study, the characteristics of sea ice initial conditions generated from a global ocean and sea ice prediction system, the Nucleus for European Modeling of the Ocean (NEMO) - Los Alamos Sea Ice Model (CICE)/ NEMOVAR were analyzed for the period June 2013 to May 2014 over the Arctic region. For the purpose, the observed and reanalyzed data were used to compare with the sea ice initial conditions. Results indicated that the variability of the monthly sea ice extent and thickness in model initial conditions were well represented as compared to the observation, while it was found that the extent and thickness of Arctic sea ice in initial data were narrower and thinner than those in reanalysis and observation for the period. The reason for the narrower sea ice extent in model initial conditions seems to be due to the fact that the initial sea ice concentration at the boundary area of sea ice was about 20 percent less than the reanalysis data. Also, the reason for the thinner sea-ice thickness in the Arctic region is due to the underestimation of Arctic sea ice thickness (about 60 cm) of the model initial conditions in the Arctic Ocean area adjacent to Greenland and Arctic archipelago where thick sea ice appears all the year round.

Sea ice properties in the Bohai Sea measured by MODIS-Aqua: 2. Study of sea ice seasonal and interannual variability

During the 2009–2010 winter, the Bohai Sea experienced its most severe sea ice event in four decades, which caused significant economic losses, affected marine transportation and fishery, and impacted the entire marine ecosystem in the region. Measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite from 2002 to 2010 and surface atmosphere temperature (SAT) data from the National Centers for Environmental Prediction (NCEP) are used to study and quantify the extreme sea ice event in the 2009–2010 winter and the interannual variability of the regional sea ice properties, as well as the relationship between sea ice and the climate variability in the Bohai Sea. The mean sea ice reflectance from MODIS-Aqua visible and near-infrared wavelengths are 9.33%, 13.26%, and 12.60% in the months of December 2009, January 2010, and February 2010, respectively, compared with the monthly average sea ice reflectance values (from 2002 to 2010) of 9.35%, 11.21%, and 11.41% in the same three winter months. The sea ice monthly average coverages are ~5427, ~27,414, and ~21,156km2 in these three winter months. These values are significantly higher than the averages of monthly sea ice coverage of ~2735, ~11,119, and ~10,287km2 in the Bohai Sea in December, January, and February between 2002 and 2010. Most of the sea ice coverage was located in the northern Bohai Sea. Both the intra-seasonal and interannual sea ice variability in the Bohai Sea is found to be related closely to SAT. The mechanism of anomalous SAT and intense sea ice severity are also discussed and attributed to large-scale climate changes due to the variability of the Arctic Oscillation (AO) and Siberian High (SH).

Assessment of sea ice melting rates in the Antarctic from SSM/I observations

Sea ice governs the fluxes of heat, moisture and momentum across the ocean-atmosphere interface. Because it is thin, sea ice is vulnerable to small perturbations within the ocean and the atmosphere, which considerably change the extent and thickness of the polar ice cover. Thus, sea ice is a climate change indicator. The DMSP SSM/I monthly ice concentration data over the Antarctic region have used to calculate the monthly sea ice extents (August to February) for each year during 1988-2006. Melting rates based on seasonal cycle of solar irradiance as well as the SSM/I data have been calculated. Compared to the melting rates based on seasonal cycle of solar irradiance, the SSM/I estimated melting rate, is less in the beginning of September and increases to its peak value by the end of December. The observed melting rate behaviour indicates that apart from the seasonal cycle of solar irradiance, it is controlled by other mechanisms also. The present study estimates the feedback impact factor, response time, accelerating and decelerating melting rate duration for the period 1988-2006.

Changes in Arctic clouds during intervals of rapid sea ice loss

We investigate the behavior of clouds during rapid sea ice loss events (RILEs) in the Arctic, as simulated by multiple ensemble projections of the 21st century in the Community Climate System Model (CCSM3). Trends in cloud properties and sea ice coverage during RILEs are compared with their secular trends between 2000 and 2049 during summer, autumn, and winter. The results suggest that clouds promote abrupt Arctic climate change during RILEs through increased (decreased) cloudiness in autumn (summer) relative to the changes over the first half of the 21st century. The trends in cloud characteristics (cloud amount, water content, and radiative forcing) during RILEs are most strongly and consistently an amplifying effect during autumn, the season in which RILEs account for the majority of the secular trends. The total cloud trends in every season are primarily due to low clouds, which show a more robust response than middle and high clouds across RILEs. Lead-lag correlations of monthly sea ice concentration and cloud cover during autumn reveal that the relationship between less ice and more clouds is enhanced during RILEs, but there is no evidence that either variable is leading the other. Given that Arctic cloud projections in CCSM3 are similar to those from other state-of-the-art GCMs and that observations show increased autumn cloudiness associated with the extreme 2007 and 2008 sea ice minima, this study suggests that the rapidly declining Arctic sea ice will be accentuated by changes in polar clouds.

Fram Strait sea ice volume export estimated between 2003 and 2008 from satellite data

Satellite‐based estimates of monthly sea ice volume exports through Fram Strait for the years 2003 to 2008 are presented. These are obtained from individual satellite observations of sea ice thickness, area, and drift. First, sea ice freeboard is inferred from ICESat laser altimeter observations and then converted to ice thickness estimates. Sea ice area and drift are derived from AMSR‐E 89 GHz data. Retrieved sea ice thickness estimates compare within 0.5 m with the few ULS data available in the Fram Strait. The mean, minimum, and maximum observed monthly Fram Strait sea ice volume export amounts to 217, 92, and 420 km3/month, respectively. In comparison to former Fram Strait sea ice volume export estimates obtained during the 1990s our estimates are slightly smaller (−33 km3/month) but are within the natural variability and no significant change of the total amount of Fram Strait sea ice export can be observed.

Trends in Antarctic annual sea ice retreat and advance and their relation to El Niño–Southern Oscillation and Southern Annular Mode variability

Previous studies have shown strong contrasting trends in annual sea ice duration and in monthly sea ice concentration in two regions of the Southern Ocean: decreases in the western Antarctic Peninsula/southern Bellingshausen Sea (wAP/sBS) region and increases in the western Ross Sea (wRS) region. To better understand the evolution of these regional sea ice trends, we utilize the full temporal (quasi‐daily) resolution of satellite‐derived sea ice data to track spatially the annual ice edge advance and retreat from 1979 to 2004. These newly analyzed data reveal that sea ice is retreating 31 ± 10 days earlier and advancing 54 ± 9 days later in the wAP/sBS region (i.e., total change over 1979–2004), whereas in the wRS region, sea ice is retreating 29 ± 6 days later and advancing 31 ± 6 days earlier. Changes in the wAP/sBS and wRS regions, particularly as observed during sea ice advance, occurred in association with decadal changes in the mean state of the Southern Annular Mode (SAM; negative in the 1980s and positive in the 1990s) and the high‐latitude response to El Niño–Southern Oscillation (ENSO). In general, the high‐latitude ice‐atmosphere response to ENSO was strongest when ‐SAM was coincident with El Niño and when +SAM was coincident with La Niña, particularly in the wAP/sBS region. In total, there were 7 of 11 ‐SAMs between 1980 and 1990 and the 7 of 10 +SAMs between 1991 and 2000 that were associated with consistent decadal sea ice changes in the wAP/sBS and wRS regions, respectively. Elsewhere, ENSO/SAM‐related sea ice changes were not as consistent over time (e.g., western Weddell, Amundsen, and eastern Ross Sea region), or variability in general was high (e.g., central/eastern Weddell and along East Antarctica).