Total Sea Ice Area Research Articles

An Assessment of the Antarctic Sea Ice Mass Budget Simulation in CMIP6 Historical Experiment

The sea ice formation and dissipation processes are complicated and involve many factors and mechanisms, from the basal growth/melting, the frazil ice formation, the snow ice processes to the dynamic process, etc. The contribution of different factors to the sea ice extent among different models over the Antarctic region has not been systematically evaluated. In this study, we evaluate and quantify the uncertainties of different contributors to the Antarctic Sea ice mass budget among 15 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Results show that the simulated total Antarctic Sea ice mass budget is primarily adjusted by the basal growth/melting terms, the frazil ice formation term and the snow-ice term, whereas the top melting terms, the lateral melting terms, the dynamic process and the evaporation process play secondary roles. In addition, while recent studies indicated that the contributors of the Arctic Sea ice formation/dissipation processes show strong coherency among different CMIP models, our results revealed a significant model diversity over the Antarctic region, indicating that the uncertainties of the sea ice formation and dissipation are still considerable in these state-of-the-art climate models. The largest uncertainties appear in the snow ice formation, the basal melting and the top melting terms, whose spread among different models is of the same order of magnitude as the multi-model mean. In some models, large positive bias in the snow ice terms may neutralize the strong negative bias of the basal/top melting terms, resulting in a similar value of the total Antarctic Sea ice area compared with other models, yet with an inaccurate physical process. The uncertainties in these Antarctic Sea ice formation/dissipation terms highlight the importance of further improving the sea ice dynamical and parameterization processes in the state-of-the-art models.

Influence of summer great cyclones on sea ice concentration and area in the Arctic Ocean

Summer Arctic cyclones occur frequently in the Arctic Ocean and play an important role in sea ice variability. We used a reanalysis dataset and sea ice concentration data to identify and track summer great Arctic cyclones and to quantitatively analyse the contribution of cyclones to variations in sea ice concentration and area. We further explored the process of how cyclones influence sea ice via sea surface temperature, radiation, sea ice motion and ice deformation. The results indicate that cyclones accelerate decreases in sea ice concentration and area. The higher the values of the sea ice concentration index (ratio of maximum variation in sea ice concentration change rate to the minimum value of sea ice concentration change rate caused by the cyclone) and sea ice area responsivity (ratio of sea ice area change caused by cyclones to total sea ice area change) are, the greater is the contribution of cyclones to sea ice reduction. Over time, sea ice concentration decreases, and the impacts of cyclones on sea ice concentration are enhanced. During summer great cyclones, a strong low-pressure system and wind stress lead to increases in sea ice motion, ice divergence and changes in sea surface temperature and net radiation, promoting decreases in sea ice concentration and area. This study aids in the prediction of short-term sea ice change, and is beneficial to the development of coupled atmosphere-ocean-ice models.

Statistical predictability of the Arctic sea ice volume anomaly: identifying predictors and optimal sampling locations

Abstract. This work evaluates the statistical predictability of the Arctic sea ice volume (SIV) anomaly – here defined as the detrended and deseasonalized SIV – on the interannual timescale. To do so, we made use of six datasets, from three different atmosphere–ocean general circulation models, with two different horizontal grid resolutions each. Based on these datasets, we have developed a statistical empirical model which in turn was used to test the performance of different predictor variables, as well as to identify optimal locations from where the SIV anomaly could be better reconstructed and/or predicted. We tested the hypothesis that an ideal sampling strategy characterized by only a few optimal sampling locations can provide in situ data for statistically reproducing and/or predicting the SIV interannual variability. The results showed that, apart from the SIV itself, the sea ice thickness is the best predictor variable, although total sea ice area, sea ice concentration, sea surface temperature, and sea ice drift can also contribute to improving the prediction skill. The prediction skill can be enhanced further by combining several predictors into the statistical model. Applying the statistical model with predictor data from four well-placed locations is sufficient for reconstructing about 70 % of the SIV anomaly variance. As suggested by the results, the four first best locations are placed at the transition Chukchi Sea–central Arctic–Beaufort Sea (79.5∘ N, 158.0∘ W), near the North Pole (88.5∘ N, 40.0∘ E), at the transition central Arctic–Laptev Sea (81.5∘ N, 107.0∘ E), and offshore the Canadian Archipelago (82.5∘ N, 109.0∘ W), in this respective order. Adding further to six well-placed locations, which explain about 80 % of the SIV anomaly variance, the statistical predictability does not substantially improve taking into account that 10 locations explain about 84 % of that variance. An improved model horizontal resolution allows a better trained statistical model so that the reconstructed values better approach the original SIV anomaly. On the other hand, if we inspect the interannual variability, the predictors provided by numerical models with lower horizontal resolution perform better when reconstructing the original SIV variability. We believe that this study provides recommendations for the ongoing and upcoming observational initiatives, in terms of an Arctic optimal observing design, for studying and predicting not only the SIV values but also its interannual variability.

The spatio-temporal patterns of landfast ice in Antarctica during 2006–2011 and 2016–2017 using high-resolution SAR imagery

Landfast ice is an important component of the Antarctic sea ice regime. It affects the Antarctic climate and ecological system. In this study, the first high-resolution, long time series of the landfast ice edge from 2006 to 2011 and 2016 to 2017 is presented. The dataset was produced based on the improved net gradient difference algorithm using 2470 SAR scenes from ENVISAT and Sentinel-1A/B as well as manual analysis of MODIS imagery to fill in SAR data gaps. The study results show that the landfast ice area in November for all studied years was approximately 49.49 ± 3.25 × 104 km2, accounting for about 3%–4% of the total Antarctic sea ice area. The maximum area was 55.70 × 104 km2 in November 2007, compared to the minimum area 44.01 × 104 km2 in 2011. The area in West Antarctica was about 40% of that in East Antarctica. The distribution of landfast ice in Antarctica has significant regional differences. The extent in the Indian Ocean sector is the maximum with a mean value of 16.49 ± 1.1 × 104 km2; however, the ratio of the landfast ice area to the sea ice area in the Pacific Ocean sector is the highest. Twenty-four landfast ice zones with groups of small, grounded icebergs were identified, most of which were located in East Antarctica, particularly along the Wilkes Land and Oates Land. Two cases are presented to illustrate how giant, grounded icebergs affected landfast ice. Results from this study are well suited to underpin the Antarctic climate or ecological system studies.

Emergent Relationships Among Sea Ice, Longwave Radiation, and the Beaufort High Circulation Exposed Through Parameter Uncertainty Analysis

AbstractUsing a perturbed parameter ensemble of a coupled climate model, emerging relationships are identified between sea ice area, net surface longwave radiation, and the atmospheric circulation over the Beaufort gyre. There is a strong positive correlation between sea ice area and the net longwave radiation over the ocean‐ice surface during the melting season and a negative correlation during the freezing season. The mechanisms responsible for the longwave radiation balance at the surface are mainly driven by sea ice variations in the freezing season and by clouds in the melting season. A strong positive (negative) correlation is also found between the fall (summer) total sea ice area in the Arctic and the sea level pressure over the Beaufort High region. It is argued that as sea ice coverage is lost, static stability losses are severe in fall, resulting in enhanced evaporation, vertical motions, and weakening of the general large‐scale anticyclonic circulation of the Beaufort High.

An improved ARTSIST sea ice algorithm based on 19 GHz modified 91 GHz

An enhanced ARTSIST Sea Ice (ASI) algorithm is presented based on a data fusion method of calculating total sea ice concentration from high-frequency microwave data. Algorithms that use low-frequency data to calculate total sea ice concentration are less affected by atmosphere, but their spatial resolutions tend to be lower. In contrast, algorithms using high-frequency data have higher spatial resolution but are significantly influenced by atmosphere. Although errors can be eliminated using weather filters, the concentration of mixed pixels cannot be modified. Here, an enhanced ASI algorithm uses the 19 GHz polarization difference to modify the 91 GHz polarization difference, which is substituted into the ASI algorithm to calculate total sea ice concentration. Arctic total sea ice concentration results are obtained based on Special Sensor Microwave Imager Sounder (SSMIS) data on January 3, from 2008 to 2017. Total sea ice area and average concentration using the enhanced ASI algorithm are compared to traditional ASI and NASA Team results. In the Marginal Ice Zone, there is a considerable difference between the enhanced and traditional ASI algorithm results, with the former much closer to the NASA Team results. The proposed algorithm effectively modifies the concentration of the mixed pixels in the marginal zone.

Comparison of Leads Mapping in the Arctic Ocean Between Landsat and MODIS Ice Surface Temperature Products

Abstract. Leads are linear or wedge-shaped openings in the sea ice cover. They account for about half of the sensible heat transfer from the Arctic Ocean to the atmosphere in winter, though the sea surface area covered by them is only 1%~2% of the total sea ice area, thus monitoring leads changes and mapping leads distributions become an essential role on Arctic researches. Sea ice surface temperature (IST) product from Moderate Resolution Imaging Spectroradiometer (MODIS) is the most used source of leads monitoring and mapping, however, due to the coarse spatial resolution (1km at swath level), it suffers from mixed pixel effect when describes the temperature variations on thin leads (10m~100m), thus an IST product with a finer spatial resolution is needed. Though several surface temperature retrieval algorithms had been introduced based on Landsat 8 thermal imagery, none of them were validated in Arctic sea ice region. Given that the special weather conditions such as air temperature inversion were not taken into consideration, these algorithms may not always suitable for IST acquisition in Arctic. In this paper, we applied five mainstream IST algorithms (three split window algorithms and two single channel methods) on Arctic sea ice, compared the Landsat 8 IST with corresponding MODIS IST product, and validated all the satellite ISTs by in situ temperature measurements from drifting buoys. Compared to the buoy ISTs, the single channel method through web-based atmosphere correction tool provided by Barsi et al. (2003) offers the best accuracy. The split window algorithm proposed by Du et al. (2015) ranks the second, but constrained by the banding effect due to the stripe noise. Split window algorithm introduced by Jiménez-Muñoz et al. (2014) coincides with MODIS IST product best. All of the three methods mentioned above have slightly better accuracy than MODIS IST, particular in thin leads areas, which indicated that Landsat based leads map will provide us a better insight of Arctic sea ice. All the satellite ISTs tend to underestimate the surface temperature than those measured by buoys.

Variability in Antarctic sea ice from 1998 to 2017.

This study was based on the daily sea ice concentration data from the NASA Team algorithm from 1998 to 2017. The Antarctic sea ice was analyzed from the total sea ice area (SIA), first-year ice area, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20years. The results showed that the total SIA increased by 0.0087 × 106km2year-1 (+ 2.08% dec-1) between 1998 and 2017. The multiyear ice area increased by 0.0141 × 106km2year-1 from 1998 to 2017. The first-year ice area decreased by - 0.0058 × 106km2year-1 between 1998 and 2017. On a spatial scale, the entire Antarctic was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The total sea ice and multiyear ice areas showed a decreasing trend in WA. However, the total SIA and multiyear ice area all showed an increasing trend in EA. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA.

Variability of Arctic Sea Ice (1979–2016)

This study is based on the daily sea ice concentration data from the National Snow and Ice Data Center (NSIDC; Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA) from 1979 to 2016. The Arctic sea ice is analyzed from the total sea ice area, first year ice extent, multiyear ice area, and the variability of sea ice concentration in different ranges. The results show that the total sea ice area decreased by 0.0453 × 106 km2·year−1 (−0.55%/year) between 1979 and 2016, and the variability of the sea ice area from 1997 to 2016 is significantly larger than that from 1979 to 1996. The first-year ice extent increased by 0.0199 × 106 km2·year−1 (0.36%/year) from 1997 to 2016. The multiyear ice area decreased by 0.0711 × 106 km2·year−1 (−0.66%/year) from 1979 to 2016, of which in the last 20 years is about 30.8% less than in 1979–1996. In terms of concentration, we have focused on comparing 1979–1996 and 1997–2016 in different ranges. Sea ice concentration between 0.9–1 accounted for about 39.57% from 1979 to 1996, while from 1997–2016, it accounted for only 27.75%. However, the sea ice of concentration between 0.15–0.4 exhibits no significant trend changes.

Melt Pond Conditions on Declining Arctic Sea Ice Over 1979–2016: Model Development, Validation, and Results

AbstractA melt pond (MP) distribution equation has been developed and incorporated into the Marginal Ice‐Zone Modeling and Assimilation System to simulate Arctic MPs and sea ice over 1979–2016. The equation differs from previous MP models and yet benefits from previous studies for MP parameterizations as well as a range of observations for model calibration. Model results show higher magnitude of MP volume per unit ice area and area fraction in most of the Canada Basin and the East Siberian Sea and lower magnitude in the central Arctic. This is consistent with Moderate Resolution Imaging Spectroradiometer observations, evaluated with Measurements of Earth Data for Environmental Analysis (MEDEA) data, and closely related to top ice melt per unit ice area. The model simulates a decrease in the total Arctic sea ice volume and area, owing to a strong increase in bottom and lateral ice melt. The sea ice decline leads to a strong decrease in the total MP volume and area. However, the Arctic‐averaged MP volume per unit ice area and area fraction show weak, statistically insignificant downward trends, which is linked to the fact that MP water drainage per unit ice area is increasing. It is also linked to the fact that MP volume and area decrease relatively faster than ice area. This suggests that overall the actual MP conditions on ice have changed little in the past decades as the ice cover is retreating in response to Arctic warming, thus consistent with the Moderate Resolution Imaging Spectroradiometer observations that show no clear trend in MP area fraction over 2000–2011.

Insights on Sea Ice Data Assimilation from Perfect Model Observing System Simulation Experiments

AbstractSimulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Research Testbed (DART). A series of perfect model observing system simulation experiments (OSSEs) are designed to explore DA algorithms within the ensemble Kalman filter (EnKF) and the relative importance of different observation types. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove SIC errors, with the error of total Arctic sea ice area reduced by about 60% annually. When the impact of SIC observations is strongly localized in space, the error of total volume is also modestly improved. The largest simulation improvements are produced when sea ice thickness (SIT) and SIC are jointly assimilated, with the error of total volume decreased by more than 70% annually. Assimilating multiyear sea ice concentration (MYI) can reduce error in total volume by more than 50%. Assimilating MYI produces modest improvements in snow depth (errors are reduced by around 16%), while assimilating SIC and SIT has no obvious influence on snow depth. This study also suggests that different observation types may need different localization distances to optimize DA performance.

Improved Sea Ice Forecasting through Spatiotemporal Bias Correction

A new method, called contour shifting, is proposed for correcting the bias in forecasts of contours such as sea ice concentration above certain thresholds. Retrospective comparisons of observations and dynamical model forecasts are used to build a statistical spatiotemporal model of how predicted contours typically differ from observed contours. Forecasted contours from a dynamical model are then adjusted to correct for expected errors in their location. The statistical model changes over time to reflect the changing error patterns that result from reducing sea ice cover in the satellite era in both models and observations. For an evaluation period from 2001 to 2013, these bias-corrected forecasts are on average more accurate than the unadjusted dynamical model forecasts for all forecast months in the year at four different lead times. The total area, which is incorrectly categorized as containing sea ice or not, is reduced by 3.3 × 105km2(or 21.3%) on average. The root-mean-square error of forecasts of total sea ice area is also reduced for all lead times.

Sea ice assimilation into a coupled ocean–sea ice model using its adjoint

Abstract. Satellite sea ice concentrations (SICs), together with several ocean parameters, are assimilated into a regional Arctic coupled ocean–sea ice model covering the period of 2000–2008 using the adjoint method. There is substantial improvement in the representation of the SIC spatial distribution, in particular with respect to the position of the ice edge and to the concentrations in the central parts of the Arctic Ocean during summer months. Seasonal cycles of total Arctic sea ice area show an overall improvement. During summer months, values of sea ice extent (SIE) integrated over the model domain become underestimated compared to observations, but absolute differences of mean SIE to the data are reduced in nearly all months and years. Along with the SICs, the sea ice thickness fields also become closer to observations, providing added value by the assimilation. Very sparse ocean data in the Arctic, corresponding to a very small contribution to the cost function, prevent sizable improvements of assimilated ocean variables, with the exception of the sea surface temperature.

Moving beyond the Total Sea Ice Extent in Gauging Model Biases.

Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. In this study we describe several approaches to improve how model biases in total sea ice distribution are quantified, and apply them to historically forced simulations contributed to the Coupled Model Intercomparison Project phase 5 (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent is often used to evaluate model performance. We introduce a new approach which investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using several observational data sets, we apply several new methods to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. We show that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. Our results suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the fine scale structure of sea ice characteristics, however, our "sector scale" metric aids to reduce the impact of compensating errors in hemispheric integrals.

The Response of the Southern Ocean and Antarctic Sea Ice to Freshwater from Ice Shelves in an Earth System Model

ABSTRACT The possibility that recent Antarctic sea ice expansion resulted from an increase in freshwater reaching the Southern Ocean is investigated here. The freshwater flux from ice sheet and ice shelf mass imbalance is largely missing in models that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5). However, on average, precipitation minus evaporation (P − E) reaching the Southern Ocean has increased in CMIP5 models to a present value that is about greater than preindustrial times and 5–22 times larger than estimates of the mass imbalance of Antarctic ice sheets and shelves (119–544 ). Two sets of experiments were conducted from 1980 to 2013 in CESM1(CAM5), one of the CMIP5 models, artificially distributing freshwater either at the ocean surface to mimic iceberg melt or at the ice shelf fronts at depth. An anomalous reduction in vertical advection of heat into the surface mixed layer resulted in sea surface cooling at high southern latitudes and an associated increase in sea ice area. Enhancing the freshwater input by an amount within the range of estimates of the Antarctic mass imbalance did not have any significant effect on either sea ice area magnitude or trend. Freshwater enhancement of raised the total sea ice area by 1 × 106 km2, yet this and even an enhancement of was insufficient to offset the sea ice decline due to anthropogenic forcing for any period of 20 years or longer. Further, the sea ice response was found to be insensitive to the depth of freshwater injection.

Potential sea ice predictability and the role of stochastic sea ice strength perturbations

AbstractEnsemble experiments with a climate model are carried out in order to explore how incorporating a stochastic ice strength parameterization to account for model uncertainty affects estimates of potential sea ice predictability on time scales from days to seasons. The impact of this new parameterization depends strongly on the spatial scale, lead time and the hemisphere being considered: Whereas the representation of model uncertainty increases the ensemble spread of Arctic sea ice thickness predictions generated by atmospheric initial perturbations up to about 4 weeks into the forecast, rather small changes are found for longer lead times as well as integrated quantities such as total sea ice area. The regions where initial condition uncertainty generates spread in sea ice thickness on subseasonal time scales (primarily along the ice edge) differ from that of the stochastic sea ice strength parameterization (along the coast lines and in the interior of the Arctic). For the Antarctic the influence of the stochastic sea ice strength parameterization is much weaker due to the predominance of thinner first year ice. These results suggest that sea ice data assimilation and prediction on subseasonal time scales could benefit from taking model uncertainty into account, especially in the Arctic.

Processes driving sea ice variability in the Bering Sea in an eddying ocean/sea ice model: anomalies from the mean seasonal cycle

A fine-resolution (1/10°) ocean/sea ice model configured in the Community Earth System Model framework is compared with observations and studied to determine the basin-scale and local balances controlling the variability of sea ice anomalies from the mean seasonal cycle in the Bering Sea for the time period 1980–1989. The model produces variations in total Bering Sea ice area anomalies that are highly correlated with observations. Surface air temperature, which is specified from reanalysis atmospheric forcing, strongly controls the ice volume variability in this simulation. The thermodynamic ice volume change is dominated by surface energy flux via atmosphere-ice sensible heat flux, except near the southern ice edge where it is largely controlled by ocean-ice heat flux. While thermodynamic processes dominate the variations in ice volume in the Bering Sea on the large scale, dynamic processes are important on the local scale near ice margins (both oceanic and land), where dynamic and thermodynamic ice volume changes have opposite signs and nearly cancel each other. Ice motion is generally consistent with winds driving the flow, except near certain straits in the north where ice motion largely follows ocean currents. Two key climate events, strong ice growth with cold air temperature and northerly wind anomalies in February 1984 and weak ice growth with warm air temperature and southerly wind anomalies in February 1989, are studied here in detail. While the processes controlling the ice changes are generally similar to those in other years, these large events help reveal the characteristic spatial patterns of ice growth/melt and transport anomalies.

A combined approach of remote sensing and airborne electromagnetics to determine the volume of polynya sea ice in the Laptev Sea

Abstract. A combined interpretation of synthetic aperture radar (SAR) satellite images and helicopter electromagnetic (HEM) sea-ice thickness data has provided an estimate of sea-ice volume formed in Laptev Sea polynyas during the winter of 2007/08. The evolution of the surveyed sea-ice areas, which were formed between late December 2007 and middle April 2008, was tracked using a series of SAR images with a sampling interval of 2–3 days. Approximately 160 km of HEM data recorded in April 2008 provided sea-ice thicknesses along profiles that transected sea ice varying in age from 1 to 116 days. For the volume estimates, thickness information along the HEM profiles was extrapolated to zones of the same age. The error of areal mean thickness information was estimated to be between 0.2 m for younger ice and up to 1.55 m for older ice, with the primary error source being the spatially limited HEM coverage. Our results have demonstrated that the modal thicknesses and mean thicknesses of level ice correlated with the sea-ice age, but that varying dynamic and thermodynamic sea-ice growth conditions resulted in a rather heterogeneous sea-ice thickness distribution on scales of tens of kilometers. Taking all uncertainties into account, total sea-ice area and volume produced within the entire surveyed area were 52 650 km2 and 93.6 ± 26.6 km3. The surveyed polynya contributed 2.0 ± 0.5% of the sea-ice produced throughout the Arctic during the 2007/08 winter. The SAR-HEM volume estimate compares well with the 112 km3 ice production calculated with a~high-resolution ocean sea-ice model. Measured modal and mean-level ice thicknesses correlate with calculated freezing-degree-day thicknesses with a factor of 0.87–0.89, which was too low to justify the assumption of homogeneous thermodynamic growth conditions in the area, or indicates a strong dynamic thickening of level ice by rafting of even thicker ice.

Local-level responses to sea ice change and cruise tourism in Arctic Canada's Northwest Passage

This article examines the interactions between sea ice change and patterns of cruise ship tourism through the Northwest Passage of Arctic Canada and how local communities are responding to this change. During the period 2006–2010, the Passage has emerged as the most popular expedition cruise area in the Canadian Arctic with an increase in planned cruises by 70%. This dramatic increase in cruise traffic has been, in part, facilitated by improved access as a result of decreases in sea ice. Since 1968, total sea ice area in northern route of the Northwest Passage has decreased by 11% per decade and total sea ice area in the southern of the Northwest Passage has decreased by 16% per decade. Integrating research from both social and geophysical science, this article presents an analysis of changing cruise tourism patterns through the Northwest Passage and analyses resident responses from Passage communities including Ulukhaktok, Gjoa Haven, and Pond Inlet. Discussion is focused on issues associated with infrastructure, security, protection of the marine environment, human safety, and search and rescue. This research is important to help prepare communities, policy makers, as well as the cruise sector itself, to be responsive to change in these remote locations.

Variability and trends in Laptev Sea ice outflow between 1992–2011

Abstract. Variability and trends in seasonal and interannual ice area export out of the Laptev Sea between 1992 and 2011 are investigated using satellite-based sea ice drift and concentration data. We found an average total winter (October to May) ice area transport across the northern and eastern Laptev Sea boundaries (NB and EB) of 3.48 × 105 km2. The average transport across the NB (2.87 × 105 km2) is thereby higher than across the EB (0.61 × 105 km2), with a less pronounced seasonal cycle. The total Laptev Sea ice area flux significantly increased over the last decades (0.85 × 105 km2 decade−1, p > 0.95), dominated by increasing export through the EB (0.55 × 105 km2 decade−1, p > 0.90), while the increase in export across the NB is smaller (0.3 × 105 km2 decade−1) and statistically not significant. The strong coupling between across-boundary SLP gradient and ice drift velocity indicates that monthly variations in ice area flux are primarily controlled by changes in geostrophic wind velocities, although the Laptev Sea ice circulation shows no clear relationship with large-scale atmospheric indices. Also there is no evidence of increasing wind velocities that could explain the overall positive trends in ice export. The increased transport rates are rather the consequence of a changing ice cover such as thinning and/or a decrease in concentration. The use of a back-propagation method revealed that most of the ice that is incorporated into the Transpolar Drift is formed during freeze-up and originates from the central and western part of the Laptev Sea, while the exchange with the East Siberian Sea is dominated by ice coming from the central and southeastern Laptev Sea. Furthermore, our results imply that years of high ice export in late winter (February to May) have a thinning effect on the ice cover, which in turn preconditions the occurence of negative sea ice extent anomalies in summer.