Arctic Sea Ice Thickness Research Articles

Brief communication: Arctic sea ice thickness internal variability and its changes under historical and anthropogenic forcing

Abstract. We use model simulations from the CESM1-CAM5-BGC-LE dataset to characterise the Arctic sea ice thickness internal variability both spatially and temporally. These properties, and their stationarity, are investigated in three different contexts: (1) constant pre-industrial, (2) historical and (3) projected conditions. Spatial modes of variability show highly stationary patterns regardless of the forcing and mean state. A temporal analysis reveals two peaks of significant variability, and despite a non-stationarity on short timescales, they remain more or less stable until the first half of the 21st century, where they start to change once summer ice-free events occur, after 2050.

Arctic sea ice density observation and its impact on sea ice thickness retrieval from CryoSat–2

Sea ice density is an important physics parameter for the climate models, and is a critical parameter for the retrieval of sea ice thickness (SIT) from satellite altimeters. In the context of rapid Arctic sea ice changes, the value of sea ice density may need to be adapted for SIT retrieving from satellite radar altimeters. In this study, we collected in situ sea ice density measurements from various literatures for the period between 2000 and 2015, and carried out spatio–temporal variability analyses. We then applied Ordinary Kriging interpolation technique on sea ice density distribution and calculated SIT based on a static equilibrium equation. The impact of sea ice density on retrieved SIT from CryoSat-2 altimeter was assessed. The measured sea ice density during 2000–2015 varies between 675 kg·m−3 and 954 kg·m−3, which indeed has a wider range than the values measured before 21 century. Monthly mean sea ice density in the Arctic varies between 832 kg·m−3 (August) and 916 kg·m−3 (February). The difference between CryoSat-2 derived SIT and buoy-measured SIT reduced from 24 ± 11 cm when using fixed ice densities to 7 ± 3 cm when using sea ice densities after data interpolation in March 2012–2015. It is suitable to use updated sea ice density values than to use the traditional fixed values for retrieving Arctic SIT from CryoSat–2. Analysis of Arctic in situ sea-ice density observations and application of them in SIT retrievals could benefit further research on Arctic sea ice and climate change.

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.

Using a skillful statistical model to predict September sea ice covering Arctic shipping routes

The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the mid-latitudes but also brings more opportunities for Arctic shipping routes and polar resource exploration, both of which motivate us to further understand causes of sea-ice variations and to obtain more accurate estimates of sea-ice cover in the future. Here, a novel data-driven method, the causal effect networks algorithm, is applied to identify the direct precursors of September sea-ice extent covering the Northern Sea Route and Transpolar Sea Route at different lead times so that statistical models can be constructed for sea-ice prediction. The whole study area was also divided into two parts: the northern region covered by multiyear ice and the southern region covered by seasonal ice. The forecast models of September sea-ice extent in the whole study area (TSIE) and southern region (SSIE) at lead times of 1–4 months can explain over 65% and 79% of the variances, respectively, but the forecast skill of sea-ice extent in the northern region (NSIE) is limited at a lead time of 1 month. At lead times of 1–4 months, local sea-ice concentration and sea-ice thickness have a larger influence on September TSIE and SSIE than other teleconnection factors. When the lead time is more than 4 months, the surface meridional wind anomaly from northern Europe in the preceding autumn or early winter is dominant for September TSIE variations but is comparable to thermodynamic factors for NSIE and SSIE. We suggest that this study provides a complementary approach for predicting regional sea ice and is helpful in evaluating and improving climate models.

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.

A voyage planning tool for ships sailing between Europe and Asia via the Arctic

ABSTRACT The Arctic is rapidly transforming into a navigable ocean because of global warming. Consequently, a large percentage of the sailing distance between Europe and Asia could be saved by alternatively sailing through the Arctic. However, taking Arctic routes is fraught with risks and additional costs due to sea ice. The major purpose of this study is to improve safety and fuel efficiency of Arctic ships, which is achieved by voyage optimization upon frequently updated meteorological, oceanographic and ice forecasting. The resistance model accounts for both ice thickness and ice concentration of unconsolidated Arctic sea ice in summer. Ice-induced risks defined in the Polar Operational Limit Assessment Risk Indexing System (POLARIS) are dealt as constraint. Other constraints such as avoidance of land and shallow water are also included. These functions are demonstrated by the two case study vessels sailing between Rotterdam and Shanghai via both the Arctic and the traditional routes.

Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination

AbstractWe present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.

Snow Property Controls on Modeled Ku-Band Altimeter Estimates of First-Year Sea Ice Thickness: Case Studies From the Canadian and Norwegian Arctic

Uncertainty in snow properties impacts the accuracy of Arctic sea ice thickness estimates from radar altimetry. On first-year sea ice (FYI), spatiotemporal variations in snow properties can cause the Ku-band main radar scattering horizon to appear above the snow/sea ice interface. This can increase the estimated sea ice freeboard by several centimeters, leading to FYI thickness overestimations. This article examines the expected changes in Ku-band main scattering horizon and its impact on FYI thickness estimates, with variations in snow temperature, salinity, and density derived from ten naturally occurring Arctic FYI Cases encompassing saline/nonsaline, warm/cold, simple/complexly layered snow (4–45 cm) overlying FYI (48–170 cm). Using a semi-empirical modeling approach, snow properties from these Cases are used to derive layer-wise brine volume and dielectric constant estimates, to simulate the Ku-band main scattering horizon and delays in radar propagation speed. Differences between modeled and observed FYI thickness are calculated to assess sources of error. Under both cold and warm conditions, saline snow covers are shown to shift the main scattering horizon above from the snow/sea ice interface, causing thickness retrieval errors. Overestimates in FYI thicknesses of up to 65% are found for warm, saline snow overlaying thin sea ice. Our simulations exhibited a distinct shift in the main scattering horizon when the snow layer densities became greater than 440 kg/m3, especially under warmer snow conditions. Our simulations suggest a mean Ku-band propagation delay for snow of 39%, which is higher than 25%, suggested in previous studies.

Comparison of passive microwave remote-sensing snow-depth products on Arctic sea ice

Changes in snow cover on the surface of Arctic sea ice affect the energy balance between the atmosphere and the ocean and play a vital role in the global climate system. Accurate snow depth is a precondition for representing thermodynamic processes in sea-ice systems and is helpful for estimating sea-ice thickness. To better apply Arctic snow-depth products released by different organizations, we compared four kinds of snow-depth products based on three kinds of passive microwave (PM) sensors and evaluated them against the snow depth measured by ice mass-balance buoys (IMB snow depth) and Operation Ice Bridge airborne snow radar (OIB snow depth). The results show that the snow depths from the product released by the University of Bremen (UB) are larger than those by the National Snow and Ice Data Center (NSIDC) and National Aeronautics and Space Administration (NASA), with an average difference of 10 cm. Comparing the PM remote-sensing snow depths released by UB, NSIDC and NASA against IMB and OIB snow depths, it is found that NSIDC AMSR-E snow-depth product has the highest accuracy. Although these PM remote-sensing snow-depth products released by different organizations differ in accuracy, they all reflect the spatio-temporal variation characteristics of snow depth on Arctic sea ice. These comparisons and analysis of snow-depth products from different sensors released by different organizations provide a basis for further investigation of Arctic sea-ice thickness estimation and benefit the studies of Arctic sea ice and climate change.

Arctic sea ice state estimation from thermodynamic PDE model

Recent rapid loss of the Arctic sea ice motivates the study of the Arctic sea ice thickness. Global climate model that describes the ice thickness evolution requires an accurate spatial temperature profile of the Arctic sea ice. However, measuring the complete temperature profile is not feasible within and throughout the Arctic icecap. Instead, measuring the ice thickness is doable with the acquisition of data from submarine and satellite devices. In this paper, we develop a backstepping observer algorithm to estimate the temperature profile for the Arctic sea ice model via available measurements of sea ice thickness and sea ice surface temperature. The observer is designed in a rigorous manner to drive the temperature profile estimation error to zero, for a salinity-free sea ice model. Moreover, the proposed observer is used to estimate the temperature profile of the original sea ice model with salinity via numerical simulation. The simulation results illustrate that our observer design achieves a successful estimation of the sea ice temperature profile in three days, which is ten times faster than the straightforward open-loop algorithm.

Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations

The variations in the Arctic sea ice thickness (SIT) due to climate change have both positive and negative effects on commercial human activities, the ecosystem, and the Earth’s environment. Satellite microwave remote sensing based on microwave reflection signals reflected by the sea ice surface has been playing an essential role in monitoring and analyzing the Arctic SIT and sea ice concentration (SIC) during the past decades. Recently, passive microwave satellites incorporating an L-band radiometer, such as soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP), have been used for analyzing sea ice characteristics, in addition to land and ocean research. In this study, we present a novel method to estimate thin SIT and sea ice roughness (SIR) using a conversion relationship between them, from the SMAP and SMOS data. Methodologically, the SMAP SIR is retrieved. The SMAP thin SIT and SMOS SIR are estimated using a conversion relationship between thin SIT data from SMOS data and SMAP-derived SIR, which is obtained from the spatial and temporal collocation of the SMOS thin SIT and the SIR retrieved from SMAP. Our results for the Arctic sea ice during December for four consecutive years from 2015 to 2018, show high accuracy (bias = −2.268 cm, root mean square error (RMSE) = 15.919 cm, and correlation coefficient (CC) = 0.414) between the SMOS-provided thin SIT and SMAP-derived SIT, and good agreement (bias = 0.03 cm, RMSE = 0.228 cm, and CC = 0.496) between the SMOS-estimated SIR and SMAP-retrieved SIR. Consequently, our study could be effectively used for monitoring and analyzing the variation in the Arctic sea ice.

Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat‐2

AbstractMultidecadal observations of sea ice thickness, in addition to those available for extent, are key to understanding long‐term variations and trends in the amount of Arctic sea ice. The European Space Agency's Envisat (2002–2010) and CryoSat‐2 (2010–present) satellite radar altimeter missions provide a continuous 17‐year dataset with the potential to estimate sea ice thickness. However, the satellite footprints are not equal in area and so different distributions of floes and leads are sampled by each mission. Here, we compare lead and floe sampling from Envisat and CryoSat‐2 to investigate the impact of geometric sampling differences on Arctic sea ice thickness estimates. We find that Envisat preferentially samples wider, thicker sea ice floes, and that floes in less consolidated ice regions are effectively thickened by off‐nadir ranging to leads. Consequently, Envisat sea ice thicknesses that are an average of 80 cm higher than CryoSat‐2 over first‐year ice and 23 cm higher over multiyear ice. By considering the along‐track distances between lead and floe measurements, we are able to develop a sea ice thickness correction that is based on Envisat's inability to resolve discrete surfaces relative to CryoSat‐2. This is a novel, physically based approach to addressing the bias between the satellites and reduces the average thickness difference to negligible values over first‐year and multiyear ice. Finally, we evaluate our new bias‐corrected Envisat sea ice thickness product using independent airborne, moored‐buoy and submarine data. The European Space Agency's Envisat and CryoSat‐2 satellites have the potential to produce a continuous record of Arctic sea ice thickness since 2002, but this is complicated by the fact that the satellites do not sample the sea ice surface in the same way. We find that Envisat is only able to sample larger, thicker sea ice relative to CryoSat‐2, because of its poorer resolution. In this paper we account for these differences in sampling to combine Arctic sea ice thickness estimates from two the satellite missions. Applying a sea ice thickness bias correction to Envisat data reduces the ice thickness difference between Envisat and CryoSat‐2 from an average of 53.0 to 0.5 cm

A risk-based approach for determining the future potential of commercial shipping in the Arctic

As global warming is causing a significant reduction in the extent and the thickness of Arctic sea ice, a narrative for future large-scale commercial shipping in the Arctic has started to evolve. However, while most efforts have aimed to assess the financial benefits along with potential savings in greenhouse gas emissions, few studies have explored risk patterns and safety issues associated with Arctic shipping and its current commercial alternatives in a geospatial manner. This study aims at addressing this gap in the literature, by assessing the risk factors associated with Arctic shipping, in particular, risks along the Northern Sea Route (NSR) connecting European and Asian ports. Its main component includes a global least-cost analysis, aiming to assess global and Arctic risk factors associated with commercial shipping, to derive the least risk paths from 10 major ports in Europe to 10 in Asia. To project the results into the future, and thus assess the future potential of the NSR as a viable and less risky alternative, a dataset on predicted sea ice thickness within 2010–2099, restricted the analysis based on the penetration capabilities of three selected vessel types. Ultimately, the study concludes that maritime transportation, bound for East-Asia, commencing from most northern European ports, could benefit from the NSR, not just in terms of distance savings, cost reduction and less CO2 production, but also as a safer alternative. Lastly, two recommendations drawn from our results aim to assist decision-makers in facilitating future Arctic transportation by addressing accessibility and safety issues. Highlights We explore risk patterns and safety issues associated with Arctic shipping and its current commercial alternatives in a geospatial manner. We assess the future potential of the northern sea route (NSR) as a viable and less risky alternative for European-Asian shipping. We provide a discussion on pros and cons of utilising the NSR in terms of distance savings resulting in cost reduction as well as less CO2 production, and as a safer alternative.

Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products

ABSTRACT As a fundamental climate state variable, sea ice thickness (SIT) has exhibited a declining trend over the past five decades. Here, we present a quantitative comparison of three CryoSat-2 (CS-2) SIT products from the Alfred-Wegener-Institute (AWI), the National Snow and Ice Data Centre (NSIDC), and the European Space Agency (ESA) during the growth season (October to April) from 2010 to 2018 with Operation IceBridge (OIB) data. The results show that the NSIDC SIT product is the closest to the OIB SIT, with ESA SIT exhibiting the highest bias. During each growth season, the SIT differences between AWI and NSIDC gradually decrease, while such differences between ESA and NSIDC increase for first-year ice (FYI) and decrease then increase for multiyear ice (MYI). The difference between ESA and NSIDC is larger than that between AWI and NSIDC. Moreover, the rather large differences between ESA and NSIDC are mainly located in thin ice areas. Consistent to SIT comparative results, sea ice freeboard for ESA is higher than that for OIB, AWI and NSIDC, especially FYI freeboard. Sea ice freeboard for NSIDC is the closest to that for OIB. The comparative results indicate that the sources of the differences in SIT between the products mainly originate from the sea ice density and freeboard retrieval methods. The choices of different waveform retrackers and threshold assignments significantly influence the MYI freeboard retrievals due to the relatively thick snow depth and high surface roughness over MYI.

Uncertainties in Arctic Sea Ice Thickness Associated with Different Atmospheric Reanalysis Datasets Using the CICE5 Model

Reanalysis data are known to have relatively large uncertainties in the polar region than at lower latitudes. In this study, we used a single sea-ice model (Los Alamos’ CICE5) and three sets of reanalysis data to quantify the sensitivities of simulated Arctic sea ice area and volume to perturbed atmospheric forcings. The simulated sea ice area and thickness thus volume were clearly sensitive to the selection of atmospheric reanalysis data. Among the forcing variables, changes in radiative and sensible/latent heat fluxes caused significant amounts of sensitivities. Differences in sea-ice concentration and thickness were primarily caused by differences in downward shortwave and longwave radiations. 2-m air temperature also has a significant influence on year-to-year variability of the sea ice volume. Differences in precipitation affected the sea ice volume by causing changes in the insulation effect of snow-cover on sea ice. The diversity of sea ice extent and thickness responses due to uncertainties in atmospheric variables highlights the need to carefully evaluate reanalysis data over the Arctic region.

PRELIMINARY RESULTS OF SEA ICE FREEBOARD MEASUREMENTS OF BEAUFORT SEA FROM CRYOSAT-2 ALTIMETRY

Abstract. Satellite altimetry has been used to observe the Arctic sea ice in long term and large scale, and the records show a continued decline for Arctic sea ice thickness over decades. In this study, the sea ice freeboard in Beaufort Sea of Arctic have been estimated using CryoSat-2 data, and validated with Upward Looking Sonar (ULS) data of Beaufort Gyre Exploration Project (BGEP). The results show an obvious seasonal variation of the Beaufort Sea with a high reliability estimation of the sea ice freeboard. The average height of the sea ice freeboard increase from January to March and achieve the maximum value 0.38 m in March. The sea ice melts after March and the average height of the sea ice freeboard reduces to the minimum 0.12 m in August. In the next few months the sea water begins to freeze and the average height of the sea ice freeboard will increase to the maximum value.

Sensitivity of the Arctic freshwater content and transport to model resolution

We compare the representation of the Arctic liquid and solid freshwater volumes, and their transports to/from the Arctic, using simulations with different ocean and atmosphere model resolutions from models participating in the EU H2020 PRIMAVERA project. Regarding ocean resolution, we find less liquid freshwater in the central Arctic Ocean and more in the Kara and Laptev seas in the model simulations at higher resolution compared with the model simulations at lower resolution. The mean sea ice thickness does not show a systematic behaviour across models regarding model resolution increase. In terms of atmospheric resolution, we find that both liquid freshwater and mean sea ice thickness decrease when the resolution of the atmospheric model increases. We also analyze differences of Arctic liquid freshwater volume and mean sea ice thickness caused by pre-industrial and historical atmospheric forcings. Pre-industrial simulations show less freshwater volume and increased mean sea ice thickness and export from the Arctic compared with present day simulations. Finally, we find an increasing impact of Arctic freshwater export on North Atlantic convection with increased atmospheric resolution.

On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses

Abstract. The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Abstract. Estimates of Arctic sea ice thickness have been available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid-scale ice thickness distribution (ITD) with respect to five ice thickness categories used in a sea ice component (Community Ice CodE, CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

Airborne Observations of Summer Thinning of Multiyear Sea Ice Originating From the Lincoln Sea.

To better understand recent changes of Arctic sea ice thickness and extent, it is important to distinguish between the contributions of winter growth and summer melt to the sea ice mass balance. In this study we present a Lagrangian approach to quantify summer sea ice melt in which multiyear ice (MYI) floes that were surveyed by airborne electromagnetic thickness sounding within Nares Strait during summer were backtracked, using satellite imagery, to a region in close proximity (3–20 km) to spring ice thickness surveys carried out in the Lincoln Sea. Typical modal total MYI thicknesses, including ~0.4‐m snow, ranged between 3.9 and 4.7 m in the Lincoln Sea during April. Ice‐only modal thicknesses were between 2.2 and 3.0 m in Nares Strait during August. Total thinning including snow and ice was 1.3 ± 0.1 m including 0.4 ± 0.09 m of snow melt and 0.9 ± 0.2 m of ice melt. This translates to a seasonal net heat input of 305 ± 69 MJ/m2 (262 ± 60 MJ/m2 for ice only) and seasonal net heat flux of 57 ± 13 W/m2 (45 ± 10 W/m2 for ice only), which is unlikely to be explained by solar radiation fluxes alone. Furthermore, our approach provides an improvement on traditional ice mass balance buoy estimates because it integrates melt over larger spatial scales, where melt can be highly variable due to differential melt experienced between melt ponds, bare ice, hummocks, and ridges.