Sea Ice Thickness Variability Research Articles

Assessment of sea ice thickness simulations in the CMIP6 models with CICE components

Arctic sea ice plays a critical role in modulating our global climate system and the exchange of heat fluxes in the polar region, but its impact on climate varies across different sea ice thickness (SIT) categories. Compared to sea ice cover, the performance of ice models in simulating SIT has been less evaluated, particularly in the sixth Coupled Model Intercomparison Project Phase (CMIP6). Here, we chose 12 CMIP6 models with the Community Ice Code model (CICE) components and compared their SIT simulations with the satellite observations and the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) model between 1980 and 2014. Our results show that the seasonal cycle of the PIOMAS SIT is consistent with satellite observations. Compared to the PIOMAS reanalysis, the multi-model ensemble mean (MME) well represents the sea ice extent in both the thin ice (<0.6 m) and thick ice (> 3.6 m). However, the MME SIT has larger biases in the Chukchi Sea, the Beaufort Sea, the central Arctic, and the Greenland Sea during winter and mainly in the central Arctic during summer. Both the MME and PIOMAS show decreasing trends in SIT over the entire Arctic Ocean in all seasons, but the interannual variability of SIT in MME is smaller than that in PIOMAS. Among the 12 CMIP6 models, the FIO-ESM-2.0 model shows the best simulation of the annual mean SIT, but the SAM0-UNICON and NESM3 models have the largest biases in the climatological mean SIT over the Arctic Ocean. We also demonstrate that the FIO-ESM-2.0 performs the best in the seasonal cycles of SIT. Our study suggests that more attention should be paid to the coupling of the CICE model with ocean and atmosphere models, which is vital to improving the SIT simulation in CMIP6 models and to better understanding the impact of Arctic sea ice on our climate system.

A long-term proxy for sea ice thickness in the Canadian Arctic: 1996–2020

Abstract. This study presents a long-term winter sea ice thickness proxy product for the Canadian Arctic based on a random forest regression model – applied to ice charts and scatterometer data, trained on CryoSat-2 observations, and applying an ice type–sea ice thickness correction using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) – that provides 25 years of sea ice thickness in the Beaufort Sea, Baffin Bay, and, for the first time, the Canadian Arctic Archipelago. An evaluation of the product with in situ sea ice thickness measurements shows that the presented sea ice thickness proxy product correctly estimates the magnitudes of the ice thickness and accurately captures spatial and temporal variability. The product estimates sea ice thickness within 30 to 50 cm uncertainty from the model. The sea ice thickness proxy product shows that sea ice is thinning over most of the Canadian Arctic, with a mean trend of −0.82 cm yr−1 in April over the whole study area (corresponding to 21 cm thinning over the 25-year record), but that trends vary locally. The Beaufort Sea and Baffin Bay show significant negative trends during all months, though with peaks in November (−2.8 cm yr−1) and April (−1.5 cm yr−1), respectively. The Parry Channel, which is part of the Northwest Passage and relevant for shipping, shows significant thinning in autumn. The sea ice thickness proxy product provides, for the first time, the opportunity to study long-term trends and variability in sea ice thickness in the Canadian Arctic, including the narrow channels in the Canadian Arctic Archipelago.

Evaluation of Arctic Sea Ice Thickness from a Parameter-Optimized Arctic Sea Ice–Ocean Model

Sea ice thickness (SIT) presents comprehensive information on Arctic sea ice changes and their role in the climate system. However, our understanding of SIT is limited by a scarcity of observations and inaccurate model simulations. Based on simultaneous parameter optimization with a micro genetic algorithm, the North Atlantic/Arctic Ocean–Sea Ice Model (NAOSIM) has already demonstrated advantages in Arctic sea ice simulations. However, its performance in simulating pan-Arctic SITs remains unclear. In this study, a further evaluation of Arctic SITs from NAOSIM was conducted based on a comparison with satellite and in situ observations. Generally, NAOSIM can reproduce the annual cycle and downward trend in the sea ice volume. However, deficiencies can still be found in the simulation of SIT spatial patterns. NAOSIM overestimates the SIT of thinner ice (<1.5 m) in the Beaufort Sea, underestimates the SIT of thick ice (>1.5 m) in the central Arctic and is unable to capture the upward trend in the SIT in the north of the Canadian Archipelago as well as to reproduce the intensity of the observed SIT variability. In terms of SIT simulation, NAOSIM performs better as the time approaches the optimization window (2000–2012). Therefore, in the context of rapid changes in Arctic sea ice, how to optimize this model based on limited observations still remains a challenge.

Sea Ice Dynamics in Hudson Strait and Its Impact on Winter Shipping Operations

AbstractHudson Strait is seasonally ice covered and is the only part of the Canadian Arctic where winter shipping takes place. Yet, very little is known about the thickness and dynamics of this ice pack. During winter operations, icebreakers often face besetting events, which can slow or immobilize vessels for up to a few days. Using in situ observations of ice draft and drift collected by moored sonars at two sites in Hudson Strait from 2005 to 2009, we provide the first detailed analysis of sea ice dynamics within Hudson Strait and provide insights into the processes that dictate ice thickness and internal pressure along this unique winter shipping corridor. Prevailing northwesterly winds drive south‐southeastward ice motion within the Strait, maintaining polynyas along Baffin Island on the north side of the Strait, and compressing the ice pack against Nunavik on the southern side. As a result, ice on the northern side remains young and thin throughout winter ( = 1.25 m), whereas ice on the southern side is older, heavily deformed and ∼60% thicker by March ( = 2.01 m). Intermittent reversals to southeasterly winds decompress the ice pack on the southern side, increasing the presence of leads and easing navigation through the ice pack to the port in Deception Bay. The spatial variability in sea ice thickness elucidated by the moorings is corroborated at the regional scale using satellite observations from ICESat‐2 during winter 2019, 2020, and 2021, and complimented by high‐resolution fields of sea ice motion during winter 2021.

A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn provide information on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporal resolution of radar freeboard is limited as 30 d are required in order to generate pan-Arctic coverage from CryoSat-2 and 27 d are required from Sentinel-3 satellites. This therefore hinders our ability to understand physical processes that drive sea ice thickness variability on sub-monthly timescales. In this study we exploit the consistency between CryoSat-2, Sentinel-3A, and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboard estimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian process regression to learn functional mappings between radar freeboard observations in space and time and to subsequently retrieve pan-Arctic freeboard as well as uncertainty estimates. We also employ an empirical Bayesian approach towards learning the free (hyper)parameters of the model, which allows us to derive daily estimates related to radar freeboard spatial and temporal correlation length scales. The estimated daily radar freeboard predictions are, on average across the 2018–2019 season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 1 mm (standard deviations <6 cm), and cross-validation experiments show that errors in predictions are, on average, ≤ 4 mm across the same period. We also demonstrate the improved temporal variability of a pan-Arctic daily product by comparing time series of the predicted freeboards, with 31 d running means from CryoSat-2 and Sentinel-3 freeboards, across nine sectors of the Arctic, as well as making comparisons with daily ERA5 snowfall data. Pearson correlations between daily radar freeboard anomalies and snowfall are as high as +0.52 over first-year ice and +0.41 over multi-year ice, suggesting that the estimated daily fields are able to capture real physical radar freeboard variability at sub-weekly timescales.

On the discretization of the ice thickness distribution in the NEMO3.6-LIM3 global ocean–sea ice model

Abstract. The ice thickness distribution (ITD) is one of the core constituents of modern sea ice models. The ITD accounts for the unresolved spatial variability of sea ice thickness within each model grid cell. While there is a general consensus on the added physical realism brought by the ITD, how to discretize it remains an open question. Here, we use the ocean–sea ice general circulation model, Nucleus for European Modelling of the Ocean (NEMO) version 3.6 and Louvain-la-Neuve sea Ice Model (LIM) version 3 (NEMO3.6-LIM3), forced by atmospheric reanalyses to test how the ITD discretization (number of ice thickness categories, positions of the category boundaries) impacts the simulated mean Arctic and Antarctic sea ice states. We find that winter ice volumes in both hemispheres increase with the number of categories and attribute that increase to a net enhancement of basal ice growth rates. The range of simulated mean winter volumes in the various experiments amounts to ∼30 % and ∼10 % of the reference values (run with five categories) in the Arctic and Antarctic, respectively. This suggests that the way the ITD is discretized has a significant influence on the model mean state, all other things being equal. We also find that the existence of a thick category with lower bounds at ∼4 and ∼2 m for the Arctic and Antarctic, respectively, is a prerequisite for allowing the storage of deformed ice and therefore for fostering thermodynamic growth in thinner categories. Our analysis finally suggests that increasing the resolution of the ITD without changing the lower limit of the upper category results in small but not negligible variations of ice volume and extent. Our study proposes for the first time a bi-polar process-based explanation of the origin of mean sea ice state changes when the ITD discretization is modified. The sensitivity experiments conducted in this study, based on one model, emphasize that the choice of category positions, especially of thickest categories, has a primary influence on the simulated mean sea ice states while the number of categories and resolution have only a secondary influence. It is also found that the current default discretization of the NEMO3.6-LIM3 model is sufficient for large-scale present-day climate applications. In all cases, the role of the ITD discretization on the simulated mean sea ice state has to be appreciated relative to other influences (parameter uncertainty, forcing uncertainty, internal climate variability).

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.

Interannual sea ice thickness variability in the Bay of Bothnia

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

Variability of Arctic Sea Ice Thickness Using PIOMAS and the CESM Large Ensemble

Abstract Because of limited high-quality satellite and in situ observations, less attention has been given to the trends in Arctic sea ice thickness and therefore sea ice volume than to the trends in sea ice extent. This study evaluates the spatial and temporal variability in Arctic sea ice thickness using the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Additionally, the Community Earth System Model Large Ensemble Project (LENS) is used to quantify the forced response and internal variability in the model. A dipole spatial pattern of sea ice thickness variability is shown in both PIOMAS and LENS with opposite signs of polarity between the East Siberian Sea and near the Fram Strait. As future sea ice thins, this dipole structure of variability is reduced, and the largest interannual variability is found only along the northern Greenland coastline. Under a high-emissions scenario (RCP8.5) projection, average September sea ice thickness falls below 0.5 m by the end of the twenty-first century. However, a regional analysis shows internal variability contributes to an uncertainty of 10 to 20 years for the timing of the first September sea ice thickness less than 0.5 m in the marginal seas.

GPR Survey and Physical Measurements of Sea Ice in Quilty Bay, Larsemann Hills, East Antarctica and its Correlation with Local Atmospheric Parameters

ABSTRACT Sea ice formation in Quilty Bay, East Antarctica shows a strong relationship with climatic conditions and its spatial variation is established by GPR survey over the sea ice during the austral winters. The study also evaluates the variability of sea ice thickness and extent with local weather parameters between 2010 and 2014. Profiling on sea ice in Quilty Bay shows that sea ice thickness decreases gradually towards east. The overall thickness of sea ice is considerably less at the centre of the bay as compared to the coastal side. Two types of fast ice layers have been delineated from GPR surveys i.e. top layer fast ice which incorporates very low density surface snow and underlying high density fast ice i.e. with melt water pockets. Development of sea ice is influenced by persistent easterly winds, temperature fluctuations and ocean currents. Sea ice reveal average annual cyclic trend in Quilty Bay with maximum development in the year 2013 (157.54 cm) and the minimum development has been observed in the year 2010 (99.04 cm). The cyclic pattern of average sea ice accumulation data show perfect inverse correlation with surface air temperature, with measure of reliability R2 = 0.93. However, moderate (R2 = 0.52) and good (R2 = 0.69) degree of inverse relationship is observed with surface snow and overall sea ice (snow and ice) respectively. With 0.46 °C change in temperature, there is 33% change in sea ice condition in Quilty Bay which may remain same as far as ice extents in the region. Besides this easterly winds play a vital role in controlling the snow/ice distribution patterns as reflected by isopach maps.

Interannual variability in sea-ice thickness in the pack-ice zone off Lützow–Holm Bay, East Antarctica

Under the Japanese Antarctic Research Expedition (JARE) program, sea-ice thickness has been routinely monitored off Lützow–Holm Bay (East Antarctica) during the summer (mid-December to early January) since 2000/01, using an electromagnetic induction (EM) instrument onboard the icebreaker Shirase. Analysis of these data over a 10-year period, combined with visual observations using a simplified form of the ASPeCt (Antarctic Sea ice Processes and Climate) protocol, suggests a strong interannual variability in sea-ice thickness in this region. For the repeat pack-ice observation area, where the sea-ice thickness averaged over the nine seasons is ∼1.9 m, mean thicknesses of observed sea-ice in 2010/11 and 2011/12 are exceptionally large, at ∼3.3 and ∼5.8 m, respectively. This result is strongly related to regional patterns of sea ice dynamics. Ice convergence caused by anomalous northerly winds was particularly high in 2011/12, suggesting that the extremely thick ice observed in that season resulted largely from sea-ice deformation processes (including pressure ridging). Longer-term analysis of data from the past 34 years confirms that sea-ice conditions and thickness off Lützow–Holm Bay in summer are determined mainly by the large-scale pattern of atmospheric pressure in December.

Modeling photosynthesis in sea ice‐covered waters

AbstractThe lower trophic levels of marine ecosystems play a critical role in the Earth System mediating fluxes of carbon to the ocean interior. Many of the functional relationships describing biological rate processes, such as primary productivity, in marine ecosystem models are nonlinear functions of environmental state variables. As a result of nonlinearity, rate processes computed from mean fields at coarse resolution will differ from similar computations that incorporate small‐scale heterogeneity. Here we examine how subgrid‐scale variability in sea ice thickness impacts simulated net primary productivity (NPP) in a configuration of the Community Earth System Model (CESM). CESM simulates a subgrid‐scale ice thickness distribution and computes shortwave penetration independently for each ice thickness category. However, the default model formulation uses grid‐cell mean irradiance to compute NPP. We demonstrate that accounting for subgrid‐scale shortwave heterogeneity by computing light limitation terms under each ice category then averaging the result is a more accurate invocation of the photosynthesis equations. Moreover, this change delays seasonal bloom onset and increases interannual variability in NPP in the sea ice zone in the model. The new treatment reduces annual production by about 32% in the Arctic and 19% in the Antarctic. Our results highlight the importance of considering heterogeneity in physical fields when integrating nonlinear biogeochemical reactions.

Sea Ice Enhancements to Polar WRF*

Abstract The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

Thermodynamic sea ice growth in the central Weddell Sea, observed in upward‐looking sonar data

AbstractUpward‐looking sonar (ULS) data were used to analyze thermodynamic sea ice growth. The study was carried out for an ocean region in the central Weddell Sea, for which data of sea ice thickness variability and of the oceanic heat flux through the ice are rare. In the study area the contribution of sea ice deformation to vertical ice growth is relatively small in some years. This provides the opportunity to simulate thermodynamic sea ice growth considering the influence of a snow cover and of the oceanic heat flux. To this end, a modified version of Stefan's law was used. The resulting ice thickness variations were then compared with the ULS measurements. For the investigated cases, the best consistency between data and model results was obtained assuming a snow layer of less than 5 cm thickness and average oceanic heat fluxes between 6 and 14 W m−2. It is demonstrated that in conjunction with ice drift data and analytical models for thermal sea ice growth, ULS ice thickness measurements are useful for studying the seasonal cycle of growth and decay and for inferring the magnitude of the average oceanic heat flux under sea ice.

An Arctic sea ice thickness variability revealed from satellite altimetric measurements

A modified algorithm taking into account the first year (FY) and multiyear (MY) ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat (2003-2008). Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4-5 month separation, were found. Interarmually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns (2005-2008) leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.

Characteristics of Arctic Sea-Ice Thickness Variability in GCMs

Abstract Skillful Arctic sea ice forecasts may be possible for lead times of months or even years owing to the persistence of thickness anomalies. In this study sea ice thickness variability is characterized in fully coupled GCMs and sea ice–ocean-only models (IOMs) that are forced with an estimate of observations derived from atmospheric reanalysis and satellite measurements. Overall, variance in sea ice thickness is greatest along Arctic Ocean coastlines. Sea ice thickness anomalies have a typical time scale of about 6–20 months, a time scale that lengthens about a season when accounting for ice transport, and a typical length scale of about 500–1000 km. The range of these scales across GCMs implies that an estimate of the number of thickness monitoring locations needed to characterize the full Arctic basin sea ice thickness variability field is model dependent and would vary between 3 and 14. Models with a thinner mean ice state tend to have ice-thickness anomalies that are generally shorter lived and smaller in amplitude but have larger spatial scales. Additionally, sea ice thickness variability in IOMs is damped relative to GCMs in part due to strong negative coupling between the dynamic and thermodynamic processes that affect sea ice thickness. The significance for designing prediction systems is discussed.

Climate change in Eurasian Arctic shelf seas: centennial ice cover observations

Arctic sea ice as an element of the global climate system.- Long-term changes in Arctic Seas ice extent during the twentieth century.- Variability of sea ice thickness and concentration in the twentieth century.- Consistency among sea ice extent and atmospheric and hydrospheric processes.- Possible causes of changes in climate and in Arctic Seas ice extent.- Assessment of possible changes in air temperature and sea-ice extent in the Arctic Seas in the twenty-first century.- Conclusions.

Field Investigations of Ku-Band Radar Penetration Into Snow Cover on Antarctic Sea Ice

Monitoring long-term, large-scale changes in the Antarctic sea ice thickness is not currently possible due to the sampling constraints of the ship-based and airborne observations which comprise most of the available thickness data. Satellite radar altimetry has been used to measure sea ice thickness variability in the Arctic where it is assumed that the highest amplitude radar return originates from the snow/ice interface as the Arctic snow is cold and dry; however, this may not be the case in the Antarctic due to more complex snow stratigraphy caused by warmer winter temperatures and frequent snow flooding. We present the first measurements of radar penetration into snow cover on Antarctic sea ice in the Ku-band at which satellite radar altimeters operate. Data were taken using a sled-borne radar on sea ice off East Antarctica during September and October 2007. Radar data and field measurements of snow density, thickness, wetness, and layers were taken over a range of locations including snow packs with flooding, hard crusts, and icy layers. Detailed snow pit studies showed that the snow/ice interface was the dominant scattering surface only for snow without morphological features or flooding. Analysis of transect data showed that the mean depth of the dominant scattering surface of the radar was only around 50% of the mean measured snow depth, indicating that the dominant scattering surface was not always the snow/ice interface for the Antarctic sea ice surveyed.

Decrease of sea ice thickness at Hopen, Barents Sea, during 1966–2007

Seasonal fast ice thickness at the island of Hopen (Barents Sea) was monitored over 40 years. Sea ice thickness variability as a climate indicator provides more quantitative information on the state of the ice cover than solely sea ice extent. Usually, starting to form just before December Hopen fast ice reaches maximum thickness in May (on average 0.99 m), before the ice starts to decay. Swell, currents, and winds interrupt the fast ice development at Hopen during several of the winters observed, leading to ice removal and new ice formation. Since 2000, no ice thicker than 1.0 m was observed. We find a trend in the ice thickness anomalies of −0.11 m per decade, coinciding with decreasing seasonal maximum ice thickness, and an increase in local surface air and water temperatures. This is consistent with the decreasing sea ice extent in the Barents Sea and the entire Arctic.

Comparison of Arctic sea ice thickness variability in IPCC Climate of the 20th Century experiments and in ocean–sea ice hindcasts

Arctic sea ice is an important climate component and often regarded as an early indicator of anthropogenic global change. For an assessment of coupled climate models, their performance with respect to the development of Arctic sea ice thickness during the 20th century is examined. Their behavior is compared with results from an ocean–sea ice model using the Arctic Ocean Model Intercomparison Project (AOMIP) atmospheric forcing for the period 1948–2000. In lieu of actually observed sea ice thickness, this model result under realistic atmospheric forcing serves as a benchmark for the coupled climate models. The hindcast exhibits virtually no trend in Arctic ice volume over its integration period 1948–2000. Most of the coupled climate models show a negative trend over the 20th century that accelerates towards the end of that century.