Arctic Sea Ice Thickness Research Articles

Warm Arctic, Increased Winter Sea Ice Growth?

AbstractWe explore current variability and future projections of winter Arctic sea ice thickness and growth using data from climate models and satellite observations. Winter ice thickness in the Community Earth System Model's Large Ensemble compares well against thickness estimates from the Pan‐Arctic Ice Ocean Modeling and Assimilation System and CryoSat‐2, despite some significant regional differences—for example, a high thickness bias in Community Earth System Model's Large Ensemble in the western Arctic. Differences across the available CryoSat‐2 thickness products hinder more robust validation efforts. We assess the importance of the negative conductive feedback of sea ice growth (thinner ice grows faster) by regressing October atmosphere/ice/ocean conditions against winter ice growth. Our regressions demonstrate the importance of a strong negative conductive feedback process in our current climate, which increases winter growth for thinner initial ice, but indicate that later in the 21st century this negative feedback is overwhelmed by variations in the fall atmosphere/ocean state.

Arctic‐Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice‐Ocean Model with Data Assimilation During the CryoSat‐2 Period

AbstractExploiting the complementary character of CryoSat‐2 and Soil Moisture and Ocean Salinity satellite sea ice thickness products, daily Arctic sea ice thickness estimates from October 2010 to December 2016 are generated by an Arctic regional ice‐ocean model with satellite thickness assimilated. The assimilation is performed by a Local Error Subspace Transform Kalman filter coded in the Parallel Data Assimilation Framework. The new estimates can be generally thought of as combined model and satellite thickness (CMST). It combines the skill of satellite thickness assimilation in the freezing season with the model skill in the melting season, when neither CryoSat‐2 nor Soil Moisture and Ocean Salinity sea ice thickness is available. Comparisons with in situ observations from the Beaufort Gyre Exploration Project, Ice Mass Balance Buoys, and the NASA Operation IceBridge demonstrate that CMST reproduces most of the observed temporal and spatial variations. Results also show that CMST compares favorably to the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System product and even appears to correct known thickness biases in the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System. Due to imperfect parameterizations in the sea ice model and satellite thickness retrievals, CMST does not reproduce the heavily deformed and ridged sea ice along the northern coast of the Canadian Arctic Archipelago and Greenland. With the new Arctic sea ice thickness estimates sea ice volume changes in recent years can be further assessed.

Arctic sea ice thickness, volume, and multiyear ice coverage: losses and coupled variability (1958–2018)

Large-scale changes in Arctic sea ice thickness, volume and multiyear sea ice (MYI) coverage with available measurements from submarine sonars, satellite altimeters (ICESat and CryoSat-2), and satellite scatterometers are summarized. The submarine record spans the period between 1958 and 2000, the satellite altimeter records between 2003 and 2018, and the scatterometer records between 1999 and 2017. Regional changes in ice thickness (since 1958) and within the data release area of the Arctic Ocean, previously reported by Kwok and Rothrock (2009 Geophys. Res. Lett. 36 L15501), have been updated to include the 8 years of CryoSat-2 (CS-2) retrievals. Between the pre-1990 submarine period (1958–1976) and the CS-2 period (2011–2018) the average thickness near the end of the melt season, in six regions, decreased by 2.0 m or some 66% over six decades. Within the data release area (∼38% of the Arctic Ocean) of submarine ice draft, the thinning of ∼1.75 m in winter since 1980 (maximum thickness of 3.64 m in the regression analysis) has not changed significantly; the mean thickness over the CS-2 period is ∼2 m. The 15 year satellite record depicts losses in sea ice volume at 2870 km3/decade and 5130 km3/decade in winter (February–March) and fall (October–November), respectively: more moderate trends compared to the sharp decreases over the ICESat period, where the losses were weighted by record-setting melt in 2007. Over the scatterometer record (1999–2017), the Arctic has lost more than 2 × 106 km2 of MYI—a decrease of more than 50%; MYI now covers less than one-third of the Arctic Ocean. Independent MYI coverage and volume records co-vary in time, the MYI area anomalies explain ∼85% of the variance in the anomalies in Arctic sea ice volume. If losses of MYI continue, Arctic thickness/volume will be controlled by seasonal ice, suggesting that the thickness/volume trends will be more moderate (as seen here) but more sensitive to climate forcing.

The Early Collapse of the 2017 Lincoln Sea Ice Arch in Response to Anomalous Sea Ice and Wind Forcing

AbstractOne of the most dramatic indicators of climate change is the reduction in the extent and thickness of Arctic sea ice that has resulted in an increase in wind‐driven sea ice mobility. During April and May 2017, satellite observations indicated that the ice arch that forms between Nares Strait and the Lincoln Sea collapsed. Typically, this collapse occurs in July or August allowing multiyear ice to exit the Arctic through Nares Strait. Here we show that the period of the collapse was associated with the presence of a polynya in northern Nares Strait, thin ice in the Lincoln Sea, and an unusual wind regime characterized by strong northerly flow. We propose that these anomalous conditions resulted in the arch's early collapse. If the ice in the region continues to thin, early collapses may occur more frequently with implications for the regional as well as the downstream climate and ecosystems.

Dynamical prediction of Arctic sea ice modes of variability

This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.

Contributions of Ice Thickness to the Atmospheric Response From Projected Arctic Sea Ice Loss

AbstractA large ensemble of simulations from a high‐top atmospheric general circulation model are conducted to compare the atmospheric responses from loss of Arctic sea ice thickness and sea ice concentration. The response to projected sea ice thickness loss indicates substantial surface warming over the Arctic Ocean and up to 1°C of cooling in Eurasia. While the dynamic circulation response from sea ice thickness loss is smaller in magnitude, it has a similar spatial anomaly pattern as that due to sea ice concentration loss. This pattern resembles the negative phase of the Northern Annular Mode. The simulations reveal that sea ice thickness loss enhances the thermodynamic and large‐scale circulation response from sea ice anomalies. These results stress the importance of considering a realistic sea ice thickness distribution in future atmospheric general circulation model sea ice perturbation experiments.

PARAMETERS COMPARSION OF LEADS DETECTION IN ARCTIC SEA ICE USING CRYOSAT-2 WAVEFORM DATA

Abstract. Leads are only a small part of the polar sea ice structure, but they play a dominant role on the turbulence exchange between the ocean and the atmosphere, they are also important factors about sea ice thickness inversion. Since the early 2000s, Satellite altimetry has been applied to monitor the Arctic sea ice thickness, Satellite altimetry data can be used to distinguish leads and sea ice. In this paper, four parameters including Pulse peakiness (PP), stack standard deviation (SSD), stack kurtosis (SKU) and stack skewness (SSK) are extracted from CryoSat-2 satellite altimetry waveform data. The four parameters are combined into five combinations (PP, PP&SSD, PP&SSD&SKU, PP&SSD&SSK, PP&SSD&SSK&SKU) with constrain conditions to detect the leads. The results of the five methods are compared with MODIS (moderate-resolution imagining spectroradiometer) images and show that, the combination of PP&SSD is better than the single PP, the rest of combinations are the same as the combination of PP&SSD. It turns out, there is no promotion when we add SSK and SKU, successively or simultaneously.

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.

Comparison of Mechanisms for Low-Frequency Variability of Summer Arctic Sea Ice in Three Coupled Models

Abstract In this study the mechanisms for low-frequency variability of summer Arctic sea ice are analyzed using long control simulations from three coupled models (GFDL CM2.1, GFDL CM3, and NCAR CESM). Despite different Arctic sea ice mean states, there are many robust features in the response of low-frequency summer Arctic sea ice variability to the three key predictors (Atlantic and Pacific oceanic heat transport into the Arctic and the Arctic dipole) across all three models. In all three models, an enhanced Atlantic (Pacific) heat transport into the Arctic induces summer Arctic sea ice decline and surface warming, especially over the Atlantic (Pacific) sector of the Arctic. A positive phase of the Arctic dipole induces summer Arctic sea ice decline and surface warming on the Pacific side, and opposite changes on the Atlantic side. There is robust Bjerknes compensation at low frequency, so the northward atmospheric heat transport provides a negative feedback to summer Arctic sea ice variations. The influence of the Arctic dipole on summer Arctic sea ice extent is more (less) effective in simulations with less (excessive) climatological summer sea ice in the Atlantic sector. The response of Arctic sea ice thickness to the three key predictors is stronger in models that have thicker climatological Arctic sea ice.

2014 summer Arctic sea ice thickness and concentration from shipborne observations

ABSTRACTA series of shipborne sea ice observations were performed during the Chinese National Arctic Research Expedition in the Pacific Arctic sector between 2 August 2014 and 1 September 2014. Undeformed sea ice thickness (SIT) as well as area fractions of open water, melt pond, and sea ice (Aw, Ap, and Ai) were monitored using downward-oriented and oblique-oriented cameras. The results show that SIT varied between 20 and 220 cm throughout the whole cruise, with the average and standard deviation equaling 104.9 and 29.1 cm, respectively. Mean Aw and Ai were 0.52 and 0.44 in the marginal ice zone, respectively, while mean Aw decreased to 0.23 and mean Ai increased to 0.73 in the pack ice zone. Limited variation between 0 and 0.32 in Ap was seen throughout the whole cruise. Shipborne sea ice concentration was then rectified and mapped across a large transect to validate estimates derived from the satellite sensors Special Sensor Microwave Imager/Sounder (SSMIS) (25 km) and AMSR2 (25 km). Overestimations were 9.5% and 9.9% for SSMIS and AMSR2 compared with measurements, respectively. The mean areal broadband surface albedo based on shipborne survey increased from 0.07 to 0.66 along the transect between 72°N and 81°N.

Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6

Abstract. Sea ice cover and thickness have substantially decreased in the Arctic Ocean since the beginning of the satellite era. As a result, sea ice strength has been reduced, allowing more deformation and fracturing and leading to increased sea ice drift speed. We use the version 3.6 of the global ocean–sea ice NEMO-LIM model (Nucleus for European Modelling of the Ocean coupled to the Louvain-la-Neuve sea Ice Model), satellite, buoy and submarine observations, as well as reanalysis data over the period from 1979 to 2013 to study these relationships. Overall, the model agrees well with observations in terms of sea ice extent, concentration and thickness. The seasonal cycle of sea ice drift speed is reasonably well reproduced by the model. NEMO-LIM3.6 is able to capture the relationships between the seasonal cycles of sea ice drift speed, concentration and thickness, with higher drift speed for both lower concentration and lower thickness, in agreement with observations. Model experiments are carried out to test the sensitivity of Arctic sea ice drift speed, thickness and concentration to changes in sea ice strength parameter P*. These show that higher values of P* generally lead to lower sea ice deformation and lower sea ice thickness, and that no single value of P* is the best option for reproducing the observed drift speed and thickness. The methodology proposed in this analysis provides a benchmark for a further model intercomparison related to the relationships between sea ice drift speed and strength, which is especially relevant in the context of the upcoming Coupled Model Intercomparison Project 6 (CMIP6).

Reducing Errors in Satellite-Derived Arctic Sea Ice Thicknesses

Salty snow throws off satellite-based estimates of Arctic sea ice thickness by up to 25%. A new method seeks to fix that.

Translation algorithm of the apparent conductivity using the frequency-domain electromagnetic method of a magnetic dipole

In this paper, based on the expression of the relative magnetic anomaly for a frequency-domain magnetic dipole in homogeneous half-space under quasi-static conditions, we derive higher-order approximate formulas of the apparent conductivity. At the same time, it is found that there is a translational and expansionary characteristic between the response curve of the relative magnetic anomaly for the frequency-domain magnetic dipole and underground conductivity and observation frequency. Accordingly, we propose a translation algorithm that can directly calculate the apparent conductivity. By processing the data of the theoretical models and the GEM-2 electromagnetic measurement data from the detection of the thickness of Arctic sea ice, we validate the effectiveness of this method. In comparison with the conventional and the higher-order approximate calculation formula of the apparent conductivity, this method features fast computation speed, high accuracy, and insensitivity to the instrument height. Meanwhile, it also helps explain the burial depth of the conductive layer. Therefore, it is worth popularizing and applying in practical applications.

A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data

Abstract. Sea-ice thickness on a global scale is derived from different satellite sensors using independent retrieval methods. Due to the sensor and orbit characteristics, such satellite retrievals differ in spatial and temporal resolution as well as in the sensitivity to certain sea-ice types and thickness ranges. Satellite altimeters, such as CryoSat-2 (CS2), sense the height of the ice surface above the sea level, which can be converted into sea-ice thickness. Relative uncertainties associated with this method are large over thin ice regimes. Another retrieval method is based on the evaluation of surface brightness temperature (TB) in L-band microwave frequencies (1.4 GHz) with a thickness-dependent emission model, as measured by the Soil Moisture and Ocean Salinity (SMOS) satellite. While the radiometer-based method looses sensitivity for thick sea ice (> 1 m), relative uncertainties over thin ice are significantly smaller than for the altimetry-based retrievals. In addition, the SMOS product provides global sea-ice coverage on a daily basis unlike the altimeter data. This study presents the first merged product of complementary weekly Arctic sea-ice thickness data records from the CS2 altimeter and SMOS radiometer. We use two merging approaches: a weighted mean (WM) and an optimal interpolation (OI) scheme. While the weighted mean leaves gaps between CS2 orbits, OI is used to produce weekly Arctic-wide sea-ice thickness fields. The benefit of the data merging is shown by a comparison with airborne electromagnetic (AEM) induction sounding measurements. When compared to airborne thickness data in the Barents Sea, the merged product has a root mean square deviation (RMSD) of about 0.7 m less than the CS2 product and therefore demonstrates the capability to enhance the CS2 product in thin ice regimes. However, in mixed first-year (FYI) and multiyear (MYI) ice regimes as in the Beaufort Sea, the CS2 retrieval shows the lowest bias.

Sea ice resource selection models for polar bears in the Barents Sea subpopulation

The extent, thickness and age of Arctic sea ice has dramatically declined since the late 1990s, and these trends are predicted to continue. Exploring the habitat use of sea‐ice‐dependent species can help us understand which resources they use and how their distribution responds to a changing environment. The goal of this study was to develop predictive models of the habitat use of an Arctic apex predator. Polar bear Ursus maritimus habitat use in the Barents Sea subpopulation was modelled with seasonal resource selection functions (RSFs) using satellite‐linked telemetry data from 294 collars deployed on female polar bears between 1991 and 2015. Polar bears selected habitat in the Marginal Ice Zone, with a preference for intermediate sea ice concentrations (40–80%). They spent most time in areas with relatively short travel distances to 15 or 75% ice concentration, and during spring and autumn they exhibited a preference for sea ice areas over the continental shelf or over shallower bathymetry). Predictions of the distribution of polar bears in the Barents Sea area can be made for specific sea ice scenarios using these models. Two such predictive distribution maps based on the autumn seasonal model were made and validated against two independent polar bear survey datasets collected in August 2004 and August 2015. The distribution of optimal polar bear habitat has shifted strongly northwards in all seasons of the year during the 25 yr study period.

Airborne Measurements of Snow Thickness: Using ultrawide-band frequency-modulated-continuous-wave radars

Frequency-modulated-continuous-wave (FM-CW) radar has been used extensively for the airborne measurement of snow thickness over sea ice and the mapping of annual accumulation over land ice. In contrast to conventional in situ measurements, FM-CW radar, when operated onboard an airborne platform, can be a useful tool for widearea surveys of snow deposition. Since the early 2000s, the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas (KU) has designed, developed, and deployed airborne ultrawide-band (UWB) FM-CW radars, called Snow Radars, on National Science Foundation (NSF)-, NASA-, Naval Research Laboratory (NRL)-, and Alfred Wegener Institute (AWI)-provided aircraft in both Arctic and Antarctic regions and generated a large amount of snow data products. In addition to the significant standalone value of the snow-thickness measurements, these data are being used in estimating Arctic sea ice thickness, which is a key variable in the study of atmosphere-ocean-ice interactions. This article provides a review of snow remote sensing techniques with airborne FM-CW radars to document the operating principle, design, and evolution of CReSIS' UWB FM-CW radars and discuss and promote understanding of the advantages and limitations associated with these systems.

Summer Enhancement of Arctic Sea Ice Volume Anomalies in the September-Ice Zone

Because of its persistence on seasonal time scales, Arctic sea ice thickness (SIT) is a potential source of predictability for summer sea ice extent (SIE). New satellite observations of SIT represent an opportunity to harness this potential predictability via improved thickness initialization in seasonal forecast systems. In this work, the evolution of Arctic sea ice volume anomalies is studied using a 700-yr control integration and a suite of initialized ensemble forecasts from a fully coupled global climate model. This analysis is focused on the September sea ice zone, as this is the region where thickness anomalies have the potential to impact the SIE minimum. The primary finding of this paper is that, in addition to a general decay with time, sea ice volume anomalies display a summer enhancement, in which anomalies tend to grow between the months of May and July. This summer enhancement is relatively symmetric for positive and negative volume anomalies and peaks in July regardless of the initial month. Analysis of the surface energy budget reveals that the summer volume anomaly enhancement is driven by a positive feedback between the SIT state and the surface albedo. The SIT state affects surface albedo through changes in the sea ice concentration field, melt-onset date, snow coverage, and ice thickness distribution, yielding an anomaly in the total absorbed shortwave radiation between May and August, which enhances the existing SIT anomaly. This phenomenon highlights the crucial importance of accurate SIT initialization and representation of ice–albedo feedback processes in seasonal forecast systems.

Comparison of Arctic sea ice thickness and snow depth estimates from CFSR with in situ observations

Sea ice growth is modulated by snow cover, and understanding this relationship requires an accurate determination of snow depth. However, a lack of in situ measurements complicates understanding of the interaction of snow depth with sea ice growth. We evaluated the accuracy of Climate Forecast System Reanalysis (CFSR) data for snow depth and sea ice thickness to study the change of snow depth on Arctic sea ice. We compared CFSR and snow depth data from 35 drifting buoys in 2002–2013. The mean annual cycle of CFSR snow depth corresponded well with the buoy data. However, the CFSR data had a positive bias during winter (10–20 cm) and spring (5–25 cm), and a negative bias during summer (−25–0 cm) and autumn (−5–10 cm). The CFSR data showed increases in snow depth from 1979 to 2013 over the Beaufort and northern Chukchi Seas during November. Significant positive trends in precipitation contributed to increased snow depth in this region when sea ice began to form. The results of model experiments using a 1-D thermodynamic sea ice model in the CFSR demonstrated a recommended value of snow thermal conductivity (0.16 W m−1 K−1), and suggested that the sea ice growth was effectively restricted by the recent increase in snow depth on thin ice during winter.

Ship accessibility predictions for the Arctic Ocean based on IPCC CO2 emission scenarios

Changes in the extent of Arctic sea ice, which have resulted from climate change, offer new opportunities to use the Northern Sea Route (NSR) and Northwest Passage (NWP) for shipping. However, choosing to navigate the Arctic Ocean remains challenging due to the limited accessibility of ships and the balance between economic gain and potential risk. As a result, more precise and detailed information on both weather and sea ice change in the Arctic are required. In this study, a high-resolution global AGCM was used to provide detailed information on the extent and thickness of Arctic sea ice. For this simulation, we have simulated the AMIP-type simulation for the present-day climate during 31 years from 1979 to 2009 with observed SST and Sea Ice concentration. For the future climate projection, we have performed the historical climate during 1979-2005 and subsequently the future climate projection during 2010-2099 with mean of four CMIP5 models due to the two Representative Concentration Pathway scenarios (RCP 8.5 and RCP 4.5). First, the AMIP-type simulation was evaluated by comparison with observations from the Hadley Centre sea-ice and Sea Surface Temperature (HadlSST) dataset. The model reflects the maximum (in March) and minimum (in September) sea ice extent and annual cycle. Based on this validation, the future sea ice extents show the decreasing trend for both the maximum and minimum seasons and RCP 8.5 shows more sharply decreasing patterns of sea ice than RCP 4.5. Under both scenarios, ships classified as Polar Class (PC) 3 and Open-Water (OW) were predicted to have the largest and smallest number of ship-accessible days (in any given year) for the NSR and NWP, respectively. Based on the RCP 8.5 scenario, the projections suggest that after 2070, PC3 and PC6 vessels will have year-round access across to the Arctic Ocean. In contrast, OW vessels will continue to have a seasonal handicap, inhibiting their ability to pass through the NSR and NWP.

Sea ice thickness and recent Arctic warming

AbstractThe climatic impact of increased Arctic sea ice loss has received growing attention in the last years. However, little focus has been set on the role of sea ice thickness, although it strongly determines surface heat fluxes. Here ensembles of simulations using the EC‐Earth atmospheric model (Integrated Forecast System) are performed and analyzed to quantify the atmospheric impacts of Arctic sea ice thickness change since 1982 as revealed by the sea ice model assimilation Global Ice‐Ocean Modeling and Assimilation System. Results show that the recent sea ice thinning has significantly affected the Arctic climate, while remote atmospheric responses are less pronounced owing to a high internal atmospheric variability. Locally, the sea ice thinning results in enhancement of near‐surface warming of about 1°C per decade in winter, which is most pronounced over marginal sea ice areas with thin ice. This leads to an increase of the Arctic amplification factor by 37%.