Sea Ice Thickness Measurements Research Articles

Прогноз толщины ледяного покрова в прибрежных областях Карского и Охотского морей

Based on the CICE viscoplastic sea ice model, the ice cover of the Gulf of Ob and the northwestern Okhotsk Sea was simulated for the winter seasons of 2021/2022, 2022/2023, and 2023/2024. The description of the seasonal variability of sea ice characteristics in these areas and examples of their numerical modeling are given. Using atmospheric forcing based on the WRF-ARW nonhydrostatic atmospheric model, the sea ice thickness in the northwestern Okhotsk Sea was predicted for April 2024. A comparison of the forecasts with the ice maps of the Hydrometcentre of Russia and sea ice thickness measurements at the Ayan and Bolshoy Shantar stations gave satisfactory results.

Baltic sea ice thickness estimation based on X-band SAR data and background information

Abstract In this study an operational sea ice thickness (SIT) estimation algorithm, based on HH-polarized X-band synthetic aperture radar (SAR) data, background information from the most recent, typically from the previous day, available daily Baltic Sea ice chart and the operational Finnish Meteorological Institute (FMI) thermodynamic ice model, was developed and evaluated. The algorithm was designed to complement the C-band SAR SIT algorithm developed earlier at FMI and applied daily as part of the operational Copernicus Marine Service (CMS). The X-band SIT algorithm was developed by utilizing the sea ice thickness measurements made onboard the Finnish and Swedish ice breakers during two winters seasons: 2021–2022 and 2022–2023. The former season measurements were used for defining the algorithm parameters and the later season for evaluation of the algorithm performance. According to the evaluation metrics the X-band algorithm performance is slightly better than that of the operational CMS C-band SAR SIT algorithm, indicating its suitability for operational use in CMS.

SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea

Abstract. Accurate satellite measurements of the thickness of Antarctic sea ice are urgently needed but pose a particular challenge. The Antarctic data presented here were produced using a method to derive the sea ice thickness from 1.4 GHz brightness temperatures previously developed for the Arctic, with only modified auxiliary data. The ability to observe the thickness of thin sea ice using this method is limited to cold conditions, meaning it is only reasonable during the freezing period, typically March to October. The Soil Moisture and Ocean Salinity (SMOS) level-3 sea ice thickness product contains estimates of the sea ice thickness and its uncertainty up to a thickness of about 1 m. The sea ice thickness is provided as a daily average on a polar stereographic projection grid with a sample resolution of 12.5 km, while the SMOS brightness temperature data used have a footprint size of about 35–40 km in diameter. Data from SMOS have been available since 2010, and the mission's operation has been extended to continue until at least the end of 2025. Here we compare two versions of the SMOS Antarctic sea ice thickness product which are based on different level-1 input data (v3.2 based on SMOS L1C v620 and v3.3 based on SMOS L1C 724). A validation is performed to generate a first baseline reference for future improvements of the retrieval algorithm and synergies with other sensors. Sea ice thickness measurements to validate the SMOS product are particularly rare in Antarctica, especially during the winter season and for the valid range of thicknesses. From the available validation measurements, we selected datasets from the Weddell Sea that have varying degrees of representativeness: Helicopter-based EM Bird (HEM), Surface and Under-Ice Trawl (SUIT), and stationary Upward-Looking Sonars (ULS). While the helicopter can measure hundreds of kilometres, SUIT's use is limited to distances of a few kilometres and thus only captures a small fraction of an SMOS footprint. Compared to SMOS, the ULS are point measurements and multi-year time series are necessary to enable a statistically representative comparison. Only four of the ULS moorings have a temporal overlap with SMOS in the year 2010. Based on selected averaged HEM flights and monthly ULS climatologies, we find a small mean difference (bias) of less than 10 cm and a root mean square deviation of about 20 cm with a correlation coefficient R > 0.9 for the valid sea ice thickness range between 0 and about 1 m. The SMOS sea ice thickness showed an underestimate of about 40 cm with respect to the less representative SUIT validation data in the marginal ice zone. Compared with sea ice thickness outside the valid range, we find that SMOS strongly underestimates the real values, which underlines the need for combination with other sensors such as altimeters. In summary, the overall validity of the SMOS sea ice thickness for thin sea ice up to a thickness of about 1 m has been demonstrated through validation with multiple datasets. To ensure the quality of the SMOS product, an independent regional sea ice extent index was used for control. We found that the new version, v3.3, is slightly improved in terms of completeness, indicating fewer missing data. However, it is worth noting that the general characteristics of both datasets are very similar, also with the same limitations. Archived data are available in the PANGAEA repository at https://doi.org/10.1594/PANGAEA.934732 (Tian-Kunze and Kaleschke, 2021) and operationally at https://doi.org/10.57780/sm1-5ebe10b (European Space Agency, 2023).

An Improved NLCS Algorithm Based on Series Reversion and Elliptical Model Using Geosynchronous Spaceborne–Airborne UHF UWB Bistatic SAR for Oceanic Scene Imaging

Geosynchronous spaceborne–airborne (GEO-SA) ultra-high-frequency ultra-wideband bistatic synthetic aperture radar (UHF UWB BiSAR) provides high-precision images for marine and polar environments, which are pivotal in glacier monitoring and sea ice thickness measurement for polar ocean mapping and navigation. Contrasting with traditional high-frequency BiSAR, it faces unique challenges, such as the considerable spatial variability, significant range–azimuth coupling, and vast volumes of echo data, which impede high-resolution image reconstruction. This paper presents an improved bistatic nonlinear chirp scaling (NLCS) algorithm for imaging oceanic scenes with GEO-SA UHF UWB BiSAR. This methodology extends the two-dimensional (2-D) spectrum up to the sixth order via the method of series reversion (MSR) to meet accuracy demands and then employs an elliptical model to elucidate the alterations in the azimuth frequency modulation (FM) rate mismatch. Initially, the imaging geometry and signal model are introduced, and then a separation of bistatic slant ranges based on the configuration is proposed. In addition, during range processing, after eliminating linear range cell migration (RCM), the derivation process for the sixth-order 2-D spectrum is detailed and an improved filter is applied to correct the high-order RCM. Finally, during azimuth processing, the causes of the FM rate mismatch are analyzed, a cubic perturbation function derived from the elliptical model is used for FM rate equalization, and a unified sixth-order filter is applied to complete the azimuth compression. Experimental results with point targets and natural oceanic scenes validate the outstanding efficacy of the proposed NLCS algorithm, particularly in imaging quality enhancements for GEO-SA UHF UWB BiSAR.

Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates

Abstract. In this study, a new method to assimilate freeboard (FB) derived from satellite radar altimetry is presented with the goal of improving the initial state of sea ice thickness predictions in the Arctic. In order to quantify the improvement in sea ice thickness gained by assimilating FB, we compare three different model runs: one reference run (refRun), one that assimilates only sea ice concentration (SIC) (sicRun), and one that assimilates both SIC and FB (fbRun). It is shown that estimates for both SIC and FB can be improved by assimilation, but only fbRun improved the FB. The resulting sea ice thickness is evaluated by comparing sea ice draft measurements from the Beaufort Gyre Exploration Project (BGEP) and sea ice thickness measurements from 19 ice mass balance (IMB) buoys deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The sea ice thickness of fbRun compares better than refRun and sicRun to the longer BGEP observations more poorly to the shorter MOSAiC observations. Further, the three model runs are compared to the Alfred Wegener Institute (AWI) weekly CryoSat-2 sea ice thickness, which is based on the same FB observations as those that were assimilated in this study. It is shown that the FB and sea ice thickness from fbRun are closer to the AWI CryoSat-2 values than the ones from refRun or sicRun. Finally, comparisons of the abovementioned observations and both the fbRun sea ice thickness and the AWI weekly CryoSat-2 sea ice thickness were performed. At the BGEP locations, both fbRun and the AWI CryoSat-2 sea ice thickness perform equally. The total root-mean-square error (RMSE) at the BGEP locations equals 30 cm for both sea ice thickness products. At the MOSAiC locations, fbRun's sea ice thickness performs significantly better, with a total 11 cm lower RMSE.

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.

Continuous measurement of sea ice freeboard with tide gauges and GNSS interferometric reflectometry

Sea ice thickness is an important climate indicator and is often monitored in the form of freeboard or total freeboard (with snow cover) using satellite altimetry. However, satellites often have a long revisit interval and freeboard measurements in coastal areas can be challenged by land, deformed ice and limited leads. Using a combination of tide gauge for sea level measurements and a coastal GNSS station for sea ice or snow surface height measurements based on GNSS interferometric reflectometry (GNSS-IR), I demonstrate freeboard measurements with high temporal resolution (hourly). In a 4.5-year period from late 2016 to middle 2021, freeboards measured with this method at Cape Roberts, western Ross Sea in Antarctica exhibit a clear annual cycle of ice growth and ablation in the range of ~0–40 cm, and a rapid decrease occurring in Antarctic summer when ocean water temperature increases abruptly. For places where the amplitudes of long-term tides are significantly smaller than the range of annual freeboard variation, GNSS-IR can independently measure both tides and freeboards. Compared to conventional sea ice thickness or freeboard measurements, the tide gauge and GNSS-IR combination method is safe, inexpensive, can distinguish spatial variation of freeboard over a distance of tens to hundreds of meters, and allows continuous monitoring of an area of a few square kilometers.

Mapping the thickness of sea ice in the Arctic as an example of using data from a ship-based television complex for operational hydrometeorological support of maritime activities

Sea ice charts produced by the ice services of the world are among the most widely used sources of information about sea ice conditions in the Arctic. The absolute majority of sea ice charts are based on visual expert analysis of satellite imagery accompanied by auxiliary data including ground information from coastal stations and ships navigating the Northern Sea Route (NSR). Ground measurements of sea ice thickness are necessary for validating the results of satellite imagery interpretation. Shipboard observations are highly valuable because, unlike coastal stations, the ships provide information about sea ice cover straight on the navigational routes, not in the coastal areas of land-fast ice, where the thickness values are not fully representative of the ice in the open sea. However, the current system of shipboard observations used by commercial fleets often does not meet the reliability requirements due to the human factor involved in the process of data collection. In the early 2000s, the Arctic and Antarctic Research Institute (AARI) suggested a new methodology for shipboard ice thickness measurement. A ship-based television complex (STC) was developed in order to exclude the human factor and standardize observations. The inaccuracy value was estimated as 3.8 % of the real thickness. By 2018, STC had been upgraded to a new ship-based television meteorological complex (STMC) allowing continuous automatic measurement of ice thickness and many other related hydrometeorological parameters during the entire voyage. The automatic and autonomous operation of the new equipment allows placing it on board the ship without the need for an ice specialist to be permanently present. It means that STMC can be used by commercial fleets, which constantly increase the number of Arc7 ice class vessels they use. For economic reasons, reinforced ice class vessels, whose number is growing, represent the only available infrastructure suitable for the deployment of distributed network providing operational hydrometeorological monitoring on the NSR. A comparison of STC data with AARI ice charts has revealed that real-time transmission of STC data from ships to the ice service office could increase the accuracy of ice charts and, as a consequence, the quality of the entire system of hydrometeorological informational support of maritime activities in the Arctic.

Retrieval of daily sea ice thickness from AMSR2 passive microwave data using ensemble convolutional neural networks

ABSTRACT Recently, measurement of sea ice thickness (SIT) has received increasing attention due to the importance of thinning ice in the context of global warming. Although altimeter sensors onboard satellite missions enable continuous SIT measurements over larger areas compared to in situ observations, these sensors are inadequate for mapping daily Arctic SIT because of their small footprints. We exploited passive microwave data from AMSR2 (Advanced Microwave Scanning Radiometer 2) by incorporating a state-of-the-art deep learning (DL) approach to address this limitation. Passive microwave data offer better temporal resolutions than those from a single altimeter sensors, but are rarely used for SIT estimations due to their limited physical relationship with SIT. In this study, we proposed an ensemble DL model with different modalities to produce daily pan-Arctic SIT retrievals. The proposed model determined the hidden and unknown relationships between the brightness temperatures of AMSR2 channels and SITs measured by CryoSat-2 (CS2) from the extended input features defined by our feature augmentation strategy. Although AMSR2-based SITs agreed well with CS2-derived gridded SIT values, they had similar uncertainties and errors in the CS2 SIT measurements, particularly for thin ice. However, based on quantitative validations using long-term unseen data and IceBridge data, the proposed retrieval model consistently generated SITs from AMSR2 at 25 km spatial resolution, regardless of time and space.

Sea Ice Thickness Retrieval Based on GOCI Remote Sensing Data: A Case Study

The accurate monitoring and measurement of sea ice thickness (SIT) is crucial for understanding climate change and preventing economic losses caused by sea ice disasters near coastal regions. In this study, a new method is developed to retrieve the SIT in Liaodong Bay (LDB) based on the Rayleigh-corrected reflectance from Geostationary Ocean Color Imager (GOCI) images in the winters of 2012 and 2013. Compared with previously developed SIT retrieval methods (e.g., the method based on the thermodynamic principle of sea ice) using remote sensing data, our method has significant advantages with respect to the inversion accuracy (achieving retrieval skill scores as high as 0.86) and spatiotemporal resolution. Moreover, there is no significant increase in the computational cost with this method, which makes the method suitable for operational SIT retrieval in the global ocean.

Comparison of direct measurements of sea ice thickness and snow height, CryoSat-2 observations and PIOMAS numerical estimates

The paper presents the results of comparison of contact measurements of ice thicknesses and snow heights performed at the points of the hydrological stations of the “Transarktika-2019” expedition in April 2019 north of Franz-Josef Land archipelago, with altimetry observations of the Cryosat-2 satellite and numerical estimates of the PIOMAS (Pan-Arctic numerical Ice and Ocean Modeling system and data Assimilation). A significantly better correspondence is predictably shown between the variability of the ice thickness directly measured and observed using the CryoSat-2 satellite than that for the numerical PIOMAS system estimates. A trial correction of the algorithm for calculating the ice thickness by replacing the climatic values of the ice density, snow density and height with data from direct measurements also predictably improves the quality of calculating the ice thickness from satellite observations. The mean / route mean square differences obtained for ice thicknesses (+44/+96 cm for uncorrected and +30/+95 cm for corrected CryoSat-2 satellite, –14/+81 cm for PIOMAS system) and snow height (–4/+12 cm for CryoSat-2 satellite, –15/+12 cm for PIOMAS system) show the scale of uncertainty in estimating sea ice thickness and snow height for areas dominated by medium and thick first-year ice. An anomaly of the ice thicknesses observed during the expedition is given in comparison with the background characteristics based on historical ice charting data for 1970s — 1990s, earlier High-Arctic aircraft “Sever” expeditions during 1950s — 1970s and the stated remote observations and numerical estimates for 2000s — 2019. Comparison shows that the AARI expedition was actually carried out in one of the most favorable years for ice research in the last decade for this region — the average ice thickness in April 2019 was 15 — 28 cm higher than that for the interval 2011 — 2019 with a slightly lower (1 — 2 cm) height of the snow cover. In the earlier period of the 1970 — 1990s this area was characterized by significantly thicker old ice with characteristic thicknesses ~ 60 cm more than in April 2019.

Direct inference of first-year sea ice thickness using broadband acoustic backscattering.

Accurate measurements of sea ice thickness are critical to better understand climate change, to provide situational awareness in ice-covered waters, and to reduce risks for communities that rely on sea ice. Nonetheless, remotely measuring the thickness of sea ice is difficult. The only regularly employed technique that accurately measures the full ice thickness involves drilling a hole through the ice. Other presently used methods are either embedded in or through the ice (e.g., ice mass balance buoys) or calculate thickness from indirect measurements (e.g., ice freeboard from altimetry; ice draft using sonars; total snow and ice thickness using electromagnetic techniques). Acoustic techniques, however, may provide an alternative approach to measure the total ice thickness. Here laboratory-grown sea ice thicknesses, estimated by inverting the time delay between echoes from the water-ice and ice-air interfaces, are compared to those measured using ice cores. A time-domain model capturing the dominant scattering mechanisms is developed to explore the viability of broadband acoustic techniques for measuring sea ice thickness, to compare with experimental measurements, and to investigate optimal frequencies for in situ applications. This approach decouples ice thickness estimates from water column properties and does not preclude ice draft measurements using the same data.

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.

The 2018 North Greenland polynya observed by a newly introduced merged optical and passive microwave sea-ice concentration dataset

Abstract. Observations of sea-ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea-ice concentrations. This yields a merged sea-ice concentration product combining the gap-free spatial coverage of the passive microwave sea-ice concentration and the 1 km resolution of the thermal infrared sea-ice concentration. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea-ice concentration product resolves leads at sea-ice concentrations between 60 % and 90 %. They are not resolved by the coarser passive microwave sea-ice concentration product. The benefit of the merged product is most pronounced during the formation of the polynya. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly below 0 ∘C and thus more than 20 ∘C higher than normal. Two estimates of thermodynamic sea-ice growth yield sea-ice thicknesses of 60 and 65 cm at the end of March in the area opened by the polynya. This differed from airborne sea-ice thickness measurements, indicating that sea-ice growth processes in the polynya are complicated by rafting and ridging. A sea-ice volume of 33 km3 was produced thermodynamically.

The Rapidly Changing Arctic and Its Impact on Global Climate

Arctic sea ice has significant seasonal variability. Prior to the 2000s, it retreated about 15% in summer and fully recovered in winter. However, by the year 2007, Arctic sea ice extent experienced a catastrophic decline to about 4.28×106 km2, which was 50% lower than conditions in the 1950s to the 1970s (Serreze et al., 2008). That was a record low over the course of the modern satellite record, since 1979 (note that the year 2012 became the new record low). This astonishing event drew wide-ranging attention in 2007-2009 during the 4th International Polar Year. The dramatic decline of sea ice attracts many scientists' interest and has become the focus of intense research since then. Currently, sea ice retreat is not only appearing around the marginal ice zone, but also in the pack ice inside the central Arctic (Zhao et al., 2018). In fact, premonitory signs had already been seen through other evidence. Before the disintegration of the Soviet Union, US naval submarines had been conducting an extensive survey under the sea ice and taking measurements of sea ice thickness. Their measurements revealed a gradual decrease of ice thickness to 1.8 m during winter by the end of the 20th century, in contrast to the climatological mean of 3.1m (Rothrock et al., 1999). However, this alarming result did not draw much attention since the Arctic was still severely cold at that time.

A Facet-Based Numerical Model for Simulating SAR Altimeter Echoes From Heterogeneous Sea Ice Surfaces

Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multiscale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler synthetic aperture radar (SAR) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facet-based models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, and roll) and sea ice properties (physical properties, roughness, and presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cm—potentially underestimating sea ice thickness by around 50 cm. We present a set of “ideal” waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness, and snow depth, from SAR altimeter observations.

A method for correcting seal-borne oceanographic data and application to the estimation of regional sea ice thickness

Abstract The high-latitude oceans surrounding Antarctica are substantially undersampled compared to lower latitudes. Mammal based instruments such as Conductivity-Temperature-Depth Satellite Relay Data Loggers (CTD-SRDLs) present one possible solution. Unfortunately, these are subject to instrument-dependent offsets in absolute salinity. This study investigates a set of satellite-transmitted data collected by CTD-SRDLs mounted on Weddell seals (Leptonychotes weddellii) in the South-western Ross Sea in 2011. The uncorrected salinity offset between devices was found to be up to 1.4 g kg−1, making the data unsuitable for some oceanographic studies without correction. Here, a correction method was developed that uses profiles from pairs of CTD-SRDLs that are considered to be co-located and to sample the same body of water if they occur within defined time and space windows. Using least squares, a best-fit solution to the matrix of offsets in co-located pairs was found that reduces salinity offsets between the CTD-SRDLs. These offsets are smaller than the original offsets by a factor of 10. A calibrated reference instrument, that was co-located with some of the devices, provided further improvement in the absolute accuracy of all the CTD-SRDLs. Using the corrected CTD-SRDL data we estimate the rejection of salt into the water column by sea ice formation, and derived the time evolution of sea ice thickness in the South-western Ross Sea. Our estimates of regional sea ice thickness are in agreement with direct sea ice thickness measurements taken over a limited area in November 2011, providing further affirmation of our method.

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (fp) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two ~5.2 km long profiles over FYI- and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring fp using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring fp. A stronger negative correlation was observed between FYI thickness and fp (Spearman rs = −0.85) compared to FYI roughness and fp (rs = −0.52). The association between MYI thickness and fp was also negative (rs = −0.56), whereas there was no association between MYI roughness and fp. 47% of spring fp variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high fp) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low fp). This work provides an important contribution towards the parameterizations of fp in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring fp.

Can we extend local sea-ice measurements to satellite scale? An example from the N-ICE2015 expedition

ABSTRACTKnowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% ‘thin’, 74.4% ‘level’, and 14.4% ‘deformed’. The area fractions of the simplified classes from ground- and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites.

Near-real-time Arctic sea ice thickness and volume from CryoSat-2

Abstract. Timely observations of sea ice thickness help us to understand the Arctic climate, and have the potential to support seasonal forecasts and operational activities in the polar regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release data set is typically 1 month due to the time required to determine precise satellite orbits. We use a new fast-delivery CryoSat-2 data set based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea-ice-thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with a mean thickness difference of 0.9 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast-delivery product also provides measurements of Arctic sea ice thickness within 3 days of acquisition by the satellite, and a measurement is delivered, on average, within 14, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea-ice-thickness data set provides an additional constraint for short-term and seasonal predictions of changes in the Arctic ice cover and could support industries such as tourism and transport through assimilation in operational models.