Thin Ice Area Research Articles

A Model‐Based Investigation of the Recent Rebound of Shelf Water Salinity in the Ross Sea

AbstractIntense atmosphere‐ocean‐ice interactions in the Ross Sea play a vital role in global overturning circulation by supplying saline and dense shelf waters. Since the 1960s, freshening of the Ross Sea shelf water has led to a decline in Antarctic Bottom Water formation. However, during 2012–2018, salinity of the western Ross Sea has rebounded. This study adopts a global ocean‐sea ice model to investigate the causes of this salinity rebound. Model‐based surface salinity budget analysis indicates that the salinity rebound was driven by increased brine rejection from sea ice formation, triggered by nearly equal effects of local anomalous winds and surface heat flux. The local divergent wind anomalies promoted local sea ice formation by creating a thin ice area, while cooling heat flux anomaly decreased the surface temperature, increasing sea ice production as well. This highlights the importance of understanding local climate variability in projecting future dense shelf water change.

Arctic Thin Ice Detection Using AMSR2 and FY-3C MWRI Radiometer Data

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature (TB) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath TB datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of TB signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks.

Reconstruct the AMSR-E/2 thin ice thickness algorithm to create a long-term time series of sea-ice production in Antarctic coastal polynyas

This study presented an extended time series of ice production in Antarctic coastal polynyas for 20 years of 2002–21 using high spatial resolution satellite data from passive microwave sensors: the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). For this, we reconstructed the previously presented thin ice thickness algorithms and re-estimated ice production by replacing atmospheric input data for the heat flux calculations with the newly released ECMWF Reanalysis v5 (ERA5). The consistency of ice production between AMSR-E and AMSR2, whose operation periods do not overlap, was confirmed from comparisons with ice production by the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) with relatively coarse spatial resolution. The open ocean area, which cannot be detected from the thin ice thickness algorithm, was defined using sea-ice concentration (SIC) from AMSR-E and AMSR2. The satellite derived SIC has been suggested to be underestimated in the new thin ice (polynya) area. The effect of the underestimation on the ice production estimation was evaluated. The underestimation of SIC from three major algorithms was also investigated from comparisons with the thin ice thickness presented in this study. The coastal polynya dataset covering 20 years with the higher spatial resolution presented in this study is vital for climate-change-related studies.

Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery

Abstract. Areas of thin sea ice in the polar regions not only are experiencing the highest rate of sea-ice production but also are, therefore, important hot spots for ocean ventilation as well as heat and moisture exchange between the ocean and the atmosphere. Through co-location of (1) an unsupervised waveform classification (UWC) approach applied to CryoSat-2 radar waveforms with (2) Moderate Resolution Imaging Spectroradiometer-derived (MODIS) thin-ice-thickness estimates and (3) Sentinel-1A/B synthetic-aperture radar (SAR) reference data, thin-ice-based waveform shapes are identified, referenced, and discussed with regard to a manifold of waveform shape parameters. Here, strong linear dependencies are found between binned thin-ice thickness up to 25 cm from MODIS and the CryoSat-2 waveform shape parameters that show the possibility of either developing simple correction terms for altimeter ranges over thin ice or directing adjustments to current retracker algorithms specifically for very thin sea ice. This highlights the potential of CryoSat-2-based SAR altimetry to reliably discriminate between occurrences of thick sea ice, open-water leads, and thin ice within recently refrozen leads or areas of thin sea ice. Furthermore, a comparison to the ESA Climate Change Initiative's (CCI) CryoSat-2 surface type classification with classes sea ice, lead, and unknown reveals that the newly found thin-ice-related waveforms are divided up almost equally between unknown (46.3 %) and lead type (53.4 %) classifications. Overall, the UWC results in far fewer unknown classifications (1.4 % to 38.7 %). Thus, UWC provides more usable information for sea-ice freeboard and thickness retrieval and at the same time reduces range biases from thin-ice waveforms processed as regular sea ice in the CCI classification.

Mapping of Active Frazil and Sea Ice Production in the Northern Hemisphere, With Comparison to the Southern Hemisphere

AbstractFrazil ice in the coastal polynyas has been increasingly recognized as important for efficient sea ice production, associated dense water formation, transport of particulate matter and consequent biological production. However, it has not been well understood where and to what degree active‐frazil areas occur in the Northern Hemisphere polynyas. We presented the first mapping of active frazil in the Northern Hemisphere, using sea ice data during September–May for 2002/2003–2010/2011, created from an Advanced Microwave Scanning Radiometer for the Earth Observing System thin ice algorithm that can detect active frazil areas. The North Water, Northeast Water, Anadyr, and St. Lawrence Island polynyas were found to have a relatively high occurrence rate of active frazil compared with other polynyas. We improved the estimation of sea ice production by discriminating between active frazil and thin solid ice and found that previous algorithms tended to overestimate sea ice production, particularly for polynyas dominated by thin solid ice such as the Okhotsk Northwestern polynya. We also made the first global comparison of polynya characteristics for all the major coastal polynyas. The annual ice production rate is 3–4 m yr−1 in the Northern Hemisphere polynyas and 6–9 m yr−1 in the Antarctic coastal polynyas. This large difference is mainly explained by the less frequent occurrence of active frazil and thin ice areas in the Northern Hemisphere polynyas owing to a lower occurrence of strong wind events.

PROCEDURE OF EXTRACTING THIN ICE AREAS IN THE SEA OF OKHOTSK USING MODIS DATA

Abstract. The latest IPCC report clearly stated that the human influence is the main reason of sea ice reduction in the Arctic. Importance of sea ice monitoring from space is increasing. In addition, the heat flux of ice in thin ice areas is strongly affected by the ice thickness difference. Therefore, ice thickness is one of the important parameters of sea ice. The authors have been studying on extracting thin ice areas using optical sensor such as MODIS for years. In this paper, the authors summarized the procedure of our study starting from comparing in-situ measurement of ice thickness with high resolution optical sensor RSI data, and finally developing the thin ice area extraction algorithm using MODIS. Estimating ice thickness from optical sensor data is not easy. However, through our study, the authors have verified the possibility of extracting thin ice areas using optical sensor data observed from satellites. In this study, the authors define “thin ice areas” as ice which thickness is less than about 30 cm with reflectance much lower than thick ice. The basic idea of the algorithm is to use the scatterplots of MODIS band 1 & 2 reflectance and extract thin ice areas using the distribution difference of thin ice against water, thick ice, clouds. Not all but most of the thin ice areas could be extracted using the algorithm.

A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS

Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.

A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic

NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To further improve the detection accuracy of the sea ice concentration (SIC), we demonstrate a new processing chain that can be used to convert the along-track sea ice freeboard products (ATL10) obtained by ICESat-2 into the SIC, with our initial efforts being focused on the Arctic. For this conversion, we primarily make use of the classification results from the type (sea ice or lead) and segment length data gathered from ATL10. The along-track SIC is the ratio of the area that is covered by sea ice segments to the area of all of the along-track segments. We generated a monthly gridded SIC product with a 25 km resolution and compared this to the NSIDC Climate Data Record (CDR) sea ice concentration. The highest correlation was determined to be 0.7690 in September at high latitudes and the lowest correlation was found to be 0.8595 in June at mid-latitudes. The regions with large standard deviations in summer and autumn are mainly distributed in the thin-ice areas at mid-latitudes. In the Laptev Sea and Kara Sea of east Siberia, the differences in the standard deviation were large; the maximum bias was −0.1566, in November, and the minimum bias was −0.0216, in June. ICESat-2 shows great potential for the accurate estimation of the SIC.

Improving the Met Office's Forecast Ocean Assimilation Model (FOAM) with the assimilation of satellite‐derived sea‐ice thickness data from CryoSat‐2 and SMOS in the Arctic

AbstractDerived from two complementary satellites, CryoSat‐2 and Soil Moisture and Ocean Salinity (SMOS), sea ice thickness (SIT) data are assimilated into the Met Office's global ocean–sea ice forecasting system, FOAM, using a 3D‐Var assimilation scheme, NEMOVAR. CryoSat‐2 along‐track SITs, which are converted from freeboard measurements using the model snow depth, and a daily, gridded SMOS SIT product are used in the assimilation to constrain the Arctic sea ice thickness. When using only CryoSat‐2 assimilation, SIT forecast fields within the ice pack are greatly improved with respect to independent airborne measurements. However, the positive impacts of CryoSat‐2 assimilation in thick ice regions are counteracted by an SIT overestimation in areas of thin ice, due to biased freeboard measurements there. Adding the SMOS assimilation results in much thinner SITs in those regions, which performs better than the control when compared to SIT objective analyses and mooring measurements in the Beaufort and Barents Seas. Furthermore, SMOS assimilation enhances the short‐term predictive skill of the marginal sea‐ice concentration relative to the control. This is translated into a consistent retreat of the sea‐ice covered areas in the 5‐day forecasts during March 2017, which is in better agreement with independent ice edge products. This work successfully demonstrates improvements in FOAM sea ice when SIT observations from both CryoSat‐2 and SMOS are assimilated, representing an important step towards the operational implementation of SIT assimilation within Met Office forecasting systems.

Improved SSM/I Thin Ice Algorithm with Ice Type Discrimination in Coastal Polynyas

AbstractLong-term quantification of sea ice production in coastal polynyas (thin sea ice areas) is an important issue to understand the global overturning circulation and its changes. The Special Sensor Microwave Imager (SSM/I), which has nearly 30 years of observation, is a powerful tool for that purpose owing to its ability to detect thin ice areas. However, previous SSM/I thin ice thickness algorithms differ between regions, probably due to the difference in dominant type of thin sea ice in each region. In this study, we developed an SSM/I thin ice thickness algorithm that accounts for three types of thin sea ice (active frazil, thin solid ice, and a mixture of two types), using the polarization and gradient ratios. The algorithm is based on comparison with the ice thickness derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) for 22 polynya events off the Ross Ice Shelf, off Cape Darnley, and off the Ronne Ice Shelf in the Southern Ocean. The algorithm can properly discriminate the ice type in coastal polynyas and estimate the thickness of thin sea ice (≤20 cm) with an error range of less than 6 cm. We also confirmed that the algorithm can be applied to other passive microwave radiometers with higher spatial resolution to obtain more accurate and detailed distributions of ice type and thickness. The validation of this algorithm in the Arctic Ocean suggests its applicability to the global oceans.

Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

Abstract. The frequent presence of cloud cover in polar regions limits the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of (i) present clouds as sea ice or open water (false negative) and (ii) open-water and/or thin-ice areas as clouds (false positive), which results in an underestimation of actual polynya area and subsequently derived information. Here, we present a novel machine-learning-based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water and/or thin-ice areas in a given swath solely from thermal-infrared MODIS channels and derived additional information. Compared to the reference MODIS sea-ice product for the year 2017, our data result in an overall increase of 20 % in annual swath-based coverage for the Brunt Ice Shelf polynya, attributed to an improved cloud-cover discrimination and the reduction of false-positive classifications. At the same time, the mean annual polynya area decreases by 44 % through the reduction of false-negative classifications of warm clouds as thin ice. Additionally, higher spatial coverage results in an overall better subdaily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.

MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery

The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change studies. Satellite-based Synthetic Aperture Radar (SAR) systems have shown advantages of retrieving this information. Operational ice mapping relies on visual analysis of SAR images along with ancillary data. However, these maps estimate ice types and concentrations within large-size polygons of a few tens or hundreds of kilometers, which are subjectively identified and selected by analysts. This study aims at developing an automated algorithm to identify individual MYI floes from SAR images then classify the rest of the image as FYI and other ice types. The algorithm identifies the MYI floes using extended-maximum operator, morphological image processing, and a few geometrical features. Classifying the rest of the image uses texture and neural network model. The input data is a set of Sentinel-1 A/B Extended Wide (EW) mode images, acquired between September and March 2016–2019. Although the overall accuracy (for all type classification) from the new method scored 93.26%, the accuracy from using the texture classifier only was 75.81%. The kappa coefficient from the former was higher than the latter by 0.25. Compared with the operational ice charts from the Canadian Ice Service, ice type maps from the new method show better distribution of MYI at the fine scale of individual floes. Comparison against MYI concentration from two automated algorithms that use a combination of coarse-resolution passive and active microwave data also confirms the advantage of resolving MYI floes from the fine-resolution SAR.

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

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

Observed spatial-temporal changes in the autumn navigability of the Arctic Northeast Route from 2010 to 2017

Recently, the Arctic Northeast Route (ANR), an important shipping route connecting Asia and Europe, has become more and more navigable because of the accelerating melt of sea ice in the Arctic. The exploitation of the ANR could shorten the navigational distances from North Asia to Northwestern Europe by 40% (about 2500 nautical miles) and reduce one-third (about 10 days) of the time required for maritime transport by the Royal Road, which can help to save lots of transport costs and bring large environmental benefits. It’s very important and urgent to accurately understand the changes in the navigability of the ANR for the development of the route. However, due to the lack of high-quality observed dataset, there are very few observation-based studies. Some model-based studies cannot effectively reflect the real changes in the navigability of the ANR, due to the varying degrees of the data quality issues. In this study, we applied the daily sea ice concentration product provided by the NSIDC and the reconstructed daily SMOS sea ice thickness product into the Arctic Transportation Accessibility Model (ATAM) from the Arctic Ice Regime Shipping System (AIRSS) to assess and map the daily navigation risks for open water vessels in the ANR from 2010–2017 and further analyze the spatial and temporal changes of the Autumn navigability of the route during the eight years. Assuming the navigability of a specific ice regime is affected by the sea ice conditions and the ice-breaking ability of the vessels, the ATAM model can quantify the navigability of a specific ice regime for seven different vessel types. Since the SMOS ice thickness product is more reliable in thin ice area (lower than 0.5 m), we only focus on the navigability changes of the ANR for open water vessels in this study. We found that, although the end of shipping season for open water vessels across the ANR has extended to the day of 297±4 (October 24th) since 2010 (excluding 2013), there is no significant trend in the navigability of the ANR for open water vessels in the last 8 years. Further analysis of the spatial distribution map of the navigation risk for open water vessels at the end of shipping season in different years shows that the navigability of the ANR mainly affected by the ice regime around the Eastern Siberian Sea, the Novosibirsk Islands and the Severnaya Zemlya Islands. In most years, it’s the earlier frozen ice over the three regions leading to the end of the entire ANR, in the case that most other areas are still safely navigable. We also performed detailed analysis and found that there are significant differences in the navigability changes in the three key straits for open water vessels over the ANR. The Long strait shows great inter-annual variations in the navigability, but has limit impact on the navigability of the entire route because of its late ending of the shipping season. The Sannikov Strait shows the fastest decline, but small inter-annual variations in the navigability. It is also the earliest one that ending the shipping season, and therefore has a great impact on the long-term trend of the navigability of the ANR. The intra- and inter-annual changes in the navigability of the Vlikitsky Strait are very complex. It always fluctuates dramatically, and thus has great impact on the short-term navigability of the entire ANR. In view of the complexity of the change in the ANR navigability, observed high-quality, near real-time sea ice datasets are very important for the quantification of navigation risk in the ASR.

THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OF THE NORTHERN HEMISPHERE USING ASMR2 DATA

Abstract. Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heat flux of ice in thin ice areas is strongly affected by the ice thickness difference. Therefore, ice thickness is one of the most important parameters of sea ice. In our previous study, the authors have developed a thin ice area extraction algorithm using passive microwave radiometer AMSR2 for the Sea of Okhotsk. The basic idea of the algorithm is to use the brightness temperature scatter plots of AMSR2 19 GHz polarization on difference (V-H) vs 19 GHz V polarization. The algorithm was also applicable to the Bering Sea, and could extract most of the thin n ice areas. However, two problems have become clear. One was that some of the thin ice areas were not well extracted, and the other was that some of the consolidated ice were mis-extracted as thin ice areas. In this study, the authors have improved the thin ice area extraction algorithm to solve these problems. By adjusting the parameters of the algorithm applied to the brightness temperature scatter plots of AMSR2 19 GHz polarization difference (V-H) vs 19 GHz V polarization, most of the thin ice areas were also well extracted in the Bering Sea. The authors also introduced an equation using the brightness temperatures difference of 89GHz vertical and horizontal polarization to reject the thin ice area misextracted over consolidated ice. By applying the above two methods to AMSR2 data, most of the thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf of St. Lawrence with good result. The thin ice area extracted data are planed to be approved by JAXA as a AMSR2 research product.

THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OF THE NORTHERN HEMISPHERE USING MODIS DATA

Abstract. Sea ice has an important role of reflecting the solar radiation back into space. However, once the sea ice area melts, the area starts to absorb the solar radiation which accelerates the global warming. This means that the trend of global warming is likely to be enhanced in sea ice areas. In this study, the authors have developed a method to extract thin ice area using reflectance data of MODIS onboard Terra and Aqua satellites of NASA. The reflectance of thin sea ice in the visible region is rather low. Moreover, since the surface of thin sea ice is likely to be wet, the reflectance of thin sea ice in the near infrared region is much lower than that of visible region. Considering these characteristics, the authors have developed a method to extract thin sea ice areas by using the reflectance data of MODIS (NASA MYD09 product, 2017) derived from MODIS L1B. By using the scatter plots of the reflectance of Band 1 (620 nm–670 nm) and Band 2 (841 nm–876 nm)) of MODIS, equations for extracting thin ice area were derived. By using those equations, most of the thin ice areas which could be recognized from MODIS images were well extracted in the seasonal sea ice zones in the Northern Hemisphere, namely the Sea of Okhotsk, the Bering Sea and the Gulf of Saint Lawrence. For some limited areas, Landsat-8 OLI images were also used for validation.

Arctic sea ice signatures: L-band brightness temperature sensitivity comparison using two radiation transfer models

Abstract. Sea ice is a crucial component for short-, medium- and long-term numerical weather predictions. Most importantly, changes of sea ice coverage and areas covered by thin sea ice have a large impact on heat fluxes between the ocean and the atmosphere. L-band brightness temperatures from ESA's Earth Explorer SMOS (Soil Moisture and Ocean Salinity) have been proven to be a valuable tool to derive thin sea ice thickness. These retrieved estimates were already successfully assimilated in forecasting models to constrain the ice analysis, leading to more accurate initial conditions and subsequently more accurate forecasts. However, the brightness temperature measurements can potentially be assimilated directly in forecasting systems, reducing the data latency and providing a more consistent first guess. As a first step towards such a data assimilation system we studied the forward operator that translates geophysical parameters provided by a model into brightness temperatures. We use two different radiative transfer models to generate top of atmosphere brightness temperatures based on ORAP5 model output for the 2012/2013 winter season. The simulations are then compared against actual SMOS measurements. The results indicate that both models are able to capture the general variability of measured brightness temperatures over sea ice. The simulated brightness temperatures are dominated by sea ice coverage and thickness changes are most pronounced in the marginal ice zone where new sea ice is formed. There we observe the largest differences of more than 20 K over sea ice between simulated and observed brightness temperatures. We conclude that the assimilation of SMOS brightness temperatures yields high potential for forecasting models to correct for uncertainties in thin sea ice areas and suggest that information on sea ice fractional coverage from higher-frequency brightness temperatures should be used simultaneously.

Improving sea ice thickness estimates by assimilating CryoSat‐2 and SMOS sea ice thickness data simultaneously

The impact of assimilating weekly CryoSat‐2 sea ice thickness data together with daily SMOS sea ice thickness and daily SSMIS sea ice concentration data on the sea ice fields of a coupled sea ice–ocean model of the Arctic Ocean is investigated. The sea‐ice model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter coded in the Parallel Data Assimilation Framework (PDAF). A period of three months from 1 November 2011 to 30 January 2012 is selected to assess the skill of the assimilation system in the cold season. Compared to the unassimilated solution and a solution where only sea ice concentration is assimilated, the model–data misfits are substantially reduced in areas of both thick and thin ice. The sea ice thickness estimates agree significantly better with in situ observations in the central Arctic Ocean than the sea ice thickness obtained from assimilating SMOS data alone, while the sea ice concentration shows very small improvements. The sea ice fields obtained by the joint assimilation of SMOS and CryoSat‐2 data also have lower errors in thickness and concentration than those obtained from directly assimilating a statistically merged SMOS and CryoSat‐2 sea ice thickness product. These lower errors suggest that model dynamics play a significant role in data blending.

Sea-ice production in the northern Japan Sea

Sinking of the dense water plays a significant role in the global climate system by driving thermohaline (overturning) circulation and biogeochemical cycles. Deep water convection occurs also in the Japan Sea, and the convection has been considered to be mainly caused by intense cooling of the sea surface. Another possible cause of the convection is brine rejection associated with high sea-ice production in a coastal polynya (thin sea-ice) area in the northern Japan Sea. We have developed an algorithm which detects the thin ice area and estimates the thickness using passive microwave satellite data. Based on a heat flux calculation with the satellite-derived ice thickness, the total sea-ice production in winter (December–March) averaged over 2002/03–2010/11 is estimated to be 4.27 × 1010m3. This indicates that the ice production was underestimated by about half in a previous study in which the polynya was unrealistically treated as a low ice concentration area. The main determinant factor for the total ice production is the surface air temperature in early winter (December–January), which shows a rapid warming trend of 0.7°C/decade for this 40-years. Based on a linear regression approach, the total ice production is estimated to have decreased by ~5%/decade due to air temperature warming. If brine rejection due to the ice production contributes to the deep water formation in the Japan Sea, this is consistent with the fact that the deep water formation has been decreasing for the last 50–100 years.

Circumpolar polynya regions and ice production in the Arctic: results from MODIS thermal infrared imagery from 2002/2003 to 2014/2015 with a regional focus on the Laptev Sea

Abstract. High-resolution MODIS thermal infrared satellite data are used to infer spatial and temporal characteristics of 17 prominent coastal polynya regions over the entire Arctic basin. Thin-ice thickness (TIT) distributions (≤ 20 cm) are calculated from MODIS ice-surface temperatures, combined with ECMWF ERA-Interim atmospheric reanalysis data in an energy balance model for 13 winter seasons (2002/2003 to 2014/2015; November to March). From all available MODIS swath data, daily thin-ice thickness composites are computed in order to derive quantities such as polynya area and total thermodynamic (i.e., potential) ice production. A gap-filling approach is applied to account for cloud and data gaps in the MODIS composites. All polynya regions combined cover an average thin-ice area of 226.6 ± 36.1 × 103 km2 in winter. This allows for an average total winter-accumulated ice production of about 1811 ± 293 km3, whereby the Kara Sea region, the North Water polynya (both 15 %), polynyas on the western side of Novaya Zemlya (20 %), as well as scattered smaller polynyas in the Canadian Arctic Archipelago (all combined 12 %) are the main contributors. Other well-known sites of polynya formation (Laptev Sea, Chukchi Sea) show smaller contributions and range between 2 and 5 %. We notice distinct differences to earlier studies on pan-Arctic polynya characteristics, originating in some part from the use of high-resolution MODIS data, as the capability to resolve small-scale (> 2 km) polynyas and also large leads are increased. Despite the short record of 13 winter seasons, positive trends in ice production are detected for several regions of the eastern Arctic (most significantly in the Laptev Sea region with an increase of 6.8 km3 yr−1) and the North Water polynya, while other polynyas in the western Arctic show a more pronounced variability with varying trends. We emphasize the role of the Laptev Sea polynyas as being a major influence on Transpolar Drift characteristics through a distinct relation between increasing ice production and ice area export. Overall, our study presents a spatially highly accurate characterization of circumpolar polynya dynamics and ice production, which should be valuable for future modeling efforts of atmosphere–ice–ocean interactions in the Arctic.