Sea Ice Concentration Products Research Articles

Estimating the uncertainty of sea-ice area and sea-ice extent from satellite retrievals

Abstract. The net Arctic sea-ice area (SIA) can be estimated from the sea-ice concentration (SIC) by passive microwave measurements from satellites. To be a truly useful metric, for example of the sensitivity of the Arctic sea-ice cover to global warming, we need, however, reliable estimates of its uncertainty. Here we retrieve this uncertainty by taking into account the spatial and temporal error correlations of the underlying local sea-ice concentration products. As 1 example year, we find that in 2015 the average observational uncertainties of the SIA are 306 000 km2 for daily estimates, 275 000 km2 for weekly estimates, and 164 000 km2 for monthly estimates. The sea-ice extent (SIE) uncertainty for that year is slightly smaller, with 296 000 km2 for daily estimates, 261 000 km2 for weekly estimates, and 156 000 km2 for monthly estimates. These daily uncertainties correspond to about 7 % of the 2015 sea-ice minimum and are about half of the spread in estimated SIA and SIE from different passive microwave SIC products. This shows that random SIC errors play a role in SIA uncertainties comparable to inter-SIC-product biases. We further show that the September SIA, which is traditionally the month with the least amount of Arctic sea ice, declined by 105 000±9000 km2 a−1 for the period from 2002 to 2017. This is the first estimate of a SIA trend with an explicit representation of temporal error correlations.

Characterization of north Greenland polynyas with super-resolved passive microwave sea ice concentration

ABSTRACT Located at the easternmost sector of the Last Ice Area (LIA) with the oldest and thickest sea ice, the north of Greenland has witnessed some unprecedented polynyas since 2018, receiving considerable attention. Sea ice concentration (SIC) derived from passive microwave data can provide near daily observations of these polynyas. However, these SIC products are limited by the coarse spatial resolution of passive microwave data, with mixed pixels and large uncertainties at the ice–water divide. It is especially difficult in the case of newly formed polynyas in the form of leads with narrow openings, preventing accurate estimation of the regional energy budget and heat fluxes as well as the hindering causal analysis of the polynyas. In this paper, a novel state-of-the-art deep-learning-based super-resolution (SR) method is adopted, and passive microwave SR images, with spatial resolutions of up to 4 times the original, are generated and employed to derive super-resolved SIC (SR-SIC). Experimental results confirm the benefits of SR-SIC for finer monitoring of sea ice at both the polynya and lead scale compared to the original lower-resolution SIC, with an average 7.55% improvement in F1-Score for polynya extent and 31.17% for lead width. Using the SR-SIC, more accurate quantitative parameters of polynyas and leads are extracted to present more detailed spatial distributions at their different development stages. For example, SR-SIC with reduced mixed pixels can distinguish more SIC values below 40%, advantageous for detecting open water pixels within polynya areas and improving the accuracy of heat fluxes estimation and ice production calculation. Moreover, SR-SIC has the capability to identify leads with openings less than 3 km and to locate the finer boundaries of the polynyas. Finally, it is found that the lead development along the 2018 winter polynya is associated with regional wind, air temperature, and ice drift. The prominent summer polynya events in 2018, 2020, and 2021 may be in part caused by the thinning sea ice in the north of Greenland.

A NEW WEATHER FILTER FOR REDUCING WEATHER EFFECT IN CALCULATING SEA ICE CONCENTRATION FROM AMSR2 DATA

Abstract. Ice concentration is one of the most fundamental parameters of sea ice which can be calculated from brightness temperatures measured by passive microwave radiometers such as AMSR2 onboard satellites. However, the presence of atmospheric water vapor, cloud liquid water, etc. is likely to increase the brightness temperatures over open water. As a result, sometimes, certain ice concentrations are calculated over open water covered with heavy clouds. The authors call this kind of area as “false sea ice” and the phenomenon is called the weather effect. In the AMSR2 ice concentration product, a weather filter is used to reduce the weather effect. However, it is known that false sea ice appears occasionally in some areas even in the summertime in AMSR2 ice concentration products. In this study, the authors have modified the original weather filter and added a new weather filter utilizing the following two equations which are TB(23GHzV)<−0.75×(TB(36GHzV)−TB(36GHzH))+253K and TB(36GHzV)−TB(36GHzH) < 57K. If the brightness temperature of the pixel meets the above two equations, we set the ice concentration of the pixel as 0%. By adding the new weather filter, most of the false sea ice that appeared in the ASMR2 ice concentration products could be rejected. JAXA has decided to add this weather filter for producing AMSR2 standard sea ice concentration products.

A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors

Abstract. Sea ice concentration (SIC) is the main geophysical variable for quantifying change in sea ice in the polar regions. A continuous SIC product is key to informing climate and ecosystem studies in the polar regions. Our study generates a new SIC product covering the Arctic and Antarctic from November 2010 to December 2019. It is the first long-term SIC product derived from the Microwave Radiation Imager (MWRI) sensors on board the Chinese FengYun-3B, FengYun-3C, and FengYun-3D satellites, after a recent re-calibration of brightness temperature. We modified the previous Arctic Radiation and Turbulence Interaction Study Sea Ice (ASI) dynamic tie point algorithm mainly by changing input brightness temperature and initial tie points. The MWRI-ASI SIC was compared to the existing ASI SIC products and validated using ship-based SIC observations. Results show that the MWRI-ASI SIC mostly coincides with the ASI SIC obtained from the Special Sensor Microwave Imager series sensors, with overall biases of −1 ± 2 % in the Arctic and 0.5 ± 2 % in the Antarctic, respectively. The overall mean absolute deviation between the MWRI-ASI SIC and ship-based SIC is 16 % and 17 % in the Arctic and Antarctic, respectively, which is close to the existing ASI SIC products. The trend of sea ice extent (SIE) derived from the MWRI-ASI SIC closely agrees with the trends of the Sea Ice Index SIEs provided by the Ocean and Sea Ice Satellite Application Facility (OSI SAF) and the National Snow and Ice Data Center (NSIDC). Therefore, the MWRI-ASI SIC is comparable with other SIC products and may be applied alternatively. The MWRI-ASI SIC dataset is available at https://doi.org/10.1594/PANGAEA.945188 (Chen et al., 2022b).

DNN‐Based Retrieval of Arctic Sea Ice Concentration From GNSS‐R and Its Effects on the Synoptic‐Scale Forecasting as Supplementary Observation Source

AbstractUsing delay‐Doppler maps of Global Navigation Satellite Systems Reflectometry (GNSS‐R) from the TechDemoSat‐1 satellite and considering sea ice and ocean interaction, an innovative method for retrieval of Arctic sea ice concentration (SIC) based on a deep neural network is proposed. This retrieval method shows the potential of future GNSS‐R applications for Arctic missions. Compared with SIC products from Hamburg University, the root mean square errors (RMSE) of retrieved results in March and June 2016 are 0.0284 and 0.0415, respectively. When the retrieved GNSS‐R SIC data are added into the assimilation as supplementary passive microwave remote‐sensing data, it has a positive influence on improving the accuracy of the Arctic SIC forecast. Especially in some edge regions of sea ice, when compared to only assimilating the remote‐sensing data, the regional RMSE of joint assimilation has a maximum decrease of approximately 17% in the 24‐hr forecast time, and over 5% in 72‐hr.

Daily Sea Ice Concentration Product over Polar Regions Based on Brightness Temperature Data from the HY-2B SMR Sensor

Polar sea ice profoundly affects atmospheric and oceanic circulation and plays a significant role in climate change. Sea ice concentration (SIC) is a key geophysical parameter used to quantify these changes. In this study, we determined SIC products for the Arctic and Antarctic from 2019 to 2021 using data from the Chinese marine satellite Haiyang 2B (HY-2B) with an improved bootstrap algorithm. Then the results were compared with similar operational SIC products and ship-based data. Our findings demonstrate the effectiveness of the improved algorithm for accurately determining SIC in polar regions. Additionally, the results of the study demonstrate that the SIC product obtained through the improved bootstrap algorithm has a high correlation with other similar SIC products. The daily average SIC of the different products showed similar inter-annual trends for both the Arctic and Antarctic regions. Comparison of the different SIC products showed that the Arctic BT-SMR SIC was slightly lower than the BT-SSMIS and BT-AMSR2 SIC products, while the difference between Antarctic SIC products was more pronounced. The lowest MAE was between the BT-SSMIS SIC and BT-SMR SIC in both regions, while the largest MAE was between the NT-SMR and BT-SMR in the Arctic, and between the NT-SSMIS and BT-SMR in the Antarctic. The SIE and SIA time series showed consistent trends, with a greater difference in SIA than SIC and a slight difference in SIA between the BT-AMSR2 and BT-SMR in the Arctic. Evaluation of the different SIC products using ship-based observation data showed a high correlation between the BT-SMR SIC and the ship-based SIC of approximately 0.85 in the Arctic and 0.88 in the Antarctic. The time series of dynamic tie-points better reflected the seasonal variation in sea ice radiation characteristics. This study lays the foundation for the release of long-term SIC product series from the Chinese autonomous HY-2B satellite, which will ensure the continuity of polar sea ice records over the past 40 years despite potential interruptions.

Classification of Arctic Sea Ice Type in CFOSAT Scatterometer Measurements Using a Random Forest Classifier

The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.

Evolution of the dynamics, area, and ice production of the Amundsen Sea Polynya, Antarctica, 2016–2021

Abstract. Polynyas are key sites of ice production during the winter and are important sites of biological activity and carbon sequestration during the summer. The Amundsen Sea Polynya (ASP) is the fourth largest Antarctic polynya, has recorded the highest primary productivity, and lies in an embayment of key oceanographic significance. However, knowledge of its dynamics, and of sub-annual variations in its area and ice production, is limited. In this study we primarily utilize Sentinel-1 synthetic aperture radar (SAR) imagery, sea ice concentration products, and climate reanalysis data, along with bathymetric data, to analyze the ASP over the period November 2016–March 2021. Specifically, we analyze (i) qualitative changes in the ASP's characteristics and dynamics, as well as quantitative changes in (ii) summer polynya area, and (iii) winter polynya area and ice production. From our analysis of SAR imagery we find that ice produced by the ASP becomes stuck in the vicinity of the polynya and sometimes flows back into the polynya, contributing to its closure and limiting further ice production. The polynya forms westward off a persistent chain of grounded icebergs that are located at the site of a bathymetric high. Grounded icebergs also influence the outflow of ice and facilitate the formation of a “secondary polynya” at times. Additionally, unlike some polynyas, ice produced by the polynya flows westward after formation, along the coast and into the neighboring sea sector. During the summer and early winter, broader regional sea ice conditions can play an important role in the polynya. The polynya opens in all summers, but record-low sea ice conditions in 2016/17 cause it to become part of the open ocean. During the winter, an average of 78 % of ice production occurs in April–May and September–October, but large polynya events often associated with high, southeasterly or easterly winds can cause ice production throughout the winter. While passive microwave data or daily sea ice concentration products remain key for analyzing variations in polynya area and ice production, we find that the ability to directly observe and qualitatively analyze the polynya at a high temporal and spatial resolution with Sentinel-1 imagery provides important insights about the behavior of the polynya that are not possible with those datasets.

Super-Resolution-Aided Sea Ice Concentration Estimation From AMSR2 Images by Encoder–Decoder Networks With Atrous Convolution

Passive microwave data is an important data source for the continuous monitoring of Arctic-wide sea ice concentration (SIC). However, its coarse spatial resolution leads to blurring effects at the ice-water divides, resulting in the great challenges of fine-scale and accurate SIC estimation, especially for regions with low SIC. Besides, the SIC derived by operational algorithms using high-frequency passive microwave observations has great uncertainties in open water or marginal ice zones due to atmospheric effects. In this article, a novel framework is proposed to achieve accurately SIC estimation with improved spatial details from original low-resolution Advanced Microwave Scanning Radiometer 2 (AMSR2) images, with joint the super-resolution (SR) and SIC estimation network. Based on the SR network, the spatial resolution of original AMSR2 images can be improved by four times, benefiting to construct AMSR2 SR features with more high-frequency information for SIC estimation. The SIC network with an encoder–decoder structure and atrous convolution, is employed to accurately perform the SIC retrieval by considering the characteristics of passive microwave images in the Arctic sea ice region. Experimental results show that the proposed SR-Aided SIC estimation approach can generate accurate SIC with more detailed sea ice textures and much sharper sea ice edges. With respect to MODIS SIC products distributed in Arctic scale, the proposed model achieves a root-mean-square error (RMSE) of 5.94% and mean absolute error (MAE) of 3.04%, whereas the Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) SIC results have three and two times greater values of RMSE and MAE.

Automatic High-Accuracy Sea Ice Monitoring in the Arctic Using MODIS Data

High accuracy sea ice monitoring is of great significance in responding to global climate change and guiding safe navigation. The MODIS sensors carried by Aqua and Terra satellites have great potential in high-resolution monitoring of sea ice in polar regions. This study developed an automatic high-accuracy sea ice concentration (SIC) detection algorithm for the MODIS data. We used a machine learning algorithm that combines spectral and texture features to obtain high-precision sea ice recognition results. Then, according to the cloud characteristics in MODIS images and the full coverage of AMSR2 SICs, outlier removal and cloud area filling are carried out. Finally, the SIC result was calculated according to the distribution of ice types. We assessed the accuracy of our high-accuracy SIC product by comparing with the MOD29/MYD29 products and validating with the Landsat 8/9 images. We found that the deviation increased with the decrease of SIC value. The deviation are larger in the ice melting area and the ice edges area, but smaller in the area completely covered by ice. Compared with MOD29/MYD29 SICs, the SICs obtained in this study have higher accuracy, with an average RMSD of 7.64%. It can detect the details of relatively small leads, ice edges and fragmented ice areas. The high-accuracy SIC product we obtained is expected to provide long-term SIC records of high quality.

CGAN Based Improved ASI Retrieval Algorithm for Antarctic Sea Ice Concentration

Sea ice change is closely related to the change of global atmosphere and ocean circulation, which plays an important role in the study of global climate change. Sea ice concentration is one of the important parameters to study the temporal and spatial change of sea ice. Accurately retrieving sea ice concentration is the innovation of this paper. At present, the high-resolution microwave-detected sea ice concentration product was provided by the University of Bremen, which was derived by the Arctic Radiation and Turbulence Interaction Study (ARTSIST) Sea Ice (ASI) algorithm based on the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) 89-GHz brightness temperature data. The AMSR-E/AMSR-2 89-GHz brightness temperature data has higher spatial resolution, but it is often affected by cloud and water vapor, which affects the recognition and subsequent use of ground feature. Although the weather filters can remove some errors in the edge regions of the sea water and the sea ice, the errors of the sea ice concentration in other regions cannot be removed. The generative model of Conditional Generative Adversarial Network (CGAN) increases the utilization of image feature information through skip connection, which improves the removal of the influence of cloud and water vapor. The discriminative model can retain the image feature information and realize the non-linear mapping from the image to the image. The loss function can reduce the pixel-level loss, which can remove the influence of cloud and water vapor. Therefore, this paper proposed an improved ASI algorithm based on CGAN. Firstly, the relatively stable relationship between the 89-GHz brightness temperature data which is not disturbed or less affected by the external environment and the 36-GHz brightness temperature data was determined, and the 89-GHz brightness temperature data with large interference was screened. Secondly, based on the 36-GHz brightness temperature data with high reliability, the 89-GHz brightness temperature data with large interference was corrected through CGAN. Finally, the ASI algorithm was used to retrieve sea ice concentration. Compared with sea ice concentration retrieved by the ASI algorithm, the results showed that the improved ASI algorithm based on CGAN was feasible. Compared with sea ice distribution obtained from the Landsat 8 OLI-L1T data, the improved ASI algorithm based on CGAN significantly improves the inversion accuracy of sea ice concentration. The improved ASI algorithm based on CGAN makes use of the reliable 36-GHz brightness temperature data, which greatly reduces the error caused by cloud and water vapor, and the method effectively corrects sea ice concentration of the pixels affected by the external environment. Therefore, the improved ASI algorithm based on CGAN realizes high spatial resolution and significantly improves the inversion accuracy of sea ice concentration.

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.

Weddell Sea polynya analysis using SMOS–SMAP apparent sea ice thickness retrieval

Abstract. The Weddell Sea is known to feature large openings in its winter sea ice field, otherwise known as open-ocean polynyas. An area within the Weddell Sea region that has repeatedly featured open-ocean polynyas in the past is that which encompasses the Maud Rise seamount. Within this area, after 40 years of intermittent, smaller openings, a larger, more persistent polynya appeared in early September 2017​​​​​​​ and remained open for approximately 80 d until spring ice melt. In this study we present proof that polynya-favorable activity in the Maud Rise area is taking place more frequently and on a larger scale than previously assumed. By investigating thin (< 50 cm) apparent sea ice thickness (ASIT) retrieved from the satellite microwave sensors Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), we find an anomaly of thin sea ice spanning an area comparable to the polynya of 2017 over Maud Rise which occurred in September 2018. In this paper, we look at sea ice above Maud Rise in August and September of 2017 and 2018 as well as all years from 2010 until 2020 in an 11-year time series. Using fifth-generation ECMWF Reanalysis (ERA5) surface wind reanalysis data, we corroborate previous findings (e.g., Campbell et al., 2019; Francis et al., 2019; Wilson et al., 2019) on the strong impact that storm activity can have on sea ice above Maud Rise and help consolidate the theory that the evolution of the Weddell Sea polynya is controlled by local atmospheric as well as oceanographic variability. Based on the results presented, we propose that the Weddell Sea polynya, rather than being a binary phenomenon with one principal cause, is a dynamic process caused by various different preconditioning factors that must occur simultaneously for it to appear and persist. Moreover, we show that rather than an abrupt stop to anomalous activity above Maud Rise in 2017, the very next year shows signs of polynya-favorable activity that, although insufficient to open the polynya, were present in the region. This phenomenon, as we have shown in the 11-year SMOS record, was not unique to 2018 and was also identified in 2010, 2013 and 2014. It is demonstrated that L-band microwave radiometry from the SMOS and SMAP satellites can provide additional useful information, which helps to better understand dynamic sea ice processes like polynya events when compared to the use of satellite sea ice concentration products alone.

Satellite passive microwave sea-ice concentration data set intercomparison using Landsat data

Abstract. We report on results of an intercomparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations. For this we use SIC estimated from >350 images acquired in the visible–near-infrared frequency range by the joint National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS) Landsat sensor during the years 2003–2011 and 2013–2015. Conditions covered are late winter/early spring in the Northern Hemisphere and from late winter through fall freeze-up in the Southern Hemisphere. Among the products investigated are the four products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms SICCI-2 and OSI-450. We stress the importance to consider intercomparison results across the entire SIC range instead of focusing on overall mean differences and to take into account known biases in PMW SIC products, e.g., for thin ice. We find superior linear agreement between PMW SIC and Landsat SIC for the 25 and the 50 km SICCI-2 products in both hemispheres. We discuss quantitatively various uncertainty sources of the evaluation carried out. First, depending on the number of mixed ocean–ice Landsat pixels classified erroneously as ice only, our Landsat SIC is found to be biased high. This applies to some of our Southern Hemisphere data, promotes an overly large fraction of Landsat SIC underestimation by PMW SIC products, and renders PMW SIC products overestimating Landsat SIC particularly problematic. Secondly, our main results are based on SIC data truncated to the range 0 % to 100 %. We demonstrate using non-truncated SIC values, where possible, can considerably improve linear agreement between PMW and Landsat SIC. Thirdly, we investigate the impact of filters often used to clean up the final products from spurious SIC over open water due to weather effects and along coastlines due to land spillover. Benefiting from the possibility to switch on or off certain filters in the SICCI-2 and OSI-450 products, we quantify the impact land spillover filtering can have on evaluation results as shown in this paper.

PMDRnet: A Progressive Multiscale Deformable Residual Network for Multi-Image Super-Resolution of AMSR2 Arctic Sea Ice Images

The extent of the area covered by polar sea ice is an important indicator of global climate change. Continuous monitoring of Arctic sea ice concentration (SIC) primarily relies on passive microwave images. However, passive microwave images have coarse spatial resolution, resulting in SIC production with significant blurring at the ice–water divides. In this article, a novel multi-image super-resolution (MISR) network called progressive multiscale deformable residual network (PMDRnet) is proposed to improve the spatial resolution of sea ice passive microwave images according to the characteristics of both passive microwave images and sea ice motions. To achieve image alignment with complex and large Arctic sea ice motions, we design a novel alignment module that includes a progressive alignment strategy and a multiscale deformable convolution alignment unit. In addition, the temporal attention mechanism is used to adaptively fuse the effective spatiotemporal information across image sequence. The sea ice-related loss function is designed to provide more detailed sea ice information of the network to improve super-resolution performance and further benefit finer Arctic SIC results. Experimental results demonstrate that PMDRnet significantly outperforms the current state-of-the-art MISR methods and can generate super-resolved SIC products with finer texture features and much sharper sea ice edges. The code and datasets of PMDRnet are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://doi.org/10.5061/dryad.k3j9kd590</uri> .

Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% &lt; SIC &lt; 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

Daily sea ice concentration product based on brightness temperature data of FY-3D MWRI in the Arctic

ABSTRACT A daily sea ice concentration (SIC) product in the Arctic, derived from the brightness temperature (TB) data of the Microwave Radiation Imager (MWRI) sensor aboard on the FY-3D satellite, is described in this paper. The MWRI TB raw swath data were first processed into daily gridded data and then corrected using the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor. An ASI algorithm, which uses daily dynamic tie points, was adopted to calculate daily SIC at 12.5 km polar stereographic projection from January 2018 to June 2020. Our generated MWRI SIC product was compared with the AMSR2 SIC based on the ASI algorithm that uses fixed tie points. For more detailed comparison, we then compared our MWRI SIC with the SIC from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. The mean bias between our MWRI SIC and AMSR2 SIC is 4.24%. The absolute values of biases between the daily MWRI SIC and MODIS SIC range from 0.14% to 10.76%, better than the MWRI SIC product based on the NT2 algorithm published by the Chinese National Satellite Meteorological Center. The results show that our MWRI SIC product has a good quality and can be used as a basic dataset for sea ice extent records. The dataset is available at http://www.dx.doi.org/10.11922/sciencedb.00137.

A Blended Sea Ice Concentration Product from AMSR2 and VIIRS

An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.

Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Abstract. The neXtSIM-F (neXtSIM forecast) forecasting system consists of a stand-alone sea ice model, neXtSIM (neXt-generation Sea Ice Model), forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation of sea ice concentration. It uses the novel brittle Bingham–Maxwell (BBM) sea ice rheology, making it the first forecast based on a continuum model not to use the viscous–plastic (VP) rheology. It was tested in the Arctic for the time period November 2018–June 2020 and was found to perform well, although there are some shortcomings. Despite drift not being assimilated in our system, the sea ice drift is good throughout the year, being relatively unbiased, even for longer lead times like 5 d. The RMSE in speed and the total RMSE are also good for the first 3 or so days, although they both increase steadily with lead time. The thickness distribution is relatively good, although there are some regions that experience excessive thickening with negative implications for the summertime sea ice extent, particularly in the Greenland Sea. The neXtSIM-F forecasting system assimilates OSI SAF sea ice concentration products (both SSMIS and AMSR2) by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. The assimilation greatly improves the sea ice extent for the forecast duration.

Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.