National Snow And Ice Data Center Research Articles

Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977

Abstract. The Electrically Scanning Microwave Radiometer (ESMR) instrument onboard the NIMBUS 5 satellite was a one-channel microwave radiometer that measured the 19.35 GHz horizontally polarized brightness temperature (TB) from 11 December 1972 to 16 May 1977. The original tape archive data in swath projection have recently been made available online by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Even though the ESMR was a predecessor of modern multi-frequency radiometers, there are still parts of modern processing methodologies which can be applied to the data to derive the sea ice extent globally. Here, we have reprocessed the entire dataset using a modern processing methodology that includes the implementation of pre-processing filtering, dynamical tie points, and a radiative transfer model (RTM) together with numerical weather prediction (NWP) for atmospheric correction. We present the one-channel sea ice concentration (SIC) algorithm and the model for computing temporally and spatially varying SIC uncertainty estimates. Post-processing steps include resampling to daily grids, land-spillover correction, the application of climatological masks, the setting of processing flags, and the estimation of sea ice extent, monthly means, and trends. This sea ice dataset derived from the NIMBUS 5 ESMR extends the sea ice record with an important reference from the mid-1970s. To make it easier to perform a consistent analysis of sea ice development over time, the same grid and land mask as used for EUMETSAT's OSI-SAF SMMR-based sea-ice climate data record (CDR) were used for our ESMR dataset. SIC uncertainties were included to further ease comparison to other datasets and time periods. We find that our sea ice extent in the Arctic and Antarctic in the 1970s is generally higher than those available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC), which were derived from the same ESMR dataset, with mean differences of 240 000 and 590 000 km2, respectively. When comparing monthly sea ice extents, the largest differences reach up to 2 million km2. Such large differences cannot be explained by the different grids and land masks of the datasets alone and must therefore also result from the differences in data filtering and algorithms, such as the dynamical tie points and atmospheric correction. The new ESMR SIC dataset has been released as part of the ESA Climate Change Initiative (ESA CCI) program and is publicly available at https://doi.org/10.5285/34a15b96f1134d9e95b9e486d74e49cf (Tonboe et al., 2023).

Evaluation of Summertime Passive Microwave and Reanalysis Sea‐Ice Concentration in the Central Arctic

AbstractPassive microwave (PM) observations have been used to monitor ice retreat in the Arctic. However, various PM sea ice concentration (SIC) algorithms are prone to underestimate ice fraction during summer. We evaluated the accuracy of 2002–2019 low SICs in the Central Arctic Ocean of four PM products from the University of Bremen, the National Snow and Ice Data Center (NSIDC), and the Ocean and Sea Ice Satellite Application Facility (OSI SAF), and two reanalysis data sets from the fifth generation of European ReAnalysis (ERA5) and the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2). Three reference fields were used: (a) Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) true‐color composites, (b) MODIS sea ice extent, and (c) multi‐product ensemble (MPE‐SIC) comprising the median of collocated SIC estimates. Our results indicate SICs derived from the Advanced Microwave Scanning Radiometer ‐ Earth Observing System (AMSR‐E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) high frequency channels have the best accuracy. Reanalysis SICs indicate almost identical patterns as their remote sensing inputs. The assessment shows that the Bremen (+1.06%) and NSIDC (+0.99%) SICs are higher than the median field, while the OSI‐401 (−6.65%) and OSI‐408 (−4.64%) have negative mean deviations. The mean error of MODIS‐derived SIC (−0.80%) is smaller than PM SICs. These small mean values belie wide distributions of values. The correlation coefficients of pairs of time series of Low sea‐Ice Concentration Index range from 0.37 to 0.96.

Spatial-temporal variations of one-year ice in Antarctic different regions, 1988–2020

In order to increase the comparability of Antarctic sea ice changes, we proposed a new method to quantitatively assess the spatial–temporal variation characteristics of Antarctic one-year ice based on daily Antarctic sea ice concentration data provided by the National Snow and Ice Data Center (NSIDC) from 1988 to 2020. According to the threshold value of 15% sea ice concentration, the definition of one-year ice and multiyear ice, we divided the Antarctic into 5 regions (seawater regions, stable one-year ice regions, one-year ice and seawater coexistence regions, multiyear ice regions, and one-year ice and multiyear ice coexistence regions) for 33 years and finally conducted the spatio-temporal analysis of the one-year ice in three regions (stable one-year ice regions, one-year ice and seawater coexistence regions, and one-year ice and multiyear ice coexistence regions). We examined the changes in sea ice parameters over 33 years. The results showed that from 1988 to 2020, in the stable one-year ice regions the Antarctic melt onset date had a slight delay trend with 0.2504 days year−1 and the melt end date had a slightly advance trend with 0.0524 days year−1, in the one-year ice and seawater coexistence regions the Antarctic annual average sea ice concentration had a weak decreasing trend with a rate of 0.0003/year (The maximal sea ice concentration is 0.238724 in 2010, and the minimal sea ice concentration is 0.166427 in 2019), in one-year ice and multiyear ice coexistence regions the multiyear ice increased at a rate of 0.0039 × 106 km2 and the annual melt index had a slight upward trend with 0.0022 × 108 km2 year−1.

Retrieval of Arctic Sea Ice Motion from FY-3D/MWRI Brightness Temperature Data

Sea ice motion (SIM) has significant implications for sea–air interactions, thermohaline circulation, and the development of the Arctic passage. This research proposes an improved SIM retrieval method from Fengyun-3D’s (FY-3D) microwave radiometer imager’s (MWRI) brightness temperature (Tb) data based on the modified classical maximum cross-correlation (MCC) method and the multisource data merging method. This study utilized buoy data to establish the search area range, applied distinct thresholds across various Arctic regions, and merged the buoy data, reanalysis wind data, and SIM retrieved from FY-3D/MWRI Tb data. In 2019, for the final Arctic SIM results retrieved from the MWRI 89 GHz and 36.5 GHz Tb data, the root-mean-square error (RMSE) and the mean average error (MAE) in the east–west direction were 2.07 cm/s and 1.38 cm/s and those in the north–south direction were 1.96 cm/s and 1.15 cm/s, compared to the ice-tethered profiler (ITP) data. Compared with the daily average data of the National Snow and Ice Data Center (NSIDC), the RMSE and MAE of the SIM results obtained in this study were 0.74 cm/s and 0.93 cm/s in the east–west direction, and 0.56 cm/s and 0.72 cm/s in the north–south direction, respectively. The monthly average of the SIM retrieved from the MWRI Tb data in this research also showed a good agreement with the monthly average of the NSIDC SIM product. The comparison showed that the MWRI Tb data could be used to retrieve the Arctic SIM, and the Arctic SIM retrieval method presented in this paper was accurate and general.

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).

Monitoring ice flow velocity of Petermann glacier combined with Sentinel-1 and −2 imagery

Synthetic Aperture Radar (SAR) images are commonly used to monitor glacier flow velocity at Greenland Ice Sheet (GrIS). However, offset-tracking with SAR imagery in summer usually show poor quality because the rapid ice surface freezing-melting cycles contaminating the surface backscattering characteristic, while optical images are less sensitive to this phenomenon. In this study, we combine Sentinel-1 and -2 images to create the glacier velocity time series for the Petermann glacier, located in the northern GrIS. Firstly, the offset-tracking technique is employed to acquire the initial deformation fields with SAR and optical sensors separately, each SAR and/or optical acquisition is tracked with its closest next three acquisitions. Next, after removing the bad matchings, the least squares method based on connected components is employed to calculate the time series of glacier velocity for Sentinel-1 and −2, separately. Finally, these two kinds of derived time series are integrated with a weighted least squares method, where weights are evaluated according to the estimated RMSEs in the last step. Error propagation analysis suggests RMSEs of the single pair of Sentinel-1 and −2 images offset-tracking are ∼ 0.22 m and ∼ 2.5 m for Petermann glaciers. Standard deviation of the difference between Sentinel-1 and Sentinel-2 measured velocity are ∼ 0.25 m/day. Compared with 6-day velocity fields product, NSIDC (National Snow and Ice Data Center) −0766, which is only derived with Sentinel-1observations, our results show good agreement and less defects in summer. The differences are ∼ 0.20 m/day in non-melting seasons and ∼ 0.34 m/day in summer. Longitudinal velocity differences growing in 2019 and 2020 at ∼ 20 Km up to the terminus are consistency with the crevasse expansion, indicating another calving event is approaching. This research finds that the fusion of Sentinel-1 and −2 offset-tracking results improves the completeness of the ice movement time series for polar glaciers.

SVM-Based Sea Ice Extent Retrieval Using Multisource Scatterometer Measurements

This study aims to explore the joint usage of multisource scatterometer measurements in polar sea water and ice discrimination. All radar backscatter measurements from current operating satellite scatterometers are considered, including the C-band ASCAT scatterometer on board the MetOp series satellites, the Ku-band scatterometer on board the HY-2B satellite (HSCAT), and the Ku-band scatterometer on board the CFOSAT satellite (CSCAT). By performing seven experiments that use the same support vector machine (SVM) classifier method but with different input data, we find that the SVM model with all available HSCAT, CSCAT, and ASCAT scatterometer data as inputs gives the best performance. In addition to the SVM outputs, we employ the image erosion/dilation techniques and area growth method to reduce misclassifications of sea water and ice. The sea ice extent obtained in this study shows a good agreement with the National Snow and Ice Data Center (NSIDC) sea ice concentration data from the years 2019 to 2021. More specifically, the sea ice areas are closer to the sea ice areas calculated using 15% as the threshold for NSIDC sea ice concentration data in both Arctic and Antarctic. The sea ice edges acquired by the multisource scatterometer show a close correlation with sea ice edges from the Sentinel-1 Synthetic Aperture Radar (SAR) images. In addition, we found that the coverage of multisource scatterometer data in a half-day is usually above 97%, and more importantly, the sea ice areas obtained on the basis of half-day and daily multisource scatterometer data are very close to each other. The presented work can serve as guidance on the usage of all available scatterometer measurements in sea ice monitoring.

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.

Spatio-temporal monitoring of the iceberg D28 using SCATSAT-1 data

Abstract The study of the icebergs and their movements is one of many applications of scatterometer data in the study of the ecosystems of polar regions. SCATSAT-1 is the Indian Space Research Organisation’s (ISRO’s) Ku-band (13.515625 GHz) scatterometer. Using enhanced resolution Gamma0H (horizontally polarised incidence angle normalised backscattering coefficient) data of SCATSAT-1, we observed the movement of iceberg D28 and its interaction with wind, ocean currents and sea ice for one and a half years of its journey (JD 269, 2019 to JD 051, 2021). The data sets used are as follows: (1) SCATSAT-1 level-4 Gamma0H; (2) OSCAR (Ocean Surface Current Analysis Real-time) third-degree resolution ocean surface currents; (3) hourly wind speed data of ERA5; 4) NSIDC (National Snow and Ice Data Center) sea ice concentration data; and (5) NSIDC Polar Path-finder Daily EASE-Grid Sea Ice Motion Vectors, Version-3. For this study, we divide the continent into five different regions/sectors. It is found that the trajectory of the iceberg is influenced by the resultant of the wind and ocean current, at different scales in these regions. Moreover, sea ice motion can also change the course of iceberg. From the on-screen digitisation of the iceberg, the average area of the iceberg is found to be approximately 1509.82 km2 with approximate dimensions of 27 km × 55.5 km. We conclude that spatial and temporal behaviours of the iceberg can be ascertained from the scatterometer data.

Разработка программного модуля построения карт распределения вероятностей встречи с морским льдом для изучения дальневосточных морей

The article presents a description of the developed software module for creating maps of the probability distribution of encountering sea ice. The module allows to process collections of raster map files based on satellite data and obtain vector maps of the probability distribution of encountering sea ice. Source data is Japan Meteorological Agency (JMA) maps provided in raster format without georeferencing, and National Snow and Ice Data Center (NSIDC) maps provided in GeoTIFF format with georeferencing. The module implements an algorithm that allows to calculate the probability of ice occurrence of a given concentration interval at each point (pixel) of a raster map, and then build polygonal vector maps in SHP-format based on the array of probabilities. The algorithm for constructing polygonal objects provides for a sequential enumeration of all horizontal adjacent pairs of elements of the array of probabilities, followed by determining the boundaries of the polygon. Detection of the first points of the polygon boundary allows to start a cyclic search for adjacent points that meet the boundary condition and creating a complete list of points of a closed polygon. Similarly, all other polygons are searched within the water area specified by the color mask. At the last stage, the lists of Cartesian coordinates of points are converted into geographical coordinates. Based on them, polygonal objects are formed in the SHP-file format. Polygon features define areas of probability of encountering ice that belong to the same interval, and include numeric attributes with values for the boundaries of the probability intervals. The generated vector maps allow further data analysis in GIS applications. The developed software was used to build maps of the probability distribution of encountering ice in the waters of the Far Eastern seas.

Isometric mapping algorithm based GNSS-R sea ice detection

<abstract> <p>Global navigation satellite system reflectometry (GNSS-R) is based on satellite signals' multipath interference effect and has developed as one of the important remote sensing technologies in sea ice detection. An isometric mapping (ISOMAP)-based method is proposed in this paper as a development in sea ice detection approaches. The integral delay waveforms (IDWs), selected from February to April in 2018, derived from TechDemoSat-1 (TDS-1) Delay-Doppler maps (DDMs) are applied to open water and sea ice classification. In the first, the model for extracting low-dimensional coordinates of IDWs employs the randomly selected 187666 IDW samples, which are 30% of the whole IDW dataset. Then, low-dimensional coordinates of IDWs are used to train three different classifiers of support vector machine (SVM) and gradient boosting decision tree (GBDT), linear discriminant algorithm (LDA) and K-nearest neighbors (KNN) for determining the sea ice and sea water. The remaining 437889 samples, about 70% of the whole datasets, are used to validate with the ground surface type from the National Snow and Ice Data Center (NSIDC) data provided by the National Oceanic and Atmospheric Administration (NOAA). The algorithm performance is evaluated, and the overall accuracy of SVM, GBDT, LDA and KNN are 99.44%, 85.58%, 91.88% and 98.82%, respectively. The sea ice detection methods are developed, and the accuracy of detection is improved in this paper.</p> </abstract>

Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager.

Sea ice is an important marine phenomenon in the Arctic region, and it is of great importance to study the motion of Arctic sea ice in the present day when its melting is accelerated by global warming. This study proposes a method to retrieve the motion of sea ice based on the maximum cross-correlation (MCC) and the successive correction method (SCM). The proposed method can apply different scales of search ranges to template matching according to the location of sea ice in the Arctic area. In addition, the data assimilation method can assign different weights to different data. We used 36.5 GHz and 89 GHz brightness temperature (Tb) data from the microwave radiometer imager (MWRI) aboard the Fengyun-3D (FY-3D) satellite, for the first time in the literature, to retrieve the sea ice motion in the Beaufort Sea from January to April 2019. The retrieved sea ice motion results were in good agreement with those obtained from the motion of the buoys. Compared with the data from the buoys, the root mean-squared error (RMSE) of the sea ice motion retrieved from FY-3D/MWRI Tb data was 1.1418 cm/s in the zonal direction and 1.0481 cm/s in the meridional direction, and the mean absolute error (MAE) between them was 0.7166 cm/s in the zonal direction and 0.6777 cm/s in the meridional direction. The RMSE between the sea ice motion obtained from the National Snow and Ice Data Center (NSIDC) and the motion of the buoys was 0.9515 cm/s in the zonal direction and 0.67003 cm/s in the meridional direction, and the MAE between them was 0.6576 cm/s in the zonal direction and 0.4922 cm/s in the meridional direction. The RMSE of daily average velocity from the FY-3D/MWRI results and NSIDC data product was 2.2726 cm/s in zonal and 1.9270 cm/s in meridional, and the MAE was 1.5103 cm/s in zonal and 1.1071 cm/s in zonal. The density of the merged data was higher than that obtained from a single polarization or frequency in this paper. The results indicate that FY-3D/MWRI Tb data can retrieve the sea ice motion successfully.

A 41-year (1979–2019) passive-microwave-derived lake ice phenology data record of the Northern Hemisphere

Abstract. Seasonal ice cover is one of the important attributes of lakes in middle- and high-latitude regions. The annual freeze-up and breakup dates as well as the duration of ice cover (i.e., lake ice phenology) are sensitive to the weather and climate; hence, they can be used as an indicator of climate variability and change. In addition to optical, active microwave, and raw passive microwave data that can provide daily observations, the Calibrated Enhanced-Resolution Brightness Temperature (CETB) dataset available from the National Snow and Ice Data Center (NSIDC) provides an alternate source of passive microwave brightness temperature (TB) measurements for the determination of lake ice phenology on a 3.125 km grid. This study used Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) data from the CETB dataset to extract the ice phenology for 56 lakes across the Northern Hemisphere from 1979 to 2019. According to the differences in TB between lake ice and open water, a threshold algorithm based on the moving t test method was applied to determine the lake ice status for grids located at least 6.25 km away from the lake shore, and the ice phenology dates for each lake were then extracted. When ice phenology could be extracted from more than one satellite over overlapping periods, results from the satellite offering the largest number of observations were prioritized. The lake ice phenology results showed strong agreement with an existing product derived from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) data (2002 to 2015), with mean absolute errors of ice dates ranging from 2 to 4 d. Compared with near-shore in situ observations, the lake ice results, while different in terms of spatial coverage, still showed overall consistency. The produced lake ice record also displayed significant consistency when compared to a historical record of annual maximum ice cover of the Laurentian Great Lakes of North America. From 1979 to 2019, the average complete freezing duration and ice cover duration for lakes forming a complete ice cover on an annual basis were 153 and 161 d, respectively. The lake ice phenology dataset – a new climate data record (CDR) – will provide valuable information to the user community about the changing ice cover of lakes over the last 4 decades. The dataset is available at https://doi.org/10.1594/PANGAEA.937904 (Cai et al., 2021).

Generating large-scale sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission using the Environment and Climate Change Canada automated sea ice tracking system

Abstract. As Arctic sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of sea ice. Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014 and Sentinel-1B (S1) in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent five spaceborne SAR sensors that together routinely cover the pan-Arctic sea ice domain. Here, we describe, apply, and validate the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS) that routinely generates large-scale sea ice motion (SIM) over the pan-Arctic domain using SAR images from S1 and RCM. We applied the ECCC-ASITS to the incoming image streams of S1 and RCM from March 2020 to October 2021 using a total of 135 471 SAR images and generated new SIM datasets (7 d 25 km and 3 d 6.25 km) by combining the image stream outputs of S1 and RCM (S1 + RCM). Results indicate that S1 + RCM SIM provides more coverage in the Hudson Bay, Davis Strait, Beaufort Sea, Bering Sea, and directly over the North Pole compared to SIM from S1 alone. Based on the resolvable S1 + RCM SIM grid cells, the 7 d 25 km spatiotemporal scale is able to provide the most complete picture of SIM across the pan-Arctic from SAR imagery alone, but considerable spatiotemporal coverage is also available from 3 d 6.25 SIM products. S1 + RCM SIM is resolved within the narrow channels and inlets of the Canadian Arctic Archipelago, filling a major gap from coarser-resolution sensors. Validating the ECCC-ASITS using S1 and RCM imagery against buoys indicates a root-mean-square error (RMSE) of 2.78 km for dry ice conditions and 3.43 km for melt season conditions. Higher speeds are more apparent with S1 + RCM SIM as comparison with the National Snow and Ice Data Center (NSIDC) SIM product and the Ocean and Sea Ice Satellite Application Facility (OSI SAF) SIM product indicated an RMSE of u=4.6 km d−1 and v=4.7 km d−1 for the NSIDC and u=3.9 km d−1 and v=3.9 km d−1 for OSI SAF. Overall, our results demonstrate the robustness of the ECCC-ASITS for routinely generating large-scale SIM entirely from SAR imagery across the pan-Arctic domain.

An Intercomparison of Satellite Derived Arctic Sea Ice Motion Products

Arctic sea ice motion information provides an important scientific basis for revealing the changing mechanism of Arctic sea ice and assessing the navigational safety of Arctic waterways. For now, many satellite derived Arctic sea ice motion products have been released but few studies have conducted comparisons of these products. In this study, eleven satellite sea ice motion products from the Ocean and Sea Ice Satellite Application Facility (OSI SAF), the National Snow and Ice Data Center (NSIDC), and the French Research Institute for the Exploitation of the Seas (Ifremer) were systematically evaluated and compared based on buoys from the International Arctic Buoy Program (IABP) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) over 2018–2020. The results show that the mean absolute errors (MAEs) of ice speed for these products are 1.15–2.26 km/d and the MAEs of ice motion angle are 14.93–23.19°. Among all products, Ifremer_AMSR2 achieves the best accuracy in terms of speed error, NSIDC_Pathfinder shows the lowest angle error and OSI-405-c_Merged performs best in sea-ice drift trajectory reconstruction. Moreover, season, region, data source, ice drift tracking algorithm, and time interval all influence the accuracy of these products: (1) The sea ice motion bias in the freezing season (1.04–1.96 km/d and 11.93–22.41°) is smaller than that in the melting season (1.13–3.90 km/d and 14.41–27.41°) for most of the products. (2) Most products perform worst in East Greenland, where ice movements are fast and complex. (3) The accuracies of the products derived from AMSR-2 remotely sensed data are better than those from other data sources. (4) The continuous maximum cross-correlation (CMCC) algorithm outperforms the maximum cross-correlation (MCC) algorithm in sea ice drift retrieval. (5) The MAEs of sea ice motion with longer time interval are relatively smaller. Overall, the results indicate that the eleven remote sensing Arctic sea ice drift products are of practical use for data assimilation and model validation if uncertainties are appropriately considered. Furthermore, this study provides some improvement directions for sea ice drift retrieval from satellite data.

Comparison of Hemispheric and Regional Sea Ice Extent and Area Trends from NOAA and NASA Passive Microwave-Derived Climate Records

Three passive microwave-based sea ice products archived at the National Snow and Ice Data Center (NSIDC) are compared: (1) the NASA Team (NT) algorithm product, (2) Bootstrap (BT) algorithm product, and (3) a new version (Version 4) of the NOAA/NSIDC Climate Data Record (CDR) product. Most notable for the CDR Version 4 is the addition of the early passive microwave record, 1979 to 1987. The focus of this study is on long-term trends in monthly extent and area. In addition to hemispheric trends, regional analysis is also carried out, including use of a new Northern Hemisphere regional mask. The results indicate overall good consistency between the products, with all three products showing strong statistically significant negative trends in the Arctic and small borderline significant positive trends in the Antarctic. Regionally, the patterns are similar, except for a notable outlier of the NT area having a steeper trend in the Central Arctic, likely related to increasing surface melt. Other differences are due to varied approaches to quality control, e.g., weather filtering and correction of mixed land-ocean grid cells. Another factor, particularly in regards to NT trends with BT or CDR, is the inter-sensor calibration approach, which yields small discontinuities between the products. These varied approaches yield small differences in trends. In the Arctic, such differences are not critical, but in the Antarctic, where overall trends are near zero and borderline statistically significant, the differences are potentially important in the interpretation of trends.

A Data-Driven Deep Learning Model for Weekly Sea Ice Concentration Prediction of the Pan-Arctic During the Melting Season

This study proposes a purely data-driven model for the weekly prediction of daily sea ice concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season. The model, SICNet, adopts an encoder–decoder framework with fully convolutional networks (FCNs) and can predict the SIC (covering <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$320\times224$ </tex-math></inline-formula> grids, each with a resolution of 25 km) one-week lead with high accuracy. We design a temporal–spatial attention module (TSAM) to help SICNet capture spatiotemporal dependencies from SIC sequences. The satellite-derived SIC data of 33 years (1988–2020) from the National Snow and Ice Data Center (NSIDC) are employed to train and test the model, 1988–2015 for training, and 2016–2020 for testing. SICNet achieves the mean absolute error (MAE) of 2.67%, the mean absolute percentage error (MAPE) of 8.67%, and the Nash–Sutcliffe efficiency (NSE) of 0.9784 in weekly predicting of SIC during the melting season. SICNet achieves better performance than existing deep-learning-based models. The TSAM reduced the MAE from 2.73% to 2.67%. We evaluate the model’s performance by recursively predicting, from seven- to 28-day leads. We employ the binary accuracy (BACC) metric to measure the accuracy of the predicted sea ice extent (SIE) and compare SICNet with the anomaly persistence (Persist). SICNet shows better performance than Persist with an average BACC on the 28th day of 2016–2019 over 90% (90.17%). For the 28-day lead predictions of three extreme minimum SIE in September 2007, 2012, and 2020, SICNet outperforms Persist with an average improvement of 1.84% in BACC and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.16 milkm^{2}$ </tex-math></inline-formula> in the SIE error.

Investigating the effect of carbon dioxide on the acidity of the Ocean

There is a significant effect of carbon dioxide on the acidification of the ocean. This research focuses on the acidification of the ocean and its effect on the animal life in the ocean. Also, it focuses on the effect of carbon dioxide concentration in the atmosphere on the ocean acidification. The data are collected from the research institutions and laboratories, such as National Snow and Ice Data Center (NSIDC), Japan, National Oceanic and Atmospheric Administration (NOAA), USA, Mauna Loa Observatory in Hawaii, and other sources of research about acidification of ocean. The results show that the acidity increases with increasing the amount of carbon dioxide in the atmosphere. This is because ocean absorbs nearly 50% of carbon dioxide from the atmosphere. Carbonate ions (CO32-) will be used in forming carbonic acid, which will increase the acidity of the water. Increasing the acidity of water will affect building of the animal Skeleton. It is recommended to reduce the amount of carbon dioxide in the atmosphere; therefore the acidity will be decreased in the ocean.

Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications

AbstractTwenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land-cover. When introduced in 1995, the finest resolution global datasets of these variables were on a 0.5° × 0.5° latitude-longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arcsecond × 10-arcsecond latitude-longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude-longitude (approximately 10 km) gridded meteorological climatologies (1981-2019, European Centre for Medium-Range Weather Forecasts [ECMWF] ReAnalysis, 5thGeneration Land [ERA5-Land]) using MicroMet, a spatially distributed, high-resolution, micro-meteorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification 2.0) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).

Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions

Abstract. Arctic sea ice drift motion affects the global material balance, energy exchange and climate change and seriously affects the navigational safety of ships along certain channels. Due to the Arctic's special geographical location and harsh natural conditions, observations and broad understanding of the Arctic sea ice motion are very limited. In this study, sea ice motion data released by the National Snow and Ice Data Center (NSIDC) were used to analyze the climatological, spatial and temporal characteristics of the Arctic sea ice drift from 1979 to 2018 and to understand the multiscale variation characteristics of the three major Arctic sea ice drift patterns. The empirical orthogonal function (EOF) analysis method was used to extract the three main sea ice drift patterns, which are the anticyclonic sea ice drift circulation pattern on the scale of the Arctic basin, the average sea ice transport pattern from the Arctic Ocean to the Fram Strait, and the transport pattern moving ice between the Kara Sea (KS) and the northern coast of Alaska. By using the ensemble empirical mode decomposition (EEMD) method, each temporal coefficient series extracted by the EOF method was decomposed into multiple timescale sequences. We found that the three major drift patterns have four significant interannual variation periods of approximately 1, 2, 4 and 8 years. Furthermore, the second pattern has a significant interdecadal variation characteristic with a period of approximately 19 years, while the other two patterns have no significant interdecadal variation characteristics. Combined with the atmospheric and oceanic geophysical variables, the results of the correlation analysis show that the first EOF sea ice drift pattern is mainly related to atmospheric environmental factors, the second pattern is related to the joint action of atmospheric and oceanic factors, and the third pattern is mainly related to oceanic factors. Our study suggests that the ocean environment also has a strong correlation with sea ice movement. Especially for some sea ice transport patterns, the correlation even exceeds atmospheric forcing.