Search

Sea ice velocity impacts the distribution of sea ice, and the flux of exported sea ice through the Fram Strait increases with increasing ice velocity. Therefore, improving the accuracy of estimates of the sea ice velocity is important. We introduce a pyramid algorithm into the Horn-Schunck optical flow (HS-OF) method (to develop the PHS-OF method). Before calculating the sea ice velocity, we generate multilayer pyramid images from an original brightness temperature image. Then, the sea ice velocity of the pyramid layer is calculated, and the ice velocity in the original image is calculated by layer iteration. Winter Arctic sea ice velocities from 2014 to 2016 are obtained and used to discuss the accuracy of the HS-OF method and PHS-OF (specifically the 2-layer PHS-OF (2LPHS-OF) and 4-layer PHS-OF (4LPHS-OF)) methods. The results prove that the PHS-OF method indeed improves the accuracy of sea ice velocity estimates, and the 2LPHS-OF scheme is more appropriate for estimating ice velocity. The error is smaller for the 2LPHS-OF velocity estimates than values from the Ocean and Sea Ice Satellite Application Facility and the Copernicus Marine Environment Monitoring Service, and estimates of changes in velocity by the 2LPHS-OF method are consistent with those from the National Snow and Ice Data Center. Sea ice undergoes two main motion patterns, i.e., transpolar drift and the Beaufort Gyre. In addition, cyclonic and anticyclonic ice drift occurred during winter 2016. Variations in sea ice velocity are related to the open water area, sea ice retreat time and length of the open water season.

This study is based on daily sea-ice concentration (SIC) data collected from the National Snow and Ice Data Center between 1989 and 2018. We analyzed the concentration, total sea ice extent (SIE), multiyear ice extent, and first- and second-year ice extent of Polar (Arctic and Antarctic) sea ice. The results showed that, over the analyzed 30 year span, the spatial distribution of SIC decreased in the Arctic, while it showed high regional variability within the Antarctic. The total SIE decreased in the polar regions at a rate of $- 0.051\,\,\times 10^{6}$ km2 yr−1 (−2.07% per decade) and the multiyear ice extent at a rate of $-0.086\times 10^{6}$ km2 yr−1 (−12.31% per decade), while the first- and second-year ice extent increased at a rate of $0.054\,\times \,10^{6}$ km2 yr−1 (2.13% per decade).

We investigate the spatio-temporal characteristics of the austral spring Ross Ice Shelf Polynya (RISP) and its relationship with atmospheric circulation anomalies based on the sea ice concentration data from the National Snow and Ice Data Center (NSIDC) and the ERA5 reanalysis data. The RISP area series is derived by choosing 25% sea ice concentration as the threshold. Then the principal spatial patterns of the RISP are acquired by using the empirical orthogonal function (EOF) analysis. The leading EOF (EOF1) has a spatial distribution of a strong negative core in the northeastern RISP with a weak positive core near the Cape Adare, explaining 48.1% of the variance. This mode matches the wind pattern of the northwesterly on the western Ross Sea while southwesterly on the eastern Ross Sea, which reflects the zonal variation of the RISP. There is also a strong positive correlation between the RISP area and the PC1. The EOF2 explains 27.4% of the variance, and is mainly characterized by a large region of low values in the northwestern Ross Sea with a small region of high value near Cape Colbeck. Matching the strong southerly over the western Ross Sea, the EOF2 partly depicts the meridional variation of the RISP. We further explored the connections between the RISP and sea level pressure, and found that a deeper and more eastward Amundsen Sea Low (ASL) in November often coincides with a larger and more eastward RISP in December. This lagged response is partly generated through the influence of the ASL on the RISP in November.

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.

Independent datasets consistently indicate a significant correlation between the sea ice variability in the Bering Sea during melt season and the summer rainfall variability in the Lake Baikal area and Northeastern China. In this study, four sea ice datasets (HadISST1, HadISST2.2, ERA-Interim and NOAA/NSIDC) and two global precipitation datasets (CRU V4.01 and GPCP V2.3) are used to investigate co-variations between melt season (March–April–May–June, MAMJ) Bering Sea ice cover (BSIC) and summer (June–July–August, JJA) East Asian precipitation. All datasets demonstrate a significant correlation between the MAMJ BSIC and the JJA rainfall in Lake Baikal-Northeastern China (Baikal-NEC). Based on the reanalysis datasets and the numerical sensitivity experiments performed in this study using Community Atmospheric Model version 5 (CAM5), a mechanism to understand how the MAMJ BSIC influences the JJA Baikal-NEC rainfall is suggested. More MAMJ BSIC triggers a wave train and causes a positive sea level pressure (SLP) anomaly over the North Atlantic during MAMJ. The high SLP anomaly, associated with an anti-cyclonic wind stress circulation anomaly, favors the appearance of sea surface temperature (SST) anomalies in a zonal dipole-pattern in the North Atlantic during summer. The dipole SST anomaly drives a zonally orientated wave train, which causes a high anomaly geopotential height at 500 hPa over the Sea of Japan. As a result, the mean East Asian trough moves westward and a low geopotential height anomaly occurs over Baikal-NEC. This prevailing regional low pressure anomaly together with enhanced moisture transport from the western North Pacific and convergence over Baikal-NEC, positively influences the increased rainfall in summer.

We report on results of a systematic inter-comparison 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 the Arctic during summer. The products are compared against SIC and net ice surface fraction (ISF) – SIC minus the per-grid-cell melt pond fraction (MPF) on sea ice – as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of 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. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20 %–25 % for groups I and III and up to 30 %–35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from group I products agrees with MODIS within 2 %–5 % accuracy during the entire melt period from May through September. Group II and IV products overestimate MODIS Arctic average SIC by 5 %–10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates MODIS Arctic average SIC by 5 %–10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for (i) the observed differences between PMW SIC and MODIS ISF and for (ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role – particularly for groups I and II – and recommend conducting further research in this field.

The accuracy of sea-ice motion products provided by the National Snow and Ice Data Center (NSIDC) and the Ocean and Sea Ice Satellite Application Facility (OSI-SAF) was validated with data collected by ice drifters that were deployed in the western Arctic Ocean in 2014 and 2016. Data from both NSIDC and OSI-SAF products exhibited statistically significant (p< 0.001) correlation with drifter data. The OSI-SAF product tended to overestimate ice speed, while underestimation was demonstrated for the NSIDC product, especially for the melt season and the marginal ice zone. Monthly Lagrangian trajectories of ice floes were reconstructed using the products. Larger spatial variability in the deviation between NSIDC and drifter trajectories was observed than that of OSI-SAF, and seasonal variability in the deviation for NSIDC was observed. Furthermore, trajectories reconstructed using the NSIDC product were sensitive to variations in sea-ice concentration. The feasibility of using remote-sensing products to characterize sea-ice deformation was assessed by evaluating the distance between two arbitrary positions as estimated by the products. Compared with the OSI-SAF product, relative errors are lower (

A new version of sea ice motion and age products includes several significant upgrades in processing, corrects known issues with the previous version, and updates the time series through 2018, with regular updates planned for the future. First, we provide a history of these NASA products distributed at the National Snow and Ice Data Center. Then we discuss the improvements to the algorithms, provide validation results for the new (Version 4) and older versions, and intercompare the two. While Version 4 algorithm changes were significant, the impact on the products is relatively minor, particularly for more recent years. The changes in Version 4 reduce motion biases by ∼ 0.01 to 0.02 cm s−1 and error standard deviations by ∼ 0.3 cm s−1. Overall, ice speed increased in Version 4 over Version 3 by 0.5 to 2.0 cm s−1 over most of the time series. Version 4 shows a higher positive trend for the Arctic of 0.21 cm s−1 per decade compared to 0.13 cm s−1 per decade for Version 3. The new version of ice age estimates indicates more older ice than Version 3, especially earlier in the record, but similar trends toward less multiyear ice. Changes in sea ice motion and age derived from the product show a significant shift in the Arctic ice cover, from a pack with a high concentration of older ice to a sea ice cover dominated by first-year ice, which is more susceptible to summer melt. We also observe an increase in the speed of the ice over the time series ≥ 30 years, which has been shown in other studies and is anticipated with the annual decrease in sea ice extent.

The Antarctic grounding zone, which is the transition between the fully grounded ice sheet to freely floating ice shelf, plays a critical role in ice sheet stability, mass budget calculations, and ice sheet model projections. It is therefore important to continuously monitor its location and migration over time. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries: the inland limit of tidal flexure (PointF), inshore limit of hydrostatic equilibrium (PointH), and the break in slope (PointIb). This dataset was derived from automated techniques developed in this study, using ICESat-2 laser altimetry repeat tracks between 30March2019 and 30September2020. The new grounding zone product has a near-complete coverage of the Antarctic Ice Sheet with a total of 21 346 PointF, 18 149 PointH, and 36 765 PointIblocations identified, including the difficult-to-survey grounding zones, such as the fast-flowing glaciers draining into the Amundsen Sea embayment. The locations of newly derived ICESat-2 landward limit of tidal flexure agree well with the most recent differential synthetic aperture radar interferometry (DInSAR) observations in 2018, with a mean absolute separation and standard deviation of 0.02 and 0.02 km, respectively. By comparing the ICESat-2-derived grounding zone with the previous grounding zone products, we find a grounding line retreat of up to 15 km on the Crary Ice Rise of Ross Ice Shelf and a pervasive landward grounding line migration along the Amundsen Sea embayment during the past 2 decades. We also identify the presence of ice plains on the Filchner–Ronne Ice Shelf and the influence of oscillating ocean tides on grounding zone migration. The product derived from this study is available athttps://doi.org/10.5523/bris.bnqqyngt89eo26qk8keckglww(Li et al., 2021) and is archived and maintained at the National Snow and Ice Data Center.

MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products have been available since 2000 – following the 1999 launch of the Terra MODIS and the 2002 launch of the Aqua MODIS – and include global snow-cover extent (SCE) (swath, daily, and 8 d composites) at 500 m and ∼5 km spatial resolutions. These products are used extensively in hydrological modeling and climate studies. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has provided improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products at a 375 m spatial resolution have been available since 2011 and are currently being reprocessed for Collection 2 (C2). Both the MODIS C6.1 and the VIIRS C2 products will be available for download from the National Snow and Ice Data Center beginning in early 2020 with the complete time series available in 2020. To address the need for a cloud-reduced or cloud-free daily SCE product for both MODIS and VIIRS, a daily cloud-gap-filled (CGF) snow-cover algorithm was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500 m resolution CGF SCE map products from MODIS. VNP10A1F is the daily, 375 m resolution CGF SCE map product from VIIRS. These CGF products include quality-assurance data such as cloud-persistence statistics showing the age of the observation in each pixel. The objective of this paper is to introduce the new MODIS and VIIRS standard CGF daily SCE products and to provide a preliminary evaluation of uncertainties in the gap-filling methodology so that the products can be used as the basis for a moderate-resolution Earth science data record (ESDR) of SCE. Time series of the MODIS and VIIRS CGF products have been developed and evaluated at selected study sites in the US and southern Canada. Observed differences, although small, are largely attributed to cloud masking and differences in the time of day of image acquisition. A nearly 3-month time-series comparison of Terra MODIS and S-NPP VIIRS CGF snow-cover maps for a large study area covering all or parts of 11 states in the western US and part of southwestern Canada reveals excellent correspondence between the Terra MODIS and S-NPP VIIRS products, with a mean difference of 11 070 km2, which is ∼0.45 % of the study area. According to our preliminary validation of the Terra and Aqua MODIS CGF SCE products in the western US study area, we found higher accuracy of the Terra product compared with the Aqua product. The MODIS CGF SCE data record beginning in 2000 has been extended into the VIIRS era, which should last at least through the early 2030s.

Global climate models, focused on projecting anthropogenic warming, have not detected an increase in sea surface temperature (SST) at low latitudes comparable to the observed one. This appears to be one reason for the discrepancy between the model estimates of warming and reduction of the sea ice extent in the Arctic and the observed changes in the climate system. In previous studies, it was shown that short-term manifestations of the impact of low latitudes on the Arctic climate were identified in 2–3 weeks as a result of strengthening of atmospheric circulation patterns. In this paper, for the first time, a climatic relationship was established among an increase in SST, air temperature, and water vapor content at low latitudes, and a decrease in sea ice extent in the Arctic. ECMWF Re-Analysis data (ERA-Interim, ERA5), Hadley Centre Sea Ice and Sea Surface Temperature data set (HadISST), sea ice archives of the World Centers NSIDC (USA), and Arctic and Antarctic Research Institute (Russia), observations of water temperature in the Kola section (33°30’ E), calculated sea ice parameters using the Arctic and Antarctic Research Institute coupled ice-ocean circulation model (AARI–IOCM). Methods of multivariate correlation analysis, calculating spectra and coherence, and creating correlation graphs were used to obtain the results. For the first time, estimates of the effect of heat transport from low to high latitudes on climate change and sea ice extent in the Arctic over the past 40 years have been obtained, explaining a significant part of their variability. The increase in heat transport is affected by an increase in SST at low latitudes, where a significant part of the solar heat is accumulated. Due to the increase in SST, the amount of heat transported by the ocean and the atmosphere from low latitudes to the Arctic increases, leading to an increase in the air temperature, water vapor content, downward longwave radiation at high latitudes, and a decrease in the thickness and extent of winter sea ice. Potential topics include, but are not limited to: the role of heat and moisture transport in the Arctic warming, effect of SST at low latitudes on transports, linkage of warming in low latitudes and in shrinking of the Arctic sea ice.

In recent years, the possibility of estimating Soil Moisture (SM) at different resolution scales improved greatly with the launch of the latest satellite microwave sensors and in particular of the Soil Moisture Active and Passive (SMAP) radar + radiometer and Sentinel-1 (S-1) Synthetic Aperture Radar (SAR). However, the tradeoff between temporal and spatial resolution offered by each of these two sensors is still unable to meet the requirements of many users. The SM SMAP products are available through the NSIDC data portal at resolution varying between 1 and 36 Km [1, 2]. This study exploited the possibility of merging SMAP, S-1 and COSMO-SkyMed X-band SAR (CSK) data through Artificial Neural Networks (ANN) for obtaining SM products at high spatial resolution, with the aim of evaluating the benefits of assimilating higher resolution SM into hydrological models. The algorithm has been implemented and validated on a test area in Tuscany (Val d’Elsa, center coordinates of Ponte a Elsa: 43°41′20.37″N 10°53′42.38″E), in central Italy. The area is characterized by a partially hilly landscape, including agricultural and urban areas areas and forests, with heterogeneities that set important constraints to the potential of SMAP observations for SM monitoring. The SMAP, S-1 and CSK acquisitions available between 2019 and 2020 on the area have been considered for the algorithm development. The reference SM values for validation purposes have been derived from in-situ observations carried out in the framework of the ASI ‘Algoritmi’ project [3], which also provided the CSK images considered in this study. The improvement of spatial resolution of the output SMC product is still under investigation; however, the preliminary results seem showing that the method is able to map SM from the SMAP, S-1 and CSK synergy at a resolution better than 100m, with correlation coefficient R≃0.89 and RMSE≃0.025 m3/m3.

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, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent 5 spaceborne SAR sensors, that together routinely cover the pan-Arctic sea ice domain. Here, we utilized over 60,000 SAR images from Sentinel-1AB (S1) and RCM to generate large-scale sea ice motion (SIM) estimates over the pan-Arctic domain from March to December, 2020. On average, 4.5 million SIM vectors from S1 and RCM were automatically detected per week for 2020 and when combined (S1+RCM) they facilitated the generation of 7-day, 25 km SIM products across the pan-Arctic domain. S1+RCM SIM provided more coverage in Hudson Bay, Davis Strait, Beaufort Sea, Bering Sea, and over the North Pole compared to SIM from S1 alone. S1+RCM SIM was able to be resolved within the narrow channels and inlets across the pan-Arctic alleviating the main limitation of coarser resolution sensors. S1+RCM SIM provided larger ice speeds with a mean difference (MD) of 1.3 km/day compared to the National Snow and Ice Data Center (NSIDC) SIM product and a MD of 0.76 km/day compared to Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) SIM product. S1+RCM was also able to better resolve SIM in the marginal ice zone compared to the NSIDC and OSA-SAF SIM products. Overall, our results demonstrate that combining SIM from multiple spaceborne SAR satellites allows for large-scale SIM to be routinely generated across the pan-Arctic domain.

Brightness temperature data from wave Imaging Radiometer using Aperture Synthesis (MIRAS) on board the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission have been used to derive the thickness of thin sea ice for the Arctic freeze-up period. To control the long-term geophysical quality for level 3 SMOS sea ice thickness products we derive a regional extent parameter that can be compared to independent standard ice extent products such as the NSIDC sea ice index. This metric allows to identify first-order quality problems such as data gaps and to observe the evolution of the Arctic sea ice growth in key regions. The regionalized SMOS sea ice thickness extent corresponds in general well with the corresponding NSIDC Sea Ice Index. The occurrence of severe RFI problems has so far mainly been limited to the initial period of the SMOS measurements during the season 2010/2011. Otherwise the comparison does not reveal any significant quality problems of the SMOS sea ice thickness data.

This study proposes a fully-convolutional-networks-based (FCN-based) model to predict the daily Arctic SIC in the melt season. The FCN-based model adopts an encoder-decoder framework with FCN layers as the basic unit. We use the SIC series of the last seven days to predict the SIC of the coming day. The Arctic SIC series of 31 years (1988–2018) from the NSIDC are employed to train and evaluate the model. Experiments show that the FCN-based model is capable of accurately predicting the daily Arctic SIC of all grids (320× 224) in an end-to-end way, with an MAE under 1%. The FCN-based model shows apparent advantages over the newly published convolution-neural-network-based (CNN-based) model in prediction accuracy, efficiency, and resource occupation.

This article presents the study of a passive acoustic dataset recorded on the Chukchi Shelf from October 2016 to July 2017 during the Canada Basin Acoustic Propagation Experiment (CANAPE). The study focuses on the low-frequency (250–350 Hz) ambient noise (after individual transient signals are removed) and its environmental drivers. A specificity of the experimental area is the Beaufort Duct, a persistent warm layer intrusion of variable extent created by climate change, which favors long-range acoustic propagation. The Chukchi Shelf ambient noise shows traditional polar features: it is quieter and wind force influence is reduced when the sea is ice-covered. However, the study reveals two other striking features. First, if the experimental area is covered with ice, the ambient noise drops by up to 10 dB/Hz when the Beaufort Duct disappears. Further, a large part of the noise variability is driven by distant cryogenic events, hundreds of kilometers away from the acoustic receivers. This was quantified using correlations between the CANAPE acoustic data and distant ice-drift magnitude data (National Snow and Ice Data Center).

The Pacific sector of the Arctic Ocean (PA, hereafter) is a region sensitive to climate change. Given the alarming changes in sea ice cover during recent years, knowledge of sea ice loss with respect to ice advection and melting processes has become critical. With satellite-derived products from the National Snow and Ice Center (NSIDC), a 38-year record (1979–2016) of the loss in sea ice area in summer within the Pacific-Arctic (PA) sector due to the two processes is obtained. The average sea ice outflow from the PA to the Atlantic-Arctic (AA) Ocean during the summer season (June–September) reaches 0.173×106 km2, which corresponds to approximately 34 % of the mean annual export (October to September). Over the investigated period, a positive trend of 0.004×106 km2 yr−1 is also observed for the outflow field in summer. The mean estimate of sea ice retreat within the PA associated with summer melting is 1.66×106 km2, with a positive trend of 0.053×106 km2 yr−1. As a result, the increasing trends of ice retreat caused by outflow and melting together contribute to a stronger decrease in sea ice coverage within the PA (0.057×106 km2 yr−1) in summer. In percentage terms, the melting process accounts for 90.4 % of the sea ice retreat in the PA in summer, whereas the remaining 9.6 % is explained by the outflow process, on average. Moreover, our analysis suggests that the connections are relatively strong (R=0.63), moderate (R=-0.46), and weak (R=-0.24) between retreat of sea ice and the winds associated with the dipole anomaly (DA), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO), respectively. The DA participates by impacting both the advection (R=0.74) and melting (R=0.55) processes, whereas the NAO affects the melting process (R=-0.46).

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.

Variability and trends of Fram Strait sea ice area and volume exports are examined for the period of 1990–2010. Simulations from a high-resolution version of the MPIOM model (STORM project) reproduce area and volume export well when compared with NSIDC and ICESat satellite data and in-situ ice thickness observations. The fluxes derived from ice thickness and drift satellite products vary considerably, indicating a high uncertainty in these estimates which we mostly assign to the drift observations. The model captures the observed average seasonal cycles and interannual variability of ice export. The simulated mean annual sea ice area export is 860 × 103 km2 a− 1 (1990–2010), and the correlation with the NSIDC-based area fluxes is r = 0.67. The simulated mean annual volume export is 3.3 × 103 km3 a− 1 (1990–2010), close to the ICESat/ULS values, with a correlation of r = 0.58. The simulated monthly area export has a significant positive trend of + 10% per decade, explained by wind forcing. The major contribution to the robust trend in area export between June and September. Fram Strait ice volume export variability is mainly controlled by ice drift with a dominant role of the Transpolar Drift and, to a lesser extent thickness variability. The area export increase reflects increasing ice-drift speed, but is balanced with a reduced thickness over time when it comes to volume export, giving no significant trend in volume export. The spatial variability of ice drift indicates that the export influences a large area upstream in the Trans-Polar Drift stream, and that high volume export events lead to a thinner thickness there. The central Arctic is well connected drift-wise to the Fram Strait via the Transpolar Drift while for thickness, the region north of Greenland is dominated and controlled by the Fram Strait ice export.

The measurement of glacier velocity fields using repeat satellite imagery has become a standard method of cryospheric research. However, the reliable discovery of important glacier velocity variations on a large scale is still problematic because time series span different time intervals and are partly populated with erroneous velocity estimates. In this study we build upon existing glacier velocity products from the GoLIVE dataset (https://nsidc.org/data/golive, last access: 26 February 2019) and compile a multi-temporal stack of velocity data over the Saint Elias Mountains and vicinity. Each layer has a time separation of 32 days, making it possible to observe details such as within-season velocity change over an area of roughly 150 000 km2. Our methodology is robust as it is based upon a fuzzy voting scheme applied in a discrete parameter space and thus is able to filter multiple outliers. The multi-temporal data stack is then smoothed to facilitate interpretation. This results in a spatiotemporal dataset in which one can identify short-term glacier dynamics on a regional scale. The goal is not to improve accuracy or precision but to enhance extraction of the timing and location of ice flow events such as glacier surges. Our implementation is fully automatic and the approach is independent of geographical area or satellite system used. We demonstrate this automatic method on a large glacier area in Alaska and Canada. Within the Saint Elias and Kluane mountain ranges, several surges and their propagation characteristics are identified and tracked through time, as well as more complicated dynamics in the Wrangell Mountains.

The process of formation and rotting of ice on lakes is an integral part of the hydrological cycle of many lakes. The conditions of the ice regime significantly influence the ecological system of lakes. The article includes calculation and analysis of errors in the determination of the spatial ice distribution (spatial resolution of 4–6 km) on Lake Onego, Lake Ladoga, Lake Segozero and Lake Vigozero within the period of 2006−2017 according to National Snow and Ice Data Center (NSIDC), National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service (NOAA NESDIS) data with regard to reliable Moderate Resolution Imaging Spectroradiometer (MODIS) data (spatial resolution of 500 m). It was established that within the monitoring period, NSIDC data have the minimum mean values of errors in determining the spatial distribution of ice on lakes (3−10%) compared to NOAA NESDIS data (11−19%) and are also of more practical interest in estimating the ice coverage of lakes. The dependence of the mean value of errors that occur in the determination of the spatial distribution of ice (according to NSIDC, NOAA and NESDIS data) on the actual value of ice coverage (according to MODIS) was revealed. The results show that the NSIDC data allow estimating adequately the phases of the ice regime; however, the formation of a daily time series of ice coverage during freeze-up and break-up phases is possible only with a significant error (mean value of absolute deviations according to MODIS data is up to 35%).

Studying of characteristics of the Lake Onega ice regime and investigating of the climate influence on the formation and destruction of the ice cover requires a continuous chronological series of data on the Lake ice coverage. Ice cover is the percentage of the ice area to the total area of the lake. In 1955–1990, calculations of the ice coverage of the Lake were based on the use of the results of airborne ice reconnaissance. On average for this period, from 5 to 15 values were annually obtained, which was not enough for a comprehensive analysis of the ice coverage variability. In this paper, for the frst time, a daily series of values of the ice coverage of the Lake Onega for the period 2000–2018 had been formed basing on the results of a combined analysis of the following satellite data sets: NSIDC, NOAA NESDIS, corrected by data of the satellite MODIS sensor. Values from November to May were grouped from the above data sets without regards for years of observations, and then the regression analysis of these values made possible to create a model (a polynomial of the 8th degree) of the chronological course of the ice coverage during the period of the ice phenomena existence on the Lake Onega. Te coefcient of determination of the model is 0.74, and the error in determining the ice coverage is 21%. Te average dates of the beginning, end, and duration of periods of the formation (from November 25th to January 19th), the destruction (from April 13th to May 17th) of the ice cover, as well as the total freeze-up time (from January 20th to April 12th) on the Lake Onega for the period 2000–2018 was determined. Te period of ice phenomena on the Lake Onega on average lasts for almost half a year (175 days), of which a signifcant part of the time is a complete freeze-up (84 days). It was found that the rate of formation of the ice cover (1.76% per day) on the Lake Onega is 1.65 times smaller than the rate of its destruction (2.90% per day), which approximately corresponds to similar results obtained for the Lake Ladoga (1.5 times).

Special issue Applications of GNSS Reflectometry for Earth Observation II.-- 19 pages, 17 figures, 4 tables, supplementary material https://doi.org/10.3390/rs13061139.-- Data used in this study will be publicly and freely available for everyone through the Copernicus system as part of the FSSCat mission, CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved, This work was supported by 2017 ESA S3 challenge and Copernicus Masters overall winner award (“FSSCat” project). this work has been (partially) sponsored by project SPOT: Sensing with Pioneering Opportunistic Techniques grant RTI2018-099008-B-C21/AEI/10.13039/501100011033, and by the Unidad de Excelencia Maria de Maeztu MDM-2016-0600. This work has also been (partially) sponsored by the Spanish Ministry of Science and Innovation through the project ESP2017-89463-C3, and by the Centro de Excelencia Severo Ochoa (CEX2019-000928-S), and by the CSIC Plataforma Temática Interdisciplinar de Teledetección (PTI-Teledetect). David Llavería receives support from a FPU fellowship from the Spanish Ministry of Education FPU18/06107.; Joan Francesc Munoz-Martin receives support from the grant for recruitment of early stage research staff FI-DGR 2018 of the AGAUR—Generalitat de Catalunya (FEDER), Spain; C.H. receives support of a fellowship from “la Caixa” Foundation (ID 100010434) with the fellowship code LCF/BQ/DI18/11660050, and funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673

Sea ice is formed when ocean water freezes at a temperature of −1.8 °C. This ice formed over the ocean plays a crucial role in the global climate system. Most of the solar radiation which is incident on sea ice is reflected, and thus, it acts as the earth’s polar refrigerator by limiting the heat absorption. However, it is found over the decades that sea ice is declining dramatically, and this can cause various negative impacts. Firstly, the warming up of the poles disrupts the earth’s overall heat flow, and secondly, it alters the wind pattern by pushing more ice towards the Atlantic. The study aims at extracting the rate of growth and attainment of sea ice over various seas of the Arctic region. The seas mainly considered for the purpose of study are East Siberian, Kara and Barents. Using sea ice thickness data from NSIDC, for the year 2012–13 and 2018–19, various episodes of sea ice thickness over the region are distinctly identified and compared with each other. It is found that the fluctuation in sea ice thickness is different for each sea mainly due to its topography. Despite the cool nature of the Arctic, it is also found that unexpected warmth comes beneath the Arctic Ocean because of the high heat capacity of water, which in turn serves as the causative factor for mild climates in the coastal regions. On the contrary, land has a lower heat capacity, so it gets heated up quickly during the day and cools down with dawn. Not only the heat capacity factor but also other parameters such as pressure, temperature, wind, humidity and its related precipitation also contribute to the variation in sea ice thickness.

In this work, we will present results from a sea ice concentration and age retrieval algorithm using microwave radiances. The algorithm has four notable features: (1) the use of a 11 GHz channel, (2) satellite brightness temperatures that have been calibrated in an absolute sense, (3) algorithm parameterization in terms of surface emissivity rather than brightness temperature, and (4) detection of sea ice

Sea ice in Baffin Bay plays an important role in the deep water formation in the Labrador Sea and contributes to the variation of the Atlantic meridional overturning circulation (AMOC) on larger scales. To quantify the sea ice volume variations in Baffin Bay, a major driver of the deep water formation, three state-of-the-art sea ice models (CMST, NAOSIM, and PIOMAS) are investigated in the melt and freezing season from 2011 to 2016. An ensemble of three estimates of the sea ice volume fluxes in Baffin Bay is generated from the three modeled sea ice thickness and NSIDC satellite derived ice drift data. Results show that the net increase of the ensemble mean sea ice volume (SIV) in Baffin Bay occurs from October to April with the largest SIV increase in December (116 ± 16 km3 month−1) and the reduction occurs from May to September with the largest SIV decline in July (−160 ± 32 km3 month−1). The maximum SIV inflow occurs in winter in all the model data consistently. The ensemble mean SIV inflow (322 ± 4 km3) reaches its maximum in winter 2013 caused by high ice velocities while the largest SIV outflow (244 ± 61 km3) occurs in spring of 2014. The long-term annual mean ice volume inflow and outflow are 437(± 53) km3 and 339(± 68) km3, respectively. Our analysis also reveals that on average, sea ice in Baffin Bay melts from May to October with a net reduction of 335 km3 in volume while it freezes from November to April with a net increase of 251 km3.

Sea ice drift plays a central role in the Arctic climate and ecology through its effects on the ice cover, thermodynamics, and energetics of northern marine ecosystems. Due to the challenges of accessing the Arctic, remote sensing has been used to obtain large-scale longitudinal data. These data are often associated with errors and biases that must be considered when incorporated into research. However, obtaining reference data for validation is often prohibitively expensive or practically unfeasible. We used the motion of 20 passively drifting high-accuracy GPS telemetry collars originally deployed on polar bears, Ursus maritimus, in western Hudson Bay, Canada, to validate a widely used sea ice drift dataset produced by the National Snow and Ice Data Center (NSIDC). Our results showed that the NSIDC model tended to underestimate the horizontal and vertical (i.e., u and v) components of drift. Consequently, the NSIDC model underestimated magnitude of drift, particularly at high ice speeds. Modelled drift direction was unbiased; however, it was less precise at lower drift speeds. Research using these drift data should consider integrating these biases into their analyses, particularly where absolute ground speed or direction is necessary. Further investigation is required into the sources of error, particularly in under-examined areas without in situ data.

The Ross Sea region, including three main polynya areas in McMurdo Sound, Terra Nova Bay, and in front of the Ross Ice Shelf, has experienced a significant increase in sea ice extent in the first four decades of satellite observations. Here, we use Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) to estimate 894 high-resolution sea ice motion fields of the Western Ross Sea in order to explore ice-atmosphere interactions based on sequential high-resolution Advanced Synthetic Aperture Radar (ASAR) images from the Envisat satellite acquired between 2002–2012. Validation of output motion vectors with manually drawn vectors for 24 image pairs show Pearson correlation coefficients of 0.92 ± 0.09 with a mean deviation in direction of −3.17 ± 6.48 degrees. The high-resolution vectors were also validated against the Environment and Climate Change Canada sea ice motion tracking algorithm, resulting in correlation coefficients of 0.84 ± 0.20 and the mean deviation in the direction of −0.04 ± 17.39 degrees. A total of 480 one-day separated velocity vector fields have been compared to an available NSIDC low-resolution sea ice motion vector product, showing much lower correlations and high directional differences. The high-resolution product is able to better identify short-term and spatial variations, whereas the low-resolution product underestimates the actual sea ice velocities by 47% in this important near-coastal region. The large-scale pattern of sea ice drift over the full time period is similar in both products. Improved image coverage is still desired to capture drift variations shorter than 24 h.

The diminishing extent of Arctic sea ice is a key indicator of climate change as well as being an accelerant for future global warming. Since 1978, Arctic sea ice has been measured using satellite-based microwave sensing; however, different measures of Arctic sea ice extent have been made available based on differing algorithmic transformations of raw satellite data. We propose and estimate a dynamic factor model that combines four of these measures in an optimal way and accounts for their differing volatility and cross-correlations. We then use the Kalman smoother to extract an optimal combined measure of Arctic sea ice extent. It turns out that almost all weight is put on the NSIDC Sea Ice Index, confirming and enhancing confidence in the Sea Ice Index and the NASA Team algorithm on which it is based.

This ethnographic study at the US National Snow and Ice Data Center (NSIDC) follows a group of scientists and communications specialists as they compose visualizations and analyses of near-real-time Arctic sea ice data. Research participants collectively make scientific judgments about near-real-time data in a highly visible public venue with ‘relational agility’. They balance multiple phenomena including knowledge of how sceptics attack climate science, reflexivity about the conventions through which sea ice data is gathered, the needs of journalists working in a news cycle paced by Twitter, and the liveliness and vitality of sea ice itself. Relational agility, understood as a way of coordinating the social in relation to this plurality of contingent practices and processes, provides insight into the science and politics of nonlinear climate change.

The article presents a comprehensive multilevel accuracy evaluation of global snow water equivalent (SWE) datasets performed for the territory of the Northern Dvina River basin, where snow plays an important role in the formation of spring runoff and the increase in winter runoff during recent decades due to ongoing climate change. Six global snow water equivalent datasets, available on the National Snow and Ice Data Center website (all currently available global SWE data), were used in this study. Snow survey data of the Roshydromet observation network were used as ground-based observations. The accuracy of dataset values of SWE was evaluated by comparison with ground observations in several successive stages. In the first stage, the global SWE values were compared with the observational data for several selected weather stations, i. e., the dataset values of SWE belonging to a particular grid cell were compared with the snow survey values of a weather station located in this cell. In the second stage, the SWE values averaged over one of the main subbasins of the Northern Dvina, the Small Northern Dvina River basin, were analyzed. The final stage was the SWE comparison for the entire Northern Dvina River basin. The methodology for ranking data depending on their accuracy based on comparison with ground-based observations of several basic parameters of quantitative changes in SWE during periods of formation and before the start of melting (maximum SWE) was specially developed to implement the accuracy evaluation and SWE comparison. A comprehensive comparative analysis performed based on the developed methodology revealed that the accuracy of global SWE data increases with the investigation area (i.e., the volume of information used) for which the comparison is being carried out. The closest to the observed SWE values for all datasets were obtained for the whole Northern Dvina River basin. The ranking methodology and the implemented analysis helped to determine the most promising global SWE data for future use.

In the Northern Hemisphere, seasonal changes in surface freeze–thaw (FT) cycles are an important component of surface energy, hydrological and eco-biogeochemical processes that must be accurately monitored. This paper presents the weekly polar-gridded Aquarius passive L-band surface freeze–thaw product (FT-AP) distributed on the Equal-Area Scalable Earth Grid version 2.0, above the parallel 50° N, with a spatial resolution of 36 km  ×  36 km. The FT-AP classification algorithm is based on a seasonal threshold approach using the normalized polarization ratio, references for frozen and thawed conditions and optimized thresholds. To evaluate the uncertainties of the product, we compared it with another satellite FT product also derived from passive microwave observations but at higher frequency: the resampled 37 GHz FT Earth Science Data Record (FT-ESDR). The assessment was carried out during the overlapping period between 2011 and 2014. Results show that 77.1 % of their common grid cells have an agreement better than 80 %. Their differences vary with land cover type (tundra, forest and open land) and freezing and thawing periods. The best agreement is obtained during the thawing transition and over forest areas, with differences between product mean freeze or thaw onsets of under 0.4 weeks. Over tundra, FT-AP tends to detect freeze onset 2–5 weeks earlier than FT-ESDR, likely due to FT sensitivity to the different frequencies used. Analysis with mean surface air temperature time series from six in situ meteorological stations shows that the main discrepancies between FT-AP and FT-ESDR are related to false frozen retrievals in summer for some regions with FT-AP. The Aquarius product is distributed by the U.S. National Snow and Ice Data Center (NSIDC) at https://nsidc.org/data/aq3_ft/versions/5 with the DOI https://doi.org/10.5067/OV4R18NL3BQR.

The Fram Strait (FS) is the primary region of sea ice export from the Arctic Ocean and thus plays an important role in regulating the amount of sea ice and fresh water entering the North Atlantic seas. A 5 a (2011–2015) sea ice thickness record retrieved from CryoSat-2 observations is used to derive a sea ice volume flux via the FS. Over this period, a mean winter accumulative volume flux (WAVF) based on sea ice drift data derived from passivemicrowave measurements, which are provided by the National Snow and Ice Data Center (NSIDC) and the Institut Francais de Recherche pour d’Exploitation de la Mer (IFREMER), amounts to 1 029 km3 (NSIDC) and 1 463 km3 (IFREMER), respectively. For this period, a mean monthly volume flux (area flux) difference between the estimates derived from the NSIDC and IFREMER drift data is–62 km3 per month (–18×106 km2 per month). Analysis reveals that this negative bias is mainly attributable to faster IFREMER drift speeds in comparison with slower NSIDC drift data. NSIDC-based sea ice volume flux estimates are compared with the results from the University of Bremen (UB), and the two products agree relatively well with a mean monthly bias of (5.7±45.9) km3 per month for the period from January 2011 to August 2013. IFREMER-based volume flux is also in good agreement with previous results of the 1990s. Compared with P1 (1990/1991–1993/1994) and P2 (2003/2004–2007/2008), the WAVF estimates indicate a decline of more than 600 km3 in P3 (2011/2012–2014/2015). Over the three periods, the variability and the decline in the sea ice volume flux are mainly attributable to sea ice motion changes, and second to sea ice thickness changes, and the least to sea ice concentration variations.

American Geophysical Union (AGU) Fall Meeting, 9-13 December 2019, San Francisco.-- Arctic+ salinity daily 9-day maps will be distributed in WGS 84 / NSIDC EASE-Grid 2.0 North EPSG:6931 (25 km) for years 2011-2019. Martínez, Justino; Gabarró, Carolina; Turiel, Antonio; 2019; >Arctic Sea Surface Salinity L3 maps (V.3.0) [Dataset]>; DIGITAL.CSIC; http://dx.doi.org/10.20350/digitalCSIC/9065, The Arctic Ocean contains only a 1% of the world's ocean water, but the rivers that flow out into it account for the 10% of the volume world's rivers freshwater. The upper layer of fresher water facilitates the creation of sea ice and plays an important role in the position of the jet stream and storms over the northern hemisphere [ISBN, 978-82-7971-097-4]. Remote sensing measurements are of special importance in the Arctic since in situ data is very scarce there. SMOS and SMAP are currently providing sea surface salinity (SSS) measures, but only the product provided by Barcelona Expert Center (BEC) is a dedicated product for the Arctic region. The product that we present in this work is an improvement of the BEC Arctic v2.0. The new version 3.0 has as the primary objective the describing better the river discharges. The spatial grid used is WGS84/NSIDC EASE-Grid 2.0 North for the all stages of the processing chain. This procedure avoids spatial interpolation, favoring the definition of river mouths. The salinity retrieval is based on the Debiased non-bayesian method [doi:10.1016/j.rse.2017.02.023] and similarly to what is done in the processing of altimetric data, SMOS salinity is corrected using a reference calculated from the own SMOS data for each latitude, longitude, pass orientation and antenna measuring position. Arctic v3.0 differs from current method [doi:10.3390/rs10111772] in two important points: the reference is computed for brightness temperature instead of SSS and the antenna has been divided in a more homogeneous grid. Other improvements concern to data filtering and propagation of the radiometric errors to SSS. All these improvements provide level 3 maps less noisy, increasing the effective resolution of salinity gradients. Freshwater gradients are much better resolved than in previous version (Fig. 1). Comparison with JPL SMAP product is also planned as a first step to generate a combined product. This work is funded by ESA Arctic + project and also includes the assimilation of the resulting SSS product in the ocean-sea ice data assimilation system TOPAZ as the next version TOPAZ5. A preliminary study [doi:10.5194/os-2018-163] has been performed concluding that BEC product could be a good candidate to be assimilated by TOPAZ. Moreover, some preliminary tests with a pre-release v3.0 version will start shortly

Changes in snow cover on the surface of Arctic sea ice affect the energy balance between the atmosphere and the ocean and play a vital role in the global climate system. Accurate snow depth is a precondition for representing thermodynamic processes in sea-ice systems and is helpful for estimating sea-ice thickness. To better apply Arctic snow-depth products released by different organizations, we compared four kinds of snow-depth products based on three kinds of passive microwave (PM) sensors and evaluated them against the snow depth measured by ice mass-balance buoys (IMB snow depth) and Operation Ice Bridge airborne snow radar (OIB snow depth). The results show that the snow depths from the product released by the University of Bremen (UB) are larger than those by the National Snow and Ice Data Center (NSIDC) and National Aeronautics and Space Administration (NASA), with an average difference of 10 cm. Comparing the PM remote-sensing snow depths released by UB, NSIDC and NASA against IMB and OIB snow depths, it is found that NSIDC AMSR-E snow-depth product has the highest accuracy. Although these PM remote-sensing snow-depth products released by different organizations differ in accuracy, they all reflect the spatio-temporal variation characteristics of snow depth on Arctic sea ice. These comparisons and analysis of snow-depth products from different sensors released by different organizations provide a basis for further investigation of Arctic sea-ice thickness estimation and benefit the studies of Arctic sea ice and climate change.

Accurate information on snowpack conditions is crucial for understanding changes in the hydrological cycle and its impacts on the climate system, especially at the middle and high latitudes. Two satellite-derived products that cover Eurasia from 1979 to 2006, i.e., the snow water equivalent (SWE) data provided by the National Snow and Ice Data Center (NSIDC; SWENSIDC) and the GlobSnow dataset provided by the Finnish Meteorological Institute (FMI) (SWEGS) are selected for examination in the present study. The performances of these datasets in representing snowpack conditions are evaluated by comparing the datasets with the observations of snow depth (SD) recorded in the Historical Soviet Daily Snow Depth (HSDSD; SDHSDSD) dataset. The results indicate that the SWEGS dataset is more consistent with the SDHSDSD dataset over northern Eurasia than the SWENSIDC dataset. In particular, the SWENSIDC dataset exhibits large discrepancies in northern Europe and western Siberia (NE-WS), indicating problems or even errors in the SWENSIDC dataset. Based on snow formation criteria (e.g., temperature and precipitation), we further explore and confirm the existence of problems in the SWENSIDC dataset. These problems may be associated with the retrieval method used to generate this dataset; this method is based on a static algorithm. Our findings suggest that satellite-derived SWE datasets (e.g., SWENSIDC) should be used with caution when investigating the impacts of snow in different research fields (e.g., climatology, hydrology, and ecology).

The NASA Soil Moisture Active Passive (SMAP) mission was launched on January 31st, 2015. The spacecraft was to provide high-resolution (3 km and 9 km) global soil moisture estimates at regular intervals by combining for the first time L-band radiometer and radar observations. On July 7th, 2015, a component of the SMAP radar failed and the radar ceased operation. However, before this occurred the mission was able to collect and process ~2.5 months of the SMAP high-resolution active-passive soil moisture data (L2SMAP) that coincided with the Northern Hemisphere's vegetation green-up and crop growth season. In this study, we evaluate the SMAP high-resolution soil moisture product derived from several alternative algorithms against in situ data from core calibration and validation sites (CVS), and sparse networks. The baseline algorithm had the best comparison statistics against the CVS and sparse networks. The overall unbiased root-mean-square-difference is close to the 0.04 m3/m3 the SMAP mission requirement. A 3 km spatial resolution soil moisture product was also examined. This product had an unbiased root-mean-square-difference of ~0.053 m3/m3. The SMAP L2SMAP product for ~2.5 months is now validated for use in geophysical applications and research and available to the public through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC). The L2SMAP product is packaged with the geo-coordinates, acquisition times, and all requisite ancillary information. Although limited in duration, SMAP has clearly demonstrated the potential of using a combined L-band radar-radiometer for proving high spatial resolution and accurate global soil moisture.

This paper presents a technique of ship route optimization in the Antarctic sea ice region using Dijkstra's algorithm. It studies some of the major parameters that cause resistance to the ship movement through sea ice. It aims to optimize a safe ship route in the Antarctic sea ice between Bharati and Maitri (Indian Research Stations in Antarctica) coasts. The study region is divided into grids that results into unique nodes, which are studied for sea ice parameters and wind velocity. Resistances caused due to ice and wind are considered to calculate expected ship velocity between nodes. The calculated ship velocities are then used to estimate durations to cover distances from a node to all the neighboring nodes. It forms a mathematical route from source to destination through various nodes with time as weight that is then optimized through Dijkstra's algorithm to obtain optimal route. Satellite remote sensing data derived sea ice parameters (Scatsat-1 derived sea ice extent and NSIDC sea ice concentration) and modeled wind velocity (from ECMWF) have been used as inputs in this model. Ship parameters of Kapitan Khlebnikov (Russian icebreaker) are used to test and compare the proposed model. Results of model are validated with the actual ship route navigated by the 33rd Indian Scientific Expedition to Antarctica (33ISEA) ship. The proposed algorithm, which provides an optimal route when ship parameters are known in advance, can be useful to support future ship navigation in the Antarctic sea ice including Indian Scientific Expeditions to Antarctica.

Analog archival data can supplement modern digital research, but only if those data are preserved, described, and migrated to appropriate formats. The National Snow and Ice Data Center (NSIDC) at the University of Colorado Boulder (CU) is responsible for managing, archiving, and disseminating cryospheric and polar data. The clear majority of these data are digital, but the NSIDC also houses a collection of historical archival materials that include measurements related to the earth's glaciated regions prior to the development of modern instrumentation. Their formats, however, are not conducive to contemporary analysis, rendering them ostensibly “lost” to research. This paper describes a series of efforts to provide access to these collections that date back to their original acquisition, as long ago as the mid-nineteenth century, with focus primarily on activities over the last 15 years. The most recent effort was funded by the Council on Library & Information Resources and won the 2016 International Data Rescue Award. The intent is to highlight key challenges, and our proposed own solutions to those challenges, in designing a digitization project centered on providing online access to analog data in glaciological, geomorphological, and related research.

Massive amounts of remotely sensed data are being generated at an unprecedented rate, offering new opportunities for data-driven scientific discovery in the Earth sciences and related domains. However, due to the sheer volume of remotely sensed data and the lack of effective data analytics tools, most data remain in the dark, with little to no quality assurance and limited access to high-level analytical tools. Anomaly detection, which aims to find scenarios that differ from the norm, is of particular importance when analyzing remotely sensed data. However, most previous work has focused on identifying individual anomalies, and required prior knowledge of the ground truth for supervised learning. In this work, we propose an unsupervised anomaly detection framework that requires no prior knowledge and is capable of detecting anomalous events, which we define as groups of outlier objects differing contextually from their spatial and temporal neighbors. Such contextual anomalies can be useful in discovering both hidden quality issues in the data and real natural events of significance. We demonstrate the effectiveness of our framework via Web-based tools developed for visualizing and analyzing such contextual anomalies, using two types of data. The techniques and tools developed in this project are generally usable for a diverse set of satellite products and will be made publicly available with the support of the National Snow and Ice Data Center (NSIDC).

The aim of the present study is to assess the full effect on the albedo of both sea ice extent decrease and snowline retreat in the Arctic during the last three decades. Averaged over the globe, the overall warming effect due to Arctic land and ocean albedo change corresponds to adding about 44% to the direct effect of human CO2 emissions during the same period. In fact, the area and thickness of Arctic sea ice have both been declining in this time frame. This has caused feedbacks affecting the whole global climate system. One such is albedo feedback of sea ice shrinking which was previously estimated (Pistone et al., 2014) to add about 25% to the direct warming effect of anthropogenic CO2 emissions. In this study, we demonstrate that the role of snowline retreat in albedo decrease is comparable to that of sea ice shrinking. To this aim, we estimate the radiative forcing (W/m2) due to snow and ice decrease during 34 years (1982–2015) from the analysis of changes of observed albedo based on the Clouds and the Earth's Radiant Energy System Energy Balanced And Filled (CERES EBAF) dataset, paired with sea ice and snow cover data from the US National Snow & Ice Data Center (NSIDC).

Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

The National Aeronautics and Space Administration (NASA)’s Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) acquired unique aircraft data on atmospheric radiation and sea ice properties during the critical late summer to autumn sea ice minimum and commencement of refreezing. The C-130 aircraft flew 15 missions over the Beaufort Sea between 4 and 24 September 2014. ARISE deployed a shortwave and longwave broadband radiometer (BBR) system from the Naval Research Laboratory; a Solar Spectral Flux Radiometer (SSFR) from the University of Colorado Boulder; the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) from the NASA Ames Research Center; cloud microprobes from the NASA Langley Research Center; and the Land, Vegetation and Ice Sensor (LVIS) laser altimeter system from the NASA Goddard Space Flight Center. These instruments sampled the radiant energy exchange between clouds and a variety of sea ice scenarios, including prior to and after refreezing began. The most critical and unique aspect of ARISE mission planning was to coordinate the flight tracks with NASA Cloud and the Earth’s Radiant Energy System (CERES) satellite sensor observations in such a way that satellite sensor angular dependence models and derived top-of-atmosphere fluxes could be validated against the aircraft data over large gridbox domains of order 100–200 km. This was accomplished over open ocean, over the marginal ice zone (MIZ), and over a region of heavy sea ice concentration, in cloudy and clear skies. ARISE data will be valuable to the community for providing better interpretation of satellite energy budget measurements in the Arctic and for process studies involving ice–cloud–atmosphere energy exchange during the sea ice transition period.

2017-382 Formed in 1999, the International Ice Charting Working Group (IICWG) is a consortium of the ice services from all over the world. The operational ice services representing nations from both the Northern and Southern Hemispheres, including all eight Arctic Council nations, come together with a common goal of promoting cooperation between the world’s ice centers on all matters concerning sea ice and icebergs. In 2013, the IICWG began to focus on how the ice services can support an emergency response in ice-laden waters. This focus was on providing current and forecasted information on sea ice and iceberg conditions in the area of the incident as well as for the shipping routes approaching the area. With rapidly changing sea ice, ice information would be critical to planning an effective response. The IICWG formed a partnership with the International Tanker Owners Pollution Federation Limited (ITOPF) and Oil Spill Response Limited (OSRL) in 2015 to expand the awareness of the ice services and their capabilities to the oil companies and oil spill removal organizations. In 2016, the IICWG conducted an unannounced emergency notification exercise to validate the contact information for each of the ice services. In an effort to ensure responders would be able to access the most accurate contact information, IICWG published this ice services’ contact list worldwide in classification society periodicals. The preparations of these nations culminated in a Tabletop Exercise at the group’s annual meeting in October 2016 in Ottawa, Ontario, Canada. The exercise scenario involved a major oil spill response in ice-infested waters with participants from the ice services, maritime users, satellite data providers, and OSRL. The lessons learned from this exercise will prepare all of the ice services for a coordinated oil spill response. The IICWG also collaborated with the Arctic Council Emergency Prevention, Preparedness, and Response Working Group for future exercises and planning.

Indian scientists have been involved in studies of cryosphere in Polar Regions from past 35 years. For continuous monitoring and expanding research in the fields of atmospheric science, geosciences, glaciology, environmental sciences, etc. specific to these regions, the ESSO - National Centre for Antarctic and Ocean Research (ESSO-NCAOR) has established three permanent research stations in Antarctica. This paper addresses efforts by Indian scientists in last five years in cryospheric studies on the Antarctica using remote sensing data. Inventories of Antarctic glaciers and ice sheet features prepared by Indian researchers have facilitated for detailed long-term studies on glacier dynamics, sea ice, fluctuations of the continental ice margin and snouts of glaciers. For safe and optimal navigation of ships, a sea ice advisory has been also set up. This paper also addresses recent efforts of using high-resolution earth observation satellite data which were acquired for geospatial mapping of cryospheric features and glacial landforms and for delineation of wind induced snow deposition zones in part of the Antarctica. Spatiotemporal dynamics of surface melting over Antarctic was further studied by using moderate resolution satellite data.