Comparisons of passive microwave remote sensing sea ice concentrations with ship-based visual observations during the CHINARE Arctic summer cruises of 2010-2018

In order to apply satellite data to guiding navigation in the Arctic more effectively, the sea ice concentrations (SIC) derived from passive microwave (PM) products were compared with ship-based visual observations (OBS) collected during the Chinese National Arctic Research Expeditions (CHINARE). A total of 3 667 observations were collected in the Arctic summers of 2010, 2012, 2014, 2016, and 2018. PM SIC were derived from the NASA-Team (NT), Bootstrap (BT) and Climate Data Record (CDR) algorithms based on the SSMIS sensor, as well as the BT, enhanced NASA-Team (NT2) and ARTIST Sea Ice (ASI) algorithms based on AMSR-E/AMSR-2 sensors. The daily arithmetic average of PM SIC values and the daily weighted average of OBS SIC values were used for the comparisons. The correlation coefficients (CC), biases and root mean square deviations (RMSD) between PM SIC and OBS SIC were compared in terms of the overall trend, and under mild/normal/severe ice conditions. Using the OBS data, the influences of floe size and ice thickness on the SIC retrieval of different PM products were evaluated by calculating the daily weighted average of floe size code and ice thickness. Our results show that CC values range from 0.89 (AMSR-E/AMSR-2 NT2) to 0.95 (SSMIS NT), biases range from -3.96% (SSMIS NT) to 12.05% (AMSR-E/AMSR-2 NT2), and RMSD values range from 10.81% (SSMIS NT) to 20.15% (AMSR-E/AMSR-2 NT2). Floe size has a significant influence on the SIC retrievals of the PM products, and most of the PM products tend to underestimate SIC under smaller floe size conditions and overestimate SIC under larger floe size conditions. Ice thickness thicker than 30 cm does not have a significant influence on the SIC retrieval of PM products. Overall, the best (worst) agreement occurs between OBS SIC and SSMIS NT (AMSR-E/AMSR-2 NT2) SIC in the Arctic summer.Peer reviewe

Assessment of the synoptic variability of the Antarctic marginal ice zone with in Situ observations

Knowledge of sea ice variability, which contributes to the detection of climate change trends, stems primarily from remote sensing information. However, sea ice in the Southern Ocean is characterised by large variability that remains unresolved and limits our confidence on the remotely sensed products. Although one of the biggest seasonal changes on Earth is the annual advance and retreat of the Antarctic sea ice cover, relatively little attention has been given to the processes by which the marginal ice zone (MIZ) edge forms and responds to synoptic events. This study aimed to assess the seasonal sea ice extent (SIE) of the MIZ by comparing sea ice observations estimated from aboard ship to high resolution passive microwave (PM) satellite imagery when transecting the MIZ. To achieve this, sea ice concentration (SIC) was derived from two AMSR (Advanced Microwave Scanning Radiometer ) products; the ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI-AMSR ) and the bootstrap (BST-AMSR ). Theice concentration estimated from these PM satellite products was assessed against SIC observations collected from the S.A. Agulhas II (using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol). This assessment took place over summer and winter for the years 2016 and 2017. After evaluating how well these PM-SIC estimates compared against the ASPeCt SIC observations, we found that there was good correlation over summer MIZ conditions, while over winter MIZ conditions the correlation was relatively poor. This highlighted winter limitations inherent in PM SIC estimates. Therefore, from these comparison results, an analysis of the seasonal SIE was accomplished while being aware of the winter limitations linked to the PM products. We inferred that the MIZ acts as an indicator for what the evolution of winter SIE might look like over the following months. In addition to winter limitations associated with PM-SIC retrievals, the ASPeCt SIC estimates, based on human interpretation of the sea ice conditions, was limited because of subjective bias. This resulted in the development of an algorithm to automatically acquire SIC from image stills and videos. This method can be used to obtain quantitative seaice data from vessels of opportunity without the need to have trained personnel on-board. In summary, this study assesses seasonal MIZ SIE within the Atlantic sector after highlighting the limitations associated with various SIC-retrieval methods

Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

Impact of Local Winter Cooling on the Melt of Pine Island Glacier, Antarctica

The rapid thinning of the ice shelves in the Amundsen Sea is generally attributed to basal melt driven by warm water originating from the continental slope. We examine the hypothesis that processes taking place on the continental shelf contribute significantly to the interannual variability of the ocean heat content and ice shelf melt rates. A numerical model is used to simulate the circulation of ocean heat and the melt of the ice shelves over the period 2006–2013. The fine model grid (grid spacing 1.5 km) explicitly resolves the coastal polynyas and mesoscale processes. The ocean heat content of the eastern continental shelf exhibits recurrent decreases around September with a magnitude that varies from year to year. The heat loss is primarily caused by surface heat fluxes along the eastern shore in areas of low ice concentration (polynyas). The cold winter water intrudes underneath the ice shelves and reduces the basal melt rates. Ocean temperatures upstream (i.e., at the shelf break) are largely constant over the year and cannot account for the cold events. The cooling is particularly marked in 2012 and its effect on the ocean heat content remains visible over the following years. The study suggests that ocean-atmosphere interactions in coastal polynyas contribute to the interannual variability of the melt of Pine Island Glacier

Influence of Tides and Mesoscale Eddies in the Ross Sea

The Ross Sea is the most biologically productive region in the Southern Ocean. Primary production is controlled by dissolved iron (dFe), a limiting micronutrient. The main focus of this thesis, motivated by the PRISM-RS project, is to investigate how tides and mesoscale eddies affect the pathways of dFe to the surface ocean. A regional ocean model with four hindcast simulations are used. Tidal forcing is added to simulations and mesoscale eddies are resolved by changing the horizontal grid resolution from 5 to 1.5 km. Simulations cover 1.5 years, ending at the time of the PRISM-RS cruise in early 2012. An extended 20 year simulation provides an estimate of model variability and significance. The model is validated using hydrographic data from the PRISM-RS cruise and climatological values of water mass volumes. Compared to observations, simulations show a salinity offset at depth, that can be attributed to freshening of the Ross Sea in recent years. The model represents water mass volumes well, but has a reduced amount of Ice Shelf Water. Analysis of eddy formation in the model indicates that the weak stratification produces small and short-lived mesoscale eddies in the Ross Sea. The increased resolution approximately doubles the number of eddies seen in one year of simulation and significantly increases the baroclinic eddy kinetic energy. The effect of tidal forcing on sea ice is investigated using a new method to extract a diurnal signal from satellite swath data. In the northwest corner of the Ross Sea continental shelf, strong tidal divergence causes the sea ice to decrease by 20% in winter. Simulation results show a strong heat flux that generates sea ice during spring tide conditions. The supply of dFe in simulations is calculated using four passive tracer dyes representing sources of dFe: sea ice, glacial ice, Circumpolar Deep Water, and benthic supply. The simulation without tides at 5 km resolution estimates the total supply of dFe to the surface at 6.63 μmol m-2 yr-1. Tides increase this by 20%, eddies decrease it by 15%, and the net change from both is not significant. Spatially, the pattern of dFe supply varies significantly between all simulations

Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions

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

Antarctic sea ice thickness and snow-to-ice conversion from atmospheric reanalysis and passive microwave snow depth

Passive microwave snow depth, ice concentration, and ice motion estimates are combined with snowfall from the European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis (ERA-40) from 1979-2001 to estimate the prevalence of snow-to-ice conversion (snow-ice formation) on level sea ice in the Antarctic for April-October. Snow ice is ubiquitous in all regions throughout the growth season. Calculated snow-ice thicknesses fall within the range of estimates from ice core analysis for most regions. However, uncertainties in both this analysis and in situ data limit the usefulness of snow depth and snow-ice production to evaluate the accuracy of ERA-40 snowfall. The East Antarctic is an exception, where calculated snow-ice production exceeds observed ice thickness over wide areas, suggesting that ERA-40 precipitation is too high there. Snow-ice thickness variability is strongly controlled not just by snow accumulation rates, but also by ice divergence. Surprisingly, snow-ice production is largely independent of snow depth, indicating that the latter may be a poor indicator of total snow accumulation. Using the presence of snow-ice formation as a proxy indicator for near-zero freeboard, we examine the possibility of estimating level ice thickness from satellite snow depths. A best estimate for the mean level ice thickness in September is 53 cm, comparing well with 51 cm from ship-based observations. The error is estimated to be 10-20 cm, which is similar to the observed interannual and regional variability. Nevertheless, this is comparable to expected errors for ice thickness determined by satellite altimeters. Improvement in satellite snow depth retrievals would benefit both of these methods

Comparison of Passive Microwave Data with Shipborne Photographic Observations of Summer Sea Ice Concentration along an Arctic Cruise Path

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (Aw, Ap, and Ai, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing Ap, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively