ALBERT

Description: 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 for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from −0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and −3.7 % (ASI), but similar for the Antarctic, −5.4 % and −5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends.

Description: We estimate the uncertainty of satellite-retrieved Arctic sea-ice thickness, sea-ice volume, and their trends stemming from the lack of reliable snow-thickness observations. To do so, we simulate a Cryosat2-type ice-thickness retrieval in an ocean-model simulation forced by atmospheric reanalysis, pretending that only freeboard is known as model output. We then convert freeboard to sea-ice thickness using different snow climatologies and compare the resulting sea-ice thickness retrievals to each other and to the real sea-ice thickness of the reanalysis-forced simulation. We find that different snow climatologies cause significant differences in the obtained ice thickness and ice volume. In addition, we show that Arctic ice-volume trends derived from ice-thickness retrievals using any snow-depth climatology are highly unreliable because the estimated trend in ice volume can strongly be influenced by the neglected interannual variability in snow volume. ©2018. American Geophysical Union. All Rights Reserved.

Description: In April 2015, an ice-tethered conductivity-temperature-depth (CTD) profiler and a down-looking Acoustic Doppler Current Profiler (ADCP) were deployed from the landfast ice near the tidewater glacier terminus of the Flade Isblink Glacier in the Wandel Sea, NE Greenland. The three week timeseries showed that water dynamics and the thermohaline structure were modified considerably during a storm event on 22–24 April when northerly winds exceeded 15 m/s. The storm initiated downwelling-like water dynamics characterized by on-shore water transport in the surface (0–40 m) layer and compensating off-shore flow at intermediate depths. After the storm, currents reversed in both layers, and the relaxation phase of downwelling lasted ~4 days. Although current velocities did not exceed 5 cm/s, the enhanced circulation during the storm caused cold turbid intrusions at 75–95 m depth that are likely attributed to sub-glacial water from the Flade Isblink Ice Cap. It was also found that the semidiurnal periodicities in the temperature and salinity time series were associated with the lunar semidiurnal tidal flow. The vertical structure of tidal currents corresponded to the first baroclinic mode of the internal tide with a velocity minimum at ~40 m. The tidal ellipses rotate in opposite directions above and below this depth and cause a divergence of tidal flow which was observed to induce semidiurnal internal waves of about 3 m height at the front of the glacier terminus. Our findings provide evidence that shelf-basin interaction and tidal forcing can potentialy modify coastal Wandel Sea waters even though they are isolated from the atmosphere by landfast sea ice almost year round. The northerly storms over the continental slope cause an enhanced circulation facilitating a release of cold and turbid sub-glacial water to the shelf. The tidal flow may contribute to the removal of such water from the glacial terminus.

Description: An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of microwave radiometer data from NASA's Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from October 1978 to April 2015 and updates and further developments are planned for the next phase of the project. The methodology for computing the sea ice concentration uses (1) numerical weather prediction (NWP) data input to a radiative transfer model for reduction of the impact of weather conditions on the measured brightness temperatures; (2) dynamical algorithm tie points to mitigate trends in residual atmospheric, sea ice, and water emission characteristics and inter-sensor differences/biases; and (3) a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new sea ice concentration uncertainty algorithm has been developed to estimate the spatial and temporal variability in sea ice concentration retrieval accuracy. A comparison to US National Ice Center sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate sea ice concentrations between open water and 100 % ice. The sea ice concentration climate data record is available for download at www.osi-saf.org, including documentation.

Description: Sea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction. We derive gridded daily sea-ice concentrations from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between ice floes, and the ice-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea-ice concentration, which is the melt-pond fraction plus the ice-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-ice concentrations are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near90GHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-ice concentration by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea ice with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt-pond fraction of 20 %. In presence of real open water between ice floes, the sea-ice concentration is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-ice concentration and by 20 % across all algorithms at 80 % sea-ice concentration. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are more sensitive to melt ponds could be optimized more easily because the influence of unknown snow and sea-ice surface property variations is less pronounced.

Description: A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection scheme is presented. The advection scheme accounts for the observed divergence/convergence and freezing/melting of sea ice and predicts consequent generation/loss of new ice. The algorithm uses daily gridded sea ice drift and sea ice concentration products from the Ocean and Sea Ice Satellite Application Facility and produces gridded fractions of sea ice of a specific age. Thus, each grid cell of the output product contains a frequency distribution of sea ice age allowing to apply mean, median, weighted average or other statistical measures. The produced SIA maps and time series are compared to the National Snow and Ice Data Center SIA product. Several improvements related to the usage of the new ice drift product, constraining the algorithm by the observed ice concentration and preventing undersampling by the Eulerian scheme are demonstrated. Muliyear ice (MYI) concentration is computed as a sum of all multi-year ice fractions and compared to the MYI products based on passive and active microwave and SAR products.

Description: The first-ever conductivity–temperature–depth (CTD) observations on the Wandel Sea shelf in northeastern Greenland were collected in April–May 2015. They were complemented by CTDs taken along the continental slope during the Norwegian FRAM 2014–2015 drift. The CTD profiles are used to reveal the origin of water masses and interactions with ambient water from the continental slope and the tidewater glacier outlet. The subsurface water is associated with the Pacific water outflow from the Arctic Ocean. The underlying halocline separates the Pacific water from a deeper layer of polar water that has interacted with the warm Atlantic water outflow through the Fram Strait, recorded below 140 m. Over the outer shelf, the halocline shows numerous cold density-compensated intrusions indicating lateral interaction with an ambient polar water mass across the continental slope. At the front of the tidewater glacier outlet, colder and turbid water intrusions were observed at the base of the halocline. On the temperature–salinity plots these stations indicate a mixing line that is different from the ambient water and seems to be conditioned by the ocean–glacier interaction. Our observations of Pacific water are set within the context of upstream observations in the Beaufort Sea and downstream observations from the Northeast Water Polynya, and clearly show the modification of Pacific water during its advection across the Arctic Ocean. Moreover, ambient water over the Wandel Sea slope shows different thermohaline structures indicating the different origin and pathways of the on-shore and off-shore branches of the Arctic Ocean outflow through the western Fram Strait.

Description: Accurate sea-ice concentration (SIC) data are a pre-requisite to reliably monitor the polar sea-ice covers. Over the last four decades, many algorithms have been developed to retrieve the SIC from satellite microwave radiometry, some of them applied to generate long-term data products. We report on results of a systematic inter-comparison of ten global SIC data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global winter-time near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the observed inter-product consistency and differences of the inter-comparison results. Group I consists of data sets using the self-optimizing EUMETSAT-OSISAF – ESA-CCI algorithms. Group II includes data using the NASA-Team 2 and Comiso-Bootstrap algorithms, and the NOAA-NSIDC sea-ice concentration climate data record (CDR). The standard NASA-Team and the ARTIST Sea Ice (ASI) algorithms are put into a separate group III because of their often quite diverse results. Within group I and II evaluation results and intra-product differences are mostly very similar. For instance, among group I products, SIA agrees within ±100 000 km2 in both hemispheres during maximum and minimum sea-ice cover. Among group II products, satellite- minus ship-based SIC differences agree within ±0.7 %. Standing out with large negative differences to other products and evaluation data is the standard NASA-Team algorithm, in both hemispheres. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to the 100 % reference SIC with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic only range from −0.2  to +0.9 %. The standard deviation is smaller in the Arctic for the quoted group I products: 1.9 % to 2.9 % and Antarctic: 2.5 % to 3.1 %, than for group II products: Arctic: 3.6 % to 5.0 %, Antarctic: 4.5 % to 5.4 %. Products of group I exhibit larger overall satellite- minus ship-based SIC differences than group II in both hemispheres. However, compared to group II, group I products’ standard deviations are smaller, correlations higher and evaluation results are less sensitive to seasonal changes. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to re-construct the un-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for, e.g., surface heat-flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and suggest advancing studies about the influence of these weather filters on SIA and SIE time-series and their trends.

Description: We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: first, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz and either 6 or 19 GHz), in their horizontal resolution (25 or 50 km), and in the time period they cover. We introduce the underlying algorithms and provide an evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.