ALBERT

Description: A ground-based ultra-wideband radiometer operating at 540, 900, 1380, and 1740 MHz was used to measure microwave thermal emissions from an Arctic sea ice floe as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition. The instrument was deployed on a drifting ice floe near 86°N, 120°E in leg 1 of the expedition (December 2019) and observed second-year ice (potentially with refrozen melt ponds) that experienced new ice growth at its base over a ten-day period. Measured circularly polarized brightness temperatures were compared with the predictions of a radiative transfer (RT) model for a layered medium consisting of ocean, growing new ice, desalinated remnant second-year ice/refrozen melt pond, and snow layers. Characteristics of the sea ice composition used in the model were determined from 〈italic〉in-situ〈/italic〉 measurements. Comparisons of the measured and modeled wideband brightness temperatures showed good agreement consistently over the observation period and for various off-nadir observation angles. The results demonstrate the capabilities of 0.5–2 GHz microwave radiometry for observing sea ice properties and also show the impact of a saline ice layer at the ice bottom on the measured brightness temperature.

Description: The sea ice on the oceans in the Arctic and Antarctic is a relatively thin blanket that significantly influences the exchange between the ocean and the atmosphere. The sea ice thickness is a major parameter, which is of great importance for diagnosis and prediction. Determining seasonal and interannual variations in sea ice thickness was the primary objective of ESA's CryoSat Earth Explorer mission. ESA's second Earth Explorer mission, SMOS, provides L-band brightness temperature data that can also be used to infer the thickness of the sea ice, although that was not its primary objective. Both missions complement each other strongly in terms of spatiotemporal sampling and their sensitivity to different ice thickness regimes. In order to further improve the synergistic use of low-frequency radiometric data for sea ice applications, it is imperative to better characterize the uncertainties and covariances associated with the retrieval. A key factor is a thorough understanding of the physical processes that determine the emissivity of sea ice in order to improve the forward model used for retrieval. A thermodynamic model is used to estimate the vertical temperature profile through the snow and sea ice. Therefore, additional meteorological data such as from atmospheric reanalyses and parameterizations of snow and sea ice properties must be taken into account. Natural sea ice is not a homogeneous medium of uniform sea ice and snow thickness, but can only be described by statistical distribution functions on different spatial scales. Thin ice and open water in leads within the compact pack ice also have a significant influence on the brightness temperature measured by SMOS. In order to take all these effects into account, the forward model or the observation operator must be of the appropriate complexity. The inversion to determine the geophysical sea ice parameters can be optimized with a-priori information and parameterizations as well as with information from other satellite sensors. The presentation will focus on a review of the current retrieval method used to generate the AWI-ESA level 3 and level 4 Sea Ice Thickness products and the way forward to improve the emissivity model and to define a common basis metrics validation to assess algorithms evolution considering that in-situ validation data is only sparsely available.

Description: The quantification of the sea ice mass balance as the marine part of the cryosphere by satellite observations depend on sea ice thickness data records for the entire ice-covered oceans. The challenges to this task are numerous. Sea ice itself is a highly dynamic medium with a significant variability at meter scale and a strong seasonal cycle which significantly impacts it remote sensing signature. Satellite sensors must therefore provide precise observations at high spatial resolution to observe the full spread of the sea ice thickness distribution and its governing processes such as the dynamic deformation. Average thickness values for larger areas are sufficient for mass balance estimates, however, available methods such as satellite altimetry and passive microwave remote sensing rely on indirect methods and auxiliary information and are often not able to provide information with an acceptable uncertainty for certain or thickness categories or during the presence of surface melt. In addition, suitable satellite sensors in orbits that enabling sea ice thickness retrieval in the inner Arctic Ocean have been in service only until recently in comparison to satellites capable of observing sea ice area. Thus, the assessment of the sea ice mass balance for longer time series is often based on reanalysis models and not Earth Observation data. The sea ice community also traditionally expresses the total sea ice budget volume and not mass. We will therefore present an available sea ice volume data record that is derived by data fusion of CryoSat-2 radar altimeter and SMOS L-Band passive microwave-based sea ice thickness information. Both methods have a complementary sensitivity to different thickness classes and optimal interpolation is employed for gap-less sea ice thickness information in the northern hemisphere since November 2010. The data record is generated for the ESA funded MOS & CryoSat-2 Sea Ice Data Product Processing and Dissemination Service (CS2SMOS-PDS). We discuss the characteristics of the data set and provide an overview of intended evolutions of the data set, specifically improvements to the spatial resolutions, a potential extension to the southern hemisphere and the addition of other available satellite sensors to the optimal interpolation. Within the context of the mass balance of the cryosphere we will share our thoughts on the significance of the CryoSat-2/SMOS based sea ice volume time series for climate applications in the context of its comparable short temporal and how this information can be presented more consistently to other components of the cryosphere.

Description: Arctic sea-ice thickness and salinity retrievals are simulated to explore the performance of nadir-observing microwave radiometry operating with up to 16 frequency channels in the 0.5–2-GHz frequency range. A radiative transfer model is used to create lookup tables of the circularly polarized thermal emissions of first-year (FY) and multiyear (MY) sea ice, and the performance of two distinct retrieval methods is examined. The first method retrieves only sea-ice thicknesses, while the second retrieves both ice thickness and ice salinity. Retrieval errors are simulated for both FY and MY sea ice as a function of ice thickness, salinity, and temperature to investigate the impact of radiometric uncertainty, the frequency channels used, and any errors in ancillary information. To gain further insight into Arctic scale retrieval performance, a simulated brightness temperature dataset is produced for Arctic sea ice for the period October 2020–March 2021 using sea-ice thicknesses from the SMOS-CryoSat-2 algorithm. Compared to existing sea-ice thickness retrievals obtained from 1.4-GHz microwave radiometers, the results demonstrate that 0.5–2-GHz radiometry can achieve higher sensitivity to a sea-ice thickness within the range 0.5–1.5 m for FY sea ice and enable the retrieval of multiple sea-ice parameters (thickness and salinity) simultaneously.

Description: In the polar oceans in winter, fractures and leads are the hotspots of exchange between the ocean and atmosphere which are otherwise well separated by sea ice. By altering the heat, gas, and momentum fluxes they play a crucial role in atmospheric, ecological, and oceanic processes. At the same time, leads represent a part of the present state of strain of the ice cover, opening up the possibility to study ice rheology. The transient nature of leads and their narrow appearance has set limits to the detection of leads from satellites. Different approaches using active and passive sensors from the microwave and infrared spectrum are employed so far to observe leads by means of satellite data. They make use of the strong contrast between leads and the surrounding ice pack in (i) surface temperature, (ii) microwave backscatter, (iii) emission or (iv) a change in ice drift speed. With the increasing availability of high-resolution SAR data for the Arctic, we explored the potential to use SAR derived sea ice deformation to estimate lead fractions. We calculated sea ice drift and divergence with a spatial resolution of 1.4 km from daily Sentinel-1 scenes. We obtained the divergence-based lead fraction of a region by summing up all positive divergence pixels multiplied by the respective time step length. We derived a second lead fraction product from the deformation fields that calculates the position of linear kinematic features (LKFs) first. The advantage is a skilled noise reduction, and a tracking algorithm of the deformation zones. We compared divergence- and LKF-based lead fractions to several other established lead fraction products in the Transpolar Drift along the drift track of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) between October 2019 to April 2020. We used lead fractions from helicopter-borne infrared surveys at a grid resolution of 5 m, classified Sentinel-1 (SAR) scenes at 80 m, MODIS (thermal infrared) at 1 km, AMSR2 (passive microwaves) at 3.25 km, and CryoSat-2 (altimeter in Ku-band) at 12.5 km. Since the methods rely on different physical properties of the water and ice in leads and are affected by different constraints, derived mean lead fractions vary by 1-2 magnitudes between the products. For example, infrared, SAR and microwave radiometer-based algorithms do not only detect open-water leads but also leads with thin ice up to a certain thickness, which differs between the products. Common lead events were identified across products. The time series mostly indicated a phase of increased lead activity during freeze-up in autumn 2019 and spring 2020. We used the different lead fraction time series to estimate new ice formation in the leads and compared the results to ice thickness and oceanographic measurements obtained during the MOSAiC campaign. Results yield lower and upper bounds for ice formation and brine expulsion in and from leads. Due to the wide range of lead fractions obtained from different methods, we conclude that the specific lead fraction product must be chosen depending on research question. Divergence- and LKF-based lead fractions provide valuable information in addition to established lead fraction products at high spatial resolution and independent of cloud coverage.