The overall aim of the ESA funded Arktalas study is to: Use satellite measurements in synergy with in situ data and modelling tools to characterize and quantify the dominant processes driving change in the Arctic sea ice and the Arctic Ocean. Today it is understood that the changes in the Arctic climate results from a large number of cross-disciplinary interactions and mutual feedbacks between the atmosphere, land, ocean and sea ice [Goosse et al., 2018]. Some of the dominant contributing processes include: - the ice-albedo feedback; - enhanced meridional energy transport; - changes in clouds and water vapour; - weak vertical mixing in the Arctic winter inversion; and a new regime for exchange of momentum, heat and gases between the atmosphere and the ocean. Understanding these processes and their mutual interaction and feedbacks and making a better representation or parameterisation in climate models are crucial to reduce the uncertainty in coupled climate model simulations. In this context, the Arktalas Hoavva project is tailored to the following specific objectives: (i) Characterization of the Arctic Amplification and its impact; (ii) Characterization of the impact of more persistent and larger area open water on sea ice dynamics; (iii) Understanding, characterization and prediction of the impact of extreme event storms in sea-ice formation; and (iv) Understanding, characterization and prediction of the Arctic ocean spin-up.

In this presentation the main achievements of the Arktalas Hoavva project study will be highlighted around seven scientific papers, notably:
- Emerging apparent Arctic amplification and its environmental impact: Attribution through remote-sensing data
- Driving mechanisms of an extreme winter sea-ice breakup event in the Beaufort Sea
- Wind-wave attenuation under sea ice in the Arctic: A review of remote sensing capabilities
- Impact of the sea ice friction on ocean tides in the Arctic Ocean, modelling insights at various time and space scales
- Response of Total and Eddy Kinetic Energy to the recent spin up of the Beaufort Gyre
- Ocean eddy signature on SAR-derived sea ice drift and vorticity
- Changes in the Arctic Ocean: Knowledge gaps and Impact of future satellite missions

Common for these papers are the collocated observations based on native high-resolution satellite sensor synergy together with in-situ data and models. Moreover, in combination with expected advances in AI and the promising outlook for new satellite missions targeting the sea ice and Arctic Ocean this will strengthen the understanding of complex processes leading to better simulations as well as model forecasting skills.

Sea-ice floating upon the Arctic ocean is a constantly moving, growing and melting surface. The seasonal cycle of sea ice volume has an average change of 10 000 Km$^3$ or 9 billion tonnes of sea ice. The role of dynamic redistribution of sea ice, the process by which it flows and deforms when blown by winds and floating upon ocean currents, has been observable during winter growth by the incorporation of satellite remote sensing of ice thickness and drift. Recent advances in the processing of CryoSat2 radar have allowed for the retrieval of summer ice thickness. This allows for a full seasonal cycle of purely remote sensing derived observational volume budget analysis.

We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide maps if ice growth, melt and dynamic redistribution. Ten winter growth and summer melt seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. We reveal key circulation patterns that contribute to summer melt and minimum sea ice volume and extent. Specifically, we show the importance of ice drift to the interannual variability in Arctic sea-ice volume, and the regional distribution of sea ice growth and melt rates. The estimates of specific areas of sea ice growth and, for the first time, sea-ice melt, provides key information for sea ice predictability and climate model validation. We make our product and code available to the community in monthly pan-Arctic NetCDF files for the entire October 2010 to April 2020 period.

We present a new Arctic sea level dataset (https://doi.org/10.24400/527896/a01-2020.001) based on the optimal interpolation of three satellite radar altimetry missions.
A dedicated processing is applied on measurements from SARAL/AltiKa, CryoSat-2 and Sentinel-3A to identify radar echoes coming from leads in the ice covered regions of the Arctic Ocean. After a data editing and application of instrumental, environmental and geophysical corrections tailored for the Arctic Ocean, these echoes are combined with open ocean echoes through an optimal interpolation scheme to map sea surface anomalies. The resulting gridded sea level anomaly fields provide an unprecedented resolution for this type of products:
the final gridded fields cover all latitudes north of 50°N, on a 25 km EASE2 grid, with one grid every three days over four years from July 2016 to June 2020.
We benefit from the use of an adaptive retracker on SARAL/AltiKa. This retracking algorithm is able to process both specular (leads) and borwnian (open ocean) echoes with one phyisical model. Using this retracker on SARAL/AltiKa removes the need to estimate an empirical bias between open ocean an ice covered areas. Therefore SARAL/AltiKa measurements provide a consistent baseline for the cross-calibation of CryoSat-2 and Sentinel-3A, for which leads are retracked using an empirical algorithm (TFMRA).
When compared to independent tide gauge data available in the basin, the combined product exhibits a much better performance and temporal resolution than any of our single mission dataset. This dataset has already been used to document new Atlantic water pathways north of Svalbard (https://doi.org/10.1029/2020JC016825).
Processing details and validation results are documented in Prandi et al., 2021 (https://doi.org/10.5194/essd-2021-123).
This product, supported by CNES, is a prototype for the future generation of regional Arctic CMEMS-SLTAC products that will generate both gridded sea level anomaly fields and cross calibrated along track products for data assimilation.
Future evolutions of this regional Arctic Ocean product will benefit from the inclusion of upcoming satellite radar altimetry missions such Sentinel-3 C&D, and CRISTAL.

The liquid freshwater content of the Beaufort Gyre, the largest Arctic Ocean freshwater reservoir, has increased by 40% in the last two decades (Solomon et al. 2021). The global thermohaline circulation and climate depend on the export of liquid freshwater from the Arctic Ocean. By freshening the upper sub-polar North Atlantic, the excess freshwater can impact the large-scale ocean circulation (Zhang et al. 2021). Major Arctic rivers, such as the Mackenzie, play a crucial role in the Arctic's freshwater circulation. However, the precise impact of any increase in Arctic freshwater runoff remains unclear due to the scarcity of measurements in the region. Thanks to dedicated satellite missions, we can now gain access to polar regions and monitor their state and changes.

In this work, we used the new SMOS capability to accurately measure the Sea Surface Salinity (SSS) in the Arctic Ocean in order to monitor the freshwater entering the Beaufort Gyre. We used the relationship between optical data and SSS to assess the freshened surface layer and thus calculated the freshwater content of the freshened surface layer. To evaluate the freshened surface layer, we exploited the relationship between the absorption coefficient of dissolved organic materials at 443 nm and SSS, and then combine it with the TOPAZ model output to compute the freshwater content of the freshened surface layer. The SSS product used is the Arctic SMOS SSS product BEC v.31, generated as part of the ESA-funded ARCTIC+ SSS ITT project, in conjunction with the TOPAZ model. To evaluate the freshened surface layer, we exploited the relationship between the absorption coefficient of dissolved organic materials at 443 nm and SSS. We used SMOS Arctic+ SSS together with the TOPAZ model to compute the freshwater content of the freshened surface layer. We analyzed the temporal evolution of freshwater content from 2012 to 2020 during the ice-free period. The first results highlight a clear freshwater content trend during the SMOS era in the Beaufort Sea. Our research demonstrates how remote sensing can assist us in monitoring the changing freshwater content of the Arctic Ocean and determining the impact of river discharge on the circulation in the Beaufort Gyre.

The upcoming EU Copernicus Imaging Microwave Radiometer (CIMR) satellite mission will measure Earth’s polarimetric microwave emission at five different frequencies between 1.4 and 37 GHz. It is the first time that this frequency range will be available on one satellite platform allowing simultaneous observations. Retrievals of surface and atmosphere parameters from measurements at these frequencies have a long-standing tradition. They provide cloud and daylight independent surface observations with full daily coverage of polar regions. For example, the more than 40-years long time series of sea ice area, retrieved from 19 and 37 GHz observations, forms one of the longest directly observed climate data records that exist today. Retrievals of sea surface temperature (SST) and water vapor have a similar history. However, current satellite microwave radiometers suffer from their coarse spatial resolution of at maximum 10 to 50 km depending on frequency. CIMR with its 8 m diameter deployable antenna reflector will be a major step forward and will offer spatial resolution better than 5 km at 37 GHz, better than 15 km at 7 GHz, and better than 50 km at 1.4 GHz.
Here we provide results of a multi-parameter retrieval using optimal estimation (OE). Measurements from the AMSR2 and SMOS sensors, which together cover the same frequency range as CIMR but at lower spatial resolution, are used to demonstrate the approach. The eight parameters (1) sea ice concentration, (2) thin ice thickness, (3) multi-year ice fraction, (4) ice surface temperature, (5) integrated water vapor, (6) liquid water path, and, over ocean, (7) wind speed, and (8) sea surface temperature and their uncertainties are retrieved simultaneously and in a consistent way. The accuracy of sea ice concentration is similar or better than current single-parameter retrievals. And the multi-parameter approach allows a smooth and physically consistent transition of all parameters from the open ocean to dense pack ice. For example, the “open ocean” parameters wind speed and SST, while having lower accuracy in our approach compared to single-parameter retrievals, can be retrieved close to the ice edge and in low ice concentration areas. The dominant frequency of 1.4 GHz for the thin ice thickness retrieval is currently measured from a different platform (SMOS) while measurements for all the other frequencies are from AMSR2. This poses some challenges for the consistency of brightness temperature measurements for the current AMSR-2/SMOS multi-parameter demonstrator product that will be resolved with the launch of CIMR.
Another challenge is the highly variable surface emissivity of sea ice, which depends on many factors including snow properties, salinity, roughness, temperature and more. Currently the sea ice emissivity variability is only considered in dependence of ice type and frequency dependent penetration depth. Here, we will present first results of an inclusion of a sea ice and snow emission model (MEMLS) in the OE forward model and its inversion. By this a higher, more realistic, variability of sea ice emissivity is obtained. The inclusion of a snow and ice forward model also allows to include snow depth as a 9th retrieved parameter in the OE scheme and preliminary snow depth results will be shown.
The quality of the retrieval for the single parameters will depend on how linearly independent they are from each other in the radiometric brightness temperature space the 10 channels (5 frequencies at vertical and horizontal polarization) span. Some parameters are radiometrically quite correlated and an information content analysis will show how much information, and thus trust, we can have in each retrieved parameter.

Wind-generated waves have a strong interaction with sea ice that is critical for air-sea
exchanges, operations at sea and marine life, and is not fully understood. In particular
the dissipation of wave energy is not well quantified and its possible effect on upper ocean mixing and ice drift are still mysterious. The growing but still limited amount of in situ observations is a clear limitation in our scientific understanding. Remote sensing in the
Arctic Marginal Ice Zone, including recent analyses of ICESat-2 laser altimeter, have shown
the frequent presence of waves under the ice. Here we show that, in cloud-free conditions,
Sentinel-2 also exhibits brightness modulations consistent with the presence of waves induced tilting of the ice surface, and we use Sentinel-1 and numerical models to put these observation in a broader context and propose ways to quantitatively calibrate observation of wave heights under sea ice. Including also the SWIM instrument on CFOSAT, a pan-Arctic monitoring system of waves under ice can probably be assembled from existing assets, possibly with some minor adjustments in their acquisition modes. Although data from optical systems is available much less frequently, they provide unique information that is highly beneficial to the interpretation of radar data. A routine analysis of their data can have large benefits for the understanding of properties of both waves and sea ice.