Improving passive microwave sea ice concentration algorithms for coastal areas: applications to the Baltic Sea

Sea ice concentration can be retrieved from passive microwave data using the NASA Team algorithm or the Artist Sea Ice (ASI) algorithm, for example. The brightness temperature measurements obtained from the Special Sensor Microwave Imager (SSM/I) instrument or the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) are commonly used for this purpose. Due to the coarse resolution of these instruments considerable systematic ice concentration errors in coastal regions occur. In the vicinity of the coast the instrument footprints usually contain both land and sea surfaces. Compared to sea surfaces, land surfaces are characterized by higher emissivities and lower polarization differences at the involved microwave channels. Thus, a systematic overestimation of coastal ice concentration is caused. In this paper, a method is developed to remove the land impact on the observed radiation. Combining a high-resolution data set for the shoreline and the antenna gain function the brightness temperature contribution originating from land surfaces can be identified. The brightness temperature related to the ocean fraction within the considered footprint can then be extracted. This separation technique is applied to SSM/I measurements in the Baltic Sea and the resulting ice concentration fields are compared to high-resolution satellite images. The highly complex shoreline of the Baltic Sea region provides an ideal area for testing the method. However, the presented approach can as well be applied to Arctic coastal regions. It is shown that the method considerably improves ice concentration retrieval in regions influenced by land surfaces without removing actually existing sea ice

An algorithm to detect sea ice leads by using AMSR-E passive microwave imagery

Leads are major sites of energy fluxes and brine releases at the air-ocean interface of sea-ice covered oceans. This study presents an algorithm to detect leads wider than 3 km in the entire Arctic Ocean. The algorithm detects 50 % of the lead area that was visible in optical MODIS satellite images. Passive microwave imagery from the Advanced Microwave Scanning Radiometer – Earth Observation System (AMSR-E) is used, allowing daily observations due to the fact that AMSR-E does not depend on daylight or cloud conditions. Using the unique signatures of thin ice in the brightness temperature ratio between the 89 GHz and 19 GHz channels, the algorithm is able to detect thin ice areas in the ice cover and is optimized to detect leads. Leads are mapped for the period from 2002 to 2011 excluding the summer months, and validated qualitatively by using MODIS, Envisat ASAR, and CryoSat-2 data. Several frequently recurring large scale lead patterns are found, especially in regions where sea ice is known to drift out of the Arctic Ocean

SMOS Sea Ice Thickness Data Product Quality Control by Comparison with the Regional Sea Ice Extent

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

Carbonate precipitation in brine ? a potential trigger for tropospheric ozone depletion events

International audienceTropospheric ozone depletion events (ODEs) at high latitudes were discovered 20 years ago and are attributed to bromine explosions. However, an unresolved issue is the explanation of how the acid-catalyzed reaction cycle is triggered in atmospheric particles derived from alkaline sea water. By simulating the chemistry occuring in polar regions over recently formed sea ice, we can model successfully the transformation of inert sea-salt bromide to reactive bromine monoxide (BrO) and the subsequent ODE when precipitation of calcium carbonate from freezing sea water is taken into account. In addition, we found the temperature dependence of the equilibrium BrCl+Br??Br2Cl? to be important

Carbonate precipitation in brine ? the trigger for tropospheric ozone depletion events

International audienceTropospheric ozone depletion events (ODEs) at high latitudes were discovered 20 years ago and are attributed to bromine explosions. However, an outstanding and unresolved issue is the explanation of how the acid-catalyzed reaction cycle is triggered in atmospheric particles derived from alkaline sea water. By simulating the chemistry occuring in polar regions over recently formed sea ice, we can model successfully the transformation of inert sea-salt bromide to reactive bromine monoxide (BrO) and the subsequent ODE when precipitation of calcium carbonate from freezing sea water is taken into account. In addition, we found the temperature dependence of the equilibrium BrCl+Br?Br2Cl? to be important

A lead-width distribution for Antarctic sea ice: a case study for the Weddell Sea with high-resolution Sentinel-2 images

Using Copernicus Sentinel-2 images we derive a statistical lead-width distribution for the Weddell Sea. While previous work focused on the Arctic, this is the first lead-width distribution for Antarctic sea ice. Previous studies suggest that the lead-width distribution follows a power law with a positive exponent; however their results for the power-law exponents are not all in agreement with each other. To detect leads we create a sea-ice surface-type classification based on 20 carefully selected cloud-free Sentinel-2 Level-1C products, which have a resolution of 10 m. The observed time period is from November 2016 until February 2018, covering only the months from November to April. We apply two different fitting methods to the measured lead widths. The first fitting method is a linear fit, while the second method is based on a maximum likelihood approach. Here, we use both methods for the same lead-width data set to observe differences in the calculated power-law exponent. To further investigate influences on the power-law exponent, we define two different thresholds: one for open-water-covered leads and one for open-water-covered and nilas-covered leads. The influence of the lead threshold on the exponent is larger for the linear fit than for the method based on the maximum likelihood approach. We show that the exponent of the lead-width distribution ranges between 1.110 and 1.413 depending on the applied fitting method and lead threshold. This exponent for the Weddell Sea sea ice is smaller than the previously observed exponents for the Arctic sea ice.</p

SMOS sea ice thickness - a review and way forward

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

Changes in summer sea ice, albedo, and portioning of surface solar radiation in the Pacific sector of Arctic Ocean during 1982-2009

SSM/I sea ice concentration and CLARA black-sky composite albedo were used to estimate sea ice albedo in the region 70 degrees N-82 degrees N, 130 degrees W-180 degrees W. The long-term trends and seasonal evolutions of ice concentration, composite albedo, and ice albedo were then obtained. In July-August 1982-2009, the linear trend of the composite albedo and the ice albedo was -0.069 and -0.046 units per decade, respectively. During 1 June to 19 August, melting of sea ice resulted in an increase of solar heat input to the ice-ocean system by 282 MJ.m(-2) from 1982 to 2009. However, because of the counter-balancing effects of the loss of sea ice area and the enhanced ice surface melting, the trend of solar heat input to the ice was insignificant. The summer evolution of ice albedo matched the ice surface melting and ponding well at basin scale. The ice albedo showed a large difference between the multiyear and first-year ice because the latter melted completely by the end of a melt season. At the SHEBA geolocations, a distinct change in the ice albedo has occurred since 2007, because most of the multiyear ice has been replaced by first-year ice. A positive polarity in the Arctic Dipole Anomaly could be partly responsible for the rapid loss of summer ice within the study region in the recent years by bringing warmer air masses from the south and advecting more ice toward the north. Both these effects would enhance ice-albedo feedback.Peer reviewe