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

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

Satellite observations of long range transport of a large BrO plume in the Arctic

Ozone Depletion Events (ODE) during polar springtime are a well known phenomenon in the Arctic and Antarctic boundary layer. They are caused by the catalytic destruction of ozone by halogens producing reactive halogen oxides like bromine monoxide (BrO). The key halogen bromine can be rapidly transferred into the gas phase in an autocatalytic process – the so called "Bromine Explosion". However, the exact mechanism, which leads to an initial bromine release as well as the influence of transport and chemical processes on BrO, is still not clearly understood. <br><br> In this study, BrO measurements from the satellite instrument GOME-2 are used together with model calculations with the dispersion model FLEXPART to study an arctic BrO event in March 2007, which could be tracked over several days and a large area. Full BrO activation was observed within one day east of Siberia with subsequent transport to Hudson Bay. The event was linked to a cyclone with very high surface wind speeds, which could have been involved in the production and lifting of aerosols or blowing snow. Considering the short life time of BrO, transported aerosols or snow can also provide the surface for BrO recycling within the plume for several days. The evolution of the BrO plume could be reproduced by FLEXPART simulations of a passive tracer indicating that the activated air mass was transported all the way from Siberia to Hudson Bay. To localise the most probable transport height, model runs initialised in different heights have been performed showing similar transport patterns throughout the troposphere but best agreement with the measurements between the surface and 3 km. The influence of changes in tropopause height on measured BrO values has been considered, but cannot completely explain the observed high BrO values. Backward trajectories from the area of BrO initialisation show upward lifting from the surface up to 3 km and no indication for intrusion of stratospheric air. These observations are consistent with a scenario in which bromine in the air mass was activated on the surface within the cyclone, lifted upwards and transported over several thousand kilometres to Hudson Bay

Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

The microwave interferometric radiometer of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission measures at a frequency of 1.4 GHz in the L-band. In contrast to other microwave satellites, low frequency measurements in L-band have a large penetration depth in sea ice and thus contain information on the ice thickness. Previous ice thickness retrievals have neglected a snow layer on top of the ice. Here, we implement a snow layer in our emission model and investigate how snow influences L-band brightness temperatures and whether it is possible to retrieve snow thickness over thick Arctic sea ice from SMOS data. We find that the brightness temperatures above snow-covered sea ice are higher than above bare sea ice and that horizontal polarisation is more affected by the snow layer than vertical polarisation. In accordance with our theoretical investigations, the root mean square deviation between simulated and observed horizontally polarised brightness temperatures decreases from 20.9 K to 4.7 K, when we include the snow layer in the simulations. Although dry snow is almost transparent in L-band, we find brightness temperatures to increase with increasing snow thickness under cold Arctic conditions. The brightness temperatures' dependence on snow thickness can be explained by the thermal insulation of snow and its dependence on the snow layer thickness. This temperature effect allows us to retrieve snow thickness over thick sea ice. For the best simulation scenario and snow thicknesses up to 35 cm, the average snow thickness retrieved from horizontally polarised SMOS brightness temperatures agrees within 0.1 cm with the average snow thickness measured during the IceBridge flight campaign in the Arctic in spring 2012. The corresponding root mean square deviation is 5.5 cm, and the coefficient of determination is r(2) = 0.58

SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea

Accurate satellite measurements of the thickness of Antarctic sea ice are urgently needed but pose a particular challenge. The Antarctic data presented here were produced using a method to derive the sea ice thickness from 1.4 GHz brightness temperatures previously developed for the Arctic, with only modified auxiliary data. The ability to observe the thickness of thin sea ice using this method is limited to cold conditions, meaning it is only reasonable during the freezing period, typically March to October. The Soil Moisture and Ocean Salinity (SMOS) level-3 sea ice thickness product contains estimates of the sea ice thickness and its uncertainty up to a thickness of about 1 m. The sea ice thickness is provided as a daily average on a polar stereographic projection grid with a sample resolution of 12.5 km, while the SMOS brightness temperature data used have a footprint size of about 35–40 km in diameter. Data from SMOS have been available since 2010, and the mission's operation has been extended to continue until at least the end of 2025. Here we compare two versions of the SMOS Antarctic sea ice thickness product which are based on different level-1 input data (v3.2 based on SMOS L1C v620 and v3.3 based on SMOS L1C 724). A validation is performed to generate a first baseline reference for future improvements of the retrieval algorithm and synergies with other sensors. Sea ice thickness measurements to validate the SMOS product are particularly rare in Antarctica, especially during the winter season and for the valid range of thicknesses. From the available validation measurements, we selected datasets from the Weddell Sea that have varying degrees of representativeness: Helicopter-based EM Bird (HEM), Surface and Under-Ice Trawl (SUIT), and stationary Upward-Looking Sonars (ULS). While the helicopter can measure hundreds of kilometres, SUIT's use is limited to distances of a few kilometres and thus only captures a small fraction of an SMOS footprint. Compared to SMOS, the ULS are point measurements and multi-year time series are necessary to enable a statistically representative comparison. Only four of the ULS moorings have a temporal overlap with SMOS in the year 2010. Based on selected averaged HEM flights and monthly ULS climatologies, we find a small mean difference (bias) of less than 10 cm and a root mean square deviation of about 20 cm with a correlation coefficient R &gt; 0.9 for the valid sea ice thickness range between 0 and about 1 m. The SMOS sea ice thickness showed an underestimate of about 40 cm with respect to the less representative SUIT validation data in the marginal ice zone. Compared with sea ice thickness outside the valid range, we find that SMOS strongly underestimates the real values, which underlines the need for combination with other sensors such as altimeters. In summary, the overall validity of the SMOS sea ice thickness for thin sea ice up to a thickness of about 1 m has been demonstrated through validation with multiple datasets. To ensure the quality of the SMOS product, an independent regional sea ice extent index was used for control. We found that the new version, v3.3, is slightly improved in terms of completeness, indicating fewer missing data. However, it is worth noting that the general characteristics of both datasets are very similar, also with the same limitations. Archived data are available in the PANGAEA repository at https://doi.org/10.1594/PANGAEA.934732 (Tian-Kunze and Kaleschke, 2021) and operationally at https://doi.org/10.57780/sm1-5ebe10b (European Space Agency, 2023).</p

Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition

Warm air intrusions over Arctic sea ice can change the snow and ice surface conditions rapidly and can alter sea ice concentration (SIC) estimates derived from satellite-based microwave radiometry without altering the true SIC. Here we focus on two warm moist air intrusions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition that reached the research vessel Polarstern in mid-April 2020. After the events, SIC deviations between different satellite products, including climate data records, were observed to increase. Especially, an underestimation of SIC for algorithms based on polarization difference was found. To examine the causes of this underestimation, we used the extensive MOSAiC snow and ice measurements to model computationally the brightness temperatures of the surface on a local scale. We further investigated the brightness temperatures observed by ground-based radiometers at frequencies 6.9 GHz, 19 GHz, and 89 GHz. We show that the drop in the retrieved SIC of some satellite products can be attributed to large-scale surface glazing, that is, the formation of a thin ice crust at the top of the snowpack, caused by the warming events. Another mechanism affecting satellite products, which are mainly based on gradient ratios of brightness temperatures, is the interplay of the changed temperature gradient in the snow with snow metamorphism. From the two analyzed climate data record products, we found that one was less affected by the warming events. The low frequency channels at 6.9 GHz were less sensitive to these snow surface changes, which could be exploited in future to obtain more accurate retrievals of sea ice concentration. Strong warm air intrusions are expected to become more frequent in future and thus their influence on SIC algorithms will increase. In order to provide consistent SIC datasets, their sensitivity to warm air intrusions needs to be addressed