Monitoring surface soil moisture (SSM) variability is essential for understanding hydrological processes, vegetation growth, and interactions between land and atmosphere. Due to sparse distribution of in-situ soil moisture networks, over the last two decades, several active and passive radar satellite missions have been launched to provide information that can be used to estimate surface conditions and subsequently soil moisture content of the upper few cm soil layers. Some recent studies reported the potential of satellite altimeter backscatter to estimate SSM, especially in arid and semi-arid regions. They also pointed out some difficulties of such technique including: (i) the noisy behavior of the backscatter estimations mainly caused by surface water in the radar footprint , (ii) the assumptions for converting altimetry backscatter to SSM, and (iii) the need for interpolating between the tracks. In this study, we introduce a new inversion framework to retrieve soil moisture information from along-track altimetry measurements. First, 20 Hz along-track nadir radar backscatter is estimated by post-processing waveforms from Jason-2 (Ku-and C-Band during 2008-2014) and Envisat (Ku-and S-Band during 2002-2008). This provides backscatter measurements every ∼300 m along-track within every ∼10 days from Jason, and every ∼35 days from Envisat observations. Empirical orthogonal base-functions (EOFs) are then derived from soil moisture simulations of a hydrological model, and used as constraints within the inversion. Finally, along-track altimetry reconstructed surface soil moisture (ARSSM) storage is inverted by fitting these EOFs to the altimeter backscatter. The framework is tested in arid and semi-arid Western Australia, for which a high resolution hydrological model (the Australian Water Resource Assessment, AWRA

Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo and the Arctic freshwater budget, and it influences the Arctic climate: it is a fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge.A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath > 1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high-inclination dawn-dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger "hole at the pole". Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a precursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

No abstract

Abstract. Snow lying on top of sea ice plays an important role in the radiation budget because of its high albedo, the Arctic freshwater budget, and influences the Arctic climate: it is fundamental climate variable. Importantly, accurate snow depth products are required to convert satellite altimeter measurements of ice freeboard to sea ice thickness (SIT). Due to the harsh environment and challenging accessibility, in situ measurements of snow depth are sparse. The quasi-synoptic frequent repeat coverage provided by satellite measurements offers the best approach to regularly monitor snow depth on sea ice. A number of algorithms are based on satellite microwave radiometry measurements and simple empirical relationships. Reducing their uncertainty remains a major challenge. A High Priority Candidate Mission called the Copernicus Imaging Microwave Radiometer (CIMR) is now being studied at the European Space Agency. CIMR proposes a conically scanning radiometer having a swath > 1900 km and including channels at 1.4, 6.9, 10.65, 18.7 and 36.5 GHz on the same platform. It will fly in a high inclination dawn-dusk orbit coordinated with the MetOp-SG(B). As part of the preparation for the CIMR mission, we explore a new approach to retrieve snow depth on sea ice from multi-frequency satellite microwave radiometer measurements using a neural network approach. Neural networks have proven to reach high accuracies in other domains and excel in handling complex, non-linear relationships. We propose one neural network that only relies on AMSR2 channel brightness temperature data input and another one using both AMSR2 and SMOS data as input. We evaluate our results from the neural network approach using airborne snow depth measurements from Operation IceBridge (OIB) campaigns and compare them to products from three other established snow depth algorithms. We show that both our neural networks outperform the other algorithms in terms of accuracy, when compared to the OIB data and we demonstrate that plausible results are obtained even outside the algorithm training period and area. We then convert CryoSat freeboard measurements to SIT using different snow products including the snow depth from our networks. We confirm that a more accurate snow depth product derived using our neural networks leads to more accurate estimates of SIT, when compared to the SIT measured by a laser altimeter at the OIB campaign. Our network with additional SMOS input yields even higher accuracies, but has the disadvantage of a larger “hole at the pole”. Our neural network approaches are applicable over the whole Arctic, capturing first-year ice and multi-year ice conditions throughout winter. Once the networks are designed and trained, they are fast and easy to use. The combined AMSR2 + SMOS neural network is particularly important as a pre-cursor demonstration for the Copernicus CIMR candidate mission highlighting the benefit of CIMR.

Abstract. Icebergs account for half of all ice loss from Antarctica and, once released, present a hazard to maritime operations. Their melting leads to a redistribution of cold fresh water around the Southern Ocean which, in turn, influences water circulation, promotes sea ice formation, and fosters primary production. In this study, we combine CryoSat-2 satellite altimetry with MODIS and Sentinel-1 satellite imagery and meteorological data to track changes in the area, freeboard, thickness, and volume of the B30 tabular iceberg between 2012 and 2018. We track the iceberg elevation when it was attached to Thwaites Glacier and on a further 106 occasions after it calved using Level 1b CryoSat data, which ensures that measurements recorded in different acquisition modes and within different geographical zones are consistently processed. From these data, we map the iceberg's freeboard and estimate its thickness taking snowfall and changes in snow and ice density into account. We compute changes in freeboard and thickness relative to the initial average for each overpass and compare these to estimates from precisely located tracks using the satellite imagery. This comparison shows good agreement (correlation coefficient 0.87) and suggests that colocation reduces the freeboard uncertainty by 1.6 m. We also demonstrate that the snow layer has a significant impact on iceberg thickness change. Changes in the iceberg area are measured by tracing its perimeter, and we show that alternative estimates based on arc lengths recorded in satellite altimetry profiles and on measurements of the semi-major and semi-minor axes also capture the trend, though with a 48 % overestimate and a 15 % underestimate, respectively. Since it calved, the area of B30 has decreased from 1500±60 to 426±27 km2, its mean freeboard has fallen from 49.0±4.6 to 38.8±2.2 m, and its mean thickness has reduced from 315±36 to 198±14 m. The combined loss amounts to an 80 %±16 % reduction in volume, two thirds (69 %±14 %) of which is due to fragmentation and the remainder (31 %±11 %) of which is due to basal melting.

<p>Icebergs impact the physical and biological properties of the ocean along their drift trajectory by releasing cold fresh meltwater and nutrients. This facilitates sea ice formation, fosters biological production and influences the local ocean circulation. The intensity of the impact depends on the amount of meltwater. A68 was the sixth largest iceberg ever recorded in satellite observations, and hence had a significant potential to impact its environment. It calved from the Larsen-C Ice Shelf in July 2017, drifted through the Weddell and Scotia Sea and approached South Georgia at the end of 2020. Finally, it disintegrated near South Georgia in early 2021. Although this is a common trajectory for Antarctic icebergs, the sheer size of A68A elevates its potential to impact ecosystems around South Georgia through release of fresh water and nutrients, through blockage and through collision with the benthic habitat.</p><p>In this study we combine satellite imagery data from Sentinel 1, Sentinel 3 and MODIS and satellite altimetry from CryoSat-2 and ICESat-2 to chart changes in the A68A iceberg’s area, freeboard, thickness, volume and mass over its lifetime to assess its disintegration and melt rate in different environments. We find that A68A thinned from 235 ± 9 to 168 ± 10 m, on average, and lost 802 ± 35 Gt of ice in 3.5 years. While the majority of this loss is due to fragmentation into smaller icebergs, which do not melt instantly, 254 ± 17 billion tons are released through melting at the iceberg’s base - a lower bound estimate for the fresh water input into the ocean. Basal melting peaked at 7.2 ± 2.3 m/month in the Northern Scotia Sea. In the vicinity of South Georgia we estimate that 152 ± 61 Gt of freshwater were released over 96 days, potentially altering the local ocean properties, plankton occurrence and conditions for predators. The iceberg may also have scoured the sea floor briefly. Our detailed maps of the A68A iceberg thickness change will be useful to investigate the impact on the Larsen-C Ice Shelf, and for more detailed studies on the effects of meltwater and nutrients released off South Georgia. Our results could also help to model the disintegration of other large tabular icebergs that take a similar path and to include their impact in ocean models.</p>

<p>Giant icebergs can greatly impact the mass, freshwater and nutrient budgets of the ocean. They can deposit large amounts of freshwater at great distances from their origins, impacting upper-ocean stratification and mixing, and they can be important vectors for micronutrient delivery with impacts on primary production and carbon drawdown. Their impacts on advection, productivity and blocking of flows can be critical for zooplankton and regional ecosystem functioning, with consequences for higher trophic levels and local fisheries. Their breakouts from ice shelves create new opportunities for biological colonisation and carbon sinks and their collisions with the seabed (iceberg scour) can shape local and regional benthic biodiversity patterns and influence carbon sequestration.</p><p>In 2017, the A68 iceberg (around 6000 km<sup>2</sup>) calved from the Larsen C Ice Shelf on the Antarctic Peninsula. It subsequently moved eastward and northward, crossing the Scotia Sea to move, virtually intact, to within 300 km of the island of South Georgia (SG) in late 2020. This caused concern, following the impact of a previous iceberg, A38, on the SG ecosystem in 2003-2004. Further, given the advances in observing technology since the time of the previous iceberg, it afforded an unparalleled opportunity to study in detail the impacts of giant bergs on the ocean physical, biogeochemical and biological systems.</p><p>Diverse datasets were collected in response to this event. A research cruise on RRS James Cook was mobilised, to study the iceberg as it approached SG and fragmented into multiple smaller pieces. These measurements included physical parameters (including oxygen isotopes to inform on freshwater sources), dissolved inorganic nutrients, biosilica concentration, and composition of the phytoplankton community to inform bloom dynamics and primary production by the input of terrigenous material. Ocean gliders, deployed from the ship, surveyed the largest iceberg fragment in extremely close proximity and followed this for the remainder of its life, deconvolving the iceberg influence from frontal dynamics and assisting in understanding meltwater influence. Concurrently, Earth Observation (EO) techniques were employed including Sentinel-1 SAR imagery, Planet Labs very high-resolution optical imagery, MODIS Aqua and Terra imagery and satellite radar and laser altimetry. A sediment trap deployed on a mooring downstream of SG will be utilised to investigate the carbon export from the cruise period to that of the previous 10 years while enhanced observations on higher predator colonies will compare their foraging paths and breeding performance to those of previous years.</p><p>This presentation will discuss preliminary findings from the study of A68, including EO-derived quantifications of changing iceberg morphology, ice loss from fragmentation and basal melting, and the significance of fractures in dictating collapse fissures. Physical oceanographic data from the ship and gliders are used to determine the impact on water column stability, mixing and circulation on a range of scales. Biogeochemical and biological data reveal the impact of interacting processes on phytoplankton community biomass and species composition. Ecosystem implications and future directions of investigation will be outlined.</p><p> </p>

Abstract. Icebergs account for half of all ice loss from Antarctica and, once released, present a hazard to maritime operations. Their melting leads to a redistribution of cold fresh water around the Southern Ocean which, in turn, influences water circulation, promotes sea ice formation, and fosters primary production. In this study, we combine CryoSat-2 satellite altimetry with MODIS and Sentinel-1 satellite imagery to track changes in the area, freeboard, thickness, and volume of the B30 tabular iceberg between 2012 and 2018. We track the iceberg elevation when it was attached to Thwaites Glacier and on a further 106 occasions after it calved using Level 1b CryoSat data, which ensures that measurements recorded in different modes and within different geographical zones are consistently processed. From these data, we mapped the icebergs freeboard and estimated its thickness taking snowfall and changes in snow and ice density into account. We compute changes in freeboard and thickness relative to the initial average for each overpass and compare this time series to precisely located tracks using the satellite imagery. This comparison shows that our time series of iceberg freeboard change is in good agreement with the geolocated overpasses (correlation coefficient 0.87), and suggests that geolocation reduces the uncertainty by 1.6 m. We also demonstrate that the snow layer has a significant impact on iceberg thickness change. Changes in the iceberg area are measured by tracing its perimeter and we show that alternative estimates based on arc lengths recorded in satellite altimetry profiles and on measurements of the semi-major and semi-minor axes also capture the trend, though with a 48 % overestimate and a 15 % underestimate, respectively. Since it calved, the area of B30 has decreased from 1500 +/− 60 to 426 +/− 27 km2, its mean freeboard has fallen from 49.0 +/− 4.6 to 38.8 +/− 2.2 m, and its mean thickness has reduced from 315 ± 36 to 198 ± 14 m. The combined loss amounts to an 80 +/− 16 % reduction in volume, two thirds (69 ± 14 %) of which is due to fragmentation and the remainder (31 ± 11 %) is due to basal melting.