Advances in altimetric snow depth estimates using bi-frequency SARAL and CryoSat-2 Ka–Ku measurements

Abstract. Although snow depth on sea ice is a key parameter for sea ice thickness (SIT) retrieval, there currently does not exist reliable estimations. In the Arctic, nearly all SIT products use a snow depth climatology (the modified Warren-99 climatology, W99m) constructed from in situ data obtained prior to the first significant impacts of climate change. In the Antarctic, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the altimetric snow depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the Ka-band frequency SARAL/Altika and the Ku-band frequency CryoSat-2. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modelling and Assimilation System (PIOMAS) or its equivalent in the Antarctic the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model, and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in the Arctic)), the Advanced Microwave Scanning Radiometer-2 (AMSR2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka–Ku product (only in the Arctic). The ASD product is further validated in the Arctic against the ice mass balance (IMB) buoys, the CryoSat Validation Experiment (CryoVEx) and Operation Ice Bridge's (OIB) airborne measurements. These comparisons demonstrate that ASD is a relevant snow depth solution, with spatiotemporal patterns consistent with those of the alternative Ka–Ku DuST product but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data: comparisons with OIB's airborne snow radar in the Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in the Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction in the average SIT of about 30 cm.

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Advances in altimetric snow depth estimates using bi-frequency SARAL/CryoSat-2 Ka/Ku measurements

10.5194/tc-2021-79 ◽

2021 ◽

Author(s):

Florent Garnier ◽

Sara Fleury ◽

Gilles Garric ◽

Jérôme Bouffard ◽

Michel Tsamados ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Ocean Modeling ◽

The Arctic ◽

Validation Data ◽

Band Frequency ◽

In Situ Data ◽

Wide Range ◽

Assimilation System

Abstract. Although snow depth on sea ice is a key parameter for Sea Ice Thickness (SIT), there currently does not exist reliable estimations. In Arctic, nearly all SIT products use a snow depth climatology (the Warren-99 modified climatology, W99m) constructed from in-situ data obtained prior to the first significant impacts of climate change. In Antarctica, the lack of information on snow depth remains a major obstacle in the development of reliable SIT products. In this study, we present the latest version of the Altimetric Snow Depth (ASD) product computed over both hemispheres from the difference of the radar penetration into the snow pack between the CryoSat-2 Ku-band and the SARAL Ka-band frequency radars. The ASD solution is compared against a wide range of snow depth products including model data (Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) or its equivalent in Antarctica the Global Ice-Ocean Modeling and Assimilation System (GIOMAS), the MERCATOR model and NASA's Eulerian Snow On Sea Ice Model (NESOSIM, only in Arctic)), the Advanced Microwave Scanning Radiometer 2 (AMSR-2) passive radiometer data, and the Dual-altimeter Snow Thickness (DuST) Ka-Ku product (only in Arctic). It is validated in the Arctic against in-situ and airborne validation data. These comparisons demonstrate that ASD provide a consistent snow depth solution, with space and time patterns comparable with those of the alternative Ka-Ku DuST product, but with a mean bias of about 6.5 cm. We also demonstrate that ASD is consistent with the validation data. Comparisons with Operation Ice Bridge's (OIB) airborne snow radar in Arctic during the period of 2014–2018 show a correlation of 0.66 and a RMSE of about 6 cm. Furthermore, a first-guess monthly climatology has been constructed in Arctic from the ASD product, which shows a good agreement with OIB during 2009–2012. This climatology is shown to provide a better solution than the W99m climatology when compared with validation data. Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013–2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to different snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction of the average SIT of about 30 cm.

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Variability scaling and consistency in airborne and satellite altimetry measurements of Arctic sea ice

The Cryosphere ◽

10.5194/tc-14-751-2020 ◽

2020 ◽

Vol 14 (2) ◽

pp. 751-767

Author(s):

Shiming Xu ◽

Lu Zhou ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Spatial Scales ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Noise Levels ◽

Wide Range

Abstract. Satellite and airborne remote sensing provide complementary capabilities for the observation of the sea ice cover. However, due to the differences in footprint sizes and noise levels of the measurement techniques, as well as sea ice's variability across scales, it is challenging to carry out inter-comparison or consistently study these observations. In this study we focus on the remote sensing of sea ice thickness parameters and carry out the following: (1) the analysis of variability and its statistical scaling for typical parameters and (2) the consistency study between airborne and satellite measurements. By using collocating data between Operation IceBridge and CryoSat-2 (CS-2) in the Arctic, we show that consistency exists between the variability in radar freeboard estimations, although CryoSat-2 has higher noise levels. Specifically, we notice that the noise levels vary among different CryoSat-2 products, and for the European Space Agency (ESA) CryoSat-2 freeboard product the noise levels are at about 14 and 20 cm for first-year ice (FYI) and multi-year ice (MYI), respectively. On the other hand, for Operation IceBridge and NASA's Ice, Cloud, and land Elevation Satellite (ICESat), it is shown that the variability in snow (or total) freeboard is quantitatively comparable despite more than a 5-year time difference between the two datasets. Furthermore, by using Operation IceBridge data, we also find widespread negative covariance between ice freeboard and snow depth, which only manifests on small spatial scales (40 m for first-year ice and about 80 to 120 m for multi-year ice). This statistical relationship highlights that the snow cover reduces the overall topography of the ice cover. Besides this, there is prevalent positive covariability between snow depth and snow freeboard across a wide range of spatial scales. The variability and consistency analysis calls for more process-oriented observations and modeling activities to elucidate key processes governing snow–ice interaction and sea ice variability on various spatial scales. The statistical results can also be utilized in improving both radar and laser altimetry as well as the validation of sea ice and snow prognostic models.

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Assessment of sea ice simulations in the CMIP5 models

The Cryosphere ◽

10.5194/tc-9-399-2015 ◽

2015 ◽

Vol 9 (1) ◽

pp. 399-409 ◽

Cited By ~ 94

Author(s):

Q. Shu ◽

Z. Song ◽

F. Qiao

Keyword(s):

Sea Ice ◽

Climate Models ◽

Linear Trend ◽

Ocean Modeling ◽

The Arctic ◽

Cmip5 Models ◽

Output Data ◽

Sea Ice Extent ◽

The Antarctic ◽

Assimilation System

Abstract. The historical simulations of sea ice during 1979 to 2005 by the Coupled Model Intercomparison Project Phase 5 (CMIP5) are compared with satellite observations, Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output data and Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) output data in this study. Forty-nine models, almost all of the CMIP5 climate models and earth system models with historical simulation, are used. For the Antarctic, multi-model ensemble mean (MME) results can give good climatology of sea ice extent (SIE), but the linear trend is incorrect. The linear trend of satellite-observed Antarctic SIE is 1.29 (±0.57) × 105 km2 decade−1; only about 1/7 CMIP5 models show increasing trends, and the linear trend of CMIP5 MME is negative with the value of −3.36 (±0.15) × 105 km2 decade−1. For the Arctic, both climatology and linear trend are better reproduced. Sea ice volume (SIV) is also evaluated in this study, and this is a first attempt to evaluate the SIV in all CMIP5 models. Compared with the GIOMAS and PIOMAS data, the SIV values in both the Antarctic and the Arctic are too small, especially for the Antarctic in spring and winter. The GIOMAS Antarctic SIV in September is 19.1 × 103 km3, while the corresponding Antarctic SIV of CMIP5 MME is 13.0 × 103 km3 (almost 32% less). The Arctic SIV of CMIP5 in April is 27.1 × 103 km3, which is also less than that from PIOMAS SIV (29.5 × 103 km3). This means that the sea ice thickness simulated in CMIP5 is too thin, although the SIE is fairly well simulated.

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Snow depth on Antarctic sea ice: a Lagrangian model-based approach

10.5194/egusphere-egu21-16088 ◽

2021 ◽

Author(s):

Isobel R. Lawrence ◽

Andy Ridout ◽

Andrew Shepherd

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Global Climate ◽

Lagrangian Model ◽

The Arctic ◽

Balance Model ◽

The Past ◽

Antarctic Sea Ice ◽

Regional Trends ◽

The Antarctic

Snow on Antarctic sea ice is an important yet poorly resolved component of the global climate system. Whilst much attention over the past few years has been dedicated to producing reanalysis-forced models of snow on sea ice in the Arctic, none currently exist for the Southern Hemisphere. Here we present a Lagrangian-framework model of snow depth on Antarctic sea ice, in which &#8220;parcels&#8221; of ice accumulate snow as they drift around the ocean according to daily ice motion vectors. Snow accumulates from two sources; (i) snowfall from ERA5 atmospheric reanalysis and (ii) snow blown off the Antarctic continent, which we estimate using the RACMO2 ice sheet mass balance model. Ice parcels lose snow via wind-redistribution into leads and through snow-ice formation. We validate our dynamic snow product against ship-based measurements from the ASPeCT data archive, and we compare our long-term climatology against estimates derived from passive microwave (AMSR-E/2) satellites. Finally, we assess regional trends in snow depth over the past four decades and investigate whether these are driven by changes in snowfall or divergence/convergence of the Antarctic sea ice pack.&#160;

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Snow Depth and Air Temperature Seasonality on Sea Ice Derived From Snow Buoy Measurements

Frontiers in Marine Science ◽

10.3389/fmars.2021.655446 ◽

2021 ◽

Vol 8 ◽

Author(s):

Marcel Nicolaus ◽

Mario Hoppmann ◽

Stefanie Arndt ◽

Stefan Hendricks ◽

Christian Katlein ◽

...

Keyword(s):

Sea Ice ◽

Snow Cover ◽

Air Temperature ◽

Snow Depth ◽

Snow Accumulation ◽

Arctic Sea Ice ◽

Weddell Sea ◽

The Arctic ◽

Validation Data ◽

Antarctic Sea Ice

Snow depth on sea ice is an essential state variable of the polar climate system and yet one of the least known and most difficult to characterize parameters of the Arctic and Antarctic sea ice systems. Here, we present a new type of autonomous platform to measure snow depth, air temperature, and barometric pressure on drifting Arctic and Antarctic sea ice. “Snow Buoys” are designed to withstand the harshest environmental conditions and to deliver high and consistent data quality with minimal impact on the surface. Our current dataset consists of 79 time series (47 Arctic, 32 Antarctic) since 2013, many of which cover entire seasonal cycles and with individual observation periods of up to 3 years. In addition to a detailed introduction of the platform itself, we describe the processing of the publicly available (near real time) data and discuss limitations. First scientific results reveal characteristic regional differences in the annual cycle of snow depth: in the Weddell Sea, annual net snow accumulation ranged from 0.2 to 0.9 m (mean 0.34 m) with some regions accumulating snow in all months. On Arctic sea ice, the seasonal cycle was more pronounced, showing accumulation from synoptic events mostly between August and April and maxima in autumn. Strongest ablation was observed in June and July, and consistently the entire snow cover melted during summer. Arctic air temperature measurements revealed several above-freezing temperature events in winter that likely impacted snow stratigraphy and thus preconditioned the subsequent spring snow cover. The ongoing Snow Buoy program will be the basis of many future studies and is expected to significantly advance our understanding of snow on sea ice, also providing invaluable in situ validation data for numerical simulations and remote sensing techniques.

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Variability Scaling and Consistency of Airborne and Satellite Altimetry Measurements of Arctic Sea Ice

10.5194/tc-2019-220 ◽

2019 ◽

Author(s):

Shiming Xu ◽

Lu Zhou ◽

Bin Wang

Keyword(s):

Remote Sensing ◽

Sea Ice ◽

Snow Depth ◽

Spatial Scales ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

First Year ◽

Noise Levels ◽

Wide Range

Abstract. Satellite and airborne remote sensing provide complementary capabilities for the observation of the sea ice cover. However, due to the differences in footprint sizes and noise levels of the measurement techniques, as well as sea ice's variability across scales, it is challenging to carry out inter-comparison or consistency study of these observations. In this study we focus on the remote sensing of sea ice thickness parameters, and carry out: (1) the analysis of variability and its statistical scaling for typical parameters, and (2) the consistency study between airborne and satellite measurements. By using collocating data between Operation IceBridge and CryoSat-2 in the Arctic, we show that there exists consistency between the variability of radar freeboard estimations, although CryoSat-2 has higher noise levels. Specifically, we notice that the noise levels vary among different CryoSat-2 products, and for ESA CryoSat-2 freeboard product the noise levels are at about 14 and 20 cm for first-year and multiyear ice, respectively. On the other hand, for Operation IceBridge and ICESat, it is shown that the variability of snow (or total) freeboard is quantitatively comparable, despite over 5 years' the time difference between the two datasets. Furthermore, by using Operation IceBridge data, we also find wide-spread negative covariance between ice freeboard and snow depth, which only manifest at small spatial scales (40 m for first-year ice and about 80 to 120 m for MYI). This statistical relationship highlights that the snow cover reduces the overall topography of the ice cover. Besides, there is prevalent positive covariability between snow depth and snow freeboard across a wide range of spatial scales. The variability and consistency analysis calls for more process-oriented observations and modeling activities to elucidate key processes governing snow-ice interaction and sea ice variability on various spatial scales. The statistical results can also be utilized in improving both radar and laser altimetry, as well as the validation of sea ice and snow prognostic models.

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Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

Remote Sensing ◽

10.3390/rs11232864 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2864 ◽

Cited By ~ 4

Author(s):

Jiping Liu ◽

Yuanyuan Zhang ◽

Xiao Cheng ◽

Yongyun Hu

Keyword(s):

Neural Network ◽

Sea Ice ◽

Snow Depth ◽

Deep Neural Network ◽

Arctic Basin ◽

Temporal Variations ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Thickness ◽

Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

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The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

10.5194/egusphere-egu21-12240 ◽

2021 ◽

Author(s):

Isolde Glissenaar ◽

Jack Landy ◽

Alek Petty ◽

Nathan Kurtz ◽

Julienne Stroeve

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Satellite Altimetry ◽

Region Of Interest ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Baffin Bay ◽

The Impact

The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.

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Simulation of Snow on Arctic Sea Ice Using a Coupled Snow–Ice Model

Journal of Hydrometeorology ◽

10.1175/2009jhm1112.1 ◽

2010 ◽

Vol 11 (1) ◽

pp. 199-210 ◽

Cited By ~ 10

Author(s):

Yi-Ching Chung ◽

Stéphane Bélair ◽

Jocelyn Mailhot

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Single Layer ◽

Coupled System ◽

The Arctic ◽

Ice Thickness ◽

The Arctic Ocean ◽

Snow Model ◽

Ice Model

Abstract The new Recherche Prévision Numérique (NEW-RPN) model, a coupled system including a multilayer snow thermal model (SNTHERM) and the sea ice model currently used in the Meteorological Service of Canada (MSC) operational forecasting system, was evaluated in a one-dimensional mode using meteorological observations from the Surface Heat Budget of the Arctic Ocean (SHEBA)’s Pittsburgh site in the Arctic Ocean collected during 1997/98. Two parameters simulated by NEW-RPN (i.e., snow depth and ice thickness) are compared with SHEBA’s observations and with simulations from RPN, MSC’s current coupled system (the same sea ice model and a single-layer snow model). Results show that NEW-RPN exhibits better agreement for the timing of snow depletion and for ice thickness. The profiles of snow thermal conductivity in NEW-RPN show considerable variability across the snow layers, but the mean value (0.39 W m−1 K−1) is within the range of reported observations for SHEBA. This value is larger than 0.31 W m−1 K−1, which is commonly used in single-layer snow models. Of particular interest in NEW-RPN’s simulation is the strong temperature stratification of the snowpack, which indicates that a multilayer snow model is needed in the SHEBA scenario. A sensitivity analysis indicates that snow compaction is also a crucial process for a realistic representation of the snowpack within the snow/sea ice system. NEW-RPN’s overestimation of snow depth may be related to other processes not included in the study, such as small-scale horizontal variability of snow depth and blowing snow processes.

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Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations

The Cryosphere ◽

10.5194/tc-14-2189-2020 ◽

2020 ◽

Vol 14 (7) ◽

pp. 2189-2203

Author(s):

H. Jakob Belter ◽

Thomas Krumpen ◽

Stefan Hendricks ◽

Jens Hoelemann ◽

Markus A. Janout ◽

...

Keyword(s):

Sea Ice ◽

Source Region ◽

European Space Agency ◽

The Arctic ◽

Ice Thickness ◽

Laptev Sea ◽

Validation Data ◽

Data Set ◽

Sea Ice Thickness ◽

The Laptev Sea

Abstract. The gridded sea ice thickness (SIT) climate data record (CDR) produced by the European Space Agency (ESA) Sea Ice Climate Change Initiative Phase 2 (CCI-2) is the longest available, Arctic-wide SIT record covering the period from 2002 to 2017. SIT data are based on radar altimetry measurements of sea ice freeboard from the Environmental Satellite (ENVISAT) and CryoSat-2 (CS2). The CCI-2 SIT has previously been validated with in situ observations from drilling, airborne remote sensing, electromagnetic (EM) measurements and upward-looking sonars (ULSs) from multiple ice-covered regions of the Arctic. Here we present the Laptev Sea CCI-2 SIT record from 2002 to 2017 and use newly acquired ULS and upward-looking acoustic Doppler current profiler (ADCP) sea ice draft (VAL) data for validation of the gridded CCI-2 and additional satellite SIT products. The ULS and ADCP time series provide the first long-term satellite SIT validation data set from this important source region of sea ice in the Transpolar Drift. The comparison of VAL sea ice draft data with gridded monthly mean and orbit trajectory CCI-2 data, as well as merged CryoSat-2–SMOS (CS2SMOS) sea ice draft, shows that the agreement between the satellite and VAL draft data strongly depends on the thickness of the sampled ice. Rather than providing mean sea ice draft, the considered satellite products provide modal sea ice draft in the Laptev Sea. Ice drafts thinner than 0.7 m are overestimated, while drafts thicker than approximately 1.3 m are increasingly underestimated by all satellite products investigated for this study. The tendency of the satellite SIT products to better agree with modal sea ice draft and underestimate thicker ice needs to be considered for all past and future investigations into SIT changes in this important region. The performance of the CCI-2 SIT CDR is considered stable over time; however, observed trends in gridded CCI-2 SIT are strongly influenced by the uncertainties of ENVISAT and CS2 and the comparably short investigation period.

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