Snow depth uncertainty and its implications on satellite derived Antarctic sea ice thickness

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel falls within 0.02 m snow water equivalent (swe) of in situ measurements across the entire study area, but exhibits deviations of 0.05 m swe from these measurements in the east where large topographic features appear to have caused a positive bias in snow depth. AMSR-E provides swe values half that of SnowModel for the majority of the sea ice growth season. The coarser resolution ERA-Interim, not segmented for sea ice freeze up area reveals a mean swe value 0.01 m higher than in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on the assumptions involved in separating snow and ice freeboard. With various assumptions about the radar penetration into the snow cover, the sea ice thickness estimates vary by up to 2 m. However, we find the best agreement between CS-2 derived and in situ thickness when a radar penetration of 0.05-0.10 m into the snow cover is assumed.

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Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

The Cryosphere ◽

10.5194/tc-13-1409-2019 ◽

2019 ◽

Vol 13 (4) ◽

pp. 1409-1422

Author(s):

Daniel Price ◽

Iman Soltanzadeh ◽

Wolfgang Rack ◽

Ethan Dale

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Snow Accumulation ◽

In Situ Measurements ◽

Ice Thickness ◽

Growth Season ◽

Mcmurdo Sound ◽

Sea Ice Thickness ◽

Antarctic Sea Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

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In situ measurements of the direct-current conductivity of Antarctic Sea ice: implications for airborne electromagnetic Sounding of Sea-ice thickness

Annals of Glaciology ◽

10.3189/172756406781811772 ◽

2006 ◽

Vol 44 ◽

pp. 217-223 ◽

Cited By ~ 15

Author(s):

J.E. Reid ◽

A. Pfaffling ◽

A.P. Worby ◽

J.R. Bishop

Keyword(s):

Sea Ice ◽

Direct Current ◽

Sea Water ◽

Vertical Direction ◽

In Situ Measurements ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Direct Current Conductivity

AbstractAirborne, Ship-borne and Surface low-frequency electromagnetic (EM) methods have become widely applied to measure Sea-ice thickness. EM responses measured over Sea ice depend mainly on the Sea-water conductivity and on the height of the Sensor above the Sea-ice–sea-water interface, but may be Sensitive to the Sea-ice conductivity at high excitation frequencies. We have conducted in Situ measurements of direct-current conductivity of Sea ice using Standard geophysical geoelectrical methods. Sea-ice thickness estimated from the geoelectrical Sounding data was found to be consistently underestimated due to the pronounced vertical-to-horizontal conductivity anisotropy present in level Sea ice. At five Sites, it was possible to determine the approximate horizontal and vertical conductivities from the Sounding data. The average horizontal conductivity was found to be 0.017 Sm–1, and that in the vertical direction to be 9–12 times higher. EM measurements over level Sea ice are Sensitive only to the horizontal conductivity. Numerical modelling has Shown that the assumption of zero Sea-ice conductivity in interpretation of airborne EM data results in a negligible error in interpreted thickness for typical level Antarctic Sea ice.

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About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

The Cryosphere Discussions ◽

10.5194/tcd-9-4893-2015 ◽

2015 ◽

Vol 9 (5) ◽

pp. 4893-4923 ◽

Cited By ~ 1

Author(s):

S. Schwegmann ◽

E. Rinne ◽

R. Ricker ◽

S. Hendricks ◽

V. Helm

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Altimeter Data ◽

Ice Thickness ◽

Radar Altimeter ◽

Growth Season ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Abstract Knowledge ◽

Modal Values

Abstract. Knowledge about Antarctic sea-ice volume and its changes over the past decades has been sparse due to the lack of systematic sea-ice thickness measurements in this remote area. Recently, first attempts have been made to develop a sea-ice thickness product over the Southern Ocean from space-borne radar altimetry and results look promising. Today, more than 20 years of radar altimeter data are potentially available for such products. However, data come from different sources, and the characteristics of individual sensors differ. Hence, it is important to study the consistency between single sensors in order to develop long and consistent time series over the potentially available measurement period. Here, the consistency between freeboard measurements of the Radar Altimeter 2 on-board Envisat and freeboard measurements from the Synthetic-Aperture Interferometric Radar Altimeter on-board CryoSat-2 is tested for their overlap period in 2011. Results indicate that mean and modal values are comparable over the sea-ice growth season (May–October) and partly also beyond. In general, Envisat data shows higher freeboards in the seasonal ice zone while CryoSat-2 freeboards are higher in the perennial ice zone and near the coasts. This has consequences for the agreement in individual sectors of the Southern Ocean, where one or the other ice class may dominate. Nevertheless, over the growth season, mean freeboard for the entire (regional separated) Southern Ocean differs generally by not more than 2 cm (5 cm, except for the Amundsen/Bellingshausen Sea) between Envisat and CryoSat-2, and the differences between modal freeboard lie generally within ±10 cm and often even below.

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Using CloudSat snowfall rate observations to constrain and characterize the uncertainties of Arctic snow-on-sea-ice

10.5194/egusphere-egu2020-11436 ◽

2020 ◽

Author(s):

Alex Cabaj ◽

Paul Kushner ◽

Alek Petty ◽

Stephen Howell ◽

Christopher Fletcher

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Arctic Ocean ◽

Ice Model

Snow on Arctic sea ice plays multiple&#8212;and sometimes contrasting&#8212;roles in several feedbacks between sea ice and the global climate system. For example, the presence of snow on sea ice may mitigate sea ice melt by increasing the sea ice albedo and enhancing the ice-albedo feedback. Conversely, snow can inhibit sea ice growth by insulating the ice from the atmosphere during the sea ice growth season. In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. In particular, snow contributes to uncertainties in retrievals of sea ice thickness from satellite altimetry measurements, such as those from ICESat-2. Snow-on-sea-ice models can produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are absent over most of the Arctic Ocean, so it can be difficult to determine which reanalysis snowfall product is best suited to be used as input for a snow-on-sea-ice model.In the absence of in-situ snowfall rate measurements, measurements from satellite instruments can be used to quantify snowfall over the Arctic Ocean. The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rates can be retrieved. This instrument provides the most extensive high-latitude snowfall rate observation dataset currently available. CloudSat&#8217;s near-polar orbit enables it to make measurements at latitudes up to 82&#176;N, with a 16-day repeat cycle, over the time period from 2006-2016.We present a calibration of reanalysis snowfall to CloudSat observations over the Arctic Ocean, which we then apply to reanalysis snowfall input for the NASA Eulerian Snow On Sea Ice Model (NESOSIM). This calibration reduces the spread in snow depths produced by NESOSIM when different reanalysis inputs are used. In light of this calibration, we revise the NESOSIM parametrizations of wind-driven snow processes, and we characterize the uncertainties in NESOSIM-generated snow depths resulting from uncertainties in snowfall input. We then extend this analysis further to estimate the resulting uncertainties in sea ice thickness retrieved from ICESat-2 when snow depth estimates from NESOSIM are used as input for the retrieval.

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Observation and thermodynamic modeling of the influence of snow cover on landfast sea ice thickness in Prydz Bay, East Antarctica

10.5194/egusphere-egu2020-1715 ◽

2020 ◽

Author(s):

Jiechen Zhao ◽

Bin Cheng ◽

Timo Vihma ◽

Qinghua Yang ◽

Fengming Hui ◽

...

Keyword(s):

Sea Ice ◽

Snow Cover ◽

Annual Cycle ◽

Snow Depth ◽

Snow Accumulation ◽

East Antarctica ◽

Prydz Bay ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Landfast Sea Ice

The observed snow depth and ice thickness on landfast sea ice in Prydz Bay, East Antarctica, were used to determine the role of snow in (a) the annual cycle of sea ice thickness at a fixed location (SIP) where snow usually blows away after snowfall and (b) early summer sea ice thickness within the transportation route surveys (TRS) domain farther from coast, where annual snow accumulation is substantial. The annual mean snow depth and maximum ice thickness had a negative relationship (r = &#8722;0.58, p < 0.05) at SIP, indicating a primary insulation effect of snow on ice thickness. However, in the TRS domain, this effect was negligible because snow contributes to ice thickness. A one-dimensional thermodynamic sea ice model, forced by local weather observations, reproduced the annual cycle of ice thickness at SIP well. During the freeze season, the modeled maximum difference of ice thickness using different snowfall scenarios ranged from 0.53&#8211;0.61 m. Snow cover delayed ice surface and ice bottom melting by 45 and 24 days, respectively. The modeled snow ice and superimposed ice accounted for 4&#8211;23% and 5&#8211;8% of the total maximum ice thickness on an annual basis in the case of initial ice thickness ranging from 0.05&#8211;2 m, respectively.

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About the consistency between Envisat and CryoSat-2 radar freeboard retrieval over Antarctic sea ice

The Cryosphere ◽

10.5194/tc-10-1415-2016 ◽

2016 ◽

Vol 10 (4) ◽

pp. 1415-1425 ◽

Cited By ~ 17

Author(s):

Sandra Schwegmann ◽

Eero Rinne ◽

Robert Ricker ◽

Stefan Hendricks ◽

Veit Helm

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Altimeter Data ◽

Ice Thickness ◽

Radar Altimeter ◽

Growth Season ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Abstract Knowledge ◽

Modal Values

Abstract. Knowledge about Antarctic sea-ice volume and its changes over the past decades has been sparse due to the lack of systematic sea-ice thickness measurements in this remote area. Recently, first attempts have been made to develop a sea-ice thickness product over the Southern Ocean from space-borne radar altimetry and results look promising. Today, more than 20 years of radar altimeter data are potentially available for such products. However, the characteristics of individual radar types differ for the available altimeter missions. Hence, it is important and our goal to study the consistency between single sensors in order to develop long and consistent time series. Here, the consistency between freeboard measurements of the Radar Altimeter 2 on board Envisat and freeboard measurements from the Synthetic-Aperture Interferometric Radar Altimeter on board CryoSat-2 is tested for their overlap period in 2011. Results indicate that mean and modal values are in reasonable agreement over the sea-ice growth season (May–October) and partly also beyond. In general, Envisat data show higher freeboards in the first-year ice zone while CryoSat-2 freeboards are higher in the multiyear ice zone and near the coasts. This has consequences for the agreement in individual sectors of the Southern Ocean, where one or the other ice class may dominate. Nevertheless, over the growth season, mean freeboard for the entire (regionally separated) Southern Ocean differs generally by not more than 3 cm (8 cm, with few exceptions) between Envisat and CryoSat-2, and the differences between modal freeboards lie generally within ±10 cm and often even below.

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Uncertainties in Antarctic sea-ice thickness retrieval from ICESat

Annals of Glaciology ◽

10.3189/2015aog69a736 ◽

2015 ◽

Vol 56 (69) ◽

pp. 107-119 ◽

Cited By ~ 31

Author(s):

Stefan Kern ◽

Gunnar Spreen

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Sensitivity Study ◽

Weddell Sea ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Earth Observing System ◽

Antarctic Sea Ice ◽

Snow Properties ◽

The Mean

AbstractA sensitivity study was carried out for the lowest-level elevation method to retrieve total (sea ice + snow) freeboard from Ice, Cloud and land Elevation Satellite (ICESat) elevation measurements in the Weddell Sea, Antarctica. Varying the percentage (P) of elevations used to approximate the instantaneous sea-surface height can cause widespread changes of a few to ˃10cm in the total freeboard obtained. Other input parameters have a smaller influence on the overall mean total freeboard but can cause large regional differences. These results, together with published ICESat elevation precision and accuracy, suggest that three times the mean per gridcell single-laser-shot error budget can be used as an estimate for freeboard uncertainty. Theoretical relative ice thickness uncertainty ranges between 20% and 80% for typical freeboard and snow properties. Ice thickness is computed from total freeboard using Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) snow depth data. Average ice thickness for the Weddell Sea is 1.73 ± 0.38 m for ICESat measurements from 2004 to 2006, in agreement with previous work. The mean uncertainty is 0.72 ± 0.09 m. Our comparison with data of an alternative approach, which assumes that sea-ice freeboard is zero and that total freeboard equals snow depth, reveals an average sea-ice thickness difference of ∼0.77m.

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Evaluation of AMSR-E snow depth product over East Antarctic sea ice using in situ measurements and aerial photography

Journal of Geophysical Research Atmospheres ◽

10.1029/2007jc004181 ◽

2008 ◽

Vol 113 (C5) ◽

Cited By ~ 41

Author(s):

Anthony P. Worby ◽

Thorsten Markus ◽

Adam D. Steer ◽

Victoria I. Lytle ◽

Robert A. Massom

Keyword(s):

Sea Ice ◽

Aerial Photography ◽

Snow Depth ◽

In Situ Measurements ◽

Antarctic Sea Ice

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Estimating Early-Winter Antarctic Sea Ice Thickness From Deformed Ice Morphology

10.5194/tc-2019-140 ◽

2019 ◽

Cited By ~ 1

Author(s):

M. Jeffrey Mei ◽

Ted Maksym ◽

Hanumant Singh

Keyword(s):

Neural Network ◽

Sea Ice ◽

Relative Error ◽

Snow Depth ◽

Large Scale ◽

Small Scale ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Mean Relative Error ◽

Antarctic Sea Ice

Abstract. Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness can be estimated from surface elevation measurements, such as those from airborne/satellite LiDAR, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic sea ice thickness have errors as high as ~ 50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate sea ice thickness at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate sea ice thickness at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ~ 15 %). Moreover, the neural network does not require specifying snow depth/density, which increases its potential applications to other LiDAR datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season, which is much lower than the mean relative error for a linear fit (errors up to 47 %). This method may be extended to lower-resolution, larger-footprint data such as such as IceBridge, and suggests a possible avenue to reduce errors in satellite estimates of Antarctic sea ice thickness from ICESat-2 over current methods, especially at smaller scale.

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An Evaluation of Antarctic Sea Ice Thickness from the Global Ice-Ocean Modeling and Assimilation System based on In-situ and Satellite Observations

10.5194/tc-2021-228 ◽

2021 ◽

Author(s):

Sutao Liao ◽

Hao Luo ◽

Jinfei Wang ◽

Qian Shi ◽

Jinlun Zhang ◽

...

Keyword(s):

Sea Ice ◽

Satellite Observations ◽

Ocean Modeling ◽

Ice Thickness ◽

Earth System ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

The Earth ◽

Assimilation System

Abstract. Antarctic sea ice is an important component of the Earth system. However, its role in the Earth system is still not very clear due to limited Antarctic sea ice thickness (SIT) data. A reliable sea ice reanalysis can be useful to study Antarctic SIT and its role in the Earth system. Among various Antarctic sea ice reanalysis products, the Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output is widely used in the research of Antarctic sea ice. As more Antarctic SIT observations with quality control are released, a further evaluation of Antarctic SIT from GIOMAS is conducted in this study based on in-situ and satellite observations. Generally, though only sea ice concentration is assimilated, GIOMAS can basically reproduce the observed variability of sea ice volume and its changes in the trend before and after 2013, indicating that GIOMAS is a good option to study the long-term variation of Antarctic sea ice. However, due to deficiencies in model and asymmetric changes in SIT caused by assimilation, GIOMAS underestimates Antarctic SIT especially in deformed ice regions, which has an impact on not only the mean state of SIT but also the variability. Thus, besides the further development of model, assimilating additional sea ice observations (e.g., SIT and sea ice drift) with advanced assimilation methods may be conducive to a more accurate estimation of Antarctic SIT.

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