Implications of surface flooding on airborne thickness measurements of snow on sea ice

Abstract. Snow thickness observations from airborne snow radars, such as the NASA’s Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow thickness measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime, caused by deep snow on thin sea ice. During the Norwegian young sea ICE expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on March 19, 2015, during which ground truth data were collected during an OIB overflight. Snow and ice thickness measurements were obtained across a two dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snow pack were observed during the N-ICE2015 expedition, due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking and saturation into the basal snow layers, causing more diffuse scattering and influenced the airborne radar signal to detect the radar main scattering horizon well above the snow/sea ice interface, resulting in a subsequent underestimation of total snow thickness, if only radar-based information is used. The average airborne snow thickness was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow thickness, a 0.06 m underestimate in snow thickness by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow thickness distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that, with frequent flooding of the snow-ice interface in specific regions of the Arctic in the future, it may result in an underestimate of snow thickness or an overestimate of ice freeboard, measured from radar altimetry, thereby affecting the accuracy of sea ice thickness estimates.

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Implications of surface flooding on airborne estimates of snow depth on sea ice

The Cryosphere ◽

10.5194/tc-15-2819-2021 ◽

2021 ◽

Vol 15 (6) ◽

pp. 2819-2833

Author(s):

Anja Rösel ◽

Sinead Louise Farrell ◽

Vishnu Nandan ◽

Jaqueline Richter-Menge ◽

Gunnar Spreen ◽

...

Keyword(s):

Sea Ice ◽

Validation Studies ◽

Snow Depth ◽

The Arctic ◽

Airborne Radar ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Thickness Measurements ◽

Snow And Ice

Abstract. Snow depth observations from airborne snow radars, such as the NASA's Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow depth measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime caused by deep snow on thin sea ice. During the Norwegian young sea Ice, Climate and Ecosystems (ICE) expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on 19 March 2015. This study collected ground truth data during an OIB overflight. Snow and ice thickness measurements were obtained across a two-dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snowpack were observed during the N-ICE2015 expedition due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking into and saturation of the basal snow layers. This causes the airborne radar signal to undergo more diffuse scattering, resulting in the location of the radar main scattering horizon being detected well above the snow–ice interface. This leads to a subsequent underestimation of snow depth; if only radar-based information is used, the average airborne snow depth was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow depth, a 0.06 m underestimate in snow depth by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow depth distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that seawater flooding of the snow–ice interface leads to underestimations of snow depth or overestimations of sea ice freeboard measured from radar altimetry, in turn impacting the accuracy of sea ice thickness estimates.

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Snow-depth observations by adventurers traveling on Arctic sea ice

Annals of Glaciology ◽

10.3189/172756411795931552 ◽

2011 ◽

Vol 52 (57) ◽

pp. 369-376 ◽

Cited By ~ 12

Author(s):

Sebastian Gerland ◽

Christian Haas

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Basin ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Snow Thickness ◽

Snow And Ice

AbstractSnow depth is a key parameter for assessing the sea-ice mass budget in the Arctic and for the surface energy balance at the atmosphere–snow–ice–ocean interfaces. However, scientific expeditions to the high Arctic Ocean are rare, and for large parts of the year no snow and ice data are collected in situ in most regions. Therefore any additional in situ observations of snow depth are of interest to the scientific community. Arctic adventurers and tourists are among the most frequent visitors to the Arctic Ocean and North Pole. If properly trained and carefully adhering to standard protocols, they could collect valuable snow-depth data from large regions. Here we analyse such data from four Arctic basin ski traverses carried out between 1994 and 2007. Individual datasets show characteristic regional differences of snow thickness, which provide invaluable information for the validation of models and satellite data. the observations were made applying a continuously upgraded observation guideline scheme. Earlier observations were based on a relatively broad view of easily observable snow and ice parameters. Improvements included requirements of more detailed snow-thickness surveys in order to observe the spatial variability over varying sea-ice surfaces in a better way. Possibilities for, and limitations of, ice-thickness estimates and measurements are also discussed. Often, assessment of ice thickness is more problematic since measurements are either time-consuming or biased, meaning that possibilities for collecting large datasets are limited.

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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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Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Geoscientific Model Development ◽

10.5194/gmd-9-2239-2016 ◽

2016 ◽

Vol 9 (6) ◽

pp. 2239-2254 ◽

Cited By ~ 3

Author(s):

Yao Yao ◽

Jianbin Huang ◽

Yong Luo ◽

Zongci Zhao

Keyword(s):

Sea Ice ◽

Heat Budget ◽

Wrf Model ◽

The Arctic ◽

Wave Radiation ◽

Ice Thickness ◽

Sea Ice Thickness ◽

The Impact ◽

Ice Model ◽

Snow And Ice

Abstract. Sea ice plays an important role in the air–ice–ocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice scheme, which is the only option in the current Weather Research and Forecasting (WRF) model (version 3.6.1), has a problem of energy imbalance due to its simplification in snow processes and lack of ablation and accretion processes in ice. Validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations, Noah underestimates the sea ice temperature which can reach −10 °C in winter. Sensitivity tests show that this bias is mainly attributed to the simulation within the ice when a time-dependent ice thickness is specified. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGHTSI) uses more realistic thermodynamics for snow and ice. Most importantly, HIGHTSI includes the ablation and accretion processes of sea ice and uses an interpolation method which can ensure the heat conservation during its integration. These allow the HIGHTSI to better resolve the energy balance in the sea ice, and the bias in sea ice temperature is reduced considerably. When HIGHTSI is coupled with the WRF model, the simulation of sea ice temperature by the original Polar WRF is greatly improved. Considering the bias with reference to SHEBA observations, WRF-HIGHTSI improves the simulation of surface temperature, 2 m air temperature and surface upward long-wave radiation flux in winter by 6, 5 °C and 20 W m−2, respectively. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information results in the best simulation among the available methods. If no observational information is available, we present a new method in which the sea ice thickness is initialized from empirical estimation and its further change is predicted by a complex thermodynamic sea ice model. The ice thickness simulated by this method depends much on the quality of the initial guess of the ice thickness and the role of the ice dynamic processes.

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Improving the WRF model's simulation over sea ice surface through coupling with a complex thermodynamic sea ice model

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-10305-2015 ◽

2015 ◽

Vol 8 (12) ◽

pp. 10305-10337 ◽

Cited By ~ 1

Author(s):

Y. Yao ◽

J. Huang ◽

Y. Luo ◽

Z. Zhao

Keyword(s):

Sea Ice ◽

Wrf Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Cold Bias ◽

Sea Ice Thickness ◽

The Impact ◽

Ice Model ◽

Snow And Ice

Abstract. Sea ice plays an important role in the air–ice–ocean interaction, but it is often represented simply in many regional atmospheric models. The Noah sea ice model, which has been widely used in the Weather Research and Forecasting (WRF) model, exhibits cold bias in simulating the Arctic sea ice temperature when validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) in situ observations. According to sensitivity tests, this bias is attributed not only to the simulation of snow depth and turbulent fluxes but also to the heat conduction within snow and ice. Compared with the Noah sea ice model, the high-resolution thermodynamic snow and ice model (HIGHTSI) has smaller bias in simulating the sea ice temperature. HIGHTSI is further coupled with the WRF model to evaluate the possible added value from better resolving the heat transport and solar penetration in sea ice from a complex thermodynamic sea ice model. The cold bias in simulating the surface temperature over sea ice in winter by the original Polar WRF is reduced when HIGHTSI rather than Noah is coupled with the WRF model, and this also leads to a better representation of surface upward longwave radiation and 2 m air temperature. A discussion on the impact of specifying sea ice thickness in the WRF model is presented. Consistent with previous research, prescribing the sea ice thickness with observational information would result in the best simulation among the available methods. If no observational information is available, using an empirical method based on the relationship between sea ice concentration and sea ice thickness could mimic the large-scale spatial feature of sea ice thickness. The potential application of a thermodynamic sea ice model in predicting the change in sea ice thickness in a RCM is limited by the lack of sea ice dynamic processes in the model and the coarse assumption on the initial value of sea ice thickness.

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Fluctuating Arctic Sea Ice Thickness Changes Estimated by an In Situ Learned and Empirically Forced Neural Network Model

Journal of Climate ◽

10.1175/2007jcli1787.1 ◽

2008 ◽

Vol 21 (4) ◽

pp. 716-729 ◽

Cited By ~ 19

Author(s):

G. I. Belchansky ◽

D. C. Douglas ◽

N. G. Platonov

Keyword(s):

Neural Network ◽

Sea Ice ◽

Arctic Sea Ice ◽

Global Climate Models ◽

Spatiotemporal Variability ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Ice Volume

Abstract Sea ice thickness (SIT) is a key parameter of scientific interest because understanding the natural spatiotemporal variability of ice thickness is critical for improving global climate models. In this paper, changes in Arctic SIT during 1982–2003 are examined using a neural network (NN) algorithm trained with in situ submarine ice draft and surface drilling data. For each month of the study period, the NN individually estimated SIT of each ice-covered pixel (25-km resolution) based on seven geophysical parameters (four shortwave and longwave radiative fluxes, surface air temperature, ice drift velocity, and ice divergence/convergence) that were cumulatively summed at each monthly position along the pixel’s previous 3-yr drift track (or less if the ice was <3 yr old). Average January SIT increased during 1982–88 in most regions of the Arctic (+7.6 ± 0.9 cm yr−1), decreased through 1996 Arctic-wide (−6.1 ± 1.2 cm yr−1), then modestly increased through 2003 mostly in the central Arctic (+2.1 ± 0.6 cm yr−1). Net ice volume change in the Arctic Ocean from 1982 to 2003 was negligible, indicating that cumulative ice growth had largely replaced the estimated 45 000 km3 of ice lost by cumulative export. Above 65°N, total annual ice volume and interannual volume changes were correlated with the Arctic Oscillation (AO) at decadal and annual time scales, respectively. Late-summer ice thickness and total volume varied proportionally until the mid-1990s, but volume did not increase commensurate with the thickening during 1996–2002. The authors speculate that decoupling of the ice thickness–volume relationship resulted from two opposing mechanisms with different latitudinal expressions: a recent quasi-decadal shift in atmospheric circulation patterns associated with the AO’s neutral state facilitated ice thickening at high latitudes while anomalously warm thermal forcing thinned and melted the ice cap at its periphery.

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Multi-Frequency Satellite Approaches for Snow on Sea Ice: first results from the POLAR+ Snow on Sea Ice ESA project

10.5194/egusphere-egu21-12341 ◽

2021 ◽

Author(s):

Michel Tsamados ◽

Keyword(s):

Sea Ice ◽

Primary Objective ◽

The Arctic ◽

Ice Thickness ◽

Laser Altimeter ◽

Sea Ice Thickness ◽

Model Studies ◽

Polar Snow ◽

The Impact ◽

Snow Thickness

Abstract: We propose new methods for multi-frequency snow thickness retrievals building on the legacy of the Arctic+ Snow project where we developed two products: the dual-altimetry Snow Thickness (DuST) and the Snow on Drifting Sea Ice (SnoDSI). The primary objective of this project is to investigate multi-frequency approaches to retrieve snow thickness over all types of sea ice surfaces in the Arctic and provide a state-of-the-art snow product. Our approach follows ESA ITT recommendations to prioritise satellite-based products and will benefit from the recent &#8216;golden era in polar altimetry&#8217; with the successful launch of the laser altimeter ICESat-2 in 2018 complementing data provided by the rich fleet of radar altimeters, CryoSat-2, Sentinel-3 A/B, AltiKa. Our primary objective is to produce an optimal snow product over the recent &#8216;operational&#8216; period. This will be complemented by additional snow products covering a longer periods of climate relevance and making use of historical altimeters (Envisat, ICESat-1) and passive microwave radiometers for comparison purposes (SMOS, AMSRE, AMSR-2). In addition to snow thickness, and as a secondary objective, we will explore other snow characteristics (snow density, snow metamorphism, scattering horizon, roughness, etc) and compare these results with in-situ, airborne and other snow on sea ice products including from model studies and reanalysis on drifting sea ice products. In preparation to future multi-frequency mission we will put an emphasis on uncertainty analysis of our snow product, the impact of the snow on the sea ice thickness retrieval, and on climate physics via model runs with snow initialisation and data assimilation. Finally, learning from past and present campaings (i.e. CryoVex, MOSAiC) we will propose methodologies for effective future snow and sea ice thickness airborne validation campaigns via innovative inverse modelling approaches and airborne retrackers.&#160;

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Thickness and density of snow-covered sea ice and hydrostatic equilibrium assumption from in situ measurements in Fram Strait, the Barents Sea and the Svalbard coast

Annals of Glaciology ◽

10.3189/172756411795931598 ◽

2011 ◽

Vol 52 (57) ◽

pp. 261-270 ◽

Cited By ~ 35

Author(s):

Sanja Forsström ◽

Sebastian Gerland ◽

Christina A. Pedersen

Keyword(s):

Sea Ice ◽

Barents Sea ◽

Sea Water ◽

Hydrostatic Equilibrium ◽

Ice Thickness ◽

Fram Strait ◽

Sea Ice Thickness ◽

The Barents Sea ◽

Snow And Ice

AbstractModern satellite measurements of sea-ice thickness are based on altimeter measurements of the difference in elevation between the snow or ice surface and the local sea surface. For retrieval of sea-ice thickness, it is assumed that the ice is in hydrostatic equilibrium, and that the snow load on the ice and the density of the sea ice and sea water are known. This study presents data from in situ sea-ice thickness drillings and snow and ice density measurements from Fram Strait, the Barents Sea and the Svalbard coast, in the European Arctic. the error in the altimetry ice thickness products is assessed based on the spatial variability of snow and ice density and snow thickness data. Ice thickness uncertainty related to snow depth was found to be ∼40 cm (radar altimeter) and ∼90 cm (laser altimeter), while uncertainty related to ice density is 25 cm for both techniques. the assumption of hydrostatic equilibrium at the scales of the measurements (10–100 m) was found to hold better in the case of level landfast ice near Svalbard than for Fram Strait drift ice, which consists of mixed ice types, where the deviation between the calculated and measured ice thicknesses was on average ∼0.5 m.

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In-situ automatic observations of ice thickness of seas

Metrology and Measurement Systems ◽

10.2478/v10178-012-0051-6 ◽

2012 ◽

Vol 19 (3) ◽

pp. 583-592 ◽

Cited By ~ 1

Author(s):

Yinke Dou ◽

Xiaomin Chang

Keyword(s):

Sea Ice ◽

New Technologies ◽

Capacitive Sensor ◽

Automatic Monitoring ◽

Ice Thickness ◽

In Situ Observation ◽

Glacier Surface ◽

Sea Ice Thickness ◽

Surface Ice

Abstract Ice thickness is one of the most critical physical indicators in the ice science and engineering. It is therefore very necessary to develop in-situ automatic observation technologies of ice thickness. This paper proposes the principle of three new technologies of in-situ automatic observations of sea ice thickness and provides the findings of laboratory applications. The results show that the in-situ observation accuracy of the monitor apparatus based on the Magnetostrictive Delay Line (MDL) principle can reach ±2 mm, which has solved the “bottleneck” problem of restricting the fine development of a sea ice thermodynamic model, and the resistance accuracy of monitor apparatus with temperature gradient can reach the centimeter level and research the ice and snow substance balance by automatically measuring the glacier surface ice and snow change. The measurement accuracy of the capacitive sensor for ice thickness can also reach ±4 mm and the capacitive sensor is of the potential for automatic monitoring the water level under the ice and the ice formation and development process in water. Such three new technologies can meet different needs of fixed-point ice thickness observation and realize the simultaneous measurement in order to accurately judge the ice thickness.

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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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