Snow dune growth increases polar heat fluxes

Abstract. Falling snow often accumulates in dunes. These bedforms are found on up to 14 % of the surface of Earth, and appear occasionally on other planets. They have been associated with increased heat ﬂuxes and rapid sea ice melting (Petrich et al., 2012; Popović et al., 2018). Their formation, however, is poorly understood (Filhol and Sturm, 2015; Kochanski et al., 2019a; Sharma et al., 2019). Here, we use ﬁeld observations to show that dune growth is controlled by snowfall rate and wind speed. We then use numerical experiments to generate simulated dune topographies under varied wind and snowfall conditions, and use those to quantify conductive and radiative heat ﬂuxes through snow. Our results show that dune growth leads to decreased snow cover, more variable snow depth, and signiﬁcant increases in surface energy ﬂuxes. We provide quantitative results that will allow modelers to account for the impact of snow bedforms in snow, sea ice, and climate simulations. In addition, this work offers a starting point for process-based studies of one of the most widespread bedforms on Earth.

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Averaging-Related Biases in Monthly Latent Heat Fluxes

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-11-00184.1 ◽

2012 ◽

Vol 29 (7) ◽

pp. 974-986 ◽

Cited By ~ 10

Author(s):

Paul J. Hughes ◽

Mark A. Bourassa ◽

Jeremy J. Rolph ◽

Shawn R. Smith

Keyword(s):

Latent Heat ◽

Classical Method ◽

Surface Flux ◽

Heat Fluxes ◽

Western Boundary ◽

State University ◽

Boundary Current ◽

Surface Energy Fluxes ◽

Temporal Averaging ◽

The Impact

Abstract Seasonal-to-multidecadal applications that require ocean surface energy fluxes often require accuracies of surface turbulent fluxes to be 5 W m−2 or better. While there is little doubt that uncertainties in the flux algorithms and input data can cause considerable errors, the impact of temporal averaging has been more controversial. The biases resulting from using monthly averaged winds, temperatures, and humidities in the bulk aerodynamic formula (i.e., the so-called classical method) to estimate the monthly mean latent heat fluxes are shown to be substantial and spatially varying in a manner that is consistent with most prior work. These averaging-related biases are linked to nonnegligible submonthly covariances between the wind, temperature, and humidity. To provide additional insight into the averaging-related bias, the methodology behind the third-generation Florida State University monthly mean surface flux product (FSU3) is detailed to highlight additional sources of errors in gridded datasets. The FSU3 latent heat fluxes suffer from this averaging-related bias, which can be as large as 90 W m−2 in western boundary current regions during winter and can exceed 40 W m−2 in synoptically active portions of the tropics. The regional impacts of these biases on the mixed layer temperature tendency are shown to demonstrate that the error resulting from applying the classical method is physically substantial.

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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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The impact of snow products on detecting trends in sea ice thickness during the CryoSat-2 era

10.5194/egusphere-egu2020-18304 ◽

2020 ◽

Author(s):

Heidi Sallila ◽

Samantha Buzzard ◽

Eero Rinne ◽

Michel Tsamados

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Ice Age ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Seasonal Snow ◽

Uncertainty Estimates ◽

The One ◽

The Impact ◽

First Time

Retrieval of sea ice depth from satellite altimetry relies on knowledge of snow depth in the conversion of freeboard measurements to sea ice thickness. This remains the largest source of uncertainty in calculating sea ice thickness. In order to go beyond the use of a seasonal snow climatology, namely the one by Warren created from measurements collected during the drifting stations in 1937 and 1954&#8211;1991, we have developed as part of an ESA Arctic+ project several novel snow on sea ice pan-Arctic products, with the ultimate goal to resolve for the first time inter-annual and seasonal snow variability.Our products are inter-compared and calibrated with each other to guarantee multi-decadal continuity, and also compared with other recently developed snow on sea ice modelling and satellite based products. Quality assessment and uncertainty estimates are provided at a gridded level and as a function of sea ice cover characteristics such as sea ice age, and sea ice type.We investigate the impact of the spatially and temporally varying snow products on current satellite estimates of sea ice thickness and provide an update on the sea ice thickness uncertainties. We pay particular attention to potential biases of the seasonal ice growth and inter-annual trends.

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Permeability of growing sea ice - observations, modelling and some implications for thinning Arctic sea ice

10.5194/egusphere-egu2020-5863 ◽

2020 ◽

Author(s):

Sönke Maus

Keyword(s):

Sea Ice ◽

Pore Space ◽

Arctic Sea Ice ◽

Heat Fluxes ◽

Onset Of Convection ◽

Permeability Model ◽

Electrically Conducting ◽

Melt Pond ◽

Starting Point

The permeability of sea ice is an important property with regard to the role of sea ice in the earth system. It controls fluid flow within sea ice, and thus processes like melt pond drainage, desalination and to some degree heat fluxes between the ocean and the atmosphere. It also impacts the role of sea ice in hosting sea ice algae and organisms, and the uptake and release of nutrients and pollutants from Arctic surface waters. However, as it is difficult to measure in the field, observations of sea ice permeability are sparse and vary, even for similar porosity, over orders of magnitude. Here I present progress on this topic in three directions. First, I present results from numerical simulations of the permeability of young sea ice based on 3-d X-ray microtomographic images (XRT). These results provide a relationship between permeability and brine porosity of young columnar sea ice for the porosity range 2 to 25 %. The simulations also show that this ice type is permeable and electrically conducting down to a porosity of 2 %, considerably lower than what has been proposed in previous work. Second, the XRT-based simulations are compared to predictions based on a novel crystal growth modelling approach, finding good agreement. Third, the permeability model provides a relationship between sea ice growth velocity and permeability. Based on this relationshiop interesting aspects of the growth of permeable sea ice can be deduced: The predictions consistently explain observations of the onset of convection from growing sea ice. They also allow for an evaluation of expected permeability changes for a thinning sea ice cover in a warmer climate. As the model is strictly valid for growing and cooling sea ice, the results are mostly relevant for sea ice desalination processes during winter. Modelling permeability of summer ice (and melt pond drainage) will require more observations of the pore space evolution in warming sea ice, for which the present results can be considered as a resonable starting point.

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Simulated Ka- and Ku-band radar altimeter height and freeboard estimation on snow-covered Arctic sea ice

The Cryosphere ◽

10.5194/tc-15-1811-2021 ◽

2021 ◽

Vol 15 (4) ◽

pp. 1811-1822

Author(s):

Rasmus T. Tonboe ◽

Vishnu Nandan ◽

John Yackel ◽

Stefan Kern ◽

Leif Toudal Pedersen ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Radar Altimeter ◽

Sea Ice Thickness ◽

Scattering Model ◽

Radar Scattering ◽

The Impact ◽

Ku Band

Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and the impact on the Ka- and Ku-band radar scattering horizon or the “track point” (i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (>0.1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka- and Ku-band dual-frequency CRISTAL radar altimeter missions.

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The effect of snow density on modelled seasonal evolution of snow depth on the Arctic sea ice

10.5194/egusphere-egu21-14095 ◽

2021 ◽

Author(s):

Jie Su ◽

Hao Yin ◽

Bin Cheng ◽

Timo Vihma

Keyword(s):

Sea Ice ◽

Mass Balance ◽

Snow Depth ◽

Arctic Sea Ice ◽

Time Dependent ◽

The Arctic ◽

Constant Density ◽

Snow Density ◽

New Approach ◽

The Impact

Due to its high surface albedo, strong thermal insulation and complex temporal and spatial distribution, snow on top of sea ice plays an important role in the air-ice-ocean interaction in polar regions and high latitudes. Accurate snow mass balance calculations are needed to better understand the evolution of sea ice and polar climate. Snow depth is affected by many factors, but in thermodynamic models many of them are treated in a relatively simple manner. One of such factors is snow density.&#160; In reality, it varies a lot in space and time but a constant bulk snow density is often used to convert precipitation (snow water equivalence) to snow depth. The densification of snow is considered to affect snow depth mainly by altering snow thermal properties rather than directly on snow depth.Based on the mass conservation principle, a one-dimensional high-resolution ice and snow thermodynamic model was applied to investigate the impact of snow density on snow depth along drift trajectories of 26 sea ice mass balance buoys (IMB) deployed in various parts of the Arctic Ocean. The ERA-Interim reanalysis data are used as atmospheric forcing for the ice model. In contrast to the bulk snow density approach, with a constant density of 330 kg/m3 (T1) or 200kg/m3 (T2), our new approach considers new and old snow with different time dependent densities (T3). The calculated results are compared with the snow thickness observed by the IMBs. The average snow depth observed by 26 IMBs during the snow season was 20&#177;14 cm. Applying the bulk density (T1 and T2) or time dependent separate snow densities (T3), the modelled average snow depths are 16&#177;13 cm, 22&#177;17cm and 17&#177;12cm, respectively. For the cases during snow accumulate period, the new approach (T3) has similar result with T1 and improved the modelled snow depth obviously from that of T2.

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Response of sea-ice models to perturbations in surface heat flux

Annals of Glaciology ◽

10.3189/s0260305500014026 ◽

1997 ◽

Vol 25 ◽

pp. 193-197 ◽

Cited By ~ 5

Author(s):

T. E. Arbetter ◽

J. A. Curry ◽

M. M. Holland ◽

J. A. Maslanik

Keyword(s):

Climate Change ◽

Heat Flux ◽

Sea Ice ◽

Surface Heat ◽

Heat Fluxes ◽

Ice Dynamics ◽

Surface Heat Fluxes ◽

Climate Simulations ◽

Sensitivity Studies ◽

Flux Perturbation

There are currently a variety of one- and two-dimensional sea-ice models being used for climate simulations and sensitivity studies. Though all the models can be timed to simulate current-day conditions to some degree of accuracy, the responses of each model to perturbations in forcing from the atmosphere or ocean are different. Thus, climate-change prediction depends on the choice of sea-ice model. In this study, the sensitivities of various sea-ice models to external heat-flux perturbations are examined in a systematic manner. Starting from similar baseline annual thicknesses, each model is subjected to an applied heat-flux perturbation to assess icemelt. Separate experiments are conducted to compare the response of each model to heat fluxes applied at the atmospheric and the oceanic interfaces. It is found that the magnitude of the heat-flux perturbation required to melt ice varies greatly among different models, with the largest difference arising between models that include ice dynamics vs those that do not. Most models show an asymmetry in the response to heat-flux perturbations applied at the top and bottom surfaces of the ice. This study has implications for the choice of sea-ice models used for climate-change simulations. It also gives insight to the accuracy required for observations and model simulations of the surface heat fluxes.

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Arctic Ocean sea ice snow depth evaluation and bias sensitivity in CCSM

The Cryosphere ◽

10.5194/tc-7-1887-2013 ◽

2013 ◽

Vol 7 (6) ◽

pp. 1887-1900 ◽

Cited By ~ 12

Author(s):

B. A. Blazey ◽

M. M. Holland ◽

E. C. Hunke

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Arctic Sea Ice ◽

The Arctic ◽

Blowing Snow ◽

Climate System Model ◽

The Arctic Ocean ◽

Future Work ◽

The Impact

Abstract. Sea ice cover in the Arctic Ocean is a continued focus of attention. This study investigates the impact of the snow overlying the sea ice in the Arctic Ocean. The impact of snow depth biases in the Community Climate System Model (CCSM) is shown to impact not only the sea ice, but also the overall Arctic climate. Following the identification of seasonal biases produced in CCSM simulations, the thermodynamic transfer through the snow–ice column is perturbed to determine model sensitivity to these biases. This study concludes that perturbations on the order of the observed biases result in modification of the annual mean conductive flux through the snow–ice column of 0.5 W m2 relative to an unmodified simulation. The results suggest that the ice has a complex response to snow characteristics, with ice of different thicknesses producing distinct reactions. Our results indicate the importance of an accurate simulation of snow on the Arctic sea ice. Consequently, future work investigating the impact of current precipitation biases and missing snow processes, such as blowing snow, densification, and seasonal changes, is warranted.

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On the numerical resolution in a thermodynamic sea-ice model

Journal of Glaciology ◽

10.3189/172756502781831449 ◽

2002 ◽

Vol 48 (161) ◽

pp. 301-311 ◽

Cited By ~ 9

Author(s):

Bin Cheng

Keyword(s):

Sea Ice ◽

Spatial Resolution ◽

Heat Fluxes ◽

Shortwave Radiation ◽

Warm Up ◽

Surface Heat Fluxes ◽

Numerical Resolution ◽

Main Components ◽

The Impact ◽

Ice Model

AbstractThe numerical integration of the heat-conduction equation is one of the main components in a thermodynamic sea-ice model. The spatial resolution in the ice normally varies from a minimum of three layers up to a few tens of layers. The temporal resolution varies from a few minutes up to hours. In this paper the impact of numerical resolution on the prediction of a one-dimensional thermodynamic ice model is studied. Analytical solutions for idealized cases were derived and compared with the numerical results. For the full ice model, groups of simulations were made, applying average climatic weather-forcing data corresponding to the ice-freezing, ice-thermal equilibrium and ice warm-up seasons. Special attention was paid to the effect of model spatial resolution. Early in the freezing season, the influence of resolution on model predictions is not significant. When the shortwave radiation becomes large, its absorption within the ice or snow cover was found to modulate the effect of numerical resolution on predictions of ice temperature and surface heat fluxes (e.g. the model run with a coarse spatial resolution yielded large daily variations in surface temperature). Resolution also affects the in-ice temperature profile. For process studies, a two-layer scheme for the solar radiation penetrating into the ice is suitable for a fine-spatial-resolution ice model.

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Simulated Ka- and Ku-band radar altimeter scattering horizon on snow-covered Arctic sea ice

10.5194/tc-2020-196 ◽

2020 ◽

Author(s):

Rasmus T. Tonboe ◽

Vishnu Nandan ◽

John Yackel ◽

Stefan Kern ◽

Leif Toudal Pedersen ◽

...

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Radar Altimeter ◽

Sea Ice Thickness ◽

Scattering Model ◽

Radar Scattering ◽

The Impact ◽

Ku Band

Abstract. Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and its impact on the Ka- and Ku-band radar scattering horizon or the "track point" (i.e. the scattering layer depth detected by the radar re-tracker), are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and Archimedes’ principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round robin data package, NASA’s Operation Ice Bridge (OIB) data and climatology, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth, however, the simulated track point difference is much smaller than what is reported in the literature from the CryoSat-2 Ku-band and SARAL/AltiKa Ka-band satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences are sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track-point difference when the snow is thin and then decreasing the difference when the snow is thick (> 10 cm). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for 1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and 2) the impact of variability in measured snow depth on the derived ice thickness. For both 1 and 2, the impact of using a snow climatology versus the actual snow depth is relatively small on the measured freeboard, by only raising the measured freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals from combining currently operational and upcoming Ka- and Ku-band dual-frequency radar altimeter missions, such as ESA’s Copernicus High Priority Candidate Mission CRISTAL.

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