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

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

<p>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.  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.</p><p>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/m<sup>3</sup> (T1) or 200kg/m<sup>3</sup> (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±14 cm. Applying the bulk density (T1 and T2) or time dependent separate snow densities (T3), the modelled average snow depths are 16±13 cm, 22±17cm and 17±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.</p>

scholarly journals The impact of a seasonally ice free Arctic Ocean on the temperature, precipitation and surface mass balance of Svalbard

2012 ◽  
Vol 6 (1) ◽  
pp. 35-50 ◽  
Author(s):  
J. J. Day ◽  
J. L. Bamber ◽  
P. J. Valdes ◽  
J. Kohler
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Mass Balance ◽  
21St Century ◽  
Arctic Sea Ice ◽  
Lower Atmosphere ◽  
The Arctic ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
The Impact

Abstract. The observed decline in summer sea ice extent since the 1970s is predicted to continue until the Arctic Ocean is seasonally ice free during the 21st Century. This will lead to a much perturbed Arctic climate with large changes in ocean surface energy flux. Svalbard, located on the present day sea ice edge, contains many low lying ice caps and glaciers and is expected to experience rapid warming over the 21st Century. The total sea level rise if all the land ice on Svalbard were to melt completely is 0.02 m. The purpose of this study is to quantify the impact of climate change on Svalbard's surface mass balance (SMB) and to determine, in particular, what proportion of the projected changes in precipitation and SMB are a result of changes to the Arctic sea ice cover. To investigate this a regional climate model was forced with monthly mean climatologies of sea surface temperature (SST) and sea ice concentration for the periods 1961–1990 and 2061–2090 under two emission scenarios. In a novel forcing experiment, 20th Century SSTs and 21st Century sea ice were used to force one simulation to investigate the role of sea ice forcing. This experiment results in a 3.5 m water equivalent increase in Svalbard's SMB compared to the present day. This is because over 50 % of the projected increase in winter precipitation over Svalbard under the A1B emissions scenario is due to an increase in lower atmosphere moisture content associated with evaporation from the ice free ocean. These results indicate that increases in precipitation due to sea ice decline may act to moderate mass loss from Svalbard's glaciers due to future Arctic warming.

scholarly journals Fate of sea ice in the 'New Arctic': A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

2021 ◽  
Author(s):  
Sean Horvath ◽  
Linette Boisvert ◽  
Chelsea Parker ◽  
Melinda Webster ◽  
Patrick Taylor ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Mass Balance ◽  
Air Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Ice Mass Balance ◽  
Temperature And Pressure

Abstract. Since the early 2000s, sea ice has experienced an increased rate of decline in thickness and extent and transitioned to a seasonal ice cover. This shift to thinner, seasonal ice in the 'New Arctic' is accompanied by a reshuffling of energy flows at the surface. Understanding the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with daily sea ice parcel drift tracks produced in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states. Building on previous work, this dataset includes remotely sensed radiative and turbulent fluxes from which the surface energy budget can be calculated. Additionally, flags indicate when sea ice parcels travel within cyclones, recording distance and direction from the cyclone center. The database drift track was evaluated by comparison with sea ice mass balance buoys. Results show ice parcels generally remain within 100km of the corresponding buoy, with a mean distance of 82.6 km and median distance of 54 km. The sea ice mass balance buoys also provide recordings of sea ice thickness, snow depth, and air temperature and pressure which were compared to this database. Ice thickness and snow depth typically are less accurate than air temperature and pressure due to the high spatial variability of the former two quantities when compared to a point measurement. The correlations between the ice parcel and buoy data are high, which highlights the accuracy of this Lagrangian database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic. Applications such as these may shed light on the atmosphere-snow-sea ice interactions in the changing Arctic environment.

scholarly journals Estimating the snow depth, the snow–ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and ice mass balance buoy data

2019 ◽  
Vol 13 (4) ◽  
pp. 1283-1296 ◽  
Author(s):  
Lise Kilic ◽  
Rasmus Tage Tonboe ◽  
Catherine Prigent ◽  
Georg Heygster
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Effective Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Interface Temperature ◽  
Microwave Frequencies ◽  
Arctic Sea ◽  
Ice Mass Balance

Abstract. Mapping sea ice concentration (SIC) and understanding sea ice properties and variability is important, especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (Teff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to the atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow–ice interface temperature (TSnow−Ice) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from ice mass balance buoys (IMBs) and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of 5.1 cm, using a multilinear regression with the TBs at 6, 18, and 36 V. The TSnow−Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 or 6 V. The root mean square errors (RMSEs) obtained are 2.87 and 2.90 K respectively, with 10 and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow−Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snowpacks. Teff is estimated from the TSnow−Ice with a RMSE of less than 1 K.

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

2013 ◽  
Vol 7 (6) ◽  
pp. 1887-1900 ◽  
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.

scholarly journals Estimating the snow depth, the snow-ice interface temperature, and the effective temperature of Arctic sea ice using Advanced Microwave Scanning Radiometer 2 and Ice Mass Balance buoys data

2018 ◽  
Author(s):  
Lise Kilic ◽  
Rasmus Tage Tonboe ◽  
Catherine Prigent ◽  
Georg Heygster
Keyword(s):  
Sea Ice ◽  
Mass Balance ◽  
Effective Temperature ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Interface Temperature ◽  
Microwave Frequencies ◽  
Arctic Sea ◽  
Ice Mass Balance

Abstract. Mapping Sea Ice Concentration (SIC) and understanding sea ice properties and variability is important especially today with the recent Arctic sea ice decline. Moreover, accurate estimation of the sea ice effective temperature (Teff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow-ice interface temperature (TSnow-Ice) and the Teff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from Ice Mass Balance (IMB) buoys and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of ~ 6 cm using a multilinear regression with the TBs at 6 V, 18 V, and 36 V. The TSnow-Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 V or 6 V. The Root Mean Square Errors (RMSEs) obtained are 1.69 and 1.95 K respectively, with the 10 V and 6 V TBs. The Teff at microwave frequencies between 6 and 89 GHz is expressed as a function of TSnow-Ice using data from a thermodynamical model combined with the Microwave Emission Model of Layered Snow-packs. Teffs are estimated from the TSnow-Ice with a RMSE of less than 1 K.

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

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
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.

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

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

<p>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.</p><p>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.</p><p>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.</p>

scholarly journals Cryospheric Impacts of Soviet River Diversion Schemes

1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
River Discharge ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
The Impact

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

scholarly journals Climate Policy Implications of Nonlinear Decline of Arctic Land Permafrost and Sea Ice

2017 ◽  
Author(s):  
Dmitry Yumashev ◽  
Chris Hope ◽  
Kevin Schaefer ◽  
Kathrin Riemann-Campe ◽  
Fernando Iglesias-Suarez ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Policy ◽  
Arctic Sea Ice ◽  
Integrated Assessment Model ◽  
Assessment Model ◽  
Policy Implications ◽  
The Arctic ◽  
Solar Absorption ◽  
Albedo Feedback ◽  
The Impact

Arctic feedbacks will accelerate climate change and could jeopardise mitigation efforts. The permafrost carbon feedback releases carbon to the atmosphere from thawing permafrost and the sea ice albedo feedback increases solar absorption in the Arctic Ocean. A constant positive albedo feedback and zero permafrost feedback have been used in nearly all climate policy studies to date, while observations and models show that the permafrost feedback is significant and that both feedbacks are nonlinear. Using novel dynamic emulators in the integrated assessment model PAGE-ICE, we investigate nonlinear interactions of the two feedbacks with the climate and economy under a range of climate scenarios consistent with the Paris Agreement. The permafrost feedback interacts with the land and ocean carbon uptake processes, and the albedo feedback evolves through a sequence of nonlinear transitions associated with the loss of Arctic sea ice in different months of the year. The US’s withdrawal from the current national pledges could increase the total discounted economic impact of the two Arctic feedbacks until 2300 by $25 trillion, reaching nearly $120 trillion, while meeting the 1.5 °C and 2 °C targets will reduce the impact by an order of magnitude.

Winter Arctic sea ice freeboard, snow depth and thickness variability from ICESat-2 and NESOSIM

2021 ◽  
Author(s):  
Alek Petty ◽  
Nicole Keeney ◽  
Alex Cabaj ◽  
Paul Kushner ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Ice Cloud ◽  
Ice Drift ◽  
Thickness Variability ◽  
Arctic Ice

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

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