Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic

2021 ◽  
Vol 40 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Haihua Chen ◽  
Lele Li ◽  
Lei Guan
Keyword(s):  
Brightness Temperature ◽  
Snow Depth ◽  
The Arctic ◽  
Cross Calibration

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

2010 ◽  
Vol 11 (1) ◽  
pp. 199-210 ◽  
Author(s):  
Yi-Ching Chung ◽  
Stéphane Bélair ◽  
Jocelyn Mailhot
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Single Layer ◽  
Coupled System ◽  
The Arctic ◽  
Ice Thickness ◽  
The Arctic Ocean ◽  
Snow Model ◽  
Ice Model

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

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>

scholarly journals The detectability of sea-ice leads in satellite data as a function of atmospheric conditions and measurement scale

1993 ◽  
Vol 17 ◽  
pp. 227-232 ◽  
Author(s):  
J. Key ◽  
R. Stone ◽  
J. Maslanik ◽  
E. Ellefsen
Keyword(s):  
Sea Ice ◽  
Brightness Temperature ◽  
Satellite Data ◽  
The Arctic ◽  
Transfer Model ◽  
Atmospheric Effects ◽  
Ice Crystal ◽  
Model Calculations ◽  
Ice Leads ◽  
The Impact

The release of heat from sea-ice leads is an important component of the heat budget in the Arctic, but the impact of leads on regional scale climate is difficult to assess without information on their distribution in both space and time. Remote sensing of leads using satellite data, specifically AVHRR thermal and Landsat visible imagery, is examined with respect to one lead parameter: lead width. Atmospheric effects are illustrated through the concept of thermal contrast transmittance, where the brightness temperature contrast between leads of various ice thicknesses and the surrounding multi-year ice is simulated using a radiative transfer model. Calculations are made as a function of aerosol, ice crystal precipitation, and cirrus cloud optical depths. For example, at ice crystal optical depths of more than about 1.5 under mean January conditions in the central Arctic, the brightness temperature differences between 2 m and 5 cm thick ice are similar to the ice temperature variability so that there would be insufficient contrast to distinguish a lead from the surrounding ice. The geometrical aspects of the sensor are also simulated by degrading Landsat data so that the effect of sensor field-of-view on retrieved lead width statistics can be assessed. Large leads tend to “grow” with increased pixel size while small leads disappear. Changes in lead width and orientation distributions can readily be seen.

scholarly journals The influence of climate and hydrological variables on opposite anomaly in active-layer thickness between Eurasian and North American watersheds

2013 ◽  
Vol 7 (2) ◽  
pp. 631-645 ◽  
Author(s):  
H. Park ◽  
J. Walsh ◽  
A. N. Fedorov ◽  
A. B. Sherstiukov ◽  
Y. Iijima ◽  
...  
Keyword(s):  
Layer Thickness ◽  
Active Layer ◽  
North American ◽  
Land Surface ◽  
Snow Depth ◽  
The Arctic ◽  
Surface Model ◽  
Active Layer Thickness ◽  
Hydrological Variables ◽  
Permafrost Regions

Abstract. This study not only examined the spatiotemporal variations of active-layer thickness (ALT) in permafrost regions during 1948–2006 over the terrestrial Arctic regions experiencing climate changes, but also identified the associated drivers based on observational data and a simulation conducted by a land surface model (CHANGE). The focus on the ALT extends previous studies that have emphasized ground temperatures in permafrost regions. The Ob, Yenisey, Lena, Yukon, and Mackenzie watersheds are foci of the study. Time series of ALT in Eurasian watersheds showed generally increasing trends, while the increase in ALT in North American watersheds was not significant. However, ALT in the North American watersheds has been negatively anomalous since 1990 when the Arctic air temperature entered into a warming phase. The warming temperatures were not simply expressed to increases in ALT. Since 1990 when the warming increased, the forcing of the ALT by the higher annual thawing index (ATI) in the Mackenzie and Yukon basins has been offset by the combined effects of less insulation caused by thinner snow depth and drier soil during summer. In contrast, the increasing ATI together with thicker snow depth and higher summer soil moisture in the Lena contributed to the increase in ALT. The results imply that the soil thermal and moisture regimes formed in the pre-thaw season(s) provide memory that manifests itself during the summer. The different ALT anomalies between Eurasian and North American watersheds highlight increased importance of the variability of hydrological variables.

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

2020 ◽  
Vol 14 (2) ◽  
pp. 751-767
Author(s):  
Shiming Xu ◽  
Lu Zhou ◽  
Bin Wang
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Snow Depth ◽  
Spatial Scales ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
First Year ◽  
Noise Levels ◽  
Wide Range

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

Rapid neoglaciation on Ellesmere Island promoted by enhanced summer snowfall in a transient climate model simulation of the middle-late-Holocene

The Holocene ◽  
2020 ◽  
Vol 30 (10) ◽  
pp. 1474-1480
Author(s):  
Stephen J Vavrus ◽  
Feng He ◽  
John E Kutzbach ◽  
William F Ruddiman
Keyword(s):  
Snow Depth ◽  
Late Holocene ◽  
Climate Model ◽  
Model Simulation ◽  
Short Circuit ◽  
Ellesmere Island ◽  
The Arctic ◽  
Holocene Thermal Maximum ◽  
Model Driven ◽  
The Arctic Oscillation

Arctic neoglaciation following the Holocene Thermal Maximum is an important feature of late-Holocene climate. We investigated this phenomenon using a transient 6000-year simulation with the CESM-CAM5 climate model driven by orbital forcing, greenhouse gas concentrations, and a land use reconstruction. During the first three millennia analyzed here (6–3 ka), mean Arctic snow depth increases, despite enhanced greenhouse forcing. Superimposed on this secular trend is a very abrupt increase in snow depth between 5 and 4.9 ka on Ellesmere Island and the Greenland coasts, in rough agreement with the timing of observed neoglaciation in the region. This transition is especially extreme on Ellesmere Island, where end-of-summer snow coverage jumps from nearly 0 to virtually 100% in 1 year, and snow depth increases to the model’s imposed maximum within 15 years. This climatic shift involves more than the Milankovitch-based expectation of cooler summers causing less snow melt. Coincident with the onset of the cold regime are two consecutive summers with heavy snowfall on Ellesmere Island that help to short-circuit the normal seasonal melt cycle. These heavy snow seasons are caused by synoptic-scale, cyclonic circulation anomalies over the Arctic Ocean and Canadian Archipelago, including an extremely positive phase of the Arctic Oscillation. Our study reveals that a climate model can produce sudden climatic transitions in this region prone to glacial inception and exceptional variability, due to a dynamic mechanism (more summer snowfall induced by an extreme circulation anomaly) that augments the traditional Milankovitch thermodynamic explanation of orbitally induced glacier development.

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