scholarly journals The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis

2018 ◽  
Vol 11 (11) ◽  
pp. 4577-4602 ◽  
Author(s):  
Alek A. Petty ◽  
Melinda Webster ◽  
Linette Boisvert ◽  
Thorsten Markus
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Horizontal Resolution ◽  
The Arctic ◽  
Blowing Snow ◽  
Sea Ice Concentration ◽  
Near Surface ◽  
Ice Model

Abstract. The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currently configured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean through the accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two (vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection–divergence, blowing snow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis-derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim, and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus median daily snowfall dataset from these reanalyses. The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate–strong correlations and root mean squared errors of  ∼ 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

scholarly journals The NASA Eulerian Snow on Sea Ice Model (NESOSIM): Initial model development and analysis

2018 ◽  
Author(s):  
Alek A. Petty ◽  
Melinda Webster ◽  
Linette Boisvert ◽  
Thorsten Markus
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Model Development ◽  
Source Model ◽  
Horizontal Resolution ◽  
Sea Ice Concentration ◽  
Near Surface ◽  
Ice Model ◽  
Strong Agreement

Abstract. The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new open source model that produces daily estimates of the depth and density of snow on sea ice across the polar oceans. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two (vertical) snow layers and several simple parameterizations to represent the key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking), during the accumulation season (August through April). In this study, we present the NESOSIM formulation, initial calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in-situ data collected on drifting ice stations during the 1980s. NESOSIM demonstrates very strong agreement with the in-situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. The results exhibit strong sensitivity to the reanalysis-derived snowfall forcing data, with the MERRA/JRA-55 (ASR) derived snow depths generally higher (lower) than ERA-Interim. We derive a new median daily snowfall dataset from these three reanalysis datasets to improve reliability in our input snowfall data. NESOSIM is run for a contemporary period (2000 to 2015) and compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009–2015, showing moderate/strong agreement, especially in the 2012–2015 comparisons.

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

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

<p><span>Snow on Arctic sea ice plays multiple—and sometimes contrasting—roles in several feedbacks between sea ice and the global climate </span><span>system.</span><span> For example, the presence of snow on sea ice may mitigate sea ice melt by</span><span> increasing the sea ice albedo </span><span>and enhancing the ice-albedo feedback. Conversely, snow can</span><span> in</span><span>hibit sea ice growth by insulating the ice from the atmosphere during the </span><span>sea ice </span><span>growth season. </span><span>In addition to its contribution to sea ice feedbacks, snow on sea ice also poses a challenge for sea ice observations. </span><span>In particular, </span><span>snow </span><span>contributes to uncertaint</span><span>ies</span><span> in retrievals of sea ice thickness from satellite altimetry </span><span>measurements, </span><span>such as those from ICESat-2</span><span>. </span><span>Snow-on-sea-ice models can</span><span> produce basin-wide snow depth estimates, but these models require snowfall input from reanalysis products. In-situ snowfall measurements are a</span><span>bsent</span><span> over most of the Arctic Ocean, so it can be difficult to determine which reanalysis </span><span>snowfall</span><span> product is b</span><span>est</span><span> suited to be used as</span><span> input for a snow-on-sea-ice model.</span></p><p><span>In the absence of in-situ snowfall rate measurements, </span><span>measurements from </span><span>satellite instruments can be used to quantify snowfall over the Arctic Ocean</span><span>. </span><span>The CloudSat satellite, which is equipped with a 94 GHz Cloud Profiling Radar instrument, measures vertical radar reflectivity profiles from which snowfall rate</span><span>s</span><span> can be retrieved. </span> <span>T</span><span>his instrument</span><span> provides the most extensive high-latitude snowfall rate observation dataset currently available. </span><span>CloudSat’s near-polar orbit enables it to make measurements at latitudes up to 82°N, with a 16-day repeat cycle, </span><span>over the time period from 2006-2016.</span></p><p><span>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 w</span><span>hen</span><span> different reanalysis inputs </span><span>are used</span><span>. </span><span>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. </span><span>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.</span></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.

Sea Ice Enhancements to Polar WRF*

2015 ◽  
Vol 143 (6) ◽  
pp. 2363-2385 ◽  
Author(s):  
Keith M. Hines ◽  
David H. Bromwich ◽  
Lesheng Bai ◽  
Cecilia M. Bitz ◽  
Jordan G. Powers ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Surface Model ◽  
Sea Ice Thickness ◽  
Near Surface ◽  
The Arctic Ocean

Abstract The Polar Weather Research and Forecasting Model (Polar WRF), a polar-optimized version of the WRF Model, is developed and made available to the community by Ohio State University’s Polar Meteorology Group (PMG) as a code supplement to the WRF release from the National Center for Atmospheric Research (NCAR). While annual NCAR official releases contain polar modifications, the PMG provides very recent updates to users. PMG supplement versions up to WRF version 3.4 include modified Noah land surface model sea ice representation, allowing the specification of variable sea ice thickness and snow depth over sea ice rather than the default 3-m thickness and 0.05-m snow depth. Starting with WRF V3.5, these options are implemented by NCAR into the standard WRF release. Gridded distributions of Arctic ice thickness and snow depth over sea ice have recently become available. Their impacts are tested with PMG’s WRF V3.5-based Polar WRF in two case studies. First, 20-km-resolution model results for January 1998 are compared with observations during the Surface Heat Budget of the Arctic Ocean project. Polar WRF using analyzed thickness and snow depth fields appears to simulate January 1998 slightly better than WRF without polar settings selected. Sensitivity tests show that the simulated impacts of realistic variability in sea ice thickness and snow depth on near-surface temperature is several degrees. The 40-km resolution simulations of a second case study covering Europe and the Arctic Ocean demonstrate remote impacts of Arctic sea ice thickness on midlatitude synoptic meteorology that develop within 2 weeks during a winter 2012 blocking event.

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 Snow-depth observations by adventurers traveling on Arctic sea ice

2011 ◽  
Vol 52 (57) ◽  
pp. 369-376 ◽  
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.

Parameter calibration and uncertainty analysis for snow depths from the NASA Eulerian Snow On Sea Ice Model and derived sea ice thickness from ICESat-2

2021 ◽  
Author(s):  
Alex Cabaj ◽  
Paul Kushner ◽  
Alek Petty
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Blowing Snow ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Arctic Sea ◽  
Ice Model

<p><span>Snow on Arctic sea ice plays many, sometimes contrasting roles in Arctic climate feedbacks. During the sea ice growth season, the presence of snow on sea ice can enhance ice growth by increasing the sea ice albedo, or conversely, inhibit sea ice growth by insulating the ice from the cold atmosphere. Furthermore, estimates of snow depth on Arctic sea ice are also a key input for deriving sea ice thickness from altimetry measurements, such as satellite lidar altimetry measurements from ICESat-2. Due to the logistical challenges of making measurements in as remote a region as the Arctic, snow depth on Arctic sea ice is difficult to observationally constrain.<br><br>The NASA Eulerian Snow On Sea Ice Model (NESOSIM) can be used to provide snow depth and density estimates over Arctic sea ice with pan-Arctic coverage within a relatively simple framework. The latest version of NESOSIM, version 1.1, is a 2-layer model with simple representations of the processes of accumulation, wind packing, loss due to blowing snow, and redistribution due to sea ice motion. Relative to version 1.0, NESOSIM 1.1 features an extended model domain, and reanalysis snowfall input scaled to observed snowfall retrieved from CloudSat satellite radar reflectivity measurements.<br><br>In this work, we present a systematic calibration, and an accompanying estimate in the uncertainty of the free parameters in NESOSIM, targeting airborne snow radar measurements from Operation IceBridge. We further investigate uncertainties in snow depth and the resulting uncertainties in derived sea ice thickness from ICESat-2 altimetry measurements using NESOSIM snow depths.</span></p>

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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.

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