Ship-ice collision analysis to define ice model according to the IACS Polar Rule

2013 ◽  
pp. 205-212
Author(s):  
M Kwak ◽  
J Choi ◽  
O Hwang ◽  
Y Oh
Keyword(s):  
Ice Model

Roughness Implementation in FENSAP-ICE: Model Calibration and Influence on Ice Shapes

2003 ◽  
Vol 40 (6) ◽  
pp. 1212-1215 ◽  
Author(s):  
Heloise Beaugendre ◽  
Francois Morency ◽  
Wagdi G. Habashi ◽  
Pascal Benquet
Keyword(s):  
Model Calibration ◽  
Ice Shapes ◽  
Ice Model

scholarly journals Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

Wave-influenced formation of new ice: Model building and a test case

2021 ◽  
Vol 167 ◽  
pp. 101878
Author(s):  
Che Yue ◽  
Hayley H. Shen
Keyword(s):  
Model Building ◽  
Test Case ◽  
Ice Model

The geography of numerical mixing in a suite of global ocean models

2021 ◽  
Author(s):  
Ryan Holmes ◽  
Jan Zika ◽  
Stephen Griffies ◽  
Andrew Hogg ◽  
Andrew Kiss ◽  
...  
Keyword(s):  
Ocean Circulation ◽  
General Circulation ◽  
Horizontal Resolution ◽  
Global Ocean ◽  
Vertical Diffusivity ◽  
Mixing Parameters ◽  
Ocean Models ◽  
Numerical Mixing ◽  
Comparable Magnitude ◽  
Ice Model

<p>Numerical mixing, the physically spurious diffusion of tracers due to the numerical discretization of advection, is known to contribute to biases in ocean circulation models. However, quantifying numerical mixing is non-trivial, with most studies utilizing specifically targeted experiments in idealized settings. Here, we present a precise method based on water-mass transformation for quantifying numerical mixing, including its spatial structure, that can be applied to any conserved variable in global general circulation ocean models. The method is applied to a suite of global MOM5 ocean-sea ice model simulations with differing grid spacings and sub-grid scale parameterizations. In all configurations numerical mixing drives across-isotherm heat transport of comparable magnitude to that associated with explicitly-parameterized mixing. Numerical mixing is prominent at warm temperatures in the tropical thermocline, where it is sensitive to the vertical diffusivity and resolution. At colder temperatures, numerical mixing is sensitive to the presence of explicit neutral diffusion, suggesting that much of the numerical mixing in these regions acts as a proxy for neutral diffusion when it is explicitly absent. Comparison of equivalent (with respect to vertical resolution and explicit mixing parameters) 1/4-degree and 1/10-degree horizontal resolution configurations shows only a modest enhancement in numerical mixing at the eddy-permitting 1/4-degree resolution. Our results provide a detailed view of numerical mixing in ocean models and pave the way for future improvements in numerical methods.</p>

scholarly journals A system of conservative regridding for ice–atmosphere coupling in a General Circulation Model (GCM)

2014 ◽  
Vol 7 (3) ◽  
pp. 883-907 ◽  
Author(s):  
R. Fischer ◽  
S. Nowicki ◽  
M. Kelley ◽  
G. A. Schmidt
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Grid Cell ◽  
Surface Model ◽  
Ice Flow ◽  
Surface Mass ◽  
Flow Models ◽  
Ice Surface ◽  
Ice Model

Abstract. The method of elevation classes, in which the ice surface model is run at multiple elevations within each grid cell, has proven to be a useful way for a low-resolution atmosphere inside a general circulation model (GCM) to produce high-resolution downscaled surface mass balance fields for use in one-way studies coupling atmospheres and ice flow models. Past uses of elevation classes have failed to conserve mass and energy because the transformation used to regrid to the atmosphere was inconsistent with the transformation used to downscale to the ice model. This would cause problems for two-way coupling. A strategy that resolves this conservation issue has been designed and is presented here. The approach identifies three grids between which data must be regridded and five transformations between those grids required by a typical coupled atmosphere–ice flow model. This paper develops a theoretical framework for the problem and shows how each of these transformations may be achieved in a consistent, conservative manner. These transformations are implemented in Glint2, a library used to couple atmosphere models with ice models. Source code and documentation are available for download. Confounding real-world issues are discussed, including the use of projections for ice modeling, how to handle dynamically changing ice geometry, and modifications required for finite element ice models.

scholarly journals Lithosphere and upper-mantle structure of the southern Baltic Sea estimated from modelling relative sea-level data with glacial isostatic adjustment

Solid Earth ◽  
2014 ◽  
Vol 5 (1) ◽  
pp. 447-459 ◽  
Author(s):  
H. Steffen ◽  
G. Kaufmann ◽  
R. Lampe
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Upper Mantle ◽  
Glacial Isostatic Adjustment ◽  
Relative Sea Level ◽  
Southern Baltic Sea ◽  
Lithospheric Thickness ◽  
Isostatic Adjustment ◽  
Mantle Viscosity ◽  
Ice Model

Abstract. During the last glacial maximum, a large ice sheet covered Scandinavia, which depressed the earth's surface by several 100 m. In northern central Europe, mass redistribution in the upper mantle led to the development of a peripheral bulge. It has been subsiding since the begin of deglaciation due to the viscoelastic behaviour of the mantle. We analyse relative sea-level (RSL) data of southern Sweden, Denmark, Germany, Poland and Lithuania to determine the lithospheric thickness and radial mantle viscosity structure for distinct regional RSL subsets. We load a 1-D Maxwell-viscoelastic earth model with a global ice-load history model of the last glaciation. We test two commonly used ice histories, RSES from the Australian National University and ICE-5G from the University of Toronto. Our results indicate that the lithospheric thickness varies, depending on the ice model used, between 60 and 160 km. The lowest values are found in the Oslo Graben area and the western German Baltic Sea coast. In between, thickness increases by at least 30 km tracing the Ringkøbing-Fyn High. In Poland and Lithuania, lithospheric thickness reaches up to 160 km. However, the latter values are not well constrained as the confidence regions are large. Upper-mantle viscosity is found to bracket [2–7] × 1020 Pa s when using ICE-5G. Employing RSES much higher values of 2 × 1021 Pa s are obtained for the southern Baltic Sea. Further investigations should evaluate whether this ice-model version and/or the RSL data need revision. We confirm that the lower-mantle viscosity in Fennoscandia can only be poorly resolved. The lithospheric structure inferred from RSES partly supports structural features of regional and global lithosphere models based on thermal or seismological data. While there is agreement in eastern Europe and southwest Sweden, the structure in an area from south of Norway to northern Germany shows large discrepancies for two of the tested lithosphere models. The lithospheric thickness as determined with ICE-5G does not agree with the lithosphere models. Hence, more investigations have to be undertaken to sufficiently determine structures such as the Ringkøbing-Fyn High as seen with seismics with the help of glacial isostatic adjustment modelling.

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.

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