Effects of using the BBM rheology in the neXtSIM-F forecast platform

Author(s):  
Timothy Williams ◽  
Anton Korosov ◽  
Pierre Rampal ◽  
Olason Einar ◽  
Laurent Bertino

<p>The neXtSIM-F forecast platform entered into service as part of CMEMS (as product ARCTIC_ANALYSISFORECAST_PHY_ICE_002_011) in July 2020, using the neXtSIM sea ice model . It is a stand-alone sea ice model, forced with atmospheric fields from ECMWF and with ocean fields from TOPAZ4. At that time (July 2021) the model was using the Maxwell Elasto Brittle (MEB) sea ice rheology in its dynamical core. In December 2020, the forecast was upgraded to use the Brittle Bingham Maxwell (BBM) rheology, result in significant improvements to the physical results and in numerical performance and stability. We will present results obtained using this new rheology.</p>

2015 ◽  
Vol 91 ◽  
pp. 23-37 ◽  
Author(s):  
Sylvain Bouillon ◽  
Pierre Rampal
Keyword(s):  
Sea Ice ◽  

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  

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.


2011 ◽  
Vol 24 (13) ◽  
pp. 3484-3519 ◽  
Author(s):  
Leo J. Donner ◽  
Bruce L. Wyman ◽  
Richard S. Hemler ◽  
Larry W. Horowitz ◽  
Yi Ming ◽  
...  

Abstract The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for the atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol–cloud interactions, chemistry–climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical system component of earth system models and models for decadal prediction in the near-term future—for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model. Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud droplet activation by aerosols, subgrid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. Its horizontal resolution is approximately 200 km, and its vertical resolution ranges approximately from 70 m near the earth’s surface to 1 to 1.5 km near the tropopause and 3 to 4 km in much of the stratosphere. Most basic circulation features in AM3 are simulated as realistically, or more so, as in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks remains problematic, as in AM2. The most intense 0.2% of precipitation rates occur less frequently in AM3 than observed. The last two decades of the twentieth century warm in CM3 by 0.32°C relative to 1881–1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of 0.56° and 0.52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol–cloud interactions, and its warming by the late twentieth century is somewhat less realistic than in CM2.1, which warmed 0.66°C but did not include aerosol–cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud–aerosol interactions to limit greenhouse gas warming.


2010 ◽  
Vol 11 (1) ◽  
pp. 199-210 ◽  
Author(s):  
Yi-Ching Chung ◽  
Stéphane Bélair ◽  
Jocelyn Mailhot

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.


1963 ◽  
Vol 4 (36) ◽  
pp. 789-807 ◽  
Author(s):  
Peter Schwerdtfecer

Abstract Compared with freshwater ice, whose physical properties are well known, sea ice is a relatively complex substance whose transition to a completely solid mixture of pure ice and solid salts is completed only at extremely low temperatures rarely encountered in nature. The physical properties of sea ice are thus strongly dependent on salinity, temperature and time. Many of these properties are still not fully understood or accurately known, particularly those important for the understanding of a natural ice cover. The specific heat for example is an important term in the calculation of the heat energy content of a cover. However, Malmgren (1927), whose calculated values of the specific heat of sea ice are in general use, neglected the direct contribution of the brine present in inclusions. Re-examination of the question of specific and latent heats of sea ice has led to distinguishing between the freezing and melting points and enabled significant observations in this range. Similarly, because the thermal conductivity is a necessary parameter in the description of the thermal behaviour of ice. the sea-ice model suggested by Anderson (1958) has been modified and extended in the present work to the case of saline ice containing air bubbles. This enabled the completion of calculations of density and conductivity. In order to illustrate the theoretically calculated values. measurements were made on sea-ice samples to determine the specific heat, density and thermal conductivity.


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