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

Author(s):  
Alek A. Petty ◽  
Melinda Webster ◽  
Linette Boisvert ◽  
Thorsten Markus
2019 ◽  
Author(s):  
Andrew E. Kiss ◽  
Andrew McC. Hogg ◽  
Nicholas Hannah ◽  
Fabio Boeira Dias ◽  
Gary B. Brassington ◽  
...  

Author(s):  
Andrew F. Roberts ◽  
Elizabeth C. Hunke ◽  
Richard Allard ◽  
David A. Bailey ◽  
Anthony P. Craig ◽  
...  

A new collaborative organization for sea-ice model development, the CICE Consortium, has devised quality control procedures to maintain the integrity of its numerical codes' physical representations, enabling broad participation from the scientific community in the Consortium's open software development environment. Using output from five coupled and uncoupled configurations of the Los Alamos Sea Ice Model, CICE, we formulate quality control methods that exploit common statistical properties of sea-ice thickness, and test for significant changes in model results in a computationally efficient manner. New additions and changes to CICE are graded into four categories, ranging from bit-for-bit amendments to significant, answer-changing upgrades. These modifications are assessed using criteria that account for the high level of autocorrelation in sea-ice time series, along with a quadratic skill metric that searches for hemispheric changes in model answers across an array of different CICE configurations. These metrics also provide objective guidance for assessing new physical representations and code functionality. This article is part of the theme issue ‘Modelling of sea-ice phenomena’.


2020 ◽  
Author(s):  
Yong-Fei Zhang ◽  
Cecilia M. Bitz ◽  
Jeffrey L. Anderson ◽  
Nancy S. Collins ◽  
Timothy J. Hoar ◽  
...  

2018 ◽  
Author(s):  
Alek A. Petty ◽  
Melinda Webster ◽  
Linette Boisvert ◽  
Thorsten Markus

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.


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