scholarly journals Quality control for community-based sea-ice model development

2018 ◽  
Vol 376 (2129) ◽  
pp. 20170344 ◽  
Author(s):  
Andrew F. Roberts ◽  
Elizabeth C. Hunke ◽  
Richard Allard ◽  
David A. Bailey ◽  
Anthony P. Craig ◽  
...  
Keyword(s):  
Quality Control ◽  
Sea Ice ◽  
Model Development ◽  
Computationally Efficient ◽  
Development Environment ◽  
Efficient Manner ◽  
Sea Ice Thickness ◽  
Control Procedures ◽  
High Level ◽  
Ice Model

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’.

scholarly journals Optimization of a Sea Ice Model Using Basinwide Observations of Arctic Sea Ice Thickness, Extent, and Velocity

2006 ◽  
Vol 19 (7) ◽  
pp. 1089-1108 ◽  
Author(s):  
Paul A. Miller ◽  
Seymour W. Laxon ◽  
Daniel L. Feltham ◽  
Douglas J. Cresswell
Keyword(s):  
Sea Ice ◽  
Parameter Space ◽  
National Laboratory ◽  
Arctic Sea Ice ◽  
Strength Parameter ◽  
Ice Thickness ◽  
Dimensional Parameter ◽  
Sea Ice Thickness ◽  
Basin Scale ◽  
Ice Model

Abstract A stand-alone sea ice model is tuned and validated using satellite-derived, basinwide observations of sea ice thickness, extent, and velocity from the years 1993 to 2001. This is the first time that basin-scale measurements of sea ice thickness have been used for this purpose. The model is based on the CICE sea ice model code developed at the Los Alamos National Laboratory, with some minor modifications, and forcing consists of 40-yr ECMWF Re-Analysis (ERA-40) and Polar Exchange at the Sea Surface (POLES) data. Three parameters are varied in the tuning process: Ca, the air–ice drag coefficient; P*, the ice strength parameter; and α, the broadband albedo of cold bare ice, with the aim being to determine the subset of this three-dimensional parameter space that gives the best simultaneous agreement with observations with this forcing set. It is found that observations of sea ice extent and velocity alone are not sufficient to unambiguously tune the model, and that sea ice thickness measurements are necessary to locate a unique subset of parameter space in which simultaneous agreement is achieved with all three observational datasets.

scholarly journals A Multithickness Sea Ice Model Accounting for Sliding Friction

2006 ◽  
Vol 36 (9) ◽  
pp. 1719-1738 ◽  
Author(s):  
Alexander V. Wilchinsky ◽  
Daniel L. Feltham ◽  
Paul A. Miller
Keyword(s):  
Sea Ice ◽  
Sliding Friction ◽  
Kinematic Model ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Model Code ◽  
Ice Strength ◽  
Discrete Element Simulations ◽  
Ice Model

Abstract A multithickness sea ice model explicitly accounting for the ridging and sliding friction contributions to sea ice stress is developed. Both ridging and sliding contributions depend on the deformation type through functions adopted from the Ukita and Moritz kinematic model of floe interaction. In contrast to most previous work, the ice strength of a uniform ice sheet of constant ice thickness is taken to be proportional to the ice thickness raised to the 3/2 power, as is revealed in discrete element simulations by Hopkins. The new multithickness sea ice model for sea ice stress has been implemented into the Los Alamos “CICE” sea ice model code and is shown to improve agreement between model predictions and observed spatial distribution of sea ice thickness in the Arctic.

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 A new snow thermodynamic scheme for large-scale sea-ice models

2011 ◽  
Vol 52 (57) ◽  
pp. 337-346 ◽  
Author(s):  
Olivier Lecomte ◽  
Thierry Fichefet ◽  
Martin Vancoppenolle ◽  
Marcel Nicolaus
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Weddell Sea ◽  
Temperature Profiles ◽  
Sea Ice Thickness ◽  
One Dimensional ◽  
Snow Stratigraphy ◽  
Set Up ◽  
Snow Temperature ◽  
Ice Model

AbstractThis paper assesses the capabilities of a new one-dimensional snow scheme developed for the thermodynamic component of the Louvain-la-Neuve sea-Ice Model (LIM). the model is validated at Point Barrow, Alaska, and at Ice Station Polarstern (ISPOL) in the western Weddell Sea, Southern Ocean. the new snow thermodynamic scheme leads to better snow internal temperature profiles, with a set-up-dependent increase in the correlation between simulated and observed temperature profiles. on average over all runs, these correlations are 27% better with the six-layer configuration. the model’s ability to reproduce observed temperatures improves with the number of snow layers, but stabilizes after a threshold layer number is reached. the lowest and highest values for this threshold are 3 (at Point Barrow) and 6 (at ISPOL), respectively. Overall, the improvement of the model’s ability to simulate sea-ice thickness is not as significant as for snow temperature, probably because of the rather crude representation of the snow stratigraphy in the model.

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