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Title: CICE, The Los Alamos Sea Ice Model

Abstract

The Los Alamos sea ice model (CICE) is the result of an effort to develop a computationally efficient sea ice component for a fully coupled atmosphere–land–ocean–ice global climate model. It was originally designed to be compatible with the Parallel Ocean Program (POP), an ocean circulation model developed at Los Alamos National Laboratory for use on massively parallel computers. CICE has several interacting components: a vertical thermodynamic model that computes local growth rates of snow and ice due to vertical conductive, radiative and turbulent fluxes, along with snowfall; an elastic-viscous-plastic model of ice dynamics, which predicts the velocity field of the ice pack based on a model of the material strength of the ice; an incremental remapping transport model that describes horizontal advection of the areal concentration, ice and snow volume and other state variables; and a ridging parameterization that transfers ice among thickness categories based on energetic balances and rates of strain. It also includes a biogeochemical model that describes evolution of the ice ecosystem. The CICE sea ice model is used for climate research as one component of complex global earth system models that include atmosphere, land, ocean and biogeochemistry components. It is also used for operational sea icemore » forecasting in the polar regions and in numerical weather prediction models.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
Contributing Org.:
Los Alamos National Laboratory
OSTI Identifier:
1364126
Report Number(s):
CICE; 005315WKSTN00
C17053 / LA-CC-06-012 (previous releases)
DOE Contract Number:
AC52-06NA25396
Resource Type:
Software
Software Revision:
00
Software Package Number:
005315
Software CPU:
WKSTN
Open Source:
Yes
OSS BSD3 https://github.com/CICE-Consortium
Source Code Available:
Yes
Other Software Info:
The CICE model has been developed since 1994 and enhanced greatly through collaborations with members of the sea ice modeling community outside of LANL. This release moves the code into an open source repository for ongoing use and development via the CICE Consortium. Since the most recent CICE release in March 2015, some parts of the code were refactored to isolate vertical columns that can be computed without reference to neighboring grid cells or other code infrastructure.
Related Software:
netCDF, pio
Country of Publication:
United States

Citation Formats

Hunke, Elizabeth, Lipscomb, William, Jones, Philip, Turner, Adrian, Jeffery, Nicole, and Elliott, Scott. CICE, The Los Alamos Sea Ice Model. Computer software. https://www.osti.gov//servlets/purl/1364126. Vers. 00. USDOE. 12 May. 2017. Web.
Hunke, Elizabeth, Lipscomb, William, Jones, Philip, Turner, Adrian, Jeffery, Nicole, & Elliott, Scott. (2017, May 12). CICE, The Los Alamos Sea Ice Model (Version 00) [Computer software]. https://www.osti.gov//servlets/purl/1364126.
Hunke, Elizabeth, Lipscomb, William, Jones, Philip, Turner, Adrian, Jeffery, Nicole, and Elliott, Scott. CICE, The Los Alamos Sea Ice Model. Computer software. Version 00. May 12, 2017. https://www.osti.gov//servlets/purl/1364126.
@misc{osti_1364126,
title = {CICE, The Los Alamos Sea Ice Model, Version 00},
author = {Hunke, Elizabeth and Lipscomb, William and Jones, Philip and Turner, Adrian and Jeffery, Nicole and Elliott, Scott},
abstractNote = {The Los Alamos sea ice model (CICE) is the result of an effort to develop a computationally efficient sea ice component for a fully coupled atmosphere–land–ocean–ice global climate model. It was originally designed to be compatible with the Parallel Ocean Program (POP), an ocean circulation model developed at Los Alamos National Laboratory for use on massively parallel computers. CICE has several interacting components: a vertical thermodynamic model that computes local growth rates of snow and ice due to vertical conductive, radiative and turbulent fluxes, along with snowfall; an elastic-viscous-plastic model of ice dynamics, which predicts the velocity field of the ice pack based on a model of the material strength of the ice; an incremental remapping transport model that describes horizontal advection of the areal concentration, ice and snow volume and other state variables; and a ridging parameterization that transfers ice among thickness categories based on energetic balances and rates of strain. It also includes a biogeochemical model that describes evolution of the ice ecosystem. The CICE sea ice model is used for climate research as one component of complex global earth system models that include atmosphere, land, ocean and biogeochemistry components. It is also used for operational sea ice forecasting in the polar regions and in numerical weather prediction models.},
url = {https://www.osti.gov//servlets/purl/1364126},
doi = {},
year = {Fri May 12 00:00:00 EDT 2017},
month = {Fri May 12 00:00:00 EDT 2017},
note =
}

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  • This is the software User's Manual for the zbgc_colpkg modifications in version 5. It is used to model the effect of aerosols on ice deposits.
  • Cited by 7
  • Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual modelmore » parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.« less
  • Polar primary production unfolds in a dynamic sea ice environment, and the interactions of sea ice with ocean support and mediate this production. In spring, for example, fresh melt water contributes to the shoaling of the mixed layer enhancing ice edge blooms. In contrast, sea ice formation in the fall reduces light penetration to the upper ocean slowing primary production in marine waters. Polar biogeochemical modeling studies typically consider these types of ice-ocean interactions. However, sea ice itself is a biogeochemically active medium, contributing a significant and, possibly, essential source of primary production to polar regions in early spring andmore » fall. Here we present numerical simulations using the Los Alamos Sea Ice Model (CICE) with prognostic salinity and sea ice biogeochemistry. This study investigates the relationship between sea ice multiphase physics and sea ice productivity. Of particular emphasis are the processes of gravity drainage, melt water flushing, and snow loading. During sea ice formation, desalination by gravity drainage facilitates nutrient exchange between ocean and ice maintaining ice algal blooms in early spring. Melt water flushing releases ice algae and nutrients to underlying waters limiting ice production. Finally, snow loading, particularly in the Southern Ocean, forces sea ice below the ocean surface driving an upward flow of nutrient rich water into the ice to the benefit of interior and freeboard communities. Incorporating ice microphysics in CICE has given us an important tool for assessing the importance of these processes for polar algal production at global scales.« less

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