Earth:CICE (sea ice model)

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Short description: Computer model that simulates sea ice

CICE (/saɪs/) is a computer model that simulates the growth, melt and movement of sea ice. It has been integrated into many coupled climate system models as well as global ocean and weather forecasting models and is often used as a tool in Arctic and Southern Ocean research.[1][2][3][4][5][6][7][8][9] CICE development began in the mid-1990s by the United States Department of Energy (DOE), and it is currently maintained and developed by a group of institutions in North America and Europe known as the CICE Consortium.[10] Its widespread use in earth system science in part owes to the importance of sea ice in determining Earth's planetary albedo, the strength of the global thermohaline circulation in the world's oceans, and in providing surface boundary conditions for atmospheric circulation models, since sea ice occupies a significant proportion (4-6%) of earth's surface.[11][12] CICE is a type of cryospheric model.

Development

File:CESM-01-05 Thickness Antarctic.tif

Development of CICE began in 1994 by Elizabeth Hunke at Los Alamos National Laboratory (LANL).[12][15] Since its initial release in 1998 following development of the Elastic-Viscous-Plastic (EVP) sea ice rheology within the model,[16] it has been substantially developed by an international community of model users and developers. Enthalpy-conserving thermodynamics and improvements to the sea ice thickness distribution were added to the model between 1998 and 2005.[17][18][19] The first institutional user outside of LANL was Naval Postgraduate School[15] in the late-1990s, where it was subsequently incorporated into the Regional Arctic System Model (RASM) in 2011.[20][21] The National Center for Atmospheric Research (NCAR) was the first to incorporate CICE into a global climate model in 2002,[22] and developers of the NCAR Community Earth System Model (CESM) have continued to contribute to CICE innovations[23][24][25] and have used it to investigate polar variability in Earth's climate system.[13] The United States Navy began using CICE shortly after 2000 for polar research and sea ice forecasting and it continues to do so today.[3][26] Since 2000, CICE development or coupling to oceanic and atmospheric models for weather and climate prediction has occurred at the University of Reading,[27] University College London,[28] the U.K. Met Office Hadley Centre,[29] Environment and Climate Change Canada,[7] the Danish Meteorological Institute,[4] the Commonwealth Science and Industrial Research Organisation,[30] and Beijing Normal University,[8] among other institutions. As a result of model development in the global community of CICE users, the model's computer code now includes a comprehensive saline ice physics and biogeochemistry library that incorporates mushy-layer thermodynamics,[31][32] anisotropic continuum mechanics,[33] Delta-Eddington radiative transfer,[34] melt-pond physics[35][36] and land-fast ice.[37] CICE version 6 is open-source software and was released in 2018 on GitHub.[38]

Keystone Equations

There are two main physics equations solved using numerical methods in CICE that underpin the model's predictions of sea ice thickness, concentration and velocity, as well as predictions made with many equations not shown here giving, for example, surface albedo, ice salinity, snow cover, divergence, and biogeochemical cycles. The first keystone equation is Newton's second law for sea ice:

[math]\displaystyle{ m\frac{d \mathbf{u}}{d t}=-mf\mathrm{k}\times\mathbf{u}+\tau_a+\tau_w-m\mathrm{\hat{g}}\nabla\mu+\nabla\cdot\mathbf{\sigma} }[/math]

where [math]\displaystyle{ m }[/math] is the mass per unit area of saline ice on the sea surface, [math]\displaystyle{ \mathbf{u} }[/math] is the drift velocity of the ice, [math]\displaystyle{ f }[/math] is the Coriolis parameter, [math]\displaystyle{ \mathrm{k} }[/math] is the upward unit vector normal to the sea surface, [math]\displaystyle{ \tau_a }[/math] and [math]\displaystyle{ \tau_w }[/math] are the wind and water stress on the ice, respectively, [math]\displaystyle{ \mathrm{\hat{g}} }[/math] is acceleration due to gravity, [math]\displaystyle{ \mu }[/math] is sea surface height and [math]\displaystyle{ \mathbf{\sigma} }[/math] is internal ice the two-dimensional stress tensor within the ice.[16] Each of the terms require information about the ice thickness, roughness, and concentration, as well as the state of the atmospheric and oceanic boundary layers. Ice mass per unit area [math]\displaystyle{ m }[/math] is determined using the second keystone equation in CICE, which describes evolution of the sea ice thickness distribution [math]\displaystyle{ g(h) }[/math] for different thicknesses [math]\displaystyle{ h }[/math] spread of the area for which sea ice velocity is calculated above:[18]

[math]\displaystyle{ \frac{dg}{dt}=\theta + \psi - g(\nabla \cdot \mathbf{u}) }[/math]

where [math]\displaystyle{ \theta }[/math] is the change in the thickness distribution due to thermodynamic growth and melt, [math]\displaystyle{ \psi }[/math] is redistribution function due to sea ice mechanics and is associated with internal ice stress [math]\displaystyle{ \mathbf{\sigma} }[/math], and [math]\displaystyle{ -g(\nabla \cdot \mathbf{u}) }[/math] describes advection of sea ice in a Lagrangian reference frame.[18][19] From this, ice mass is given by:

[math]\displaystyle{ m=\rho\int_0^\infty h \, g(h) \, dh }[/math]

for density [math]\displaystyle{ \rho }[/math] of sea ice.[38]

Code Design

File:2019 MPAS sea ice grid.tif

CICE version 6 is coded in FORTRAN90. It is organized into a dynamical core (dycore) and a separate column physics package called Icepack, which is maintained as a CICE submodule on GitHub.[39] The momentum equation and thickness advection described above are time-stepped on a quadrilateral Arakawa B-grid within the dynamical core, while Icepack solves diagnostic and prognostic equations necessary for calculating radiation physics, hydrology, thermodynamics, and vertical biogeochemistry, including terms necessary to calculate [math]\displaystyle{ \tau_a }[/math], [math]\displaystyle{ \tau_w }[/math], [math]\displaystyle{ \mathbf{\sigma} }[/math], [math]\displaystyle{ \theta }[/math], and [math]\displaystyle{ \psi }[/math] defined above. CICE can be run independently, as in the first figure on this page, but is frequently coupled with earth systems models through an external flux coupler, such as the CESM Flux Coupler from NCAR[22] for which results are shown in the second figure for the CESM Large Ensemble. The column physics were separated into Icepack for the version 6 release to permit insertion into earth system models that use their own sea ice dynamical core, including the new DOE Energy Exascale Earth System Model (E3SM),[38][40] which uses an unstructured grid in the sea ice component of the Model for Prediction Across Scales (MPAS),[41][42] as demonstrated in the final figure.

See also

References

  1. ↑ Roberts, Andrew; Hunke, Elizabeth; Allard, Richard; Bailey, David; Craig, Anthony; Lemieux, Jean-François; Turner, Matthew (2018). "Quality control for community-based sea-ice model development". Philosophical Transactions of the Royal Society A 376 (2129): 17. doi:10.1098/rsta.2017.0344. PMID 30126915. Bibcode: 2018RSPTA.37670344R. 
  2. ↑ Walters, D. N.; Hunke, E. C.; Harris, C. M.; West, A. E.; Ridley, J. K.; Keen, A. B.; Hewitt, H. T.; Rae, J. G. L. (2015-07-24). "Development of the Global Sea Ice 6.0 CICE configuration for the Met Office Global Coupled model" (in en). Geoscientific Model Development 8 (7): 2221–2230. doi:10.5194/gmd-8-2221-2015. ISSN 1991-959X. Bibcode: 2015GMD.....8.2221R. 
  3. ↑ 3.0 3.1 Metzger, E. Joseph; Smedstad, Ole Martin; Thoppil, Prasad; Hurlburt, Harley; Cummings, James; Walcraft, Alan; Zamudio, Luis; Franklin, Deborah et al. (2014-09-01). "US Navy Operational Global Ocean and Arctic Ice Prediction Systems". Oceanography 27 (3): 32–43. doi:10.5670/oceanog.2014.66. ISSN 1042-8275. 
  4. ↑ 4.0 4.1 "DMI Ocean Models [HYCOM"]. http://ocean.dmi.dk/models/hycom.uk.php. 
  5. ↑ Canada, Environment and Climate Change (2009-11-12). "Latest ice conditions". https://www.canada.ca/en/environment-climate-change/services/ice-forecasts-observations/latest-conditions.html. 
  6. ↑ "ESRL : PSD : PSD Arctic Sea Ice Forecast". https://www.esrl.noaa.gov/psd/forecasts/seaice/about.html. 
  7. ↑ 7.0 7.1 Lemieux, Jean-François; Beaudoin, Christiane; Dupont, Frédéric; Roy, François; Smith, Gregory C.; Shlyaeva, Anna; Buehner, Mark; Caya, Alain et al. (2016). "The Regional Ice Prediction System (RIPS): verification of forecast sea ice concentration" (in en). Quarterly Journal of the Royal Meteorological Society 142 (695): 632–643. doi:10.1002/qj.2526. ISSN 1477-870X. Bibcode: 2016QJRMS.142..632L. 
  8. ↑ 8.0 8.1 Stocker, Thomas (2013). Climate change 2013 : the physical science basis : Working Group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change, Working Group I. Cambridge, United Kingdom: Cambridge University Press. ISBN 9781107661820. OCLC 875970367. 
  9. ↑ Horvat, Christopher; Jones, David Rees; Iams, Sarah; Schroeder, David; Flocco, Daniela; Feltham, Daniel (2017). "The frequency and extent of sub-ice phytoplankton blooms in the Arctic Ocean". Science Advances 3 (3): e1601191. doi:10.1126/sciadv.1601191. ISSN 2375-2548. PMID 28435859. Bibcode: 2017SciA....3E1191H. 
  10. ↑ Background and supporting information for the CICE Consortium: CICE-Consortium/About-Us, CICE Consortium, 2018-08-27, https://github.com/CICE-Consortium/About-Us, retrieved 2018-12-21 
  11. ↑ Thomas, David (2017). Sea Ice. Wiley-Blackwell. ISBN 978-1118778388. 
  12. ↑ 12.0 12.1 Hunke, Elizabeth (2017). "Rothschild Lecture: Large-scale sea ice modeling: societal needs and community development". Lecture at the Isaac Newton Institute for Mathematical Sciences, University of Cambridge, U.K.. https://www.newton.ac.uk/seminar/20170911160017001. 
  13. ↑ 13.0 13.1 Kay, J. E.; Deser, C.; Phillips, A.; Mai, A.; Hannay, C.; Strand, G.; Arblaster, J. M.; Bates, S. C. et al. (2015). "The Community Earth System Model (CESM) Large Ensemble Project: A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability". Bulletin of the American Meteorological Society 96 (8): 1333–1349. doi:10.1175/bams-d-13-00255.1. ISSN 0003-0007. Bibcode: 2015BAMS...96.1333K. 
  14. ↑ Meier, W. N.; Fetterer (2017) (in en). NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3 | National Snow and Ice Data Center. NSIDC. doi:10.7265/n59p2ztg. https://nsidc.org/data/g02202/versions/3. 
  15. ↑ 15.0 15.1 "A Brief History of CICE Milestones and Collaborations". February 12, 2018. https://github.com/CICE-Consortium/About-Us/wiki/History. 
  16. ↑ 16.0 16.1 Hunke, E. C.; Dukowicz, J. K. (1997). "An Elastic–Viscous–Plastic Model for Sea Ice Dynamics". Journal of Physical Oceanography 27 (9): 1849–1867. doi:10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2. Bibcode: 1997JPO....27.1849H. https://digital.library.unt.edu/ark:/67531/metadc677137/. 
  17. ↑ Bitz, C. M.; Lipscomb, William H. (1999). "An energy-conserving thermodynamic model of sea ice" (in en). Journal of Geophysical Research: Oceans 104 (C7): 15669–15677. doi:10.1029/1999JC900100. ISSN 2156-2202. Bibcode: 1999JGR...10415669B. 
  18. ↑ 18.0 18.1 18.2 Lipscomb, William H. (2001-07-15). "Remapping the thickness distribution in sea ice models". Journal of Geophysical Research: Oceans 106 (C7): 13989–14000. doi:10.1029/2000jc000518. ISSN 0148-0227. Bibcode: 2001JGR...10613989L. 
  19. ↑ 19.0 19.1 Lipscomb, William H.; Hunke, Elizabeth C. (2005). "Modeling Sea Ice Transport Using Incremental Remapping". Monthly Weather Review 132 (6): 1341–1354. doi:10.1175/1520-0493(2004)132<1341:msitui>2.0.co;2. ISSN 0027-0644. 
  20. ↑ Roberts, Andrew; Craig, Anthony; Maslowski, Wieslaw; Osinski, Robert; Duvivier, Alice; Hughes, Mimi; Nijssen, Bart; Cassano, John et al. (2015). "Simulating transient ice-ocean Ekman transport in the Regional Arctic System Model and Community Earth System Model" (in en). Annals of Glaciology 56 (69): 211–228. doi:10.3189/2015AoG69A760. Bibcode: 2015AnGla..56..211R. 
  21. ↑ Jin, Meibing; Deal, Clara; Maslowski, Wieslaw; Matrai, Patricia; Roberts, Andrew; Osinski, Robert; Lee, Younjoo J.; Frants, Marina et al. (2018). "Effects of Model Resolution and Ocean Mixing on Forced Ice-Ocean Physical and Biogeochemical Simulations Using Global and Regional System Models" (in en). Journal of Geophysical Research: Oceans 123 (1): 358–377. doi:10.1002/2017JC013365. ISSN 2169-9291. Bibcode: 2018JGRC..123..358J. 
  22. ↑ 22.0 22.1 Kauffman, Brian G.; Large, William G. (August 1, 2002). "The CCSM Coupler Version 5.0.1". https://github.com/CICE-Consortium/CICE/blob/master/doc/PDF/KL_NCAR2002.pdf. 
  23. ↑ Holland, Marika; Bailey, David; Briegleb, Bruce; Light, Bonnie; Hunke, Elizabeth (2012). "Improved Sea Ice Shortwave Radiation Physics in CCSM4: The Impact of Melt Ponds and Aerosols on Arctic Sea Ice". Journal of Climate 25 (5): 1413–1430. doi:10.1175/JCLI-D-11-00078.1. Bibcode: 2012JCli...25.1413H. 
  24. ↑ Jahn, Alexandra; Sterling, Kara; Holland, Marika M.; Kay, Jennifer E.; Maslanik, James A.; Bitz, Cecilia M.; Bailey, David A.; Stroeve, Julienne et al. (2012). "Late-Twentieth-Century Simulation of Arctic Sea Ice and Ocean Properties in the CCSM4". Journal of Climate 25 (5): 1431–1452. doi:10.1175/jcli-d-11-00201.1. ISSN 0894-8755. Bibcode: 2012JCli...25.1431J. 
  25. ↑ Hurrell, James W.; Holland, M. M.; Gent, P. R.; Ghan, S.; Kay, Jennifer E.; Kushner, P. J.; Lamarque, J.-F.; Large, W. G. et al. (2013). "The Community Earth System Model: A Framework for Collaborative Research". Bulletin of the American Meteorological Society 94 (9): 1339–1360. doi:10.1175/bams-d-12-00121.1. ISSN 0003-0007. Bibcode: 2013BAMS...94.1339H. 
  26. ↑ Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin (2015). "Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System". Journal of Geophysical Research: Oceans 120 (12): 8327–8345. doi:10.1002/2015jc011283. ISSN 2169-9275. Bibcode: 2015JGRC..120.8327H. 
  27. ↑ Tsamados, M.; Feltham, D. L.; Wilchinsky, A. V. (2013). "Impact of a new anisotropic rheology on simulations of Arctic sea ice" (in en). Journal of Geophysical Research: Oceans 118 (1): 91–107. doi:10.1029/2012JC007990. ISSN 2169-9291. Bibcode: 2013JGRC..118...91T. http://centaur.reading.ac.uk/30719/1/Feltham2012b.pdf. 
  28. ↑ Wilchinsky, Alexander V.; Feltham, Daniel L.; Miller, Paul A. (2006). "A Multithickness Sea Ice Model Accounting for Sliding Friction". Journal of Physical Oceanography 36 (9): 1719–1738. doi:10.1175/jpo2937.1. ISSN 0022-3670. Bibcode: 2006JPO....36.1719W. 
  29. ↑ Ridley, Jeff K.; Blockley, Edward W.; Keen, Ann B.; Rae, Jamie G. L.; West, Alex E.; Schroeder, David (2018-02-27). "The sea ice model component of HadGEM3-GC3.1". Geoscientific Model Development 11 (2): 713–723. doi:10.5194/gmd-11-713-2018. ISSN 1991-9603. Bibcode: 2018GMD....11..713R. 
  30. ↑ Uotila, P.; O’Farrell, S.; Marsland, S. J.; Bi, D. (2012-07-01). "A sea-ice sensitivity study with a global ocean-ice model". Ocean Modelling 51: 1–18. doi:10.1016/j.ocemod.2012.04.002. ISSN 1463-5003. Bibcode: 2012OcMod..51....1U. 
  31. ↑ Feltham, D. L.; Untersteiner, N.; Wettlaufer, J. S.; Worster, M. G. (2006). "Sea ice is a mushy layer" (in en). Geophysical Research Letters 33 (14). doi:10.1029/2006GL026290. ISSN 1944-8007. Bibcode: 2006GeoRL..3314501F. http://centaur.reading.ac.uk/34911/1/grl21578.pdf. 
  32. ↑ Turner, Adrian K.; Hunke, Elizabeth C. (2015). "Impacts of a mushy-layer thermodynamic approach in global sea-ice simulations using the CICE sea-ice model". Journal of Geophysical Research: Oceans 120 (2): 1253–1275. doi:10.1002/2014jc010358. ISSN 2169-9275. Bibcode: 2015JGRC..120.1253T. 
  33. ↑ Wilchinsky, Alexander V.; Feltham, Daniel L. (2006-06-01). "Anisotropic model for granulated sea ice dynamics". Journal of the Mechanics and Physics of Solids 54 (6): 1147–1185. doi:10.1016/j.jmps.2005.12.006. ISSN 0022-5096. Bibcode: 2006JMPSo..54.1147W. 
  34. ↑ Briegleb, Bruce P. (1992). "Delta-Eddington approximation for solar radiation in the NCAR community climate model" (in en). Journal of Geophysical Research: Atmospheres 97 (D7): 7603–7612. doi:10.1029/92JD00291. ISSN 2156-2202. Bibcode: 1992JGR....97.7603B. 
  35. ↑ Flocco, Daniela; Feltham, Daniel L.; Turner, Adrian K. (2010). "Incorporation of a physically based melt pond scheme into the sea ice component of a climate model" (in en). Journal of Geophysical Research: Oceans 115 (C8). doi:10.1029/2009JC005568. ISSN 2156-2202. Bibcode: 2010JGRC..115.8012F. http://centaur.reading.ac.uk/30570/1/Feltham2010.pdf. 
  36. ↑ Hunke, Elizabeth C.; Hebert, David A.; Lecomte, Olivier (2013-11-01). "Level-ice melt ponds in the Los Alamos sea ice model, CICE". Ocean Modelling. Arctic Ocean 71: 26–42. doi:10.1016/j.ocemod.2012.11.008. ISSN 1463-5003. Bibcode: 2013OcMod..71...26H. 
  37. ↑ Lemieux, Jean-François; Dupont, Frédéric; Blain, Philippe; Roy, François; Smith, Gregory C.; Flato, Gregory M. (2016). "Improving the simulation of landfast ice by combining tensile strength and a parameterization for grounded ridges" (in en). Journal of Geophysical Research: Oceans 121 (10): 7354–7368. doi:10.1002/2016JC012006. ISSN 2169-9291. Bibcode: 2016JGRC..121.7354L. 
  38. ↑ 38.0 38.1 38.2 CICE Consortium (December 3, 2018). "CICE Documentation (v6)". https://media.readthedocs.org/pdf/cice-consortium-cice/cice6.0.0/cice-consortium-cice.pdf. 
  39. ↑ "Icepack Documentation — Icepack documentation". https://icepack.readthedocs.io/en/latest/. 
  40. ↑ "Energy Exascale Earth System Model (E3SM)" (in en-US). https://e3sm.org/. 
  41. ↑ Ringler, Todd; Petersen, Mark; Higdon, Robert L.; Jacobsen, Doug; Jones, Philip W.; Maltrud, Mathew (2013). "A multi-resolution approach to global ocean modeling". Ocean Modelling 69: 211–232. doi:10.1016/j.ocemod.2013.04.010. ISSN 1463-5003. Bibcode: 2013OcMod..69..211R. 
  42. ↑ "Model for Prediction Across Scales". https://mpas-dev.github.io/. 

External links



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