Andrew Roberts, Alexandra Jahn, Adrian Turner

Naval Postgraduate School; University of Colorado at Boulder; Los Alamos National Laboratory

Applied Research Topic: 

An ICESat-2 emulator for the Los Alamos sea ice model (CICE) to evaluate DOE, NCAR, and DOD sea ice predictions for the Arctic.

Potential Applications: 

Sea ice forecasting; national defense environmental forecasting; coordinated disaster response: oil spill mitigation, field campaigns; improved climate projections at all latitudes


This project will develop an ICESat-2 emulator for the Los Alamos Sea Ice Model (CICE) to facilitate detailed comparisons between measured and modeled sea ice freeboard in Earth System Models, including in the Regional Arctic System Model (RASM) and Community Earth System Model (CESM). The purpose of this emulator is to sample simulated sea ice freeboard and snow cover in a comparable way to the method used to measure real sea ice and snow cover by the Advanced Topographic Laser Altimeter System (ATLAS) aboard ICESat-2.

Currently, Earth System Models (ESMs) generate sea ice state statistics, such as concentration, thickness and snow cover, differently from the way ATLAS will sample them in the real world. Whereas ATLAS will sample sea ice freeboard in three pairs of strong/weak tracks, each pair separated by 3km, and each strong/weak track separated by 90m, ESMs generate mean sea ice and snow thickness statistics by sampling all model grid cells at all time steps. These different sampling regimes will make detailed ESM evaluation difficult with ICESat-2 measurements, since average seasonal sea ice freeboard and snow products generated from ATLAS data will not be directly comparable with modeled ice mass averages. This project will address the problem by creating an ICESat-2 emulator in CICE that samples modeled sea ice freeboard and snow cover at the same time of the day and in the same proximity as ATLAS measurements. In so doing, this limits the problem, for example, that ATLAS will sample locations closer to the poles more frequently than lower latitude Arctic locations, whereas standard models statistics will not. CICE is widely used for numerical sea ice predictions spanning short-term (7 day) forecasts to centennial sea ice projections.

As part of this project, we will create tools to quickly compare CICE freeboard track data with ATLAS measurements, which will improve the ability to evaluate model skill and uncertainty in a large collection of models that use CICE, including RASM, CESM, the U.S. Department of Energy's Model for Prediction Across Scales (MPAS), and the U.S. Navy's ice forecasting system. The ICESat-2 emulator will be released publicly as a module within CICE, and will be tested in collaboration with the primary end users within the U.S. Departments of Energy and Defense, National Center for Atmospheric Research, and research universities engaged in Earth System Modeling.

SDT Member Partner: 
End Users: 

U.S. Department of Energy (POC: Elizabeth Hunke); National Center for Atmospheric Research (POC: Marika Holland, Jennifer Kay); U.S. Department of Defense (POC: Wieslaw Maslowski, Ruth Preller); University of Colorado Boulder (POC: John Cassano)

  1. AON (2010), Arctic Observing Network Program Status Report. Results from the Third AON Principal Investigators (PI) Meeting, 30 November - 2 December (2009), 170pp.
  2. Arctic Council (2009), Arctic Marine Shipping Assessment, Arctic Council, 187pp.
  3. Berkman P. A., O. R. Young (2009). Governance and environmental change in the Arctic Ocean. Science, 324, 339-340.
  4. Bitz, C. M., and L. M. Polvani (2012), Antarctic climate response to stratospheric ozone depletion in a fine resolution ocean climate model, Geophys. Res. Lett., 39(20), doi: 10.1029/2012GL053393.
  5. Bony, S., M. Webb, C. Bretherton, S. A. Klein, P. Siebesma, G. Tselioudis, and M. Zhang (2011), Towards a better evaluation and understanding of clouds and cloud feedbacks in CMIP5 models, CLIVAR Exchanges, 2(56), 20-24.
  6. DOD (2013). Department of Defense Arctic Strategy, United States Department of Defense, USA, 14pp.
  7. Gautier D. L., et al. (2009). Assessment of undiscovered oil and gas in the Arctic, Science, 324, 1175-1179.
  8. Hurrell, J. W. and coauthors (2013), The Community Earth System Model: A Framework for Collaborative Research, Bull. Am. Meteorol. Soc., 94(9), 1339–1360, doi:10.1175/BAMS-D-12-00121.1.
  9. IPCC, 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovern- mental Panel on Climate Change,
  10. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.), Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535pp.
  11. Jahn, A., L. B. Tremblay, R. Newton, M. M. Holland, L. A. Mysak, and I. A. Dmitrenko (2010), A tracer study of the Arctic Ocean‰ЫЄs liquid freshwater export variability, J. Geophys. Res., 115.
  12. Jahn, A., K. Sterling, M. M. Holland, J. E. Kay, J. A. Maslanik, C. M. Bitz, D. A. Bailey, J.
  13. Stroeve, E. C. Hunke, W. H. Lipscomb, and D. A. Pollak (2012), Late 20th century simulation of Arctic sea ice and ocean properties in the CCSM4, J. Clim., 25(5), 1431–1452.
  14. Jahn, A., and M. M. Holland (2013), Implications of Arctic sea ice changes for North Atlantic deep convection and the meridional overturning circulation in CCSM4-CMIP55 simulations, Geophys. Res. Let., 40(6), 1206–1211.
  15. Jahn, A., K. Lindsay, X. Giraud, E. Brady, N. Gruber, B. Otto-Bliesner, Z. Liu (2014), Carbon isotopes in the CESM, to be submitted to Geophysical Model Development in September 2014.
  16. Kay, J. E., M. M. Holland, and A. Jahn (2011), Inter-annual to multi-decadal Arctic sea ice extent trends in a warming world, Geophys. Res. Let., 38.
  17. Kay, J. E., B. Hillman, S. Klein, Y. Zhang, B. Medeiros, G. Gettelman, R. Pincus, B. Eaton, J. 16
  18. Boyle, R. Marchand, and T. Ackerman (2012), Exposing global cloud biases in the
  19. Community Atmosphere Model (CAM) using satellite observations and their corresponding instrument simulators, J. Climate, 25, 5190–5207.
  20. Maslowski, W., J. Clement Kinney, M. Higgins, and A. Roberts (2012), The Future of Arctic Sea Ice, Annu. Rev. Earth Planet. Sci., 40, 625–654, doi:10.1146/annurev-earth-042711-105345.
  21. National Science and Technology Council (2013). Arctic Research Plan: FY2013 - FY2017, Executive Office of the President, USA, 95pp.
  22. Office of the President (2013). National Strategy for the Arctic Region, Executive Office of the President, May 2013, USA.
  23. Osinski, R., W. Maslowski, A.F. Roberts, J. Clement Kinney and A. Craig, (2014): On the sensitivity of sea ice states to variable parameter space in the Regional Arctic System Model, Ann. Glac., conditionally accepted.
  24. Roberts, A. and coauthors (2010), A Science Plan for Regional Arctic System Modeling, A report to the National Science Foundation from the International Arctic Science Community, International Arctic Research Center Technical Papers 10-0001. University of Alaska Fairbanks, 47pp.
  25. Roberts, A., J. Cherry, R. DМ¦scher, S. Elliott, and L. Sushama (2011), Exploring the Potential for Arctic System Modeling, Bull. Am. Meteorol. Soc., 92(2), 203–206, doi:10.1175/2010bams2959.1.
  26. Roberts, A., A. Craig, W. Maslowski, R. Osinski, A. DuVivier, M. Hughes, B. Nijssen, J. Cassano and M. Brunke (2014), Simulating transient ice-ocean Ekman transport in the Regional Arctic System Model and Community Earth System Model, Ann. Glac., accepted.
  27. Task Force Climate Change (2013), U.S. Navy Arctic Roadmap 2014-2030. United States Department of Defense, USA, 42pp.
  28. Tilmes, S., A. Jahn, J.E. Kay, M. Holland, J.-F. Lamarque (2014), Can regional climate engineering save the summer Arctic Sea-Ice?, GRL, Vol 41, Issue 3, doi: 10.1002/2013GL058731
  29. Turner, A. K. and E. C. Hunke (2013), Two modes of sea-ice gravity drainage: a parameterization for large-scale modeling, Journal of Geophysical Research, 118, 2279-2294
  30. Turner, A. K. and E. C. Hunke (2014), Impacts of a mushy-layer thermodynamic approach in global sea-ice simulations using the CICE sea-ice model, Journal of Geophysical Research, submitted.
  31. Vavrus, S., D. Bailey, B. Blazey, M. M. Holland, A. Jahn, and J. Maslanik (2012), The simulation of 21st century Arctic climate in the CCSM4, J. Climate, 25(8), 2696–2710.
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