Arctic sea ice interannual variability and change

Zachary Labe - Research Website - @ZLabe

Changes in Arctic sea ice extent and concentration are well documented within the satellite record (1979). However, quantifying pan-Arctic sea ice thickness and volume is challenging as a result of limited observations until recently through ICESat (2003-2009) and CryoSat-2 (2011-present). The Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) is a POP ocean and sea ice model that has been widely validated in reproducing a sea ice thickness and volume record consistent with the spatial and temporal variability of our limited observations. This project will provide a comprehensive overview of long-term trends and variability in sea ice volume using PIOMAS in addition to ICESat/CryoSat-2. We are particularly interested in the the distribution of sea ice and regional trends, which may be important for feedbacks with the large-scale atmospheric circulation.

For more information: [Summary of Labe et al. 2018]

• Data/: Additional data files (ascii and netCDF4) modified from original sources. Data includes calculations from EOF analysis, linear trends, and nearest-neighbor interpolation
• Scripts/: Main Python scripts/functions used in data analysis and plotting. These scripts are not compatible with Python 3+.
• requirements.txt: List of environments and modules associated with the most recent version of this project. A Python Anaconda2 Distribution was used for our analysis. All AGCM experiments were processed through resources on CISL's Cheyenne supercomputer. Tools including NCL, CDO, and NCO were also used for initial data manipulation.

• CESM Large Ensemble Project (LENS) : [DATA]
  • Kay, J. E and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 1333–1349, doi:10.1175/BAMS-D-13-00255.1, [Publication]
• CryoSat-2 : [DATA]
  • Laxon, S. W., and Coauthors, 2013: CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophysical Research Letters, 40 (4), 732–737, doi:10.1002/grl.50193, [Publication]
• Ice, Cloud, and land Elevation Satellite (ICESat) : [DATA]
  • Kwok, R., 2004: ICESat observations of Arctic sea ice: A first look. Geophysical Research Letters, 31 (16), L16 401, doi:10.1029/2004GL020309, [Publication]
• Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) : [DATA]
  • Zhang, J., and D. A. Rothrock, 2003: Modeling Global Sea Ice with a Thickness and Enthalpy Distribution Model in Generalized Curvilinear Coordinates. Monthly Weather Review, 131 (5), 845–861, doi:10.1175/1520-0493(2003)131<0845:MGSIWA>2.0.CO;2 [Publication]
• Submarine sea ice thickness : [DATA]
  • Lindsay, R., 2010: New Unified Sea Ice Thickness Climate Data Record. Eos, Transactions Amer578 ican Geophysical Union, 91 (44), 405, doi:10.1029/2010EO440001, [Publication]

• Labe, Z.M., G. Magnusdottir, and H.S. Stern (2018), Variability of Arctic sea ice thickness using PIOMAS and the CESM Large Ensemble, Journal of Climate, DOI:10.1175/JCLI-D-17-0436.1 [HTML][BibTeX] [SUMMARY]

• [2] Labe, Z.M., G. Magnusdottir, and H.S. Stern. Variability and future projections of Arctic sea ice thickness. Understanding the Causes and Consequences of Polar Amplification Workshop, Aspen Global Change Institute, Aspen, CO (Jun 2017). [RECORDING] [SLIDES]
• [1] Labe, Z.M., G. Magnusdottir, and H.S. Stern. SArctic Sea Ice Thickness Variability and the Large-scale Atmospheric Circulation Using Satellite Observations, PIOMAS, and the CESM Large Ensemble, 14th Conference on Polar Meteorology and Oceanography, 97th Annual Meeting of the American Meteorological Society (Jan 2017). [ABSTRACT] [POSTER]