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Python data processing and plotting scripts from Petty et al., (2018), published in The Cryosphere.

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The Arctic sea ice cover of 2016: A year of record-low highs and higher-than-expected lows

Alek Petty

Python scripts used to produce Arctic sea ice concentration budgets and monthly extent/area rankings. Results from this analysis were published in The Cryosphere.

Citation: Petty, A. A., J. C. Stroeve, P. R. Holland, L. N. Boisvert, A. C. Bliss, N. Kimura, W. N. Meier, (2018) The Arctic sea ice cover of 2016: A year of record-low highs and higher-than-expected lows, The Cryosphere, 12, 1–20, doi:10.5194/tc-12-1-2018. (open access peer review paper)

Scripts

Individual descriptions should be included at the top of each script.

Python 2.7 was used for all processing. I have not tested these scripts in Python 3.

I use Conda to intall/manage the various Python packages. Check out the file 'packages.txt' for a list of the Python package versions I used to run these Scripts. I will run this in a conda environment and provide that information shortly.

Information about installing Conda/Python, and a brief introduction to using Python can be found on my NASA Cryospheric Sciences meetup repo: https://github.com/akpetty/cryoscripts.

Data

The gridded forecast datasets were generated from the following, publically available datasets:

Sea ice concentration data (NASA Team, final): http://nsidc.org/data/nsidc-0051
Sea ice concentration data (NASA Team, near real-time): https://nsidc.org/data/nsidc-0081

2016 KIMURA ice drifts have been added to the Data folder. The rest of the KIMURA ice drift data will be added shortly.

Contact me if you any any questions (alek.a.petty@nasa.gov)!

Alek

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Python data processing and plotting scripts from Petty et al., (2018), published in The Cryosphere.

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