The Cryosphere
The Cryosphere
TC
Articles | Volume 16, issue 5
Articles | Volume 16, issue 5
The Cryosphere, 16, 1653–1673, 2022
https://doi.org/10.5194/tc-16-1653-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 16, 1653–1673, 2022
https://doi.org/10.5194/tc-16-1653-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 5
Research article
05 May 2022
Research article | 05 May 2022

Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations

William Gregory et al.

Supplement

https://doi.org/10.5194/tc-16-1653-2022-supplement

Data sets

Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data D. J. Cavalieri, C. L. Parkinson, P. Gloersen, H. J. and Zwally https://doi.org/10.5067/8GQ8LZQVL0VL

Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS J. Comiso https://doi.org/10.5067/7Q8HCCWS4I0R

Global Sea Ice Concentration Climate Data Record v2.0 - Multimission OSI-SAF https://doi.org/10.15770/EUM_SAF_OSI_0008

Global sea ice concentration interim climate data record 2016-onwards (v2.0, 2017) EUMETSAT Ocean and Sea Ice Satellite Application Facility https://osi-saf.eumetsat.int/products/osi-430-b-complementing-osi-450

ERA5 monthly averaged data on single levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut https://doi.org/10.24381/cds.f17050d7

PIOMAS Variables on Model Grid Polar Science Center http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/model_grid

Model code and software

William-gregory/CMIP6: Accompanying code for: "Network connectivity between the winter Arctic Oscillation and summer sea ice in CMIP6 models and observations" (v1.0) W. Gregory https://doi.org/10.5281/zenodo.6514306

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Short summary
This research was conducted to better understand how coupled climate models simulate one of the large-scale interactions between the atmosphere and Arctic sea ice that we see in observational data, the accurate representation of which is important for producing reliable forecasts of Arctic sea ice on seasonal to inter-annual timescales. With network theory, this work shows that models do not reflect this interaction well on average, which is likely due to regional biases in sea ice thickness.
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