Articles | Volume 16, issue 5
The Cryosphere, 16, 1653–1673, 2022
https://doi.org/10.5194/tc-16-1653-2022
The Cryosphere, 16, 1653–1673, 2022
https://doi.org/10.5194/tc-16-1653-2022
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.

Related authors

A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations
William Gregory, Isobel R. Lawrence, and Michel Tsamados
The Cryosphere, 15, 2857–2871, https://doi.org/10.5194/tc-15-2857-2021,https://doi.org/10.5194/tc-15-2857-2021, 2021
Short summary

Related subject area

Discipline: Sea ice | Subject: Climate Interactions
The contribution of melt ponds to enhanced Arctic sea-ice melt during the Last Interglacial
Rachel Diamond, Louise C. Sime, David Schroeder, and Maria-Vittoria Guarino
The Cryosphere, 15, 5099–5114, https://doi.org/10.5194/tc-15-5099-2021,https://doi.org/10.5194/tc-15-5099-2021, 2021
Short summary
Analyzing links between simulated Laptev Sea sea ice and atmospheric conditions over adjoining landmasses using causal-effect networks
Zoé Rehder, Anne Laura Niederdrenk, Lars Kaleschke, and Lars Kutzbach
The Cryosphere, 14, 4201–4215, https://doi.org/10.5194/tc-14-4201-2020,https://doi.org/10.5194/tc-14-4201-2020, 2020
Short summary
Clouds damp the radiative impacts of polar sea ice loss
Ramdane Alkama, Patrick C. Taylor, Lorea Garcia-San Martin, Herve Douville, Gregory Duveiller, Giovanni Forzieri, Didier Swingedouw, and Alessandro Cescatti
The Cryosphere, 14, 2673–2686, https://doi.org/10.5194/tc-14-2673-2020,https://doi.org/10.5194/tc-14-2673-2020, 2020
Short summary

Cited articles

Abe, S. and Suzuki, N.: Complex-network description of seismicity, Nonlin. Processes Geophys., 13, 145–150, https://doi.org/10.5194/npg-13-145-2006, 2006. a
Albert, R. and Barabási, A.-L.: Statistical mechanics of complex networks, Rev. Mod. Phys., 74, 47​​​​​​​, https://doi.org/10.1103/RevModPhys.74.47, 2002. a
Allard, R. A., Farrell, S. L., Hebert, D. A., Johnston, W. F., Li, L., Kurtz, N. T., Phelps, M. W., Posey, P. G., Tilling, R., Ridout, A., and Wallcraft, A. J.​​​​​​​: Utilizing CryoSat-2 sea ice thickness to initialize a coupled ice-ocean modeling system, Adv. Space Res., 62, 1265–1280, https://doi.org/10.1016/j.asr.2017.12.030, 2018. a
Årthun, M., Onarheim, I. H., Dörr, J., and Eldevik, T.: The seasonal and regional transition to an ice-free Arctic, Geophys. Res. Lett., 48, e2020GL090825, https://doi.org/10.1029/2020GL090825, 2021. a
Balan-Sarojini, B., Tietsche, S., Mayer, M., Balmaseda, M., Zuo, H., de Rosnay, P., Stockdale, T., and Vitart, F.: Year-round impact of winter sea ice thickness observations on seasonal forecasts, The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, 2021. a
Download
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.