Articles | Volume 13, issue 7
The Cryosphere, 13, 2001–2022, 2019
https://doi.org/10.5194/tc-13-2001-2019
The Cryosphere, 13, 2001–2022, 2019
https://doi.org/10.5194/tc-13-2001-2019
Research article
19 Jul 2019
Research article | 19 Jul 2019

Induced surface fluxes: a new framework for attributing Arctic sea ice volume balance biases to specific model errors

Alex West et al.

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Cited articles

Anderson, M., Bliss, A., and Drobot, S.: Snow Melt Onset Over Arctic Sea Ice from SMMR and SSM/I-SSMIS Brightness Temperatures, Version 3, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Centerm https://doi.org/10.5067/22NFZL42RMUO, 2011 (updated 2012). 
Bitz, C. M.: Some Aspects of Uncertainty in Predicting Sea Ice Thinning, in: Arctic Sea Ice Decline: Observations, Projections, Mechanisms, and Implications, edited by: DeWeaver, E. T., Bitz, C. M., and Tremblay, L.-B., American Geophysical Union, Washington, D.C., https://doi.org/10.1029/180GM06, 2008. 
Bitz, C. M. and Roe, G. H.: A Mechanism for the High Rate of Sea Ice Thinning in the Arctic Ocean, J. Climate, 17, 3623–3632, https://doi.org/10.1175/1520-0442(2004)017<3623:AMFTHR>2.0.CO;2, 2004. 
Bitz, C. M., Holland, M. M., Hunke, E. C., and Moritz, R. M.: Maintenance of the Sea-Ice Edge, J. Climate, 18, 2903–2921, https://doi.org/10.1175/JCLI3428.1, 2005. 
Boeke, R. C. and Taylor, P. C.: Evaluation of the Arctic surface radiation budget in CMIP5 models, J. Geophys. Res.-Atmos., 121, 8525–8548, https://doi.org/10.1002/2016JD025099, 2016. 
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Short summary
This study presents a framework for examining the causes of model errors in Arctic sea ice volume, using HadGEM2-ES as a case study. Simple models are used to estimate how much of the error in energy arriving at the ice surface is due to error in key Arctic climate variables. The method quantifies how each variable affects sea ice volume balance and shows that for HadGEM2-ES an annual mean low bias in ice thickness is likely due to errors in surface melt onset.