Induced surface fluxes: A new framework for attributing Arctic sea ice volume balance biases to specific model errors
dc.contributor.author | West, A | |
dc.contributor.author | Collins, M | |
dc.contributor.author | Blockley, E | |
dc.contributor.author | Ridley, J | |
dc.contributor.author | Bodas-Salcedo, A | |
dc.date.accessioned | 2019-11-29T14:36:26Z | |
dc.date.issued | 2019-07-19 | |
dc.description.abstract | A new framework is presented for analysing the proximate causes of model Arctic sea ice biases, demonstrated with the CMIP5 model HadGEM2-ES (Hadley Centre Global Environment Model version 2 - Earth System). In this framework the Arctic sea ice volume is treated as a consequence of the integrated surface energy balance, via the volume balance. A simple model allows the local dependence of the surface flux on specific model variables to be described as a function of time and space. When these are combined with reference datasets, it is possible to estimate the surface flux bias induced by the model bias in each variable. The method allows the role of the surface albedo and ice thickness-growth feedbacks in sea ice volume balance biases to be quantified along with the roles of model bias in variables not directly related to the sea ice volume. It shows biases in the HadGEM2-ES sea ice volume simulation to be due to a bias in spring surface melt onset date, partly countered by a bias in winter downwelling longwave radiation. The framework is applicable in principle to any model and has the potential to greatly improve understanding of the reasons for ensemble spread in the modelled sea ice state. A secondary finding is that observational uncertainty is the largest cause of uncertainty in the induced surface flux bias calculation. | en_GB |
dc.description.sponsorship | Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme | en_GB |
dc.description.sponsorship | European Union Horizon 2020 | en_GB |
dc.description.sponsorship | Natural Environment Research Council (NERC) | en_GB |
dc.identifier.citation | Vol. 13, pp. 2001 - 2022 | en_GB |
dc.identifier.doi | 10.5194/tc-13-2001-2019 | |
dc.identifier.grantnumber | GA01101 | en_GB |
dc.identifier.grantnumber | 727862 | en_GB |
dc.identifier.grantnumber | NE/N018486/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/39874 | |
dc.language.iso | en | en_GB |
dc.publisher | European Geosciences Union (EGU) / Copernicus Publications | en_GB |
dc.rights | © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. | en_GB |
dc.title | Induced surface fluxes: A new framework for attributing Arctic sea ice volume balance biases to specific model errors | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-11-29T14:36:26Z | |
dc.identifier.issn | 1994-0416 | |
dc.description | This is the final version. Available on open access from EGU via the DOI in this record | en_GB |
dc.description | Code availability. The code used to create the fields of induced surface flux bias is written in Python and is provided as a Supplement (directory “ISF”). The code used to create Figs. 1–9, as well as Fig. B1, is also provided (directory “Figures”). In addition, the routines used to estimate errors in the ISF analysis are provided (directory “Analysis”). Finally, the code used to create Table 1 is provided (directory “Tables”). A set of auxiliary routines used by most of the above are also provided (directory “Library”). Most routines make use of the open-source Iris library, and several make use of the open-source Cartopy library. | en_GB |
dc.description | Data availability. Monthly mean ice thickness, ice fraction, snow thickness and surface radiation, as well as daily surface temperature and surface radiation, for the historical simulations of HadGEM2-ES, are available from the CMIP5 archive at https://cmip.llnl.gov/cmip5/data_portal.html (last access: February 2018). NSIDC ice concentration and melt onset data can be downloaded at http://nsidc.org/data/NSIDC-0051 (last access: May 2017; Cavalieri et al., 1996) and http://nsidc.org/data/NSIDC-0105 (last access: March 2016; Anderson et al., 2011) respectively. PIOMAS ice thickness data can be downloaded at http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/data/ (last access: March 2016; Zhang and Rothrock, 2003). ERAI surface radiation data can be downloaded at http://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/ (last access: September 2016; Dee et al., 2011). ISCCP-FD surface radiation data are available at https://isccp.giss.nasa.gov/projects/browse_fc.html (last access: October 2015; Zhang et al., 2004). CERES surface radiation data are available at https://climatedataguide.ucar.edu/climate-data/ceres-ebaf. (last access: August 2014; Loeb et al., 2009) | en_GB |
dc.identifier.journal | Cryosphere | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2019-05-29 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2019-07-19 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-11-29T14:33:58Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2019-11-29T14:36:30Z | |
refterms.panel | B | en_GB |
refterms.depositException | publishedGoldOA |
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