Abstract
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.
Highlights
The Arctic sea ice cover has witnessed rapid change during the past 30 years, with a decline in the September extent of 1.05 × 106 km2 per decade from 1986 to 2015 (HadISST1.2, Rayner et al, 2003)
Given reference datasets for independent variables, fields of induced surface flux bias can be calculated from the underlying model bias; these in theory sum to the total surface flux bias
The induced surface flux (ISF) analysis enables model biases in sea ice growth and melt rate to be attributed in detail to different causes
Summary
The Arctic sea ice cover has witnessed rapid change during the past 30 years, with a decline in the September extent of 1.05 × 106 km per decade from 1986 to 2015 (HadISST1.2, Rayner et al, 2003). The method allows the contributions to model biases in ice growth and melt caused by the surface–albedo feedback, the ice thickness– growth feedback and “forcings” to be independently quantified In this way it can be seen how model biases in the external forcings drive model bias in the sea ice volume balance, offering a valuable tool for setting sea ice state biases in context and for understanding spread in sea ice simulation within multi-model ensembles. The fields of induced surface flux bias (ISF bias) can be averaged in time or space to determine the large-scale effects of particular model biases This bypasses nonlinearities in the dependence of surface flux on some model metrics. A variety of reference datasets demonstrate the large observational uncertainties present in the Arctic, which affect our ability to attribute sea ice volume balance bias with the ISF framework.
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