Abstract

Abstract. Projections of Arctic sea ice thickness (SIT) have the potential to inform stakeholders about accessibility to the region, but are currently rather uncertain. The latest suite of CMIP5 global climate models (GCMs) produce a wide range of simulated SIT in the historical period (1979–2014) and exhibit various biases when compared with the Pan-Arctic Ice–Ocean Modelling and Assimilation System (PIOMAS) sea ice reanalysis. We present a new method to constrain such GCM simulations of SIT via a statistical bias correction technique. The bias correction successfully constrains the spatial SIT distribution and temporal variability in the CMIP5 projections whilst retaining the climatic fluctuations from individual ensemble members. The bias correction acts to reduce the spread in projections of SIT and reveals the significant contributions of climate internal variability in the first half of the century and of scenario uncertainty from the mid-century onwards. The projected date of ice-free conditions in the Arctic under the RCP8.5 high emission scenario occurs in the 2050s, which is a decade earlier than without the bias correction, with potentially significant implications for stakeholders in the Arctic such as the shipping industry. The bias correction methodology developed could be similarly applied to other variables to reduce spread in climate projections more generally.

Highlights

  • Global climate models (GCMs) are the primary tool for making climate predictions on seasonal to decadal timescales, and climate projections over the century (Flato et al., 2013)

  • By constraining CMIP5 simulations with the Pan-Arctic Ice–Ocean Modelling and Assimilation System (PIOMAS) reanalysis we have demonstrated the following

  • – GCMs simulate a wide range of sea ice thickness (SIT) in the historical period and exhibit various spatial and temporal biases when compared with the PIOMAS reanalysis

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Summary

Introduction

Global climate models (GCMs) are the primary tool for making climate predictions on seasonal to decadal timescales, and climate projections over the century (Flato et al., 2013). Boe et al (2009); Christensen et al (2008); Ho et al (2011); Mahlstein and Knutti (2012); Vrac and Friederichs (2014); Watanabe et al (2012), and references therein) which have mainly been applied to temperature and precipitation These existing methods need refining for sea ice as SIT is a challenging variable. The study uses multiple ensemble members from the same model when performing the BC, something that is often not utilised in other studies This is important as it enables an assessment of the role of internal variability in future projections to be made. In this paper we use the Pan-Arctic Ice–Ocean Modelling and Assimilation System (PIOMAS) (Zhang and Rothrock, 2003) as a reanalysis-based estimate of recent SIT, along with climate projections from a subset of six GCMs from the CMIP5 archive

PIOMAS
Global climate models
Bias correction methodology
Additive correction
Multiplicative correction
Mean multiplicative correction
Mean and variance correction
Bias corrected sea ice thickness projections
Temporal perspective example
Historical spatial perspective
CMIP5 subset multi-model sea ice thickness projections
Sources of uncertainty in projections of sea ice thickness
Reduced spread in timing of ice-free conditions
Summary
Findings
Discussion
Full Text
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