Abstract

Abstract. Sea ice drift plays a central role in the Arctic climate and ecology through its effects on the ice cover, thermodynamics, and energetics of northern marine ecosystems. Due to the challenges of accessing the Arctic, remote sensing has been used to obtain large-scale longitudinal data. These data are often associated with errors and biases that must be considered when incorporated into research. However, obtaining reference data for validation is often prohibitively expensive or practically unfeasible. We used the motion of 20 passively drifting high-accuracy GPS telemetry collars originally deployed on polar bears, Ursus maritimus, in western Hudson Bay, Canada, to validate a widely used sea ice drift dataset produced by the National Snow and Ice Data Center (NSIDC). Our results showed that the NSIDC model tended to underestimate the horizontal and vertical (i.e., u and v) components of drift. Consequently, the NSIDC model underestimated magnitude of drift, particularly at high ice speeds. Modelled drift direction was unbiased; however, it was less precise at lower drift speeds. Research using these drift data should consider integrating these biases into their analyses, particularly where absolute ground speed or direction is necessary. Further investigation is required into the sources of error, particularly in under-examined areas without in situ data.

Highlights

  • Many research fields increasingly depend on remote sensing to collect environmental data

  • National Snow and Ice Data Center (NSIDC) drift speeds were slower than collar drift speeds in 63.1 % of the vectors, and only 10.4 % of NSIDC drift speeds were within

  • This study provides the first error estimates of any sea ice drift model in Hudson Bay

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Summary

Introduction

Many research fields increasingly depend on remote sensing to collect environmental data. Measurement errors and assimilation biases can lead to large inaccuracies (Reichle, 2008). Quantifying error in remotely sensed data can be used to improve these data products (Cressie et al, 2009) and is important for data assimilation and the development of new products (Meier et al, 2000; Sumata et al, 2014, 2015a). Assessing these errors is challenging, in remote areas that are difficult to ground truth

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