Application of a SeaWinds/QuikSCAT sea ice melt algorithm for assessing melt dynamics in the Canadian Arctic Archipelago

Abstract

A remotely sensed sea ice melt algorithm utilizing SeaWinds/QuikSCAT (QuikSCAT) data is developed and applied to sea ice the Canadian Arctic Archipelago (CAA) from 2000 to 2004. The extended advanced very high resolution radiometer Polar Pathfinder (APP‐x) data set is used to identify spatially coupled relationships between sea ice melt and radiative forcings. In situ data from the Collaborative Interdisciplinary Cryospheric Experiment (C‐ICE) (2000, 2001, and 2002) and the Canadian Arctic Shelf Exchange Study (CASES) (2004) are used to validate APP‐x data during the melt period. QuikSCAT‐detected maps of melt onset, pond onset, and drainage are created from 2000 to 2004, and results indicate considerable interannual variability of melt dynamics within the CAA. In some years, melt stages are positively spatially autocorrelated, whereas other years exhibit a negative or no spatial autocorrelation. QuikSCAT‐detected stages of melt are found to be influenced by interannual varying amounts and timing of radiative forcing making prediction difficult. The spatiotemporal variability of ice melt also influences the distribution of ice within the CAA. The lower‐latitude regions of the CAA are shown to have accumulated increasing concentrations of multiyear ice from 2000 to 2005. This paper concludes with a discussion of the interplay between thermodynamic and dynamic sea ice processes likely to have contributed to this trend.

First-year sea ice melt pond fraction estimation from dual-polarisation C-band SAR – Part 2: Scaling in situ to Radarsat-2

“…Optical data are limited by the persistent stratus cloud cover present in the Arctic during summer (Inoue et al, 2005), and retrieval algorithms are limited by assumptions made regarding the predefined spectral behaviour of subscale surface types open water, melt pond and snow/ice . Passive and active microwave radiometers and scatterometers provide synoptic estimations of timings related to pond formation, regardless of cloud cover and weather conditions (Comiso and Kwok, 1996;Howell et al, 2006). These data are potentially usable in large-scale models, though spatial resolutions of several kilometres, combined with signal contamination by land and open water , make automated retrievals problematic.…”

Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery

“…They also require cloud-free conditions, limiting their applicability in the Arctic due to the persistent cloud cover present during summer. Satellite microwave radiometers and scatterometers can on the other hand penetrate clouds, but their resolution is in general too coarse for automated melt pond monitoring (Comiso and Kwok, 1996;Howell et al, 2006).…”

Multi‐year sea‐ice conditions in the western Canadian arctic archipelago region of the northwest passage: 1968–2006

“…Blocking landfast sea ice plays a dominant role in CAA sea-ice dynamics by restricting movement; sea ice can only move when a route of ice-free water is available (Marko, 1977). The annual melt process is greatly affected by the spatio-temporal distribution of the ice type and concentration makeup of the sea-ice matrix in the CAA (Howell et al, 2006).…”