Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network

A Rösel,G Birnbaum,L Kaleschke

doi:10.5194/tc-6-431-2012

Abstract

Abstract. Melt ponds on sea ice strongly reduce the surface albedo and accelerate the decay of Arctic sea ice. Due to different spectral properties of snow, ice, and water, the fractional coverage of these distinct surface types can be derived from multispectral sensors like the Moderate Resolution Image Spectroradiometer (MODIS) using a spectral unmixing algorithm. The unmixing was implemented using a multilayer perceptron to reduce computational costs. Arctic-wide melt pond fractions and sea ice concentrations are derived from the level 3 MODIS surface reflectance product. The validation of the MODIS melt pond data set was conducted with aerial photos from the MELTEX campaign 2008 in the Beaufort Sea, data sets from the National Snow and Ice Data Center (NSIDC) for 2000 and 2001 from four sites spread over the entire Arctic, and with ship observations from the trans-Arctic HOTRAX cruise in 2005. The root-mean-square errors range from 3.8 % for the comparison with HOTRAX data, over 10.7 % for the comparison with NSIDC data, to 10.3 % and 11.4 % for the comparison with MELTEX data, with coefficient of determination ranging from R2=0.28 to R2=0.45. The mean annual cycle of the melt pond fraction per grid cell for the entire Arctic shows a strong increase in June, reaching a maximum of 15 % by the end of June. The zonal mean of melt pond fractions indicates a dependence of the temporal development of melt ponds on the geographical latitude, and has its maximum in mid-July at latitudes between 80° and 88° N. Furthermore, the MODIS results are used to estimate the influence of melt ponds on retrievals of sea ice concentrations from passive microwave data. Results from a case study comparing sea ice concentrations from ARTIST Sea Ice-, NASA Team 2-, and Bootstrap-algorithms with MODIS sea ice concentrations indicate an underestimation of around 40 % for sea ice concentrations retrieved with microwave algorithms.

Highlights

In boreal summer, melt ponds are a common feature on Arctic sea ice and they can cover up to 50 to 60 % of the sea ice area (Fetterer and Untersteiner, 1998; Eicken et al, 2004)
The validation of the Moderate Resolution Image Spectroradiometer (MODIS) melt pond data set was conducted with aerial photos from the MELTEX campaign 2008 in the Beaufort Sea, data sets from the National Snow and Ice Data Center (NSIDC) for 2000 and 2001 from four sites spread over the entire Arctic, and with ship observations from the trans-Arctic HOTRAX cruise in 2005
The root-mean-square errors range from 3.8 % for the comparison with HOTRAX data, over 10.7 % for the comparison with NSIDC data, to 10.3 % and 11.4 % for the comparison with MELTEX data, with coefficient of determination ranging from R2 = 0.28 to R2 = 0.45

Summary

Introduction

Melt ponds are a common feature on Arctic sea ice and they can cover up to 50 to 60 % of the sea ice area (Fetterer and Untersteiner, 1998; Eicken et al, 2004). On a flat topography of first-year ice and in an early melt stage the melt pond fraction can even rise up to 90 % (Perovich et al, 2011a). Melting caused by shortwave insolation and surface air temperatures above the freezing point during summer results in the development of melt ponds on the sea ice surface and a decrease of the surface albedo from approximately 0.8 to 0.5 which excites additional heat uptake (Curry et al, 1995; Perovich and Tucker, 1997). The appearance of melt ponds has a significant influence on Earth’s radiation balance (Maslanik et al, 2007; Perovich et al, 2007; Nicolaus et al, 2010) and an impact on the strength of the ice-albedo feedback (Tschudi et al, 2008). In order to better constrain the role of sea ice for the Arctic amplification and Earth’s climate system, it is important to quantify the large-scale distribution of melt ponds (e.g. Holland et al, 2006; Eisenman and Wettlaufer, 2009; Notz, 2009; Tietsche et al, 2011; Serreze, 2011; Serreze et al, 2011; Kurtz et al, 2011; Perovich et al, 2011b).

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Conclusion