Abstract
Abstract. Arctic summer sea ice experiences rapid changes in its sea-ice concentration, surface albedo, and the melt pond fraction. This affects the energy balance of the region and demands an accurate knowledge of those surface characteristics in climate models. In this paper, the broadband albedo (300–3000 nm) of Arctic sea ice is derived from MEdium Resolution Imaging Spectrometer (MERIS) optical swath data by transforming the spectral albedo as an output from the Melt Pond Detector (MPD) algorithm with a newly developed spectral-to-broadband conversion (STBC). The new STBC replaces the previously applied spectral averaging method to provide a more accurate broadband albedo product, which approaches the accuracy of 0.02–0.05 required in climate simulations and allows a direct comparison to broadband albedo values from climate models. The STBC is derived empirically from spectral and broadband albedo measurements over landfast ice. It is validated on a variety of simultaneous spectral and broadband field measurements over Arctic sea ice, is compared to existing conversion techniques, and performs better than the currently published algorithms. The root-mean-square deviation (RMSD) between broadband albedo that was measured and converted by the STBC is 0.02. Other conversion techniques, the spectral averaging method and the linear combination of albedo values from four MERIS channels, result in higher RMSDs of 0.09 and 0.05, respectively. The improved MERIS-derived broadband albedo values are validated with airborne measurements. Results show a smaller RMSD of 0.04 for landfast ice than the RMSD of 0.07 for drifting ice. The MERIS-derived broadband albedo is compared to broadband albedo from ERA5 reanalysis to examine the albedo parameterization used in ERA5. Both albedo products agree over large scales and in temporal patterns. However, consistency in point-to-point comparison is rather poor, with differences up to 0.20, correlations between 0.69 and 0.79, and RMSDs in excess of 0.10. Differences in sea-ice concentration and cloud-masking uncertainties play a role, but most discrepancies can be attributed to climatological sea-ice albedo values used in ERA5. They are not adequate and need revising, in order to better simulate surface heat fluxes in the Arctic. The advantage of the resulting broadband albedo data set from MERIS over other published data sets is the accompanied data set of available melt pond fraction. Melt ponds are the main reason for the sea-ice albedo change in summer but are currently not represented in climate models and atmospheric reanalysis. Additional information about melt evolution, together with accurate albedo retrievals, can aid the challenging representation of sea-ice optical properties in those models in summer.
Highlights
Broadband albedo of sea ice, including snow-covered sea ice, is a key parameter in Arctic climate studies
We introduce a spectral-to-broadband conversion (STBC), which replaces the conversion method proposed by Istomina et al (2015) for calculating broadband albedo in the waveband 300–3000 nm from spectral albedo retrieved from MEdium Resolution Imaging Spectrometer (MERIS) swath observations via the Melt Pond Detector (MPD) algorithm (Zege et al, 2015)
Averages of broadband albedo over Arctic sea ice are derived from MERIS observations in three steps: MERIS swath Level 1b data over Arctic sea ice are used by the Melt Pond Detector (MPD) algorithm to derive time-synchronous spectral albedo values which are averaged daily afterwards (Sect. 2.1)
Summary
Broadband albedo of sea ice, including snow-covered sea ice, is a key parameter in Arctic climate studies. The satellite-retrieved albedo products underlie uncertainties, depending on the number and spectral distribution of satellite channels (i.e., narrowbands) available, the temporal and spatial resolution of the measurement (Table 1), and the approach to retrieve the broadband albedo (kerneldriven, bidirectional reflectance distribution function, BRDF, methods or anisotropy reflectance correction, ARC, methods, both combined with a narrow-to-broadband conversion, NTBC, direct estimation methods, and methods using radiative transfer theory, RTT) (Liang, 2001; Qu et al, 2014) The latter dependency is caused by pre-assumed surface and atmospheric conditions in most retrieval methods.
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