Broadband albedo of Arctic sea ice from MERIS optical data

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.

Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica

“…Generally, MODIS observations underestimated the anisotropy of the reflection by the snow surfaces. This has possible implications for satellite retrievals of surface albedo and atmospheric parameters over snow surfaces (e.g., AOD and cloud properties), which strongly depend on a correct angular modeling of the surface reflectance (e.g., Qu et al, 2015).…”

“…However, they are restricted in terms of the number T. Carlsen et al: Parameterizing snow HDRF measurements in Antarctica of available observation angles and spectral bands as well as their temporal resolution. The processing of using measurements from polar-orbiting satellites to monitor the broadband surface albedo typically requires three steps (e.g., Qu et al, 2015): atmospheric correction, modeling of the angular reflectance, and narrow-to-broadband conversion. During the first step, the TOA reflectance is converted into a surface reflectance by correcting for gaseous and aerosol scattering and absorption applying radiative transfer modeling (e.g., Vermote and Kotchenova, 2008).…”

Year-round impact of winter sea ice thickness observations on seasonal forecasts

“…Blockley and Peterson (2018) reported for the first time the positive impact of winter SIT initialization on the skill of seasonal forecasts for summer sea ice forecasts using a fully coupled atmosphere-oceansea-ice model. All of these studies used either the European Space Agency's CryoSat-2 (CS2) radar altimeter freeboard SIT measurements alone (Laxon et al, 2013;Hendricks et al, 2016) or measurements merged with SMOS radiometric measurements (Kaleschke et al, 2012;Tian-Kunze et al, 2014) in a dataset called CS2SMOS (Ricker et al, 2017).…”

Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica

“…The BRDF describes the scattering of electromagnetic (EM) radiation from one incident direction into another direction of the hemisphere. By using a linear combination of the discrete spectral band measurements with specific weighting, the broadband surface albedo is calculated (e.g., Brest and Goward, 1987;Klein and Stroeve, 2002;Liang et al, 2003;Pohl et al, 2020). The largest uncertainty in this three-step process is introduced by the angular dependence of the surface BRDF, especially when the reflection of the underlying surface is highly anisotropic.…”