Arctic sea ice albedo. Terhikki Manninen Aku Riihelä Vesa Laine Kaj Andersson (VTT)

Transcription

1 Arctic sea ice albedo Terhikki Manninen Aku Riihelä Vesa Laine Kaj Andersson (VTT)

2 Surface albedo Defined as the fraction of (solar) radiation reflected by a surface An Essential Climate Variable as defined by GCOS Plays a major role in the surface energy budget of Earth Snow and ice have a high albedo, bare soils and water (for example) have a low albedo Most cost-effectively monitored from satellites

3 Sea ice albedo processing using optical satellite instruments (AVHRR) Processing steps: AVHRR visible and near-infrared radiances into TOA reflectances Cloud (and shadow) masking (using PPS developed by SMHI and based on all the AVHRR channels) Atmospheric correction BRDF correction (averaging the overpass data) Narrow to broadband conversion End product: pentad and monthly averaged surface albedos

4 Atmospheric correction Atmospheric effects (path radiance) need to be removed from the observed TOA reflectances to generate direct-illumination-only surface reflectances for black-sky albedo computation. The Simplified Model for Atmospheric Correction (SMAC) is our algorithm of choice for this operation [Rahman & Dedieu, 1994]. Inputs TOA reflectances in the visible and near-infrared imager bands Aerosol Optical Depth (AOD) content of the atmosphere, currently constant at 0.1 Total ozone column (O3), currently constant at 0.35 (atm cm) Total column water vapour content, from ECMWF ERA-Interim reanalysis (reprocessed AVHRR) or DWD GME/ECMWF model runs (g/cm^2) Surface pressure, from ECMWF ERA-Interim (reproc. AVHRR) or DWD GME/ECMWF model runs (hpa)

5 Anisotropy sampling of snow The reflectance anisotropy properties of snow vary widely with snow type! Very difficult to model universally without universal data on snow physical characteristics Our solution: Do not apply a model, in stead sample the anisotropy directly and consider the mean of the samples to represent the albedo. The strategy works if we have enough samples of the BRDF which fortunately is the case when using AVHRR in the high latitudes (where snow exists)! Reflectance sampling distribution at Summit Camp, Greenland Ice Sheet, summer Radial distance shows viewing zenith angles, azimuthal angles as shown.

6 Narrow-to-broadband conversion Satellite imagers cover only a part of the solar spectrum algorithms needed to convert observed (spectral) albedo to full broadband albedo! NTBC algorithms separated by instrument (SEVIRI / AVHRR) and land cover (vegetation, snow, water) Vegetation-AVHRR: Liang (2000) Vegetation-SEVIRI: Van Leeuwen & Roujean (2002) Snow: Xiong et al. (2002) AVHRR channels 1&2 Water (LUT-based): Jin et al. (2004)

7 Surface broadband albedo (SAL) Broadband albedo over the solar spectrum Dedicated algorithms for different land cover and snow/ice Weekly/pentad and monthly products for: AVHRR Europe AVHRR Arctic SEVIRI Full Disc Click to edit Master text styles Second level Third level Fourth level Fifth level

8 CM-SAF SAL Arctic time series

9 Validation of the dataset over snow/ice Comparisons to SHEBA (and Tara) campaign albedo data Good accuracy overall (10-15% relative) Late-summer retrievals challenging

10 Shortwave broadband black-sky surface albedo estimation for Arctic sea ice using passive microwave radiometer data FMI: Laine, V., T. Manninen, A. Riihelä, and VTT: K. Andersson J. Geophys. Res., 116, D16124, doi: /2011jd015700, Publication Date: 31 August

11 Albedo estimation using passive microwaves (AMSR-E) Sea ice is a mixture of ice, brine pockets, air bubbles (and biological organisms such as algae). The ice may be bare or covered by dry snow or wet snow. These quantities affect both microwave emissivity and ice surface albedo. Surface albedo is directly related to the sea ice concentration, which is related to microwave emission. Hypothesis: Albedo can be related to microwave emission.

12 Evolution of albedo and microwave emission Perovich et al., 2002, Grenfell et al. 1998

13 Snow penetration depth in the optical wavelength range for various snow properties (Wiscombe and Warren 1980) Snow type Grain radius Density Penetration depth Fluffy new snow 50 µm 0.1 g cm-3 20 cm Old snow 200 µm 0.4 g cm-3 20 cm Old melting snow 1000 µm 0.4 g cm-3 50 cm Penetration depths (cm) for sea ice and snow with liquid water contents of 1 and 2% for the AMSR-E frequencies of 6.9, 18.7 and 36.5 GHz (Ulaby et al. 1982) Surface type 6.9 GHz (75x43) 18.7 GHz (27x16) 36.5 GHz (14x8) First year ice Multi year ice Snow (1%) Snow (2%)

14 Effect of melt points on albedo of sea ice Eicken et al., 2004

15 Bi-weekly spatial and temporal averages for each common rectangular sub-regions from AMSR-E and AVHRR data has been employed in the analysis. The correspondence has been tested using independent data from the bigger (red) sub-region where the data from the six smaller sub regions are removed.

16 Third degree polynomial curve fit to the cloud masked AVHRRalbedo and AMSR-E brightness temperature data points. TB = TBi ci ( 1 c p ) + TBp ci c p + TBw ( 1 ci )

17 The scatterplot of the AVHRR albedo versus the estimated AMSR-E albedo.

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19 Variability in the sea surface albedo in the time series of weekly averages for the summer 2007.

20 Thank you for your attention! pentad SAL