Abstract

The size and shape of snow grains directly impacts the reflection by a snowpack. In this article, different approaches to retrieve the optical-equivalent snow grain size (ropt) or, alternatively, the specific surface area (SSA) using satellite, airborne, and ground-based observations are compared and used to evaluate ICON-ART (ICOsahedral Nonhydrostatic—Aerosols and Reactive Trace gases) simulations. The retrieval methods are based on optical measurements and rely on the ropt-dependent absorption of solar radiation in snow. The measurement data were taken during a three-week campaign that was conducted in the North of Greenland in March/April 2018, such that the retrieval methods and radiation measurements are affected by enhanced uncertainties under these low-Sun conditions. An adjusted airborne retrieval method is applied which uses the albedo at 1700 nm wavelength and combines an atmospheric and snow radiative transfer model to account for the direct-to-global fraction of the solar radiation incident on the snow. From this approach, we achieved a significantly improved uncertainty (<25%) and a reduced effect of atmospheric masking compared to the previous method. Ground-based in situ measurements indicated an increase of ropt of 15 µm within a five-day period after a snowfall event which is small compared to previous observations under similar temperature regimes. ICON-ART captured the observed change of ropt during snowfall events, but systematically overestimated the subsequent snow grain growth by about 100%. Adjusting the growth rate factor to 0.012 µm2 s−1 minimized the difference between model and observations. Satellite-based and airborne retrieval methods showed higher ropt over sea ice (<300 µm) than over land surfaces (<100 µm) which was reduced by data filtering of surface roughness features. Moderate-Resolution Imaging Spectroradiometer (MODIS) retrievals revealed a large spread within a series of subsequent individual overpasses, indicating their limitations in observing the snow grain size evolution in early spring conditions with low Sun.

Highlights

  • The enhanced sensitivity of the Arctic climate system regarding global warming, referred to as Arctic Amplification, is associated with several feedback mechanisms [1,2,3]

  • For 25 March, the analysis reveals that the interquartile ranges (IQR), indicated by the gray boxes, cover different ropt -ranges, especially for the snow grain size and pollution amount (SGSP) retrievals of the ModerateResolution Imaging Spectroradiometer (MODIS) data from 13:50 UTC and 16:45 UTC

  • Since the measurement uncertainties are affected by Sun and sensor viewing directions, which were variable between the different MODIS overpasses, we can assume that these uncertainties contribute to the observed variation of the retrieved snow grain size

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Summary

Introduction

The enhanced sensitivity of the Arctic climate system regarding global warming, referred to as Arctic Amplification, is associated with several feedback mechanisms [1,2,3]. The ground-based methods mostly refer to the measurements of the surface albedo, whereas satellite data provide the bidirectional reflectance distribution function (BRDF). Both reflection properties are influenced by e.g., the surface roughness and the snow grain shape and orientation. For satellite-based remote sensing of the snow grain size, the deviation of the snow BRDF from that of an ideal plane surface, may lead to an underestimation (overestimation) of the retrieved SSA (ropt ). Since low-Sun observations are prevalent especially in Arctic spring and autumn, and an evaluation of weather and climate models require observations of larger spatial scales, this study estimates the variations of different snow grain size retrievals for these extreme conditions.

PAMARCMiP Campaign
Ground-Based Measurements by the IceCube System
Airborne Measurements by SMART
Satellite Measurements
Sea Ice Conditions
Meteorological Conditions
Overview
Snow Radiative Transfer Model—TARTES
Atmospheric Radiative Transfer Model—libRadtran
Weather and Climate Model—ICON-ART
Parametrization of SSA Evolution
XBAER Retrieval of Snow Grain Size Using Satellite-Based Sentinel-3 Data
SGSP Retrieval of Snow Grain Size Using Satellite-Based MODIS Data
Snow Grain Size Retrieval Using Airborne SMART Data
Relevance of Atmospheric Effect Correction on SMART Retrieval
Temporal Variability
Retrieved Maps of Snow Grain Size
Statistical Comparison for Smooth Snow Surfaces
Discussion
Uncertainties of the SGSP Satellite Retrieval
SMART Measurement Uncertainty and Retrieval Sensitivity
Effect of Snow Particle Shape
Wavelength Choice and Penetration Depth
Summary and Conclusions
Full Text
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