Abstract
AbstractTurbulent fluxes make a substantial and growing contribution to the energy balance of ice surfaces globally, but are poorly constrained owing to challenges in estimating the aerodynamic roughness length (z0). Here, we used structure from motion (SfM) photogrammetry and terrestrial laser scanning (TLS) surveys to make plot-scale 2-D and 3-D microtopographic estimations ofz0and upscale these to mapz0across an ablating mountain glacier. At plot scales, we found spatial variability inz0estimates of over two orders of magnitude with unpredictablez0trajectories, even when classified into ice surface types. TLS-derived surface roughness exhibited strong relationships with plot-scale SfMz0estimates. At the glacier scale, a consistent increase inz0of ~0.1 mm d−1was observed. Space-for-time substitution based on time since surface ice was exposed by snow melt confirmed this gradual increase inz0over 60 d. These measurements permit us to propose a scale-dependent temporalz0evolution model where unpredictable variability at the plot scale gives way to more predictable changes ofz0at the glacier scale. This model provides a critical step towards deriving spatially and temporally distributed representations ofz0that are currently lacking in the parameterisation of distributed glacier surface energy balance models.
Highlights
The physical roughness of a surface exerts drag on the air moving over it, leading to instabilities that drive turbulence within a wind profile and vertical mixing of air through turbulent eddies (Cuffey and Paterson, 2010)
While not the dominant source of melt energy, turbulent fluxes can have a substantial contribution to a glacier surface energy balance (SEB), : (i) at high latitudes in the northern hemisphere where ice surfaces at lower altitudes are exposed to high summer air temperatures; and (ii) in maritime conditions where windy and cloudy conditions reduce the role of short-wave and long-wave radiation, e.g. Scandinavia and the West coast of New Zealand (Ishikawa and others, 1992; Giesen and others, 2014; Conway and Cullen, 2016)
A statistically significant relationship between 2-D and 3-D z0 estimates averaged for all directions was present for all surveys combined (Spearman’s rank ρ = 0.838, p < 0.01, n = 59)
Summary
The physical roughness of a surface exerts drag on the air moving over it, leading to instabilities that drive turbulence within a wind profile and vertical mixing of air through turbulent eddies (Cuffey and Paterson, 2010). Over ice surfaces, such turbulence can deliver energy to the glacier surface in two ways:. In areas where both summer and winter temperatures remain extremely low, turbulent fluxes tend to be either very small or act as a net sink of melt energy (Sicart and others, 2005, Bravo and others, 2017)
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