Mapping the aerodynamic roughness of the Greenland Ice Sheet surface using ICESat-2: evaluation over the K-transect

Abstract

Abstract. The aerodynamic roughness of heat, moisture, and momentum of a natural surface are important parameters in atmospheric models, as they co-determine the intensity of turbulent transfer between the atmosphere and the surface. Unfortunately this parameter is often poorly known, especially in remote areas where neither high-resolution elevation models nor eddy-covariance measurements are available. In this study we adapt a bulk drag partitioning model to estimate the aerodynamic roughness length (z0m) such that it can be applied to 1D (i.e. unidirectional) elevation profiles, typically measured by laser altimeters. We apply the model to a rough ice surface on the K-transect (west Greenland Ice Sheet) using UAV photogrammetry, and we evaluate the modelled roughness against in situ eddy-covariance observations. We then present a method to estimate the topography at 1 m horizontal resolution using the ICESat-2 satellite laser altimeter, and we demonstrate the high precision of the satellite elevation profiles against UAV photogrammetry. The currently available satellite profiles are used to map the aerodynamic roughness during different time periods along the K-transect, that is compared to an extensive dataset of in situ observations. We find a considerable spatio-temporal variability in z0m, ranging between 10−4 m for a smooth snow surface and 10−1 m for rough crevassed areas, which confirms the need to incorporate a variable aerodynamic roughness in atmospheric models over ice sheets.

Observed and Parameterized Roughness Lengths for Momentum and Heat Over Rough Ice Surfaces

“…The constant was set to K m = 0.1 in order to match the z 0m observations during the ablation season. A more precise calibration of K m would require several repeated surveys of the ice obstacles at a single location using for example, photogrammetry, as in Van Tiggelen et al (2021). We assume a fixed decrease in ice obstacle height due to sublimation only when there is no melt:…”

“…If we consider the obstacles along a fixed wind fetch direction, and we assume that all the obstacles have the same height, then λ = fH mod /100, with f the number of obstacles per 100 m profile length, and H mod the modeled height of the ice obstacles. As an approximation, we take f = 8 obstacles per 100 m, based on UAV surveys over this rough ice area (Van Tiggelen et al., 2021). We then model the total obstacle height as: $$${H}_{mod}={H}_{\mathit{ice}}-{H}_{\mathit{snow}},$$$ where H ice is the height of the ice obstacles and H snow is the snow depth, either taken from AWS observations or from RCM output.…”

“…A more precise calibration of K m would require several repeated surveys of the ice obstacles at a single location using for example, photogrammetry, as in Van Tiggelen et al. (2021). We assume a fixed decrease in ice obstacle height due to sublimation only when there is no melt: $$${\Delta }{H}_{\mathit{subl}}=\left\{\begin{array}{ll}{K}_{s}\ \,\left(\mathrm{m}\mathrm{m}\,\mathrm{d}\mathrm{a}{\mathrm{y}}^{-\mathrm{1}}\right)\quad \hfill & \text{if}\,{M}_{\mathit{melt}}=0,\hfill \\ 0\,\left(\mathrm{m}\mathrm{m}\,\mathrm{d}\mathrm{a}{\mathrm{y}}^{-\mathrm{1}}\right)\quad \hfill & \text{otherwise}.\hfill \end{array}\right.$$$ …”

“…Based on previous drag modeling work (Raupach, 1992; Van Tiggelen et al., 2021), z 0 m is known to directly depend on both the average height of the roughness obstacles after high‐pass filtering of the topography ( H mod ), and on the obstacle frontal area index ( λ ). If we consider the obstacles along a fixed wind fetch direction, and we assume that all the obstacles have the same height, then λ = fH mod /100, with f the number of obstacles per 100 m profile length, and H mod the modeled height of the ice obstacles.…”

Time‐lapse photogrammetry reveals hydrological controls of fine‐scale High‐Arctic glacier surface roughness evolution

“…However, these insightful length‐scales correspond to those of supraglacial rill and stream widths and their spacing, cryoconite holes, foliation and other ice structure, and, importantly, to the scale‐dependency of ice surface roughness between length‐scales of 0.1 to ~2 m (Rees & Arnold, 2006). Nonetheless, roughness variability at finer scales is essential to inform the response of, and uncertainties in data retrieved from assorted satellite platforms (Fitzpatrick et al, 2019; Rees & Arnold, 2006; van Tiggelen et al, 2021). Yet, despite such critical questions, assessments of bare‐ice topographic dynamics, and their drivers, at high resolution remain lacking.…”