Distributed Global Debris Thickness Estimates Reveal Debris Significantly Impacts Glacier Mass Balance

Abstract

Supraglacial debris affects glacier mass balance as a thin layer enhances surface melting, while a thick layer reduces it. While many glaciers are debris-covered, global glacier models do not account for debris because its thickness is unknown. We provide the first globally distributed debris thickness estimates using a novel approach combining sub-debris melt and surface temperature inversion methods. Results are evaluated against observations from 22 glaciers. We find the median global debris thickness is ~0.15 ± 0.06 m. In all regions, the net effect of accounting for debris is a reduction in sub-debris melt, on average, by 37%, which can impact regional mass balance by up to 0.40 m water equivalent (w.e.) yr-1. We also find recent observations of similar thinning rates over debris-covered and clean ice glacier tongues is primarily due to differences in ice dynamics. Our results demonstrate the importance of accounting for debris in glacier modeling efforts.

What Can Thermal Imagery Tell Us About Glacier Melt Below Rock Debris?

“…I compare this approach to two recently proposed/published methods from Rowan et al (2021) and Rounce et al (2021).…”

“…I also evaluate the approach used by Rounce et al (2021) where debris thickness is estimated using the Hill equation,…”

“…I quantify the success of the proposed methods and consider factors that cause method failure, specifically, by indirectly solving for debris layer saturation. I then compare these results to two recently proposed/published methods from Rowan et al (2021) and Rounce et al (2021).…”

“…Earth's mountain glaciers are 7.3% debris-covered (Herreid and Pellicciotti, 2020), yet estimates of debris thickness, the key variable governing sub-debris melt rates, are only now being derived at large scales (Kraaijenbrink et al, 2017;Rounce et al, 2021) and their accuracy will benefit from further validation. Debris thickness can be derived from a high density network of excavation point measurements (e.g., Anderson et al, 2021), naturally occurring cross sections (e.g., Nicholson and Mertes, 2017) or ground penetrating radar (e.g., Nicholson et al, 2018); however, these methods are time and resource intensive and impractical at large spatial scales.…”

“…Debris thickness can be derived from a high density network of excavation point measurements (e.g., Anderson et al, 2021), naturally occurring cross sections (e.g., Nicholson and Mertes, 2017) or ground penetrating radar (e.g., Nicholson et al, 2018); however, these methods are time and resource intensive and impractical at large spatial scales. Proposed methods to derive debris thickness at wider spatial scales are 1) empirical relations between debris thickness and satellite thermal data (e.g., Ranzi et al, 2004;Mihalcea et al, 2008a;Kraaijenbrink et al, 2017); 2) debris thickness derived from satellite thermal data as the residual of a physically-based surface temperature inversion (Foster et al, 2012;Rounce and McKinney, 2014;Schauwecker et al, 2015); 3) a sub-debris melt inversion (Ragettli et al, 2015;Rounce et al, 2018); and 4), a combination of both a sub-debris melt inversion and a surface temperature inversion (Rounce et al, 2021). While studies using moderate/coarse resolution thermal data acquired from satellites is common, the use of field-based oblique or airborne/unmanned aerial vehicle (UAV) acquired high resolution thermal data is surprisingly rare in glaciology (Hopkinson et al, 2010;Aubry-Wake et al, 2015Herreid and Pellicciotti, 2018;Kraaijenbrink et al, 2018), and none of these studies used their thermal data to explicitly solve for debris thickness and/or glacier melt rates.…”