Abstract
Abstract. Nine density-dependent empirical thermal conductivity relationships for firn were compared against data from three automatic weather stations at climatically different sites in East Antarctica (Dome A, Eagle, and LGB69). The empirical relationships were validated using a vertical, 1D thermal diffusion model and a phase-change-based firn diffusivity estimation method. The best relationships for the abovementioned sites were identified by comparing the modeled and observed firn temperature at a depth of 1 and 3 m, and from the mean heat conductivities over two depth intervals (1–3 and 3–10 m). Among the nine relationships, that proposed by Calonne et al. (2011) appeared to show the best performance. The density- and temperature-dependent relationship given in Calonne et al. (2019) does not show clear superiority over other density-dependent relationships. This study provides a useful reference for firn thermal conductivity parameterizations in land modeling or snow–air interaction studies on the Antarctica ice sheet.
Highlights
In the Earth’s climate system, snow cover has two important physical properties, its high albedo and its low thermal conductivity
Alternative approaches include Fourier analysis methods that can estimate thermal diffusivity and reconstruct snow thermal histories from temperature measurements (Oldroyd et al, 2013), considering that the bulk/apparent heat diffusivity can be more effectively described than the whole physical process of snow metamorphism, as assumed by needle probe measurement studies (Calonne et al, 2011)
S4, S5, and S6, we show the comparisons of observed and modeled firn temperature using nine different density–conductivity relationships at the Dome A, Eagle, and LGB69 automatic weather stations (AWSs)
Summary
In the Earth’s climate system, snow cover has two important physical properties, its high albedo and its low thermal conductivity. Both properties modulate heat exchange between the atmosphere and the surface (Dutra et al, 2010). Alternative approaches include Fourier analysis methods that can estimate thermal diffusivity and reconstruct snow thermal histories from temperature measurements (Oldroyd et al, 2013), considering that the bulk/apparent heat diffusivity can be more effectively described than the whole physical process of snow metamorphism, as assumed by needle probe measurement studies (Calonne et al, 2011). The numerical inverse method (optimal control theory) is another possible approach for recovering thermal diffusivity using a least squares method (Sergienko et al, 2008) or a recursive optimization approach (Oldroyd et al, 2013)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
