Radar inference of the bulk properties of glacier
beds, most notably identifying basal melting, is, in general,
derived from the basal reflection coefficient. On the scale of
an ice sheet, unambiguous determination of basal reflection
is primarily limited by uncertainty in the englacial attenuation
of the radio wave, which is an Arrhenius function of
temperature. Existing bed-returned power algorithms for deriving
attenuation assume that the attenuation rate is regionally
constant, which is not feasible at an ice-sheet-wide scale.
Here we introduce a new semi-empirical framework for deriving
englacial attenuation, and, to demonstrate its efficacy,
we apply it to the Greenland Ice Sheet. A central feature
is the use of a prior Arrhenius temperature model to estimate
the spatial variation in englacial attenuation as a first
guess input for the radar algorithm. We demonstrate regions
of solution convergence for two input temperature fields and
for independently analysed field campaigns. The coverage
achieved is a trade-off with uncertainty and we propose that
the algorithm can be “tuned” for discrimination of basal melt
(attenuation loss uncertainty ∼ 5 dB). This is supported by
our physically realistic (∼ 20 dB) range for the basal reflection
coefficient. Finally, we show that the attenuation solution
can be used to predict the temperature bias of thermomechanical
ice sheet models and is in agreement with known model
temperature biases at the Dye 3 ice core.