Assessing output errors of ice flow models is a major
challenge that needs to be addressed if we are to
increase our confidence level in projections of mass
balance in Antarctica and Greenland. Major inputs to
ice flow models include geometry (ice thickness and
surface elevation), constitutive laws and boundary
conditions (geothermal flux, basal drag coefficient,
surface temperature). These inputs can be either
measured, in which case they carry errors due to
instruments, or inferred using inverse methods (such
as basal drag which is inverted using InSAR surface
velocities) in which case they carry additional
errors generated by the inversion process itself. In
both cases, these input errors will result in
uncertainties that propagate throughout a forward
model, and that influence output diagnostics. In
order to estimate the resulting error margins on
diagnostics such as mass flux, we develop a new
framework based on the Design Analysis Kit for
Optimization and Terascale Applications (DAKOTA),
which we interface to the Ice Sheet System Model
(ISSM). We present results on the Pine Island
Glacier, West Antarctica, for which we evaluate error
margins of mass flux across the whole glacier, given
currently known error margins on ice thickness, basal
friction and ice hardness. Our results suggest errors
in these inputs propagate linearly through the ice
flow model, providing a way to 1) calibrate
measurement requirements for field campaigns
collecting data such as bedrock or surface topography
2) quantify uncertainties in projections of mass
balance and 3) assess the sensitivity of model
outputs to input parameters. This new error
propagation model should help quantify confidence
levels that we assign to model projections for the
mass balance of Antarctica and Greenland, which will
ultimately improve our projections of future sea
level rise in a warming climate.