Articles | Volume 9, issue 5
https://doi.org/10.5194/gmd-9-1891-2016
https://doi.org/10.5194/gmd-9-1891-2016
Model description paper
 | 
20 May 2016
Model description paper |  | 20 May 2016

An optimized treatment for algorithmic differentiation of an important glaciological fixed-point problem

Daniel N. Goldberg, Sri Hari Krishna Narayanan, Laurent Hascoet, and Jean Utke

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Cited articles

Arthern, R. J., Hindmarsh, R. C. A., and Williams, C. R.: Flow speed within the Antarctic ice sheet and its controls inferred from satellite observations, J. Geophys. Res.-Earth, 120, 1171–1188, https://doi.org/10.1002/2014JF003239, 2015.
Bartholomew-Biggs, M., Brown, S., Christianson, B., and Dixon, L.: Automatic differentiation of algorithms, J. Comput. Appl. Math., 124, 171–190, https://doi.org/10.1016/S0377-0427(00)00422-2, 2000.
Blatter, H.: Velocity and stress fields in grounded glaciers: a simple algorithm for including deviatoric stress gradients, J. Glaciol., 41, 333–344, 1995.
Christianson, B.: Reverse accumulation and attractive fixed points, Optim. Method. Softw., 3, 311–326, https://doi.org/10.1080/10556789408805572, 1994.
Christianson, B.: Reverse accumulation and implicit functions, Optim. Method. Softw., 9, 307–322, https://doi.org/10.1080/10556789808805697, 1998.
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Short summary
Geophysical adjoint models are powerful tools, allowing sensitivity studies that are not possible otherwise, and enabling optimized fit of models to observing data sets. The complexity involved requires the use of algorithmic differentiation (AD) software, but AD adjoint calculation for ice models can be slow, with prohibitive memory requirements. In this paper, we present a method to improve the performance of ice model adjoint generation, in terms of timing, memory load, and accuracy.
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