Abstract
Earth system models rely on past observations and knowledge to simulate future climate states. Because of the inherent complexity, a substantial uncertainty exists in model-based predictions. Evaluation and improvement of model codes are one of the priorities of climate science research. Automatic Differentiation enables analysis of sensitivities of predicted outcomes to input parameters by calculating derivatives of modeled functions. The resulting sensitivity knowledge can lead to improved parameter calibration. We present our experiences in applying OpenAD to the Fortran-based crop model code in the Community Land Model (CLM). We identify several issues that need to be addressed in future developments of tangent-linear and adjoint versions of the CLM.
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Acknowledgements
This work was supported by the U.S. Dept. of Energy Office of Biological and Environmental Research under the project of Climate Science for Sustainable Energy Future (CSSEF) and by the U.S. Dept. of Energy Office of Science under Contract No. DE-AC02-06CH11357. We thank our collaborators Rao Kotamarthi (ANL), Peter Thornton (ORNL), and our CSSEF colleagues for helpful discussions about the CLM.
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Mametjanov, A. et al. (2012). Applying Automatic Differentiation to the Community Land Model. In: Forth, S., Hovland, P., Phipps, E., Utke, J., Walther, A. (eds) Recent Advances in Algorithmic Differentiation. Lecture Notes in Computational Science and Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30023-3_5
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DOI: https://doi.org/10.1007/978-3-642-30023-3_5
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