Publication: Applying Automatic Differentiation to the Community Land Model
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Applying Automatic Differentiation to the Community Land Model

- incollection -
 

Author(s)
Azamat Mametjanov , Boyana Norris , Xiaoyan Zeng , Beth Drewniak , Jean Utke , Mihai Anitescu , Paul Hovland

Published in
Recent Advances in Algorithmic Differentiation

Editor(s)
Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther

Year
2012

Publisher
Springer

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.

Cross-References
Forth2012RAi

AD Tools
OpenAD

BibTeX
@INCOLLECTION{
         Mametjanov2012AAD,
       title = "Applying Automatic Differentiation to the Community Land Model",
       doi = "10.1007/978-3-642-30023-3_5",
       author = "Azamat Mametjanov and Boyana Norris and Xiaoyan Zeng and Beth Drewniak and Jean Utke
         and Mihai Anitescu and Paul Hovland",
       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.",
       pages = "47--57",
       crossref = "Forth2012RAi",
       booktitle = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       volume = "87",
       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2012",
       ad_tools = "OpenAD"
}


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