A. Mametjanov, B. Norris, X. Zeng, B. Drewniak, J. Utke, M. Anitescu, P. Hovland, "Applying Automatic Differentiation to the Community Land Model," Advances in Algorithmic Differentiation, Proceedings of the 6th International Conference on Automatic Differentiation (AD'12), vol. 87, Fort Collins, CO, Springer, 2012, pp. 47-57. Also Preprint ANL/MCS-P1993-0112, January 2012. [pdf]
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.