Articles | Volume 10, issue 6
https://doi.org/10.5194/gmd-10-2425-2017
https://doi.org/10.5194/gmd-10-2425-2017
Model evaluation paper
 | 
29 Jun 2017
Model evaluation paper |  | 29 Jun 2017

Evaluation of the transport matrix method for simulation of ocean biogeochemical tracers

Karin F. Kvale, Samar Khatiwala, Heiner Dietze, Iris Kriest, and Andreas Oschlies

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Cited articles

Antonov, J., Seidov, D., Boyer, T. P., Locarnini, R. A., Mishonov, A., Garcia, H., Baranova, O., Zweng, M. M., and Johnson, D.: World Ocean Atlas 2009, Volume 2: Salinity, Tech. rep., NOAA Atlas NESDIS 69, U.S. Government Printing Office, Washington, DC, 2010.
Balay, S., Gropp, W. D., McInnes, L. C., and Smith, B. F.: PETSc Users Manual, Tech. Rep. ANL-95/11 – Revision 2.1.5, Argonne National Laboratory, 2003.
Coleman, T. F. and Moré, J. J.: Estimation of sparse Jacobian matrices and graph coloring problems, SIAM J. Numer. Anal., 20, 187–209, 1983.
Curtis, A. R., Powell, M. J. D., and Reid, J. K.: On the estimation of sparse Jacobian matrices, J. Inst. Math. Appl., 13, 117–119, 1974.
Duteil, O., Koeve, W., Oschlies, A., Bianchi, D., Galbraith, E., Kriest, I., and Matear, R.: A novel estimate of ocean oxygen utilisation points to a reduced rate of respiration in the ocean interior, Biogeosciences, 10, 7723–7738, https://doi.org/10.5194/bg-10-7723-2013, 2013.
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Short summary
Computer models of ocean biology and chemistry are becoming increasingly complex, and thus more expensive, to run. One solution is to approximate the behaviour of the ocean physics and store that information outside of the model. That offline information can then be used to calculate a steady-state solution from the model's biology and chemistry, without waiting for a traditional online integration to complete. We show this offline method reproduces online results and is 100 times faster.