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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 2
Geosci. Model Dev., 6, 345–352, 2013
https://doi.org/10.5194/gmd-6-345-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 6, 345–352, 2013
https://doi.org/10.5194/gmd-6-345-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 12 Mar 2013

Development and technical paper | 12 Mar 2013

Using model reduction to predict the soil-surface C18OO flux: an example of representing complex biogeochemical dynamics in a computationally efficient manner

W. J. Riley W. J. Riley
  • Earth Sciences Division, Lawrence Berkeley National Laboratory, Bldg 84-1134, 1 Cyclotron Road, Berkeley, California 94720, USA

Abstract. Earth system models (ESMs) must calculate large-scale interactions between the land and atmosphere while accurately characterizing fine-scale spatial heterogeneity in water, carbon, and other nutrient dynamics. We present here a high-dimension model representation (HDMR) approach that allows detailed process representation of a coupled carbon and water tracer (the δ18O value of the soil-surface CO2 flux (δ Fs)) in a computationally tractable manner. δ Fs depends on the δ18O value of soil water, soil moisture and temperature, and soil CO2 production (all of which are depth dependent), and the δ18O value of above-surface CO2. We tested the HDMR approach over a growing season in a C4-dominated pasture using two vertical soil discretizations. The difference between the HDMR approach and the full model solution in the three-month integrated isoflux was less than 0.2% (0.5 mol m−2 ‰), and the approach is up to 100 times faster than the full numerical solution. This type of model reduction approach allows representation of complex coupled biogeochemical processes in regional and global climate models and can be extended to characterize subgrid-scale spatial heterogeneity.

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