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


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. Geosci. Model Dev., 6, 345-352, 2013 doi:10.5194/gmd-6-345-2013

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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Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development and evaluation of numerical models of the Earth System and its components. Manuscript types considered for peer-reviewed publication are:

  • Geoscientific model descriptions, from box models to GCMs;
  • Development and Technical papers, describing development such as new parameterisations or technical aspects of running models such as the reproducibility of results;
  • Papers describing new standard experiments for assessing model performance, or novel ways of comparing model results with observational data;
  • Model intercomparison descriptions, including experimental details and project protocols.

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