Structural uncertainty arising from ensembles of carbon models
Jennifer
L
Dungan, NASA Ames Research Center, jennifer.l.dungan@nasa.gov
(Presenting)
Weile
Wang, California State University, Monterey Bay, weile.wang@gmail.com
Andrew
Michaelis, California State University, Monterey Bay, amac@hyperplane.org
Ramakrishna
Nemani, NASA Ames Research Center, rama.nemani@nasa.gov
Numerous efforts have begun to characterize a variety of sources of uncertainty in carbon flux estimates from both forward-modeling and inverse modeling approaches. These efforts have addressed aspects of parametric uncertainty and bias and random error in input variables. An additional source of uncertainty is structural, created by the variety of approaches taken to select and characterize the most important biogeochemical processes. To begin to explore this structural uncertainty for some widely used models, we used the standard versions of BGC, CASA, TOPS and LPJ, run with a consistent set of inputs for the period 1982-2006. We focused on a few variables in particular, namely leaf area index, evapotranspiration and net primary production. Measures of uncertainty include summary functions such as RMSE, but can be more fully understood using two-dimensional histograms that show complex, nonlinear features. Though ensemble modeling of this type cannot yield probabilistic statements (Tebaldi and Knutti (2007), it serves to highlight the problems inherent in relying on only one modeling approach.
C. Tebaldi and R. Knutti, The use of the multi-model ensemble in probabilistic climate projections, Philosophical Transactions of the Royal Society A, 365:2053-2075, 2007.
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