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| Titel |
CH4 parameter estimation in CLM4.5bgc using surrogate global optimization |
| VerfasserIn |
J. Müller, R. Paudel, C. A. Shoemaker, J. Woodbury, Y. Wang, N. Mahowald |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 10 ; Nr. 8, no. 10 (2015-10-20), S.3285-3310 |
| Datensatznummer |
250116607
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| Publikation (Nr.) |
 copernicus.org/gmd-8-3285-2015.pdf |
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| Zusammenfassung |
| Over the anthropocene methane has increased dramatically. Wetlands are one of
the major sources of methane to the atmosphere, but the role of changes in
wetland emissions is not well understood. The Community Land Model (CLM) of
the Community Earth System Models contains a module to estimate methane
emissions from natural wetlands and rice paddies. Our comparison of CH4
emission observations at 16 sites around the planet reveals, however, that
there are large discrepancies between the CLM predictions and the
observations. The goal of our study is to adjust the model parameters in
order to minimize the root mean squared error (RMSE) between model
predictions and observations. These parameters have been selected based on a
sensitivity analysis. Because of the cost associated with running the CLM
simulation (15 to 30 min on the Yellowstone Supercomputing Facility), only
relatively few simulations can be allowed in order to find a near-optimal
solution within an acceptable time. Our results indicate that the parameter
estimation problem has multiple local minima. Hence, we use a computationally
efficient global optimization algorithm that uses a radial basis function
(RBF) surrogate model to approximate the objective function. We use the
information from the RBF to select parameter values that are most promising
with respect to improving the objective function value. We show with pseudo
data that our optimization algorithm is able to make excellent progress with
respect to decreasing the RMSE. Using the true CH4 emission observations
for optimizing the parameters, we are able to significantly reduce the
overall RMSE between observations and model predictions by about 50 %.
The methane emission predictions of the CLM using the optimized parameters agree better with the observed methane emission data in northern and tropical latitudes. With the optimized parameters, the methane emission predictions are higher in northern latitudes than when the default parameters are used. For the tropics,
the optimized parameters lead to lower emission predictions than the default parameters. |
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