Articles | Volume 8, issue 10
Geosci. Model Dev., 8, 3285–3310, 2015
Geosci. Model Dev., 8, 3285–3310, 2015

Development and technical paper 20 Oct 2015

Development and technical paper | 20 Oct 2015

CH4 parameter estimation in CLM4.5bgc using surrogate global optimization

J. Müller1, R. Paudel2, C. A. Shoemaker3, J. Woodbury3, Y. Wang3, and N. Mahowald2 J. Müller et al.
  • 1Center for Computational Sciences and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
  • 2Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA
  • 3School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA

Abstract. 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.

Short summary
We tune the CH4-related parameters of the Community Land Model (CLM) using surrogate global optimization in order to reduce the discrepancies between the CLM predictions and observed CH4 emissions. This is the first application of a surrogate optimization method to calibrate a global climate model. We found that the observation data drives the model to predict more CH4 emissions in the northern latitudes and less in the tropics.