Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites. Issue 3 (22nd March 2017) - Post, Hanna; Vrugt, Jasper A.; Fox, Andrew; Vereecken, Harry; Hendricks Franssen, Harrie‐Jan
Abstract: The Community Land Model (CLM) contains many parameters whose values are uncertain and thus require careful estimation for model application at individual sites. Here we used Bayesian inference with the DiffeRential Evolution Adaptive Metropolis (DREAM(zs) ) algorithm to estimate eight CLM v.4.5 ecosystem parameters using 1 year records of half‐hourly net ecosystem CO2 exchange (NEE) observations of four central European sites with different plant functional types (PFTs). The posterior CLM parameter distributions of each site were estimated per individual season and on a yearly basis. These estimates were then evaluated using NEE data from an independent evaluation period and data from "nearby" FLUXNET sites at ~600 km distance to the original sites. Latent variables (multipliers) were used to treat explicitly uncertainty in the initial carbon‐nitrogen pools. The posterior parameter estimates were superior to their default values in their ability to track and explain the measured NEE data of each site. The seasonal parameter values reduced with more than 50% (averaged over all sites) the bias in the simulated NEE values. The most consistent performance of CLM during the evaluation period was found for the posterior parameter values of the forest PFTs, and contrary to the C3‐grass and C3‐crop sites, the latent variables of the initial pools further enhanced the quality‐of‐fit. The carbon sink function of the forest PFTs significantly increased with the posterior parameter estimates. We thus conclude that land surface model predictions of carbon stocks and fluxes require careful consideration of uncertain ecological parameters and initial states. Key Points: The CLM parameters, estimated separately for four plant functional types, correlated with initial carbon‐nitrogen pools Parameter estimates improved model performance for an independent evaluation period and at independent sites Parameter estimates were more reliable for forest than for C3‐crop and C3‐grass PFTs and less dependent on the initial model states