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Terrestrial ecosystem models (TEMs) contain the coupling of many biogeochemical processes with a large number of parameters involved. In many cases those parameters are highly uncertain. In order to reduce those uncertainties, parameter estimation methods can be applied, which allow the model to be constrained against observations. We compare the performance and results of two such parameter estimation techniques - the Metropolis algorithm (MA) which is a Markov Chain Monte Carlo (MCMC) method and the adjoint approach as it is used in the Carbon Cycle Data Assimilation System (CCDAS). Both techniques are applied here to derive the posterior probability density function (PDF) for 19 parameters of the Biosphere Energy Transfer and Hydrology (BETHY) scheme. We also use the MA to sample the posterior parameter distribution from the adjoint inversion. This allows us to assess if the commonly made assumption in variational data assimilation, that everything is normally distributed, holds. The comparison of the posterior parameter PDF derived by both metfiods shows that in most cases an approximation of the PDF by a normal distribution as used by the adjoint approach is a valid assumption. The results also indicate that the global minimum has been identified by both methods for the given set up. However, the adjoint approach outperforms the MA by several orders of magnitude in terms of computational time. Both methods show good agreement in the PDF of estimated net carbon fluxes for the decades of the 1980s and 1990s [ABSTRACT FROM AUTHOR]

Better estimates of the net exchange of CO2 between the atmosphere and the terrestrial biosphere are urgently needed to improve predictions of future CO2 levels in the atmosphere. The carbon cycle data assimilation system (CCDAS) offers the capability of inversion, while it is at the same time based on a process model that can be used independent of observational data. CCDAS allows the assimilation of atmospheric CO2 concentrations into the terrestrial biosphere model BETHY, constraining its process parameters via an adjoint approach. Here, we investigate the effect of spatial differentiation of a universal carbon balance parameter of BETHY on posterior net CO2 fluxes and their uncertainties. The parameter, β, determines the characteristics of the slowly decomposing soil carbon pool and represents processes that are difficult to model explicitly. Two cases are studied with an assimilation period of 1979 to 2003. In the base case, there is a separate β for each plant functional type (PFT). In the regionalization case, β is differentiated not only by PFT, but also according to each of 11 large continental regions as used by the TransCom project. We find that the choice of spatial differentiation has a profound impact not only on the posterior (optimized) fluxes and their uncertainties, but even more so on the spatial covariance of the uncertainties. Differences are most pronounced in tropical regions, where observations are sparse. While regionalization leads to an improved fit to the observations by about 20% compared to the base case, we notice large spatial variations in the posterior net CO2 flux on a grid cell level. The results illustrate the need for universal process formulations in global-scale atmospheric CO2 inversion studies, at least as long as the observational network is too sparse to resolve spatial fluctuations at the regional scale. [ABSTRACT FROM AUTHOR]

The use of complex mechanisms is increasing within models describing a range of important chemical processes, including combustion. Parameters describing chemical reaction rates and thermodynamics can often be very uncertain. Highlighting the main parameters contributing to predictive uncertainty is an important part of model development. However, due to the computational cost of the models and their nonlinearity, traditional methods for sensitivity analysis are often not suitable. The high-dimensional model representation (HDMR) method was developed to express the input–output relationship of a complex model with a high-dimensional input space. A fully functional surrogate model can be constructed with low computational effort. First- and second-order sensitivity indices can then be calculated in an automatic way, over large input parameter ranges. These provide an importance ranking for the input parameters. An extension to the existing set of HDMR tools is developed in this work, where the number of HMDR component functions can be reduced to explore large dimensional input spaces very efficiently. The HDMR tools are demonstrated for a case study of a one-dimensional low-pressure premixed methane flame model doped with trace sulfur and nitrogen species. Uncertainties in rate constants and thermodynamic data are considered, leading to a study of 176 input parameters. Using the new HDMR tools, the use of screening methods such as the Morris method, which aim to identify unimportant parameters beforehand, can generally be avoided. However, in certain cases, a combination of a screening method and HDMR is computationally more efficient than using HDMR alone. The final ranking of important parameters is shown to be critically dependent on the uncertainty ranges chosen due to the nonlinearity of the model. The study demonstrates that the proposed HDMR method provides a powerful tool for general application to global sensitivity analysis of complex chemical mechanisms. © 2008 Wiley Periodicals, Inc. Int J Chem Kinet 40: 742–753, 2008 [ABSTRACT FROM AUTHOR]