Maarten C. Braakhekke

Comparing observations and process-based simulations of biosphere-atmosphere exchanges on multiple timescales

Terrestrial biosphere models are indispensable tools for analyzing the biosphere-atmosphere exchange of carbon and water. Evaluation of these models using site level observations scrutinizes our current understanding of biospheric responses to meteorological variables. Here we propose a novel model-data comparison strategy considering that CO2 and H2O exchanges fluctuate on a wide range of timescales. Decomposing simulated and observed time series into subsignals allows to quantify model performance as a function of frequency, and to localize model-data disagreement in time. This approach is illustrated using site level predictions from two models of different complexity, Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and Lund-Potsdam-Jena (LPJ), at four eddy covariance towers in different climates. Frequency-dependent errors reveal substantial model-data disagreement in seasonal-annual and high-frequency net CO2 fluxes. By localizing these errors in time we can trace these back, for example, to overestimations of seasonal-annual periodicities of ecosystem respiration during spring greenup and autumn in both models. In the same frequencies, systematic misrepresentations of CO2 uptake severely affect the performance of LPJ, which is a consequence of the parsimonious representation of phenology. ORCHIDEE shows pronounced model-data disagreements in the high-frequency fluctuations of evapotranspiration across the four sites. We highlight the advantages that our novel methodology offers for a rigorous model evaluation compared to classical model evaluation approaches. We propose that ongoing model development will benefit from considering model-data (dis)agreements in the time-frequency domain

The use of radiocarbon to constrain current and future soil organic matter turnover and transport in a temperate forest

We investigated the merits of radiocarbon measurements for estimating soil organic matter (SOM) turnover and vertical transport for a temperate deciduous forest in Germany. Eleven parameters, defining decomposition and transport in the soil carbon model SOMPROF, were estimated using a Bayesian approach based on organic carbon measurements and radiocarbon concentration of SOM and heterotrophic respiration. The addition of radiocarbon data had strong effects on the parameters, most importantly a reduction of the decomposition and production rate of the slowest SOM pool by an order of magnitude, and a similar reduction in advective SOM transport. The modified parameters further led to changes in the partitioning of SOM over the different model pools. The calibration results were subsequently used to perform transient soil carbon projections for the period 1901-2100. These simulations were run with parameter sets from calibrations both with and without radiocarbon. The results show an increase over time of topsoil carbon and a decrease in the subsoil, adding to a net gain overall. Near the end of the 21st century, total carbon stocks stabilize and -- for the radiocarbon-constrained model -- start to decrease. However, the changes are small compared to the total stocks. The model results for the calibrations with and without radiocarbon are in general quite similar, but the latter shows notably higher heterotrophic respiration fluxes. Constraining the model with radiocarbon yielded only a small reduction of uncertainty for the total carbon stocks, while for the individual depth compartments, the uncertainty was increased.