Ecosystem biogeochemistry model parameterization: Do more flux data result in a better model in predicting carbon flux?

Reliability of terrestrial ecosystem models highly depends on the quantity and quality of the data that have been used to calibrate the models. Nowadays, in situ observations of carbon fluxes are abundant. However, the knowledge of how much data (data length) and which subset of the time series data (data period) should be used to effectively calibrate the model is still lacking. This study uses the AmeriFlux carbon flux data to parameterize the Terrestrial Ecosystem Model (TEM) with an adjoint-based data assimilation technique for various ecosystem types. Parameterization experiments are thus conducted to explore the impact of both data length and data period on the uncertainty reduction of the posterior model parameters and the quantification of site and regional carbon dynamics. We find that: (1) the model is better constrained when it uses two-year data comparing to using one-year data. Further, two-year data is sufficient in calibrating TEM's carbon dynamics, since using three-year data could only marginally improve the model performance at our study sites; (2) the model is better constrained with the data that have a higher “climate variability” than that having a lower one. The climate variability is used to measure the overall possibility of the ecosystem to experience all climatic conditions including drought and extreme air temperatures and radiation; (3) the U.S. regional simulations indicate that the effect of calibration data length on carbon dynamics is amplified at regional and temporal scales, leading to large discrepancies among different parameterization experiments, especially in July and August. Our findings are conditioned on the specific model we used and the calibration sites we selected. The optimal calibration data length may not be suitable for other models. However, this study demonstrates that there may exist a threshold for calibration data length and simply using more data would not guarantee a better model parameterization and prediction. More importantly, climate variability might be an effective indicator of information within the data, which could help data selection for model parameterization. We believe our findings will benefit the ecosystem modeling community in using multiple-year data to improve model predictability.