Influences of calibration data length and data period on model parameterization and quantification of terrestrial ecosystem carbon dynamics

Abstract. 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. In this study we use the AmeriFlux carbon flux data to parameterize the Terrestrial Ecosystem Model (TEM) using an adjoint based data assimilation technique for five different ecosystem types including deciduous broadleaf forest, coniferous forest, grassland, shrubland and boreal forest. We hypothesize that calibration data covering various climate conditions for the ecosystems (e.g. drought and wet; high and low air temperature) can reduce the uncertainty of the model parameter space. Here parameterization is 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 long enough 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 with a lower one. The climate variability is used to measure the overall possibility of the ecosystem to experience various climate conditions including drought and extreme air temperatures and radiation; (3) the US 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. This study shall help the eddy flux observation community in conducting field observations. The study shall also benefit the ecosystem modeling community in using multiple-year data to improve model parameterization and predictability.