Constraining ecosystem model with Adaptive Metropolis algorithm using boreal forest site eddy covariance measurements

We examined parameter optimization in JSBACH ecosystem model, applied for two boreal forest sites (Hyytiälä and Sodankylä) in Finland. We identified and tested key parameters in soil hydrology and forest water and carbon exchange related formulations and optimized them using the Adaptive Metropolis algorithm (AM) for Hyytiälä with a five year calibration period (2000–2004) followed by a four year validation period (2005–2008). Sodankylä acted as an independent validation site, where optimizations were not made. 5 The tuning provided estimates for full distribution of possible parameter, along with information about correlation, sensitivity and identifiability. Some parameters were correlated with each other due to phenomenological connection between carbon uptake and water stress or other connections due to the set-up of the model formulations. The latter holds especially for vegetation phenology parameters. The least identifiable parameters include phenology parameters, parameters connecting relative humidity and soil dryness, and the field capacity of the skin reservoir. These soil parameters were masked by the large 10 contribution from vegetation transpiration. In addition to leaf area index and maximum carboxylation rate, the most effective parameters adjusting GPP and ET fluxes in seasonal tuning were related to soil wilting point, drainage and moisture stress imposed on vegetation. For daily and half-hourly tunings the most important parameters were the ratio of leaf internal CO2 concentration to external CO2 and the parameter connecting relative humidity and soil dryness. Effectively the seasonal tuning transferred water from soil moisture into ET, and 15 daily and half-hourly tunings reversed this process. The seasonal tuning improved the month-to-month development of GPP and ET, and produced the most stable estimates of water use efficiency. When compared to the seasonal tuning, the daily tuning is worse on the seasonal scale. However, daily parametrization reproduced the observations for average diurnal cycle best, except the GPP for Sodankylä validation period, where half-hourly tuned parameters were better. In general, the daily tuning providing the most reduction in model20 data mismatch. The models response to drought was unaffected by our parametrizations and further studies are needed into enhancing the dry response in JSBACH.