Intercomparison of methods to estimate GPP based on CO2 and COS flux measurements.

Knowing the components of ecosystem scale carbon exchange is crucial in order to develop better models and future predictions of the terrestrial carbon cycle. However, there are several uncertainties and unknowns related to current photosynthesis estimates. In this study, we test the use of four different methods for quantifying photosynthesis at the ecosystem scale, of which two are based on carbon dioxide (CO₂) and two on carbonyl sulfide (COS) flux measurements. The CO_2- based methods use traditional flux partitioning and artificial neural networks to separate the net CO₂ flux into respiration and photosynthesis. The COS-based methods make use of a unique five year COS flux data set at a boreal forest and include two different approaches to determine the leaf scale uptake ratio of COS and CO₂ (LRU), of which one (LRU_CAP) was developed in this study. LRU_CAP was based on stomatal conductance theories, while the other was based on an empirical relation to measured radiation (LRU_PAR). We found that for the measurement period 2013-2017 the artificial neural networks method gave a GPP estimate very close to that of traditional flux partitioning at all time scales. COS-based methods gave on average higher GPP estimates than the CO₂-based estimates on daily (23 and 7 % higher, if using LRU_PAR or LRU_CAP in GPP calculation, respectively) and monthly scales (20 and 3 % higher), as well as a higher cumulative sum over three months in all years (on average 25 and 3 % higher). LRU_CAP was higher than measured LRU at high radiation leading to an underestimated GPP during midday. However, in general it compared better with the CO₂-based methods than LRU_PAR-based GPP calculations. The applicability of LRU_CAP at other measurement sites is potentially better than that of LRU_PAR since its parameters are based on literature values and simple meteorological measurements, while the radiation relation in LRU_PAR might be site-specific. This, however, requires further testing at other measurement sites. [ABSTRACT FROM AUTHOR]