Intercomparison of methods to estimate gross primary production based on CO₂ and COS flux measurements

Separating 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 evaluate four different methods for estimating photosynthesis at a boreal forest at the ecosystem scale, of which two are based on carbon dioxide (CO₂) flux measurements and two on carbonyl sulfide (COS) flux measurements. The CO₂-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 5-year COS flux data set and involve two different approaches to determine the leaf-scale relative uptake ratio of COS and CO₂ (LRU), of which one (LRU_CAP) was developed in this study. LRU_CAP was based on a previously tested stomatal optimization theory (CAP), while LRU_PAR was based on an empirical relation to measured radiation. For the measurement period 2013-2017, the artificial neural network method gave a GPP estimate very close to that of traditional flux partitioning at all timescales. On average, the COS-based methods gave higher GPP estimates than the CO₂-based estimates on daily (23% and 7% higher, using LRU_PAR and LRU_CAP, respectively) and monthly scales (20% and 3% higher), as well as a higher cumulative sum over 3 months in all years (on average 25% and 3% higher). LRU_CAP was higher than LRU estimated from chamber measurements at high radiation, leading to underestimation of midday GPP relative to other GPP methods. In general, however, use of LRU_CAP gave closer agreement with CO₂-based estimates of GPP than use of LRUPAR. When extended to other sites, LRU_CAP may be more robust than LRU_PAR because it is based on a physiological model whose parameters can be estimated from simple measurements or obtained from the literature. In contrast, the empirical radiation relation in LRU_PAR may be more site-specific. However, this requires further testing at other measurement sites.