Reply on RC1

Abstract

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 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 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 (LRUCAP) was developed in this study. LRUCAP was based on stomatal conductance theories, while the other was based on an empirical relation to measured radiation (LRUPAR). 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 LRUPAR or LRUCAP 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). LRUCAP 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 LRUPAR -based GPP calculations. The applicability of LRUCAP at other measurement sites is potentially better than that of LRUPAR since its parameters are based on literature values and simple meteorological measurements, while the radiation relation in LRUPAR might be site-specific. This, however, requires further testing at other measurement sites.