Search

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 (CO 2 ) flux measurements 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 CO 2 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 CO 2 (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 CO 2 -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 CO 2 -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., Competing Interests: Competing interests. The contact author has declared that none of the authors has any competing interests.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO 2 ) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance. Our study indicates that spatial climate covariation drives the global carbon cycle response.