A long-standing challenge in studying the global carbon cycle has been understanding the factors controlling inter–annual variation (IAV) of carbon fluxes related to vegetation photosynthesis and respiration, and improving their representations in existing biogeochemical models. Here, we compared an optimality-based mechanistic model and a semi-empirical light use efficiency model to understand how current models can be improved to simulate IAV of gross primary production (GPP). Both models simulated hourly GPP and were parameterized for (1) each site–year, (2) each site with an additional constraint on IAV (CostIAV), (3) each site, (4) each plant–functional type, and (5) globally. This was followed by forward runs using calibrated parameters, and model evaluations at different temporal scales across 198 eddy covariance sites. Both models performed better on hourly scale than annual scale for most sites. Specifically, the mechanistic model substantially improved when drought stress was explicitly included. Most of the variability in model performances was due to model types and parameterization strategies. The semi-empirical model produced statistically better hourly simulations than the mechanistic model, and site–year parameterization yielded better annual performance for both models. Annual model performance did not improve even when parameterized using CostIAV. Furthermore, both models underestimated the peaks of diurnal GPP in each site–year, suggesting that improving predictions of peaks could produce a comparatively better annual model performance. GPP of forests were better simulated than grassland or savanna sites by both models. Our findings reveal current model deficiencies in representing IAV of carbon fluxes and guide improvements in further model development.

As lakes receive and transform significant amounts of terrestrial carbon, they often act as source of atmospheric CO2. Yet, long-term measurements of lake-atmosphere CO2 exchange with high temporal resolution are sparse. In this study, we measured the CO2 exchange of a small lake situated in complex mountainous topography in the Austrian Alps continuously for one year. We used the eddy covariance (EC) and the boundary layer model (BLM) approaches to estimate the lake’s CO2 source or sink strength and to analyze differences between these methods. Overall, CO2 fluxes were small and EC measurements indicated influence of low-frequency contributions. Results from both the EC and the BLM methods indicated the lake to be a small source of atmospheric CO2 with highest emissions in fall. During night-time, the CO2 concentration gradient at the air-water interface decreased due to an increase in atmospheric CO2 above the lake, likely caused by cold and CO2-rich air draining from the surrounding land. Consequently, BLM fluxes were lower during night-time than during daytime. This diel pattern was lacking in the EC flux measurements because the EC instruments deployed at the shore of the lake did not capture low nocturnal lake CO2 fluxes due to the local wind regime. Overall, this study exemplifies the relevance of the surrounding landscape for lake-atmosphere flux measurements. We conclude that estimating CO2 evasion from lakes situated in complex topography needs to explicitly account for biases in EC flux measurements caused by low-frequency contributions and local wind systems.