Does growing atmospheric CO2 explain increasing carbon sink in a boreal coniferous forest?

The terrestrial net ecosystem productivity (NEP) has increased during the past three decades, but the mechanisms responsible are still unclear. We analyzed 17 years (2001-2017) of eddy-covariance measurements of NEP, evapotranspiration (ET) and light and water use efficiency from a boreal coniferous forest in Southern Finland for trends and inter-annual variability (IAV). The forest was a mean annual carbon sink (252 [ ± 42] gC m-2a-1 ), and NEP increased at rate +6.4-7.0 gC m-2a-1 (or ca. +2.5% a-1 ) during the period. This was attributed to the increasing gross-primary productivity GPP and occurred without detectable change in ET. The start of annual carbon uptake period was advanced by 0.7 d a-1 , and increase in GPP and NEP outside the main growing season contributed ca. one-third and one-fourth of the annual trend, respectively. Meteorological factors were responsible for the IAV of fluxes but did not explain the long-term trends. The growing season GPP trend was strongest in ample light during the peak growing season. Using a multi-layer ecosystem model, we showed that direct CO2 fertilization effect diminishes when moving from leaf to ecosystem, and only 30-40% of the observed ecosystem GPP increase could be attributed to CO2 . The increasing trend in leaf-area index (LAI), stimulated by forest thinning in 2002, was the main driver of the enhanced GPP and NEP of the mid-rotation managed forest. It also compensated for the decrease of mean leaf stomatal conductance with increasing CO2 and LAI, explaining the apparent proportionality between observed GPP and CO2 trends. The results emphasize that attributing trends to their physical and physiological drivers is challenged by strong IAV, and uncertainty of LAI and species composition changes due to the dynamic flux footprint. The results enlighten the underlying mechanisms responsible for the increasing terrestrial carbon uptake in the boreal zone.

Three-phase hierarchical model-based and hybrid inference.

Global commitments to mitigating climate change and halting biodiversity loss require reliable information about Earth's ecosystems. Increasingly, such information is obtained from multiple sources of remotely sensed data combined with data acquired in the field. This new wealth of data poses challenges regarding the combination of different data sources to derive the required information and assess uncertainties. In this article, we show how predictors and their variances can be derived when hierarchically nested models are applied. Previous studies have developed methods for cases involving two modeling steps, such as biomass prediction relying on tree-level allometric models and models linking plot-level field data with remotely sensed data. This study extends the analysis to cases involving three modeling steps to cover new important applications. The additional step might involve an intermediate model, linking field and remotely sensed data available from a small sample, for making predictions that are subsequently used for training a final prediction model based on remotely sensed data:•In cases where the data in the final step are available wall-to-wall, we denote the approach three-phase hierarchical model-based inference (3pHMB),•In cases where the data in the final step are available as a probability sample, we denote the approach three-phase hierarchical hybrid inference (3pHHY).