Testing for a CO₂fertilization effect on growth of Canadian boreal forests

Abstract

[1] The CO 2 fertilization hypothesis stipulates that rising atmospheric CO 2 has a direct positive effect on net primary productivity (NPP), with experimental evidence suggesting a 23% growth enhancement with a doubling of CO 2 . Here, we test this hypothesis by comparing a bioclimatic model simulation of NPP over the twentieth century against tree growth increment (TGI) data of 192 Pinus banksiana trees from the Duck Mountain Provincial Forest in Manitoba, Canada. We postulate that, if a CO 2 fertilization effect has occurred, climatically driven simulations of NPP and TGI will diverge with increasing CO 2 . We use a two-level scaling approach to simulate NPP. A leaf-level model is first used to simulate high-frequency responses to climate variability. A canopy-level model of NPP is then adjusted to the aggregated leaf-level results and used to simulate yearly plot-level NPP. Neither model accounts for CO 2 fertilization. The climatically driven simulations of NPP for 1912-2000 are effective for tracking the measured year-to-year variations in TGI, with 47.2% of the variance in TGI reproduced by the simulation. In addition, the simulation reproduces without divergence the positive linear trend detected in TGI over the same period. Our results therefore do not support the attribution of a portion of the historical linear trend in TGI to CO 2 fertilization at the level suggested by current experimental evidence. A sensitivity analysis done by adding an expected CO 2 fertilization effect to simulations suggests that the detection limit of the study is for a 14% growth increment with a doubling of atmospheric CO 2 concentration.

The pyrogeography of eastern boreal Canada from 1901 to 2012 simulated with the LPJ-LMfire model

“…Our simulated 18 % growth enhancement, with a 32.5 % increase in CO 2 concentration between 1901 and 2012, was higher than the 15 and 14 % growth increases that have been proposed by Hickler et al (2008) and Girardin et al (2011), respectively. LPJ-LMfire is highly sensitive to atmospheric CO 2 concentration and interpreting its impacts must be carried out with caution (Girardin et al, 2011).…”

“…Our simulated 18 % growth enhancement, with a 32.5 % increase in CO 2 concentration between 1901 and 2012, was higher than the 15 and 14 % growth increases that have been proposed by Hickler et al (2008) and Girardin et al (2011), respectively. LPJ-LMfire is highly sensitive to atmospheric CO 2 concentration and interpreting its impacts must be carried out with caution (Girardin et al, 2011). That being said, our results suggest that CO 2 -induced enhancement of forest productivity can be offset by fires and climate, which is consistent with the results of Hayes et al (2011) and Kelly et al (2016).…”

“…In terrestrial ecosystem models, changes in atmospheric CO 2 concentration in the recent past and future often have a more important influence on vegetation than does climate change (Girardin et al, 2011). Therefore, their inclusion has a very important effect on simulated changes in productivity.…”

No growth stimulation of Canada’s boreal forest under half-century of combined warming and CO 2 fertilization

“…Adding to this uncertainty is the discrepancy over recent changes in the productivity of boreal and other northern latitude forests. Some empirical evidence suggests increases in the forest productivity (12)(13)(14), whereas other studies suggest decreasing productivity over the last decades (7,8,(15)(16)(17). Furthermore, inversion and process-based ecosystem models indicate large carbon sinks (7,8), whereas field-based bottom-up approaches suggest smaller carbon sinks or small carbon sources (3, 18), or large sinks (19).…”

“…However, a recent comparison of commonly used NDVI datasets showed marked differences in their representation of mean seasonal vegetation productivity and decadal-long trends (21), which stresses the necessity for large-scale, ground-based observations. Ground-based observations of vegetation productivity may originate from permanent sample plots (13,17) or from tree-ring analyses (12,14,16,23), which differ in the temporal resolution (decadal vs. annual) and spatial scale (stand vs. tree). Both are prone to bias because of unbalanced spatial sampling.…”