Land vegetation is currently taking up large amounts of atmospheric CO2, possibly due to tree growth stimulation. Extant models predict that this growth stimulation will continue to cause a net carbon uptake this century. However, there are indications that increased growth rates may shorten trees′ lifespan and thus recent increases in forest carbon stocks may be transient due to lagged increases in mortality. Here we show that growth-lifespan trade-offs are indeed near universal, occurring across almost all species and climates. This trade-off is directly linked to faster growth reducing tree lifespan, and not due to covariance with climate or environment. Thus, current tree growth stimulation will, inevitably, result in a lagged increase in canopy tree mortality, as is indeed widely observed, and eventually neutralise carbon gains due to growth stimulation. Results from a strongly data-based forest simulator confirm these expectations. Extant Earth system model projections of global forest carbon sink persistence are likely too optimistic, increasing the need to curb greenhouse gas emissions.

Tree rings are thought to be a powerful tool to reconstruct historical growth changes and have been widely used to assess tree responses to global warming. Demographic inferences suggest, however, that typical sampling procedures induce spurious trends in growth reconstructions. Here we use the world’s largest single tree-ring dataset (283,536 trees from 136,621 sites) from Quebec, Canada, to assess to what extent growth reconstructions based on these - and thus any similar - data might be affected by this problem. Indeed, straightforward growth rate reconstructions based on these data suggest a six-fold increase in radial growth of black spruce ( Picea mariana ) from ~0.5 mm yr −1 in 1800 to ~2.5 mm yr −1 in 1990. While the strong correlation (R 2 = 0.98) between this increase and that of atmospheric CO 2 could suggest a causal relationship, we here unambiguously demonstrate that this growth trend is an artefact of sampling biases caused by the absence of old, fast-growing trees (cf. “ slow-grower survivorship bias ”) and of young, slow-growing trees (cf. “ big-tree selection bias ”) in the dataset. At the moment, we cannot envision how to remedy the issue of incomplete representation of cohorts in existing large-scale tree-ring datasets. Thus, innovation will be needed before such datasets can be used for growth rate reconstructions.