The reforestation-TCRE: A metric to quantify the effect of reforestation on global temperature

With a well-studied potential to remove CO₂ from the atmosphere, reforestation is a CO₂ removal intervention common to net-zero CO₂ pathways, policies, and the voluntary CO₂ offset market. However, the relationship between a reforestation-based CO₂ removal and temperature change is complicated by the biogeophsyical effects of reforestation on temperature, which have a demonstrated uncertainty across climate models. Furthermore, reforestation is a land-based intervention occurring in specific geographic locations and the relationship between reforestation within a specific locality and global temperature change is not well-defined.

Here we address these concerns by asking whether the TCRE framework - the fundamental metric relating anthropogenic CO₂ emissions to global temperature change - and its regional variant can be applied to measure the effect of reforestation-based CO₂ removal on global temperature. We conduct idealized net-zero CO₂ simulations in a climate model of intermediate complexity (the UVic ESCM) to quantify the reforestation-TCRE across large-scales of reforestation. We measure reforestation-based CO₂ removals by assessing both the change in above-ground and the change in above and below-ground CO₂ in reforested areas as compared to a counter-factual simulation without reforestation. We further isolate the biogeophyical effects of reforestation to constrain the reforestation-TCRE to only the carbon-effects of reforestation. We expect our results to show that the reforestation-TCRE is not equal and opposite to the TCRE, which is accountable to the biogephsical effects of reforestation and asymmetries between the climate effects of a reforestation-based CO₂ removal and an anthropogenic CO₂ emission. Despite the short-coming, we expect our results to provide a metric for calculating a direct relationship between reforestation-based CO₂ removal and global temperature change that is relatable to net-zero frameworks and potentially reproducible across climate models.