Implementing forest-based natural climate solutions (NCS) in a global vegetation model to better constrain global potentials

Natural climate solutions (NCS) are often promoted as cost-effective and readily available mitigation measures to slow down global warming. The largest emission reduction potentials are estimated for forest-based NCS such as reforestation, avoided deforestation, and improved forest management. Yet, uncertainties are high regarding the magnitude and permanence of negative or avoided emissions, given i.a. uncertainties in implementation and governance of these measures, extrapolating global potentials from limited case study data, and effects of climate change on forest carbon stocks.

We set out to better constrain the biophysical potential of forest-based NCS using the dynamic global vegetation model LPJmL. The model has a long track record of simulating the effects of climate and climate change on the carbon, water and nitrogen cycle of forests and other terrestrial ecosystems. Whereas the management of agricultural systems was already well-represented, the model so far had no explicit representation of any forest management.

We implemented forest harvest, but more importantly replaced the use of a single average individual representing all trees in a grid-cell with an explicit representation of age classes in order to improve simulation of forest (re-)growth after management (harvest or land-use abandonment) and after natural disturbance events (e.g. fire, drought).

We show results for the historical period and future scenarios contrasting simulations with and without forest harvest and demonstrate the importance of including age classes.