Surface biophysical-carbon-climate sensitivities to tropical deforestation: Do the rates matter in both science and policy?
Abstract
Deforestation pathways are simulated using a range of annual rates - 5%, 2%, 1% and 0.5% - paired with a preservation target of 10% for each tropical tree type.
Sensitivity analyses are conducted using the Community Climate System Model 4 (CCSM4) with prognostic carbon-nitrogen and dynamic vegetation (CNDV). Trends and means in biophysical, carbon and climate variables are quantified during and after deforestation, then compared across the different rates. Analyses
focus on tropical land, the Amazon Basin, Central Africa, and Southeast Asia. Results show that sensitivities to deforestation can be approximated as linear in this model, and vary more across regions than across rates. Most sensitive to deforestation are ground temperatures over the Amazon Basin, which increase 3 to 4K per million km2 of tree cover lost with both biophysical and CO2 feedbacks. In addition, locally, it was found that biophysical feedbacks alone can have considerable impact on climate. In the Amazon, the increase in ground temperatures with only biophysical feedbacks is as much as 55%, and the decrease in precipitation is as much as 61% of the combined biophysical and carbon impacts.
These results are brought to bear on climate policy, particularly on the REDD+ mechanism. Modeling results support an enhanced scope, the use of projected baselines, and the validity of streamlined monitoring methodologies. The significance of local biophysical impacts further suggests, firstly, that developing countries have more to gain from the concentrated climate good compared to Annex I countries only benefit from the global public good from reduced deforestation emissions; and secondly, that carbon-based metrics are inadequate for the forestry sector, as well as for land cover/land use change in general. For forestry-based climate mitigation initiatives to move forward in a holistic manner, guidance is required from both science and policy.
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