Local biogeophysical effects of deforestation
- 1Vrije Universiteit Brussel, Faculty of engineering, Hydrology and hydraulic engineering, Belgium (sdeherto@vub.ac.be)
- 2Ludwig-Maximilians-University Munich, Department of Geography, Munich, Germany.
- 3Max Planck Institute for Meteorology, Hamburg, Germany.
- 4Vrije Universiteit Amsterdam, Institute for Environmental studies, Amsterdam, Netherlands.
- 5ETH Zurich, Institute for Atmospheric and Climate Science, 8092 Zurich, Switzerland.
- 6Climate Analytics, Berlin, Germany.
The impact of deforestation on climate is mostly pronounced through net carbon emissions (biogeochemical effects), leading to a global warming. However, deforestation also alters the water and energy cycles (biogeophysical effects), which can cause a local warming or cooling depending on the region. This can potentially offset or even exacerbate the initial global warming signal caused by the biogeochemical effect. The results of earth system models show a large spread on the magnitude of biogeophysical effects and can even vary on the sign of these impacts for some regions. Thus, uncovering the uncertainty related to the biogeophysical effect of deforestation is crucial, to better understand the potential of afforestation as a means for land-based climate mitigation.
We investigate the biogeophysical effects of deforestation on climate by conducting idealised deforestation experiments consisting of a 150-year simulation. Greenhouse gas forcing is held constant at present-day levels to disentangle between the climatic effects from land use and from those due to anthropogenic climate change. The experiment is conducted by three different Earth System Models (MPI-ESM, EC-EARTH and CESM) to quantify inter-model uncertainty and potentially uncover specific model biases.
A recently-developed checkerboard approach is applied to disentangle the local and non-local effect (i.e. remote impacts of deforestation due to changes in atmospheric dynamics) from deforestation (Winckler et al. 2019). This enables us to better determine the uncertainties across the models as well as to validate the local biogeophysical effects of deforestation using observational datasets. This is the first time that the checkerboard approach is applied on multi-model climate simulations and thus serves as a benchmark for the applicability of this approach.
References:
Winckler, J., Reick, C.H., Luyssaert, S., Cescatti, A., Stoy, P.C., Lejeune, Q., Raddatz, T., Chlond, A., Heidkamp, M., Pongratz, J., Different Response of surface temperature and air temperature to deforestation in climate models, Journal of Earth System Dynamics, doi: https://doi.org/10.5194/esd-2018-66
How to cite: De Hertog, S., Vanderkelen, I., Havermann, F., Guo, S., Pongratz, J., Manola, I., Coumou, D., Davin, E., Seneviratne, S., Lejeune, Q., Menke, I., Schleussner, C., and Thiery, W.: Local biogeophysical effects of deforestation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1248, https://doi.org/10.5194/egusphere-egu2020-1248, 2020.
