Paper/Poster/Presentation Title
Keywords
Climate; deforestation; Earth system model
Start Date
15-9-2020 2:00 PM
End Date
15-9-2020 2:20 PM
Abstract
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 additional warming 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). Through this approach the local effect is determined by comparing the effects from a pixel where land use change occurred versus a nearby pixel where this is not the case. This approach 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 study will be 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.
Local biogeophysical effects of deforestation
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 additional warming 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). Through this approach the local effect is determined by comparing the effects from a pixel where land use change occurred versus a nearby pixel where this is not the case. This approach 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 study will be 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.
Stream and Session
false