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Journal Article

Nonlocal effects dominate the global mean surface temperature response to the biogeophysical effects of deforestation

MPS-Authors
/persons/resource/persons201864

Winckler,  Johannes
Emmy Noether Junior Research Group Forest Management in the Earth System, The Land in the Earth System, MPI for Meteorology, Max Planck Society;
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;

/persons/resource/persons37304

Reick,  Christian H.
Global Vegetation Modelling, The Land in the Earth System, MPI for Meteorology, Max Planck Society;

/persons/resource/persons37296

Pongratz,  Julia
Emmy Noether Junior Research Group Forest Management in the Earth System, The Land in the Earth System, MPI for Meteorology, Max Planck Society;

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Supplementary Material (public)

2019_GRL_nonlocal_winckler.tar.gz
(Supplementary material), 17MB

grl58469-sup-0001-2018gl080211-text_si-s01.pdf
(Supplementary material), 7MB

Citation

Winckler, J., Lejeune, Q., Reick, C. H., & Pongratz, J. (2019). Nonlocal effects dominate the global mean surface temperature response to the biogeophysical effects of deforestation. Geophysical Research Letters, 46, 745-755. doi:10.1029/2018GL080211.


Cite as: http://hdl.handle.net/21.11116/0000-0002-D328-3
Abstract
Deforestation influences surface temperature locally (“local effects”), but also at neighboring or remote regions (“nonlocal effects”). Observations indicate that local effects induce a warming in most locations, while many climate models show a global mean cooling when simulating global deforestation. We show that a nonlocal cooling in models, which is excluded from observations, may strongly contribute to these conflicting results. For the MPI‐ESM, the globally averaged nonlocal cooling exceeds the globally averaged local warming by a factor of three, for global deforestation but also for realistic areal extents and spatial distributions of deforestation. Furthermore, the globally averaged nonlocal effects dominate the local effects in realistic scenarios across a range of climate models. We conclude that observations alone are not sufficient to capture the full biogeophysical effects, and climate models are needed to better understand and quantify the full effects of deforestation before they are considered in strategies for climate mitigation.