Articles | Volume 21, issue 4
https://doi.org/10.5194/cp-21-773-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/cp-21-773-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Apr 2025
Research article |
| 10 Apr 2025
| 10 Apr 2025
Pattern scaling of simulated vegetation change in northern Africa during glacial cycles
Mateo Duque-Villegas
CORRESPONDING AUTHOR
Max Planck Institute for Meteorology, Hamburg, Germany
International Max Planck Research School on Earth System Modelling, Hamburg, Germany
Martin Claussen
Max Planck Institute for Meteorology, Hamburg, Germany
Meteorological Institute, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
Thomas Kleinen
Max Planck Institute for Meteorology, Hamburg, Germany
Jürgen Bader
Institute of Oceanography, Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
Christian H. Reick
Max Planck Institute for Meteorology, Hamburg, Germany
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Guilherme L. Torres Mendonça, Julia Pongratz, and Christian H. Reick
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Linear response functions are a powerful tool to both predict and investigate the dynamics of a system when subjected to small perturbations. In practice, these functions must often be derived from perturbation experiment data. Nevertheless, current methods for this identification require a tailored perturbation experiment, often with many realizations. We present a method that instead derives these functions from a single realization of an experiment driven by any type of perturbation.
Guilherme L. Torres Mendonça, Julia Pongratz, and Christian H. Reick
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We apply a new identification method to derive the response functions that characterize the sensitivity of the land carbon cycle to CO2 perturbations in an Earth system model. By means of these response functions, which generalize the usually employed single-valued sensitivities, we can reliably predict the response of the land carbon to weak perturbations. Further, we demonstrate how by this new method one can robustly derive and interpret internal spectra of timescales of the system.
Leonore Jungandreas, Cathy Hohenegger, and Martin Claussen
Clim. Past, 17, 1665–1684, https://doi.org/10.5194/cp-17-1665-2021, https://doi.org/10.5194/cp-17-1665-2021, 2021
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We investigate the impact of explicitly resolving convection on the mid-Holocene West African Monsoon rain belt by employing the ICON climate model in high resolution. While the spatial distribution and intensity of the precipitation are improved by this technique, the monsoon extents further north and the mean summer rainfall is higher in the simulation with parameterized convection.
Philipp de Vrese, Tobias Stacke, Thomas Kleinen, and Victor Brovkin
The Cryosphere, 15, 1097–1130, https://doi.org/10.5194/tc-15-1097-2021, https://doi.org/10.5194/tc-15-1097-2021, 2021
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With large amounts of carbon stored in frozen soils and a highly energy-limited vegetation the Arctic is very sensitive to changes in climate. Here our simulations with the land surface model JSBACH reveal a number of offsetting factors moderating the Arctic's net response to global warming. More importantly we find that the effects of climate change may not be fully reversible on decadal timescales, leading to substantially different CH4 emissions depending on whether the Arctic warms or cools.
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Short summary
We simulate the last glacial cycle with a comprehensive model of the Earth system and investigate vegetation cover change in northern Africa during the last four African Humid Periods (AHPs). We find a common pattern of vegetation change and relate it with climatic factors in order to discuss how vegetation might have evolved during even older AHPs. This scaling relationship we find according to past AHPs fails to account for projected changes in northern Africa under strong greenhouse gas warming.
We simulate the last glacial cycle with a comprehensive model of the Earth system and...
