Non-matching predictions from different models simulating the effects of elevated atmospheric CO2 on the Amazon forest’s functional diversity

Blanco, C.C., Rius, B.F., Darela-Filho, J.P., Cardeli, B., Aleixo, I., Scheiter, S., Langan, L., Joshi, J., et al. (2024). Non-matching predictions from different models simulating the effects of elevated atmospheric CO2 on the Amazon forest’s functional diversity. DOI:10.5194/egusphere-egu24-22186. In: EGU General Assembly 2024, 14-19 April 2024, Vienna.

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Abstract

The continuous rising of atmospheric carbon dioxide (CO2) concentration is undoubtedly affecting the resilience of tropical forests worldwide. However, the magnitude of such effects is poorly known, limiting our capacity to assess the vulnerability of tropical forests and to improve their representation by models. Functional diversity (FD) is an important component of biodiversity enhancing ecosystem resilience, as high FD can provide higher response diversity and capacity to buffer against climate change. How FD is represented by different Dynamic Global Vegetation Models (DGVMs) may affect how such models predict the impacts of environmental changes on hyperdiverse ecosystems. We compared simulations of five trait-based DGVMs (i.e., with flexible, variable traits) constrained with data from the Amazon rainforest in the scope of the AmazonFACE project. Simulations were conducted considering initial high or low diversity scenarios under ambient and elevated CO2 (400 ppm and 600 ppm, respectively). We searched for correspondence between the functional identity of simulated plant strategies and their ecophysiological performances under elevated CO2. As models take different approaches to simulating functional trait distributions and they differ in their structure and in the trade-offs implemented, we found important intermodel differences in simulated results. Nevertheless, we took advantage of these differences in order to assess the most likely scenarios in terms of functional composition under elevated CO2, as well as to give feedback for better harmonization of model inputs and outputs and future model improvements. In the face of the pessimistic scenarios that project a continuous increase in CO2 levels, resolving the divergent responses among model predictions is critical, given the global importance of the Amazon rainforest's biodiversity and climate regulation, as well as the approximately 30 million people that directly or indirectly depend on the forest for their well-being.

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