Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2

• Published in: International journal of biometeorology 2016-07, Vol.60 (7), p.945-955
• Main Authors: Rezende, L. F. C, B. C. Arenque, S. T. Aidar, M. S. B. Moura, C. Von Randow, E. Tourigny, R. S. C. Menezes, J. P. H. B. Ometto
• Format: Article
• Language: English
• Subjects: acclimation; Acclimatization; Anatomy & physiology; Animal Physiology; Atmosphere; Atmospheric sciences; bioclimatology; Biogeochemical cycles; Biological and Medical Physics; Biophysics; carbon; Carbon Dioxide; carbon dioxide enrichment; carboxylation; Climate models; Earth and Environmental Science; ecosystems; energy transfer; Environment; Environmental Health; evolution; Evolutionary biology; Flowers & plants; Land use; land use change; Meteorology; Models, Theoretical; momentum; Original Paper; phenology; Photosynthesis; Physiology; Plant Physiological Phenomena; Plant Physiology; Plants; Temperature; uncertainty; Vegetation
• ISSN: 0020-7128; 1432-1254
• DOI: 10.1007/s00484-015-1087-6

Dynamic global vegetation models (DGVMs) simulate surface processes such as the transfer of energy, water, CO₂, and momentum between the terrestrial surface and the atmosphere, biogeochemical cycles, carbon assimilation by vegetation, phenology, and land use change in scenarios of varying atmospheric CO₂ concentrations. DGVMs increase the complexity and the Earth system representation when they are coupled with atmospheric global circulation models (AGCMs) or climate models. However, plant physiological processes are still a major source of uncertainty in DGVMs. The maximum velocity of carboxylation (Vcₘₐₓ), for example, has a direct impact over productivity in the models. This parameter is often underestimated or imprecisely defined for the various plant functional types (PFTs) and ecosystems. Vcₘₐₓ is directly related to photosynthesis acclimation (loss of response to elevated CO₂), a widely known phenomenon that usually occurs when plants are subjected to elevated atmospheric CO₂ and might affect productivity estimation in DGVMs. Despite this, current models have improved substantially, compared to earlier models which had a rudimentary and very simple representation of vegetation–atmosphere interactions. In this paper, we describe this evolution through generations of models and the main events that contributed to their improvements until the current state-of-the-art class of models. Also, we describe some main challenges for further improvements to DGVMs.