Including vegetation dynamics in an atmospheric chemistry-enabled general circulation model: linking LPJ-GUESS (v4.0) with the EMAC modelling system (v2.53)

• Published in: Geoscientific Model Development
• Main Authors: M. Forrest, H. Tost, J. Lelieveld, T. Hickler
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: Geology; QE1-996.5; Arctic; arctic methane; Climate change; permafrost
• Online Access: https://doi.org/10.5194/gmd-13-1285-2020; https://doaj.org/article/c34d8df876184cf0b68a4041bfa21fcf

Central to the development of Earth system models (ESMs) has been the coupling of previously separate model types, such as ocean, atmospheric, and vegetation models, to address interactive feedbacks between the system components. A modelling framework which combines a detailed representation of these components, including vegetation and other land surface processes, enables the study of land–atmosphere feedbacks under global climate change. Here we present the initial steps of coupling LPJ-GUESS, a dynamic global vegetation model, to the atmospheric chemistry-enabled atmosphere–ocean general circulation model EMAC. The LPJ-GUESS framework is based on ecophysiological processes, such as photosynthesis; plant and soil respiration; and ecosystem carbon, nitrogen, and water cycling, and it includes a comparatively detailed individual-based representation of resource competition, plant growth, and vegetation dynamics as well as fire disturbance. Although not enabled here, the model framework also includes a crop and managed-land scheme, a representation of arctic methane and permafrost, and a choice of fire models; and hence it represents many important terrestrial biosphere processes and provides a wide range of prognostic trace-gas emissions from vegetation, soil, and fire. We evaluated an online one-way-coupled model configuration (with climate variable being passed from EMAC to LPJ-GUESS but no return information flow) by conducting simulations at three spatial resolutions (T42, T63, and T85). These were compared to an expert-derived map of potential natural vegetation and four global gridded data products: tree cover, biomass, canopy height, and gross primary productivity (GPP). We also applied a post hoc land use correction to account for human land use. The simulations give a good description of the global potential natural vegetation distribution, although there are some regional discrepancies. In particular, at the lower spatial resolutions, a combination of low-temperature and low-radiation biases in the ...