Abstract
Abstract. 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 growing season of the EMAC climate at high latitudes causes an underestimation of vegetation extent. Quantification of the agreement with the gridded datasets using the normalised mean error (NME) averaged over all datasets shows that increasing the spatial resolution from T42 to T63 improved the agreement by 10 %, and going from T63 to T85 improved the agreement by a further 4 %. The highest-resolution simulation gave NME scores of 0.63, 0.66, 0.84, and 0.53 for tree cover, biomass, canopy height, and GPP, respectively (after correcting tree cover and biomass for human-caused deforestation which was not present in the simulations). These scores are just 4 % worse on average than an offline LPJ-GUESS simulation using observed climate data and corrected for deforestation by the same method. However, it should be noted that the offline LPJ-GUESS simulation used a higher spatial resolution, which makes the evaluation more rigorous, and that excluding GPP from the datasets (which was anomalously better in the EMAC simulations) gave 10 % worse agreement for the EMAC simulation than the offline simulation. Gross primary productivity was best simulated by the coupled simulations, and canopy height was the worst. Based on this first evaluation, we conclude that the coupled model provides a suitable means to simulate dynamic vegetation processes in EMAC.
Highlights
Simulation models are at the forefront of Earth systems research
We evaluated an online one-way-coupled model configuration by conducting simulations at three spatial resolutions (T42, T63, and T85)
Models have increasingly been coupled to each other to provide dynamic multidirectional fluxes between models, as opposed to prescribing simple non-interacting boundary conditions. This approach has yielded atmosphere–ocean general circulation models (AOGCMs) which are utilised to understand the dynamics of the physical components of the climate system (Flato et al, 2013)
Summary
Simulation models are at the forefront of Earth systems research. Historically, such models were initially developed to simulate one component of the Earth system in isolation, such as ocean and atmospheric general circulation models (GCMs) or dynamic global vegetation models (DGVMs), with prescribed boundary conditions at the interfaces to other Earth system components. The model has since been extended to include expanded representations of atmospheric chemistry; a coupled ocean model with dynamic sea ice (Pozzer et al, 2011); ocean biogeochemistry (Kern, 2013); an alternative base model for the atmospheric circulation (Baumgaertner et al, 2016); regional downscaling via a two-way coupling (Kerkweg et al, 2018) with the COSMO weather forecast model (Baldauf et al, 2011); and a multitude of processes such as representations for aerosols, aerosol–radiation and aerosol–cloud (Tost, 2017) interactions, and many more (all of which are integrated via the MESSy infrastructure) By bringing together these two modelling systems, our intent is to produce a fully featured ESM, which benefits from the continuous development of all components. To investigate resolution-dependent climate biases in EMAC, simulations with three spatial resolutions were performed, and their performance relative to observed data were compared
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