The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 1: Model description and pre-industrial simulation
Earth system models (ESMs) that incorporate carbon–climate feedbacks
represent the present state of the art in climate modelling. Here, we
describe the Australian Community Climate and Earth System Simulator
(ACCESS)-ESM1, which comprises atmosphere (UM7.3), land (CABLE), ocean
(MOM4p1), and sea-ice (CICE4.1) components with OASIS-MCT coupling, to which
ocean and land carbon modules have been added. The land carbon model (as part
of CABLE) can optionally include both nitrogen and phosphorous limitation on
the land carbon uptake. The ocean carbon model (WOMBAT, added to MOM)
simulates the evolution of phosphate, oxygen, dissolved inorganic carbon,
alkalinity and iron with one class of phytoplankton and zooplankton. We
perform multi-centennial pre-industrial simulations with a fixed atmospheric
CO2 concentration and different land carbon model configurations
(prescribed or prognostic leaf area index). We evaluate the equilibration of
the carbon cycle and present the spatial and temporal variability in key
carbon exchanges. Simulating leaf area index results in a slight warming of
the atmosphere relative to the prescribed leaf area index case. Seasonal and
interannual variations in land carbon exchange are sensitive to whether leaf
area index is simulated, with interannual variations driven by variability in
precipitation and temperature. We find that the response of the ocean carbon
cycle shows reasonable agreement with observations. While our model
overestimates surface phosphate values, the global primary productivity
agrees well with observations. Our analysis highlights some deficiencies
inherent in the carbon models and where the carbon simulation is negatively
impacted by known biases in the underlying physical model and consequent
limits on the applicability of this model version. We conclude the study with
a brief discussion of key developments required to further improve the
realism of our model simulation
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