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Carbon cycle feedbacks were often quantified through the carbon-concentration and carbon-climate feedbacks with the assumption of no significant interaction between the two feedbacks in most previous studies. Here we calculated the strength of the interactions between the two responses using simulations of models participated in the phase 6 of the Coupled Model Intercomparison Project (CMIP6). We found that the nonlinear interaction contributed 11% of the land-atmosphere carbon exchange on average with large intermodel variation (from -20% to +162%). This nonlinear interaction is largely driven by the pattern of net primary production (NPP), with shifts in heterotrophic respiration that dampen the overall positive interactions from NPP. Photosynthetic rate per unit leaf area alone cannot adequately explain a wide variation of interactions in global NPP simulated by CMIP6 models. Plant respiration and processes that regulate leaf area are also important contributors to the interactions. Dominant factors that induce carbon-concentration and carbon-climate interactions are highly variable among models. One of those dominant factors is nutrient limitation. Using additional simulations of ACCESS-ESM1.5 that include both nitrogen and phosphorus limitation, we found that the estimated interactions by ACCESS-ESM1.5 with or without nutrient limitations covered the large intermodel variations among the CMIP6 models. It remains largely unknown how nutrient limitation complicates ecosystem's responses to simultaneously CO 2 fertilization and warming at the global scale. Our modeling results point to a potential important role of nutrients, especially phosphorus on the nonlinear interactions. Yet, more studies are needed on ecosystem responses to concurrent changes in nutrient availability, atmospheric CO 2 concentration, and warming., (© 2021 Commonwealth of Australia. Global Change Biology © 2021 John Wiley & Sons Ltd.)

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 that combines existing ocean and land carbon models into the physical climate model to simulate exchanges of carbon between the land, atmosphere and ocean. The land carbon model can optionally include both nitrogen and phosphorous limitation on the land carbon uptake. The ocean carbon model simulates the evolution of nitrate, oxygen, dissolved inorganic carbon, alkalinity and iron with one class of phytoplankton and zooplankton. From two multi-centennial simulations of the pre-industrial period with different land carbon model configurations, we evaluate the equilibration of the carbon cycle and present the spatial and temporal variability in key carbon exchanges. For the land carbon cycle, leaf area index is simulated reasonably, and seasonal carbon exchange is well represented. Interannual variations of land carbon exchange are relatively large, driven by variability in precipitation and temperature. We find that the response of the ocean carbon cycle shows reasonable agreement with observations and very good agreement with existing Coupled Model Intercomparison Project (CMIP5) models. While our model over estimates surface nitrate values, the 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. We conclude the study with a brief discussion of key developments required to further improve the realism of our model simulation. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystem models are employed to calculate the sources and sinks of carbon dioxide between land and atmosphere. These models may be heavily parameterised. Where reliable estimates are unavailable for a parameter, it remains highly uncertain; uncertainty of parameters can substantially contribute to overall model output uncertainty. This paper builds on the work of the terrestrial Carbon Cycle Data Assimilation System (CCDAS), which, here, assimilates atmospheric CO2 concentrations to optimise 19 parameters of the underlying terrestrial ecosystem model (Biosphere Energy Transfer and Hydrology scheme, BETHY). Previous experiments have shown that the identified minimum may contain non-physical parameter values. One way to combat this problem is to use constrained optimisation and so avoid the optimiser searching non-physical regions. Another technique is to use penalty terms in the cost function, which are added when the optimisation searches outside of a specified region. The use of parameter transformations is a further method of avoiding this problem, where the optimisation is carried out in a transformed parameter space, thus ensuring that the optimal parameters at the minimum are in the physical domain. We compare these different methods of achieving meaningful parameter values, finding that the parameter transformation method shows consistent results and that the other two provide no useful results. [ABSTRACT FROM AUTHOR]

The article discusses the carbon cycle modelling using the Carbon Cycle Data Assimilation System (CCDAS) that evaluates the Biosphere Energy Transfer and Hydrology scheme (BETHY). It discusses the impact of terrestrial biosphere on global carbon cycle and the accumulation of carbon dioxide (CO2). It reports that the method serves as a parameter for carbon cycle-climate and for future climate change.

Better estimates of the net exchange of CO2 between the atmosphere and the terrestrial biosphere are urgently needed to improve predictions of future CO2 levels in the atmosphere. The carbon cycle data assimilation system (CCDAS) offers the capability of inversion, while it is at the same time based on a process model that can be used independent of observational data. CCDAS allows the assimilation of atmospheric CO2 concentrations into the terrestrial biosphere model BETHY, constraining its process parameters via an adjoint approach. Here, we investigate the effect of spatial differentiation of a universal carbon balance parameter of BETHY on posterior net CO2 fluxes and their uncertainties. The parameter, β, determines the characteristics of the slowly decomposing soil carbon pool and represents processes that are difficult to model explicitly. Two cases are studied with an assimilation period of 1979 to 2003. In the base case, there is a separate β for each plant functional type (PFT). In the regionalization case, β is differentiated not only by PFT, but also according to each of 11 large continental regions as used by the TransCom project. We find that the choice of spatial differentiation has a profound impact not only on the posterior (optimized) fluxes and their uncertainties, but even more so on the spatial covariance of the uncertainties. Differences are most pronounced in tropical regions, where observations are sparse. While regionalization leads to an improved fit to the observations by about 20% compared to the base case, we notice large spatial variations in the posterior net CO2 flux on a grid cell level. The results illustrate the need for universal process formulations in global-scale atmospheric CO2 inversion studies, at least as long as the observational network is too sparse to resolve spatial fluctuations at the regional scale. [ABSTRACT FROM AUTHOR]