AIMS Geosciences, 2018, 4(2): 126-143. doi: 10.3934/geosci.2018.2.126.

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Estimation and inversion across the spectrum of carbon cycle modeling

Faculty of Science, The University of Melbourne (Honorary fellow)

Understanding of the carbon cycle is particularly important because of the role of carbon dioxide as a greenhouse gas. Carbon cycle models play an essential role in the interpretation of observational data. The analysis of the carbon cycle involves statistical estimation in various contexts. These include various types of model calibration, including the estimation of feedbacks. A range of inverse calculations are involved in estimating the spatial and/or temporal dependence of carbon dioxide sources and sinks, given observations of concentrations. The uncertainties in these estimates propagate into uncertainties in projections of future carbon cycle behavior. These disparate analyses are discussed in terms of a modeling spectrum that runs from empirical statistical models through to reductionist mechanistic models. The use of the modeling spectrum allows a comparison of di erent modeling approaches. Comparing di erent levels of modeling can provide a basis for assessing the extent to which estimation is being applied consistently.
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Keywords Carbon cycle; inverse problems; estimation; modeling

Citation: Ian Enting. Estimation and inversion across the spectrum of carbon cycle modeling. AIMS Geosciences, 2018, 4(2): 126-143. doi: 10.3934/geosci.2018.2.126

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