Estimation and inversion across the spectrum of carbon cycle modeling

Ian Enting; Ian Enting

doi:10.3934/geosci.2018.2.126

AIMS Geosciences

2018, Volume 4, Issue 2: 126-143. doi: 10.3934/geosci.2018.2.126

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Research article

Estimation and inversion across the spectrum of carbon cycle modeling

Ian Enting ^,

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

Received: 04 December 2017 Accepted: 02 May 2018 Published: 08 May 2018

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

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Abstract

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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