Abstract

Abstract. 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.

Highlights

  • The response of the global carbon cycle to future changes in climate is highly uncertain

  • An ensemble of optimisations is performed, with each optimisation starting in slightly varied points in parameter space. If they all converge to the same minimum, we have confidence that we have found a minimum that is more likely to be a global minimum within the physical parameter space

  • We present the results of the different experiments, with a focus on the parameter transformations, as these are the experiments that successfully located a minimum within the physical parameter space

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Summary

Introduction

The response of the global carbon cycle to future changes in climate is highly uncertain. All of the models showed that future climate change would reduce the efficiency of the Earth system and in particular the land biosphere to absorb the anthropogenic carbon perturbation, with an additional CO2 of between 20 and 200 ppm between the two most extreme models by 2100. The study of Sitch et al (2008) used five dynamic global vegetation models (DGVMs) to model the contemporary terrestrial carbon cycling. They coupled the DGVMs to a fast “climate analogue model” based on the Hadley Centre General Circulation Model, and ran the coupled models to Published by Copernicus Publications on behalf of the European Geosciences Union

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