The impact of structural error on parameter constraint in a climate model
McNeall, D; Williams, J; Booth, B; et al.Betts, RA; Challenor, P; Wiltshire, A; Sexton, D
Date: 24 November 2016
Journal
Earth System Dynamics
Publisher
European Geosciences Union
Publisher DOI
Abstract
Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections
of future climate change. We use observations of forest fraction to constrain carbon cycle and land
surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error.
Using an ...
Uncertainty in the simulation of the carbon cycle contributes significantly to uncertainty in the projections
of future climate change. We use observations of forest fraction to constrain carbon cycle and land
surface input parameters of the global climate model FAMOUS, in the presence of an uncertain structural error.
Using an ensemble of climate model runs to build a computationally cheap statistical proxy (emulator) of the
climate model, we use history matching to rule out input parameter settings where the corresponding climate
model output is judged sufficiently different from observations, even allowing for uncertainty.
Regions of parameter space where FAMOUS best simulates the Amazon forest fraction are incompatible with
the regions where FAMOUS best simulates other forests, indicating a structural error in the model. We use
the emulator to simulate the forest fraction at the best set of parameters implied by matching the model to the
Amazon, Central African, South East Asian, and North American forests in turn. We can find parameters that
lead to a realistic forest fraction in the Amazon, but that using the Amazon alone to tune the simulator would
result in a significant overestimate of forest fraction in the other forests. Conversely, using the other forests to
tune the simulator leads to a larger underestimate of the Amazon forest fraction.
We use sensitivity analysis to find the parameters which have the most impact on simulator output and perform
a history-matching exercise using credible estimates for simulator discrepancy and observational uncertainty
terms. We are unable to constrain the parameters individually, but we rule out just under half of joint parameter
space as being incompatible with forest observations. We discuss the possible sources of the discrepancy in the
simulated Amazon, including missing processes in the land surface component and a bias in the climatology of
the Amazon.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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