The impact of structural error on parameter constraint in a climate model
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 uncerta...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-11-01
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| Series: | Earth System Dynamics |
| Online Access: | http://www.earth-syst-dynam.net/7/917/2016/esd-7-917-2016.pdf |
| Summary: | 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.
<br><br>
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.
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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.
<br><br>
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. |
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| ISSN: | 2190-4979 2190-4987 |