dc.contributor.author | McNeall, D | |
dc.contributor.author | Williams, J | |
dc.contributor.author | Booth, B | |
dc.contributor.author | Betts, RA | |
dc.contributor.author | Challenor, P | |
dc.contributor.author | Wiltshire, A | |
dc.contributor.author | Sexton, D | |
dc.date.accessioned | 2016-12-06T15:50:18Z | |
dc.date.issued | 2016-11-24 | |
dc.description.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 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. | en_GB |
dc.description.sponsorship | This work was supported by the Joint
UK BEIS/Defra Met Office Hadley Centre Climate Programme
(GA01101). Doug McNeall was supported on secondment
to Exeter University by the Met Office Academic Partnership
(MOAP) for part of the work. Jonny Williams was supported
by funding from Statoil ASA, Norway | en_GB |
dc.identifier.citation | Vol. 7, pp. 917 - 935 | en_GB |
dc.identifier.doi | 10.5194/esd-7-917-2016 | |
dc.identifier.uri | http://hdl.handle.net/10871/24751 | |
dc.language.iso | en | en_GB |
dc.publisher | European Geosciences Union | en_GB |
dc.rights | This is the final version of an open access article also available from EGU via the DOI in this record. This work is distributed
under the Creative Commons Attribution 3.0 License: https://creativecommons.org/licenses/by/3.0/ | en_GB |
dc.title | The impact of structural error on parameter constraint in a climate model | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2016-12-06T15:50:18Z | |
dc.identifier.issn | 2190-4979 | |
dc.identifier.eissn | 2190-4987 | |
dc.identifier.journal | Earth System Dynamics | en_GB |