Objective
Objectives :
To quantify the robustness of recent reports of an
attributable anthropogenic influence on global climate
through the application of a consistent optimal detection
methodology to a wider range of alternative climate
change mechanisms, a wider range of model predictions and
a wider range of observational data sources than
have been addressed to date.
To investigate the simulation of natural climate
variability in the coupled models used for climate change
detection and attribution to establish the extent to
which uncertainty estimates based on these models may
under- (or over-) estimate the true uncertainties, and to
develop methods of correcting any uncertainty analysis
when model variability is found to be deficient.
To quantify the implications of detection and attribution
results in terms of physically-interpretable processes
and parameters using simplified coupled ocean-atmosphere
climate models.
Brief Description of the Research Project :
A common procedure for detection and attribution studies
will be developed, based on optimal fingerprinting but
revised to (i) allow for noise in the model-predicted
patterns, (ii) incorporate diagnostic checks to ensure
that uncertainty estimates are consistent with the
dataset used in the attribution study, (iii) standardize
the choice of
truncation and treatment of missing data and (iv)
incorporate prior expectations in a maximum-likelihood
framework.
Following detailed statistical evaluation, the algorithm
will be used to provide quantitative estimates of
uncertainties in the climate response to greenhouse
gases, sulphate aerosols, ozone depletion, volcanic
aerosols and solar variability. These estimates will be
based on the analysis of the historical surface
temperature record and
vertically-resolved radiosonde record of atmospheric
temperatures using patterns derived from both the Hadley
Centre and suite of ECHAM climate models.
All current uncertainty estimates in climate change
detection and attribution depend on model simulations of
internal climate variability. The adequacy of model-simulated variability over the full range of spatio-temporal scales will be evaluated and advanced
statistical techniques will be applied to quantitify any
bias in uncertainty estimates.
Current approaches to detection and attribution involve
estimating the range of possible amplitudes of model-predicted response-patterns in the observational record.
For these results to inform model development, these
pattern-amplitude-ranges must be interpreted in terms of
physically meaningful parameters. A reduced-physics
(zonally averaged) climate model will be calibrated to
mimic the behaviour of
the GCMs under the full range of forcing scenarios. This
will allow a physical interpretation of detection and
attribution results in the framework of a non-linear
model and suggest which physical parameters are
constrained (or left unconstrained) by detection results.
Throughout, this project will provide information, as
required, to EU policy makers, both directly and through
the Intergovernmental Panel on Climate Change (IPCC).
Fields of science
Call for proposal
Data not availableFunding Scheme
CSC - Cost-sharing contractsCoordinator
OX11 0QX DIDCOT,HARWELL,CHILTON
United Kingdom