On statistical approaches to climate change analysis
Date
2008-04-21T16:40:24Z
Authors
Lee, Terry Chun Kit
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Abstract
Evidence for a human contribution to climatic changes during the past
century is accumulating rapidly. Given the strength of the evidence, it seems natural to ask
whether forcing projections can be used to forecast climate change. A Bayesian method for
post-processing forced climate model simulations that produces probabilistic hindcasts of
inter-decadal temperature changes on large spatial scales is proposed. Hindcasts produced for the
last two decades of the 20th century are shown to be skillful. The suggestion that
skillful decadal forecasts can be produced on large regional scales by exploiting the response to
anthropogenic forcing provides additional evidence that anthropogenic change in the composition of
the atmosphere has influenced our climate. In the absence of large negative volcanic forcing on the
climate system (which cannot presently be forecast), the global mean temperature for the decade
2000-2009 is predicted to lie above the 1970-1999 normal with probability 0.94. The global mean
temperature anomaly for this decade relative to 1970-1999 is predicted to be 0.35C (5-95%
confidence range: 0.21C-0.48C).
Reconstruction of temperature variability of the past centuries using climate proxy data can also
provide important information on the role of anthropogenic forcing in the observed 20th
century warming. A state-space model approach that allows incorporation of additional
non-temperature information, such as the estimated response to external forcing, to reconstruct
historical temperature is proposed. An advantage of this approach is that it permits simultaneous
reconstruction and detection analysis as well as future projection. A difficulty in using this
approach is that estimation of several unknown state-space model parameters is required. To take
advantage of the data structure in the reconstruction problem, the existing parameter estimation
approach is modified, resulting in two new estimation approaches. The competing estimation
approaches are compared based on theoretical grounds and through simulation studies. The two new
estimation approaches generally perform better than the existing approach.
A number of studies have attempted to reconstruct hemispheric mean temperature for the past
millennium from proxy climate indicators. Different statistical methods are used in these studies
and it therefore seems natural to ask which method is more reliable. An empirical comparison
between the different reconstruction methods is considered using both climate model data and
real-world paleoclimate proxy data. The proposed state-space model approach and the RegEM method
generally perform better than their competitors when reconstructing interannual variations in
Northern Hemispheric mean surface air temperature. On the other hand, a variety of methods are seen
to perform well when reconstructing decadal temperature variability. The similarity in performance
provides evidence that the difference between many real-world reconstructions is more likely to be
due to the choice of the proxy series, or the use of difference target seasons or latitudes, than
to the choice of statistical method.
Description
Keywords
Climate change, Climate prediction, Paleoclimate reconstruction, State-space model, Kalman filter and smoother, Maximum likelihood estimation, Asymptotic distribution