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The Probability of Future Global and Large-Scale Regional Climate Change

PI: Mat Collins
Institution: Met Office Hadley Centre
Additional Investigators: Linda Mearns, James Murphy, David Sexton, Claudia Tebaldi
Probabilistic methods have recently been proposed as a way of formally quantifying uncertainty in model predictions of climate change (e.g. Murphy et al., 2004; Tebaldi et al, 2004). Ensembles of model projections are gathered (the multi-model “ensemble of opportunity”) or designed (the “perturbed physics ensemble”) and individual ensemble members are given different weights according to some measure of their credibility or reliability. The ensemble members and their relative weights are then combined to produce a probability density function (PDF) of future climate change (here we use the term PDF as an umbrella term for quantities such as the frequency histogram, cumulative density function etc.). Results may be displayed graphically or, as is common, quoted in terms of an average measure together with a 5-95% uncertainty range.

PDFs are thought to be useful to both policy makers and scientists but care must be taken in their production and interpretation. No method exists which can quantify all the uncertainties in climate change predictions (models, scenarios, unrepresented feedbacks etc.) hence it must be made clear what uncertainties are accounted for. Also a number of different methods exist for generating PDFs and results can be sensitive to those choices. The sensitivity to methods, in particular what the PDF is constrained by and what it is conditional upon should be investigated and clearly stated.

Space has already been set aside in the AR4 for the inclusion of probabilistic measures of global and large-scale regional variables (section 10.6 of chapter 10 and the corresponding section of chapter 11). It is the purpose of this proposal to produce these measures in the limited time available. The following steps will be performed.

* Regions (e.g. those defined by Giorgi and Mearns, 2002), seasons, time periods and variables will be defined for analysis by all researchers via email exchange early in the project. The project will necessarily focus on a small subset of possible combinations. Data will be downloaded to the individual institutions as and when it becomes available.

* Each group will produce their own weighted ensembles for the regions, seasons and time periods specified. The multi-model and perturbed physics ensembles will be (at first) kept separate to facilitate a comparison but may ultimately be combined. The Hadley group will also produce values for the un-weighted ensemble.

* PDFs will be compared using the same measures and graphical representations. Similarities and differences will be discussed via email exchange and at the March workshop.

* Draft figures and the critical commentary will be produced after the meeting with a view to the inclusion in the AR4 and for peer-reviewed publication.

Data Required

Seasonal mean fields from control, 20th century forced and scenario integrations for model fields for which good observational datasets exists (see e.g. figure 4 of Murphy et al., 2004).


Giorgi, F. and L.O. Mearns, 2002: Calculation of average, uncertainty range and reliability of regional climate changes from AOGCM simulations via the “reliability ensemble averaging” (REA) method. J. Climate, 15, 1141-1158.

Murphy, J.M., D.M.H. Sexton, D.N. Barnett, G.S. Jones, M.J. Webb, M. Collins and D.A. Stainforth, 2004: Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 430, 768-772.

Tebaldi, C., R.L. Smith, D. Nychka and L.O. Mearns, 2004: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multi-model ensembles. Submitted.
  • Collins, M., Booth, B.B.B., Harris, G., Murphy, J.M., Sexton, D.M.H., Webb, M., 2006: Towards Quantifying Uncertainty in Transient Climate Change.. Climate Dynamics, 27, 127-147, DOI: 10.1007/s00382-006-0121-0. Abstract. Full Article. Edit.

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