Comments On A New Paper “Skill And Uncertainty In Climate Models” By Julia C. Hargreaves

We have been alerted to a new paper (h/t to Marcel Crok) which has investigated the skill of the multi-decadal global model predictions. The paper is

Julia C. Hargreaves, 2010: Skill and uncertainty in climate models. Published Online: Jun 24 2010 03:55 AM
DOI: 10.1002/wcc.58

The abstract reads

“Analyses of skill are widely used for assessing weather predictions, but the time scale and lack of validation data mean that it is not generally possible to investigate the predictive skill of today’s climate models on the multidecadal time scale. The predictions made with early climate models can, however, be analyzed, and here we show that one such forecast did have skill. It seems reasonable to expect that predictions based on today’s more advanced models will be at least as skillful. In general, assessments of predictions based on today’s climate models should use Bayesian methods, in which the inevitable subjective decisions are made explicit. For the AR4, the Intergovernmental Panel on Climate Change (IPCC) recommended the Bayesian paradigm for making estimates of uncertainty and probabilistic statements, and here we analyze the way in which uncertainty was actually addressed in the report. Analysis of the ensemble of general circulation models (GCMs) used in the last IPCC report suggests there is little evidence to support the popular notion that the multimodel ensemble is underdispersive, which would imply that the spread of the ensemble may be a reasonable starting point for estimating uncertainty. It is important that the field of uncertainty estimation is developed in order that the best use is made of current scientific knowledge in making predictions of future climate. At the same time, it is only by better understanding the processes and inclusion of these processes in the models, the best estimates of future climate will be closer to the truth .”

Excerpts from the conclusion read

“In the first section, it was argued that it is impossible to assess the skill (in the conventional sense) of current climate forecasts. Analysis of the Hansen forecast of 1988 does, however, give reasons to be hopeful that predictions from current climate models are skillful, at least in terms of the globally averaged temperature trend.”

“Bayesian predictions of future change will be obtained by combining all lines of evidence: the multimodel ensembles run for past, present, future and transient experiments; additional expert opinion; data from the present day, historical record, and paleoclimates. Although small steps have been made toward this goal, more serious attempts analyzing a broader range of variables than climate sensitivity should be a high priority.”

This is a quite revealing paper. Even without questioning the conclusion of the study with respect to their methodology, the paper admits that only the Hansen 1988 prediction is  “skillful [only] in terms of the globally averaged temperature trend.”   The article also accepts that  “it is impossible to assess the skill (in the conventional sense) of current climate forecasts”, which is why the authors introduce a Bayesian prediction method.

This study confirms how little added information the multi-decadal global climate models predictions are providing in terms of regional scale climate information, and even other global average climate metrics such as the hydrologic cycle.

An obvious conclusion from this study (which the author did not comment on) is that large amounts of money are being spent to complete global model forecasts decades into the future, yet only the global average surface temperature trend (at best) has been shown to be possible. Regional forecasts for the coming decades, so far, are without skill; e.g. as already shown by

Koutsoyiannis, D., A. Efstratiadis, N. Mamassis, and A. Christofides, 2008: On the credibility of climate predictions, Hydrological Sciences Journal, 53 (4), 671-684.

and are misleading policymakers when they are presented as skillful projections.

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