Monday, December 06, 2010

science not politics

One of the most common complaints by skeptics is that the climate models (upon which climate change predictions rest) are not very accurate. Moreover, there is a perception that the reason for this is that basic premises reflect ideology rather than physical science.

This perspective is given additional traction by the publication of this refereed journal paper, which concludes:
  • It is claimed that GCMs provide credible quantitative estimates of future climate change, particularly at continental scales and above. Examining the local performance of the models at 55 points, we found that local projections do not correlate well with observed measurements. Furthermore, we found that the correlation at a large spatial scale, i.e. the contiguous USA, is worse than at the local scale.
  • However, we think that the most important question is not whether GCMs can produce credible estimates of future climate, but whether climate is at all predictable in deterministic terms. Several publications, a typical example being Rial et al. (2004), point out the difficulties that the climate system complexity introduces when we attempt to make predictions. “Complexity” in this context usually refers to the fact that there are many parts comprising the system and many interactions among these parts. This observation is correct, but we take it a step further. We think that it is not merely a matter of high dimensionality, and that it can be misleading to assume that the uncertainty can be reduced if we analyse its “sources” as nonlinearities, feedbacks, thresholds, etc., and attempt to establish causality relationships. Koutsoyiannis (2010) created a toy model with simple, fully-known, deterministic dynamics, and with only two degrees of freedom (i.e. internal state variables or dimensions); but it exhibits extremely uncertain behaviour at all scales, including trends, fluctuations, and other features similar to those displayed by the climate. It does so with a constant external forcing, which means that there is no causality relationship between its state and the forcing. The fact that climate has many orders of magnitude more degrees of freedom certainly perplexes the situation further, but in the end it may be irrelevant; for, in the end, we do not have a predictable system hidden behind many layers of uncertainty which could be removed to some extent, but, rather, we have a system that is uncertain at its heart.
  • Do we have something better than GCMs when it comes to establishing policies for the future? Our answer is yes: we have stochastic approaches, and what is needed is a paradigm shift. We need to recognize the fact that the uncertainty is intrinsic, and shift our attention from reducing the uncertainty towards quantifying the uncertainty (see also Koutsoyiannis et al., 2009a). Obviously, in such a paradigm shift, stochastic descriptions of hydroclimatic processes should incorporate what is known about the driving physical mechanisms of the processes. Despite a common misconception of stochastics as black-box approaches whose blind use of data disregard the system dynamics, several celebrated examples, including statistical thermophysics and the modelling of turbulence, emphasize the opposite, i.e. the fact that stochastics is an indispensable, advanced and powerful part of physics. Other simpler examples (e.g. Koutsoyiannis, 2010) indicate how known deterministic dynamics can be fully incorporated in a stochastic framework and reconciled with the unavoidable emergence of uncertainty in predictions.
Now cutting through the standard academic boiler plate -- there is something better, it is our approach -- the take home message is that present GCM models are not the accurate projection they are claimed to be.  A stochastic approach may yield better results, but while that might work scientifically it is a certain non-starter politically.

A stochastic relationship is, by definition, a random process with defined probabilities.  Environmental ideology does not allow for random processes: it is entirely predicated upon the certainty of human agency upon which to promulgate its dogma of restraint and regulation.  Moreover, soci0-economic data are immune to probabilistic modeling precisely because they entail humans and free choice.

No serious economist takes the Stern report as a valid projection of the future.  But it is necessary as an input to drive the IPCC models.  Those models are not an accurate representation of past climate trends.  The error may not be large, but the global re-organization of society has been promoted by climate alarmists on the basis of temperature changes of less than this margin of error.

Admitting this constraint has two concomitant effects: it both restores the credibility of the science on climate change and removes its entire political imperative.