The risk of a climate crisis, like the risks associated with sub-prime mortgage securitisation, are calculated using complex computer models and both are too complex for the average punter to understand.
As Graham Young wrote last week in a blog post entitled ‘Sub-prime and climate change’, these models were created by clever people with PhDs in maths and physics, but they are only as good as the information feed into them. GIGO (garbage in, garbage out) is how he described both the climate models and the models that helped created the current credit crisis.
According to Richard Mackey, a sceptic from Canberra, also writing on the issues of climate change and financial systems, a key limitation with both financial and climate models is the underlying false assumption that economic and climate systems are ergodic systems – that is they normalise to an equilibrium state.
Richard Mackey wrote:
“One of the lethal critiques of the United Nation’s Intergovernmental Panel on Climate Change (IPCC) models is that the climate system can never be at anything like an equilibrium state.
All the models assume that the climate system normalises to an equilibrium state, the state modelled. As the natural processes of the climate system are non-linear and non-ergodic, small variations may result in large changes. There are negative and positive feedback loops. There is randomness in the system. As a result, the simple deterministic computer simulations on which all climate change projections are based will have little to do with the real world.
The econometric models of the Treasury are also equilibrium models.
They too assume that the economic system normalises to an equilibrium state, the state modelled by those models.
As Nobel Laureate, Douglass North, has demonstrated, the real world is vastly more complex that the simulated world of the models and is never in an equilibrium state, more precisely, never anywhere near such a state.
He argued that we live in a non-ergodic world and explained that an ergodic phenomenon has an underlying structure so stable theory that can be applied time after time, consistently, can be developed.
In contrast, the world with which we are concerned is continually changing: it is continually novel. Inconsistency over time is a feature of a non-ergodic world. The dynamics of change of the processes important to us are non-ergodic. The processes do not repeat themselves precisely. Douglass North argued that although there may be some aspects of the world that may be ergodic, most of the significant phenomena are non-ergodic.
Douglass North stressed that our capacity to deal with uncertainty effectively is essential to our succeeding in a non-ergodic world. It is crucial, therefore, that the methodologies we use to understand the exceedingly complex phenomena measured in our time series, correctly inform us of the future uncertainty of the likely pattern of development indicated by the time series." [end of quote]
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Additional Reading
In 1993 Douglass North, along with fellow economic historian, Robert W. Fogel, received the Noble Prize for Economics for pioneering work which resulted in the establishment of Institutional Economics, now a central school of modern economics. There is a substantial economic literature that identifies the fatal flaws in the neoclassical deterministic equilibrium models that the Commonwealth Treasury uses and that Ross Garnaut will rely on to tell the Australian Government of the (almost certain) economic consequences of the (almost certain) predictions of the equilibrium climate models.
North, D. C., 1999. Dealing with a Non Ergodic World: Institutional Economics, Property Rights, and the Global Environment. Duke Environmental Law and Policy Forum Vol 10 No. 1 pps 1 to 12.
Professor North's opening address at the Fourth Annual Cummings Colloquium on Environmental Law, at Duke University, April 30, 1999, is available on line here: Global Markets for Global Commons: Will Property Rights Protect the Planet?
Classical time series analysis that features in the reports of the IPCC necessarily underestimates future uncertainty. Of great relevance here is that two scientists at the Department of Civil and Environmental Engineering University of Melbourne, Dr Murray Peel and Professor Tom McMahon, have recently shown that randomness in the climate system has been on the rise since the 1950s. The authors used the time series analysis technique, Empirical Mode Decomposition (EMD), to quantify the proportion of variation in the annual temperature and rainfall time series that resulted from fluctuations at different time scales. They applied EMD to annual data for 1,524 temperature and 2,814 rainfall stations from the Global Historical Climatology Network.
Peel, M and McMahon, T. A., 2006. Recent frequency component changes in interannual climate variability, Geophysical Research Letters, Vol.33, L16810, doi:10.1029/2006GL025670
Richard Mackey’s submission to the Garnaut Climate Change Review is entitled 'Much more to the Earth’s climate dynamics than human activity' and can be read here.
Dear John
I am familiar with those debates. I also realise that a lot of the time series in use in climate research, including those used to report the dominant role of the Sun, are problematic as are the techniques of time series analysis in use in may of the published papers. Given that almost all traditional data analysis methodologies are based on linear and stationary assumptions, the analysis is bound to year equivocal results.
Demetris Koutsoyiannis is one of the few scientists who have taken seriously the challenge to use methods that let the data speak.
Norden Huang developed the methodology called Empirical Mode Decomposition (EMD). Unlike most statistical methodologies for analysing time series, EMD makes no assumptions about the linearity or stationarity of a time series. EMD lets the data speak more directly, revealing its intrinsic functional structure more clearly. It does not does not have the restrictive assumptions of linearity and stationarity that the familiar Fourier-based techniques have, because it uses Hilbert, not Fourier, transforms.
Huang et al (1998) have also highlighted the need to use analytic methodologies that reveal clearly any nonlinear relationships (that may also contain intrinsic trends) when analysing time series of natural phenomena. Huang et al (1998) showed that, necessarily, misleading conclusions will be drawn from the uncritical use of time series analytic techniques that assume relationships within the time series are linear, stationary and devoid of intrinsic trends.
Cohn and Lins (2005) brought attention to the nonlinear, non-stationary nature of climate time series data. Cohn and Lins (2005) concluded:
These findings have implications for both science and public policy. For example, with respect to temperature data, there is overwhelming evidence that the planet has warmed during the past century. But could this warming be due to natural dynamics? Given what we know about the complexity, long-term persistence, and nonlinearity of the climate system, it seems the answer might be yes. Finally, that reported trends are real yet insignificant indicates a worrisome possibility: Natural climate excursions may be much larger than we imagine. So large, perhaps, that they render insignificant the changes, human-induced or otherwise, observed during the past century.
Demetris’ work is without doubt the most thorough and comprehensive. It is informed by an understanding that builds on Poincare, Birkhoff, Kolmogorov, Hurst, Mandelbrot + some insightful workers in hydrology.
I haven’t got behind most of the published work that use problematic time series and problematic time series analysis methodologies. That would be too big a project!!
In my written work about climate dynamics I try to make the most use of papers using analysis that is commensurate with the nonlinear, nonstationary nature of the natural processes. The IPCC and related work is not in this category. As I read the science, authors in the IPCC awg/ghg school of thought stubbornly refuse to recognise that nature is nonlinear and non stationary and that all of their time series analysis is problematic. Refs
Cohn, T A. and Lins, F., 2005. Nature’s style: Naturally trendy. Geophysical Research Letters, (32), L23402.
Huang, N. E.; Shen, Z.; Long, S. R.; Wu, M. C.; Shih, H. H.; Zheng, Q.; Yen, N. C.; Tung, C. C.; and Liu, H. H., 1998. The empirical mode decomposition and Hilbert spectrum for nonlinear and non stationary time series analysis. Proceedings of the Royal Society of London Series A the Mathematical, Physical and Engineering Sciences, 454 903 995.