A climate computing revolution
Climate modelling has been undergoing a quiet revolution – and it is not one that should be allowed to go unnoticed by the long suffering public. Weather has been known to be chaotic since Edward Lorenz discovered the ‘butterfly effect’ in the 1960’s. Abrupt climate change on the other hand was thought to have happened only in the distant past and so climate was expected to evolve steadily over this century in response to ordered climate forcing.
More recent work is identifying abrupt climate changes working through the El Niño Southern Oscillation, the Pacific Decadal Oscillation, the North Atlantic Oscillation, the Southern Annular Mode, the Artic Oscillation, the Indian Ocean Dipole and other measures of ocean and atmospheric states. These are measurements of sea surface temperature and atmospheric pressure over more than 100 years which show evidence for abrupt change to new climate conditions that persist for up to a few decades before shifting again. Global rainfall and flood records likewise show evidence for abrupt shifts and regimes that persist for decades. In Australia, less frequent flooding from early last century to the mid 1940’s, more frequent flooding to the late 1970’s and again a low rainfall regime to recent times.
Anastasios Tsonis, of the Atmospheric Sciences Group at University of Wisconsin, Milwaukee, and colleagues used a mathematical network approach to analyse abrupt climate change on decadal timescales. Ocean and atmospheric indices – in this case the El Niño Southern Oscillation, the Pacific Decadal Oscillation, the North Atlantic Oscillation and the North Pacific Oscillation – can be thought of as chaotic oscillators that capture the major modes of climate variability. Tsonis and colleagues calculated the ‘distance’ between the indices. It was found that they would synchronise at certain times and then shift into a new state.
It is no coincidence that shifts in ocean and atmospheric indices occur at the same time as changes in the trajectory of global surface temperature. Our ‘interest is to understand – first the natural variability of climate – and then take it from there. So we were very excited when we realized a lot of changes in the past century from warmer to cooler and then back to warmer were all natural,’ Tsonis said.
Four multi-decadal climate shifts were identified in the last century coinciding with changes in the surface temperature trajectory. Warming from 1909 to the mid 1940’s, cooling to the late 1970’s, warming to 1998 and declining since. The shifts are punctuated by extreme El Niño Southern Oscillation events. Fluctuations between La Niña and El Niño peak at these times and climate then settles into a damped oscillation. Until the next critical climate threshold – due perhaps in a decade or two if the recent past is any indication.
James Hurrell and colleagues in a recent article in the Bulletin of the American Meteorological Society stated that the ‘global coupled atmosphere–ocean–land–cryosphere system exhibits a wide range of physical and dynamical phenomena with associated physical, biological, and chemical feedbacks that collectively result in a continuum of temporal and spatial variability. The traditional boundaries between weather and climate are, therefore, somewhat artificial.’ Somewhat artificial is somewhat of an understatement for a paradigm shift in climate science.
The weight of evidence is such that modellers are frantically revising their strategies. They are asking for an international climate computing centre and $5 billion (for 2000 times more computing power) to solve this new problem in climate forecasting. The monumental size of the task they have set themselves cannot be exaggerated.
James C. McWilliams of the Department of Atmospheric and Oceanic Sciences at the University of California discussed chaos and climate in a 2007 paper titled ‘Irreducible imprecision in atmospheric and oceanic simulations’. ‘Sensitive dependence and structural instability are humbling twin properties for chaotic dynamical systems, indicating limits about which kinds of questions are theoretically answerable’. Sensitive dependence refers to qualitative shifts in climate and models that occur as a result of small changes in initial states. Structural instabilities are qualitative shifts in modelled outcomes as a result of plausible (within the limits of accuracy of measurements) changes in boundary parameters.
The bottom line of all this is that the current generation of climate forecasting models cannot be relied on as accurate representations of future climate. It will be quite some time before the new models are good enough to model ‘sensitive dependence’ in climate. I doubt their chances at all; weather models are accurate, because of chaos theory in operation, over about 7 days at best.
An article exploring aspects of chaos theory and abrupt climate change has been posted here…