The abstract reads: (emphasis added by Pielke)
Prevailing definitions of climate are not much different from “the climate is what you expect, the weather is what you get”. Using a variety of sources including reanalyses and paleo data, and aided by notions and analysis techniques from Nonlinear Geophysics, we argue that this dictum is fundamentally wrong. In addition to the weather and climate, there is a qualitatively distinct intermediate regime extending over a factor of ≈ 1000 in scale.
For example, mean temperature fluctuations increase up to about 5 K at 10 days (the lifetime of planetary structures), then decrease to about 0.2 K at 30 years, and then increase again to about 5 K at glacial-interglacial scales. Both deterministic GCM’s with fixed forcings (“control runs”) and stochastic turbulence-based models reproduce the first two regimes, but not the third. The middle regime is thus a kind of low frequency “macroweather” not “high frequency climate”. Regimes whose fluctuations increase with scale appear unstable whereas regimes where they decrease appear stable. If we average macroweather states over periods ≈ 30 years, the results thus have low variability. In this sense, macroweather is what you expect.
We can use the critical duration of ≈ 30 years to define (fluctuating) “climate states”. As we move to even lower frequencies, these states increasingly fluctuate – appearing unstable so that the climate is not what you expect. The same methodology allows us to categorize climate forcings according to whether their fluctuations decrease or increase with scale and this has important implications for GCM’s and for climate change and climate predictions.
The conclusion reads:
Contrary to [Bryson, 1997], we have argued that the climate is not accurately viewed as the statistics of fundamentally fast weather dynamics that are constrained by quasi fixed boundary conditions. The empirically substantiated picture is rather one of unstable (high frequency) weather processes tending – at scales beyond 10 days or so and primarily due to the quenching of spatial degrees of freedom – to quasi stable (intermediate frequency, low variability) macroweather processes. Climate processes only emerge from macroweather at even lower frequencies, and this thanks to new slow internal climate processes coupled with external forcings. Their synergy yields fluctuations that on average again grow with scale and become dominant typically on time scales of 10 – 30 years up to ≈ 100 kyrs.
Looked at another way, if the climate really was what you expected, then – since one expects averages – predicting the climate would be a relatively simple matter. On the contrary, we have argued that from the stochastic point of view – and notwithstanding the vastly different time scales – that predicting natural climate change is very much like predicting the weather. This is because the climate at any time or place is the consequence of climate changes that are (qualitatively and quantitatively) unexpected in very much the same way that the weather is unexpected.
There are a series of informative comments on this paper by Judy Curry, Philip Richens, Shaun Lovejoy and others on the weblog All Models are Wrong post
In the insightful comment by Shaun Lovejoy on that weblog, he does write on one issue that I disagree with. Shaun writes
“….deterministic models (GCM’s) reproduce only weather and macroweather statistics (they do this quite well)”.
I agree on weather, but not on macroweather. Macroweather prediction has shown little, if any skill ; e.g. see the papers listed in my post
Read Dr. Pielke’ whole post here.