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What the IPCC models really say

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Translations: (Español) (Italian)

Over the last couple of months there has been much blog-viating about what the models used in the IPCC 4th Assessment Report (AR4) do and do not predict about natural variability in the presence of a long-term greenhouse gas related trend. Unfortunately, much of the discussion has been based on graphics, energy-balance models and descriptions of what the forced component is, rather than the full ensemble from the coupled models. That has lead to some rather excitable but ill-informed buzz about very short time scale tendencies. We have already discussed how short term analysis of the data can be misleading, and we have previously commented on the use of the uncertainty in the ensemble mean being confused with the envelope of possible trajectories (here). The actual model outputs have been available for a long time, and it is somewhat surprising that no-one has looked specifically at it given the attention the subject has garnered. So in this post we will examine directly what the individual model simulations actually show.

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Global Cooling-Wanna Bet?

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Translations: (Español) (Deutsch) (Italian)

By Stefan Rahmstorf, Michael Mann, Ray Bradley, William Connolley, David Archer, and Caspar Ammann

Global cooling appears to be the “flavour of the month”. First, a rather misguided media discussion erupted on whether global warming had stopped, based on the observed temperatures of the past 8 years or so (see our post). Now, an entirely new discussion is capturing the imagination, based on a group of scientists from Germany predicting a pause in global warming last week in the journal Nature (Keenlyside et al. 2008).

Specifically, they make two forecasts for global temperature, as discussed in the last paragraphs of their paper and shown in their Figure 4 (see below). The first forecast concerns the time interval 2000-2010, while the second concerns the interval 2005-2015 (*). For these two 10-year averages, the authors make the following prediction:

“… the initialised prediction indicates a slight cooling relative to 1994-2004 conditions”

Their graph shows this: temperatures in the two forecast intervals (green points shown at 2005 and 2010) are almost the same and are both lower than observed in 1994-2004 (the end of the red line in their graph).

Fig. 4 from <em data-lazy-src=

That this cooling would just be a temporary blip and would change nothing about global warming goes without saying and has been amply discussed elsewhere (e.g. here). But another question has been rarely discussed: will this forecast turn out to be correct?

We think not – and we are prepared to bet serious money on this. We have double-checked with the authors: they say they really mean this as a serious forecast, not just as a methodological experiment. If the authors of the paper really believe that their forecast has a greater than 50% chance of being correct, then they should accept our offer of a bet; it should be easy money for them. If they do not accept our bet, then we must question how much faith they really have in their own forecast.

The bet we propose is very simple and concerns the specific global prediction in their Nature article. If the average temperature 2000-2010 (their first forecast) really turns out to be lower or equal to the average temperature 1994-2004 (*), we will pay them € 2500. If it turns out to be warmer, they pay us € 2500. This bet will be decided by the end of 2010. We offer the same for their second forecast: If 2005-2015 (*) turns out to be colder or equal compared to 1994-2004 (*), we will pay them € 2500 – if it turns out to be warmer, they pay us the same. The basis for the temperature comparison will be the HadCRUT3 global mean surface temperature data set used by the authors in their paper.

To be fair, the bet needs an escape clause in case a big volcano erupts or a big meteorite hits the Earth and causes cooling below the 1994-2004 level. In this eventuality, the forecast of Keenlyside et al. could not be verified any more, and the bet is off.

The bet would also need a neutral arbiter – we propose, for example, the director of the Hadley Centre, home of the data used by Keenlyside et al., or a committee of neutral colleagues. This neutral arbiter would also decide whether a volcano or meteorite impact event is large enough as to make the bet obsolete.

We will discuss the scientific reasons for our assessment here another time – first we want to hear from Keenlyside et al. whether they accept our bet. Our friendly challenge is out – we hope they will accept it in good sportsmanship.

(*) We adopt here the definition of the 10-year intervals as in their paper, which is from 1 November of the first year to 31 October of the last year. I.e.: 2000-2010 means 1 November 2000 until 31 October 2010.

Update: We have now published part 2 of this bet with our scientific arguments.

_______________________
Update: Andy Revkin has weighed in at “dot earth”.

Update 5/11/08: so has Anna Barnett at Nature’s ‘climate feedback’ blog

Back to the future

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Translations: (Español)

A few weeks ago I was at a meeting in Cambridge that discussed how (or whether) paleo-climate information can reduce the known uncertainties in future climate simulations.

The uncertainties in the impacts of rising greenhouse gases on multiple systems are significant: the potential impact on ENSO or the overturning circulation in the North Atlantic, probable feedbacks on atmospheric composition (CO2, CH4, N2O, aerosols), the predictability of decadal climate change, global climate sensitivity itself, and perhaps most importantly, what will happen to ice sheets and regional rainfall in a warming climate.

The reason why paleo-climate information may be key in these cases is because all of these climate components have changed in the past. If we can understand why and how those changes occurred then, that might inform our projections of changes in the future. Unfortunately, the simplest use of the record – just going back to a point that had similar conditions to what we expect for the future – doesn’t work very well because there are no good analogs for the perturbations we are making. The world has never before seen such a rapid rise in greenhouse gases with the present-day configuration of the continents and with large amounts of polar ice. So more sophisticated approaches must be developed and this meeting was devoted to examining them.

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Butterflies, tornadoes and climate modelling

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Ed Lorenz hiking Many of you will have seen the obituaries (MIT, NYT) for Ed Lorenz, who died a short time ago. Lorenz is most famous scientifically for discovering the exquisite sensitivity to initial conditions (i.e. chaos) in a simple model of fluid convection, which serves as an archetype for the weather prediction problem. He is most famous outside science for the ‘The Butterfly Effect’ described in his 1972 paper “Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?”. Lorenz’s contributions to both atmospheric science and the mathematics of dynamical systems were wide ranging and seminal. He also directly touched the lives of many of us here at RealClimate, and both his wisdom, and quiet personal charm will be sorely missed.

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The global cooling mole

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By John Fleck and William Connolley

To veterans of the Climate Wars, the old 1970s global cooling canard – “How can we believe climate scientists about global warming today when back in the 1970s they told us an ice age was imminent?” – must seem like a never-ending game of Whack-a-mole. One of us (WMC) has devoted years to whacking down the mole (see here, here and here, for example), while the other of us (JF) sees the mole pop up anew in his in box every time he quotes contemporary scientific views regarding climate change in his newspaper stories.

Una traducción está disponible aqui
Tłumaczenie na polski dostępne jest tutaj.

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The IPCC model simulation archive

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In the lead up to the 4th Assessment Report, all the main climate modelling groups (17 of them at last count) made a series of coordinated simulations for the 20th Century and various scenarios for the future. All of this output is publicly available in the PCMDI IPCC AR4 archive (now officially called the CMIP3 archive, in recognition of the two previous, though less comprehensive, collections). We’ve mentioned this archive before in passing, but we’ve never really discussed what it is, how it came to be, how it is being used and how it is (or should be) radically transforming the comparisons of model output and observational data.
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Uncertainty, noise and the art of model-data comparison

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Translations: (Español) (Chinese (simplified))

Gavin Schmidt and Stefan Rahmstorf

John Tierney and Roger Pielke Jr. have recently discussed attempts to validate (or falsify) IPCC projections of global temperature change over the period 2000-2007. Others have attempted to show that last year’s numbers imply that ‘Global Warming has stopped’ or that it is ‘taking a break’ (Uli Kulke, Die Welt)). However, as most of our readers will realise, these comparisons are flawed since they basically compare long term climate change to short term weather variability.

This becomes immediately clear when looking at the following graph:

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Tropical tropospheric trends

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Once more unto the breach, dear friends, once more!

Some old-timers will remember a series of ‘bombshell’ papers back in 2004 which were going to “knock the stuffing out” of the consensus position on climate change science (see here for example). Needless to say, nothing of the sort happened. The issue in two of those papers was whether satellite and radiosonde data were globally consistent with model simulations over the same time. Those papers claimed that they weren’t, but they did so based on a great deal of over-confidence in observational data accuracy (see here or here for how that turned out) and an insufficient appreciation of the statistics of trends over short time periods.

Well, the same authors (Douglass, Pearson and Singer, now joined by Christy) are back with a new (but necessarily more constrained) claim, but with the same over-confidence in observational accuracy and a similar lack of appreciation of short term statistics.
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