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Climate Change and Tropical Cyclones (Yet Again)

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By Rasmus Benestad & Michael Mann
Hurricane Katerina
Just as Typhoon Nargis has reminded us of the destructive power of tropical cyclones (with its horrible death toll in Burma–around 100,000 according to the UN), a new paper by Knutson et al in the latest issue of the journal Nature Geosciences purports to project a reduction in Atlantic hurricane activity (principally the ‘frequency’ but also integrated measures of powerfulness).

The close timing of the Knutson et al and Typhoon Nargis is of course coincidental. But the study has been accorded the unprecedented privilege (that is, for a climate change article published during the past 7 years) of a NOAA press conference. What’s the difference this time? Well, for one thing, the title of the paper: “Simulated reduction in Atlantic hurricane frequency under twenty-first-century warming conditions” (emphasis added).

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What the IPCC models really say

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Translations: (Español) (Italian) (English)

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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Back to the future

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Translations: (Español) (English)

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)) (English)

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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