Broken models predict extreme cold snaps. (CO2 causes every sort of weather.)

Remember how CO2 is supposed to cause warmer winters, and warmer nights? Well now CO2 also produces cold snaps. No matter what weather you get, there is a citation to blame CO2. Nature (the formerly great science journal) and Northeastern University have produced another permutation of outputs from models we know are broken.

The first line in the press release is false and smugly so: “most sci­en­tists — 97 per­cent of them, to be exact — agree that the tem­per­a­ture of the planet is rising and that the increase is due to human activ­i­ties….” 10 seconds on Google would have shown — 60% of geoscientists and engineers don’t agree.

If Kodra and co were trying to be accurate, they could have said “97% of annointed climate scientists agree… “. If they were trying to be scientific, of course, they wouldn’t mention a consensus at all. If they had good evidence, they’d talk about that instead.

They dug deep in The-Book-of-Cliches for the press release. Strip away the advertising spin and I think this is the nub of the work:

“While global tem­per­a­ture is indeed increasing, so too is the vari­ability in tem­per­a­ture extremes. For instance, while each year’s average hottest and coldest tem­per­a­tures will likely rise, those aver­ages will also tend to fall within a wider range of poten­tial high and low tem­perate extremes than are cur­rently being observed. This means that even as overall tem­per­a­tures rise, we may still con­tinue to expe­ri­ence extreme cold snaps…

Essentially, by using a models that didn’t predict the pause, nor the missing hot spot, and with homogenized, reanalyzed data that probably does not resemble the observations, they found something “interesting”. The modern witchdoctors are at work. Runestones, tea-leaves, broken models, what’s the difference?

“The study used sim­u­la­tions from the most recent cli­mate models devel­oped by groups around the world for the Inter­gov­ern­mental Panel on Cli­mate Change and “reanalysis data sets,” which are gen­er­ated by blending the best avail­able weather obser­va­tions with numer­ical weather models. The team com­bined a suite of methods in a rel­a­tively new way to char­ac­terize extremes and explain how their vari­ability is influ­enced by things like the sea­sons, geo­graph­ical region, and the land-sea inter­face. The analysis of mul­tiple cli­mate model runs and reanalysis data sets was nec­es­sary to account for uncer­tain­ties in the physics and model imperfections.

So because the models don’t work, they did a lot of runs, and because there are infinite ways to reanalyze data, they used several different datasets too. (It’s not like history ever has one correct “temperature”.) They felt the average of all these errors showed something about the variability of artificial simulations of our climate. Bravo.

They also felt they should tell us this big news: “It sug­gests that the nat­ural processes that drive weather anom­alies today could con­tinue to do so in a warming future.” It would have been something special indeed if they found that nature had stopped.

This study was done by “Evan Kodra, PhD’14″. (I guess he must be quite excited about graduating then? Congrats to Evan… )

Nature:

Asymmetry of projected increases in extreme temperature distributions

Evan Kodra & Auroop R. Ganguly | doi:10.1038/srep05884

Received 05 October 2012 | Published 30 July 2014

Abstract

A statistical analysis reveals projections of consistently larger increases in the highest percentiles of summer and winter temperature maxima and minima versus the respective lowest percentiles, resulting in a wider range of temperature extremes in the future. These asymmetric changes in tail distributions of temperature appear robust when explored through 14 CMIP5 climate models and three reanalysis datasets. Asymmetry of projected increases in temperature extremes generalizes widely. Magnitude of the projected asymmetry depends significantly on region, season, land-ocean contrast, and climate model variability as well as whether the extremes of consideration are seasonal minima or maxima events. An assessment of potential physical mechanisms provides support for asymmetric tail increases and hence wider temperature extremes ranges, especially for northern winter extremes. These results offer statistically grounded perspectives on projected changes in the IPCC-recommended extremes indices relevant for impacts and adaptation studies. Producing something this bad costs a lot of money — it took some part of $10 million from an “Expe­di­tions in Com­puting Grant “. An expedition indeed. The duo used, wait for it… “com­pu­ta­tional tools from Big Data sci­ence to sys­tem­at­i­cally examine this aspect of cli­mate change for the first time.” (That’ll show those climate modelers using slide rules with Small Data science. And us naive types who use a spreadsheet.)

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