From Northeastern University via Eurekalert, and the department of modeling for 10 million dollars, this seems to be all they could come up with. Nature has a way however, of taking the the best laid plans and rendering them moot. I don’t think they’ve noted ‘the pause’ yet. There’s no paper listed, nor data references, nothing, making it one of the worst press releases I’ve seen in awhile. The press release upstream at the University is hardly any better, citing the 97% consensus as if it has anything to do with extremes modeling, but at least they gave a link to the paper where Eurekalert didn’t.
Big data confirms climate extremes are here to stay
In a paper published online today in the journal Scientific Reports, published by Nature, Northeastern researchers Evan Kodra and Auroop Ganguly found that 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, said Kodra.
“Just because you have a year that’s colder than the usual over the last decade isn’t a rejection of the global warming hypothesis,” Kodra explained.
With funding from a $10-million multi-university Expeditions in Computing grant, the duo used computational tools from big data science for the first time in order to extract nuanced insights about climate extremes.
The research also opens new areas of interest for future work, both in climate and data science. It suggests that the natural processes that drive weather anomalies today could continue to do so in a warming future. For instance, the team speculates that ice melt in hotter years may cause colder subsequent winters, but these hypotheses can only be confirmed in physics-based studies.
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.
The new results provide important scientific as well as societal implications, Ganguly noted. For one thing, knowing that models project a wider range of extreme temperature behavior will allow sectors like agriculture, public health, and insurance planning to better prepare for the future. For example, Kodra said, “an agriculture insurance company wants to know next year what is the coldest snap we could see and hedge against that. So, if the range gets wider they have a broader array of policies to consider.”
The paper:
http://www.nature.com/srep/2014/140730/srep05884/full/srep05884.html
Asymmetry of projected increases in extreme temperature distributions
Evan Kodra & Auroop R. Ganguly
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.
Figure S1
“Knowing that the models projecty . . . .” The models project nothing. At least nothing that can be relied upon.
Here’s a rule about natural statistics: Once you start keeping track of highs and lows (of anything) on a global scale there will ALWAYS be a new higher and a new lower over any given time period. How is this news?
There – I fixed it for ya.
The poor souls are contradicting IPCC predictions from 2007 for a warming world:
“There is likely to be a decline in the frequency of cold air outbreaks (i.e., periods of extreme cold lasting from several days to over a week) in NH winter in most areas.”
http://www.ipcc.ch/publications_and_data/ar4/wg1/en/faq-10-1.html
This falls under CYA protocol . Claim both ends of the spectrum and you can always be right. Psudo-Science at is best.
“The analysis of multiple climate model runs and reanalysis data sets was necessary to account for uncertainties in the physics and model imperfections.”
Well except when all the models make the same assumptions. Then the use of multiple climate model runs is just GIGO.
So their model diverges from reality and becomes more and more unstable.
So the world’s weather will become more and more unstable or they need a better model.
Would someone from the alarmist camp (surely there are plenty of you that visit this site) please explain what a “climate extreme” is?
Somebody needs to pull the feeding tubes and shut off the respirators on these bozos.
“With funding from a $10-million multi-university Expeditions in Computing grant, the duo used computational tools from big data science for the first time in order to extract nuanced insights about climate extremes.” [+emphasis]
Oh, I get it; they extracted nuanced insights. Isn’t that special.
” It suggests that the natural processes that drive weather anomalies today could continue to do so in a warming future.”
What elses would drive weather anomalies? Unnatural processes? Devil worship?
Pure PUFFERY at best.
Where are global temperatures increasing? In the models or do they have data they’re not sharing with us? Oh wait, I get it — the temps are increasing in the models. They take the output of the models and then model that. That explains it.
“…’reanalysis data sets,’ which are generated by blending the best available weather observations with numerical weather models.” Could somebody explain to me how that works? If you take (a) real-world data and (b) BS generated by your magical box, does that result in knowledge? Or more BS? For some reason I am thinking of pee in a swimming pool.
Surely when they ran cross checks on the input sources they must have discovered some suspicious concurrences. Indeed it would be very suspicious if they didn’t. That’s what big data is good for not validating models.
Slowed decline of ice in the Arctic. High concentrations of ice.
http://arctic.atmos.uiuc.edu/cryosphere/arctic.sea.ice.interactive.html
http://arctic.atmos.uiuc.edu/cryosphere/NEWIMAGES/arctic.seaice.color.000.png
Humanity and critters alike have always been at the mercy of the earths climate.
We now manage our local environments, heat or cool our living spaces to survive comfortably in the face somewhat harsh environments. I think this has reduced our tolerance and understanding of what a harsh place the earth can be.
Oh-oh. Shape shifters.
” We mixed 17 gallons of paint from different models and now we have the true color ”
And it is new and improved……buy some now…at your friendly climate science store, near you…
Limited time offer…
“duo used computational tools from big data science for the first time in order to extract nuanced insights about climate extremes.”
Man, if you need to use $10M computational tools to extract nuanced insights from the very rough, much diddled with temperature record with >50% error bars, you are wasting time and money. There are no nuances in this kind of data. Its like trying to extract the names of respondents from divorce statistics. How about the nuance of 18years and continuing of no warming that was totally unexpected. I hope your nuances account for increasing Arctic and Antarctic ice extents in the future, or will this be another big data surprise to show us after the fact.
How come after a couple decades of the this stuff the recognizable names in the heat-up industry have disappeared and they have taken to sacrificing a legion of young lambs like Kodra, Ganguly, and the recent legion of newbies. They must be behind the curtain pulling the strings on these innocent folks. Kids, get your asses out of there, you are at the bottom of the pyramid of the scam.
The problem I see: when you look at the min/max data you do NOT see more extremes. You see higher mins. The difference between the min and max has actually gotten SMALLER. In the raw data, the maxs are getting lower, while the mins are getting higher. I would call this the exact opposite of ‘more extreme”.
It seems like they’re making essentially the same argument that got Charles Murray tarred and feathered for making in a different context. Bell Curve Climate = good. Bell Curve IQ = stone the infidel.
Well diggers use thin plywood veneer to reinforce the walls of the hole as they dig deeper, lest they be buried under a collapse. The authors of this “paper” are using a veneer too thin for the task, but that hasn’t stopped them from digging.
Now they’re doing notional statistical analysis of the model outputs? Am I reading this right?
Could anything be more useless?
pay no attention to the thermometer….your insurance rates are going up
Does he mean like these extremes? Oh. Wait. Wrong century.
http://www.weather.com/news/5-extreme-temperature-drops-20130118
“Big data confirms climate extremes are here to stay”
– Oh FFS. “Big data” could confirm almost anything – as Boyd and Crawford (2011) point out:
“It is the kind of data that encourages the practice of apophenia: seeing patterns where none actually exist, simply because massive quantities of data can offer connections that radiate in all directions. Due to this, it is crucial to begin asking questions about the analytic assumptions, methodological frameworks, and underlying biases embedded in the Big Data phenomenon.”
‘Big Data’ covers a hugely heterogeneous set of methodologies, technologies, assumptions, approaches and so on. What is it with these people? It reminds me of how the definite article is used when referring to “the science” as if it is a static thing self-evident to everyone. Whenever I see such phraseology it indicates to me that I’m dealing with a willful propagandist.