Modeling in the red

From an Ohio State University press release where they see a lot of red, and little else, yet another warm certainty model:

STATISTICAL ANALYSIS PROJECTS FUTURE TEMPERATURES IN NORTH AMERICA

Upper-left panel: The posterior mean of the average temperature-change projections. Upper-right panel: The posterior standard deviation of the average temperature-change projections. Lower panels: Pixelwise posterior 2.5th (lower-left) and 97.5th (lower-right) percentiles of the average temperature-change projections. Units for all panels are in °C. [Source: Kang and Cressie (2012)]

COLUMBUS, Ohio – For the first time, researchers have been able to combine different climate models using spatial statistics – to project future seasonal temperature changes in regions across North America.

They performed advanced statistical analysis on two different North American regional climate models and were able to estimate projections of temperature changes for the years 2041 to 2070, as well as the certainty of those projections.

The analysis, developed by statisticians at Ohio State University, examines groups of regional climate models, finds the commonalities between them, and determines how much weight each individual climate projection should get in a consensus climate estimate.

Through maps on the statisticians’ website, people can see how their own region’s temperature will likely change by 2070 – overall, and for individual seasons of the year.

Given the complexity and variety of climate models produced by different research groups around the world, there is a need for a tool that can analyze groups of them together, explained Noel Cressie, professor of statistics and director of Ohio State’s Program in Spatial Statistics and Environmental Statistics.

Cressie and former graduate student Emily Kang, now at the University of Cincinnati, present the statistical analysis in a paper published in the International Journal of Applied Earth Observation and Geoinformation.

“One of the criticisms from climate-change skeptics is that different climate models give different results, so they argue that they don’t know what to believe,” he said. “We wanted to develop a way to determine the likelihood of different outcomes, and combine them into a consensus climate projection. We show that there are shared conclusions upon which scientists can agree with some certainty, and we are able to statistically quantify that certainty.”

For their initial analysis, Cressie and Kang chose to combine two regional climate models developed for the North American Regional Climate Change Assessment Program. Though the models produced a wide variety of climate variables, the researchers focused on temperatures during a 100-year period: first, the climate models’ temperature values from 1971 to 2000, and then the climate models’ temperature values projected for 2041 to 2070. The data were broken down into blocks of area 50 kilometers (about 30 miles) on a side, throughout North America.

Averaging the results over those individual blocks, Cressie and Kang’s statistical analysis estimated that average land temperatures across North America will rise around 2.5 degrees Celsius (4.5 degrees Fahrenheit) by 2070. That result is in agreement with the findings of the United Nations Intergovernmental Panel on Climate Change, which suggest that under the same emissions scenario as used by NARCCAP, global average temperatures will rise 2.4 degrees Celsius (4.3 degrees Fahrenheit) by 2070. Cressie and Kang’s analysis is for North America – and not only estimates average land temperature rise, but regional temperature rise for all four seasons of the year.

Cressie cautioned that this first study is based on a combination of a small number of models. Nevertheless, he continued, the statistical computations are scalable to a larger number of models. The study shows that climate models can indeed be combined to achieve consensus, and the certainty of that consensus can be quantified.

The statistical analysis could be used to combine climate models from any region in the world, though, he added, it would require an expert spatial statistician to modify the analysis for other settings.

The key is a special combination of statistical analysis methods that Cressie pioneered, which use spatial statistical models in what researchers call Bayesian hierarchical statistical analyses.

“We show that there are shared conclusions upon which scientists can agree with some certainty, and we are able to statistically quantify that certainty.”

The latter techniques come from Bayesian statistics, which allows researchers to quantify the certainty associated with any particular model outcome. All data sources and models are more or less certain, Cressie explained, and it is the quantification of these certainties that are the building blocks of a Bayesian analysis.

In the case of the two North American regional climate models, his Bayesian analysis technique was able to give a range of possible temperature changes that includes the true temperature change with 95 percent probability.

After producing average maps for all of North America, the researchers took their analysis a step further and examined temperature changes for the four seasons. On their website, they show those seasonal changes for regions in the Hudson Bay, the Great Lakes, the Midwest, and the Rocky Mountains.

In the future, the region in the Hudson Bay will likely experience larger temperature swings than the others, they found.

That Canadian region in the northeast part of the continent is likely to experience the biggest change over the winter months, with temperatures estimated to rise an average of about 6 degrees Celsius (10.7 degrees Fahrenheit) – possibly because ice reflects less energy away from the Earth’s surface as it melts. Hudson Bay summers, on the other hand, are estimated to experience only an increase of about 1.2 degrees Celsius (2.1 degrees Fahrenheit).

According to the researchers’ statistical analysis, the Midwest and Great Lakes regions will experience a rise in temperature of about 2.8 degrees Celsius (5 degrees Fahrenheit), regardless of season. The Rocky Mountains region shows greater projected increases in the summer (about 3.5 degrees Celsius, or 6.3 degrees Fahrenheit) than in the winter (about 2.3 degrees Celsius, or 4.1 degrees Fahrenheit).

In the future, the researchers could consider other climate variables in their analysis, such as precipitation.

This research was supported by NASA’s Earth Science Technology Office. The North American Regional Climate Change Assessment Program is funded by the National Science Foundation, the U.S. Department of Energy, the National Oceanic and Atmospheric Administration, and the U.S. Environmental Protection Agency office of Research and Development.

###

 

Advertisements

  Subscribe  
newest oldest most voted
Notify of

“One of the criticisms from climate-change skeptics is that different climate models give different results, so they argue that they don’t know what to believe,” he said.
I have never seen anyone make that statement — did Cressie just transfer from ANU, or did he think that one up all on his own?

Ken in Beaverton, OR

Looking at the groups that supported this “research” I am not surprised by the conclusions.

NEW! Models with included consensus, the must-have tool for the ambitious climate ‘scientist’.

Tired of theBS

How far into the future is this model? If it’s August, they might be right this time.

How can you statitisticize chaos? Here it is, folks. We’re gonna fry….statistically. Oh, and everyone agrees.

Trouble is that the projected temp could be -2.4C and the probability is just as valid. That’s what my computer says.

“We show that there are shared conclusions upon which scientists can agree with some certainty, and we are able to statistically quantify that certainty.”
Huh?
Hudson Bay summers, on the other hand, are estimated to experience only an increase of about 1.2 degrees Celsius (2.1 degrees Fahrenheit).
After quantifying the certainty they merely estimate that Hudson Bay will do their bidding.

DesertYote

So they looked for commonality between two models written by the same group?

JPG

But….if the models are wrong………………………………..

Geoff Sherrington

There is much overlap with this work and the prediction of national and regional economic changes. If these guys are smart enough to derive fine detail from coarse models, it should be a cinch to work in economics and become very wealthy very quickly.
I remain totally unconvinced, if for no other reason that many past temperature data bases are so corrupt that one cannot have confidence in the future. As a test, would any of these authors like to wager large personal sums of $ on the predictions?
I thought not.

Alan Clark of Dirty Oil-berta

“That Canadian region in the northeast part of the continent is likely to experience the biggest change over the winter months, with temperatures estimated to rise an average of about 6 degrees Celsius”…
Six degrees over 55 years? Big deal. The temperature rose from 7°C this morning to 26°C this afternoon.
Mine’s bigger.

G. E. Pease

“All data sources and models are more or less certain, Cressie explained, and it is the quantification of these certainties that are the building blocks of a Bayesian analysis.” ?!
From
http://mathworld.wolfram.com/BayesianAnalysis.html
“Bayesian analysis is somewhat controversial because the validity of the result depends on how valid the prior distribution is, and this cannot be assessed statistically.”
Clearly, an invalid a priori assumption of perfectly certain data sources and models always leads to invalidly “certain” Bayesian results. In other words, GIGO.

Curious George

These guys never apply their certainty models to weather forecasts.

Werner Brozek

Unless I am missing something, it appears that they assume the feedbacks due to a doubling of CO2 are positive and not negative. However if the last 10 to 15 years are any indication, and if Dr. Spencer’s views on negative feedback are true, then it seems as if they made a wrong assumption right off the bat and everything else they may say would be wrong.

davidmhoffer

Let’s take some models that have been shown to have no predictive skill what so ever, combine them, and make still more predictions. Yeah, that ought to get a Nobel prize…
I used to make snarky sarcastic remarks about shoddy science, but the drivel of late has descended to a level of absurdity such that mocking it is pointless.

Though the models produced a wide variety of climate variables, the researchers focused on temperatures during a 100-year period: first, the climate models’ temperature values from 1971 to 2000, and then the climate models’ temperature values projected for 2041 to 2070.
As Bill Tuttles remarks, these people have no clue on what sceptics have issues with.
But as the quote shows here, they cherry pick a time when temperatures went up and CO2 went up to show their case. Why don’t they look at either the ENTIRE time period of say 1950 – today instead of cherry picking the time when CO2 levels and temperatures both went up?
The answer of course which most of us realize as sceptics ….. is that the only time period from 1950-present that fits the meme of CO2 causes it to warm is this shortened time period whereas anything outside of that shows either cooling or stagnant temperatures. Instead of showing the truth and the entire picture, they remain fixated on their goal of showing a pre-determined outcome. That is what sceptics have an issue with first and foremost.
Of course, other issues in the models (GCM’s if you will) come to mind including assumed values for positive feedbacks on CO2 (or other greenhouse gases). They fix these in the GCM’s by assuming that magically other anthropological influences were present when temperatures did not cooperate with their pre-determined conclusions……and it all comes back to the problems that no one understands exactly why clouds to this day change. And so the models are all based on cherry picking this very same time period of 1970-2000 as a “golden standard” because it MUST be the time period that warming was caused by CO2.
Its such a large logical fallacy, that I don’t know even how to tell people how retarded it is. No sceptic has a problem with computer models off the bat, or that models disagree with one another, the problems stem from one common source, this predetermined outcome always surfaces and no matter what study you find, they will compare changes seen in this time period with the future as they tell us will warm up the same way.
Why is this 30 year time period so special? Why is it that 30 years is the climate golden standard when we see a sin wave of temperatures on a 60 year cycle……?
These facts just boggle the mind because the truth is easy to determine. Sure, most sceptics agree that we warmed over the 20th century. But the effect of CO2 on this is the only point we have a contention at. And yet, every study they put out never goes to the heart of this issue.
So yes, yet another worthless study. And another scientist who completely misunderstands the sceptical argument from the get-go and refuses to leave his or her echo chamber and see the forest through the trees.

otter17

Go Bucks!

sophocles

Did they incluide the sun and add it to the consensus?
No? Oops.

Neil Jones

“consensus climate estimate” A consensus of guesses models. Wow!

Toto

These regional climate model runs are based on some scenarios for CO2 levels in the future. My question is somewhat different: How many (or which) regional weather forecasting models base their outputs on current CO2 levels?

Mike McMillan

So they can predict with statistical certainty how computer climate models will predict the climate. When this technique is perfected, it might be more usefully applied to video games. Imagine knowing how Final Fantasy XII or Super Mario Bros will turn out before you begin.
“All data sources and models are more or less certain, Cressie explained…”
Are we more certain of HadCRUT3 than HadCRUT2, or 4, or USHCN more than USHCN v2?
Climate models are more sensitive to initial conditions than to CO2, and more sensitive to CO2 than reality. Perfect application of fuzzy logic to fuzzy thinking.

Phillip Bratby

There’s only one word to describe this: GIGO

Lew Skannen

“One of the criticisms from climate-change skeptics is that different climate models give different results, so they argue that they don’t know what to believe,”
Solution: Garbage In – Weighted Average Garbage Out.

Ally E.

They need the people to agree with them. All the sheep for slaughter have to walk willingly into the pen. But the sheep are hesitating at the gate, so more scare tactics are needed to herd them through. That’s not working, so it’s back to rational and consensus. If the people aren’t scared enough to accept the need for global control, then, then, then, why it might just all fall apart!

Goldie

So we take a bunch of models that can’t replicate current global temperature trends and combine them. Well if some were above the actual trend and some were below we might end up with something in the middle. Sadly all of these models are over-predicting temperature trends so I can have a pretty high confidence that this will be wrong too.

I have copied and pasted this from Christopher Booker’s excellent weekly column in the Sunday Telegraph.
“Someone whom I was delighted to meet again in Australia was Professor Ian Plimer, a prominent “climate sceptic”, who is one of Abbott’s advisers. In his latest entertaining book, How To Get Expelled From School (by asking the teachers 101 awkward scientific questions about their belief in global warming), Plimer cites a vivid illustration of how great is the threat posed to the planet by man-made CO2.
If one imagines a length of the Earth’s atmosphere one kilometre long, 780 metres of this are made up of nitrogen, 210 are oxygen and 10 metres are water vapour (the largest greenhouse gas). Just 0.38 of a metre is carbon dioxide, to which human emissions contribute one millimetre.”
This is why I am sceptical and smile to myself as these climate “scientists” constantly run around in metaphorical circles trying to prove that their computer models are right, when clearly they are not!

These folks are brilliant… uhm… religious! CO2 the newest false god. We cannot disprove their models for decades. But we should act prudently just in case they are right! The IPCC’s models continue to be wrong, yet the AGW believers still believe. Another sad day for people disguising themselves as scientists.

Complicate a climate model as much as you like, it’s still as scientifically relevant as an astrolabe.

tty

This is all Bayesian statistics. This started out as a technique to formalize “soft” data, e. g. the opinions of knowledgeable persons. This is the “prior” which is then modified by the actual data (in this case, not real data, but modelling results) and the result is the “posterior”. So this is a guess updated by another guess and ending up as shiny new consensus climate predictions.
Bayesian statistics, properly used, is a legitimate technique, but its popularity in climate science is probably due to the fact that you can get essentially any result you want by a judicious choice of prior. Nor is there any objective method for evaluating the validity of the prior, and you can go back an change the prior any number of times without having to tell anybody.

markx

Seems quite simple to me – no matter how many variable are included, how many million lines of code, no matter how airflows and ocean currents are taken into account, if the effect of increasing carbon dioxide is predicated as a ‘nett energy gain to the system’;
Guess what, when you run that model the output WILL tell you that the system will get warmer.

M Courtney

Bill Tuttle says:
May 15, 2012 at 9:11 pm
“One of the criticisms from climate-change skeptics is that different climate models give different results, so they argue that they don’t know what to believe,” he said.
Actually that is true.
It’s just that the models give different results to what actually happens that causes the doubt .
No-one cares that they disagree in many different wrong ways.

Kelvin Vaughan

Boring! Lets have some variety. It’s always warming everywhere. Red red red.
There is an old saying “Don’t put all your eggs in one basket”.

Cressie is the main man in spatial stats today, specifically spatio-temporal stats.

Alex Heyworth

Worse than GIGO. More like G2IG2O.

Alex Heyworth

Oh dear, HTML superscript doesn’t work. GsquaredIGsquaredO.

“One of the criticisms from climate-change skeptics is that different climate models give different results, so they argue that they don’t know what to believe,” he said.
I would feel more confident if his project was driven by his own critical perspective and that of other sceptical scientists.

Can someone tell me if this is called “Guess-Laundering”? I may have just been the first to coin a phrase for these new attempts to pursued useful idiots…

DirkH

benfrommo says:
May 15, 2012 at 10:40 pm
“But as the quote shows here, they cherry pick a time when temperatures went up and CO2 went up to show their case. Why don’t they look at either the ENTIRE time period of say 1950 – today instead of cherry picking the time when CO2 levels and temperatures both went up?”
Exactly. The approach might have merit but by ignoring the period 2000-2010 – the period with which all the models have problems – they are reducing their own work to a caricature.
Cherrypicking the data for the validation means that the validation is worthless. During validation of a model, you should always strive to use as much data as is available.
Furthermore, even if the validation had been done properly, this by no means proves predictive skill out to 2070. What are they thinking. If, as Mosher says, “Cressie is the main man in spatial stats today”, I would expect him to know that. If he pretends he doesn’t… well…

Daniel Vogler

LOL otter, I was about to say Go Bucks too! But I think they chose all the red to show their team colors. Scarlet and Grey! 🙂
All joking aside, I never take those model studies seriously because they are never true. Just wish the MSM would realize how far off those things are.

Stephen Richards

Steven Mosher says:
May 16, 2012 at 12:29 am
Cressie is the main man in spatial stats today, specifically spatio-temporal stats.
SO WHAT ?

M Courtney says:
May 16, 2012 at 12:06 am
A me, May 15, 2012 at 9:11 pm
“…so they argue that they don’t know what to believe,” he said.
Actually that is true.
It’s just that the models give different results to what actually happens that causes the doubt.
No-one cares that they disagree in many different wrong ways.

Cressie’s statement means he assumes that if they can just bring all their models into agreement, the 60-watt cartoon light bulb (a CFC, naturally) over our collective heads will suddenly blink “on” and we’ll cease being so contrary — which is why he’s concentrating on getting the models to agree. Therein lies the rub:
All data sources and models are more or less certain, Cressie explained…
He’s saying the models are just fine — we’re saying the models are *not* fine. The models are fundamentally flawed because they have no means of replicating all the factors influencing the climate other than by inputting assumptions, and those assumptions are colored by bias, e.g., CO2 drives temperature rather than follows it, or that an increase in temperature automatically triggers an increase in water vapor. In sessence, their macro-models rely on a host of micro-models, and all of them are programmed to run on assumptions which don’t replicate reality. And here’s Cressie saying we can fix that merely by adding more models to the mix, rather than working on refining the assumptions.
It’s not that we don’t know *which* of their climate models to believe, we don’t believe *any* of their climate models, because none of them are capable of producing either the kind of results or “high certainty” that they claim.
The garbage is built into the models — to add multiple models and then expect filet mignon to come out is unrealistic.

Otter

otter17~ You understand! Bucks, moola, $$$, cha-ching….! That’s what ‘science’ like this, is all about.

KnR

“One of the criticisms from climate-change skeptics” Any chance of them of pointing out who actual claims climate does not change, or is just a standard throw away line designed not for its scientific use but for political purposes ?
Meanwhile its the standard approach , start with base assumptions that support your views , and never mind their actual validity as this is ‘the cause ‘ that is a minor issue , then run models which tell you how bad things will get . Finish by asking for more research cash to do the same again..
.

Philip Bradley

“We show that there are shared conclusions upon which scientists can agree with some certainty, and we are able to statistically quantify that certainty.”
Certainty?
What they have measured is the extent scientists agree, which has nothing to do with certainty in what they believe. Only certainty (in a statistical sense) that they do believe it.
Scientific certainty results from predictive accuracy. Something climate models are not known for.

Phillip Bratby says:
May 15, 2012 at 11:22 pm
There’s only one word to describe this: GIGO

One step further – GIGO + lies, damn lies + Bayesian Stats = Concensus = Certainty
Ehem – Guess again

Bloke down the pub

My mother always told me that two wrongs don’t make a right, pity no-one told these guys that two wrong models don’t make a right one.
The posterior mean of the average temperature change projections
Posterior my arse.

H.R.

a.) What are they smoking??!?!
b.) Where can I buy some?

Steve Keohane

I see a lot of criticism above, with which I agree. Without comparing models to reality, statements such as this are meaningless.
They performed advanced statistical analysis on two different North American regional climate models and were able to estimate projections of temperature changes for the years 2041 to 2070, as well as the certainty of those projections.

Without a track record, there is no way estimate ‘certainty’ other than SWAG.

Bloke down the pub says:
May 16, 2012 at 2:52 am
“The posterior mean of the average temperature change projections”
Posterior my arse.

Threadwinner!

Steven Mosher says:
May 16, 2012 at 12:29 am
Cressie is the main man in spatial stats today, specifically spatio-temporal stats.

How is he with spatio-temperature stats?