Statistics research could build consensus around climate predictions
Philadelphia, PA—Vast amounts of data related to climate change are being compiled by research groups all over the world. Data from these many and varied sources results in different climate projections; hence, the need arises to combine information across data sets to arrive at a consensus regarding future climate estimates.
In a paper published last December in the SIAM Journal on Uncertainty Quantification, authors Matthew Heaton, Tamara Greasby, and Stephan Sain propose a statistical hierarchical Bayesian model that consolidates climate change information from observation-based data sets and climate models.
“The vast array of climate data—from reconstructions of historic temperatures and modern observational temperature measurements to climate model projections of future climate—seems to agree that global temperatures are changing,” says author Matthew Heaton. “Where these data sources disagree, however, is by how much temperatures have changed and are expected to change in the future. Our research seeks to combine many different sources of climate data, in a statistically rigorous way, to determine a consensus on how much temperatures are changing.”
Using a hierarchical model, the authors combine information from these various sources to obtain an ensemble estimate of current and future climate along with an associated measure of uncertainty. “Each climate data source provides us with an estimate of how much temperatures are changing. But, each data source also has a degree of uncertainty in its climate projection,” says Heaton. “Statistical modeling is a tool to not only get a consensus estimate of temperature change but also an estimate of our uncertainty about this temperature change.”
The approach proposed in the paper combines information from observation-based data, general circulation models (GCMs) and regional climate models (RCMs).
Regional analysis for climate change assessment. Image credit: Melissa Bukovsky, National Center for Atmospheric Research (NCAR/IMAGe)
Observation-based data sets, which focus mainly on local and regional climate, are obtained by taking raw climate measurements from weather stations and applying it to a grid defined over the globe. This allows the final data product to provide an aggregate measure of climate rather than be restricted to individual weather data sets. Such data sets are restricted to current and historical time periods. Another source of information related to observation-based data sets are reanalysis data sets in which numerical model forecasts and weather station observations are combined into a single gridded reconstruction of climate over the globe.
GCMs are computer models which capture physical processes governing the atmosphere and oceans to simulate the response of temperature, precipitation, and other meteorological variables in different scenarios. While a GCM portrayal of temperature would not be accurate to a given day, these models give fairly good estimates for long-term average temperatures, such as 30-year periods, which closely match observed data. A big advantage of GCMs over observed and reanalyzed data is that GCMs are able to simulate climate systems in the future.
RCMs are used to simulate climate over a specific region, as opposed to global simulations created by GCMs. Since climate in a specific region is affected by the rest of the earth, atmospheric conditions such as temperature and moisture at the region’s boundary are estimated by using other sources such as GCMs or reanalysis data.
By combining information from multiple observation-based data sets, GCMs and RCMs, the model obtains an estimate and measure of uncertainty for the average temperature, temporal trend, as well as the variability of seasonal average temperatures. The model was used to analyze average summer and winter temperatures for the Pacific Southwest, Prairie and North Atlantic regions (seen in the image above)—regions that represent three distinct climates. The assumption would be that climate models would behave differently for each of these regions. Data from each region was considered individually so that the model could be fit to each region separately.
“Our understanding of how much temperatures are changing is reflected in all the data available to us,” says Heaton. “For example, one data source might suggest that temperatures are increasing by 2 degrees Celsius while another source suggests temperatures are increasing by 4 degrees. So, do we believe a 2-degree increase or a 4-degree increase? The answer is probably ‘neither’ because combining data sources together suggests that increases would likely be somewhere between 2 and 4 degrees. The point is that that no single data source has all the answers. And, only by combining many different sources of climate data are we really able to quantify how much we think temperatures are changing.”
While most previous such work focuses on mean or average values, the authors in this paper acknowledge that climate in the broader sense encompasses variations between years, trends, averages and extreme events. Hence the hierarchical Bayesian model used here simultaneously considers the average, linear trend and interannual variability (variation between years). Many previous models also assume independence between climate models, whereas this paper accounts for commonalities shared by various models—such as physical equations or fluid dynamics—and correlates between data sets.
“While our work is a good first step in combining many different sources of climate information, we still fall short in that we still leave out many viable sources of climate information,” says Heaton. “Furthermore, our work focuses on increases/decreases in temperatures, but similar analyses are needed to estimate consensus changes in other meteorological variables such as precipitation. Finally, we hope to expand our analysis from regional temperatures (say, over just a portion of the U.S.) to global temperatures.”
To read other SIAM Nuggets, explaining current high level research involving applications of mathematics in popular science terms, go to http://connect.siam.org/category/siam-nuggets/.
Source article:
Matthew J. Heaton, Tamara A. Greasby, and Stephan R. Sain
SIAM/ASA Journal on Uncertainty Quantification, 1(1), 535–559 (Online publish date: December 17, 2013). The source article is available for free access at the link above through December 31, 2014.
About the authors:
Matthew Heaton is currently an assistant professor in the department of statistics at Brigham Young University, but the majority of this work was done while the author was a postgraduate scientist at the National Center for Atmospheric Research. Tamara Greasby is a postdoctoral researcher and Stephan Sain is a scientist at the Institute for Mathematics Applied to the Geosciences (IMAGe) at the National Center for Atmospheric Research (NCAR).
“these models give fairly good estimates for long-term average temperatures, such as 30-year periods, which closely match observed data”. Is this supposed to be funny? I guess it was intended to be a joke?
Are those climate models or weather models? How does “climate” apply to a 24-hour period?
While I do agree wholeheartedly with your conclusions, your opening statement needs some work.
While a GCM portrayal of temperature would not be accurate to a given day, these models give fairly good estimates for long-term average temperatures, such as 30-year periods, which closely match observed data.
Yeah? Then they should start by throwing out the GCMs that don’t match the current 15-year plateau in global temperature.
Nothing that contains the results of climate models could possibly pass as a substitute for data taken by observation. This is just a fudge.
I would have far more faith in say two dozen representative records from rural stations around the world that have records of over 100 years, that meet the required standard for a met station and have not been affected UHI. Just raw data only. no need for any corrections even for time of day, simply plot the changes or anomalies over the last century and average them.
There are so many fudges in the current data from HadCRU GISS etc to make the whole process very questionable.
Well, climate science keeps advancing here in the 21st Century (it just feels like the 13th).
We’ve gone from:
GIGO (Garbage In, Garbage Out) to…
MIMO (Models In, Models Out).
Somebody shoot me…
Bottom line: They want to MODEL actual temperature measurements. What’s wrong with this idea–anybody, no need to raise your hand; hint: notice the capital letters in the preceding sentence. This is nothing but an admission that the measurements themselves are inadequate to the point of uselessness. (And Bayesian probability? That’s an uncertain modelling already. In order for Bayesian statistics to prove itself, in any application, it has to reduce to a basic, ordinary probability description, and of course that means it is essentially irrelevant from the very start. It’s merely an attempt to pull oneself up by one’s own bootstraps, when one really doesn’t know enough to make intelligent statistical judgments.)
But even more fundamentally: You cannot reach consensus with criminals. You HAVE to clean house before you can make any REAL progress. But the lukewarmers have so far determinedly ignored this basic reality, to try to curry favor with the criminal system. You are aiding and abetting deluded academics and an insane and tyrannical political agenda, an unprecedented subornation of all of our institutions, and a sullying of the name of science that will take generations to remove (if our civilization lasts that long). And every time you do so, shamelessly and cluelessly, you only convince yourselves further, sink ever deeper, in your false point of view.
16 versions of DENIERS in a single post from Revkin! is that a record per-word count?
19 Feb: NYT Dot Earth: Andrew C. Revkin: A Look at the ‘Shills,’ ‘Skeptics’ and ‘Hobbyists’ Lumped Together in Climate Denialism
In his contribution to the recent discussion of Secretary of State John Kerry’s speech on global warming, David Victor of the University of California, San Diego, included this line:
[T]he whole climate science and policy community is spending too much time thinking about the denialists and imagining that if we could just muzzle or convince these outliers that policy would be different. That’s not right—in part because the denialists aren’t such a hearty band and in part because the real barriers to policy are cost and strategy.
He attached a speech he recently delivered at the Scripps Institution of Oceanography as part of a seminar series titled “Global Warming Denialism: What science has to say…
???Given that I’ve written about the limited value of facile labels of this sort (noting that I am a “recovering denialist” on another climate front), I thought it well worth posting (Victor granted permission).
His talk is titled “Why Do Smart People Disagree About Facts? Some Perspectives on Climate Denialism.” …
Here are two of his closing points:
***First, we in the scientific community need to acknowledge that the science is softer than we like to portray. The science is not “in” on climate change because we are dealing with a complex system whose full properties are, with current methods, unknowable…
***Second, under pressure from denialists we in the scientific community have spent too much time talking about consensus. That approach leads us down a path that, at the end, is fundamentally unscientific and might even make us more vulnerable to attack, including attack from our own…
http://dotearth.blogs.nytimes.com/2014/02/19/a-look-at-the-shills-skeptics-and-hobbyists-lumped-together-in-climate-denialism/?_php=true&_type=blogs&_php=true&_type=blogs&_r=1
@Richard Dupuis K 3:41
MIGO fits better, models in Gospel out.
A capital A would truly acronym this nonsense as AMIGO.
Pals publishing each others fantasies.
I have difficultly finding an A without profanity.
Adam says: @ur momisugly February 19, 2014 at 3:12 pm
On point number three. Where the are all of the Professors of Mathematics? Why are they so silent?….
>>>>>>>>>>>>>>>>>>>
You can repeat this complaint about ANY use of statistical packages for computers now a days.
I took a couple college courses in statistics so I cringed when some idiot was brought in to ‘teach’ factory workers and engineers “Six Sigma Green Belt Statistics” He never covered variable vs attributes or plotting the data to determine if the data was close enough to a Gaussian distribution that the statistical treatment in the package he was selling was appropriate.
Now any fool can plug numbers into a computer and get out pretty numbers. Unfortunately most have zero idea of what those pretty numbers actually are telling them if anything.
just what the wishcasters need: more data smearing
heaven help science
The biggest problem with consensus in this particular field is that the ones that make it up are clearly biased. Most are tied financially and/or have their reputations on the line. Their previous work, based on assumptions made over a decade ago, causes their brain to interpret new data in a biased fashion.
This would be like deciding who the NFL champs will be by consensus. Football fans are noted for their fierce loyalty to their special team and you’ll get plenty of biased results from tunnel visioned “cheerleaders” of their favorite team(s).
Climate scientist have their favorite theory too. It has caused them to ignore powerful empirical data which contradicts parts of………..or even most of that theory.
Just like we let all the NFL teams battle it out on the football field and measure the best by the results of the score at the end of the games……………..we should do the same with climate science.
Measure everything that relates to this field with non manipulated data gathering. The refs should be scientific principles. The data that doesn’t fit with a legit scientific principle, goes to the bench. That way, we can have an authentic competition, with the winner being the one with the most points scored based on a compilation of comprehensive empirical data during the period in which the data is gathered.
But then, who gets to interpret the data? Now we’re back to the consensus bs as many biased scientists will throw out everything that doesn’t match up with their pet theory.
Just too many biased and fraudulent climate scientists out there that have destroyed the credibility of this field, so that a consensus is worthless. It will take decades to repair.
“Vast amounts of data related to climate change are being compiled by research groups all over the world.”
Vast amounts of data related to climate change are being “fraudulently manufactured” by research groups all over the world.
There, fixed.
Ok, I read thru it, and I think I’ve got a handle on it. They’re going to infill data into empty cells by Kridging from cells that were empty but infilled from homogenized data, averaged from adjusted data, and baked at 325F for 45 minutes. (If somehow after all this a real signal does show up, it will be promptly discarded because it doesn’t resemble the rest of the creamy mixture.) Thank Heaven. We’re all going to be saved. /Do I really need the sarc tag?
I agree with many of the above posters. Combining models and data could be problematic if you start with the assumption that the models are correct over 30 year periods. That has not been demonstrated except for the past. And there you know the answers already.
They have finally found a way to turn a soe’s ear into a silk purse.
All you need is enough soe’s ears and then average them together.
Even the observations have been modeled! Check out the Watts et al paper on the subject. And the models presently used employ the fiddled numbers. The present work is a Dagwood sandwich.
http://wattsupwiththat.com/2012/07/29/press-release-2/
“U.S. Temperature trends show a spurious doubling due to NOAA station siting problems and post measurement adjustments.”
One deliberate adjustment over time was to push the pesky 1937 US hottest year down a few tenths to make 1998 El Nino year hotter – they knew there may be no better chance than this within several years to finally deep six that embarrassing record. Canadians, who don’t have dozens of Federal weather/climate agencies all working away like the US (why don’t you guys get this egregiously wasteful practice cleaned up in the next election) has one budget squeezed agency doing it all. Not having the resources to diddle data as much as the US, the 1930s temps still stand north of the border and man, if was 45C in 1937 in Saskatchewan, what was it in the US Great Plains and elsewhere?
43 Hottest Temperatures Ever Recorded in Canada
Date Recorded Location Temperature
July 5, 1937 Midale, Saskatchewan and 45.0 °C
July 5, 1937 Yellow Grass, Saskatchewan 45.0 °C
July 11, 1936 St. Albans, Manitoba 44.4 °C
July 11, 1936 Emerson, Manitoba 44.4 °C
July 5, 1937 Fort Qu’Appelle, Saskatchewan 44.4 °C
July 16, 1941 Lillooet, British Columbia 44.4 °C
July 16, 1941 Lytton, British Columbia 44.4 °C
July 17, 1941 Lillooet, British Columbia 44.4 °C
July 17, 1941 Lytton, British Columbia 44.4 °C
July 17, 1941 Chinook Cove, British Columbia 44.4 °C
July 29, 1934 Rock Creek, British Columbia 43.9 °C
July 5, 1936 Midale, Saskatchewan 43.9 °C
July 11, 1936 Emerson, Manitoba 43.9 °C
July 11, 1936 Morden, Manitoba 43.9 °C
July 4, 1937 Rosetown, Saskatchewan 43.9 °C
July 5, 1937 Regina, Saskatchewan 43.9 °C
July 16, 1941 Oliver, British Columbia 43.9 °C
June 23, 1900 Cannington, Saskatchewan 43.3 °C
June 25, 1919 Dauphin, Manitoba 43.3 °C
July 31, 1926 Fort Qu’Appelle, Saskatchewan 43.3 °C
July 24, 1927 Greenwood, British Columbia 43.3 °C
July 25, 1931 Fort Qu’Appelle, Saskatchewan 43.3 °C
July 5, 1936 Estevan, Saskatchewan 43.3 °C
July 7, 1936 Emerson, Manitoba 43.3 °C
July 11, 1936 Waskada, Manitoba 43.3 °C
July 11, 1936 Virden, Manitoba 43.3 °C
July 11, 1936 Brandon, Manitoba 43.3 °C
July 11, 1936 Greenfell, Saskatchewan 43.3 °C
July 5, 1937 Moose Jaw, Saskatchewan 43.3 °C
July 5, 1937 Grenfell, Saskatchewan 43.3 °C
July 5, 1937 Francis, Saskatchewan 43.3 °C
July 5, 1937 Regina, Saskatchewan 43.3 °C
July 5, 1937 Estevan, Saskatchewan 43.3 °C
July 5, 1937 Carlyle, Saskatchewan 43.3 °C
July 12, 1937 Regina, Saskatchewan 43.3 °C
July 27, 1939 Oliver, British Columbia 43.3 °C
July 17, 1941 Oliver, British Columbia 43.3 °C
July 17, 1941 Skagit River, British Columbia 43.3 °C
July 19, 1941 Elbow, Saskatchewan 43.3 °C
July 19, 1941 Lumsden, Saskatchewan 43.3 °C
August 6, 1949 Rosetown, Saskatchewan 43.3 °C
July 19, 1960 Newgate, British Columbia 43.3 °C
August 5, 1961 Maple Creek, Saskatchewan 43.3 °C
Garbage In — Garbage Out
Col Mosby says:
February 19, 2014 at 1:43 pm
“The everlasting delusion of statisticians that they can mine gold from worthless ore
simply by applying statistics to a giant boatload of crappy data and model estimates.”
==========================================================================
Real statisticians don’t believe this. I had the good fortune to work have three excellent statisticians to keep my data analysis honest during my working career. The first one told me the first day I worked with him:
“Too many engineers think statistics is a black box into which you can pour bad data and crank out good answers. It is not.”
As for Bayesian probabilities, I had to go to Google for that. Col Mosby is right on, they:
“ …aren’t even statistics, simply people giving their own guesses and weighting their belief in same.”
So while statisticians know better, apparently those who employ statisticians can get them to use enough jargon that the truth is covered up. I wonder who peer (pal?) reviewed the paper.
@John robertson 3:46
I hear ya, John.
This is nothing more than “We made a model to tell us how all our models are doing and found out they’re doing quite well!”
Sometimes profanity ain’t enough, John!
They say, “Our research seeks to combine many different sources of climate data, in a statistically rigorous way, to determine a CONSENSUS on how much temperatures are changing.”
I hacked up a kidney when I read, “Statistical modeling is a tool to not only get a CONSENSUS ESTIMATE of temperature change but also an ESTIMATE OF OUR UNCERTAINTY about this temperature change.”
Churchhill described Russia in 1939 as “a riddle, wrapped in a mystery, inside an enigma.”
Climate science in 2014 is an uncertainty, wrapped in an estimate, inside a consensus.
It should be inside… [something else]!
I can’t finish the above sentence without profanity, John!
Plus, now I’m down to only 1 kidney!
Londo says: “Science by committee”.
That is so terry pratchett (and true).
Gamecock says:
February 19, 2014 at 1:20 pm
They can’t even predict the past.
To repeat myself from a few days ago…
With Climate Science only the past is uncertain.
Proposals such this these call for the creation of a new field of study:
statistical wankology
“Our understanding of how much temperatures are changing is reflected in all the data available to us,” says Heaton. “For example, one data source might suggest that temperatures are increasing by 2 degrees Celsius while another source suggests temperatures are increasing by 4 degrees. So, do we believe a 2-degree increase or a 4-degree increase? The answer is probably ‘neither’ because combining data sources together suggests that increases would likely be somewhere between 2 and 4 degrees.“.
Leaving aside the fact that “data” appears actually to be model predictions and not data at all, surely that size of discrepancy indicates that we do not yet have a reliable figure. There might be some justification for supposing that the likely ‘correct’ figure is between say 0 deg and 6 deg, but I doubt it. Surely the only correct approach is to test the models until the range of uncertainty for at least one of them is reasonably well known and narrow enough to be useful.
Versions of climate models are used for shart term weather forecasting. The forecasts a day or two out are often pretty good, but they are still far from perfect. Forecasts more than a week out are still very unreliable and are generally not used, except for the Met in the UK who have made seasonal forecasts that are completely reliable. Completely reliable to be absolutely wrong, that is. The argument that we can’t forecast the short term weather so we can’t forecast the long term climate are IMHO logically incorrect (they are different things that may have different primary drivers), but the total failure of the Met’s seasonal forecasts does show that the models get those primary drivers seriously wrong. Since at least one of these drivers, CO2, is also a primary driver (in the models) for long term climate, it does logically follow that the Met climate models are unfit for purpose. Since all climate models AFAIK use the same primary drivers and the same basic logic, differing only in some detail and some parameterisations, it does logically follow that all climate models are unfit for purpose.
ie, we still cannot predict climate. Period.
TheLastDemocrat says:
February 19, 2014 at 1:49 pm
“One of these models is the most accurate. … Averaging the models is a way to ensure your model is poor by straying away from the best model.”
The problem is that they don’t yet know which model is the “best”, so they cannot eliminate the others.
Curious George says:
You’ve fallen for it even though you critique it. He said “how much we think temperatures are changing.” Their result is (at best) an estimate of human opinions, not of the facts that those opinions are opinions about. To reach either of the conclusions your two criticisms criticise, one has to add the hypothesis: “Our opinions are always right”.