Statistics research could build consensus around climate predictions

Feb 19, 2014
Statistics research could build consensus around climate predictions
This graphic shows a regional analysis for climate change assessment. Credit: Melissa Bukovsky, National Center for Atmospheric Research (NCAR/IMAGe)

Vast amounts of data related to climate change are being compiled by research groups all over the world. Data from these many and various 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 information from observation-based data sets and .

"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 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).

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 , 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 has all the answers. And, only by combining many different sources of 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 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."

Explore further: Temperature swings may be bigger threat to life than increased warmth

More information: Modeling Uncertainty in Climate Using Ensembles of Regional and Global Climate Models and Multiple Observation-Based Data Sets 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 below through December 31, 2014: epubs.siam.org/doi/abs/10.1137/12088505X

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User comments : 17

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COCO
2.3 / 5 (6) Feb 20, 2014
"seem to agree' - very scientific - is the AGW beast still alive - how many sheeple are out there?
ryggesogn2
2.7 / 5 (7) Feb 20, 2014
There is already a 'consensus', right?
Captain Stumpy
3.7 / 5 (6) Feb 20, 2014
@COCO
@Rygg
Do yall just read a headline and start in posting anti-agw comments to troll?
yall should have read more than the title... the first paragraph says
hence, the need arises to combine information across data sets

I dont believe that they do that now, do they?
And again
"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,"

so there is a consensus on the data, but when everyone shares their data together, a larger more detailed (as well as more complex) model can be generated, which should increase effective results in prediction
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

see? Told ya!
ryggesogn2
2.3 / 5 (9) Feb 20, 2014
Based on all the AGW hype, one would have expected all that is described in the story had already been done.
Who wrote the headline?
Why did the headline imply there was no consensus when AGWites like stump so state?
freethinking
2 / 5 (8) Feb 20, 2014
Global Warming Believers need to check with the Farmers Almanac they are more accurate than weather forecasters. Weather forecasters predicted, based on their tried and true models (which they use for Global Warming prediction), warmer than average winter, farmers almanac predicted colder than average winter.
ryggesogn2
2 / 5 (8) Feb 20, 2014
""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,"

Read more at: http://phys.org/n...html#jCp

What?
AGWites believe they know ALL. APS states it is indisputable.

"how much we think temperatures are changing.""
Changing from what? What is the reference? What IS the mean or median temperature of the Earth?
Captain Stumpy
3.9 / 5 (7) Feb 21, 2014
Who wrote the headline?
Why did the headline imply there was no consensus when AGWites like stump so state?

@Rygg
you know as well as the next person that headlines are mostly hype to draw the reader/eye to the article... it is advertising/selling in as short a span as possible
for you anti-agw's like Ryggy, read here ( http://www.thecon...ect.com/ )
Based on all the AGW hype, one would have expected all that is described in the story had already been done

I WAS RIGHT!
You neither read the article NOR what I wrote!
TROLL

just one more illiterate idiot in the food chain showing that they are incapable of comprehending basic English
or understanding basic science
check with the Farmers Almanac they are more accurate than weather forecasters

@fthinking
not in my area
almanac gets 1 in ten days correct, sometimes 2
weather is 100% accurate to 3 days out (NOAA transmissions)
ryggesogn2
2.1 / 5 (7) Feb 21, 2014
headlines are mostly hype

But this is a SCIENCE site?

Again, IF there WAS consensus AND the science was indisputable, THEN any work this story describes was a waste of time and money.

BTW, this story is NOT from a climate journal. Is is from an applied math journal so how is this relevant? After all, statisticians like Wegman and McIntyre are dismissed out of hand by AGWites for NOT being climate scientists.
AeroSR71
3.9 / 5 (7) Feb 22, 2014
Just please stop commenting rygg. It's really tiresome to read your ridiculous comments over and over again, day after day. It's clear to me that you're a layman, in the sense that you have zero formal science education. Just please let others take over the comments section, as their are many people that have great insights into these topics, but their always drowned out by people that clearly do not know wtf their talking about.
ryggesogn2
2.3 / 5 (6) Feb 22, 2014
Will AGWites stop claiming 'the science is settled' and stop claiming 'a consensus'?
Will AGWites stop claiming that statisticians like Mcintyre are not qualified to comment on AGW?
many people that have great insights into these topics

Yes, like Lindzen, Roy Spencer, McIntyre, Wegman.....
Aero, like a typical AGWite, is demanding critics shut up instead of discussing the issues.
ryggesogn2
2.6 / 5 (5) Feb 22, 2014
I think it a great idea for supposedly independent experts in applied mathematics and one would hope independent experts in modeling and simulation would collaborate with all research that relies heavily on statistics and M&S.
But AGWites must take a big humility pill and acknowledge their political tactics have not benefited real science work.
No real scientists would ever claim ANY science is settled to stifle critics. Yet that is the tactic used by AGWites and Mann himself in is lawsuit and open attacks on critical colleagues.
Maggnus
3.4 / 5 (5) Feb 22, 2014
Just please stop commenting rygg. It's really tiresome to read your ridiculous comments over and over again, day after day. It's clear to me that you're a layman, in the sense that you have zero formal science education. Just please let others take over the comments section, as their are many people that have great insights into these topics, but their always drowned out by people that clearly do not know wtf their talking about.
I agree with you, but I am getting better and better at just ignoring his conspiracist/denialist/paranoid rants. It's easy, for the most part, as almost no one pays him any attention anymore, and just seeing his name tells you that he will just be providing more mined quotes and screams that the socialists are poised to take us over or some similar delusional tripe.

His credibility is zero, so he's easy to ignore. Any response to him just feeds the troll.
ryggesogn2
2.3 / 5 (6) Feb 22, 2014
It's too bad AGWites weren't more open to collaboration with professional statisticians and M&S experts years ago. They many have saved themselves the embarrassment they now face and, even worse, the loss of credibility of science in general.
ryggesogn2
2.3 / 5 (6) Feb 22, 2014
This is the real tragedy of AGWism:
""A liar will not be believed, even when he speaks the truth.""
http://etc.usf.ed...ed-wolf/
SamB
2.3 / 5 (4) Feb 23, 2014
Yes, by all means lets get our story straight. Does not look good if each of us predict a different climate outcome. People might think this whole thing was a butterfly effect or something...
ryggesogn2
1.8 / 5 (5) Feb 23, 2014
"statisticians are looking for better ways of thinking about data, to help scientists to avoid missing important information or acting on false alarms. "Change your statistical philosophy and all of a sudden different things become important," says Steven Goodman, a physician and statistician at Stanford. "Then 'laws' handed down from God are no longer handed down from God. They're actually handed down to us by ourselves, through the methodology we adopt.""
" "P-hacking," says Simonsohn, "is trying multiple things until you get the desired result" — even unconsciously. "
"P-hacking is especially likely, he says, in today's environment of studies that chase small effects hidden in noisy data."
http://www.nature...-1.14700
aksdad
2.3 / 5 (3) Feb 24, 2014
[p]The vast array of climate data...seems to agree that global temperatures are changing[/p]

News flash! It's been changing for all of recorded human history and long before:

See here:
http://www.climat...omeC.gif

And here:
http://www.climat...20BP.gif

I fail to see how combining data from actual observations with guesstimates generated by computer models can produce useful information. It is well established, even by the IPCC, that the GCM are bad at predicting temperatures.

See this graph of the increasing variance of GCM compared to observations that was in the IPCC AR5 draft, but removed from the final report because it too obviously illustrated the problems with the GCM:

http://wpmedia.op...mp;h=507

Mixing good data (observations) with bad (GCM) produces garbage.

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