From the “If your experiment needs statistics, you ought to have done a better experiment.- Ernest Rutherford” department.
From the American Meteorological Society via press release:
AMS TO RELEASE REPORT EXPLAINING EXTREME EVENTS OF 2016
New research links notable weather and climate events to human influence
The American Meteorological Society is releasing the strongest evidence yet that humanity’s long-term release of greenhouse gases has altered recent extreme weather and climate events. In the new collection of peer-reviewed papers analyzing the link between extremes in 2016 and climate change, scientists identified three events that would not have been possible without human-caused changes in the climate.
The linkages are made in a newly released report—Explaining Extreme Events from a Climate Perspective, a supplement to the Bulletin of the American Meteorological Society. This collection of studies from 116 scientists analyzes 21 different events in 2016, from U.S. snowstorms and South African drought to oceanic hot spots and Arctic warmth. Most of the events researchers examined can be attributed, at least in part, to human-caused climate change.
Some of the analyses go beyond atmospheric and oceanic extremes to link societal or ecological impacts, including coral bleaching and crop failures, to human-caused climate change.
The American Meteorological Society will release Explaining Extreme Events in 2016 from a Climate Perspective at the AGU Fall Meeting on Wednesday, December 13, 2017 at 11:30am CDT.
The panel including editors and authors of the papers will discuss their findings about natural and human influences on the extreme events, as well as the developing maturity of attribution science.
Reporters are invited to attend the press conference in person at the Morial Convention Center in New Orleans or else via live web streaming offered by the American Geophysical Union.
Events Assessed for 2016 include:
- The notorious warm “Blob” in the Pacific Ocean
- Flash Droughts in South Africa
- Wildfires in North America and Australia
- Cold Snap in Eastern China
- Drought in Northeast Brazil
They already have released the report ahead of the press conference:
Explaining Extreme Events from a Climate Perspective
This BAMS special report presents assessments of how climate change may have affected the strength and likelihood of individual extreme events.
This fifth edition of explaining extreme events of the previous year (2015) from a climate perspective continues to provide evidence that climate change is altering some extreme event risk. Without exception, all the heat-related events studied in this year’s report were found to have been made more intense or likely due to human-induced climate change, and this was discernible even for those events strongly influenced by the 2015 El Niño. Furthermore, many papers in this year’s report demonstrate that attribution science is capable of separating the effects of natural drivers including the strong 2015 El Niño from the influences of long-term human-induced climate change.
http://ift.tt/2j2SFQ1 (size: 44MB)
You can download by chapter, here: http://ift.tt/2aqSOLS
From the introduction:
This last year has been exciting for attribution science, as the U.S. National Academy of Sciences released its report on the topic (NAS 2016). To date, it is the most comprehensive look at the state of event attribution science, including how the framing of attribution questions impacts the results. For example, in a complex event such as drought, a study of precipitation versus a study of temperature may yield different results regarding the role of climate change. The report also addresses how attribution results are presented, interpreted, and communicated. It provides the most robust description to date of the various methodologies used in event attribution and addresses the issues around both the confidence of the results and the current capabilities of near-real time attribution. No single methodology exists for the entire field of event attribution, and each event type must be examined individually. Confidence in results of an attribution analysis depends on what has been referred to as the “three pillars” of event attribution: the quality of the observational record, the ability of models to simulate the event, and our understanding of the physical processes that drive the event and how they are being impacted by climate change.
I’m not all impressed with the “three pillars”, because what typically happens is that if a model doesn’t simulate an event on the first pass, the researchers keep tweaking it until it does. Eventually, they all become “Clever Hans” in being able to respond to the weather events nature provides.
Larry Kummer of Fabius Maximus comments via email to me:
An exercise in data mining.
How many kinds of extreme weather are there? How many of these irregularly defined geographic areas are there? Combine the two into a database. A survey of one year will always find outliers at the 5% level — by chance, because there are so many possibilities.Given that, it is easy for ingenious scientists to link some of them to anthropogenic effects.
More useful would be to see if the overall class of extreme events showed trends over time. Or at least some classes of extreme events (temp, precipitation) did so. Otherwise they have not shown that anything unusually happened in 2016. Just weather.
Literally, all that is going on here is “p-hacking”, and it is well known to have bias problems.
Data dredging (also data fishing, data snooping, and p–hacking) is the use of data mining to uncover patterns in data that can be presented as statistically significant, without first devising a specific hypothesis as to the underlying causality.
After they find a statistically significant set of data, then they use the “three pillars” to assign causality, and that causality is always climate change. Problem is, there’s a built-in bias involved, for example, this 2015 paper in PLOS One explains why(bold mine):
The Extent and Consequences of P-Hacking in Science
Abstract
A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as “p-hacking,” occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.
In Wikipedia, there is this description and a curious graph:
The process of data dredging involves automatically testing huge numbers of hypotheses about a single data set by exhaustively searching — perhaps for combinations of variables that might show a correlation, and perhaps for groups of cases or observations that show differences in their mean or in their breakdown by some other variable. Conventional tests of statistical significance are based on the probability that a particular result would arise if chance alone were at work, and necessarily accept some risk of mistaken conclusions of a certain type (mistaken rejections of the null hypothesis). This level of risk is called the significance. When large numbers of tests are performed, some produce false results of this type, hence 5% of randomly chosen hypotheses turn out to be significant at the 5% level, 1% turn out to be significant at the 1% significance level, and so on, by chance alone. When enough hypotheses are tested, it is virtually certain that some will be statistically significant but misleading, since almost every data set with any degree of randomness is likely to contain (for example) some spurious correlations. If they are not cautious, researchers using data mining techniques can be easily misled by these results.
There’s this example provided:
An example of data produced by data dredging, apparently showing a close link between the best word winning a spelling bee competition and the number of people in the United States killed by venomous spiders.
There’s also the famous graph showing climate change correlates to the number of pirates.
Scientists have been searching for years for the elusive link between “climate change” and “severe weather” for example, this editorial in Nature put the onus on them back in 2012:
From Nature: Extreme weather
Better models are needed before exceptional events can be reliably linked to global warming.
As climate change proceeds — which the record summer melt of Arctic sea-ice suggests it is doing at a worrying pace — nations, communities and individual citizens may begin to seek compensation for losses and damage arising from global warming. Climate scientists should be prepared for their skills one day to be probed in court. Whether there is a legal basis for such claims, such as that brought against the energy company ExxonMobil by the remote Alaskan community of Kivalina, which is facing coastal erosion and flooding as the sea ice retreats, is far from certain, however. So lawyers, insurers and climate negotiators are watching with interest the emerging ability, arising from improvements in climate models, to calculate how anthropogenic global warming will change, or has changed, the probability and magnitude of extreme weather and other climate-related events. But to make this emerging science of ‘climate attribution’ fit to inform legal and societal decisions will require enormous research effort.
Attribution is the attempt to deconstruct the causes of observable weather and to understand the physics of why extremes such as floods and heatwaves occur. This is important basic research. Extreme weather and changing weather patterns — the obvious manifestations of global climate change — do not simply reflect easily identifiable changes in Earth’s energy balance such as a rise in atmospheric temperature. They usually have complex causes, involving anomalies in atmospheric circulation, levels of soil moisture and the like. Solid understanding of these factors is crucial if researchers are to improve the performance of, and confidence in, the climate models on which event attribution and longer-term climate projections depend.
Read the full editorial here.
Dr. Roger Pielke Jr. observed then:
The 112 scientists finally have come to a point where they figured out how to justify their claims with with “better models” and data mining, but all the correlation in the world does not equate to causation.
Meanwhile, examining one of the most fearful severe weather metrics, tornadoes, doesn’t seem to show a correlation:
But in the case of this recent BAMS special report, the researchers truly believe the correlation must be there, and belief is a powerful motivator, so they set out on a path of self-reinforcing data discovery to prove it, just like those kids at spelling bees and venomous spiders, they certainly found what they are looking for.
I weep for science.
via Watts Up With That?
December 13, 2017 at 10:45AM
Iowa Climate Science Education 