Today's post is part 6 of the THB series Weather Attribution Alchemy.
Last month, NASA's climate scientist Kate Marvel shared “things that I really struggled” about attribution of extreme events. [emphasis, links added]
She is an invited expert at the National Committee on Extreme Activities Attribution for Public Information Collection Conference.
Miracle also served as lead author in the 2023 U.S. National Climate Assessment chapter, he explained to the committee Her struggle was a result of (a) the seemingly contradictory discovery of the IPCC, which did not detect the long-term trends in most extreme weather indicators, and (b) the claim of extreme event attribution – it seems that a large change has been found in every type of extreme weather.:
No [in the IPCC] Many “we have very strong attributions to these long-term trends in human activities” and it seems to be in the face of “well, this heat wave is more likely to be severe or X% larger or worse due to human activities.” When I talk to the public, it’s something I really try to bridge.
Marvel's struggle is real.
As I have extensively documented on THB, IPCC’s findings in detection and attribution do not coincide with the title generation statements of the extreme event attribution community.
However, coordinating the difference between the two does not require struggle.
IPCC has played a (mainly) role in evaluating peer-reviewed literature on detection and attribution related to extreme weather events for decades.
By comparison, Extreme incident attributions were caused by alchemy to a large extent outside peer-reviewed literature and facilitated by press releases.
Today’s post withdraws the curtains of World Weather Attribution (WWA), which is undoubtedly one of the most successful marketing campaigns in the history of climate advocacy.
I call this a marketing campaign based on how they describe their goals:
Despite its disdain for peer evaluation, WWA found considerable appeal – its press releases have caused headlines around the world, appeared in legal documents and even widely cited in peer-reviewed literature.
WWA is also the core of the current National Academy of Sciences/Bezos Earth Fund Extreme Evertibition study, which is the same as the WWA and the committee itself.
If you read the first five parts of the THB series about weather attribution alchemy, you will soon learn that my concern is not to question the small methodological details of extreme event attribution.
Instead, my criticism is that extreme event attribution is pseudoscience, which seems to have been created to undermine the scientifically powerful detection and attribution framework of IPCC.
Despite public relations success, few people know the actual function of WWA and its role in connecting every famous weather event with climate change (it is well known that climate change is not the cause).
This is the first of two articles describing the WWA method and how these methods can be used to produce compelling results that make it headlines around the world.
Through accounting, the approach used by world weather attribution to determine the “impact of global warming on recent extreme events” is eight steps.
The first is to determine the event that has just occurred in some significant impact.
The second step involves identifying specific variables to characterize events and centrally analyzing them – For example, high temperatures exceed 40°C per day or rainfall of up to five days a year.
The third step is crucial, and this is the focus of this article:
The next step is Analyze the observations to determine the return time of the event and how it changes. This information is also needed to evaluate and bias later climate models, so in our methodology, the availability of sufficient observations is a necessary condition for attributional research.
Both sentences here are very important. Observing time series has two purposes: (1) to determine how extreme events change (i.e. to establish trends) and (2) to select which models to run as filters are related to making attribution requirements.
How long does a time series take to fully determine the return time of the event? WWA explained,
A long sequence that takes place, including activities, but dates back at least 50 years, preferably over 100 years.
It is assumed that the statistics of the observed time series are characterized by extreme value distributions (it seems that the generalized extreme value (GEV) function is usually preferred in WWA studies).
If you are interested in a great short (7 minutes) tutorial on GEV, click the video below:
WWA proposes two assumptions that are crucial to its approach and its results:
- “[T]He distributes [of historical observations] Change or scale only with the change of smooth global average surface temperature (GMST) and does not change shape”
This means that the distribution of the relevant variables is fixed (Unchanged) Except for the effects of global average surface temperature changes.
This assumption has three huge problems.
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