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Hurricane—Warming Link Is All Model, No Data

18 Tuesday Oct 2022

Posted by Nuetzel in Climate science, Hurricanes, Uncategorized

≈ 2 Comments

Tags

Carbon Forcing Models, carbon Sensitivity, Climate Alarmism, Geophysical Fluid Dynamics Laboratory, Glenn Reynolds, Greenhouse Gases, Hurricane Ian, Hurricane Models, IPCC, Model Calibration, Named Storms, National Hurricane Center, National Oceanic and Atmospheric Administration, Neil L. Frank, NOAA, Paul Driessen, Roger Pielke Jr., Ron DeSantis, Ryan Maue, Satellite Data, Tropical Cyclones

There was deep disappointment among political opponents of Florida Governor Ron DeSantis at their inability to pin blame on him for Hurricane Ian’s destruction. It was a terrible hurricane, but they so wanted it to be “Hurricane Hitler”, as Glenn Reynolds noted with tongue in cheek. That just didn’t work out for them, given DeSantis’ competent performance in marshaling resources for aid and cleanup from the storm. Their last ditch refuge was to condemn DeSantis for dismissing the connection they presume to exist between climate change and hurricane frequency and intensity. That criticism didn’t seem to stick, however, and it shouldn’t.

There is no linkage to climate change in actual data on tropical cyclones. It is a myth. Yes, models of hurricane activity have been constructed that embed assumptions leading to predictions of more hurricanes, and more intense hurricanes, as temperatures rise. But these are models constructed as simplified representations of hurricane development. The following quote from the climate modelers at the Geophysical Fluid Dynamics Laboratory (GFDL) (a division of the National Oceanic and Atmospheric Administration (NOAA)) is straightforward on this point (emphases are mine):

“Through research, GFDL scientists have concluded that it is premature to attribute past changes in hurricane activity to greenhouse warming, although simulated hurricanes tend to be more intense in a warmer climate. Other climate changes related to greenhouse warming, such as increases in vertical wind shear over the Caribbean, lead to fewer yet more intense hurricanes in the GFDL model projections for the late 21st century.

Models typically are said to be “calibrated” to historical data, but no one should take much comfort in that. As a long-time econometric modeler myself, I can say without reservation that such assurances are flimsy, especially with respect to “toy models” containing parameters that aren’t directly observable in the available data. In such a context, a modeler can take advantage of tremendous latitude in choosing parameters to include, sensitivities to assume for unknowns or unmeasured relationships, and historical samples for use in “calibration”. Sad to say, modelers can make these models do just about anything they want. The cautious approach to claims about model implications is a credit to GFDL.

Before I get to the evidence on hurricanes, it’s worth remembering that the entire edifice of climate alarmism relies not just on the temperature record, but on models based on other assumptions about the sensitivity of temperatures to CO2 concentration. The models relied upon to generate catastrophic warming assume very high sensitivity, and those models have a very poor track record of prediction. Estimates of sensitivity are highly uncertain, and this article cites research indicating that the IPCC’s assumptions about sensitivity are about 50% too high. And this article reviews recent findings that carbon sensitivity is even lower, about one-third of what many climate models assume. In addition, this research finds that sensitivities are nearly impossible to estimate from historical data with any precision because the record is plagued by different sources and types of atmospheric forcings, accompanying aerosol effects on climate, and differing half-lives of various greenhouse gases. If sensitivities are as low as discussed at the links above, it means that predictions of warming have been grossly exaggerated.

The evidence that hurricanes have become more frequent or severe, or that they now intensify more rapidly, is basically nonexistent. Ryan Maue and Roger Pielke Jr. of the University of Colorado have both researched hurricanes extensively for many years. They described their compilation of data on land-falling hurricanes in this Forbes piece in 2020. They point out that hurricane activity in older data is much more likely to be missing and undercounted, especially storms that never make landfall. That’s one of the reasons for the focus on landfalling hurricanes to begin with. With the advent of satellite data, storms are highly unlikely to be missed, but even landfalls have sometimes gone unreported historically. The farther back one goes, the less is known about the extent of hurricane activity, but Pielke and Maue feel that post-1970 data is fairly comprehensive.

The chart at the top of this post is a summery of the data that Pielke and Maue have compiled. There are no obvious trends in terms of the number of storms or their strength. The 1970s were quiet while the 90s were more turbulent. The absence of trends also characterizes NOAA’s data on U.S. landfalling hurricanes since 1851, as noted by Pail Driessen. Here is Driessen on Florida hurricane history:

“Using pressure, Ian was not the fourth-strongest hurricane in Florida history but the tenth. The strongest hurricane in U.S. history moved through the Florida Keys in 1935. Among other Florida hurricanes stronger than Ian was another Florida Keys storm in 1919. This was followed by the hurricanes in 1926 in Miami, the Palm Beach/Lake Okeechobee storm in 1928, the Keys in 1948, and Donna in 1960. We do not know how strong the hurricane in 1873 was, but it destroyed Punta Rassa with a 14-foot storm surge. Punta Rassa is located at the mouth of the river leading up to Ft. Myers, where Ian made landfall.”

Neil L. Frank, veteran meteorologist and former head of the National Hurricane Center, bemoans the changed conventions for assigning names to storms in the satellite era. A typical clash of warm and cold air will often produce thunderstorms and wind, but few of these types of systems were assigned names under older conventions. They are not typical of systems that usually produce tropical cyclones, although they can. Many of those kinds of storms are named today. Right or wrong, that gives the false impression of a trend in the number of named storms. Not only is it easier to identify storms today, given the advent of satellite data, but storms are assigned names more readily, even if they don’t strictly meet the definition of a tropical cyclone. It’s a wonder that certain policy advocates get away with saying the outcome of all this is a legitimate trend!

As Frank insists, there is no evidence of a trend toward more frequent and powerful hurricanes during the last several decades, and there is no evidence of rapid intensification. More importantly, there is no evidence that climate change is leading to more hurricane activity. It’s also worth noting that today we suffer far fewer casualties from hurricanes owing to much earlier warnings, better precautions, and better construction.

Myth Makers in Lab Coats

02 Friday Apr 2021

Posted by Nuetzel in Climate science, Research Bias, Science

≈ Leave a comment

Tags

Cambridge, Canonization Effect, Citation Bias, Climate Change, Climatology, Lee Jussim, Medical Science, Model Calibration, National Oceanic and Atmospheric Administration, Pandemic, Political Bias, Psychology Today, Publication Bias, Repication Crisis, Reporting Bias, Spin

The prestige of some elements of the science community has taken a beating during the pandemic due to hugely erroneous predictions, contradictory pronouncements, and misplaced confidence in interventions that have proven futile. We know that medical science has suffered from a replication crisis, and other areas of inquiry like climate science have been compromised by politicization. So it seemed timely when a friend sent me this brief exposition of how “scientific myths” are sometimes created, authored by Lee Jussim in Psychology Today. It’s a real indictment of the publication process in scientific journals, and one can well imagine the impact these biases have on journalists, who themselves are prone to exaggeration in their efforts to produce “hot” stories.

The graphic above appears in Jussim’s article, taken from a Cambridge study of reporting and citation biases in research on treatments for depression. But as Jussim asserts, the biases at play here are not “remotely restricted to antidepressant research”.

The first column of dots represent trial results submitted to journals for publication. A green dot signifies a positive result: that the treatment or intervention was associated with significantly improved patient outcomes. The red dots are trials in which the results were either inconclusive or the treatment was associated with detrimental outcomes. The trials were split about equally between positive and non-positive findings, but far fewer of the trials with non-positive findings were published. From the study:

“While all but one of the positive trials (98%) were published, only 25 (48%) of the negative trials were published. Hence, 77 trials were published, of which 25 (32%) were negative.“

The third column shows that even within the set of published trials, certain negative results were NOT reported or secondary outcomes were elevated to primary emphasis:

“Ten negative trials, however, became ‘positive’ in the published literature, by omitting unfavorable outcomes or switching the status of the primary and secondary outcomes.“

The authors went further by classifying whether the published narrative put a “positive spin” on inconclusive or negative results (yellow dots):

“… only four (5%) of 77 published trials unambiguously reported that the treatment was not more effective than placebo in that particular trial.“

Finally, the last column represents citations of the published trials in subsequent research, where the size of the dots corresponds to different levels of citation:

“Compounding the problem, positive trials were cited three times as frequently as negative trials (92 v. 32 citations. … Altogether, these results show that the effects of different biases accumulate to hide non- significant results from view.“

As Jussim concludes, it’s safe to say these biases are not confined to antidepressant research. He also writes of the “canonization effect”, which occurs when certain conclusions become widely accepted by scientists:

“It is not that [the] underlying research is ‘invalid.’ It is that [the] full scope of findings is mixed, but that the mixed nature of those findings does not make it into what gets canonized.“

I would say canonization applies more broadly across areas of research. For example, in climate research, empirics often take a back seat to theoretical models “calibrated” over short historical records. The theoretical models often incorporate “canonized” climate change doctrine which, on climatological timescales, can only be classified as speculative. Of course, the media and public has difficulty distinguishing this practice from real empirics.

All this is compounded by the institutional biases introduced by the grant-making process, the politicization of certain areas of science (another source of publication bias), and mission creep within government bureaucracies. In fact, some of these agencies control the very data upon which much research is based (the National Oceanic and Atmospheric Administration, for example), and there is credible evidence that this information has been systematically distorted over time.

The authors of the Cambridge study discuss efforts to mitigate the biases in published research. Unfortunately, reforms have met with mixed success at best. The anti-depressant research reflects tendencies that are all too human and perhaps financially motivated. Add to that the political motivation underlying the conduct of broad areas of research and the dimensions of the problem seem almost insurmountable without a fundamental revolution of ethics within the scientific community. For now, the biases have made “follow the science” into something of a joke.

Spate of Research Shows COVID Lockdowns Fail

27 Sunday Dec 2020

Posted by Nuetzel in Lockdowns, Public Health

≈ 4 Comments

Tags

@boriquagato, AIER, Covid-19, el gato malo, Hypothesis Testing, Ivor Cummins, Lockdowns, Model Calibration, Mortality, Non-Pharmaceutical interventions, Transmissability

For clarity, start with this charming interpretive one-act on public health policy in 2020. You might find it a little sardonic, but that’s the point. It was one of the more entertaining tweets of the day, from @boriquagato.

A growing body of research shows that stringent non-pharmaceutical interventions (NPIs) — “lockdowns” is an often-used shorthand — are not effective in stemming the transmission and spread of COVID-19. A compendium of articles and preprints on the topic was just published by the American Institute for Economic Research (AEIR): “Lockdowns Do Not Control the Coronavirus: The Evidence”. The list was compiled originally by Ivor Cummins, and he has added a few more articles and other relevant materials to the list. The links span research on lockdowns across the globe. It covers transmission, mortality, and other health outcomes, as well as the economic effects of lockdowns. AIER states the following:

“Perhaps this is a shocking revelation, given that universal social and economic controls are becoming the new orthodoxy. In a saner world, the burden of proof really should belong to the lockdowners, since it is they who overthrew 100 years of public-health wisdom and replaced it with an untested, top-down imposition on freedom and human rights. They never accepted that burden. They took it as axiomatic that a virus could be intimidated and frightened by credentials, edicts, speeches, and masked gendarmes.

The pro-lockdown evidence is shockingly thin, and based largely on comparing real-world outcomes against dire computer-generated forecasts derived from empirically untested models, and then merely positing that stringencies and “nonpharmaceutical interventions” account for the difference between the fictionalized vs. the real outcome. The anti-lockdown studies, on the other hand, are evidence-based, robust, and thorough, grappling with the data we have (with all its flaws) and looking at the results in light of controls on the population.”

We are constantly told that public intervention constitutes “leadership”, as if our well being depends upon behavioral control by the state. Unfortunately, it’s all too typical of research on phenomena deemed ripe for intervention that computer models are employed to “prove” the case. A common practice is to calibrate such models so that the outputs mimic certain historical outcomes. Unfortunately, a wide range of model specifications can be compatible with an historical record. This practice is also a far cry from empirically testing well-defined hypotheses against alternatives. And it is a practice that usually does poorly when the model is tested outside the period to which it is calibrated. Yet that is the kind of evidence that proponents of intervention are fond of using to support their policy prescriptions.

In this case, it’s even worse, with some of the alleged positive effects of NPI’s wholly made-up, with no empirical support whatsoever! So-called public health experts have misled themselves, and the public, with this kind of fake evidence, when they aren’t too busy talking out of both sides of their mouths.

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