Tag Archives: National Oceanic and Atmospheric Administration

Hurricane—Warming Link Is All Model, No Data

18 Tuesday Oct 2022

Posted by in Climate science, Hurricanes, Uncategorized

≈ Leave a comment

Tags

, , , , , , , , , , , , , , , , , , , ,

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.

Relax: Natural Variability Causes Heatwaves

30 Saturday Jul 2022

Posted by in Global Warming

≈ 2 Comments

Tags

, , , , , , , , , , , , , , ,

Lately almost any passing weather phenomenon is said to have been rooted in climate change and higher carbon concentrations. The recent heatwaves that seared parts of Europe and the U.S. are no exception, and climate change activists always find heat spells ripe for rhetorical exploitation. But while these would-be Cassandras and Gretas push their fearful narrative, there are strong reasons to doubt that these weather events are any cause for alarm. This summer’s heat waves, like all others, were of limited geographic scope, and they certainly weren’t the most severe heat waves on record in terms of either duration or magnitude. More on that below.

Data Problems

Temperature measurements tend to be exaggerated these days because so many “official” temperature records come from local airports or other urban sites rich in impervious cover and heat absorbing building materials. This gives rise to the so-called “urban heat island effect”, which refers to the elevated temperatures measured in urban versus rural areas. It’s even worse than that, however, as the vast majority of active weather stations in the U.S. are sited at “hot spots”, and many of them are poorly maintained. Data problems plague European temperature records as well.

Furthermore, official temperature records are extremely short on climatological scales, going back only about 150 years in the U.S. And these records have been “adjusted” by weather authorities like the National Oceanographic and Atmospheric Administration (NOAA), usually with the early records “cooled” relative to more recent readings. That means the long-term trend in temperatures is biased upward.

Climate Catastrophists

Nevertheless, Joe Biden has been threatening to declare a wholly unjustified “climate emergency“, perhaps thinking these dog days are the perfect time to assume a host of new emergency powers. It’s unclear whether the new “Build Back” bill making its way through Congress will be enough to satisfy the appetite of Biden’s handlers for costly and ultimately ineffective climate measures.

It’s tempting to think delirium from the heat waves is what prompted Al Gore to compare climate change skeptics to the dithering police officers in Uvalde, TX, but Gore’s fever is nothing new. We’re still waiting for the world to end, which he once predicted would occur by 2016.

Even weather reporters on TV are breathless in their descriptions of the heatwaves. They’ve certainly become dramatists for the climate-change cause. And people love good scare stories. It gives them an excuse to polish up their pitchforks! Or to be lazy and stay inside. It’s telling that so many people now quote heat index values (which combine heat and humidity), rather than actual temperatures, in the warm summer months. After all, it’s more thrilling to say it’s 105 outside than it is to say 95.

Anyway, compare the paired maps in each of the graphics below (here are links to sources for the first and second):

The temperatures are comparable, but the use of RED colors on the 2022 maps is so much more frightening! This post from Anthony Watts provides a list of links to news sources taking alarmist perspectives on the heatwaves in the U.S. and Europe, and falsely attributing the heatwaves to CO2.

Same Old High Pressure Domes

Cliff Mass offers a bone to the climate change community. He thinks perhaps 5% – 10% of the recent temperature anomaly in the UK is attributable to greenhouse gases. An effect of that magnitude is hardly worthy of government action, let alone panic. Mass says:

“Natural variability of the atmosphere was the proximate cause of the warmth and does not represent an existential threat to the population of Europe.”

The heat wave phenomenon is typical of slow-moving high-pressure systems that often develop during the summer months. These domes of high pressure vary in temperature and geographic breadth, and they are sandwiched between or adjacent to low-pressure systems with cooler temperatures. That’s been the case in both Europe and the U.S. during this summer’s heat waves, as illustrated by the following graphics, The northern hemisphere is not entirely enveloped in a heat wave.

And the rest of the globe? In the tropics (below 20 degrees latitude), June 2022 was the coolest June in 22 years, according to satellite temperature readings! Furthermore, the monthly anomaly in June was the coolest in 10 years. In the Southern Hemisphere, Australia and South America have had extremely cold winters. Antarctica had its coldest winter on record in 2021. Yet Joe Biden is under the misapprehension that we’re experiencing “a climate emergency”.

These are not the worst heat waves on record. Both the U.S. and Europe experienced higher temperatures and prolonged heat waves during the 1930s. For example, St.Louis, Missouri matched or exceeded 110 degrees four times in the 1930s, and twice in 1954, whereas the city topped out at 102 so far this year, and that was after a cool spring. There was an extreme European heat wave in 1976 that was drier and much lengthier, and others occurred in 1911 and 1906. Of course, available temperature comparisons are distorted because the early readings weren’t as impacted by urban heat islands. There are historical accounts of drastic heat waves much earlier, such as the 1500s and 1700s. Here is more heatwave history, in case you’re interested.

We’ll Be Fine

Heat isn’t the only story, of course. A wide range of other disastrous events are blamed on climate change. Wildfires are a prime example, but as we know, wildfires are not new, and the worst wildfires have more to do with poor forest management than anything else. Likewise, there is little if any association between extreme weather events and climate change. In that context, it’s also worth noting that cold weather is much deadlier than hot weather. The climate today, and going forward, presents far fewer dangers to humanity than in the past.

I did a lot of dirty, outdoor work in my youth, and it was hot! There were times just as hot as this summer, if not worse, I’d venture to say. Anyone old enough to have lived through the 1970s or even the 1950s should recognize the heatwave Chicken Littles as such.

Myth Makers in Lab Coats

02 Friday Apr 2021

Posted by in Climate science, Research Bias, Science

≈ Leave a comment

Tags

, , , , , , , , , , , , , , ,

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.

Fitting Data To Models At NOAA

08 Monday Jun 2015

Posted by in Global Warming

≈ 5 Comments

Tags

, , , , , , , , , , , ,

Dilbert Made Up Numbers

If the facts don’t suit your agenda, change them! The 18-year “hiatus” in global warming, which has made a shambles of climate model predictions, is now said to have been based on “incorrect data”, according to researchers at National Oceanic and Atmospheric Administration (NOAA). Translation: they have created new data “adjustments” that tell a story more consistent with their preferred narrative, namely, that man-made carbon emissions are forcing global temperatures upward, more or less steadily. The New York Times’ report on the research took a fairly uncritical tone, despite immediate cautions and rebuttals from a number of authorities. On balance, the NOAA claims seem rather laughable.

Ross McKitrick has an excellent discussion of the NOAA adjustments on the Watts Up With That? blog (WUWT). His post reinforces the difficulty of aggregating temperature data in a meaningful way. A given thermometer in a fixed location can yield drifting temperatures over time due to changes in the surrounding environment, such as urbanization. In addition, weather stations are dispersed in irregular ways with extremely uneven coverage, and even worse, they have come and gone over time. There are gaps in the data that must be filled. There might be international differences in reporting practices as well. Sea surface temperature measurement is subject to even greater uncertainty. They can be broadly classified into temperatures collected on buoys and those collected by ships, and the latter have been taken in a variety of ways, from samples collected in various kinds of buckets, hull sensors, engine room intakes, and deck temperatures. The satellite readings, which are a recent development, are accurate in tracking changes, but the levels must be calibrated to other data. Here’s McKitrick on the measurements taken on ships:

“… in about half the cases people did not record which method was used to take the sample (Hirahari et al. 2014). In some cases they noted that, for example, ERI readings were obtained but they not indicate the depth. Or they might not record the height of the ship when the MAT reading is taken.“

The upshot is that calculating a global mean temperature is a statistical exercise fraught with uncertainty. A calculated mean at any point in time is an estimate of a conceptual value. The estimate is one of many possible estimates around the “true” value. Given the measurement difficulties, any meaningful confidence interval for the true mean would likely be so broad as to render inconsequential the much-discussed temperature trends of the past 50 years.

McKitrick emphasizes the three major changes made by NOAA, all having to do with sea surface temperatures:

  1. NOAA has decided to apply an upward adjustment to bring buoy temperature records into line with ship temperatures. This is curious, because most researchers have concluded that the ship temperatures are subject to greater bias. Also, the frequency of buoy records has been rising as a share of total sea temperature readings.
  2. NOAA added extra weight to the buoy readings, a decision which was unexplained.
  3. They applied a relatively large downward adjustment to temperatures collected by ships during 1998-2000.

Even the difference between the temperatures measured by ships and buoys (0.12 degrees Celsius), taken at face value, has a confidence interval (95%?) that is about 29 times as large as the difference. That adjustments such as those above are made with a straight face is nothing short of preposterous.

A number of other researchers have weighed in on the NOAA adjustments. Carl Beisner summarizes some of this work. He quotes McKitrick as well as Judith Curry:

“I think that uncertainties in global surface temperature anomalies is [sic] substantially understated. The surface temperature data sets that I have confidence in are the UK group and also Berkeley Earth. This short paper in Science is not adequate to explain and explore the very large changes that have been made to the NOAA data set. The global surface temperature datasets are clearly a moving target.“

There are a number of other posts this week on WUWT regarding the NOAA adjustments. Some of the experts, like Judith Curry, emphasize the new disparities created by NOAA’s adjustments with other well-regarded temperature series. It will be interesting to see how these differences are debated. Let’s hope that the discussion is driven wholly by science and not politics, but I fear that the latter will have a major impact on the debate. It has already.