One Look at a Graph

Paul Homewood objects to the U.K. Met Office telling people that “Arctic sea ice decline continues.” Of course his opinion is echoed at WUWT.

It’s Paul Homewood whose claims are false.

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Please Vaccinate against COVID-19

The vaccine against COVID-19 reduces its spread, even for the highly contagious delta variant. Perhaps more important, the vaccinated who contract the disease are likely to have a mild case, less likely to require hospitalization, and much less likely to be killed by it.

Maybe that’s why states with low vaccination rates have higher infection rates:

and states with low vaccination rates have higher death rates:

Lower infection rates, and especially fewer hospitalizations, is what our country’s health care providers are begging for. Maybe that’s why they’re begging us to get vaccinated against COVID-19.

The life you save may be your own.

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Anti-Vaxxers, Anti-Maskers: IT’S YOUR FAULT

Over a year ago I began making graphs related to the COVID-19 epidemic. It’s not controversial to identify the most basic number to tell the story: how many new cases each day, per capita? Medical personell tend to express this as cases per day per hundred thousand population, but I prefer to use cases per day per million population. Call me quirky.

I (like many before and since) decided to color-code some of my graphs, with “red” reserved for the most severe outbreaks — so many new cases each day that it will strain the health care system in a week or less, and before too long will crush it, while filling up the morgue to overflowing. I did a little research (translation: looked around on the internet, not peer-reviewed research, but at least I used “reliable” sources like Johns Hopkins Univ. School of Public Health) and concluded that since the so-called “experts” seemed to think that was 25 cases/day/100,000 people, that’s what I’d use — but I’d call it 250 cases/day/million population. Call me quirky.

Here’s today’s map for the states of the USA:

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Hurricane Ida: Climate Change makes a Monster Storm

Warm sea water is what powers hurricanes. Usually, sea surface temperature (SST) in the Gulf of Mexico needs to exceed 29°C to intensify a hurricane, and every fraction of a degree above 29°C increases the chance — dramatically — of not just intensifying, but super-charging it, creating a “monster storm.”

Which makes one wonder … if a storm passes by, what are the odds the sea surface temperature (SST) will exceed 29°C? Or more? Have the odds changed over time? Of course SST isn’t the only factor at play, only fools say so, but only bigger fools deny its impact on tropical storms.

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3 Graphs

Earth is heating up.

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COVID-19: delta variant

The state of Maine is suffering through an explosion of COVID-19 cases, now that the delta-variant has arrived. It’s especially a pity because we were doing so well when July began, with only about 20 cases per day per million population — but now we’re up to nearly 120. It’s putting a real strain on the health care system.

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European Heat

The memory is still fresh, in the minds of Americans, of the fearful heat wave which struck the Pacific Northwest in late June, reaching temperatures never before imagined in the region. It drives home the point that the effects of climate change are here, now, to stay — and that includes more and hotter heat waves.

Convincing Americans of that is made easier by the fact that we ourselves have seen it happen in our own back yard. I suspect it’s downright easy to convince Europeans — because they’ve seen it before. More than once.

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USA Heat: Where are we Vulnerable?

My investigations suggest that the strongest influence on extreme heat is the increase in average temperature during summer; the shape of the distribution can change, and that has an effect, but change in the average value dominates. So I decided to look at how summertime heat has changed in each climate division of the conterminous USA (i.e. the “lower 48 states”), according to the data for high temperature from NOAA.

For each division, I fit a smooth curve (lowess smooth), then estimated the “summer warming” as the difference between the smoothed values now (i.e. in 2021) and at the start (i.e. in 1895). Some of them show considerable warming, in fact the northeast corner of Utah has warmed by a whopping 6.05°F:

Although most climate divisions show summer warming, not all of them do; in fact in Alabama there’s a division which shows cooling by -2.39°F:

Whichever divisions in the USA have warmed by the most, are most at risk for never-before-seen extreme heat. And here they are as red dots (bigger dots, bigger risk), with blue dots indication regions which have shown net summer cooling (rather than heating) since 1895:

Two regions stand out as being at greatest risk. First is the entire U.S. west, westward of longitude 100°W. Second is the northeast coast, northward of Washington D.C.

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Bevy of Black Swans

Cliff Mass likes to refer to the recent heat wave in the Pacific Northwest as a “black swan” event. The term refers to “Black Swan Theory,” developed by Nassim Nicholas Taleb, which wikipedia describes as a “metaphor that describes an event that comes as a surprise, has a major effect, and is often inappropriately rationalized after the fact with the benefit of hindsight.

Here’s how Taleb himself defines a “black swan” event:

What we call here a Black Swan (and capitalize it) is an event with the following three attributes.

First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility. Second, it carries an extreme ‘impact’. Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.

I stop and summarize the triplet: rarity, extreme ‘impact’, and retrospective (though not prospective) predictability. A small number of Black Swans explains almost everything in our world, from the success of ideas and religions, to the dynamics of historical events, to elements of our own personal lives.

I stop and emphasize the phrase “not prospective.”

Taleb means that there’s only retrospective explanation, there’s an absence of prospective (i.e. predictive) explanation. I’d like to remind Cliff Mass that extreme heat waves, of greater frequency and severity than seen before, has been a prediction of climate change science for decades. Therefore, according to Taleb’s own definition, the recent heat wave in the Pacific Northwest is not a black swan event.

Taleb emphasizes the folly of “explaining” an extreme event with major effect which nobody saw coming, as though we should have known all along. He’s got a point. I’ll emphasize the folly of the opposite mistake: to use “Black Swan Theory” to dismiss what scientists have been warning us about for decades, as nothing but an unpredictable outlier.

Perhaps Cliff Mass will insist that the heat wave in the Pacific Northwest was so severe, that nobody predicted a heat wave this strong. It was so much an outlier, we have to call it a “Black Swan.”

If we’re going to do that … then there are a whole lot of Black Swans popping up these days. All over the world. With a frequency, and of a blackness, far beyond what we’ve seen before, a veritable population explosion of Black Swans. The heat wave in the Pacific Northwest is far from the only example, but it is the one which Cliff Mass can’t ignore.

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Extreme Heat: the French Disconnection

I decided to apply the method used in the post about the heat wave in the Pacific Northwest, to look at ERA5 data for daily high temperature, but for other regions. Having looked at a number of spots, let me show you what it looks like for two of them which represent very different recent history of extreme heat.

One is latitude 45°N, longitude 5°E, in France, not far from the city of Lyon (which is not far from the Swiss border). The other is just north of New Orleans, at latitude 30°N, longitude 90°W. We’ll start with France, and here’s the data for temperatures 90°F and hotter:

Right off the bat it “looks like” (there’s that phrase again) there have been more such days lately. I found the highest temperature for each year (excluding 2021, which isn’t over yet), and the trend (red line in the graph with pink shading for uncertainty range) is not only real (i.e. overwhelmingly statistically significant), it’s substantial, increasing by 6.4°F from 1950 to now.

To look for changes in the distribution, first I isolated the summer months (June, July, and August), then computed anomaly (the difference from the average value for the given time of year) in order to remove the seasonal cycle. Then I split the time span (1950 to now) into three segments: from 1950 to 1975, from 1975 to 2000, and from 2000 to now (this differs from the previous analysis, in which I only used two segments). Finally I estimated the pdf (probability density function) for each interval, both by constructing a histogram and by a smoothed estimate. Here they are, with interval 1 (1950 to 1975) in blue, interval 2 (1975 to 2000) in black, and interval 3 (2000 to now) in red:

Clearly the most recent time span (2000 to now) has far greater likelihood of extreme heat (which we get from very high temperature anomalies in summer). It also “looks like” the distribution got wider, making the excursion into extreme heat even more extreme.

All three time intervals have different distributions. But interestingly, if we offset each series by its own average, to get a distribution with the same shape but with average value zero, then the first two intervals show no significant evidence of a difference in their distributions. The real difference from time span 1 to time span 2, is the small increase in its mean value.

But the third interval has a pdf which is definitely of a different shape than the other two. In particular, its variance increased (the distribution got wider). More to the point at hand, the high-temerature end of the survival function tells us the likelihood of extreme heat during each span:

This region shows exactly the kind of temperature increase, not just of the average but of the extreme-heat region, which makes it vulnerable to never-before-seen heat waves.

Turning our attention to the western hemisphere, let’s look a bit north of New Orleans at latitude 30°N, longitude 90°W. Here’s the data for temperatures 92°F and hotter:

This time, the yearly high temperature series shows no significant trend:

Performing the same procedure as before to study possible changes in the distribution, I get this for this part of the USA (interval 1 in blue, interval 2 in black, interval 3 in red):

They all look similar, and when we use the Kolmogorov-Smirnov test, none of them shows any statistically significant sign of being different from the others. This overall picture, of a location not in danger of extreme heat because it has shown neither strong warming of its average nor widening of its profile, is confirmed by a close look at the high-temperature end of the distribution:

So far, all indication are that the change in a location’s susceptibility to extreme heat is dominated by the change in its summertime average temperature. I have not yet found a case where a shape change dominates, either to creates or to suppresses a significant change in extreme heat. It’s the change in average temperature (during summer, at least) which carries the day. But … I have a lot of locations yet to study.

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