Hot Time, Summer in the City…

September 11, 2018


click here for larger image

I just cannot stop thinking about this graph that appeared with this article in the NYTimes recently.  The piece discussed how the number of hot summer days, those above 90 degrees F, are projected to increase in the future, and it allows readers to enter their town and date of birth to see how the weather has changed between then and now.

Hmmm….  Well, we all know that climate is always changing, and we all know that it is warmer now, in general, than it was 100 years ago, but beyond that what does this article and its interactive graphic tell us?

I imagine that a lot of readers misinterpret the data plot and believe that it represents the rise in temperature in NYC over the recorded period:  my experience is that most readers of these articles in the Times are not too concerned with details of data and data presentation.  In fact, it is more accurate to say that the chart shows the number of “above 90-degree F days” in NYC over the period.  That is, a count of days, not temperatures.   Except that it doesn’t show that…  On the left there is some text that says that it shows the “average number of days above 90-degrees F.”  What does that mean?

If we look at the data point for the year 2010, we find a value of about ten days.  Ten days above 90F in 2010?  You could easily check the record to see if that is accurate. But the text says that ten days is the “average number” in 2010.  In that year, there were either ten days above 90F or there were not ten days.  An average does not enter into the discussion.  That would be as if we said that June, on average, has thirty days.

The confusion is eliminated when we read the FAQ and Methodology document to which a link is provided at the end of the article:  How many people do that, do you think?  We learn that the data plot shows a twenty-year moving average of the above 90F days for each year.  For example, for the year 2000, the number of above 90F days for 1990,1991, 1992…2000…2008, 2009, 2010 are added up and and divided by twenty-one (there are twenty-one years’ values) and an average is obtained.  For 2001, the same process is used, but the summed years begin with 1991 and end with 2011.  Moving averages are often used to smooth out the data curve:  in this case, without doing it the plot would be very “spiky” with sudden changes in the number of above 90F days from year to year.  Smoothing the data gives a better idea of the trend, but it is good practice to make clear up front that you have done so, which the authors of the piece do not do.

On the other hand, what about the years 2008 through 2018?  For example, take the year 2015:  we get a twenty-year moving average by summing the data from 2005 to 2015, and adding that to the data for 2016 to 2026…  Oops!  There is NO DATA for the years after 2017!!  The kindly scientists at the Climate Impact Lab of Columbia University have used model data, simulated data, or shall we say, created data in place of actual historical data.  They do, obliquely, note this fact in their FAQ and Methodology text, but you’d never know it by looking at the graph.

Consider this:  their models show temperatures rising and above 90F days increasing, so the tendline after 2017 is rising.  But unlike the rest of the graph, that is NOT actual recorded data.  For all we know, the data record during that period is flat, or perhaps moving downward.

And speaking of flat data records, at least in NYC, the period from 1990 to 2017 (keeping in mind that the data for 2008 to 2017 is not actually the historical data) looks pretty much horizontal, i.e. constant, not increasing.  But sure enough, we can be completely confident that the upward trend that begins…next year, will come about.

Well, we cannot be completely sure because the Climate Lab also tells us – they are honest, if not forthcoming – that the results plotted here represent the data range that two-thirds of the models project.  I’m used to hearing the IPCC and other outfits talk about high or very high confidence in projections, i.e. a 90 or 95% confidence interval, but here we have a “just likely,” …mebbe… confidence interval of 66%.  Of course, this is simply a statistical sample of modeled results, described with the unspoken assumption that the models are correct, or nearly correct, or more correct than not correct… 🙂  If all the models share a few assumptions and parameters that later are disproved, then the fact that 66% predict this is hardly something to inspire confidence.  This, by the way, goes for all the climate projection models.

It would be nice if this graph for NYC were to be published every year in the NYTimes.  Then we could see each year how accurate the projections actually were.  Instead, this plot will be forgotten, and next year there will be a new batch, showing the rise in this or that frightful metric after the fateful year at hand.

Of course, it could happen exactly the way they are claiming it will.  We shall see…!

Shoot the Messenger: Comedy & Criticism with Nate Silver

November 4, 2012

Click for Richard Sala source.

Statistical reports and observed reality do not always correspond, as my favorite comics artist, Richard Sala, illustrates with the image above.  This gives an opening to right-wing critics of the statistician Nate Silver, who has consistently rated Obama the favorite in this election at his blog,  I find the attacks on him to be laughable:  yes, he says he votes Democratic; yes, he has strong opinions on the importance of state as opposed to national polls; yes, he predicts the popular vote to be rather close but still rates Obama at more than 80% likely to win.

So what?  As they say in the pundit world, “Here’s the thing…”  In a few days the election will be over and we’ll see whose predictions and analysis were good, and whose were bad.  Let’s just wait and see, heh?

Global Warming – Is it Hot in Here? Sic et Non, Dustbowls, etc.

April 22, 2005

Time to deal with the big question of the day, is it getting hotter? I’m not a scientist, and I don’t follow this question in all of its minutiae, but I have tracked it over the last fifteen years, and I am peripherally involved by virtue of some of my professional work. The other day, I attended a meeting about global warming and projected sea level rise as part of a municipal planning effort to determine what should be done to protect critical infrastructure from flooding, particularly the sewage collection and treatment system. At the start of the meeting, a professor reviewed some of the data:

The Data

Yes, well, it seems to be warming up, doesn’t it? Permafrost is melting in Alaska, polar ice caps are getting smaller, etc. Something is happening – is it a long-term trend? At one point in the meeting, a colleague, perhaps sensing skepticism on my part, leaned over and said, “Just because at this moment in time you may not be able to determine from the data that there is a long-term warming trend doesn’t mean you shouldn’t do anything.” As a policy position, this is certainly true – you can’t wait for certainty when you have to safeguard the sanitary sewage system of a huge metropolis, but as an intellectual question, “What is going on?” you certainly should withhold judgment. I had the creepy feeling that these scientists ‘know’ that warming from human activity is happening, and that this colors their view of the data.

Remember the Dustbowl of the 30s? For generations, Americans moved west, created farms on the plains, and sowed wheat. The climate favored them with sufficient rain to do so until the twin scourges of the Depression and the dustbowl hit. In fact, the weather had been in an anomalously wet period, and it simply reverted to its established pattern of more sparse rainfall – the ‘drought’ was a result of bad land use by humans. As the author of Soil and Civilization put it, that soil should never have felt the plow. When it did, and it got dry, it blew away. [Now, farmers make up for the lack of rain by pumping from the groundwater with center-pivot irrigation, which is what makes those lovely circles you see on the ground when you fly over the mid-west. Eventually, the water will be exhausted or too pricey to pump, and we’ll have another ‘drought.’] How is one to know if one is in the middle of a temporary trend?

The scientists showed a graph of mean global temperature, rising…sort of steadily from 1860. Unfortunately, this isn’t as clear as the little graph appears because mean global temperature is calculated from stations all over the world, but not evenly distributed. And in the 1800s, there were relatively few stations…and where were they? And stations in cities tend to report higher temperatures than surrounding areas – the “heat island effect.” So, as they remarked, the earlier data has much uncertainty. So, again, how do you know if the trend is long-term or a mere blip in the record?

The Models

It all comes down to the models that scientists have created of the global weather system. If you can create a computer simulation of the weather system, and if you begin your simulation in the year 1850, and if the results match the graph I mentioned above, then you have a good model, and you feel comfortable using it to predict the future, right? [This is known as hindcasting (calibration) and forecasting.] But what if there are problems with the data that you are trying to match. And what if there are elements in the weather system of which you are unaware which you don’t understand well that might bring different effects from the causes you input to your model. [Candide said, “Il n’ya pas d’effet sans cause.”] How do you know what you don’t know? The modelers put their faith in calibration – if it mimics past behavior, it’s good. Then there are the questions of how well it has to match past data for you to have confidence, etc.

This is a devilishly complicated business. There are entire books written just on the questions of assessing the data related to one element of this question, e.g., the nature of the global mean temperature record. As an engineer who is well acquainted with computer modeling, I have to say that the tenor of the remarks I hear in meetings such as these is almost always rather cavalier, while my colleagues, who are modelers, though of water, not atmosphere, cluck a bit and shake their heads: “Well, maybe…we can’t even get the East River quite right – they’re doing the global climate!?”

Sometimes I dip into the literature with my big toe: What I find there is a lot of honest discussion of all these issues. It’s in the editorials that people put on their spin. Part of this is philosophical differences…

So What of It – What is to be Done?

I can think of so many reasons why we should burn less fossil fuel: political reasons, public health reasons, environmental reasons…global warming isn’t one of them. I can’t even see that as a practical motivator; human society just can’t respond to such amorphous threats; the Indians and the Chinese are not going to forgo their automobiles just because of these warnings. Sure, warming may bring disaster for some…island dwellers…but the rest of us will get on. Nobody misses what they never had. Europeans today don’t wail about the fact that 80% of the continent used to be deep forest and now it’s gone: Future generations may not care that it’s a bit warmer. Humans are as hardy as cockroaches – we’ll survive it, though it might very well be preferable to avoid it. Even if we were to implement the Kyoto protocols, it would only minimally slow the rate of warming, if that.

So, use less energy – conserve, be efficient, develop new technology: Achieve energy independence and leave the Sultans of Crude to thrash about in their bizarre medieval cum modern world: Improve public health by reducing smog and particulate emissions; cut acid rain and improve environmental quality: develop mass transit, fight sprawl, preserve natural habitat, cultivate an environmental ethic of stewardship – all great things! Let’s do it. At least if anthropogenic warming does come about, we’ll have a more pleasant world in which to experience it!