A fascinating idea has emerged, that when heat waves or cold waves happen, global warming might make them last longer. Some think that this has already begun, an idea suggested by the recent long-lasting cold wave to hit the eastern U.S. It has also been implicated in some recent extreme hot times, such as the Moscow heat wave in 2010, notable not just for its extremity but for its long duration.
The gist of the idea is this: we already know that the Arctic is warming much faster than the globe as a whole, in fact in general high latitides tend to warm faster than the tropics. Arctic temperatures are getting closer to their more southerly cousins, and this means that the temperature difference as you travel toward the pole (the gradient) is getting smaller. The temperature gradient is what drives the jet stream, which has a big influence on how weather patterns tend to move about. When the gradient is weaker the jet stream is more meandering, which can make weather systems move more slowly, perhaps even get “stuck” in place for longer times. That means that whatever the weather brings — including hot spells and cold snaps — can last longer.
The effect may be especially pronounced in the Arctic because the reduction of sea ice cover can also profoundly affect how wind patterns behave.
It sounds to me like sound theory, but the proof is in the pudding. Whether the effect is real or not remains to be seen, but I wonder, is there evidence that it has begun already?
I retrieved daily temperature data for New York City (Central Park), in order to see how long cold and hot spells last when they do occur. It seems a good choice for a first look, because it has such a long record and it is feeling the cold that has gripped the U.S. east coast.
The question itself opens a “can of worms” because now we have to decide what makes a cold or hot spell, how to quantify it. It even requires a definition of what’s “hot” and “cold,” and I chose to define them relative to what’s “normal” for the given time of year, so a temperature of 60°F (about 15.6°C) could be part of a cold spell if it happens in July, or part of a hot spell if in January. So the first order of business is to transform temperature to temperature anomaly, the difference between a given day’s temperature and the “normal” for that time of year.
That of course raises the question, “What’s normal?” I wanted to avoid getting more/fewer/longer/shorter cold/hot spells simply because what’s “normal” changed via global warming. So I fit a lowess smooth to the temperature anomalies in order that the “normal” could change to keep up with the times. That means rather than just look at temperature anomaly (which really needs an unchanging baseline), I’m looking at what I’ll call adaptive anomaly, in which the baseline follows the estimated global warming signal.
And what, you may be wondering, makes a “cold spell” or “hot spell”? The first measure I’ve tried is days when temperature is as much as 5°C hotter or colder than “normal.”
Then, each time daily temperature went into the specified range, I counted how many days that condition persisted. This gives me a series of times at which such “excursions” happened and how long they last.
Of course there are many different ways to define such conditions, but this occurred to me as a good starting point. If there is already an obvious change in the duration of excursions, it might show in such data.
Allow me to illustrate. Here’s the daily mean temperature in New York City (Central Park) for the last 149 years:
Subtracting away the average seasonal cycle gives us the usual form of temperature anomaly:
The long-term trend is clearly visible and easily established statistically. It can be estimated with a lowess smooth, which is shown as a red line in the above graph.
I can now subtract the smoothed value from the anomalies themselves to generate adaptive anomaly:
This is, I believe, a pretty good estimate of how daily temperature has fluctuated, apart from the seasonal cycle and the global warming trend. It is in the sequence of adaptive anomalies that I will search for changes in the typical duration of hot/cold times.
What did I find? Here’s the length (in days) of each excursion of daily mean temperature by 5°C or more, either hotter or colder than normal, in the 149 years of data for New York City:
The longest-duration event was a 16-day cold spell which finally came to a close on February 3rd, 1961. The next-longest has just happened, a 14-day cold spell.
Both are in the 2nd half of the data set, one is extremely recent — does that mean hot/cold spells are getting longer? A few extreme events can be suggestive but rarely demonstrate anything other than the fact that extreme events happen, but they’re rare enough that “statistical significance” eludes us.
I’ve looked for a trend in these data, and found none. The average duration of cold/hot excursions (beyond the 5°C range) hasn’t changed at all; when the “p-value” gets up to about 0.98, any claim of statistical significance is ludicrous. The number of excursions which exceed some limit, like those 3 days long or longer, or 7 days long or longer, likewise shows no significant trend.
I also looked for any change at all in the lengths of excursions, by dividing the data into two different time spans and comparing the probability distributions of excursion duration for each. I compared pre-1945 to post-1945, pre-1980 to post-1980, and pre-2000 to post-2000. Here are histograms of pre- and post-2000 durations, with the pre-2000 distribution in blue and post- in red:
It certainly seems that excursions lasting only 1 day are less frequent while those 2 days or longer are on the rise, but is that a real difference or just a result of random fluctuation? I compared the distributions with the Kolmogorov-Smirnov test, and all three tested splits gave the same answer: no, the distribution isn’t significantly different. When the p-value gets up around 0.7, claiming statistical significance is just nonsense.
What does it mean? The most important conclusion is that we need more analysis to know. A single location is a paltry sample. New York City is not the world (despite the perspective of many New Yorkers). And we already know that climate change affects different areas differently, particularly with regard to heat waves, which are definitely on the rise in Europe, almost certainly in Asia, but have not yet shown in the United States.
But it is an important result, I think, that New York City, in the midst of the east coast cold wave, shows no sign that this latest is a change in climate conditions. It’s certainly unusual, but not unheard-of, we’ve seen similarly unusual excursions before. Maybe the take-home message is the now-known expression that “Just because climate changes, that doesn’t mean we won’t still have weather.” From what I’ve looked at the recent bitter cold is just that: weather. Extreme weather, yes, but out of line with what extreme weather has been like in the past, no.
I think analysis like this deserves a more far-reaching and systematic approach. When we look at more than just one location, using more than just one analytical approach, we’re bound to learn more. And I emphasize again that this investigation, with its limited scope, is really just a first exploratory step.
So much data, so little time …
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