Data Visualization # 12—Using Roulette to Deconstruct the ‘Climate is not the Weather’ response to climate “deniers”

If you are at all familiar with the politics and communication surrounding the global warming issue you’ll almost certainly have come across one of the most popular talking points among those who dismiss (“deny”) contemporary anthropogenic (human-caused) climate change (I’ll call them “climate deniers” henceforth). The claim goes something like this:

“If scientists can’t predict the weather a week from now, how in the world can climate scientists predict what the ‘weather’ [sic!] is going to be like 10, 20, or 50 years from now?”

Notably, the statement does possess a prima facie (i.e., “commonsensical”) claim to plausibility–most people would agree that it is easier (other things being equal) to make predictions about things are closer in time to the present than things that happen well into the future. We have a fairly good idea of the chances that the Vancouver Canucks will win at least half of their games for the remainder of the month of March 2021. We have much less knowledge of how likely the Canucks will be to win at least half their games in February 2022, February 2025, or February 2040.

Notwithstanding the preceding, the problem with this denialist argument is that it relies on a fundamental misunderstanding of the difference between climate and weather. Here is an extended excerpt from the US NOAA:

We hear about weather and climate all of the time. Most of us check the local weather forecast to plan our days. And climate change is certainly a “hot” topic in the news. There is, however, still a lot of confusion over the difference between the two.

Think about it this way: Climate is what you expect, weather is what you get.

Weather is what you see outside on any particular day. So, for example, it may be 75° degrees and sunny or it could be 20° degrees with heavy snow. That’s the weather.

Climate is the average of that weather. For example, you can expect snow in the Northeast [USA] in January or for it to be hot and humid in the Southeast [USA] in July. This is climate. The climate record also includes extreme values such as record high temperatures or record amounts of rainfall. If you’ve ever heard your local weather person say “today we hit a record high for this day,” she is talking about climate records.

So when we are talking about climate change, we are talking about changes in long-term averages of daily weather. In most places, weather can change from minute-to-minute, hour-to-hour, day-to-day, and season-to-season. Climate, however, is the average of weather over time and space.

The important message to take from this is that while the weather can be very unpredictable, even at time-horizons of only hours, or minutes, the climate (long-term averages of weather) is remarkably stable over time (assuming the absence of important exogenous events like major volcanic eruptions, for example).

Although weather forecasting has become more accurate over time with the advance of meteorological science, there is still a massive amount of randomness that affects weather models. The difference between a major snowstorm, or clear blue skies with sun, could literally be a slight difference in air pressure, or wind direction/speed, etc. But, once these daily, or hourly, deviations from the expected are averaged out over the course of a year, the global mean annual temperature is remarkably stable from year-to-year. And it is an unprecedentedly rapid increase in mean annual global temperatures over the last 250 years or so that is the source of climate scientists’ claims that the earth’s temperature is rising and, indeed, is currently higher than at any point since the beginning of human civilization some 10,000 years ago.

Although the temperature at any point and place on earth in a typical year can vary from as high as the mid-50s degrees Celsius to as low as the -80s degrees Celsius (a range of some 130 degrees Celsius) the difference in the global mean annual temperature between 2018 and 2019 was only 0.14 degrees Celsius. That incorporates all of the polar vortexes, droughts, etc., over the course of a year. That is remarkably stable. And it’s not a surprise that global mean annual temperatures tend to be stable, given the nature of the earth’s energy system, and the concept of earth’s energy budget.

In the same way that earth’s mean annual temperatures tend to be very stable (accompanied by dramatic inter-temporal and inter-spatial variation), we can see that the collective result of many repeated spins of a roulette wheel is analogously stable (with similarly dramatic between-spin variation).

A roulette wheel has 38 numbered slots–36 of which are split evenly between red slots and black slots–numbered from 1 through 36–and (in North America) two green slots which are numbered 0, and 00. It is impossible to determine with any level of accuracy the precise number that will turn up on any given spin of the roulette wheel. But, we know that for a standard North American roulette wheel, over time the number of black slots that turn up will be equal to the number of red slots that turn up, with the green slots turning up about 1/9 as often as either red or black. Thus, while we have no way of knowing exactly what the next spin of the roulette wheel will be (which is a good thing for the casino’s owners), we can accurately predict the “mean outcome” of thousands of spins, and get quite close to the actual results (which is also a good thing for the casino owners and the reason that they continue to offer the game to their clients).

Below are two plots–the upper plot is an animated plot of each of 1000 simulated random spins of a roulette wheel. We can see that the value of each of the individual spins varies considerably–from a low of 0 to a high of 36. It is impossible to predict what the value of the next spin will be.

The lower plot, on the other hand is an animated plot, the line of which represents the cumulative (i.e. “running”) mean of 1000 random spins of a roulette wheel. We see that for the first few random rolls of the roulette wheel the cumulative mean is relatively unstable, but as the number of rolls increases the cumulative mean eventually settles down to a value that is very close to the ‘expected value’ (on a North Amercian roulette wheel) of 17.526. The expected value* is simply the sum of all of the individual values 0,0, 1 through 36 divided by the total number of slots, which is 38. Over time, as we spin and spin the roulette wheel, the values from spin-to-spin may be dramatically different. Over time, though, the mean value of these spins will converge on the expected value of 17.526. From the chart below, we see that this is the case.

Created by Josip Dasović

Completing the analogy to weather (and climate) prediction, on any given spin our ability to predict what the next spin of the roulette wheel will be is very low. [The analogy isn’t perfect because we are a bit more confident in our weather predictions given that the process is not completely random–it will be more likely to be cold and to snow in the winter, for example.] But, over time, we can predict with a high degree of accuracy that the mean of all spins will be very close to 17.526. So, our inability to predict short-term events accurately does not mean that we are not able to predict long-term events accurately. We can, and we do. In roulette, and for the climate as well.

TLDR: Just because a science can’t predict something short-term does not mean that it isn’t a science. Google quantum physics and randomness and you’ll understand what Einstein was referring to when he quipped that “God does not play dice.” Maybe she’s a roulette player instead?

  • Note: This is not the same as the expected dollar value of a bet given that casinos generate pay-off matrixes that are advantageous to themselves.

One thought on “Data Visualization # 12—Using Roulette to Deconstruct the ‘Climate is not the Weather’ response to climate “deniers””

  1. The question you should be asking is why can’t scientists predict the weather a week from now. In my opinion (based on two degrees in meteorology, professional certification, and over 40 years experience), it is because they cannot model clouds well enough. That is the biggest shortcoming in the general circulation models used to project how one parameter of many can affect the climate. Most importantly, clouds are a bigger driver of climate than changes in radiation. Given that those models cannot incorporate cloud dynamics, I believe that claiming that greenhouse gas concentrations are the driver of observed climate change is a stretch at best. If that makes me a denier then I have to accept that.

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