A post at RealClimate introduces a new app, which enables the user to take a detailed look at climate data. It also raises the age-old and oft confusing issue, just what is “climate” anyway? I’ll begin the new year with my own definition, in the hope that I can impart to all the knowledge of what climate really is.
The RealClimate post author (Rasmus Benestad) defines it this way:
For many people, “climate” may seem to be an abstract concept. I have had many conversations about climate, and then realised that people often have different interpretations. In my mind, climate is the same as weather statistics (which I realise can be quite abstract to many).
Frankly, I consider that a damn good definition, essentially the same as my own:
Climate is the probability density function of weather.
The catchy way I like to say it is:
Climate is the odds. Weather is the roll of the dice.
I notice that both Rasmus Benestad and I can (and probably have more than once) write “weather statitstics” to mean the full probability structure: the odds, all the odds, and nothing but the odds — then be surprised because someone thinks that it “implies average weather.” Not an idiot, mind you, but someone who knows that climate change is so much more than just a change of the average, maybe is even keenly aware of how the change in extreme events is one of the crucial aspects. What we have here, is failure to communicate.
Because a reader objects, saying
To my mind you fell at the first fence. You wrote:
“In my mind, climate is the same as weather statistics (which I realise can be quite abstract to many).”
to me that implies average weather, which is what most meteorologist believe. But the dangers of climate change are not that the temperature will rise by 1.5C on average. The danger is from the more extreme events caused by the temperature rise, which will lead to wild fires and heat waves. The average rise in sea level of 10cm will not flood many places. It is the the storm surges 10cm higher which will cause disaster, just as it did in New Orleans. If sea level had only risen by the average, New Orleans would never have been flooded.
I don’t agree that weather statistics “implies average weather” to the exclusion of “the more extreme events.” I think it specifically includes the entire change of probabilities, which is why we call it “weather statistics” rather than just “average weather.” Certainly the average is the most prominent, one could even say most important, single statistic, but there are lots and lots of others which capture those extreme events.
But when intelligent people equate “weather statistics” with “average weather,” we can have failure to communicate. We need to stop calling it “weather statistics” and use a better description. The one I like is, “the odds” — it’s much friendlier and more familiar than “probability density function.”
The association of climate change with a change of the average, may be rooted in the fact that the most common (and probably easiest) way to show that climate has changed, is to show a change in the average that’s more than just random fluctuation. That’s because if the odds don’t change then the mean value won’t change. Just can’t happen. A change in the average which is “statistically significant” reveals a change in the mean, and a change in the mean, means somehow the odds have changed, which is exactly what climate change is.
And it’s so easy to show a change in the mean. The classic case is yearly global average temperature anomaly (I’ll use the data from NASA):
The change in average value is definitely statistically significant, and visually, it packs quite a punch. The world is getting hotter.
But, as has been emphasized and repeated, the change in the average isn’t the only aspect of climate change — it’s just one of the most obvious.
If climate is some abstract notion of “statistics” beyond just the average, if it’s a “probability density function” which is another name for “the odds” (by which I mean, all the odds for every possibility), then what does it really look like? To put it in more sensible terms, how can we show it in a way which conveys all the odds, not just the averages?
I think such views have to be tailored to what is being studied. Consider, for instance, the daily high temperature in the city of Moscow, Russia during the month of July, and begin by looking for a change in the average. I can average the high temperatures over the entire month of July, for each and every July from 1949 through 2017, and get this:
The red line is a trend estimate, and it is statistically significant (there are other ways to estimate the trend which also confirm its statistical significance). This means that the trend is not flat, i.e. the average is changing in a statistically significant way so the actual mean value is changing. Hence the odds are changing, and voila, we have climate change.
But that’s just the average. We can look at all the probabilities, for all possibilities, using something called a histogram.
We begin by dividing the range of temperatures into slots, or “bins”; I chose bins 1°C wide each. Then we count how often the value is within each bin. Finally, we divide by the total number of days we’ve observed to estimate the probability that the daily high temperature in Moscow during July will be within that bin. We can plot all these estimates in a bar graph, to show the histogram.
These are just estimates mind you, and the histogram itself is an estimate of the probability density function (i.e., the odds). It’s what climate is.
The smooth solid blue line is another estimate of the probability density function, using the same data. It assumes that the data follow the normal probability distribution (the familiar “bell curve” probability function), which indeed it does very closely. This too is only an estimate (perhaps an even better estimate than the histogram itself).
There’s climate! But it doesn’t show us climate change like the graph of monthly averages for each year did. How might we get that onto a graph like this?
One thing we can do is split the available data into two pieces: everything before the year 2000, and everything since. Then we can make a histogram (and a smooth line) for each piece separately, and plot them on the same graph (pre-2000 in blue, post-2000 in red):
This succeeds in showing the details of climate (at least as far as daily high temperature is concerned), and how it has changed between the last few decades and the half-century before that.
In my opinion, this graph is quite effective. It makes clear that the average has changed, but it also highlights that the odds of extreme events have changed dramatically. Days at 30°C or above (damn hot by Moscow standards) are now twice as common as they used to be. Days at 35°C and above, which used to be practically unheard-of, are now an uncommon but regular occurence.
The graph is also visually stimulating (in my opinion) and makes good use of color (quite eye-catching I think). I have no idea how the color-blind might be hampered by it.
Climate change denial is on its death-bed. That’s not because of the savvy messaging skillz of scientists and advocates, but because of the onslaught of climate-related disasters in the U.S. and around the world.
Not that scientists and advocates haven’t helped. They have countered much of the propaganda from fossil fuel interests and ideologues, as well as informed the public of the essential nature of the problem. But its urgency, and the severity of the danger, hasn’t really gotten through to the mass of people.
Now that people are becoming more aware, they’re hungry for a deeper understanding of what the issues are. We can help. But to do it effectively, we need to take a new approach to telling the story of science. We’re not just targeting the geeks, or the hardcore interested who might watch a series like Cosmos, we’re now doing science communication for everybody, including the farmer or factory worker who doesn’t give a rat’s ass about the spotted tree owl, but knows that this is some important stuff. Hurricanes and heat waves and wildfires and droughts and floods — some important stuff.
To help the truly new get some background which doesn’t overwhelm or bore them, but which won’t oversimplify so much as to neuter the message, is a challenge. Some of the things we need to do, may include:
As for #1, I’m quite fond of my own description. Climate is the odds. Weather is the roll of the dice.
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