Temperature varies, through day and night, from day to day, from month to month, and year to year. The most common way to note its changes is to record each day’s high temperature and low temperature, which has been done at Kremsmuenster, Austria since 1876. Here’s a snapshot of five years of that data, from 2010 through 2014:
Yes, it varies. There’s a cycle of seasons, yearly ups and down marking the heat of summer and cold of winter. On top of that, it keeps on fluctuating from day to day in apparently random fashion.
We can summarize the seasonal cycle by computing averages by month of the year, i.e. we take the average of all January days for the January average, etc. Let’s do so for both daily high temperature and daily low temperature, and plot each month’s average high in red with the average low in blue:
The dots show the monthly averages. The “whiskers” extending above and below the dots, show the range in which most days (about 95% of them) in that month end up falling. This is one version of what’s sometimes called a climate graph, showing how temperature changes throughout the year, in this case both on average (the dots) and the range within which it’s likely to fluctuate (the whiskers). [Note: this is just part of the usual “climate graph,” which generally shows monthly average rainfall values as well.]
If climate changes (at least as far as temperature is concerned), then the climate graph should change too. Here’s a side-by-side comparison of climate graphs using Kremsmuenster data, with the graph on the left representing the data prior to the year 2000, the graph on the right the data from 2000 onward:
Indeed it has changed; for every month of the year, both average overnight low (blue) and average daily high (red) temperature have increased.
The most common — and probably, most sensible — way to quantify the change is to transform high and low temperature into mean temperature anomaly.
First we define the daily mean temperature as the average of the day’s high and low (pretty simple, really). Then we choose a reference period (the “baseline” period), usually at least 30 years long, to define what the “expected” or “average” temperature is for each day of the year. This defines the climatology which captures both the overall average and the seasonal cycle, the warming and cooling that comes with the passing of the year. It’s worth pondering that what it actually captures is the average and seasonal cycle during the baseline period used to define our anomalies.
Finally we define each day’s mean temperature anomaly as the mean temperature, minus the average for that day of the year as defined by the climatology. That way we remove the yearly cycle, which makes the long-term (the all-important trend!) changes more clear — by clearing the seasonal ups and downs out of the way. Positive anomalies are hotter-than-average days, negative anomalies cooler than average.
I did all that. Then I took my daily temperature anomaly values and averaged them over each year, which gave me this:
I’ve included a solid red line showing an estimate of the trend (a lowess smooth). And trend there is! Since about 1980, temperature has been taking off like a rocket. Since the record begins, we’ve seen a net increase of about 3°C (5.4°F).
This is the point where the climate scientist talks about how much that is — it’s really a lot! — and how unusual it is — very! — to see so much increase in such a short time.
It’s also the point where a lot of people think, “What’s the big deal about 3°C? It can change by a lot more than that in a single day, and typically does. It changes by a lot more than that with the cycle of the seasons. Something like 3°C sounds modest. Heck, under some circumstances the temperature could change by 3°C and you wouldn’t even notice!
But the change doesn’t just make the afternoon a little warmer. It happens (on average, that is) to the cold times and the hot times, not just the “average” times. It especially affects the extremes, making the cold ones more rare and the coldest far more rare, while the hot days are more common, the hottest days far more so, and more extremely hot to boot. It’s in the limits, the extremes, that we see the biggest statistical effect and the greatest potential damage.
And that change isn’t just on average, it’s on average day after day, year after year, for the rest of your life and beyond, and as it turns out, such persistence can give a “modest” change a profound impact, on things that matter, on things that we’re sure to notice.
For one thing, there’s the cold.
One thing that matters, to us at least, is whether or not the daily mean temperature (that’s the average of the day’s high and low) is above freezing or not. I counted the number of days each year (from July through the following June) in the “freezing season” (when the daily mean temperature was 0°C or below), and that gave this:
Again there’s clearly a trend, again it’s not just a straight-line trend, and again I’ll approximate it with a lowess smooth as well as with a “piecewise linear” model (finding the turning points by changepoint analysis):
According to the smooth, the mean number of “freezing days” (days with mean temperature 0°C or below) has declined since the record began, by a whopping 33.5 days, according to the piecewise linear fit it’s 34.6 days. The frozen season (and everything that goes with it) is about four and a half to five weeks shorter than it used to be. Nature herself can’t help but notice.
Then there’s the heat.
I counted the number of days each year (January through December) when the temperature reached 30°C or above (for Kremsmuenster that’s very hot, enough to be a health hazard). I got this:
The highest numbers are recent, but there’s also an outburst of higher-than-normal activity early in the record, from just after 1890 to just before 1910. I suspect this is due to an inhomogeneity in the data, so let’s just look at what happened after 1910 (the hottest time is recently anyway):
There’s absolutely no doubt that the number of hot days is rising dramatically. So is the total number of excess degrees (degrees over 30°C throughout the year):
Severe heat, and all its attendant problems, is increasing in Kremsmuenster, and describing the increase as “dramatic” is not an exaggeration. People notice, and nature does too.
A shortening of the frozen season by five weeks is sure to have consequences. Increasing the expected number of dangerously hot days each year from near zero to about 14, is sure to have consequences. These changes aren’t small. They’re not even modest.
Perhaps most important, such changes are taking place not just in Kremsmuenster, but all over the world. As every part of Earth changes in sometimes unpredictable ways, each disruption to our society and to nature is like a falling domino toppling those around it, spreading disruption. The delicate web of life is a wonder to behold; setting it afire is folly to be feared.
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Reblogged this on Don't look now and commented:
I live in Tampa, Florida. I have a collection of Tropical trees that are damaged by temperatures less than 0C. In the past about every 20 years there was a strong cold spell which ver severely damaged them. There was a strong cold spell about 10 years ago when they were damaged and last year there were two days that damaged them. Longer cold events do more damage as do colder events.
How can I calculate the chances of future cold events going below 0C? A graph like the freezing days above might show it, but since in Tampa the more useful number now might be the amount of time between cold events (in years). I would like to plant a few acres in Litches which are moderately damaged by cold and would like to estimate the likelood of a year being a successful crop.
I also have some peaches which require about 100 hours of cold (below 40F) per year to produce crops. They are not reliable here any more (they were reliable 20 years ago).
Could I use the NOAA data in excell to generate graphs like yours?
Is that actual raw readings or homogenized data you are using ?
I have just been looking at graphs of remote Australian locations with a long history, and to be honest I can’t really see much warming.
I think maybe this is raw data.
I didn’t cherry pick these, just the first I found with long complete data.
Am I missing something ?
By contrast the city urban locations show strong warming
Here are two charts of temperature change in California – daily max and daily min:
A big decrease in T-max shows up in the San Joaquin Valley, California’s “breadbasket”.
And from a recent study:
“We find that irrigation has a large impact on temperature extremes, with a particularly strong cooling effect during the hottest day of the year….”
Yeah, you’re missing something.
The article was about Austria, not Australia.
The New York Times has an amazing graphic that illustrates the relationship between shifting temperature means and corresponding changes in extreme highs and lows – se https://www.nytimes.com/interactive/2017/07/28/climate/more-frequent-extreme-summer-heat.html?rref=collection/sectioncollection/climate
“I suspect this is due to an inhomogeneity in the data, so let’s just look at what happened after 1910” .
Oh no! All those deniers you’ve accused of cherry picking data to fit your preconceived ideas will have a field day with this one.
A glance at your plot of temperature anomalies vs. year for Kremsmuenster suggests that, at least qualitatively, the trend shown is similar to what I have seen for global trends for the time period presented. No surprise, the global trend is reflected in local trends.
I note that you decided to average the daily highs and lows to get a daily average. I think it would also be interesting to treat the daily highs and lows separately to see if the trends in both are the same or not. Also interesting, since you have the data, would be to look at seasonal or monthly trends over the time period and ask whether there are seasonal or monthly differences (not sure if you have already done something like that previously — I seem to recall something like that).
Thank you! could you please provide a link to the Kremsmuenster daily data? i can only find monthly data online.
[Response: It’s from ECA&D (European Climate Assessment and Dataset network):
It’s but one of thousands of stations reporting there.]
Thank you very much — there seems to be a gap in the Kremsmuenster data from ECAD between 1999 and 2002. Very strange — would anyone here know why that is, and how to fill that in? thanks again!
Answering my own question: The ECAD “blended” datasets correct this gap.