** Update: there is a companion Coldest Days Trends blog post. **
There's a lot of discussion about the increase in heatwaves and hot days in general in a warming world. Unfortunately there is no standard definition of a "heatwave". Many places in the northeastern U.S. describe heatwaves as multiple days in a row with a high temperature above 90F. In the southern Great Plains, days above 100F are more relevant. From a public health perspective, nighttime low temperature may be most meaningful.
In 2021, the EPA published a widely-shared blog post and map set of the trends in heatwaves from 1961-2019 for the 50 largest cities in the U.S. They defined heatwaves as:
[A] period of two or more consecutive days when the daily minimum apparent temperature (the actual temperature adjusted for humidity) in a particular city exceeds the 85th percentile of historical July and August temperatures (1981–2010) for that city.
The 85th percentile equates to the 9 highest values per summer (July-August). I have no real objection to their criteria. If I were to critique it though, I would say that most people informally characterize heatwaves as a measure of the peak heat, not the overnight minimum (apparent) temperature. Again, from a public health point of view, nighttime recovery is crucially important. Their metric is more heat stress than heatwave. In my opinion, heatwaves are about high temperatures. Not low temperatures. Not apparent temperatures. In most cases, the very hottest temperatures occur with locally dry airmasses. But again, there's plenty of room to slice and dice this any number of ways.
Changes in Hot Days
In May 2021, I wrote a blog post on the change in days with heavy precipitation. The USA Today newspaper did a full-length write-up on the findings. For this analysis of change in hot days, I used a similar methodology.
Methodology:
1) Find the temperature that occurs on average 20, 15, 10, and 5 days per year during a baseline period for all long-term climate stations in the U.S. (I chose 1951 to 2000 - 50 years). For a 50-year period, the 10-day per year temperature value is found by ordering all 18,263 high temperatures (assuming no missing obs) and identifying the 500th highest value.
2) For each year between 1950 and 2021, count the number of days that met or exceeded the temperatures identified in Step 1. Also identify the first and last occurrence of these temperatures each year. For example, if 90F is the temperature that occurred an average of 10 days per year during the 1951-2000 baseline period, count the number of days each year that 90F occurred and find the first/last 90F day each year.
3) If a station contained 65 or more complete years of temperature data (no more than 10 missing days in a year), it was kept for the final analysis. There were 706 U.S. stations that successfully met the criteria, 18 Canadian stations, and 0 Mexican stations.
4) Fit a linear regression line to the 72-year (1950-2021) time series of the number of annual occurrences (and the first/last dates) for each station. The different between the fitted (not observed) 2021 value and the fitted (not observed) 1950 value represents the change,
Example:
In Fig. 1, we see the change in days per year for the temperature that historically occurred 20 and 10 days per year for Boise, Idaho. Those temperatures are 96F and 98F respectively. A regression line is fitted to each time series and the begin and end points of the regression lines represent the change. In the Boise example, the 96F temperature, which occurred an average of 20 days per year during the 1951-2000 period, trended from 14 days per year to 34 days per year. An increase of 20 days per year over the 72-year period.
Fig 1. Change in the number of days per year for the temperature that historically (1951-2000) occurred 10 and 20 days per year in Boise, Idaho.
In Fig. 2 we see the same information for Boise, except it notes the length of time between the first and last occurrences of the temperature that historically occurred 20 and 10 days per year. A high temperature of 96F historically occurred 20 days per year in Boise. The trend in the length of time between the first and last 96F day of the year increased from 65 to 89 days per year. We interpret this as a lengthening of the hottest time of year by 24 days. Specifically, the typical first occurrence for Boise of 96F changed from June 24th to June 13th (11 days earlier) and the typical last occurrence of 96F changed from August 26th to September 8th (13 days later).
Fig 2. Change in the length of time (days) between the first/last occurrence of the temperature that historically (1951-2000) occurred 10 and 20 days per year in Boise, Idaho.
Maps: Modern Days per Year Count
Figs 3-6 below show the current number of days per year for the temperatures that historically occurred 20, 15, 10, and 5 days per year. If you refer back to Fig. 1, the value at the end of the trend (dotted) lines are what we are mapping below.
Analysis
Most readers are familiar with the great heatwaves of the 1930s during the Dust Bowl era. Poor agricultural practices led to a drying of the soils, which meant much more efficient solar heating (higher temperatures). In the 1950s, there were pronounced droughts in the central portion of the U.S. too. These droughts meant more sunshine and greater solar heating efficiency. I note this because the time period we begin with is 1950. This results is a muting of the trends for many regions. If we started in 1960, the change in hot days per year would be more dramatic. That said, starting in 1950 allowed for a maximum number of stations to be included in the analysis; and we should never arbitrarily pick a start date for the analysis to maximize (or minimize) the trends.
The obvious question that arises from looking at the maps are the blue areas in the Great Plains and Mississippi River valley. My assessment is that these regions had large values in the 1950s (flattens the curve) and also have seen a dramatic increase in precipitation during this same time period - which causes less solar energy to be used for warming the surface (more solar energy is used to evaporate water). Conversely, there has been a dramatic drying of the western U.S. This allows for more efficient solar heating.
Let's not lose sight of the fact that the net values are dramatically higher - even with the blue areas. The orange and blue areas do not cancel each other out. Looking at Fig. 6, we now see 7.8 days per year nationwide of the temperature that historically occurred 5 days per year. That is a greater than 50% increase!
Fig 3. Current number of days per year that the historically (1951-2020) hottest 20 days per year now occur. This is the end point of the 72-year regression line.
Fig 4. Current number of days per year that the historically (1951-2020) hottest 15 days per year now occur. This is the end point of the 72-year regression line.
Fig 5. Current number of days per year that the historically (1951-2020) hottest 10 days per year now occur. This is the end point of the 72-year regression line.
Fig 6. Current number of days per year that the historically (1951-2020) hottest 5 days per year now occur. This is the end point of the 72-year regression line.
Maps: Change in the Length of the Hot Portion of the Year
Ultimately I was interested in how the length of summer has changed with respect to peak heating. As Fig. 2 shows for Boise, we can track the length of time between the first/last occurrence of particularly hot days and see how that length changed over time. Figs 7-11 below show how the length of time of peak heating has changed.
Analysis
In general, the patterns are consistent with the maps showing the change in the number of days (Figs 3-6). I would say that the primary difference is a sharper peak in max summer heating. Using the example of the historically hottest 10 days per year, Fig. 7 shows a nationwide average increase of 44% (now 14.4 days per year vs 10 days per year). The length of time that the historically warmest 10 days per year occurs increased from 59.6 days to 67.7 days - a length increase of 13.6%.
Fig 7. Change in the length of time that the historically (1951-2020) hottest 20 days per year now occur. This is the difference between end point and beginning point of the 72-year regression line.
Fig 8. Change in the length of time that the historically (1951-2020) hottest 15 days per year now occur. This is the difference between end point and beginning point of the 72-year regression line.
Fig 9. Change in the length of time that the historically (1951-2020) hottest 10 days per year now occur. This is the difference between end point and beginning point of the 72-year regression line.
Fig 10. Change in the length of time that the historically (1951-2020) hottest 5 days per year now occur. This is the difference between end point and beginning point of the 72-year regression line.
Maps: Change in the Length of Consecutive Hot Days
This is where we will look at the changing length of heatwaves. As with before, we're using the temperature that historically occurred 20, 15, 10, and 5 days per year. Now we are looking at each year and identifying the maximum number of consecutive days that those temperatures occurred. If the 5-day per year temperature occurred 8 times in a specific year but there was at least 1 day between each occurrence, then the maximum consecutive streak would be 1 in that example. Figs. 11-14 show the change in the length of these streaks. The change is the end trend point minus the beginning trend point divided by the beginning trend point times 100. As with the other maps, there is a decline in the Mississippi River valley, but a marked increase everywhere else. A quick summary is that heat waves are longer in most places, except for the Mississippi River valley.
Fig 11. Change in the number of consecutive days at or above the temperature that historically (1951-2020) occurred 20 days per year. This is the difference between end point and beginning point of the 72-year regression line as a percentage.
Fig 12. Change in the number of consecutive days at or above the temperature that historically (1951-2020) occurred 15 days per year. This is the difference between end point and beginning point of the 72-year regression line as a percentage.
Fig 13. Change in the number of consecutive days at or above the temperature that historically (1951-2020) occurred 10 days per year. This is the difference between end point and beginning point of the 72-year regression line as a percentage.
Fig 14. Change in the number of consecutive days at or above the temperature that historically (1951-2020) occurred 5 days per year. This is the difference between end point and beginning point of the 72-year regression line as a percentage.
Mapping
The map in Fig. 15 shows the stations used in this analysis. The maps in Figs 3-10 were generated from station data using an inverse distance weighted surfacing algorithm with 50km grid cells. There is often a lot of variability in small geographical regions; therefore, a 5x5 smoothing filter was applied to all maps. No adjustment for topography was made.
Fig 15. Map of stations used in the analysis presented in this blog post.
There are 706 U.S. stations that successfully met the criteria, 18 Canadian stations, and 0 Mexican stations.
IPCC Comparison
The maps presented above are broadly consistent with the IPCC analysis of trends in hot extremes (see Fig. 16). They also looked at trends since the 1950s and found parts of central North America were the only region in the world not experiencing an increase in extreme hot temperatures.
Conclusion
The frequency and duration of the hottest days of the year are dramatically increasing in the U.S. Period. The rate and magnitude of increase differs across space. Some areas have even seen a decrease. Why does this spatial variation exist? My guess is that the 1950s were an especially warm and dry decade in the central U.S. and a dramatic increase in precipitation in this region in recent decades make achieving hot temperatures especially difficult. Conversely, the western CONUS has dried substantially in recent decades. This has worked to magnify the increase in hot days. Remember that the orange and blue areas on the maps do not cancel each other out.
The blue colors in Figs 3-14 (the analysis maps) unfortunately draw attention away from the strong warming signal in the rest of the maps. It provides "deniers" with a false narrative to latch on to. Such is life. Scientists should always publish data and analysis and accept that bad faith actors will willfully misinterpret. It is incumbent upon us to explain when the bad faith arguments are incorrect and why the people pushing those arguments are bad actors.
