Fig 1.
Hourly temperature time series for LaGuardia Airport (KLGA) in New York City from 23 July 2023 through 26 July 2023.
Vertical lines are at midnight local time. The hours at or above 30 °C for each day correspond to 2, 0, 2, and 5 hours, respectively. Starting at 2:00 PM local time on 25 July 2023, afternoon thunderstorms and the subsequent passage of a cold front yielded cooling of air temperatures from 30 °C to ∼22 °C, which persisted until after sunrise the next day.
Fig 2.
Hourly temperature distributions by year for (left) LaGuardia Airport (KLGA), New York City, and (right) Hector International Airport (KFAR), Fargo, North Dakota, for each year from 1978 to 2023.
Data from both stations are binned at 1 °F and labeled in degree C. The Y-axis temperature scales are aligned.
Fig 3.
(Top left) The number of observations below 0 °C (32 °F) and (top right) above 30 °C (86 °F), along with (bottom left) the counts of heating degree hours and (bottom right) cooling degree hours, for LaGuardia Airport (KLGA), New York City, from 1978 to 2023.
A linear regression model is used to depict the overall long-term changes for each metric. For KLGA, the linear fit equates to a loss of ∼18.5 days (∼446 hours) below freezing temperatures and gain of ∼5 days (∼112 hours) of temperatures above 30 °C (86 °F) between 1978 and 2023. The residual standard deviation describes the year-to-year variability of each metric. Residual standard deviations and p-values are shown below each subplot. The p-values for all 4 subplots are < 0.05, indicating that the trends are significant.
Fig 4.
(Top left) The number of observations below 0 °C (32 °F) and (top right) above 30 °C (86 °F), along with (bottom left) the counts of heating degree hours and (bottom right) cooling degree hours, for Pierre Regional Airport (KPIR), South Dakota, from 1978 to 2023.
A linear regression model is used to depict the overall long-term changes for each metric. The residual standard deviation describes the year-to-year variability of each metric. Despite demonstrating nonzero trend magnitudes, particularly in the winter season, these trends are insignificant due to the overall high variability. Residual standard deviations and p-values are shown below each subplot. The p-values for all 4 subplots are > 0.05, indicating that the trends are not significant.
Fig 5.
Changes in (top) the number of hours below 0 °C (32 °F) and (bottom) number of hours above 30 °C (86 °F) from 1978 to 2023 for 340 airports across the CONUS and southern Canada.
Dashed gray lines at 37 °N and 98 °W separate geographic quadrants representing the northeast, northwest, southeast, and southwest regions of the CONUS and southern Canada study area. Statistically significant trends in units of hours per decade are shown in colored circles. Warming trends are denoted in shades of red and orange for both maps. Insignificant trends are divided into categories of high variability (residual standard deviation ≥ median residual standard deviation) or low variability (residual standard deviation < median residual standard deviation). Stations with insufficient threshold hours passed all filtering procedures but have a median of < 10 threshold hours each year for the corresponding metric across the 46-year period. Basemap and country boundaries were plotted using the Mapping Toolbox for MATLAB [48]. The public domain base layer shapefiles are from Natural Earth.
Fig 6.
Changes in (top) the number of heating degree hours and (bottom) cooling degree hours from 1978 to 2023 for 340 airports across the CONUS and southern Canada.
Statistically significant regression trends are colored by magnitude in terms of degree hours per decade. Dashed gray lines at 37 °N and 98 °W separate geographic quadrants representing the northeast, northwest, southeast, and southwest regions of the CONUS and southern Canada study area. Stations without significant trends are divided into categories of high variability (residual standard deviation ≥ median residual standard deviation) or low variability (residual standard deviation < median residual standard deviation). Basemap and country boundaries as in Fig 5.
Fig 7.
Trends in heating degree hours compared to trends in cooling degree hours for subset of 214 stations that have both significant heating degree hours and cooling degree hours trends.
This subset of stations is further divided into geographic quadrants separated by 37 °N and 98 °W, representing the northeast, northwest, southeast, and southwest regions of the CONUS and southern Canada study area. For all northwest stations (orange), almost all northeast stations (green), and approximately half of both southern regions (cyan and pink), the reduction in energy usage (heating degree hours) in winter outpaces the increase in energy usage (cooling degree hours) in summer.
Fig 8.
Trend magnitude and residual standard deviation for stations with significant trends (orange dots) and with insignificant trends (black dots) as determined by the permutation test.
Median residual standard deviation is shown by the red line in each panel. (top left) Hours below 0 °C (32 °F), (top right) hours above 30 °C (86 °F), (bottom left) heating degree hours, and (bottom right) cooling degree hours.