Yes, the graph is meant to compare the deviation from average (mean) on a given day every year. Each line is a different year, the independent axis is the day of the year (e.g. July 12th), and the dependent axis is the number of standard deviations from average temp for said date.
The expected temperature varies wildly depending on the day of the year which is why is makes sense to find the average and standard deviation for the same date year over year and compare the measured values to that statistic. If you are comparing different dates, say June 3rd to February 18th, what meaningful information do you gain by saying that June 3rd was hotter than the average between those two dates? But if this June 3rd was the hottest June 3rd on record by several standard deviations, that’s notable.
Day of the year comparison is as close to apples to apples as you can get given the seasons. And it is much harder to compare year over year levels at a given date if they are graphed sequentially, especially for 44 years worth of data. So you overlay them aligning on date, set your average and deviations based on the measured temps for that date for all years, and you can see seasonal trends or outliers such as this one.
Yes, the graph is meant to compare the deviation from average (mean) on a given day every year. Each line is a different year, the independent axis is the day of the year (e.g. July 12th), and the dependent axis is the number of standard deviations from average temp for said date.
The expected temperature varies wildly depending on the day of the year which is why is makes sense to find the average and standard deviation for the same date year over year and compare the measured values to that statistic. If you are comparing different dates, say June 3rd to February 18th, what meaningful information do you gain by saying that June 3rd was hotter than the average between those two dates? But if this June 3rd was the hottest June 3rd on record by several standard deviations, that’s notable.
Day of the year comparison is as close to apples to apples as you can get given the seasons. And it is much harder to compare year over year levels at a given date if they are graphed sequentially, especially for 44 years worth of data. So you overlay them aligning on date, set your average and deviations based on the measured temps for that date for all years, and you can see seasonal trends or outliers such as this one.