I have created a map of temperature and precipitation changes in the continental US based on monthly NOAA data going back to 1895. The linear trend lines are added wherever they make sense.
If you have any questions or comments, please let me know (especially if the map does not work for you or something is not well explained).
When people talk about climate change is the US, they tend to either concentrate on what is happening with the whole country or large regions, or on very local changes that have a lot of variability. For a more detailed understanding of what exactly has been going on over the last century we can look at areas called climate divisions.
* What does the map show?
It displays overall changes in temperature and precipitation (rainfall plus snowfall) for each month since 1895. You can also see yearly and seasonal changes.
* What do the colors mean?
Shades of red/purple show an increase in temperature or precipitation. Shades of green indicate a decrease. The exact ranges for each color are displayed in the legend next to the map.
* Why are some areas of the map blank?
If an area is shown, it mean there's a strong trend - in other words, only if there is enough confidence that despite year-to-year variability, the values are climbing up or down.
* What are some examples of large changes?
February temperature since 1895: north and northeast of the country has warmed over 3F.
November precipitation since 1895: east and southeast had over 60% increase.
* How can I see the trends for myself?
Click anywhere on the map. All the data points will be shown in a chart under the map. The trend line will be overlayed on top of the data. Even if an area is blank, you can still click on it and see the chart, but there will be no trend line.
* Where did the data come from?
From NOAA (National Oceanic and Atmospheric Administration).
* Where can I learn more about US weather changes?
In June 2012, Climate Central has published a report about state-by-state temperature changes. The most comprehensive source is the national climate change section of the 2009 report on climate change impacts produced by US Global Change Research Program.
* How did you compute the trend? Did you use linear regression?
Almost. Linear regression would produce approximately the same results, but it assumes certain things about the data that may not always be true. Instead, we used what statisticians call "nonparametric estimation" - in other words, tried to determine if there is a linear trend without assuming anything about the nature of the data. Specifically, we can apply the Mann-Kendall test to see if the data have any trend at all or just change randomly without moving a lot in any direction. If the test says that there is a trend (with p-value <0.05), we can use a method similar to linear regression, the Theil-Sen estimator, to find the most likely straight line that approximates the trend.
* What is p-value?
It's a statistical indicator of how well a linear trend fits, or how well it describes a series of observations. Trend estimation formulas always produce a linear trend (a straight line), but if the data are not really changing more or less together with this straight line, it makes no sense to talk about a trend. This is why trend estimation formulas also produce another number called the p-value. The p-value is always between 0 and 1, but only very small p-values point at good fits. A cutoff value of 0.05 is often used - so if the trend estimation formulas produce a p-value of 0.05 or below, the map shows a trend, otherwise it does not. See the wikipedia articlefor more information.