Recently I tried updating this map using the current data file ("climdiv-tmpccy-v1.0.0-20220804.txt") and it didn't work. I found that NOAA is now including data from Alaska in the master file, whereas previously the Alaskan data was in a separate file. I reworked the notebook to account for this change. You will still have to change the statement beginning cdata = SemanticImport[ to reference the directory where you have placed the file of historic temperatures.

The updated notebook, and the map, are attached.

Attachments:

USClimateChange.nb

Hare's the link to the two follow on projects that I did with my kids. The first was my 7th graders idea to look at percent change rather than absolute change. The 2nd was my 9th graders idea to see how we could make predictions (which I modified to be a project about linear and quadratic approximations to the data).

https://mikesmathpage.wordpress.com/2019/06/23/follow-up-2-to-john-shonders-us-weather-data-visulaization/

Thanks so much, John, for sharing your work. Using it with my kids has been really fun. I'm really happy that this sort of data analysis can be made accessible to middle and high school kids.

Great way to get the kids interested. I did much the same as you but experimented with different start years. TBP.
BTW the missing county is entirely an Indian reservation with no county seat. (I wonder why kriging was not used).

I decided to publish my changes. I separated the temperature ranges at 0 so there's no doubt as to the direction of change. Also the start date is 20 years later to see how the trend changes.

Starting point makes a difference - 1991 is a unique starting point. Mt. Pinatubo eruption.

Nearly 20 million tons of sulfur dioxide were injected into the stratosphere in Pinatubo's 1991 eruptions, and dispersal of this gas cloud around the world caused global temperatures to drop temporarily (1991 through 1993) by about 1°F (0.5°C). (https://pubs.usgs.gov/fs/1997/fs113-97/)

I'll attach a run of your 10-year moving averages 1991-2012, not as year-specific but interesting. (Is there a way to embed the legend in the chart?)

!legend for climate by counties][1]

Attachments:

1991-2012 means.pdf

The question I thought someone was going to ask is where did they get temperature data for counties that didn’t even exist at the time. Arizona for example didn’t become a state until 1912 and yet there are the average monthly temperatures for each of its current counties back to 1895. The answer is through kriging.

Thank you. Must be my error in calculating the FIPS.

-Ed

John, I may be mistaken in decoding the file, but I believe the file shows about 17 county entries for Michigan and it has 83. For Arizona, the state code is 04 and has 15 counties. The file shows 58 counties being reported for 04. FYI. Thanks for the information as well as your analysis. Very Interesting.

Excellent analysis! A quick question, what was the standard deviation in temperature measurements? I am asking that because some Delta T are small and I wonder to what extend we can interpret them as a meaningful shift in T?

Hi John, and thanks for sharing - very educational. One thing I don't fully understand (and maybe you can explain it a bit?) is how you calculated the values for the legend.

E.g. when I'm retracing your steps, I don't see any negative values in qq:

Min[qq]
0.134336

and

zz
{0.74857, 1.3628, 1.97704, 2.59127, 3.20551, 3.81974, 4.43397, \
5.04821, 5.66244}

What am I missing?

Hi John, Thanks for sharing. It seems a great code. But unfortunately I don't have access to the website and data (see screenshot below). Please can you support me with the data? Your support will be highly appreciated ! Best Regards,....Jos