This blog is about:

- Tracking down raw, unadjusted climate data.
- Applying straight-forward analysis methods.
- Presenting results, code, and data sources.

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This blog is about:

- Tracking down raw, unadjusted climate data.
- Applying straight-forward analysis methods.
- Presenting results, code, and data sources.

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I have noticed something very unusual about the Monthly_1x1 data.

If at each grid point you first calculate the year on year temperature change when data are available at that grid point for the two dates.

Then calculate the standard deviation of all available datapoints at each month. Then divide by the square root of the number of data points at that month (this will give the standard error of the average of year on year temperature changes at each date).

Wow! All the standard errors are exceedingly close to one. This is very surprising and I think worth a closer look. I’ll send the calc if you want.

By:

Caseyon May 4, 2010at 10:32 pm

Scratch that last comment. It’s wrong. Aploogies.

By:

Caseyon May 5, 2010at 1:12 am

Will scratch comment.

Hadn’t had a chance to wrap my head around what you were computing, yet. Feel free to share whatever you find, though.

gene

By:

genezeienon May 5, 2010at 5:42 pm