Under NOAA’s own criteria, our Baltimore station is a CRN5, and the errors that they suggest for CRN 5 (≥ 5ºC) are in line with the error values published for Baltimore’s roof station in the table of the NOAA PUC6 training manual.

Anthony, i m not sure whether you answered my question. i am pretty sure i read all the resources that you linked to in the past (the link now didn t work for me)

the meaning of a term error would be rather obvious. with the signs the other way round (error>=5°C), it is not.

it could be some average error. a maximum error. daily, monthly, per year or over station lifetime..

Re # 9 sod

Shadow-induced cooling bias.

The purpose of the early Stevenson/Glashier screens was to create a shadow so the thermometer was out of direct sun. So can you make an automatic assumption that a larger shadow, from a building, falling on the screen would produce cooling? I think not. It is plausible to argue that surrounding a station with tall buildings would create a stagnant pool of hot air, in the same way as a car interior heats when the windows are up – through reduced circulation. Like a glasshouse.

I’m just about done with hypothetical models. Return to measurement of actualities.

Anthony surely knows a lot more about this than i do.
shading issues are in the very good presentation that you can find on his site. but he decided not to mention them in this case here.

in short, the air around the Stevenson screen will be much warmer, if the whole place is in sunlight. shadow will decrease the temperature.

Shadow-induced cooling bias.

The purpose of the early Stevenson/Glashier screens was to create a shadow so the thermometer was out of direct sun. So can you make an automatic assumption that a larger shadow, from a building, falling on the screen would produce cooling? I think not. It is plausible to argue that surrounding a station with tall buildings would create a stagnant pool of hot air, in the same way as a car interior heats when the windows are up – through reduced circulation. Like a glasshouse.

I’m just about done with hypothetical models. Return to measurement of actualities.

Re #32,#28 Alan Woods and #15 Steve Mosher

If there exists a sound basis for making adjustments to historical temperature data, the adjustments must be correct in physics and they can be done either on raw data (preferred in deg K) or on some form of arbitrary anomay basis derived from an arbitrary reference period, which itself might suffer from the same error of physics. The former method is scietifically better.

My objections to your comment that the adjustments had done a reasonable job led me to ask why the adjustments had taken over-warm data and made it warmer. Surely, it should have made it cooler to compensate for the physical observation. As for the overall picture, I agree with Steve Mosher. A person like you or me, remote from the actual data, simply cannot eyeball a graph and pronounce it an improvement. I tried to back my criticism with logic and physics as explained by others, probably credibly.

Can you tell me why 0.5 to 1.5 degrees of warmth was added to the early years of the records shown by Anthony? What physics dominate?

This is really important, bucause until they get the numbers right at the GISS, HADCRU etc level, it is a waste of time writing elegant papers that reference these dubious sources.

Trends have a place; they are easy to comprehend and project. But, they must be constructed on a trustworthy basis. Otherwise, stick to absolute. No known principle gives you 10/10 for making an irregular graph look smooth and level in this type of data.

Yes, there is good reason to archive then scrap a lot of USA station data. Please don’t allow it to remain to infect good work.

1. Go out and buy 15 digital thermometers that send out a temperature reading every 10 minutes wirelessly, and an attachment for your computer that gets such transmissions and records them.

2. Put the 15 digital thermometers at random locations that are all at least 5 feet away from each other, at various altitidues (near the ground, on top of the house, on the mailbox, in your car, up a tree, etc)

3. Check the reported temperatures against each other, and see what variation you get. Compare to similar airport reports of the temperature and lights equal zero sites in your area.

4. Combine the 6 readings into an hourly mean and compare for each of the 15. Compare to similar airport reports of the temperature and lights equal zero sites in your area.

5. Combine the 24 readings into a daily mean and compare for each of the 15. Compare to similar airport reports of the temperature and lights equal zero sites in your area.

6. Combine the 30 readings into a monthly mean and compare for each of the 15. Compare to similar airport reports of the temperature and lights equal zero sites in your area.

7. Start combining anomaly stats per day and month to the hourly means. Compare to similar airport reports of the temperature and lights equal zero sites in your area.

8. Start combining those into yearly stats. Compare to similar airport reports of the temperature and lights equal zero sites in your area.

Keep doing this. What do you end up with? Some number telling you what it’s kinda like outside locally, of which probably none match.

Good luck, new climatologist. Remember to never share any of your data.

Answer 1:

Compare and contrast:

Today’s average daily high is 15 C, and the record is 30 C 35 years ago.
Today’s average daily low is 5 C, and the record is -8 C 65 years ago.
Today’s high and low are expected to be 5 C and 4 C.

Wow. Global cooling; it “should have been” on average 10C warmer and 1 C colder. At least where they’re measuring it at the airport.

Question 2: Is it city growth, building energy use/dissipation, rooftop albedo variations, nearby building changes, or climate change?

Answer 2: Yes.

Question 3: Does the global mean temperature anomaly trend tell us if “The Earth” is “Warming” and “Cooling”?

Answer 3: Some people would like to think so.

I’m presenting “sites” in these posts, not regions. If I were doing regions, homogenised data would be more representative (and appropriate) of a region of 1000km as it encompasess other COOP station data influence within that radius.