Examples of How the Use of Temperature ANOMALY Data Instead of Temperature Data Can Result in WRONG Answers

This post comes a couple of weeks after the post EXAMPLES OF HOW AND WHY THE USE OF A “CLIMATE MODEL MEAN” AND THE USE OF ANOMALIES CAN BE MISLEADING (The WattsUpWithThat cross post is here.)

INTRO

I was preparing a post using Berkeley Earth Near-Surface Land Air Temperature data that included the highest-annual TMAX temperatures (not anomalies) for China…you know, the country with the highest population here on our wonder-filled planet Earth. The graph was for the period of 1900 to 2012 (FYI, 2012 is the last full year of the local TMAX and TMIN data from Berkeley Earth). Berkeley Earth’s China data can be found here, with the China TMAX data here. For a more-detailed explanation, referring to Figure 1, I was extracting the highest peak values for every year of the TMAX Data for China, but I hadn’t yet plotted the graph in Figure 1, so I had no idea what I was about to see.

Figure 1

The results are presented in Figure 2, and they were a little surprising, to say the least.

NOTE: Monthly TMAX data from Berkeley Earth are described as the “Mean of Daily High Temperature”. Conversely, their TMIN data are described as the “Mean of Daily Low Temperature”. [End note.]

Because of elevated highest-annual TMAX temperatures (not anomalies) in the early part of the 20th Century, the linear trend for that subset was basically flat at a rate of 0.006 deg C/decade, as calculated by MS EXCEL. (Yeah, I know, too many significant figures, so go ahead and read it to yourself as 0.01 deg C/decade, or 0.0 deg C/decade, if you’d prefer.)

Figure 2

Yup, that’s right. In addition to the Contiguous U.S. (Figure 3), China also had high surface temperatures in the first half of the 20th Century. (Splain that, oh true-blue believers of human-induced global warming.)

Figure 3—(It’s from an upcoming post. Stay tuned.)

THE PROBLEM WITH USING ANOMALIES

So I felt this would provide a great opportunity to present illustrations to confirm what many of us understand: The use of temperature anomalies in scientific studies can provide wrong answers…very wrong answers. That is, wrong answers to surface temperature-related questions can be caused by using temperature anomaly data instead of temperature (not anomalies) data. (Or as members of the climate science community like to call them “absolute temperatures”, assumedly to help differentiate them from anomalies. Maybe climate scientists should simply state “temperatures, not temperature anomalies” instead of “absolute temperatures”, which riles purists. Then again, “temperatures, not temperature anomalies” grows tiring when you’re reading and writing it.)

How wrong are the answers if you use anomalies, you ask? Figure 4 presents the highest annual TMAX temperature anomalies (not actual temperatures) for China, along with the annual July temperature anomalies (not actual temperatures), both for the term of 1900 to 2012. The highest annual TMAX temperature anomalies (not actual temperatures) for China show a noticeable warming rate of 0.12 deg C/decade, when, in reality, no long-term warming of the actual highest annual TMAX temperatures existed during that period.

Figure 4

Referring to the Berkeley Earth TMAX webpage for China, July shows the highest value of the monthly temperature conversion factors listed. As also shown in Figure 4, the July TMAX temperature anomalies for China give a better answer, but still not the correct one. Obviously, the highest annual TMAX temperatures for China don’t always occur in July.

THE PROBLEMS CARRY OVER TO THE GLOBAL NEAR-SURFACE LAND AIR TMAX TEMPERATURE DATA

For the sake of illustration, I ran through the same process with the GLOBAL near-land surface air TMAX temperature data from Berkeley Earth. The same basic problems exist with the global highest annual TMAX anomaly data, but the July TMAX trend values are correct. See Figures 5 and 6.

Figure 5

# # #

Figure 6

AND THEN THERE’S THE BERKELEY EARTH TAVG TEMPERATURE DATA

While we’re on the subject, do not go looking for “Mean of Daily High Temperature” (TMAX) answers using average monthly (TAVG) temperature data, Berkeley Earth’s standard near-surface land air temperature anomaly dataset. The TAVG data are the wrong data to use from Berkeley Earth when looking for TMAX answers.

This warning also carries over to the standard NCDC/NCEI or CRUTEM4 near-surface land air temperature anomaly data. They’re not TMAX datasets. If you want a TMAX dataset other than the one from Berkeley Earth, see the “Monthly observations” webpage at the KNMI Climate Explorer. They have a couple. (Thanks, Geert Jan.)

That’s it for this post. It gave me the opportunity to present Figures 2 and 3 in advance of the post I’m preparing.

Enjoy yourself in the comments below, and have a great rest of your day.

STANDARD CLOSING REQUEST

Please purchase my recently published ebooks. As many of you know, this year I published 2 ebooks that are available through Amazon in Kindle format:

And please purchase Anthony Watts’s et al. Climate Change: The Facts – 2017.

To those of you who have purchased them, thank you. To those of you who will purchase them, thank you, too.

Regards,

Bob Tisdale

via Watts Up With That?

https://ift.tt/2Liis4b

December 13, 2018 at 08:01PM

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