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.)
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.
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.)
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.
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.
# # #
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:
- Dad, Why Are You A Global Warming Denier? (For an overview, the blog post that introduced it is here.)
- Dad, Is Climate Getting Worse in the United States? (See the blog post here for an overview.)
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.
A problem with the anomaly method of deseasonalization is the assumption of the constancy of the seasonal cycle.
Full text here
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Reblogged this on Climate Collections.
the funny thing is your conclusion under Fig. 4:
“Obviously, the highest annual TMAX temperatures for China don’t always occur in July.”
If you look at the data you will see that e.g for 1902 the “annaul” TMAX (should be: the annual-max(anomaly(monthly averaged Tmax)) is the January value (anomaly of 1.931°C). This value has nothing to do with a annual TMAX vlaue (oin China).
Thus the major fault is not using the anomalies, but misinterpreting the (MAX) anomalies in a wrong way.
MFKBoulder, you began your comment, “the funny thing is your conclusion under Fig. 4:
‘Obviously, the highest annual TMAX temperatures for China don’t always occur in July.'”
There’s nothing funny about it, MFKBoulder. It’s confirmed by the data. Before I write something that’s easy for me to check, I verify it, MFKBoulder. I just didn’t bother to publish the graph that confirmed my statement:
Too bad you didn’t bother to check before you went on to write the rest of your comment, MFKBoulder. Because you’ve wasted my time.
And the rest of your comment with all the typos is difficult to read, at best. Regardless, your closing statement displays for all to see that you have a limited grasp of reality.
Good-bye. For you, that means don’t bother to come back.
You were using the word “Obviously” under your Graph 4 and there was _nothing obvious_ – next to the fact that you were comparing trends for Tmax (max_monthly anomaly of year with July anomaly) which obvioulsy do not match. And this has nothing to do with using temperature value vs. anomaly value.
What you have proved: You easly can fumble in statistics if you are not aware of what you are comparing.
MFKBoulder, you wrote, “You were using the word “Obviously” under your Graph 4 and there was _nothing obvious_ – next to the fact that you were comparing trends for Tmax (max_monthly anomaly of year with July anomaly) which obvioulsy do not match….”
You obviously have no idea what was presented or discussed.
You re no longer welcome here, because YOU WASTE MY TIME, with your blatant lack of comprehension, likely a willful lack of comprehension.
From now on your comments will go into the spam file and then be deleted.
Good-bye, waste. Good riddance.
PS: If you understood the topic, the reason would be blatantly obvious to you why, as I wrote in the post, “the highest annual TMAX temperatures (not anomalies) for China don’t always occur in July.” If you had any grasp of the real world you might assume that some years, occasionally, and based on the data I linked for China, that the highest TMAX temperatures (not anomalies) for China occur in in August.
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