Sunday, November 28, 2010

Record "hot" days out number record "cold" days

So we must be heating up!!

Of course the devil is in the details. It was brought to my attention that this paper shows that the number of record high days outnumber the record low days 2:1.

The relative increase of record high maximum temperatures compared to record low minimum temperatures in the U.S

Gerald Meehl, NCAR, Boulder, CO; and C. Tebaldi, G. Walton, D. R. Easterling, and L. R. McDaniel

Summed over the first decade of the 21st century and averaged over the U.S., the observed ratio of record high maximum temperatures to record low minimum temperatures is about two to one. That there are more record highs than lows is not surprising since observed U.S. average temperatures have been increasing over this time period, and a shift in the distribution of temperatures would dictate a greater number of record high temperatures being set than record low minimum temperatures. But is the two to one ratio particularly unique or characteristic of a warming climate? It is shown that the two to one ratio of record highs to record lows is not unique but happens to be a product of the recent time period in the non-stationary warming climate. Records that were declining uniformly earlier in the 20th century following roughly a decay of 1/n have been declining less slowly for record highs than record lows since the late 1970s, and for the recent decade the ratio has grown to about two to one. A multi-member ensemble of model simulations of U.S. climate of the 20th and early 21st centuries shows a greater ratio of about four to one for this recent time period due to more uniform warming across the U.S. in contrast to observations that have been characterized by relatively greater warming in the western U.S. compared to the eastern U.S. Following an A1B emission scenario for the 21st century, the ratio of record high maximum to record low minimum temperatures, averaged over the U.S., is projected to continue to increase, with ratios of about 20 to 1 by mid-century, and roughly 50 to 1 by the end of the century.

It was highly critized in both WUWT and World Climate Report.

This is nothing more than an accounting issue and has nothing to do with it getting hotter as you will see in this example.

Using Station 4333 I took the highest TMax for each month for each year and made a chart with months as columns and years as rows. Each cell contained the highest TMax for that month and that year. I then scanned down each month and looked for what would have been recorded as record temps. For each of the months this is what I found:

Record Hot Days


1906 (8.9)

1932 (11.7)

2005 (11.8)


1902 (5.6)

1906 (7.8)

1953 (12.2)


1902 (15)

1903 (16.7)

1910 (22.8)

1945 (25.6)


1901 (26.7)

1903 (27.8)

1915 (30.6)

1990 (31.2)


1903 (32.8)

1911 (34.4)

2010 (35.2)


1901 (36.1)

1921 (36.7)


1901 (37.2)

1913 (37.8)


1906 (36.1)

1911 (36.7)


1908 (35.6)

1931 (36.7)


1908 (27.2)

1922 (27.8)


1914 (18.3)

1924 (18.9)

1927 (19.4)

1944 (20)

1961 (23.3)


1901 (11.7)

1922 (12.2)

1923 (12.8)

1948 (13.3)

1951 (16.1)

Total record breakers


Record Cold Days


1901 (-32.2)

1903 (-34.4)

1925 (-37.8)


1904 (-33.3)

1914 (-34.4)

1923 (-36.1)

1937 (-38.3)


1905 (-26.7)

1907 (-28.3)

1936 (-28.9)

1938 (-36.7)


1903 (-10)

1907 (-11.7)

1908 (-14.4)

1911 (-15.6)

1919 (-16.1)


1902 (-7.2)


1902 (3.3)

1906 (2.8)

1910 (0)


1903 (6.7)

1923 (4.4)

1942 (3.3)


1902 (5.6)

1907 (3.9)

1913 (3.3)

1925 (2.8)

1934 (1.7)


1901 (0.6)

1904 (-2.2)

1943 (-2.8)

1951 (-3.3)


1902 (-7.2)

1905 (-8.9)

1925 (-11.7)

1933 (-12.8)


1901 (-19.4)

1917 (-22.2)

1925 (-23.9)


1902 (-31.7)

1933 (-38.9)

Total record breakers



















Here is what it means. In Jan, the first highest Tmax was in 1906 at 8.9C. Which was then broken in 1932 at 11.7C, and later broken again in 2005 at 11.8C.

Each month is like that. In all there are 37 record breakers. But notice all but 6 are before 1950. 17 are in the first decade of 1900. In spite of the claim that 2010 was the hottest on record, the record breaking summer temp hasn't been broken since 1913.

The same procedure was done for the lowest of TMin for record breaking lowest temps which you can see in the above chart.

Counting the number of record breaking temps in the month you can see the record cold ended before 1952. None since. But there have been record hot days since. So the record hot days have go on longer than the record cold days. But the record temps are all still way in the past. With TMin increasing, this is what would be expected. We just have not gotten as cold in the winters as we did at the beginning of the last century. But this does not mean we are getting hotter in the summer months. Not even close.

Now this was just on monthly record breakers, if this were done on a daily basis we would see the counts much higher, but the trend would be the same.

Yes, Meehl's paper dealt only with the last ten years, but since TMin has been increasing, and TMax decreasing, there will still be more record breaking TMax days than TMin days, especially in the winter months. This would be a consequence of the rising winter temps.

The other issue is the accounting, which plays a much bigger role in these "record breakers". As we start to keep records, the number of record breaking days starts to drop as more data is accumulated. But with 365 days, and 100 years, that 36,500 potential record breaking days for temps to go into. Thus record breakers will still occur due to random temperature variation even in a temperature regeme that is not increasing. Couple that with winter TMax and Tmin getting less cold, and you will be bound to hit record breakers for Tmax long after there are no more for Tmin. Even with summer Tmax dropping, random temperature "noise" will hit the odd record breaker simply because there isn't enough records kept so far to cover all the possible temperature ranges that can happen.

If we had 5,000 years of daily temperature data, it can be argued that in the last 1000 years there wouldn't be a single record broken, winter or summer, as all the possible temps that can be achieved would have been in the previous 4000 years.

Hence Meehl et al's paper doesn't mean anything by itself. The rest of the data trends must be also considered. And that trend is that summer TMax is dropping since 1900. And that is an inconvenient truth that the AGW faithful are desperately trying to ignore.

Anomalies Revisted

I posted a link to this blog at this other blog, which BTW is excellent,

Someone objected to my lack of use and description of anomalies stating:

Let me explain for you what anomaly means and how it is calculated.To define
a “normal” an analyst selects a period of time, typically 30 years but it could
be the entire length of the time series. I can tell you that it doesnt matter
which period you pick the answer you get is the same. Given your time
period you then create averages for that time period: You average all jan, all
feb etc etc. Then you can create an anomaly or deviation from that normal. This
mathematical operation does not change the shape of curve. it doesnt change
slopes or the magnitudes of difference between points. It merely “scales” the
curve by subtracting a constant. Why do we do this? well, you noted that some
canadian records are short and others are long and you worried about the
weighting. Well anomalies help us with that problem. If you want to actually
know how an anomaly works and why you should use them just write.

As we recall the most number of records reported at stations peaked in the 1980's. Prior to the 1950's there are very few stations. So if I wanted to get the trend for, say, Southern Ontario as a whole, and not just individual stations, this person claims anomalies will somehow compensate for the 80% missing records prior to 1980.

My position is that missing records means the data is biased by the remaining stations and skews the results into a non-realistic view of what happened.

Well, this is easy enough to test to see if missing records makes a difference.

First, we know that even in the regional area of Southern Ontario we have a wide range of temps on the same day throughout the area, as much as 5 or more degrees diff. Harrow, for example is close to the lowest point in Canada near Windsor, right on Lake Erie. Ottawa is about 7 hours drive east and some north. If you tried to average the anomalies between these two locations, and Harrow is missing more than half the years, how can the data NOT be biased towards Ottawa?

So I decided to test this with 15 stations that have data back to 1900 from all across the country (assuring a wide range of temps on the same day). To keep the records short I chose only July TMax temps.

The goal is to get the anomaly for these 15 with full intact records and plot the trend. Then for half of the lowest temp records remove all the records prior to 1980 and replot the anomaly trend to see if there is a biasing difference.

Just to make sure we are on the same page here. Each station's full range average was produced with one query (Access SQL). This became the baseline for each station. I then subtracted the daily TMax from each station matched with its baseline to get the daily anomalies.

From there, the max, average and min of each station's daily anomaly was aggregated together to produce this yearly plot for all 15 stations:

The first thing you will notice is there isn't one line. Every anomaly graph you see presented by climate scientists is just one line, how come I get three? Because I took each day's TMax from the average of the full set of TMax's for each station. TMax has a wide swing of daily temps (recall this is JUST July).

Here is an example. This is just one of those station's TMax temps for July of each year showing the highest TMax, averaged TMax and lowest TMax. In other words, the range of July TMax temps for each year:

Here is the same data zero base lined (anomaly): You will see that the shape of the lines is identical. But importantly, the range of the data is also the same. When climate scientists use anomalies, they are using the AVERAGED data line (the center one) only! So contrary that anomalies don't loose detail, they in fact do lose detail if only average is used. Think of the range they just throw out as error bars. Their anomaly actually is supposed to have "error bars" showing the full range of anomalies for each year. This removal of data from their graphs is greatly troubling. Their sloppiness to detail is also troubling. If engineers were as sloppy with detail, threw out crucial data, as climate scientists do, we would have bridges and buildings collapsing.

In fact, that is exactly what is happening. Their sloppiness to detail is forcing government policy based on sloppy data, which is contributing to the economic problems we face.

Only in climate science are scientists allowed to get away with throwing away data.

Thus, this brings us back to what this site is all about, showing ALL the data, not just the averaged.

So what about the other claim, that anomalies can "fix" missing data?

After I deleted the data on half the stations from 1900 to 1979 and replotted the anomalies I get this graph:

If you look carefully it's different. In fact, it is VERY different. The max anomaly that was dropping is now rising.

Best to subtract the two highest anomalies to see this difference:

The bias is obvious.
Let's see how that biasing affects the number they use, just the average.

The top blue graph is the average anomaly for the full dataset, no missing records. The red graph is with the missing records. Notice it changes the slope (trend) very much when data is missing.

This test clearly shows two things about using anomalies.

1) the use of just the averaged anomalies loses crucial data that shows trends not seen in the average, same as we see in just the raw temperature numbers. The trends of TMax and TMin are vital to know what is going on. Same with the use of the anomalies. Losing the highest and lowest ranges of the anomalies loses the same trends. Throwing out data is scientifically criminal.

2) anomalies cannot fill in missing data. The fewer the stations going back in time, the more the data is biased towards those stations. There is no getting away from this. Worse, there is no way for climate scientists to know how much the biasing is. They can't go back 100 years and built the missing stations and rerun the climate.

This leaves us back to what this site has been doing all along -- showing ALL the data one station at a time. It's the only way to see what has physically happened.

You cannot trust averages to give you a trend. You cannot trust anomalies to give you a trend. They only way you can get a true picture of what's going on is to look at the full range of each individual station. NO massaging of data, NO "cleaning" of the data, NO "tricks" will change that.

Wednesday, November 24, 2010

Why Mean Temp is meaningless

This has been my theme throughout this blog. But it appears some do not understand why TMean as supplied is a meaningless number. So some specific examples of why it is meaningless is needed.

TMean as supplied by Environment Canada is simply (TMax+TMin)/2. That is, it's the half way point between the highest temp and the lowest temp of the day. If the hourly temperature profile were a perfect sine wave this would not be a problem. But the hourly temperatures are not a perfect sine wave. Far from it.

In a perfect sine wave the average of all the hourly temps and the TMean would be the same. But because the daily temperature trek is not a perfect sine wave the hourly average is not the same as the TMean. TMean could be more than the hourly average, it could be less. The point being the hourly average is a number that is closer to what is physically going on. TMean is not.

I download the hourly temp data for the month of November, 2010 for Station 4333 so you can see the difference. (For those who think I cherry pick, choose any station, any month, any days you want, you will see the same problem.)

This is the temperature profile for Nov 1 to 4:
Here are the numbers:


Notice TMean is more than the average, substantially more. Thus using TMean as an indicator of how much the planet is heating up is grossly overstating the case for these days at least.

This graph for the entire month of Nov shows the TMax, TMin, average and TMean. Notice TMean is mostly higher than the average:

This graph shows the difference, average subtracted from TMean:

Adding up the differences is 2.23, which indicates that TMean is higher most of the time than the average.
Here are two examples of two days with the same TMax, TMin, TMean but very different averages:

You can see a slight change in the hourly temperature profile can dramatically change the average, but not the TMean because TMax and TMin have not changed.

This means crucial data is lost with just the three daily numbers that come from EC. If one wants to see what is actually going on with temperatures, one should be counting the number of hours at each degree temp and see what shifts have been happening over time.

Thus, I maintain that TMean is a meaningless number as an indicator of trends in temperature over time. It's not a measurement, it's a course calculation. This leaves TMax and TMin as the only physical data points one can work with, even though the yearly highest and lowest of those two values may only represent 1 hour each for the entire year.

Hence, to get a better profile of what is happening you have to look at the range of TMax and TMin for the respective seasons. Tmax ranges in the summer (to see if the planet is really "getting hotter") and TMin in the winter to see if we are getting more or less cold. This is why I present such in this blog.

Friday, November 19, 2010

Station 2973: Muenster Saskatchewan

Full range of yearly temps:

Summer full range of TMax:

Highest of summer TMax:

Lowest of TMax (not sure how coolest summer days is relevant in a world that is supposed to be heating up, but it's a flat trend):

Number of days 30C and above:

Winter TMin full range (Jan & Feb only)

Lowest TMin for each year:

About Me

jrwakefield (at) mcswiz (dot) com