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Henry’s Pool Tables on Global Warming/Cooling

February 21, 2013 in climate change

1)

I took a random sample of weather stations that had daily data
In this respect random means any place on earth, with a weather station with complete or almost complete daily data, subject to the given sampling procedure decided upon and given in 2) below.
2)

I made sure the sample was globally representative (most data sets aren’t!!!) ……
that means
a) The amount of weather stations taken from the NH must be equal to the amount weather stations taken from the SH

b) The sample must balance by latitude (as close to zero as possible)

c)The sample must also balance 70/30 in or at sea/ inland

 

d) longitude does not matter, as in the end we are looking at average yearly temps. which includes the effect of seasonal shifts and irradiation + earth rotates once every 24 hours).  So balancing on longitude is not required.

e) all continents included (unfortunately I could not get reliable daily data going back 38 years from Antarctica,so there always is this question mark about that, knowing that you never can get a “perfect” sample)

f) I made a special provision for months with missing data, not to put in a long term average, as usual in stats,  but to rather take the average of that particular month’s preceding year and year after. This is because we are studying weather patterns which might change over time.

As an example here you can see the annual average temperatures for New York JFK:

http://www.tutiempo.net/clima/New_York_Kennedy_International_Airport/744860.htm

You can copy and paste the results of the first 4 columns  in excel.

Note that in this particular case you will have to go into the months of the years 2002 and 2005 to see in which months data are missing and from there apply the correction as indicated by me + determine the average temperature for 2002 and 2005 from all twelve months of the year.

g) I did not look only at means (average daily temp.) like all the other data sets, but also at maxima and minima… …

3)

I determined at all stations the average change in temp. per annum from the average temperature recorded, over the period indicated (least square fits). The figure reported is the value before the x.

4)

the end results on the bottom of the first table (on maximum temperatures),
http://blogs.24.com/henryp/2013/02/21/henrys-pool-tables-on-global-warmingcooling/
clearly showed a drop in the speed of warming that started around 38 years ago, and continued to drop every other period I looked//…
5)

I did a linear fit, on those 4 results for the drop in the speed of global maximum temps,
ended up with y=0.0018x -0.0314, with r2=0.96
At that stage I was sure to know that I had hooked a fish:
I was at least 95% sure (max) temperatures were falling. I had wanted to take at least 50 samples but decided this would not be necessary which such high correlation.
6)

On same maxima data, a polynomial fit, of 2nd order, i.e. parabolic, gave me
y= -0.000049×2 + 0.004267x – 0.056745
r2=0.995
That is very high, showing a natural relationship, like the trajectory of somebody throwing a ball…
7)

projection on the above parabolic fit backward, ( 5 years) showed a curve:
happening around 40 years ago. You always have to be careful with forward and backward projection, but you can do so with such high correlation (0.995)
8)

ergo: the final curve must be a sine wave fit, with another curve happening, somewhere on the bottom…
http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/

9)

Now, I simply cannot be clearer about this. The only bias might have been that I selected stations with complete or near complete daily data. But even that in itself would not affect randomness in my understanding of probability theory.

10)

Either way, you could also compare my results (in the means table) with that of Dr. Spencers, or even that reported by others and you will find same 0.14 /decade since 1990 or 0.13/decade since 1980.

In addition, you can put the speed of temperature change in means and minima in binomials with more than 0.95 correlation. So, I do not have just 4 data for a curve fit, I have 3 data sets with 4 data each.They each confirm that it is cooling. And my final proposed fit for the drop in maximum temps. shows it will not stop cooling until 2039.

9 responses to Henry’s Pool Tables on Global Warming/Cooling

  1. Hi Henry, came by to say hello.
    I don’t do too well with figures- explain some?

    • The (black) figures you are looking at in the tables (allow some time to load up), represent the average change in degrees Celsius (or Kelvin) per annum, from the average temperatures measured during the period indicated. These are the slopes of the least square fit equations or “ linear trendlines” for the periods indicated, as calculated, i.e. the value before the x.

      The average temperature data from the stations were obtained from http://www.tutiempo.net. I tried to avoid stations with many missing data. Nevertheless, it is very difficult finding weather stations that have no missing data at all. If a month’s data was found missing or if I found that the average for a month was based on less than 15 days of that month’s data, I looked at the average temperatures of that month of the preceding- and following year, averaged these, and in this way estimated the temperatures of that particular month’s missing data.

      To understand how I did this you just need to understand first year statistics. I will come back to you on what these figures all mean by giving cross references to other comments. You can already see that all results for maxima, means and minima went negative some 15 years ago. This means earth is getting cooler now. But don’t worry. I don’t think we will fall in an ice age just yet. I have good hopes that my A-C curve is correct. http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/
      (but where did all my comments go on the above post? I cannot seem to be able to make contact with the administrator of this blog)

  2. CET temps totalled over each sunspot cycle compared with total sunspots.

    When Temperatures a low then maximum temperatures go up with sunspot numbers. When temperatures are high then maximum temperatures go up as the sunspot numbers go down.
    It appears there is a mechanism trying to keep the temperatures high.

    The minimum temperatures always do the opposite to sunspot numbers.

    ????

  3. Hi Kelvin! I need to see a graph of that or the paired data.I lost a lot of comments – I don’t how that happened or why.

  4. I cannot get a plot out of that that makes sense to me. Remember that in a cooling period such as now, CET runs opposite of the a-c wave
    http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/
    as determined earlier by me (post went missing)
    simply because it gets more clouds and precipitation. So paradoxically it gets warmer in CET because globally it is getting cooler.,… it is called the GH effect…..

  5. Henry

    I have been playing with the total of maximum temperatures in a cycle and the total number of sun spots.

    I have discovered there is a correlation formula on Excel and have been using it after Willis pointed out I should be using it.

    I have found a correlation of -0.99 between max and sunspots and -0.97 for minimum and sun spots for Cambridge UK.

    I then tried it on Lerwick and discovered that for the cycles before the one ending in 1964 the correlations are +0.90 and +0.92. For the cycles ending 1964 onwards the correlations are -0.81 and 0.77.

    I then tried it on the CET and got the same change at the 1964 cycle. I got 0.67 and 0.78 for cycles 1964 on and 0.71 and 0.64 prior to the 1964ending cycle.

    Do you still want me to upload the graph?If so how do I do it?

    Kelvin

  6. Hi Kelvin
    my mum has just passed on to be with the Lord and I have a lot of things to do and on my mind. My son always figures out for me how to upload a graph on my blog, I am not too good at it myself. In any case, you must first start a blog.
    Just remember: for example, if you do a plot on the drop of maximum temps (last row, first table, above, and you set the average speed of warming/cooling out against time, you can also get a binomial with very high correlation, something like 0.997, but in the end it showed that that plot would lead to such an amount of cooling such as has never seen before. This therefore led me to consider the a-c wave fit for same data:
    http://blogs.24.com/henryp/2012/10/02/best-sine-wave-fit-for-the-drop-in-global-maximum-temperatures/
    …….
    so with high correlation it may show that you are going somewhere but don’t be too quick into predicting the future from your plot….

  7. Sorry to hear about your Mum Henry. It’s not good to loose a family member, especially when it’s your Mum. My thoughts are with you.

    Regards

    Kelvin

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