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## Half Year Cyclic Variaition in RSS,UAH and GISS Anomaly

Posted by Jeff Id on October 26, 2008

From my previous post we looked at the FFT of the three main temp metrics from which Tamino noticed a strong 1 year signal in the UAH weather. From Leif Svalgaard’s comments on my other post, the solar heating effect is compensated for on a monthly basis by some function. This creates a few problems, scientists need to know the response of the earth climate to solar forcing, the difference in albedo between the hemispheres, the phase angle lag created by the thermal mass of the climate system and any feedback mechanisms associated with the response to make an accurate correction.

The phase of the one year trend I found in my last post means that (not surprisingly) the AGW guys have underestimated the annual response to solar heating in all three anomaly metrics. In their defense, they may have simply estimated the lag incorrectly. e.g. The differrence between two slightly out of phase sine waves is a sine wave.

There is also a half year signal present in all three metrics. This kind of variation could be caused by seasonal effects or corrections between the hemispheres. Below are the three Fourier transforms I did of the three metrics.

Because of the sampling rate of the R fft function, I don’t have better resolution on the peak. I may need to write my own function again but in the meantime, this is the best resolution I have.

All three measurements show a strong half year variation in temp. I ran my correlation analysis again, checking the phase correlation of a half year wavelength sine wave and again found interesting results.

The peak of each best correlation to about 1 week resolution was

RSS – 1.68 months and 7.68 months after Jan 1

Giss – 1.44 months and 7.44 months after Jan 1

UAH – 1.68 months and 7.68 months after Jan 1

The FFT signal of all three was around 0.1 degrees C in amplitude. All three measurements have a very similar underlying half year trend.
Now I wonder what the temp anomalies look like with the 1 year and half year variation removed.

1. ### John F. Pittmansaid

Looking at your previous post, this one, the #132 comment on Tsonis, and Lief’s remarks “This takes care [it is thought] of the seasonal variation of solar forcing [incl. varying distance]. Apparently, there are some problems with that assumption [as per Tamino and you]”, on the last thread, if you are able to correct for this, it would be good to see if the about 11 year suncycle is now statistically significant and can be extracted from the signal.

2. ### PaulMsaid

“Because of the sampling rate of the R fft function, I don’t have better resolution on the peak. ”

Jeff, there is something you don’t seem to understand about FFT’s. You may have been confused by Tamino, who doesnt seem to understand it either, which is odd since he has written papers about it. You cannot get better resolution of the peak unless you have a longer data set. Your data is 5 years long. That means the lowest possible frequency you can identify is a frequency of 1 cycle per 5 years, and your higher frequencies are all multiples of this. Your plots are correct – the spike comes at point number 5 on the graph. Tamino’s plots are wrong – he has too many data points. He must be somehow fabricating extra data, perhaps by doubling up the data or padding it with zeros. When I asked him about this he got confused and had his ‘brain fart’!

3. ### Alan D. McIntiresaid

In your prior post you were questioning seasonal variaton in forcing. I couldn’t find an online link giving the figures, but Ellis and Haar, in
April 20, 1978 issue of “Journal of Geophysical Research” give the following figures based on a 3 year average, and an assumed constant 1360 watt output from the Sun:

January 350.7 watts Albedo 0.308
February 347.6 watts Albedo 0.309
March 342.5 watts Albedo 0.299
April 336.8 watts Albedo 0.304
May 332.0 watts Albedo 0.314
June 329.1 watts Albedo 0.311
July 328.8 watts Albedo 0.306
August 331.1 watts Albedo 0.290
September 335.7 watts Albedo 0.287
October 341.6 watts Albedo 0.298
November 347.1 watts Albedo 0.313
December 350.5 watts Albedo 0.318

Annual 339.5 avg 0.304 avg

You can assume the I amounts are correct, subject to an adjustment of the sun’s measured wattage, but I suspect there’s a significant variation in those monthly albedo figures

4. ### Curtsaid

Sorry to be late to the party, but this brings up an issue I have been wondering about for some time, but too busy to look into properly.

Since radiative loss of a black or gray body (such as the earth’s solid or liquid surface) is proportional to the 4th power of absolute temperature, the wider the spread of temperature for a given mean (and I am quite sure all of these indices are fundamentally means), the greater the total radiative emissions.

This plausibly could lead to the result that the periods after the solstices (after due to thermal time constants) would have the highest total radiative losses and therefore the lowest mean temperatures, and the periods after the equinoxes would have the lowest total radiative losses and therefore the highest mean temperatures, providing a twice-yearly variation.

As Leif pointed out in the previous thread, a predictable and repeatable effect like this should be easily removable from an anomaly plot, even if only by empirical methods. But it seems that, as with many things in climate science, nothing is obvious.

If I understand your correlation plots above correctly, the peaks are at the time of year that I would expect, but the sign is opposite that which would occur if this had not been compensated for. So has this effect been overcompensated in the anomaly plots? As Leif suggested in the thread about the yearly variations, you would have to go back to the absolute data, not the anomalies, to see.

5. ### Jeff Idsaid

Curt,

I found an odd shift in the phase of the signal over the last 30 years and haven’t had time to sort out what is happening but at least in anomaly it isn’t stable.

I think looking at the phase reaction of the global temperature (not anomaly) data is an interesting method to calculate a change in Earth climate response directly. For instance, we know the W/m^2 vs distance to the sun. We know CO2 levels have changed. So seasonally there is a global temperature shift. The change in the magnitude of this shift could be used to calculate a change in the response of the climate.

If more radiation is captured by the CO2, a steeper rise would happen when the earth is closest to the sun. This should be detectable in the data as a direct measurement of climate change with many of the other variables removed.

I would already be working on it except I can’t conceptually get past incorporation of the thermal inertia of our climate system so my best result now would be to show a difference from before rather than a more useful result. Also, it would be another avenue which could be used by the IPCC to abuse the science with estimated numbers.

6. ### TCOsaid

“Also, it would be another avenue which could be used by the IPCC to abuse the science with estimated numbers.”

There is an awful sentiment buried within this sarcasm. It’s as if you only see this in terms of sides and a battle. Heck, you should want IPCC to make it’s case as cogently as possible. As I want the same from you. And you should be open to your doodling finding things that help them.

7. ### Jeff Idsaid

“Heck, you should want IPCC to make it’s case as cogently as possible.”

I am under no illusion as to what the IPCC represents. They are a political organization which for simple survival long term requires global warming to be man made, be very dangerous and have an expensive non-working solution. I would have thought you could see that.

8. ### TCOsaid

Blablabla. Go publish, grasshopper. Get hot. This finding was 6 months ago. Are you a blogger, community type, dick holder? Or a scientist? Either is fine. but if you are the latter, no one should get excited about your comments.

9. ### TCOsaid

former.

Sheesh. This board needs an edit feature.