1) If you average the temperature data over 1/3 of the solar cycle length, you get a curve which matches the TSI data nicely, and shows an amplitude of variation in the region of 0.2C over the solar cycle rather than the 0.08 you found. I believe the tendency of el nino to occur away from the peak of the solar cycle, often soon after minimum, and the tendency for la nina to occur at the peak of the solar cycle flattens the curve of the global temperature response to insolation, and leads to an underestimation of the solar influence on temperature.

2) You were criticised by Ray Pierre Humbert some time ago for using an ocean heating depth of 1000m for your simple model, and he said 25m was more like it. My calcs on the amount of heat-energy the ocean must have absorbed to account for the steric component of sea level rise 1993-2003 coupled with a calculation of the drop-off in temperature from surface to thermocline show you were right! Heat-energy must be mixed down to much greater depths around 1000m, probably by lunar tidal action over a longer timescale, perhaps due to the changing max declination dragging water latitudinally and causing overturning due to coriolis effect.

3)Great work, keep it up! Please let us know what result you get with 0.2C variation and 1000m ocean mixing.

Second, I tried using a mixed layer depth of 20 m, instead of 25 m. Now the lambda with the lowest sum of squared errors is lambda = 1.49 (Climate Sensitivity = 2.51), with r = 0.9607. The highest correlation occurs at lambda = 1.63 (r = 0.9618).

There’s no way to simultaneously optimize the correlation in both lambda and Cp: it’s always a trade-off (higher heat capacity implies lower sensitivity). But it’s easy to find the best fit in terms of sum of the squared errors: there’s a clear minimum at 18 m mixed layer and lambda = 1.47, which implies a climate sensitivity of 2.54. On the same arbitrary scale, the sum of the squared errors is 0.0090, substantially better than the fit that Dr. Spencer obtains. At that location, the correlation coefficient is 0.9617.

Now, I just grabbed the numbers for Tsi and Tsfc from Dr. Spencer’s plot, and implemented his model in matlab.
Roy- have I made some error?

So what delay do you use in your “Climate sensitivity” analysis; in order to get a true logarithmic temperature resonse to CO2 changes.

Personally, I prefer the mechanism that is implicit in the results of Frank Wentz et al, in SCIENCE for July-7 2007; “How much More rain Will Global Warming Bring ?”

They report that a 1deg C rise in mean global surface Temperature results in a 7% increase in total global evaporation; a 7% increase, in total atmospheric water content,; and a 7% increase in the total glohbal precipitation.
Implicit in that result; but not stated specifically by Wentz is an increase (maybe about 7%) in the total global precipitable cloud cover; presumably in the form of increased area; increased optical density; and increased cloud perisistence time; or some combination of all three.

But no way can you get 7% morre rain and snow; without having considerably more global cloud cover; and increases in cloud cover over climatic time scales (how about 30 years) ALWAYS cause cooling from increased blocking of incoming solar spectrum radiation to the ground (or water).

So I favor a “model” where H2O completely regulates the temperature of the earth. Vapor leads to warming (of the atmosphere), but maybe cooling of the ground from loss of solar energy to the atmosphere; and clouds lead to cooling by increased albedo reflection off cloud tops plus increased absorption of additional solar energy; so that IT ALWAYS GETS COLDER IN THE SHADOW ZONE.

So if I add a bit more CO2 OR A BIT MORE TSI; I simply end up with a small increase in total global cloud cover; which blocks a bigger fraction of the TSI.

And if Svensmark’s Cosmic Rays, make cloud formation a little easier; then I get more cloud cooling for less surface warming (mostly of the ocean).

You guys are making it all far too complicated.

IT’S THE WATER !

Thanks for this interesting analysis, and for opening up the comments section.

I’m intrigued that you did not mention that Tung et al. (2008, GRL) published a paper in which they used four datasets (two reanalysis products (NCEP, ERA-40) and two in-situ (GISS, HadCRUT3)) and TSI data to estimate the transient climate response. They established the existence of a response to the solar cycle in all four SAT datasets (using linear discriminant analysis) above the 95% level of confidence. TSI data from Lean et al. (1995) from 1959 to circa 2005 was used in their analyses.

Tung et al. (2008) then calculated the transient climate response (TCR, which is what you seem to be estimating here) for the HadCRUT3 data to be +2.5 K, with a corresponding equilibrium climate sensitivity (ECS) of +3.8 K.

Unless I am missing something, it is critical to note that the number you arrive at (+1.7 K) seems to be for the TCR and not the ECS. Yet the numbers from the IPCC which you cite are for the ECS (not the TCR) and the ECS is estimated to be higher than the TCR by a factor of 1.5 (Tung and Camp 2008, JGR; Stott et al. 2006, J. Climate).

Would it be possible for you to provide some error bars for your estimate of TCR, or at least quantify the uncertainty? It appears that your TCR value is close to that of Tung et al. (2008), and that there may even be some overlap once uncertainty is considered.

It did not escape my attention that your value for TCR is more than three times higher than the estimate made by Lindzen (~ +0.5 K). If I may be so bold, perhaps a more appropriate title for your post would be “Transient climate response estimated from the 11 year cycle in TSI is at lower bound of values reported in the IPCC”.

PS: Have you any plans to publish this?

Hat tip and thanks to LHarris.

It just happens that the Lunar/solar tidal cycles are indirectly linked with the orbital periods of the Jovian planets (primarily Jupiter and Saturn).

‘A more sensitive system normally responds more slowly to perturbation.’

I think this depends on how close to particular system’s resonances one is with the perturbation. In the case of the climate, it seems hard to model such resonances.

So we have to compute the response with the simple model Dr. Spencer is discussing here. I was just wondering if he had done that already.

Thanks for the reply.

http://www.ecd.bnl.gov/pubs/BNL-80226-2008-JA.pdf

Given the multitude of papers that put the value higher that 1.5, that statement in the IPCC report seems to be jsutified.