Strange.

Scroll down to SST and select the first dataset HADISST, then scroll back up and click on “Select Field”. On the next page, there are fields for Latitude and Longitude. The coordinates for NINO3.4 are 5S-5N, 170W-120W, so enter -5 & 5 for the latitudes and -170 and -120 for longitudes. (I also got into the habit of entering a zero in the “Demand at least” field somewhere along the line. It shouldn’t have any effect on the HADISST data though.) Click on “Make Time Series.” On the next page, scroll down to the third graph. It reads “Anomalies with respect to the above annual cycle”. On that same line, click on “raw data.” That next page is the raw HADISST NINO3.4 SST anomaly data. It’s in two columns that starts at “1870.0000 -0.804598”

Bob, I followed these instructions to the letter and got a series starting 1870.0000 -0.948814. When I plotted it, I got a series which looks like yours, but there is still a data discrepancy, the downtick at 1920 on your graph now appears at around 1915 on mine. Oh well, getting closer. :-)

http://s630.photobucket.com/albums/uu21/stroller-2009/?action=view&current=ssa-sst-ssn.jpg

I will download the precipitation data and run that to see if any patterns emerge. A combination of the warm and the wet (though not necessarily simultaneously) will be what we are looking for in greater tree ring width, as you noted.

Just that I had seen your link, it is just not easy for me to “read it” with respect to solar cycles.

I read the abstracts of Shaviv’s paper. I do not know whether the amplification is adequate, 7*0.1 is 0.7. It is one more piece of the jigsaw?

“Using the oceans as a calorimeter to quantify the solar radiative forcing” Nir Shaviv

It’s behind a paywall, maybe you can get institutional access?
Shaviv finds the variation in TSI over the solar cycle is amplified by an order of magnitude by processes such as decadal changes in cloud cover (Svensmark effect) and so causes a fluctuation at the frequency of the Schwabe cycle.

This is enough of a change to show up in tree rings.

Given the accumulative nature of the ocean processes Bob has identified, it seems to me it’s reasonable to look at solar data in a cumulative way to see how well it matches longer term changes in the temperature record. So I made this graph:

http://s630.photobucket.com/albums/uu21/stroller-2009/?action=view&current=sst-nino-ssa.jpg

I did link it in a reply to you earlier in the thread. I don’t know if you saw it.

I am sorry but the reference you give does not turn the suncycle effect observation into myth at the moment. It is obvious that many things work in concert or disharmony to produce PDOs and AMOs etc.

What I have been trying to clear up is whether the long known “wisdom” that generally the 11 year sun cycle is visible in tree rings is really a “myth” . No such debunking exists, at least not found easily. I would expect there should have been a paper stating clearly that “no correlation between ring tree thicknesses and sunspot cycles is found”.

I would be fine with that. BTW my correlation was not with wet+warm, rather wet or warm.

If the old conclusions though are not a myth, then somehow even though TSI varies vary little, the cycle is affecting the climate and the question is reduced to “how”.

Might this have anything to do with the changes Stephen and Erl are discussing?

http://www.pac.dfo-mpo.gc.ca/sci/sa-mfpd/downloads/indices/LOD.jpg

Touché

Anthony: Thanks.”

Touche.

How does one do ‘e acute’ on a UK keyboard ?

A change in the surface temperature of the oceans seems to do the trick.

The question is whether that surface temperaure is changed by the mechanism you describe or instead by a change in the internal activity of the oceans which causes them to release stored solar energy faster or slower.

I said this above:

1) Oceanic energy content is hugely greater than that of the air and so minor variations in the rate of energy flow from the oceans will have very large effects on the temperature of the air. In contrast it would take large changes in the air to result in a warming of the ocean surface. You partially recognise that by relying not on the air imparting warmth to the ocean surface but instead an increase in solar energy reaching the ocean surface from increased insolation due to less cloud cover. I think that is, however, the wrong way round.

2) It would take some time for increased insolation to build up in the surface waters yet the onset of a strong EL Nino is very rapid. I don’t think your mechanism would be fast enough.

3) I am not satisfied that your essentially short term scenario (consistent with the frequency of single ENSO events) can explain the 30/60 year periodicities that have been clearly observed between positive warming phases and negative cooling phases. To deal with that I think one has to have an oceanic mechanism involving variations in the rate of energy flow from ocean to air

I think something is going on within the oceans, some sort of circulation which brings bodies of water with differing thermal characteristics to the surface at different times. Ultimately solar driven.

Bob, many thanks again for sticking with me through this, and for all the valuable information you’ve added about the background to the datasets. As an exercise, I’m going to plot both series on the same graph for myself at various smoothing levels and with various median lines and see how it affects the ‘look’ of the data. I’m sure I’ll learn a lot about ‘eyeballing the data’. :o)

erlhapp (18:29:37) :
What is it that accounts for: “variations in the rate of energy flow from ocean to air.”

I think Stephen is taking the weekend off Erl.

My guess is it will have quite a lot to do with night time air temperatures and wind speeds. I came across an old post of Willis Eschenbach’s earlier on CA that would be worth a look. It the last comment on this thread about long wave radiation entering and leaving the ocean surface. Even Gavin Schmidt joins in!
http://www.climateaudit.org/?p=213

Both Kaplan and HADISST start with the same data, which is derived from ICOADS SST. Kaplan used an early version of Hadley SST data that had been corrected for the Folland bucket adjustment prior to 1945. Kaplan then applied their smoothing and infilling techniques to it.

The UCAR “Informed Guide to Climate Data Sets” on Kaplan is here if you’d like a more detailed explanation:
http://www.cgd.ucar.edu/cas/guide/Data/kaplan_sst.html

The UCAR write up about HADISST is here, but it’s incomplete:
http://www.cgd.ucar.edu/cas/guide/Data/hadisst.html
The Hadley Centre’s description is here:
http://hadobs.metoffice.com/hadisst/

Plotting the RAW Kaplan and HADISST NINO3.4 SST anomaly data shows that ENSO events appear at the same time. There are differences in magnitude, some minor, some major, but the timings of the ENSO events agree. Also note the minor differences in the ENSO-neutral years.
http://i42.tinypic.com/20iawzd.jpg

Smoothing the Kaplan and the HADISST NINO3.4 SST anomaly data with a 121-month filter brings out those underlying minor and major differences:
http://i39.tinypic.com/2lo0oly.jpg

Regards

Anna and others, tree ring data is a far better proxy for PDO than just about anything else people try to use tree rings for.

This is probably true for American tree ring data, and we’d probably find British tree ring data gave a reasonable proxy for the AMO. One thing about trees I learned which interested me is that it’s been discovered that the internal temperature of leaves stays almost constant regardless of ambient temperature. It seems likely therefore that tree ring differences are more to do with changing precipitation than temperature.

Looking at Bob Tisdale’s graphs of temperature versus precipitation the data seems to run counterintuitively to anna v’s warm=wet cool=dry adage, though on different timescales maybe it’s a different story.

http://bobtisdale.blogspot.com/2008_07_01_archive.html

But I disagree with your conclusion, “I think something is seriously wrong with the time scaling on one plot or the other.”

Bob, I really appreciate the time you’ve taken to work on this with me. Before we leave it, please take a look at this second side by side I’ve done with the time scales on our two graphs matched alongside the first one by opening them both in separate windows:

http://s630.photobucket.com/albums/uu21/stroller-2009/?action=view&current=ninos-datasets2.jpg

http://s630.photobucket.com/albums/uu21/stroller-2009/?action=view&current=ninos-datasets.jpg

As you can see, the dates now line up on the ‘datasets2′ image, but whereas the data visually agreed reasonably well before, it’s now ‘stretched and shifted’. To me, this seems more like a problem with the data and date collation than a difference between Hadley and Kaplan’s individual monthly temperature readings.