An important new paper published today in Global and Planetary Change finds that changes in CO2 follow rather than lead global air surface temperature and that “CO2 released from use of fossil fuels have little influence on the observed changes in the amount of atmospheric CO2” The paper finds the “overall global temperature change sequence of events appears to be from 1) the ocean surface to 2) the land surface to 3) the lower troposphere,” in other words, the opposite of claims by global warming alarmists that CO2 in the atmosphere drives land and ocean temperatures. Instead, just as in the ice cores, CO2 levels are found to be a lagging effect ocean warming, not significantly related to man-made emissions, and not the driver of warming. Prior research has shown infrared radiation from greenhouse gases is incapable of warming the oceans, only shortwave radiation from the Sun is capable of penetrating and heating the oceans and thereby driving global surface temperatures.
The highlights of the paper are:
► The overall global temperature change sequence of events appears to be from 1) the ocean surface to 2) the land surface to 3) the lower troposphere.
► Changes in global atmospheric CO2 are lagging about 11–12 months behind changes in global sea surface temperature.
► Changes in global atmospheric CO2 are lagging 9.5-10 months behind changes in global air surface temperature.
► Changes in global atmospheric CO2 are lagging about 9 months behind changes in global lower troposphere temperature.
► Changes in ocean temperatures appear to explain a substantial part of the observed changes in atmospheric CO2 since January 1980.
► CO2 released from use of fossil fuels have little influence on the observed changes in the amount of atmospheric CO2, and changes in atmospheric CO2 are not tracking changes in human emissions.
The paper:
The phase relation between atmospheric carbon dioxide and global temperature
- a Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, N-0316 Oslo, Norway
- b Department of Geology, University Centre in Svalbard (UNIS), P.O. Box 156, N-9171 Longyearbyen, Svalbard, Norway
- c Telenor Norway, Finance, N-1331 Fornebu, Norway
- d Department of Physics and Technology, University of Tromsø, N-9037 Tromsø, Norway
Abstract
Using data series on atmospheric carbon dioxide and global temperatures we investigate the phase relation (leads/lags) between these for the period January 1980 to December 2011. Ice cores show atmospheric CO2 variations to lag behind atmospheric temperature changes on a century to millennium scale, but modern temperature is expected to lag changes in atmospheric CO2, as the atmospheric temperature increase since about 1975 generally is assumed to be caused by the modern increase in CO2. In our analysis we use eight well-known datasets; 1) globally averaged well-mixed marine boundary layer CO2 data, 2) HadCRUT3 surface air temperature data, 3) GISS surface air temperature data, 4) NCDC surface air temperature data, 5) HadSST2 sea surface data, 6) UAH lower troposphere temperature data series, 7) CDIAC data on release of anthropogene CO2, and 8) GWP data on volcanic eruptions. Annual cycles are present in all datasets except 7) and 8), and to remove the influence of these we analyze 12-month averaged data. We find a high degree of co-variation between all data series except 7) and 8), but with changes in CO2 always lagging changes in temperature. The maximum positive correlation between CO2 and temperature is found for CO2 lagging 11–12 months in relation to global sea surface temperature, 9.5-10 months to global surface air temperature, and about 9 months to global lower troposphere temperature. The correlation between changes in ocean temperatures and atmospheric CO2 is high, but do not explain all observed changes.
I got to the party here quite late and only had a chance to read the main post and about 50 comments before I had to attend to other matters. Something told me that this might turn out to be an interesting and enlightening thread. I am now about half way through the comments, and all I can say is that this has been a wonderful thread, both scientifically and for its overall polite tone and serious attempt to deal with each others comments. Just wanted to say thank you all before comments got closed off.
John Finn says:
September 13, 2012 at 12:17 pm
“What are the values of k and Tau by the way?”
Give it up. Extrapolating beyond the limits of the data to reach an absurd conclusion is a standard “neat trick” which sways the uninitiated, but has no real bite.
Nonlinear systems operate under different conditions. They can generally be linearized about a particular operating condition, but the limits in which that approximation holds accurately cannot be known without extensive data collection outside that operating regime. Since we only have the one climate, and limited data on it, we can only say what holds for the here and now based on the data we have which accurately describes the modern era.
In your lay status, you may consider that a dodge. For one whose duties include gain scheduling to control complex plants, it is workaday and unremarkable, and you are just making a nuisance of yourself.
Bart says:
September 13, 2012 at 12:52 pm
Just a quick response to one point:
If you choose not to choose, you still have made a choice. To match up the data prior to 1958, you have to use the proxy data, which have been calibrated and time shifted in order to give you a nice, pat little narrative, i.e., the deck has been stacked.
There was not need for a choice of a bias, as one can set the startpoint for the comparison between emissions and increase in the atmosphere at any year between 1900 and 2000 with a period of at least 20 years (to avoid too much noise from the small temperature influence on the increase).
The CO2 levels in the Law Dome ice cores air were directly measured, that are not proxies. There is a time shift which was calculated by counting the ice layers (= 40 years) and direct measurements top down of the CO2 levels in the firn until closing depth and in ice at closing depth, which showed that the CO2 levels at closing depth were average 10 years older than in the atmosphere above it and similar in firn and ice. That gives a fixed time shift of 30 years between ice age and average gas age, at least over the period of interest. Moreover, there was an overlap of ~20 years (1960-1980) between ice incorporated CO2 and the direct measurements in the atmosphere. That is near 20% of the period of interest, without any need for calibration.
Bart says:
September 13, 2012 at 1:49 pm
One last remark, as I need to have some sleep, early up tomorrow:
Nonlinear systems operate under different conditions.
Well, the temperature – CO2 relationship seems to be quite linear over 420 kyr (extended to 800 kyr by the Dome C ice core):
http://www.ferdinand-engelbeen.be/klimaat/klim_img/Vostok_trends.gif
where most of the deviation from the trend is caused by the varying lags (more during cooling than during warming) of CO2 after temperature changes.
Thus there was little problem to predict the CO2 (and CH4) behaviour as result of temperature changes, until some 160 years ago…
Bart,
I did look at what you wrote earlier … but found it confusing. For example, you have
Is this the human input to CO2? In which case, shouldn’t you have something like
d(CO2)/dt = dh/dt + dn/dt
Where “n” is the “natural” contribution to CO2 ?
Then
dn/dt responds to a change in temperature, but dh/dt doesn’t
dh/dt responds (mostly) to fossil fuel use, but dn/dt doesn’t
d(CO2)/dt responds to total CO2 in the atmosphere (higher concentrations lead to faster plant growth, which tries to “scrub” excess CO2 out of the atmosphere).
So it is pretty clear that this should be a differential equation — a forced, damped, non-linear differential equation. And that will take much more to unravel that a quick blog post.
Tim Folkerts says:
September 13, 2012 at 6:43 pm
You are taking up a sledgehammer to swat flies. It is not a set of input/output relationships. It is a simple set of equations which express empirically how these functions look. They are all essentially affine functions of time, with some small superimposed variation.
The fact that the temperature variation and slope match those of the CO2 rate of change with only a single simple scale factor is what tells us that temperature is responsible, and there is no room in that relationship for significant human influence.
Bart says:
September 13, 2012 at 12:52 pm
You can’t break it up like that. The response is smooth across all frequencies. You take all, or nothing, and nothing is not a viable alternative.
There are only two distinct frequencies involved: the variability around the trend with a period of a few years at maximum and the trend with a period of several hundred years, nothing inbetween. That points to two distinct processes.
A period of several hundred years is visible in the ice cores, some with a resolution of about 20 years over the past 1000 years. These show an amplitude (MWP-LIA) of about 8 ppmv/degr.C. Not the 100 ppmv/degr.C as you suppose.
Tim Folkerts says:
September 13, 2012 at 6:43 pm
Is this the human input to CO2? In which case, shouldn’t you have something like
d(CO2)/dt = dh/dt + dn/dt
Indeed, if the human input is small, because of a huge sink capacity (as Bart assumes), then the extra natural input should be gigantic, but still doesn’t add to the overall increase, as the natural sinks still are 4 +/- 2 GtC/year larger than the natrual inputs…
Must leave now…
Ferdinand Engelbeen says:
September 13, 2012 at 11:38 pm
“There are only two distinct frequencies involved: the variability around the trend with a period of a few years at maximum and the trend with a period of several hundred years, nothing inbetween.”
It may look like that to the naked eye, but the spectrum is continuous.
“…but still doesn’t add to the overall increase…”
Because it’s magic CO2. Ei yi yi.
Bart says:
September 13, 2012 at 1:49 pm
John Finn says:
September 13, 2012 at 12:17 pm
“What are the values of k and Tau by the way?”
Give it up. Extrapolating beyond the limits of the data to reach an absurd conclusion is a standard “neat trick” which sways the uninitiated, but has no real bite.
I wasn’t intending to do any such thing. Your model is able to reach absurd conclusions from simple pbservations without any attempts at extrapolation. I’m interested in knowing what values have been determined to achieve the fit within the data limits.
You seem a bit touchy about your pride and joy, Bart. You’re not embarrassed in any way are you? It’s just that most researchers I’ve come across are more than happy to share as much information as possible about their analysis – even if it’s a complete fruit-cake job.