UPDATED: Roy Spencer on how Oceans are Driving CO2

NOTE: Earlier today I posted a paper from Joe D’Aleo on how he has found strong correlations between the oceans multidecadal oscillations, PDO and AMO, and surface temperature, followed by finding no strong correlation between CO2 and surface temperatures. See that article here:

Warming Trend: PDO And Solar Correlate Better Than CO2

Now within hours of that, Roy Spencer of the National Space Science and Technology Center at University of Alabama, Huntsville,  sends me and others this paper where he postulates that the ocean may be the main driver of CO2.

In the flurry of emails that followed, Joe D’Aleo provided this graph of CO2 variations correlated by El Nino/La Nina /Volcanic event years which is relevant to the discussion. Additionally for my laymen readers, a graph of CO2 solubility in water versus temperature is also relevant and both are shown below:

  
Click for full size images

Additionally, I’d like to point out that former California State Climatologist Jim Goodridge posted a short essay on this blog, Atmospheric Carbon Dioxide Variation, that postulated something similar.

UPDATE: This from Roy on Monday 1/28/08 see new post on C12 to C13 ratio here

I want to (1) clarify the major point of my post, and (2) report some new (C13/C12 isotope) results:

1.  The interannual relationship between SST and dCO2/dt is more than enough to explain the long term increase in CO2 since 1958.  I’m not claiming that ALL of the Mauna Loa increase is all natural…some of it HAS to be anthropogenic…. but this evidence suggests that SST-related effects could be a big part of the CO2 increase.

2.  NEW RESULTS: I’ve been analyzing the C13/C12 ratio data from Mauna Loa.  Just as others have found, the decrease in that ratio with time (over the 1990-2005 period anyway) is almost exactly what is expected from the depleted C13 source of fossil fuels.  But guess what? If you detrend the data, then the annual cycle and interannual variability shows the EXACT SAME SIGNATURE.  So, how can decreasing C13/C12 ratio be the signal of HUMAN emissions, when the NATURAL emissions have the same signal???

-Roy

Here is Roy Spencer’s essay, without any editing or commentary:

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Atmospheric CO2 Increases:

Could the Ocean, Rather Than Mankind, Be the Reason?

by

Roy W. Spencer

1/25/2008

            This is probably the most provocative hypothesis I have ever (and will ever) advance:  The long-term increases in carbon dioxide concentration that have been observed at Mauna Loa since 1958 could be driven more than by the ocean than by mankind’s burning of fossil fuels.

            Most, if not all, experts in the global carbon cycle will at this point think I am totally off my rocker.  Not being an expert in the global carbon cycle, I am admittedly sticking my neck out here.  But, at a minimum, the results I will show make for a fascinating story - even if my hypothesis is wrong.  While the evidence I will show is admittedly empirical, I believe that a physically based case can be made to support it.

            But first, some acknowledgements. Even though I have been playing with the CO2 and global temperature data for about a year, it was the persistent queries from a Canadian engineer, Allan MacRae, who made me recently revisit this issue in more detail.  Also, the writings of Tom V. Segalstad, a Norwegian geochemist, were also a source of information and ideas about the carbon cycle.

Warming Trend: PDO And Solar Correlate Better Than CO2

Note: This is my analysis of a new paper by Joe D’Aleo, I’ve tried to simplify and explain certain terms where possible so that  it can reach the broadest audience of readers. You can read the entire paper here.

Joe D’Aleo, an AMS Certified Consulting Meteorologist, one of the founders of The Weather Channel and who operates the website ICECAP took it upon himself to do an analysis of the newly released USHCN2 surface temperature data set and compare it against measured trends of CO2, Pacific Decadal Oscillation, and Solar Irradiance. to see which one matched better.

It’s a simple experiment; compare the trends by running an R² correlation on the different data sets. The result is a coefficient of determination that tells you how well the trend curves match. When the correlation is 1.0, you have a perfect match between two curves. The lower the number, the lower the trend correlation.

Understanding R2 correlation
R2 Coefficient	Match between data trends
1.0	Perfect
.90	Good
.50	Fair
.25	Poor
0 or negative	no match at all

If CO2 is the main driver of climate change this last century, it stands to reason that the trend of surface temperatures would follow the trend of CO2, and thus the R² correlation between the two trends would be high. Since NCDC has recently released the new USHCN2 data set for surface temperatures, which promises improved detection and removal of false trends introduced by change points in the data, such as station moves, it seemed like an opportune time to test the correlation.

At the same time,  R² correlation tests were run on other possible drivers of climate; Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Total Solar Irradiance (TSI).

First lets look at the surface temperature record. Here we see the familiar plot of temperature over the last century as it has been plotted by NASA GISS:

 

The temperature trend is unmistakeably upwards, and the change over the last century is about +0.8°C. 

Now lets look at the familiar carbon dioxide graph, known as the Keeling Curve, which plots atmospheric CO2 concentration measure at the Mauna Loa Observatory:

CDIAC (Carbon Dioxide Information Analysis Center - Oak Ridge National Lab) also has a data set for this that includes CO2 data back to the last century (1895) extracted from ice core samples.  That CO2 data set was plotted against the new USHCN2 surface temperature data as shown below:

A comparison of the 11year running mean of the USHCN version 2 annual mean temperatures with the running mean of CO2 from CDIAC. An r-squared of 0.44 was found.

The results were striking to say the least. An R² correlation of only 0.44 was determined, placing it between fair and poor in the fit between the two data sets.