I have previously described a basic thermodynamic model for global average temperature that I call CSALT. It consists of 5 primary factors that account for trend and natural variability. The acronym C S A L T spells out each of these factors.

C obviously stands for excess atmospheric CO2 and the trend is factored as logarithm(CO2).

S refers to the ENSO SOI index which is known to provide a significant fraction of temperature variability, without adding anything by way of a long-term trend. Both the SOI and closely correlated Atmospheric Angular Momentum (AAM) have the property that they revert to a long-term mean value.

A refers to aerosols and specifically the volcanic aerosols that cause sporadic cooling dips in the global temperature measure. Just a handful of volcanic eruptions of VEI scale of 5 and above are able to account for the major cooling noise spikes of the last century.

L refers to the length-of-day (LOD) variability that the earth experiences. Not well known, but this anomaly closely correlates to variability in temperature caused by geophysical processes. Because of conservation of energy, changes in kinetic rotational energy are balanced by changes in thermal energy, via waxing and waning of the long-term frictional processes. This is probably the weakest link in terms of fundamental understanding, with  the nascent research findings mainly coming out of NASA JPL (and check this out too).

T refers to variations in Total Solar Irradiance (TSI) due mainly to sunspot fluctuations. This is smaller in comparison to the others and around the level predicted from basic insolation physics.

Taken together, these factors account for a correlation coefficient of well over 90% for a global temperature series such as GISTEMP.

Over time I have experimented with other factors such as tidal periodicities and other oceanic dipoles such as North Atlantic Oscillation, but these are minor in comparison to the main CSALT factors. They also add more degrees of freedom, so they can also lead one astray in terms of spurious correlation.  A factor such as AMO contains parts of the temperature record itself so also needs to be treated with care.

My biggest surprise is that more scientists don’t do this kind of modeling. My guess is that it might be too basic and not sophisticated enough in its treatment of atmospheric and climate physics to attract the General Circulation Model (GCM) adherents.

So what follows is a step-by-step decomposition of the NASA GISS temperature record shown below, applying the symbolic regression tool Eureqa as an alternative to the CSALT multiple linear regression algorithm. This series has been filtered and a World War II correction has been applied.

The GISTEMP profile, interval 1940-1944 corrected as a 0.1 adjustment down.

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