CSALT is a multiple linear regression analysis for fitting global average temperature that I developed a few years ago, before the Azimuth Code Project got started. I haven't talked about it much except incidentally because the focus has been more on ENSO here.

This link is the most recent revisit to the model, providing a mechanistic view of how the model is constructed.


This is the first post in 2013. From that point onward, I only used data up to Oct 2013 for training and fitting.


This is an index to most of the CSALT posts


So it's now 2016 and I figured I would check how good the CSALT model did in capturing the temperature variation of 2014 and 2015, relying only on training from 1880 to 2013.

This is the extrapolation with updated CO2 + SOI + Aero + LOD + TSI data. Extra periodic factors capturing mainly long-periods associated with lunisolar cycles (which tended to improve the fit for 1880-2013) were left as is and simply projected forward.


The figure below is a zoomed version where you can see the plateau, and then the numbers snapping back up. The combination of factors worked to compensate for the plateau, and when they combined in a constructive phase, the modeled CO2 trend got back in line with the temperature upswing. That all happened in 2014 and 2015, which the model did a good job in projecting.


There's nothing contradictory in this model to the mainstream climate science findings. It finds a Transient Climate Response of over 2C per doubling of CO2, which is line with the Equilibrium Climate Sensitivity of 3C per doubling after the oceans equilibrate.

The reason I became interested in an ENSO model is that being able to predict ENSO dynamics should help in predict the temperature movement.