DC of the Oil Peak Climate blog suggested that reverse forecasting to earlier dates using the CSALT model may be an interesting experiment.  Considering the growing sophistication of the model, I tend to agree.

The contributing factors to CSALT are a mix of empirical forcing terms and several periodic elements suggested by climate scientists with an interest in tidal and solar topics,  including Keeling from Scripps [1], R.Ray from NASA Goddard [2], Dickey from NASA JPL [3], and going back to Brier in 1968 [4]. These fall under the category of orbital influences discussed in a previous post.   Selecting the periods of the principle orbital most commonly cited, we get the staggered view of the individual contributions shown in Figure 2.  (see http://entroplet.com/context_salt_model/navigate for an interactive version)

A group of climate skeptics including Scafetta, Morner, Tattersall, Wilson, and a few others have also shown a keen interest in the possibility of orbital influences with a recent special issue of a journal, which has since been axed by the publisher.  This appears to be a sensitive area, considering that orbital influences on climate is deemed a very subtle effect by consensus science, but a behavior that skeptics and denialists claim overshadows the well-accepted theory of GHG induced warming.

There is also a likely connection between this area of research and the Stadium Wave theory of Wyatt & Curry via the introduction of the quasi-periodic LOD connection proposed by Dickey [3].

If we consider the connection between LOD and tidal effects (see the Earth Orientation Center model),  CSALT is able to decompose the thermal signal into the diurnal and semidiurnal tidal frequencies of 18.6 years and 8.85/2 years, getting the phase accurately and the relative strengths to boot. The significance is that this alignment occurring by random chance is highly unlikely.

From Figure 1, we linearly aggregate the factors to produce the CSALT result shown below in Figure 3 (see [7]).

This leads to  the possibility of making deterministic predictions on the limited natural variability that these factors have in the decadal temperature profile.  The majority of the terms are periodic, including the “bary” term which represents the position of center of mass of the solar system identified by Scafetta [5].  The Total Solar Irradiance (TSI) is quasi-periodic with empirical support that it will continue to follow the Hale cycle, with the 11 year Schwabe harmonic providing the principle heuristic for estimating the peaks in the sunspot activity.

Some of the cyclical terms are minor, such as Quasi-Biennial Oscillation (QBO) and the 80year/20year signal suggested by Treloar [6] that I was able to dig out of a residual analysis. On a Principal Component Analysis scree plot, these would appear below the knee.  In particular, the QBO can be traded out for a Venus periodicity of 8 years suggested by Scafetta [5] with a higher significance level.

The remaining terms are a mix of what appear to be chaotic or deterministic factors. The Southern Oscillation Index (SOI), Atmospheric Angular Momentum (AAM), and volcanic Aerosol are largely unpredictable, with the first two being bounded by a strong reversion to the mean tendency.  Volcanic aerosol activity is sporadic with the potential for large transient cooling excursions.

The Length of day (LOD) component suggests the longer-term pseudo-cycles of anywhere from 40 to 80 years. Estimates of LOD go back to nearly 1600, but obviously have greater uncertainty the further back one goes.

That leaves the forcing function of CO2 as the remaining secular, nearly-deterministic factor remaining. The Climate Explorer provides estimates dating back to 1000 AD.

That brings us to the first reverse forecast (or hindcast) that I have attempted so far. I used the GISTEMP data to generate the CSALT fit based on a data starting-point of 1880, and then hindcasted it back to the year 1850 where the HadCRUT temperature time-series starts, see Figure 4.

Before 1866, SOI and AAM data is missing so anything before that does not classify as a complete hindcast. At least the model will hindcast  fairly well in the 14 years before 1880, which is a very promising development.

The bottom-line is that the CSALT model may work well as a forecasting tool, if we can come up with predictions, or at a minimum, a set of bounds for  the chaotic, quasi-periodic, and sporadic elements which include SOI, AAM, LOD, TSI, and volcanic forcings.

Related

• The CSALT Model
• The Cause of the Pause is explained by thermodynamic Laws.

References

[1] C. D. Keeling and T. P. Whorf, “The 1,800-year oceanic tidal cycle: A possible cause of rapid climate change,” Proceedings of the National Academy of Sciences, vol. 97, no. 8, pp. 3814–3819, 2000.
[2] R. D. Ray, “Decadal climate variability: Is there a tidal connection?,” Journal of climate, vol. 20, no. 14, pp. 3542–3560, 2007.
[3] J. O. Dickey, S. L. Marcus, and O. de Viron, “Closure in the Earth’s angular momentum budget observed from subseasonal periods down to four days: No core effects needed,” Geophys. Res. Lett., vol. 37, no. 3, p. L03307, Feb. 2010.
[4] G. W. Brier, “Long‐range prediction of the zonal westerlies and some problems in data analysis,” Reviews of Geophysics, vol. 6, no. 4, pp. 525–551, 1968.
[5] N. Scafetta, “Empirical evidence for a celestial origin of the climate oscillations and its implications,” Journal of Atmospheric and Solar-Terrestrial Physics, vol. 72, no. 13, pp. 951–970, Aug. 2010.
[6] N. C. Treloar, “Luni‐solar tidal influences on climate variability,” International journal of climatology, vol. 22, no. 12, pp. 1527–1542, 2002.
[7] (Note: I have an ongoing challenge to the folks at The Blackboard to anyone that can easily distinguish between the model and the data. This isn’t completely fair, but it does get them riled up)