The fit of the CSALT model to global temperature data is so close that it makes some sense to do a detailed analysis of the occasional glitches in the data. This could tell us if there are other noise components that could fill the gap.
Initially what I want to do is a subjective interpretation — if one can isolate the parts of the model that don’t work so well, this may open up areas for further investigation. For example, was the large warming spike at the end of WWII (starting in late 1943) real or was it due to calibration issues ?
(added note: this is the initial post covering the CSALT model, index to other posts)
It would be nice to have a simple climatology tool that illustrates the trend of modern-day global warming, while also providing a view of the underlying mechanisms. What I came up with is the CSALT model, which is based on some of the analysis from a recent blog post titled Climate Variability and Inferring Global Warming.
What CSALT does right now is act as a reverse weather forecaster that can hindcast the global average temperature accurately without using any direct measures of temperature.
The Dynamic Context Server features an interactive Bakken Oil model showing the Red Queen effect. The model uses historical oil well count and cumulative production to estimate average well output over time and then project that a number of months into the future.
The North Dakota Mineral Resources Department releases monthly data which we use to fit against. The start of the model is set to when the oil production statistics began and continues to the recent month 378:
The Earth’s historical global mean temperature record shows much variability, often changing by tenths of a degree from year-to-year. If this variability is somewhat cyclical, one could reason that the long term would approach a mean value of zero, indicating that as many positive temperature fluctuations occur as negative fluctuations. That describes the reality of noisy fluctuations — noise is a detectable signal but it does not necessarily impact the outcome of a trend.
A bias-free noise should not contribute to the long term trend in the global temperature time series either. The premise is that if we can remove this noise from the historical record, then we can make a more precise estimate of the underlying trend .