Prompted by the interest VS has rekindled in fundamental analysis of the temperature series at Bart’s and Lucia’s blogs, below are a small set of core ‘problems’ facing statistical climate science (CS) — kind of a challenge.
Remember a deterministic trend is one brought about by a changing value of the mean, due to a change in an equilibrium value for example (ie non-stationary). A stochastic trend is due the accumulation of random variations; all parameters are stationary.
Problem 1. If temperature is adequately represented by a deterministic trend due to increasing GHGs, why be concerned with the presence of a unit root?
Problem 2. Cointegration was developed in economics to deal with a problem of spurious correlation between series with stochastic trends. Why should spurious correlation be a concern if the trends in temperature and GHGs are deterministic?
Problem 3. Why is the concept of ‘climate’ distinguished from the concept of ‘weather’ by an arbitrary free parameter, usually involved in averaging or smoothing or ‘scale’ transformations of 10 to 30 years?
Problem 4. Why has a community of thousands or tens of thousands of climate scientists not managed to improve certainty in core areas in any significant way in more than a decade (eg the climate sensitivity caused by CO2 doubling as evidenced by little change in the IPCC bounds)?
Problem 5. Why do so many of the forecasts of CS fail (see C3 for list)?