This is why we decomposed the temperature data into a slow, non-linear trend line (shown here) and
a stochastic component — a standard procedure that even makes it onto the cover picture of a data analysis textbook, as well as being described in a climate time series analysis textbook.
Those two effects (edge of the observed range,
no stochastic component) add about 1C to the bottom range of the terminal distribution.
> «Moreover, since they have
no stochastic component» huh??
Strictly an initial condition ensemble or a perturbed parameter ensemble is not
a stochastic component per strict definition of stochastic but I am not sure I see any way in which these fail to fill the purposes of a stochastic component (and they have other benefits / uses as well).
Moreover, since they have
no stochastic component, the terminal range fails to include one standard deviation of downside natural variability.