Abstract
Relationships between climate variability and climate sensitivity are to
be expected where the damping of a climatic anomaly is due to a change
in the energy balance of the planet, such that the
Fluctuation-Dissipation theorem heuristically applies [Leith, 1975].
A recent attempt to relate Equilibrium Climate Sensitivity (ECS) to
global temperature variability over the historical period suggested a
surprisingly tight emergent constraint on ECS [Cox et al., 2018].
However, the sensitivity-variability relationship in that study was
partially hidden by anthropogenic forcing over the historical period.
Here we examine instead CMIP5 control runs. These runs have no external
forcing and therefore provide a much cleaner test of proposed links
between internal variability and sensitivity. It has been noted before
that there is a positive correlation between decadal temperature
variability and climate sensitivity across climate models [Colman &
Power, 2018]. Questions remained however as to how robust this
relationship is across different model ensembles, what mechanisms are
responsible for it, and whether it can be used as an emergent constraint
on climate sensitivity. We examine the relationship between decadal
variability and ECS using models of varying complexity, including CMIP5
control runs and a range of conceptual energy balance models for which
analytical solutions are presented. Based on these results, a general
mechanism becomes apparent and the shape of the relationship is
determined to be more quadratic than linear. The nonlinearity has
implications for using this relationship as an emergent constraint,
where an incorrect assumption of linearity might lead to biased
estimates. A further surprising implication of the study is that a
slowdown in global warming does not necessarily imply that climate
sensitivity is lower than previously estimated. Models with a higher
sensitivity, but which broadly reproduce the long-term record of global
warming, are actually more likely to have slow-down periods than models
with lower sensitivity.