  Earth's climate can be understood as a dynamical system that changes due to
external forcing and internal couplings. Essential climate variables, such as
surface air temperature, describe this dynamics. Our current interglacial, the
Holocene (11,700 years ago to today), has been characterized by great stability
of global mean temperature prior to anthropogenic warming. However, the
mechanisms and spatiotemporal patterns of fluctuations around this mean, called
temperature variability, are poorly understood despite their socio-economic
relevance for climate change mitigation and adaptation. Here, we examine
discrepancies between temperature variability from model simulations and
paleoclimate reconstructions by clarifying the stability of local and global
surface air temperature on the timescale of years to centuries. To this end, we
contrast power spectral densities (PSD) and their power-law scaling using
simulated and observation-based temperature series of the last six thousand
years. We further introduce the spectral gain to disentangle the
externally-forced and internally-generated variability as a function of
timescale. It is based on our estimate of the joint PSD of radiative forcing,
which exhibits a scale break around the period of ten years. We find that local
temperature series from paleoclimate reconstructions tend to show unstable
behavior on periods of 10 to 200 years, while simulated temperatures almost
exclusively show stable behavior. Conversely, the PSD and spectral gain of
global mean temperature are consistent across data sets. Our results point to
the limitation of climate models to fully represent local temperature
statistics over decades to centuries. By highlighting the key characteristics
of temperature variability, we pave a way to better constrain possible changes
in temperature variability with global warming and assess future climate risks.
