Probing the timescale dependency of local and global variations in surface air temperature from climate simulations and reconstructions of the last millennia

TL;DR

This study compares climate model simulations and paleoclimate reconstructions to understand the timescale-dependent variability of surface air temperature over the last 6000 years, revealing differences in local temperature persistence and model limitations.

Contribution

It introduces a spectral gain method to distinguish forced and internal variability and highlights discrepancies in local temperature variability between models and reconstructions.

Findings

Local temperature shows stronger persistence in reconstructions.

Global temperature variability is consistent across data sets.

Models underrepresent local temperature statistics over decades to centuries.

Abstract

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 yr ago to today), has been characterized by small variations in 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. Here, we examine discrepancies between temperature variability from model simulations and paleoclimate reconstructions by categorizing the scaling behavior 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 6000 yr. 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 7 yr. We find that local temperature series from paleoclimate reconstructions show a different scaling behavior than simulated ones, with a tendency towards stronger persistence (i.e., correlation between successive values within a time series) on periods of 10 to 200 yr. 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.