Slow Modes of Global Temperature Variability and Their Impact on Climate Sensitivity Estimates

Internal climate variability confounds estimates of the climate response to forcing but offers an opportunity to examine the dynamics controlling Earth's energy budget. This study analyzes the time-evolving impact of modes of low-frequency internal variability on global-mean surface temperature (GMST) and top-of-atmosphere (TOA) radiation in preindustrial control simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The results show that the slow modes of variability with the largest impact on decadal GMST anomalies are focused in high-latitude ocean regions, where they have a minimal impact on global TOA radiation. When these regions warm, positive shortwave cloud and sea ice-albedo feedbacks largely cancel the negative feedback of outgoing longwave radiation, resulting in a weak net radiative feedback. As a consequence of the weak net radiative feedback, less energy is required to sustain these long-lived temperature anomalies. In contrast to these weakly radiating high-latitude modes, El Nino-Southern Oscillation (ENSO) has a large impact on the global energy budget, such that it remains the dominant influence on global TOA radiation out to decadal and longer time scales, despite its primarily interannual time scale. These results show that on decadal and longer time scales, different processes control internal variability in GMST than control internal variability in global TOA radiation. The results are used to quantify the impact of low-frequency internal variability and ENSO on estimates of climate sensitivity from historical GMST and TOA-radiative-imbalance anomalies.

Automated summary

Internal climate variability has a significant impact on global mean surface temperature and top-of-atmosphere radiation, with slow modes of variability in high-latitude ocean regions affecting GMST anomalies. On the other hand, El Nino-Southern Oscillation (ENSO) plays a major role in the global energy budget. These findings provide insights into the influences of low-frequency internal variability and ENSO on climate sensitivity estimates.