Fingerprinting low-frequency Last Millennium temperature variability in forced and unforced climate models

Abstract

Constraining unforced and forced climate variability impacts interpretations of past climate variations and predictions of future warming. However, comparing General Circulation Models (GCMs) and Last Millennium Holocene hydroclimate proxies reveals significant mismatches between simulated and reconstructed low-frequency variability at multi-decadal and longer timescales. This mismatch suggests that existing simulations underestimate either external or internal drivers of climate variability. In addition, large differences arise across GCMs in both the magnitude and spatial pattern of low-frequency climate variability. Dynamical understanding of forced and unforced variability is expected to contribute to improved interpretations of paleoclimate variability. To that end, we develop a framework for fingerprinting spatiotemporal patterns of temperature variability in forced and unforced simulations. This framework relies on two frequency-dependent metrics: (1) degrees of freedom (≡N) and (2) spatial coherence. First, we use N and spatial coherence to characterize variability across a suite of both pre-industrial control (unforced) and last-millennium (forced) GCM simulations. Overall, we find that, at low frequencies and when forcings are added, regional independence in the climate system decreases, reflected in fewer N and higher coherence between local and global-mean surface-temperature. We then present a simple three-box moist-static-energy-balance model for temperature variability, which is able to emulate key frequency-dependent behavior in the GCMs. This suggests that temperature variability in the GCM ensemble can be understood through Earth’s energy budget and down-gradient energy transport, and allows us to identify sources of polar-amplified variability. Finally, we discuss insights the three-box model can provide into model-to-model GCM differences.