Observations of planetary heating since the 1980s from multiple independent datasets

Allison, L. C. ORCID: https://orcid.org/0000-0002-9271-1197, Palmer, M. D. ORCID: https://orcid.org/0000-0001-7422-198X, Allan, R. ORCID: https://orcid.org/0000-0003-0264-9447, Hermanson, L. ORCID: https://orcid.org/0000-0002-1062-6731, Liu, C. ORCID: https://orcid.org/0000-0002-6663-452X and Smith, D. M. ORCID: https://orcid.org/0000-0001-5708-694X (2020) Observations of planetary heating since the 1980s from multiple independent datasets. Environmental Research Communications, 2 (10). p. 101001. ISSN 2515-7620 doi: 10.1088/2515-7620/abbb39

Abstract/Summary

Time series of global mean surface temperature are widely used to measure the rate of climate change that results from Earth's energy imbalance. However, studies based on climate model simulations suggest that on annual-to-decadal timescales global ocean heat content is a more reliable indicator. Here we examine the observational evidence for this, drawing together multiple datasets that span the past ~30 years. This observational analysis strongly supports the model-based finding that global ocean heat content and sea level are more reliable than surface temperature for monitoring Earth's energy accumulation on these timescales. Global ocean temperature anomalies in the 0–100 m and 100–250 m layers are negatively correlated (r = −0.36), primarily explained by the influence of the Tropical Pacific, and a clearer heating signal is revealed by integrating over deeper ocean layers. The striking agreement between multiple independent datasets represents unequivocal evidence of ongoing planetary heating.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/93369
Identification Number/DOI	10.1088/2515-7620/abbb39
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Walker Institute Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO) Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	IOP Science
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