期刊
NATURE COMMUNICATIONS
卷 12, 期 1, 页码 -出版社
NATURE RESEARCH
DOI: 10.1038/s41467-021-23543-9
关键词
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资金
- National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) [GEO-1240507]
- Penn State Center for Climate Risk Management
- RIT College of Science Dean's Research Initiation Grants program
- NSF [1603051]
- National Science Foundation of China [41888101]
- RCN NSF award [OCE-16-36005]
Earth-system sensitivity (ESS) describes the long-term temperature response to a given change in atmospheric CO2 and, as such, is a crucial parameter to assess future climate change. Here, the authors use a Bayesian model with data from the last 420 Myrs to reduce uncertainties and estimate ESS to be around 3.4 degrees C.
The long-term temperature response to a given change in CO2 forcing, or Earth-system sensitivity (ESS), is a key parameter quantifying our understanding about the relationship between changes in Earth's radiative forcing and the resulting long-term Earth-system response. Current ESS estimates are subject to sizable uncertainties. Long-term carbon cycle models can provide a useful avenue to constrain ESS, but previous efforts either use rather informal statistical approaches or focus on discrete paleoevents. Here, we improve on previous ESS estimates by using a Bayesian approach to fuse deep-time CO2 and temperature data over the last 420 Myrs with a long-term carbon cycle model. Our median ESS estimate of 3.4 degrees C (2.6-4.7 degrees C; 5-95% range) shows a narrower range than previous assessments. We show that weaker chemical weathering relative to the a priori model configuration via reduced weatherable land area yields better agreement with temperature records during the Cretaceous. Research into improving the understanding about these weathering mechanisms hence provides potentially powerful avenues to further constrain this fundamental Earth-system property. Earth-system sensitivity (ESS) describes the long-term temperature response for a given change in atmospheric CO2 and, as such, is a crucial parameter to assess future climate change. Here, the authors use a Bayesian model with data from the last 420 Myrs to reduce uncertainties and estimate ESS to be around 3.4 degrees C.
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