» Articles » PMID: 38114458

Solar Cycle As a Distinct Line of Evidence Constraining Earth's Transient Climate Response

Overview
Journal Nat Commun
Specialty Biology
Date 2023 Dec 19
PMID 38114458
Authors
Affiliations
Soon will be listed here.
Abstract

Severity of warming predicted by climate models depends on their Transient Climate Response (TCR). Inter-model spread of TCR has persisted at ~ 100% of its mean for decades. Existing observational constraints of TCR are based on observed historical warming response to historical forcing and their uncertainty spread is just as wide, mainly due to forcing uncertainty, and especially that of aerosols. Contrary, no aerosols are involved in solar-cycle forcing, providing an independent, tighter, constraint. Here, we define a climate sensitivity metric: time-dependent response regressed against time-dependent forcing, allowing phenomena with dissimilar time variations, such as the solar cycle with 11-year cyclic forcing, to be used to constrain TCR, which has a linear time-dependent forcing. We find a theoretical linear relationship between the two. The latest coupled atmosphere-ocean climate models obey the same linear relationship statistically. The proposed observational constraint on TCR is about [Formula: see text] as narrow as existing constraints. The central estimate, 2.2 C, is at the midpoint of the spread of the latest generation of climate models, which are more sensitive than those of the previous generations.

References
1.
Hope C . The $10 trillion value of better information about the transient climate response. Philos Trans A Math Phys Eng Sci. 2015; 373(2054). DOI: 10.1098/rsta.2014.0429. View

2.
Meehl G, Senior C, Eyring V, Flato G, Lamarque J, Stouffer R . Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci Adv. 2020; 6(26):eaba1981. PMC: 7314520. DOI: 10.1126/sciadv.aba1981. View

3.
Tokarska K, Stolpe M, Sippel S, Fischer E, Smith C, Lehner F . Past warming trend constrains future warming in CMIP6 models. Sci Adv. 2020; 6(12):eaaz9549. PMC: 7080456. DOI: 10.1126/sciadv.aaz9549. View

4.
Wu Z, Huang N, Long S, Peng C . On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc Natl Acad Sci U S A. 2007; 104(38):14889-94. PMC: 1986583. DOI: 10.1073/pnas.0701020104. View

5.
Tung K, Zhou J . Using data to attribute episodes of warming and cooling in instrumental records. Proc Natl Acad Sci U S A. 2013; 110(6):2058-63. PMC: 3568361. DOI: 10.1073/pnas.1212471110. View