Abstract
Abstract. Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5–95 % range) ∘C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.
Highlights
A key goal in climate science is to evaluate how sensitive the global mean temperature anomaly is, which is done via the following equation: λeff(t) = (Rtotal(t) − N (t))/ T (t), (1)where both Rtotal and T are defined as zero at some preindustrial state
CRUT5 and NODC), revealing that the Planck feedback, fast feedback, and multi-decadal feedback strengths are insensitive to the choice of ocean heat content dataset used within the likelihood function (Fig. 2a–c compare red and grey)
Our analysis reveals a transient climate response (TCR) of 1.5 (1.3 to 1.8 at 90 % range) ◦C when constrained by the HadCRUT5 temperature reconstruction with either ocean heat content dataset (Table 1)
Summary
The aim here is to perform Bayesian probabilistic evaluations of both S and transient climate response (TCR in K), using observational constraints on global surface temperature and ocean heat content anomalies to constrain a model framework that includes time-varying climate feedbacks (Eqs. 1 and 2). We utilize a numerical model that allows multiple climate feedbacks to individually respond to radiative forcing over different timescales (Goodwin, 2018), such that λeff varies over time (Eqs. 1 and 2). Each posterior ensemble applies a different combination of historic reconstructions of global surface temperature anomaly (either HadCRUT5 or HadCRUT5 without statistical infilling of geographically absent data, hereafter HadCRUT5 (no infill); Morice et al, 2021; Fig. 1a) and a reconstruction of the ocean heat content anomaly (either Cheng et al, 2017, hereafter Cheng et al, or Levitus et al, 2012, hereafter NODC; Fig. 1b). All of our posterior ensembles are extracted using the additional constraints from HadSST4 (Kennedy et al, 2019) and the Global Carbon Budget (le Quéré et al, 2018) for sea surface temperature and ocean carbon uptake anomalies, respectively (see Supplement)
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