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Abstract. Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature isto anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorlyconstrained, in part because historic observations and future climate projections consider the climate system under different response timescaleswith potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since themid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components actingover multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedbackcombined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. Weextract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCRdistributions 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 (1.6to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year responsetimescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper boundon S than historic observations are otherwise consistent with.

Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets