Including the efficacy of land ice changes in deriving climate sensitivity from paleodata

Abstract

Abstract. The equilibrium climate sensitivity (ECS) of climate models is calculated as the equilibrium global mean surface air warming resulting from a simulated doubling of the atmospheric CO2 concentration. In these simulations, long-term processes in the climate system, such as land ice changes, are not incorporated. Hence, climate sensitivity derived from paleodata has to be compensated for these processes, when comparing it to the ECS of climate models. Several recent studies found that the impact these long-term processes have on global temperature cannot be quantified directly through the global radiative forcing they induce. This renders the prevailing approach of deconvoluting paleotemperatures through a partitioning based on radiative forcings inaccurate. Here, we therefore implement an efficacy factor ε[LI] that relates the impact of land ice changes on global temperature to that of CO2 changes in our calculation of climate sensitivity from paleodata. We apply our refined approach to a proxy-inferred paleoclimate dataset, using ε[LI]=0.45-0.20+0.34 based on a multi-model assemblage of simulated relative influences of land ice changes on the Last Glacial Maximum temperature anomaly. The implemented ε[LI] is smaller than unity, meaning that per unit of radiative, forcing the impact on global temperature is less strong for land ice changes than for CO2 changes. Consequently, our obtained ECS estimate of 5.8±1.3 K, where the uncertainty reflects the implemented range in ε[LI], is ∼50 % higher than when differences in efficacy are not considered.

Glacial cooling and climate sensitivity revisited

“…We also calculate a third solution for ECS that assumes a lower ice sheet e cacy (") 42 , in which R ICE is multiplied by 0.65. This value of " comes from assuming an average fractional influence of the ice sheet (!)…”

“…11 in ref. 42 to calculate the corresponding ", based on the mean values of R GHG and R ICE given above. LGM global temperature change and climate sensitivity derived from data assimilation.…”

“…To calculate an ECS that approximates the classical "Charney" definition, we must consider, in addition to greenhouse gas forcing ( R GHG ) the slow feedback processes that a↵ect LGM climate, which following ref. 36 Recently, it has been argued that ice sheets are not as "e↵ective" as greenhouse gases in terms of global radiative forcing, because their impact might be concentrated at high latitudes 41,42 . We explore this possibility by presenting a distribution of ECS with an ice sheet e cacy (") of 0.65 (see Methods) ( Fig.…”

LGM climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity

“…Rohling et al (2012) proposed a framework to obtain ECS from reconstructions of paleo-temperatures and climatic forcings in which slow (timescales > 100 years) feedback processes such as changes in GHGs, LISs, Earth's orbits, and land use are considered as climate forcings rather than feedbacks. This approach has been widely used to directly calculate ECS from proxy reconstructions of the LGM and glacial-interglacial cycles with estimates ranging from 2.6 to 8.1°C (Friedrich, Timmermann, Tigchelaar, Elison Timm, & Ganopolski, 2016;Köhler et al, 2017;Stap, Köhler, & Lohmann, 2019;Tierney, Zhu, et al, 2019;von der Heydt, Köhler, van de Wal, & Dijkstra, 2014).…”

“…In the context of the LGM, both the radiative forcing due to changes in GHGs, LISs, vegetation, and aerosols and their efficacy (impact on GMST) must be known. Of these forcings, GHGs and LISs have been considered in most LGMbased ECS estimations (Friedrich et al, 2016;Köhler et al, 2017;Schmittner et al, 2011;Stap et al, 2019;von der Heydt et al, 2014). Previous estimates of the LGM LIS forcing account for albedo changes associated with the presence of LISs and the exposure of shelves due to the lowered sea level (Figure 1a), yielding a shortwave forcing ranging from -1.5 to -5.2 W m -2 (Braconnot et al, 2012;Braconnot & Kageyama, 2015;Friedrich et al, 2016;Hansen, Sato, Russell, & Kharecha, 2013;Köhler et al, 2010;Taylor et al, 2007;Tierney, Zhu, et al, 2019).…”