Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models

Abstract

Radiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. This study investigates intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the west Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6.

Climate Sensitivity and Feedback of a New Coupled Model (K-ACE) to Idealized CO2 Forcing

“…A useful method to calculate ECS via abrupt experiment is proposed by Gregory et al [48], which is estimated by comparing the response of the top of atmosphere (TOA) radiative flux and surface air temperature (ECS value as one-half of the x-intercept and the total climate feedback parameter as the slope of the regression). This method has been widely used to provide ECS of climate models [25][26][27]30]. Shortwave (SW) and Longwave (LW) feedback parameters are calculated using a similar concept; however, the TOA radiative fluxes are applied anomalies instead of total value.…”

An Assessment of Earth's Climate Sensitivity Using Multiple Lines of Evidence

“…Note these values differ slightly from those in Armour (2017) and Lewis and Curry (2018) who estimated S based on a regression over Years 21-150 following abrupt CO 2 quadrupling rather than Years 1-150 as done here. Using the early portion of abrupt4xCO 2 simulations as an analog for historical warming and following the methods of Lewis and Curry (2018), Dong et al (2020) find an average radiative feedback change of Δλ = +0.1 W m −2 K −1 (−0.2 to +0.3 W m −2 K −1 range across models) for CMIP5 models and Δλ = +0.1 W m −2 K −1 (−0.1 to +0.3 W m −2 K −1 range across models) for CMIP6 models.…”

“…Since the sensitivity causes us to infer S > S hist because of the "warm-getting-warmer" pattern in the historical record, an overestimated cloud sensitivity would imply an overestimate of S. However, during paleoclimate periods, where warm regions changed less than cool regions, the same error could lead to an underestimate of S. We therefore find that codependency between paleo and historical evidence is "buffered." Codependencies are also possible whereby errors in cloud physics more generally could affect both the historical transfer function and process understanding; however, given that there are a wide range of cloud feedback behavior and transfer functions implied across GCMs, a codependency should appear as a correlation between the two, but available evidence does not suggest a correlation (Dong et al, 2020) although this merits further investigation. So we conclude that uncertainty in the cloud sensitivity to SST patterns is not an evident codependency concern.…”

“…(c) Global mean TOA radiative response induced by perturbing SSTs in one region at a time, calculated as anomalous TOA radiative fluxes in response to local SST perturbations in NCAR's Community Atmosphere Model Version 5 (CAM5)(Zhou et al, 2017; see alsoDong et al, 2019, for comparison to CAM4). (d) Relationship between historical feedbacks λ hist and the long-term λ in coupled CMIP5 and CMIP6 models using values from analysis inLewis and Curry (2018) andDong et al (2020) (blue points), respectively, and for atmosphere-only simulations from Andrews et al (2018) (orange points).…”

Changes in Local and Global Climate Feedbacks in the Absence of Interactive Clouds: Southern Ocean–Climate Interactions in Two Intermediate-Complexity Models

“…Over time l(t) becomes less negative in most GCMs (Geoffroy et al 2013;Andrews et al 2015;Gregory et al 2015;Rugenstein et al 2016a;Yoshimori et al 2016;Rose and Rayborn 2016;Proistosescu and Huybers 2017;Sherwood et al 2020;Dong et al 2020), indicating an increasing climate sensitivity (5 21/l). This feedback time dependence has been attributed mainly to contributions of cloud and lapse-rate feedback changes (Rose et al 2014;Andrews et al 2015;Rose and Rayborn 2016;Zhou et al 2016;Ceppi and Gregory 2017;Zhou et al 2017;Andrews and Webb 2018;Dong et al 2019).…”