Spatial Radiative Feedbacks from Internal Variability Using Multiple Regression

Abstract

The sensitivity of the climate to CO2 forcing depends on spatially varying radiative feedbacks that act both locally and nonlocally. We assess whether a method employing multiple regression can be used to estimate local and nonlocal radiative feedbacks from internal variability. We test this method on millennial-length simulations performed with six coupled atmosphere–ocean general circulation models (AOGCMs). Given the spatial pattern of warming, the method does quite well at recreating the top-of-atmosphere flux response for most regions of Earth, except over the Southern Ocean where it consistently overestimates the change, leading to an overestimate of the sensitivity. For five of the six models, the method finds that local feedbacks are positive due to cloud processes, balanced by negative nonlocal shortwave cloud feedbacks associated with regions of tropical convection. For four of these models, the magnitudes of both are comparable to the Planck feedback, so that changes in the ratio between them could lead to large changes in climate sensitivity. The positive local feedback explains why observational studies that estimate spatial feedbacks using only local regressions predict an unstable climate. The method implies that sensitivity in these AOGCMs increases over time due to a reduction in the share of warming occurring in tropical convecting regions and the resulting weakening of associated shortwave cloud and longwave clear-sky feedbacks. Our results provide a step toward an observational estimate of time-varying climate sensitivity by demonstrating that many aspects of spatial feedbacks appear to be the same between internal variability and the forced response.

The Influence of Warming Patterns on Passive Ocean Heat Uptake

“…Since the UVic model captures the average efficiency in CMIP5 quite well (which implies that it may also emulate the large-scale, average circulation dynamics in CMIP5), it must therefore be an inferior emulator for those CMIP5 models in which ventilation dynamics vary widely from the ensemble mean ( Figure 2). Sea surface warming patterns are known to regulate the climate's transient response to forcing through their interaction with radiative feedbacks (Andrews et al, 2015;Armour et al, 2013;Bloch-Johnson et al, 2020;Ceppi & Gregory, 2019;Dong et al, 2019;Gregory & Andrews, 2016;Rose et al, 2014). Our results show that warming patterns additionally influence the climate response by damping the ocean's ability to absorb heat, on average, in coupled models.…”

“…Sea surface warming patterns are known to regulate the climate's transient response to forcing through their interaction with radiative feedbacks (Andrews et al, 2015; Armour et al, 2013; Bloch‐Johnson et al, 2020; Ceppi & Gregory, 2019; Dong et al, 2019; Gregory & Andrews, 2016; Rose et al, 2014). Our results show that warming patterns additionally influence the climate response by damping the ocean's ability to absorb heat, on average, in coupled models.…”

“…Here we will specifically focus on the influence of spatial patterns of surface warming on OHU p , motivated by the critical role warming patterns play in pacing the atmosphere's radiative response to forcing. Specifically, radiative feedbacks vary strongly in space, such that their collective global effect, that is, λ in Equation , depends sensitively on where surface warming occurs (Armour et al, 2013; Rose et al, 2014) through both local and nonlocal effects (Andrews et al, 2015; Bloch‐Johnson et al, 2020; Ceppi & Gregory, 2019; Dong et al, 2019; Gregory & Andrews, 2016; Stuecker et al, 2018). This is termed a “pattern effect” (Stevens et al, 2016; Xie, 2020), whereby depends not only on (i.e., Equation ) but also on its spatial pattern (i.e., T ( x , y )).…”

Comparison of Equilibrium Climate Sensitivity Estimates From Slab Ocean, 150‐Year, and Longer Simulations

“…Ocean heat uptake influences the pattern of surface temperature (Haugstad et al, 2017), which in turn determines the strength of climate feedback due to the spatially heterogeneous nature of these feedbacks (Armour et al, 2013). Specifically, the increase in feedback with time appears to be in large part due to the movement of the pattern of warming away from regions of tropical convection, regions that tends to induce particularly negative climate feedbacks (Bloch‐Johnson et al, 2020; Dong et al, 2019; Zhou et al, 2017). Feedback temperature dependence, as mentioned above, can also change the slope of Δ F against Δ T .…”

Seasonal Loops Between Local Outgoing Longwave Radiation and Surface Temperature

“…Going to even longer variability, recent work found that the longwave feedback inferred from internal variability in CMIP5 Global Climate Models only approaches its equilibrium value on time scales longer than  10 years (Lutsko & Takahashi, 2018). A plausible explanation is that the spatial pattern of warming/ cooling associated with internal variability differs from the warming pattern under 2 CO forcing (Andrews et al, 2018), and that warming in different parts of the world triggers distinct and non-local feedbacks (e.g., surface warming in the West Pacific can change tropospheric emission in the East Pacific without directly warming the surface in the East Pacific; Bloch-Johnson et al, 2020;Dong et al, 2019). Since these dynamics imply that local atmospheric changes under internal variability are not necessarily driven by changes in local surface temperature, the same dynamics that cause seasonal OLR loops might thus also occur on longer timescales, which would complicate inferences about Earth's long-term feedbacks that are based on simple linear regression.…”