Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators

TL;DR

The prospects for and advantages from using ESMs and ML emulators to study fast processes in global climate are discussed, and it is shown that the responses from historically trained emulators agree with those produced by full-physics Earth System Models.

Abstract

The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typically activated in response to changes in ocean temperatures on decadal timescales and manifest as changes in climatic state with no recent historical analogue. However, fast feedbacks are activated in response to rapid atmospheric physical processes on weekly timescales, and they are already operative in the present-day climate. This distinction implies that the physics of fast radiative feedbacks is present in the historical meteorological reanalyses used to train many recent successful machine-learning-based (ML) emulators of weather and climate. In addition, these feedbacks are functional under the historical boundary conditions pertaining to the top-of-atmosphere radiative balance and sea-surface temperatures. Together, these factors imply that we can use historically trained ML weather emulators to study the response of radiative-convective equilibrium (RCE), and hence the global hydrological cycle, to perturbations in carbon dioxide and other well-mixed greenhouse gases. Without retraining on prospective Earth system conditions, we use ML weather emulators to quantify the fast precipitation response to reduced and elevated carbon dioxed concentrations with no recent historical precedent. We show that the responses from historically trained emulators agree with those produced by full-physics Earth System Models (ESMs). In conclusion, we discuss the prospects for and advantages from using ESMs and ML emulators to study fast processes in global climate.

Figures (21)

• Figure 1: Response of CMIP Models to Abrupt CO${}_2$ Quadrupling. The response of 8 ESMs to the abrupt4xCO2 experiment from the Coupled Model Intercomparison Project (CMIP) v6 Eyring2016. Lead time is shown as days since CO${}_2$ was quadrupled. Listed in Table \ref{['table:cmip_table']}, models were selected based on availability of data at daily resolution and sufficient metadata to determine the branch points of the instantaneous quadrupling simulations from the corresponding pre-industrial control simulations. Panel (a) shows the response of ocean surface temperature, and panel (b) shows the response of precipitation. For precipitation and temperature, the $\Delta$ refers to the global-mean differences between the abrupt4xCO2 simulations and the pre-industrial control simulations from which these were branched. Shading denotes $\pm1$ standard deviation across the multi-model ensemble.
• Figure 2: Diagram of ACE-RRTMG Rollout. Panel (a) shows the standard rollout of ACE with no perturbations for CO${}_2$ applied. The instantaneous model states are depicted at times $t_0 , t_1, \text{and}\ t_2$ with stylized maps depicting various prognosed variables. Panel (b) shows ACE-RRTMG with modifications to simulate the effects of different levels of CO${}_2$. $Q$ is the net clear-sky radiative heating [${}^\circ$K], and $Q^{\text{net}}$ is the sum of the clear-sky longwave and shortwave heating rates [${}^\circ$K/s]. At each timestep, RRTMG is used to calculate $\Delta Q$, the heating perturbation due to an instantaneous change in CO${}_2$ from $1\times$ to $N\times$ its concentration at 2010 CE. The heating rate perturbations are applied to the temperature fields at each pressure level in the ACE rollout.
• Figure 3: Heating Rate Perturbations from 1$\times$CO${}_2$ & 4$\times$CO${}_2$. The longwave, shortwave, and net heating rate perturbations are shown for the E3SM Atmosphere Model EAMv2 in panel (a) and ACE coupled to RRTMG in panel (b). The net heating rate perturbation (4$\times$CO${}_2$ - 1$\times$CO${}_2$) is shown for EAMv2 and ACE in (c). Note the different scales in the x axis in (c) compared to (a) and (b).
• Figure 4: Global Mean Fast Feedback in Response to Instantaneous Changes in CO${}_2$ Concentrations. Panel (a): the fast response to increased CO${}_2$ from the E3SM Atmosphere Model; panel (b): the corresponding response from our modified ACE architecture including column diagnosis and prognosis. See Section \ref{['ssec:ace_modification']} and Figure \ref{['fig:architecture_diagram']} for a description of these modifications. Colors denote the response to instantaneous multiplicative changes in CO${}_2$ concentration relative to a $1\times$ baseline. Dashed lines denote latent heat flux, and solid lines denote precipitation. Responses are calculated using 96-member ensembles of 1-month-long simulations from EAMv2 and columnwise ACE.
• Figure 5: Spatial Pattern of Precipitation and Latent Heat Flux Fast Feedback Response to 8$\times$CO${}_2$. Spatial patterns are calculated from the first seven days after CO${}_2$ is instantaneously octupled. Panels (a) and (b): response from the physics-based E3SM Atmosphere Model; panels (c) and (d): response from our modified ACE architecture with column diagnostics and prognostics. Panels (a) and (c): spatial pattern of the response of precipitation; panels (b) and (d): corresponding response of latent heat flux converted to equivalent precipitation units.