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The combination of precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics. The model has several idealizations, including that all condensates immediately fall out of the atmosphere, and is run across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a 10-fold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of “tropical cyclone worlds” to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly “turning on” mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill. 

Much of our understanding of atmospheric circulation comes from relationships between aspects of the circulation and the mean state of the atmosphere. In particular, the concept of mean available potential energy (MAPE) has been used previously to relate the strength of the extratropical storm tracks to the zonal-mean temperature and humidity distributions. Here, we calculate for the first time the MAPE of the zonally varying (i.e., three-dimensional) time-mean state of the atmosphere including the effects of latent heating. We further calculate a local MAPE by restricting the domain to an assumed eddy size, and we partition this local MAPE into convective and nonconvective components. Local convective MAPE maximizes in the subtropics and midlatitudes, in many cases in regions of the world that are known to have intense convection. Local nonconvective MAPE has a spatial pattern similar to the Eady growth rate, although local nonconvective MAPE has the advantage that it takes into account latent heating. Furthermore, the maximum potential ascent associated with local nonconvective MAPE is related to the frequency of warm conveyor belts (WCBs), which are ascending airstreams in extratropical cyclones with large impacts on weather. This maximum potential ascent can be calculated based only on mean temperature and humidity, and WCBs tend to start in regions of high maximum potential ascent on a given day. These advances in the use of MAPE are expected to be helpful to connect changes in the mean state of the atmosphere, such as under global warming, to changes in important aspects of extratropical circulation. 

A promising approach to improve climate‐model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data‐driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to an atmospheric model. Here, we learn an NN parameterization from a high‐resolution atmospheric simulation in an idealized domain by accurately calculating subgrid terms through coarse graining. The NN parameterization has a structure that ensures physical constraints are respected, such as by predicting subgrid fluxes instead of tendencies. The NN parameterization leads to stable simulations that replicate the climate of the high‐resolution simulation with similar accuracy to a successful random‐forest parameterization while needing far less memory. We find that the simulations are stable for different horizontal resolutions and a variety of NN architectures, and that an NN with substantially reduced numerical precision could decrease computational costs without affecting the quality of simulations.

 

Solar geoengineering that aims to offset global warming could nonetheless alter atmospheric temperature gradients and humidity and thus affect the extratropical storm tracks. Here, we first analyze climate model simulations from experiment G1 of the Geoengineering Model Intercomparison Project, in which a reduction in incoming solar radiation balances a quadrupling of CO₂. The Northern Hemisphere extratropical storm track weakens by a comparable amount in G1 as it does for increased CO₂only. The Southern Hemisphere storm track also weakens in G1, in contrast to a strengthening and poleward shift for increased CO₂. Using mean available potential energy, we show that the changes in zonal‐mean temperature and humidity are sufficient to explain the different responses of storm‐track intensity. We also demonstrate similar weakening in a more complex geoengineering scenario. Our results offer insight into how geoengineering affects storm tracks, highlighting the potential for geoengineering to induce novel climate changes.