Global warming is expected to lead to a large increase in atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. The intensity of precipitation extremes is widely held to increase proportionately to the increase in atmospheric water vapor content. Here, we show that this is not the case in 21st-century climate change scenarios simulated with climate models. In the tropics, precipitation extremes are not simulated reliably and do not change consistently among climate models; in the extratropics, they consistently increase more slowly than atmospheric water vapor content. We give a physical basis for how precipitation extremes change with climate and show that their changes depend on changes in the moist-adiabatic temperature lapse rate, in the upward velocity, and in the temperature when precipitation extremes occur. For the tropics, the theory suggests that improving the simulation of upward velocities in climate models is essential for improving predictions of precipitation extremes; for the extratropics, agreement with theory and the consistency among climate models increase confidence in the robustness of predictions of precipitation extremes under climate change.global warming ͉ hydrological cycle ͉ rainfall ͉ extreme events I n simulations of 21st century climate change scenarios, mean precipitation generally increases in the deep tropics and extratropics and decreases in the subtropics (1-3). However, precipitation extremes (defined, for example, as a high percentile of daily precipitation) increase almost across the globe (2, 3), with expected societal impacts such as increased flooding and soil erosion (4). Precipitation extremes are widely held to increase proportionately to the mean atmospheric water vapor content (5, 6), or to the amount of water vapor converging at the base of storms (7). Global-mean water vapor content increases strongly in global warming simulations, at a rate of ϳ7.5% K Ϫ1 with respect to surface temperature, approximately consistent with a constant effective relative humidity (1). Precipitation extremes are thought to increase at a similar rate, or maybe even more rapidly if the strength of the updrafts associated with extreme precipitation events increases as the climate warms (5, 6).However, although precipitation extremes in simulations increase as the climate warms, their rate of increase varies with latitude and is generally not equal to the rate of increase in atmospheric water vapor content (6). Simulations of a wide range of climates with an idealized general circulation model show that precipitation extremes outside the subtropics scale more similarly to mean precipitation than to water vapor content (8). In simulations with comprehensive climate models, the rate of increase in precipitation extremes varies widely among models, particularly in the tropics (2). The variations among models in the tropics indicate that simulated precipitation extremes may depend sensitively on the parameterizatio...

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[1] Water vapor is not only Earth's dominant greenhouse gas. Through the release of latent heat when it condenses, it also plays an active role in dynamic processes that shape the global circulation of the atmosphere and thus climate. Here we present an overview of how latent heat release affects atmosphere dynamics in a broad range of climates, ranging from extremely cold to extremely warm. Contrary to widely held beliefs, atmospheric circulation statistics can change nonmonotonically with global-mean surface temperature, in part because of dynamic effects of water vapor. For example, the strengths of the tropical Hadley circulation and of zonally asymmetric tropical circulations, as well as the kinetic energy of extratropical baroclinic eddies, can be lower than they presently are both in much warmer climates and in much colder climates. We discuss how latent heat release is implicated in such circulation changes, particularly through its effect on the atmospheric static stability, and we illustrate the circulation changes through simulations with an idealized general circulation model. This allows us to explore a continuum of climates, to constrain macroscopic laws governing this climatic continuum, and to place past and possible future climate changes in a broader context. Citation: Schneider, T., P. A. O'Gorman, and X. J. Levine (2010), Water vapor and the dynamics of climate changes, Rev. Geophys., 48, RG3001,

The response of precipitation extremes to climate change is considered using results from theory, modeling, and observations, with a focus on the physical factors that control the response. Observations and simulations with climate models show that precipitation extremes intensify in response to a warming climate. However, the sensitivity of precipitation extremes to warming remains uncertain when convection is important, and it may be higher in the tropics than the extratropics. Several physical contributions govern the response of precipitation extremes. The thermodynamic contribution is robust and well understood, but theoretical understanding of the microphysical and dynamical contributions is still being developed. Orographic precipitation extremes and snowfall extremes respond differently from other precipitation extremes and require particular attention. Outstanding research challenges include the influence of mesoscale convective organization, the dependence on the duration considered, and the need to better constrain the sensitivity of tropical precipitation extremes to warming.

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The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high-resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here we focus on these issues using idealized tests in which an ML-based parameterization is trained on output from a conventional parameterization and its performance is assessed in simulations with a GCM. We use an ensemble of decision trees (random forest) as the ML algorithm, and this has the advantage that it automatically ensures conservation of energy and nonnegativity of surface precipitation. The GCM with the ML convective parameterization runs stably and accurately captures important climate statistics including precipitation extremes without the need for special training on extremes. Climate change between a control climate and a warm climate is not captured if the ML parameterization is only trained on the control climate, but it is captured if the training includes samples from both climates. Remarkably, climate change is also captured when training only on the warm climate, and this is because the extratropics of the warm climate provides training samples for the tropics of the control climate. In addition to being potentially useful for the simulation of climate, we show that ML parameterizations can be interrogated to provide diagnostics of the interaction between convection and the large-scale environment. Plain Language SummarySmall-scale features such as clouds are typically represented in climate models by simplified physical models, and these simplified models introduce errors and uncertainties. A promising alternative approach is to use machine learning to train a statistical model to represent small-scale processes based on output from expensive physics-based models that better represent the small-scale processes. Here we use idealized tests to explore the implications of incorporating a machine-learning model of atmospheric convection in a climate model. We find that such an approach can give accurate simulations of mean climate and heavy rainfall events. The machine-learning model does not work well for global warming if it is only trained on the current climate. However, it does work well for global warming if trained on both the current and warmer climates, and it works surprisingly well if only trained on the warmer climate. We also show that the machine-learning model can be used to better understand the underlying physical processes.

A wide range of hydrological cycles and general circulations was simulated with an idealized general circulation model (GCM) by varying the optical thickness of the longwave absorber. While the idealized GCM does not capture the full complexity of the hydrological cycle, the wide range of climates simulated allows the systematic development and testing of theories of how precipitation and moisture transport change as the climate changes. The simulations show that the character of the response of the hydrological cycle to variations in longwave optical thickness differs in different climate regimes.The global-mean precipitation increases linearly with surface temperature for colder climates, but it asymptotically approaches a maximum at higher surface temperatures. The basic features of the precipitation-temperature relation, including the rate of increase in the linear regime, are reproduced in radiativeconvective equilibrium simulations. Energy constraints partially account for the precipitation-temperature relation but are not quantitatively accurate.Large-scale condensation is most important in the midlatitude storm tracks, and its behavior is accounted for using a stochastic model of moisture advection and condensation. The precipitation associated with large-scale condensation does not scale with mean specific humidity, partly because the condensation region moves upward and meridionally as the climate warms, and partly because the mean condensation rate depends on isentropic specific humidity gradients, which do not scale with the specific humidity itself.The local water vapor budget relates local precipitation to evaporation and meridional moisture fluxes, whose scaling in the subtropics and extratropics is examined. A delicate balance between opposing changes in evaporation and moisture flux divergence holds in the subtropical dry zones. The extratropical precipitation maximum follows the storm track in warm climates but lies equatorward of the storm track in cold climates.