Mean precipitation change from a deepening troposphere.

Global climate models robustly predict that global mean precipitation should increase at roughly 2-3% [Formula: see text], but the origin of these values is not well understood. Here we develop a simple theory to help explain these values. This theory combines the well-known radiative constraint on precipitation, which says that condensation heating from precipitation is balanced by the net radiative cooling of the free troposphere, with an invariance of radiative cooling profiles when expressed in temperature coordinates. These two constraints yield a picture in which mean precipitation is controlled primarily by the depth of the troposphere, when measured in temperature coordinates. We develop this theory in idealized simulations of radiative-convective equilibrium and also demonstrate its applicability to global climate models.

Acceleration of tropical cyclogenesis by self-aggregation feedbacks.

Idealized simulations of tropical moist convection have revealed that clouds can spontaneously clump together in a process called self-aggregation. This results in a state where a moist cloudy region with intense deep convection is surrounded by extremely dry subsiding air devoid of deep convection. Because of the idealized settings of the simulations where it was discovered, the relevance of self-aggregation to the real world is still debated. Here, we show that self-aggregation feedbacks play a leading-order role in the spontaneous genesis of tropical cyclones in cloud-resolving simulations. Those feedbacks accelerate the cyclogenesis process by a factor of 2, and the feedbacks contributing to the cyclone formation show qualitative and quantitative agreement with the self-aggregation process. Once the cyclone is formed, wind-induced surface heat exchange (WISHE) effects dominate, although we find that self-aggregation feedbacks have a small but nonnegligible contribution to the maintenance of the mature cyclone. Our results suggest that self-aggregation, and the framework developed for its study, can help shed more light into the physical processes leading to cyclogenesis and cyclone intensification. In particular, our results point out the importance of the longwave radiative cooling outside the cyclone.

Predicting fatal heat and humidity using the heat index model.

A unique wet-bulb temperature of 35°C is often used as the threshold for human survivability, but recent experiments have shown that a person's core temperature starts to rise at a wide range of critical wet-bulb temperatures. Here, it is shown that the model underlying the heat index correctly predicts those critical wet-bulb temperatures, explaining 95% of the variance in the values observed in laboratory heat-stress experiments. This is the first time the heat-index model has been validated against physiological data from laboratory experiments. For light and moderate exertion in an indoor setting, the heat index model predicts that the critical wet-bulb temperature ranges from 20°C to 32°C, depending on the relative humidity, consistent with experimental results. For the same setting and exertion, the heat index model predicts fatal wet-bulb temperatures ranging from 24°C to 37°C.NEW & NOTEWORTHY Recent experiments have identified the critical combinations of heat and humidity, in an indoor setting, above which an individual is unable to maintain a standard core temperature, indicating severe heat stress. It is shown here why this state of severe heat stress cannot be predicted using the wet-bulb temperature. Instead, it is shown that the recently extended heat index model can explain nearly all of the variance in the observed critical combinations of temperature and humidity, and can be used to calculate fatal combinations.

Amazon windthrow disturbances are likely to increase with storm frequency under global warming.

Forest mortality caused by convective storms (windthrow) is a major disturbance in the Amazon. However, the linkage between windthrows at the surface and convective storms in the atmosphere remains unclear. In addition, the current Earth system models (ESMs) lack mechanistic links between convective wind events and tree mortality. Here we find an empirical relationship that maps convective available potential energy, which is well simulated by ESMs, to the spatial pattern of large windthrow events. This relationship builds connections between strong convective storms and forest dynamics in the Amazon. Based on the relationship, our model projects a 51 ± 20% increase in the area favorable to extreme storms, and a 43 ± 17% increase in windthrow density within the Amazon by the end of this century under the high-emission scenario (SSP 585). These results indicate significant changes in tropical forest composition and carbon cycle dynamics under climate change.

Evolving CO2 Rather Than SST Leads to a Factor of Ten Decrease in GCM Convergence Time.

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the C O 2 mixing ratio to equilibrate climate, has been implemented in a cloud-resolving model. In this article, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium C O 2 for SSTs ranging from 290 to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations.

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.

Climate change. Projected increase in lightning strikes in the United States due to global warming.

Lightning plays an important role in atmospheric chemistry and in the initiation of wildfires, but the impact of global warming on lightning rates is poorly constrained. Here we propose that the lightning flash rate is proportional to the convective available potential energy (CAPE) times the precipitation rate. Using observations, the product of CAPE and precipitation explains 77% of the variance in the time series of total cloud-to-ground lightning flashes over the contiguous United States (CONUS). Storms convert CAPE times precipitated water mass to discharged lightning energy with an efficiency of 1%. When this proxy is applied to 11 climate models, CONUS lightning strikes are predicted to increase 12 ± 5% per degree Celsius of global warming and about 50% over this century.