The vertical velocities of convective clouds are of great practical interest because of their influence on many phenomena, including severe weather and stratospheric moistening. However, the magnitudes of forces giving rise to these vertical velocities are poorly understood, and the dominant balance is in dispute. Here, an algorithm is used to extract thousands of cloud thermals from a large-eddy simulation of deep and tropical maritime convection. Using a streamfunction to define natural boundaries for these thermals, the dominant balance in the vertical momentum equation is revealed. Cloud thermals rise with a nearly constant speed determined by their buoyancy and the standard drag law with a drag coefficient of 0.6. Contrary to suggestions that cloud thermals might be slippery, with a dominant balance between buoyancy and acceleration, cloud thermals are found here to be sticky, with a dominant balance between buoyancy and drag.
The contemporaneous pointwise product of convective available potential energy (CAPE) and precipitation is shown to be a good proxy for lightning. In particular, the CAPE × P proxy for lightning faithfully replicates seasonal maps of lightning over the contiguous United States, as well as the shape, amplitude, and timing of the diurnal cycle in lightning. Globally, CAPE × P correctly predicts the distribution of flash rate densities over land, but it does not predict the pronounced land‐ocean contrast in flash rate density; some factor other than CAPE or P is responsible for that land‐ocean contrast.
Abstract. It has become increasingly common for researchers to utilize methods that identify weather features in climate models. There is an increasing recognition that the uncertainty associated with choice of detection method may affect our scientific understanding. For example, results from the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) indicate that there are a broad range of plausible atmospheric river (AR) detectors and that scientific results can depend on the algorithm used. There are similar examples from the literature on extratropical cyclones and tropical cyclones. It is therefore imperative to develop detection techniques that explicitly quantify the uncertainty associated with the detection of events. We seek to answer the following question: given a “plausible” AR detector, how does uncertainty in the detector quantitatively impact scientific results? We develop a large dataset of global AR counts, manually identified by a set of eight researchers with expertise in atmospheric science, which we use to constrain parameters in a novel AR detection method. We use a Bayesian framework to sample from the set of AR detector parameters that yield AR counts similar to the expert database of AR counts; this yields a set of “plausible” AR detectors from which we can assess quantitative uncertainty. This probabilistic AR detector has been implemented in the Toolkit for Extreme Climate Analysis (TECA), which allows for efficient processing of petabyte-scale datasets. We apply the TECA Bayesian AR Detector, TECA-BARD v1.0.1, to the MERRA-2 reanalysis and show that the sign of the correlation between global AR count and El Niño–Southern Oscillation depends on the set of parameters used.
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