Predicting Cloud-To-Ground Lightning in the Western United States From the Large-Scale Environment Using Explainable Neural Networks.

Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud-to-ground (CG) lightning across the WUS during June-September from the spatial patterns of seven large-scale meteorological variables from reanalysis (1995-2022). Individually trained CNN models at each 1° × 1° grid cell ( = 285 CNNs) show high skill at predicting CG lightning days across the WUS (median AUC = 0.

The Deep Ocean's Carbon Exhaust.

The deep ocean releases large amounts of old, pre-industrial carbon dioxide (CO) to the atmosphere through upwelling in the Southern Ocean, which counters the marine carbon uptake occurring elsewhere. This Southern Ocean CO release is relevant to the global climate because its changes could alter atmospheric CO levels on long time scales, and also affects the present-day potential of the Southern Ocean to take up anthropogenic CO. Here, year-round profiling float measurements show that this CO release arises from a zonal band of upwelling waters between the Subantarctic Front and wintertime sea-ice edge.

Correlation Between Sea-Level Rise and Aspects of Future Tropical Cyclone Activity in CMIP6 Models.

Future coastal flood hazard at many locations will be impacted by both tropical cyclone (TC) change and relative sea-level rise (SLR). Despite sea level and TC activity being influenced by common thermodynamic and dynamic climate variables, their future changes are generally considered independently. Here, we investigate correlations between SLR and TC change derived from simulations of 26 Coupled Model Intercomparison Project Phase 6 models.