Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropical cyclone, Hurricane Florence. Mean total overland rainfall amounts associated with the forecasted storm’s core were increased by 4.9 ± 4.6% with local maximum amounts experiencing increases of 3.8 ± 5.7% due to climate change. A slight increase in the forecasted storm size of 1 to 2% was also attributed. This work reviews our forecasted attribution statement with the benefit of hindsight, demonstrating credibility of advance attribution statements for tropical cyclones.

Tropical cyclones (TCs) can subject an area to heavy precipitation for many hours, or even days, worsening the risk of flooding, which creates dangerous conditions for residents of the U.S. East and Gulf Coasts. To study the representation of TC-related precipitation over the eastern United States in current-generation global climate models, a novel analysis methodology is developed to track TCs and extract their associated precipitation using an estimate of their dynamical outer size. This methodology is applied to three variable-resolution (VR) configurations of the Community Atmosphere Model, version 5 (CAM5), with high-resolution domains over the North Atlantic and one low-resolution conventional configuration, as well as to a combination of reanalysis and observational precipitation data. Metrics and diagnostics such as TC counts, intensities, outer storm sizes, and annual mean total and extreme precipitation are compared between the CAM5 simulations and reanalysis/observations. The high-resolution VR configurations outperform the global low-resolution configuration for all variables in the North Atlantic. Realistic TC intensities are produced by the VR configurations. The total North Atlantic TC counts are lower than observations but better than reanalysis.