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Abstract Increasing severity of extreme heat is a hallmark of climate change. Its impacts depend on temperature but also on moisture and solar radiation, each with distinct spatial patterns and vertical profiles. Here, we consider these variables’ combined effect on extreme heat stress, as measured by the environmental stress index, using a suite of high-resolution climate simulations for historical (1980–2005) and future (2074–2099, Representative Concentration Pathway 8.5 (RCP8.5)) periods. We find that observed extreme heat stress drops off nearly linearly with elevation above a coastal zone, at a rate that is larger in more humid regions. Future projections indicate dramatic relative increases whereby the historical top 1% summer heat stress value may occur on about 25%–50% of future summer days under the RCP8.5 scenario. Heat stress increases tend to be larger at higher latitudes and in areas of greater temperature increase, although in the southern and eastern US moisture increases are nearly as important. Imprinted on top of this dominant pattern we find secondary effects of smaller heat stress increases near ocean coastlines, notably along the Pacific coast, and larger increases in mountains, notably the Sierra Nevada and southern Appalachians. This differential warming is attributable to the greater warming of land relative to ocean, and to larger temperature increases at higher elevations outweighing larger water-vapor increases at lower elevations. All together, our results aid in furthering knowledge about drivers and characteristics that shape future extreme heat stress at scales difficult to capture in global assessments. 

Topological data analysis (TDA) combines concepts from algebraic topology, machine learning, statistics, and data science which allow us to study data in terms of their latent shape properties. Despite the use of TDA in a broad range of applications, from neuroscience to power systems to finance, the utility of TDA in Earth science applications is yet untapped. The current study aims to offer a new approach for analyzing multi-resolution Earth science datasets using the concept of data shape and associated intrinsic topological data characteristics. In particular, we develop a new topological approach to quantitatively compare two maps of geophysical variables at different spatial resolutions. We illustrate the proposed methodology by applying TDA to aerosol optical depth (AOD) datasets from the Goddard Earth Observing System, Version 5 (GEOS-5) model over the Middle East. Our results show that, contrary to the existing approaches, TDA allows for systematic and reliable comparison of spatial patterns from different observational and model datasets without regridding the datasets into common grids. 

Key spatiotemporal patterns of monthly scale temperature variability are characterized over southern South America usingk‐means clustering. The resulting clusters reveal patterns of temperature variability, referred to as temperature variability states. Analysis is performed over summer and winter months separately using data covering the period 1980–2015. Results for both seasons show four primary temperature variability states. In both seasons, one state is primarily characterized by warm temperature anomalies across the domain while another is characterized by cold anomalies. The other two patterns tend to be characterized by a warm north–cold south and cold north–warm south feature. This suggests two primary modes of temperature variability over the region. Composites of synoptic‐scale meteorological patterns (wind, geopotential height, and moisture fields) are computed for months assigned to each cluster to diagnose the driving meteorology associated with these variability states. Results suggest that low‐level temperature advection promoted by anomalies in atmospheric circulation patterns is a key process for driving these variability states. Moisture‐related processes also are shown to play a role, especially in summer. The El Niño–Southern Oscillation and the Southern Annular Mode exhibit some relationship with temperature variability state frequency, with some states more common during amplified phases of these two modes than others. However, the climate modes are not a primary driver of the temperature variability states.