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Trends in infectious disease incidence provide important information about epidemic dynamics and prospects for control. Higher-frequency variation around incidence trends can shed light on the processes driving epidemics in complex populations, as transmission heterogeneity, shifting landscapes of susceptibility, and fluctuations in reporting can impact the volatility of observed case counts. However, measures of temporal volatility in incidence, and how volatility changes over time, are often overlooked in population-level analyses of incidence data, which typically focus on moving averages. Here we present a statistical framework to quantify temporal changes in incidence dispersion and to detect rapid shifts in the dispersion parameter, which may signal new epidemic phases. We apply the method to COVID-19 incidence data in 144 United States (US) counties from January 1st, 2020 to March 23rd, 2023. Theory predicts that dispersion should be inversely proportional to incidence, however our method reveals pronounced temporal trends in dispersion that are not explained by incidence alone, but which are replicated across counties. In particular, dispersion increased around the major surge in cases in 2022, and highly overdispersed patterns became more frequent later in the time series. These increases potentially indicate transmission heterogeneity, changes in the susceptibility landscape, or that there were changes in reporting. Shifts in dispersion can also indicate shifts in epidemic phase, so our method provides a way for public health officials to anticipate and manage changes in epidemic regime and the drivers of transmission. 

The physics of the heat-trapping properties of CO ${}_2$were established in the mid-19th century, as fossil fuel burning rapidly increased atmospheric CO ${}_2$levels. To date, however, research has not probed when climate change could have been detected if scientists in the 19th century had the current models and observing network. We consider this question in a thought experiment with state-of-the-art climate models. We assume that the capability to make accurate measurements of atmospheric temperature changes existed in 1860, and then apply a standard “fingerprint” method to determine the time at which a human-caused climate change signal was first detectable. Pronounced cooling of the mid- to upper stratosphere, mainly driven by anthropogenic increases in carbon dioxide, would have been identifiable with high confidence by approximately 1885, before the advent of gas-powered cars. These results arise from the favorable signal-to-noise characteristics of the mid- to upper stratosphere, where the signal of human-caused cooling is large and the pattern of this cooling differs markedly from patterns of intrinsic variability. Even if our monitoring capability in 1860 had not been global, and high-quality stratospheric temperature measurements existed for Northern Hemisphere mid-latitudes only, it still would have been feasible to detect human-caused stratospheric cooling by 1894, only 34 y after the assumed start of climate monitoring. Our study provides strong evidence that a discernible human influence on atmospheric temperature has likely existed for over 130 y. 

Abstract We investigate changes in the vertical structure of the ocean temperature annual cycle amplitude (TEMP_AC) down to a depth of 300 m, providing important insights into the relative contributions of anthropogenic and natural influences. Using observations and phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we perform a detection and attribution analysis by applying a standard pattern-based “fingerprint” method to zonal-mean TEMP_ACanomalies for three major ocean basins. In all model historical simulations and observational datasets, TEMP_ACincreases significantly in the surface layer, except in the Southern Ocean, and weakens within the subsurface ocean. There is a decrease in TEMP_ACbelow the annual-mean mixed layer depth, mainly due to a deep-reaching winter warming signal. The temporal evolution of signal-to-noise (S/N) ratios in observations indicates an identifiable anthropogenic fingerprint in both surface and interior ocean annual temperature cycles. These findings are consistent across three different observational datasets, with variations in fingerprint detection time likely related to differences in dataset coverage, interpolation method, and accuracy. Analysis of CMIP6 single-forcing simulations reveals the dominant influence of greenhouse gases and anthropogenic aerosols on TEMP_ACchanges. Our identification of an anthropogenic TEMP_ACfingerprint is robust to the selection of different analysis periods. S/N ratios derived with model data only are consistently larger than ratios calculated with observational signals, primarily due to model versus observed TEMP_ACdifferences in the Atlantic. Human influence on the seasonality of surface and subsurface ocean temperature may have profound consequences for fisheries, marine ecosystems, and ocean chemistry. Significance StatementThe seasonal cycle is a fundamental aspect of our climate, and gaining insight into how anthropogenic forcing has impacted seasonality is of scientific, economic, and societal importance. Using observations and CMIP6 model simulations, this research applies a pattern-based detection and attribution method to ocean temperature annual cycle amplitude (TEMP_AC) down to 300 m across three major ocean basins. Key findings reveal significant increases in surface layer TEMP_ACexcept in the Southern Ocean and a weakening of TEMP_ACwithin the subsurface ocean. Importantly, the analysis confirms human influence on TEMP_AC. These findings underscore the profound influence of human-caused climate change on the world’s oceans and have important implications for marine ecosystems, fisheries, and ocean chemistry. 

Abstract We present initial results from extremely well-resolved 3D magnetohydrodynamical simulations of idealized galaxy clusters, conducted using the AthenaPK code on the Frontier exascale supercomputer. These simulations explore the self-regulation of galaxy groups and cool-core clusters by cold gas-triggered active galactic nucleus (AGN) feedback incorporating magnetized kinetic jets. Our simulation campaign includes simulations of galaxy groups and clusters with a range of masses and intragroup and intracluster medium properties. In this paper, we present results that focus on a Perseus-like cluster. We find that the simulated clusters are self-regulating, with the cluster cores staying at a roughly constant thermodynamic state and AGN jet power staying at physically reasonable values (≃10⁴⁴–10⁴⁵erg s^–1) for billions of years without a discernible duty cycle. These simulations also produce significant amounts of cold gas, with calculations having strong magnetic fields generally both promoting cold gas formation and allowing cold gas out to much larger cluster-centric radii (≃100 kpc) than simulations with weak or no fields (≃10 kpc), and also having more filamentary cold gas morphology. We find that AGN feedback significantly increases the strength of magnetic fields at the center of the cluster. We also find that the magnetized turbulence generated by the AGN results in turbulence where the velocity power spectra are tied to AGN activity, whereas the magnetic energy spectra are much less impacted after reaching a stationary state. 

Abstract High‐tide flooding—minor, disruptive coastal inundation—is expected to become more frequent as sea levels rise. However, quantifying just how quickly high‐tide flooding rates are changing, and whether some places experience more high‐tide flooding than others, is challenging. To quantify trends in high‐tide flooding from tide‐gauge observations, flood thresholds—elevations above which flooding begins—must be specified. Past studies of high‐tide flooding in the United States have used different data sets and approaches for specifying flood thresholds, only some of which directly relate to coastal impacts, which has lead to sometimes conflicting and ambiguous results. Here we present a novel method for quantifying, with uncertainty, high‐tide flooding thresholds along the United States coast based on sparsely available impact‐based flood thresholds. We use those newly modeled thresholds to make an updated assessment of changes in high‐tide flooding across the United States over the past few decades. From 1990–2000 to 2010–2020, high‐tide flooding rates almost certainly (probability ) increased along the United States East Coast, Gulf Coast, California, and Pacific Islands, while they very likely decreased along Alaska during that time; significant changes in high‐tide flooding rates between the two decades were not detected in Oregon, Washington, and the Caribbean. Averaging spatially, we find that high‐tide flooding rates probably more than doubled nationally between 1990–2000 and 2010–2020. Our approach lays a foundation for future studies to more accurately model high‐tide flood thresholds and trends along the global coastline. 

The genusPseudogymnoascusincludes several species frequently isolated from extreme environments worldwide, including cold environments such as Antarctica. This study describes three new species ofPseudogymnoascus—P. russussp. nov.,P. irelandiaesp. nov., andP. ramosussp. nov.—isolated from Antarctic soils. These species represent the firstPseudogymnoascustaxa to be formally described from Antarctic soil samples, expanding our understanding of fungal biodiversity in this extreme environment. Microscopic descriptions of asexual structures from living cultures, along with measurements of cultural characteristics and growth on various media types at different temperatures, identify three distinct new species. In addition, phylogenetic analyses based on five gene regions (ITS, LSU, MCM7, RPB2, TEF1) and whole-genome proteomes place these new species within three distinct previously described clades:P. irelandiaein clade K,P. ramosusin clade Q, andP. russusin clade B. These results provide further evidence of the extensive undescribed diversity ofPseudogymnoascusin high-latitude soils. This study contributes to the growing body of knowledge on Antarctic mycology and the broader ecology of psychrophilic and psychrotolerant fungi.