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Convective available potential energy (CAPE), a metric associated with severe weather, is expected to increase with warming, but we have lacked a framework that describes its changes in the populated midlatitudes. In the tropics, theory suggests mean CAPE should rise following the Clausius–Clapeyron (C–C) relationship at ∼6%/K. In the heterogeneous midlatitudes, where the mean change is less relevant, we show that CAPE changes are larger and can be well‐described by a simple framework based on moist static energy surplus, which is robust across climate states. This effect is highly general and holds across both high‐resolution nudged regional simulations and free‐running global climate models. The simplicity of this framework means that complex distributional changes in future CAPE can be well‐captured by a simple scaling of present‐day data using only three parameters.

 

Cattle farming is a major source of global food production and livelihoods that is being impacted by climate change. However, despite numerous studies reporting local-scale heat impacts, quantifying the global risk of heat stress to cattle from climate change remains challenging. We conducted a global synthesis of documented heat stress for cattle using 164 records to identify temperature-humidity conditions associated with decreased production and increased mortality, then projected how future greenhouse gas emissions and land-use decisions will limit or exacerbate heat stress, and mapped this globally. The median threshold for the onset of negative impacts on cattle was a temperature-humidity index of 68.8 (95% C.I.: 67.3–70.7). Currently, almost 80% of cattle globally are exposed to conditions exceeding this threshold for at least 30 days a year. For global warming above 4°C, heat stress of over 180 days per year emerges in temperate regions, and year-round heat stress expands across all tropical regions by 2100. Limiting global warming to 2°C, limits expansion of 180 days of heat stress to sub-tropical regions. In all scenarios, severity of heat stress increases most in tropical regions, reducing global milk yields. Future land-use decisions are an important driver of risk. Under a low environmental protection scenario (SSP3-RCP7.0), the greatest expansion of cattle farming is projected for tropical regions (especially Amazon, Congo Basin, and India), where heat stress is projected to increase the most. This would expose over 500 million more cattle in these regions to severe heat risk by 2090 compared to 2010. A less resource-intensive and higher environmental protection scenario (SSP1-RCP2.6) reduces heat risk for cattle by at least 50% in Asia, 63% in South America, and 84% in Africa. These results highlight how societal choices that expand cattle production in tropical forest regions are unsustainable, both worsening climate change and exposing hundreds of millions more cattle to large increases in severe, year-round heat stress. 

Clouds play a critical role in the Earth's energy budget and their potential changes are one of the largest uncertainties in future climate projections. However, the use of satellite observations to understand cloud feedbacks in a warming climate has been hampered by the simplicity of existing cloud classification schemes, which are based on single-pixel cloud properties rather than utilizing spatial structures and textures. Recent advances in computer vision enable the grouping of different patterns of images without using human-predefined labels, providing a novel means of automated cloud classification. This unsupervised learning approach allows discovery of unknown climate-relevant cloud patterns, and the automated processing of large datasets. We describe here the use of such methods to generate a new AI-driven Cloud Classification Atlas (AICCA), which leverages 22 years and 800 terabytes of MODIS satellite observations over the global ocean. We use a rotation-invariant cloud clustering (RICC) method to classify those observations into 42 AI-generated cloud class labels at ~100 km spatial resolution. As a case study, we use AICCA to examine a recent finding of decreasing cloudiness in a critical part of the subtropical stratocumulus deck, and show that the change is accompanied by strong trends in cloud classes. 

The contributions of asymptomatic infections to herd immunity and community transmission are key to the resurgence and control of COVID-19, but are difficult to estimate using current models that ignore changes in testing capacity. Using a model that incorporates daily testing information fit to the case and serology data from New York City, we show that the proportion of symptomatic cases is low, ranging from 13 to 18%, and that the reproductive number may be larger than often assumed. Asymptomatic infections contribute substantially to herd immunity, and to community transmission together with presymptomatic ones. If asymptomatic infections transmit at similar rates as symptomatic ones, the overall reproductive number across all classes is larger than often assumed, with estimates ranging from 3.2 to 4.4. If they transmit poorly, then symptomatic cases have a larger reproductive number ranging from 3.9 to 8.1. Even in this regime, presymptomatic and asymptomatic cases together comprise at least 50% of the force of infection at the outbreak peak. We find no regimes in which all infection subpopulations have reproductive numbers lower than three. These findings elucidate the uncertainty that current case and serology data cannot resolve, despite consideration of different model structures. They also emphasize how temporal data on testing can reduce and better define this uncertainty, as we move forward through longer surveillance and second epidemic waves. Complementary information is required to determine the transmissibility of asymptomatic cases, which we discuss. Regardless, current assumptions about the basic reproductive number of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) should be reconsidered. 

Predicting arbovirus re-emergence remains challenging in regions with limited off-season transmission and intermittent epidemics. Current mathematical models treat the depletion and replenishment of susceptible (non-immune) hosts as the principal drivers of re-emergence, based on established understanding of highly transmissible childhood diseases with frequent epidemics. We extend an analytical approach to determine the number of ‘skip’ years preceding re-emergence for diseases with continuous seasonal transmission, population growth and under-reporting. Re-emergence times are shown to be highly sensitive to small changes in low R 0 (secondary cases produced from a primary infection in a fully susceptible population). We then fit a stochastic Susceptible–Infected–Recovered (SIR) model to observed case data for the emergence of dengue serotype DENV1 in Rio de Janeiro. This aggregated city-level model substantially over-estimates observed re-emergence times either in terms of skips or outbreak probability under forward simulation. The inability of susceptible depletion and replenishment to explain re-emergence under ‘well-mixed’ conditions at a city-wide scale demonstrates a key limitation of SIR aggregated models, including those applied to other arboviruses. The predictive uncertainty and high skip sensitivity to epidemiological parameters suggest a need to investigate the relevant spatial scales of susceptible depletion and the scaling of microscale transmission dynamics to formulate simpler models that apply at coarse resolutions.