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Abstract Urban overheating presents significant challenges to public health and energy sustainability. Conventional radiative cooling strategies, such as cool roofs with high albedo, lead to undesired winter cooling and increased space heating demand for cities with cold winters, a phenomenon known as heating energy penalty. A novel roof coating with high albedo and temperature‐adaptive emissivity (TAE)—low emissivity during cold conditions and high emissivity during hot conditions—has the potential to mitigate winter heating energy penalty. In this study, we implement this roof coating in a global climate model to evaluate its impact on air temperature and building energy demand for space heating and cooling in global cities. Adopting roofs with TAE increases global urban air temperature by up to +0.54°C in the winter (99th percentile; mean change +0.16°C) but has negligible effects on summer urban air temperature (mean change +0.05°C). Combining TAE with high albedo effectively provides summer cooling and does not increase building energy demand in the winter, particularly for mid‐latitude cities. Sensitivities of air temperature to changes in emissivity and albedo are associated with local “apparent” net longwave radiation and incoming solar radiation, respectively. We propose a simple parameterization of air temperature responses to emissivity and albedo to facilitate the development of city‐specific radiative mitigation strategies. This study emphasizes the necessity of developing mitigation approaches specific to local cloudiness. 

ABSTRACT Identifying species with disproportionate effects on other species under press perturbations is essential, yet how species traits and community context drive their ‘keystone‐ness’ remain unclear. We quantified keystone‐ness as linearly approximated per capita net effect derived from normalised inverse community matrices and as non‐linear per capita community biomass change from simulated perturbations in food webs with varying biomass structure. In bottom‐heavy webs (negative relationship between species' body mass and their biomass within the web), larger species at higher trophic levels tended to be keystone species, whereas in top‐heavy webs (positive body mass to biomass relationship), the opposite was true and the relationships between species' energetic traits and keystone‐ness were weakened or reversed compared to bottom‐heavy webs. Linear approximations aligned well with non‐linear responses in bottom‐heavy webs, but were less consistent in top‐heavy webs. These findings highlight the importance of community context in shaping species' keystone‐ness and informing effective conservation actions. 

Abstract Increasing the albedo of urban surfaces, through strategies like white roof installations, has emerged as a promising approach for urban climate adaptation. Yet, modeling these strategies on a large scale is limited by the use of static urban surface albedo representations in the Earth system models. In this study, we developed a new transient urban surface albedo scheme in the Community Earth System Model and evaluated evolving adaptation strategies under varying urban surface albedo configurations. Our simulations model a gradual increase in the urban surface albedo of roofs, impervious roads, and walls from 2015 to 2099 under the SSP3‐7.0 scenario. Results highlight the cooling effects of roof albedo modifications, which reduce the annual‐mean canopy urban heat island intensity from 0.8°C in 2015 to 0.2°C by 2099. Compared to high‐density and medium‐density urban areas, higher albedo configurations are more effective in cooling environments within tall building districts. Additionally, urban surface albedo changes lead to changes in building energy consumption, where high albedo results in more indoor heating usage in urban areas located beyond 30°N and 25°S. This scheme offers potential applications like simulating natural albedo variations across urban surfaces and enables the inclusion of other urban parameters, such as surface emissivity. 

ABSTRACT China's pursuit of carbon neutrality targets hinges on a profound shift towards low-carbon energy, primarily reliant on intermittent and variable, yet crucial, solar and wind power sources. In particular, low-solar-low-wind (LSLW) compound extremes present a critical yet largely ignored threat to the reliability of renewable electricity generation. While existing studies have largely evaluated the impacts of average climate-induced changes in renewable energy resources, comprehensive analyses of the compound extremes and, particularly, the underpinning dynamic mechanisms remain scarce. Here we show the dynamic evolution of compound LSLW extremes and their underlying mechanisms across China via coupling multi-model simulations with diagnostic analysis. Our results unveil a strong topographic dependence in the frequency of compound LSLW extremes, with a national average frequency of 16.4 (10th–90th percentile interval ranges from 5.3 to 32.6) days/yr, when renewable energy resources in eastern China are particularly compromised (∼80% lower than that under an average climate). We reveal a striking increase in the frequency of LSLW extremes, ranging from 12.4% under SSP126 to 60.2% under SSP370, primarily driven by both renewable energy resource declines and increasingly heavily-tailed distributions, resulting from weakened meridional temperature (pressure) gradient, increased frequency of extremely dense cloud cover and additional distinctive influence of increased aerosols under SSP370. Our study underscores the urgency of preparing for significantly heightened occurrences of LSLW events in a warmer future, emphasizing that such climate-induced compound LSLW extreme changes are not simply by chance, but rather projectable, thereby underscoring the need for proactive adaptation strategies. Such insights are crucial for countries navigating a similar transition towards renewable energy.