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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 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. 

Abstract. High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth system models (ESMs) and ultra-high-resolution urban climate modeling, over large domains. Here, we present U-Surf, a first-of-its-kind 1 km resolution present-day (circa 2020) global continuous urban surface parameter dataset. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for satisfying dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet and canopy level. Generated using a systematically unified workflow, U-Surf ensures internal consistency among key parameters, making it the first globally coherent urban canopy surface dataset. U-Surf significantly improves the representation of the urban land heterogeneity both within and across cities globally; provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs; enables detailed city-to-city comparisons across the globe; and supports next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf can also be used as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to advance the research frontier of urban system science, climate-sensitive urban design, and coupled human–Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247598 (Cheng et al., 2024).