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More than half of the worlds population now live in cities, which are known to be heat islands. While daytime urban heat islands (UHIs) are traditionally thought to be the consequence of less evaporative cooling in cities, recent work sparks new debate, showing that geographic variations of daytime UHI intensity were largely explained by variations in the efficiency with which urban and rural areas convect heat from the land surface to the lower atmosphere. Here, we reconcile this debate by demonstrating that the difference between the recent finding and the traditional paradigm can be explained by the difference in the attribution methods. Using a new attribution method, we find that spatial variations of daytime UHI intensity are more controlled by variations in the capacity of urban and rural areas to evaporate water, suggesting that strategies enhancing the evaporation capability such as green infrastructure are effective ways to mitigate urban heat.

The complexities of urban climate and environmental challenges have garnered significant attention in the 21st century. Numerical simulations, offering high spatiotemporal resolution meteorological data, are essential tools in meteorological research and atmospheric science. Accurate representation of urban morphology parameters is crucial for enhancing the precision of these simulations in urban areas. Despite the availability of urban canopy parameter (UCP) data for 44 major cities in the United States and 60 in China for the weather research and forecasting (WRF) model, a comprehensive global dataset representing urban morphology remains absent. This study addresses this gap by leveraging existing global three-dimensional vector data of buildings, including footprints and heights, to compile a global 1 km spatially continuous UCP (GloUCP) dataset for the WRF model. Our findings indicate that GloUCP not only surpasses existing datasets in accuracy but also provides superior spatial coverage. In key urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Guangdong-Hong Kong-Macao Greater Bay Area in China, GloUCP offers detailed and reliable urban morphological information that closely aligns with reference datasets, outperforming other available sources. Similarly, in U.S. cities like Seattle, San Francisco, and Philadelphia, GloUCP consistently achieves lower RMSE values and higher correlation coefficients, demonstrating its robustness in modeling diverse urban environments. Furthermore, GloUCP's capability to effectively capture the vertical distribution of buildings, particularly in high-rise areas, highlights its utility in urban climate modeling and related applications. As UCPs are pivotal in regulating atmospheric responses to urbanization, the availability of this globally consistent urban description is a crucial prerequisite for advancing model development and informing climate-sensitive urban planning policies. The GloUCP dataset, converted to WRF binary file format, is available for download at https://doi.org/10.6084/m9.figshare.27011491 (Liao et al., 2024). [ABSTRACT FROM AUTHOR]

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, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing 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. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the 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 are also relevant 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 promote the research frontier on urban systems 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.11247599 (Cheng et al., 2024). [ABSTRACT FROM AUTHOR]

It is well known that urban areas are typically hotter than the surrounding (vegetated) rural areas. However, the contribution of urbanization to the trends of extreme temperature events such as heat waves (HWs) is less understood. Using a homogenized meteorological dataset drawn from nearly 2,000 stations in China, we find that urban and rural areas have different HW trends and the urban-rural contrast of HW trends varies across climate regimes. In wet climates, the increasing trends of HWs in urban areas are greater than those in rural areas, suggesting a positive contribution of urbanization to HW trends. In arid regions, the urbanization contribution to HW trends is smaller and even negative. The stronger urbanization contribution to HW trends in wet climates is linked to the smaller variability of urban heat island intensity. This study highlights the important role of local hydroclimate in modulating the urbanization contribution to extreme temperatures.

Understanding urban vertical structures, particularly building heights, is essential for examining the intricate interaction between humans and their environment. Such datasets are indispensable for a variety of applications, including climate modeling, energy consumption analysis, and socioeconomic activities. Despite the importance of this information, previous studies have primarily focused on estimating building heights regionally on a grid scale, often resulting in datasets with limited coverage or spatial resolution. This limitation hampers comprehensive global analyses and the ability to generate actionable insights on finer scales. In this study, we developed a global building height map (3D-GloBFP) at a building footprint scale by leveraging Earth Observation (EO) datasets and advanced machine learning techniques. Our approach integrated multisource remote sensing features and building morphology features to develop height estimation models using the eXtreme Gradient Boosting (XGBoost) regression method across diverse global regions. This methodology allowed us to estimate the heights of individual buildings worldwide, culminating in the creation of the first global three-dimensional (3-D) building footprints (3D-GloBFP). Our evaluation results show that the height estimation models perform exceptionally well on a worldwide scale, with R 2 ranging from 0.66 to 0.96 and root mean square errors (RMSEs) ranging from 1.9 m to 14.6 m across 33 subregions. Comparisons with other datasets demonstrate that our 3D-GloBFP closely matches the distribution and spatial pattern of reference heights. Our derived 3-D global building footprint map shows a distinct spatial pattern of building heights across regions, countries, and cities, with building heights gradually decreasing from the city center to the surrounding rural areas. Furthermore, our findings indicate the disparities in built-up infrastructure (i.e., building volume) across different countries and cities. China is the country with the most intensive total built-up infrastructure (5.28×1011 m3, accounting for 23.9 % of the global total), followed by the United States (3.90×1011 m3, accounting for 17.6 % of the global total). Shanghai has the largest volume of built-up infrastructure (2.1×1010 m3) of all representative cities. The derived building-footprint scale height map (3D-GloBFP) reveals the significant heterogeneity of urban built-up environments, providing valuable insights for studies in urban socioeconomic dynamics and climatology. The 3D-GloBFP dataset is available at https://doi.org/10.5281/zenodo.11319913 (Building height of the Americas, Africa, and Oceania in 3D-GloBFP) (Che et al., 2024a), https://doi.org/10.5281/zenodo.11397015 (Building height of Asia in 3D-GloBFP) (Che et al., 2024b), and https://doi.org/10.5281/zenodo.11391077 (Building height of Europe in 3D-GloBFP) (Che et al., 2024c). [ABSTRACT FROM AUTHOR]

Natural plants contain numerous chemical compounds that are beneficial to human health. The berries from the Lycium genus are widely consumed and are highly nutritious. Moreover, their chemical constituents have attracted attention for their health-promoting properties. In East Asia, there are three varieties of the Lycium genus (Lycium barbarum L., Lycium chinense Miller, and L. ruthenicum Murray) that possess medicinal value and are commonly used for treating chronic diseases and improving metabolic disorders. These varieties are locally referred to as "red Goji berries" or "black Goji berries" due to their distinct colors, and they differ in their chemical compositions, primarily in terms of carotenoid and anthocyanin content. The pharmacological functions of these berries include anti-aging, antioxidant, anti-inflammatory, and anti-exercise fatigue effects. This review aims to analyze previous and recent studies on the active ingredients and pharmacological activities of these Lycium varieties, elucidating their signaling pathways and assessing their impact on the gut microbiota. Furthermore, the potential prospects for using these active ingredients in the treatment of COVID-19 are evaluated. This review explores the potential targets of these Lycium varieties in the treatment of relevant diseases, highlighting their potential value in drug development. [ABSTRACT FROM AUTHOR]