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Climate change scenarios established by the Intergovernmental Panel on Climate Change have developed a significant tool for analyzing, modeling, and predicting future climate change impacts in different research fields after more than 30 years of development and refinement. In the wake of future climate change, the changes in forest structure and functions have become a frontier and focal area of global change research. This study mainly reviews and synthesizes climate change scenarios and their applications in forest ecosystem research over the past decade. These applications include changes in (1) forest structure and spatial vegetation distribution, (2) ecosystem structure, (3) ecosystem services, and (4) ecosystem stability. Although climate change scenarios are useful for predicting future climate change impacts on forest ecosystems, the accuracy of model simulations needs to be further improved. Based on existing studies, climate change scenarios are used in future simulation applications to construct a biomonitoring network platform integrating observations and predictions for better conservation of species diversity. Key words: global change, carbon stocks, forest vulnerability, CMIP6, land surface models, 1. Introduction Based on the Sixth Assessment Report from the Intergovernmental Panel on Climate Change (IPCC), the recent worldwide average surface temperature for 2011-2020 was 1.09 [degrees]C warmer than that [...]

Urban ecosystems are complex systems with anthropogenic features that generate considerable C[O.sub.2] emissions, which contributes to global climate change. Quantitative estimates of the carbon footprint of urban ecosystems are crucial for developing low-carbon development policies to mitigate climate change. Herein, we reviewed more than 195 urban carbon footprint and carbon footprint related studies, collated the recent progress in carbon footprint calculation methods and research applications of the urban ecosystem carbon footprint, analyzed the research applications of the carbon footprint of different cities, and focused on the need to study the urban ecosystem carbon footprint from a holistic perspective. Specifically, we aimed to: (i) compare the strengths and weaknesses of five existing carbon footprint calculation methods [life cycle assessment, input-output analysis, hybrid life cycle assessment, carbon footprint calculator, and Intergovernmental Panel on Climate Change (IPCC)]; (ii) analyze the status of current research on the carbon footprint of different urban subregions based on different features; and (iii) highlight new methods and areas of research on the carbon footprint of future urban ecosystems. Not all carbon footprint accounting methods are applicable to the carbon footprint determination of urban ecosystems; although the IPCC method is more widely used than the others, the hybrid life cycle assessment method is more accurate. With the emergence of new science and technology, quantitative methods to calculate the carbon footprint of urban ecosystems have evolved, becoming more accurate. Further development of new technologies, such as big data and artificial intelligence, to assess the carbon footprint of urban ecosystems is anticipated to help address the emerging challenges in urban ecosystem research effectively to achieve carbon neutrality and urban sustainability under global change. Key words: accounting systems, climate change, carbon sequestration, C[O.sub.2] emissions assessment, hybrid life cycle assessment. Les ecosystemes urbains sont des systemes complexes presentant des caracteristiques anthropiques qui generent des emissions considerables de C[O.sub.2] contribuant aux changements climatiques mondiaux. Les estimations quantitatives de l'empreinte carbone des ecosystemes urbains sont cruciales pour l'elaboration de politiques de developpement a faible emission de carbone visant a attenuer les changements climatiques. Dans cet article, les auteurs ont passe en revue plus de 195 etudes sur l'empreinte carbone urbaine et reliees a l'empreinte carbone urbaine, collige les progres recents en matiere de methodes de calcul de l'empreinte carbone et les applications de la recherche sur l'empreinte carbone des ecosystemes urbains, analyse les applications de recherche de l'empreinte carbone de differentes villes et mis l'accent sur la necessite d'etudier l'empreinte carbone des ecosystemes urbains d'un point de vue holistique. Plus precisement, ils ont cherche a: (i) comparer les forces et les faiblesses de cinq methodes existantes de calcul de l'empreinte carbone [evaluation du cycle de vie, analyse entree-sortie, evaluation hybride du cycle de vie, calculateur de l'empreinte carbone et celle du Groupe d'experts intergouvernemental sur revolution du climat (GIEC)]; (ii)analyser l'etat de la recherche actuelle sur l'empreinte carbone de differentes sous-regions urbaines en fonction de differentes caracteristiques; et (iii) mettre en lumiere de nou-velles methodes et de nouveaux domaines de recherche sur l'empreinte carbone des futurs ecosystemes urbains. Toutes les methodes de comptabilisation de l'empreinte carbone ne sont pas applicables a la determination de l'empreinte carbone des ecosystemes urbains; bien que la methode du GIEC soit plus largement utilisee que les autres, la methode hybride d'evaluation du cycle de vie est plus precise. Avec les avancees de la science et l'emergence de nouvelles technologies, les methodes quantitatives de calcul de l'empreinte carbone des ecosystemes urbains ont evolue et sont devenues plus precises. Le developpement ulterieur de nouvelles technologies, telles que celles qui utilisent les donnees massives et l'intelligence artificielle, pour evaluer l'empreinte carbone des ecosystemes urbains devrait aider a relever efficacement les nouveaux defisde la recherche sur les ecosystemes urbains afind'atteindre la neutralite carbone et la durabilite urbaine dans le contexte des changements climatiques mondiaux. [Traduit par la Redaction] Mots-cles: systemes de comptabilite, changements climatiques, sequestration du carbone, evaluation des emissions de CO2, analysehybrideducycledevie., Introduction Approximately half of the Earth's terrestrial surface is currently affected by human activities that are accelerating C[O.sub.2] emissions and increasingly becoming detrimental for several ecosystems (Lambin et al. 2003). [...]

Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM2.5) due to their low cost, wide coverage, and all-weather capability. Estimation of PM2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons (p < 0.01) and different spatial resolutions (p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM2.5 monitoring stations and historical PM2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions. [ABSTRACT FROM AUTHOR]

Plants interact to the seasonality of their environments, and changes in plant phenology have long been regarded as sensitive indicators of climatic change. Plant phenology modeling has been shown to be the simplest and most useful tool to assess phenol-climate shifts. Temperature, solar radiation, and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic, and theoretical approaches have often been used for the parameterization of plant phenology models. The statistical approaches correlate the timing of phenological events to environmental factors or heat unit accumulations. The approaches have the simplified calculation procedures, correct phenological mechanism assumptions, but limited applications and predictive abilities. The mechanistic approaches describe plant phenology with the known or assumed 'cause-effect relationships' between biological processes and key driving variables. The mechanistic approaches have the improved parameter processes, realistic assumptions, broad applications, and effective predictions. The theoretical approaches assume cost-benefit tradeoff strategies in trees. These methods are capable of capturing and quantifying the potential impacts and consequences of global climate change and human activity. However, certain limitations still exist related to our understanding of phenological mechanisms in relation to (1) interactions between plants and their specific climates, (2) the integration of both field observational and remote sensing data with plant phenology models across taxa and ecosystem type, (3) amplitude clarification of scale-related sensitivity to global climate change, and (4) improvements in parameterization processes and the overall reduction of modeling uncertainties to forecast impacts of future climate change on plant phenological dynamics. To improve our capacity in the prediction of the amplitude of plant phenological responses with regard to both structural and functional sensitivity to future global climate change, it is important to refine modeling methodologies by applying long-term and large-scale observational data. It is equally important to consider other less used but critical factors (such as heredity, pests, and anthropogenic drivers), apply advanced model parameterization and data assimilation techniques, incorporate process-based plant phenology models as a dynamic component into global vegetation dynamic models, and test plant phenology models against long-term ground observations and high-resolution satellite data across different spatial and temporal scales. Key words: plant phenology, climate change, NDVI, ecological forecasting, process-based model. Les plantes interagissent avec la saisonnalite de leur environnement et on a longtemps considere les changements de la phenologie comme indicateurs du changement climatique. On a montre que la modelisation de la phenologie des plantes constitue le moyen le plus simple et le plus utile pour evaluer les glissements pheno-climatiques. On assume que la temperature, la radiation solaire et la disponibilite de l'eau constituent des facteurs determinants controlant la phenologie des plantes. On a egalement souvent utilise des approches statistiques, mecanistes et theoriques pour parametrer les modeles de phenologie vegetale. Les approches statistiques font la correlation entre l'incidence des evenements phenologiques, les facteurs environnementaux ou les accumulations d'unites thermiques. Ces approches statistiques ont l'avantage d'utiliser des methodes de calcul simplifiees et de corriger les mecanismes phenologiques assumes mais ont des capacites d'application et de prediction limitees. Les approches mecanistes decrivent la phenologie des plantes avec les relations de causes a effets connues ou presumees impliquant les processus biologiques et des variables determinantes. Les approches mecanistes ont l'avantage d'ameliorer le parametrage des processus, d'etablir des postulats realistes et de fournir des applications et des predictions valables. L'approche theorique se base sur des strategies cout benefice dans l'arbre. Ces methodes peuvent integrer et quantifier les impacts potentiels et les consequences du changement climatique global et de l'activite humaine. Cependant, il existe toujours certaines limitations reliees a notre comprehension des mecanismes phenologiques en relation avec (1) les interactions entre les plantes et les climats specifiques, (2) l'integration a la fois des donnees de terrain et de teledetection avec les modeles phenologiques vegetaux a travers les taxons et les types d'ecosystemes, (3) la clarification de l'amplitude et de la sensibilite reliees a l'echelle du changement climatique global et (4) l'amelioration des processus de parametrisation et la reduction generale des incertitudes de la modelisation pour predire les impacts du changement climatique futur sur la dynamique phenologique des plantes. Pour ameliorer notre capacite a predire l'amplitude des reactions phenologique des plantes en relation avec la sensibilite a la fois structurale et fonctionnelle du futur changement climatique, il faut raffiner les methodologies de modelisation en appliquant des donnees a long terme et a grande echelle. Il faut egalement considerer des facteurs moins utilises mais critiques (comme l'heredite, les pestes et les determinants anthropiques), appliquer la parametrisation de modeles avances et des techniques d'assimilation des donnees, incorporer des modeles de phenologie vegetale bases sur des processus comme composante dynamique des modeles globaux dynamiques de la vegetation et tester les modeles de phenologie vegetale contre des observations a long terme ainsi que les donnees satellites a haute resolution sur differentes echelles spatiales et temporelles. [Traduit par la Redaction] Mots-cles: phenologie vegetale, changement climatique, NDVI, previsions ecologiques, modele base sur des processus., 1. Introduction Phenology is the scientific study of periodic plant and animal life cycle events (such as flowering, breeding, and migration) and how these are influenced by seasonal and interannual [...]