• Energy Research

AbstractVegetation growth is affected by past growth rates and climate variability. However, the impacts of vegetation growth carryover (VGC; biotic) and lagged climatic effects (LCE; abiotic) on tree stem radial growth may be decoupled from photosynthetic capacity, as higher photosynthesis does not always translate into greater growth. To assess the interaction of tree‐species level VGC and LCE with ecosystem‐scale photosynthetic processes, we utilized tree‐ring width (TRW) data for three tree species: Castanopsis eyrei (CE), Castanea henryi (CH, Chinese chinquapin), and Liquidambar formosana (LF, Chinese sweet gum), along with satellite‐based data on canopy greenness (EVI, enhanced vegetation index), leaf area index (LAI), and gross primary productivity (GPP). We used vector autoregressive models, impulse response functions, and forecast error variance decomposition to analyze the duration, intensity, and drivers of VGC and of LCE response to precipitation, temperature, and sunshine duration. The results showed that at the tree‐species level, VGC in TRW was strongest in the first year, with an average 77% reduction in response intensity by the fourth year. VGC and LCE exhibited species‐specific patterns; compared to CE and CH (diffuse‐porous species), LF (ring‐porous species) exhibited stronger VGC but weaker LCE. For photosynthetic capacity at the ecosystem scale (EVI, LAI, and GPP), VGC and LCE occurred within 96 days. Our study demonstrates that VGC effects play a dominant role in vegetation function and productivity, and that vegetation responses to previous growth states are decoupled from climatic variability. Additionally, we discovered the possibility for tree‐ring growth to be decoupled from canopy condition. Investigating VGC and LCE of multiple indicators of vegetation growth at multiple scales has the potential to improve the accuracy of terrestrial global change models.

Climate change is posing dramatic effects on terrestrial vegetation dynamics. The links between vegetation phenology or vegetation activity (growth) and climate change have been widely reported, yet, less is known about the impacts of phenological shifts on vegetation growth. Urban settings characterized by urban heat island and CO2 dome are often used as ideal natural laboratories to understand how vegetation responds to global climate change. Here we assessed the impacts of phenology changes on vegetation growth in China using satellite phenology metrics and gross primary production (GPP) data from 2003 to 2018 and urban-natural contrast analysis. Compared with natural environments, phenological metrics (e.g., start/end of growing season (SOS/EOS), and the length of growing season (GSL), etc.) were observed to change more dramatically in urban environments. Furthermore, we found that GPP in both settings increased over time but with a higher increment in the urban environments, and the urban-natural vegetation productivity gap had been diminishing at a rate of 16.9 ± 6.76 g C m-2 y-1. The narrowing of the urban-natural GPP difference over time can be attributed to a more advanced SOS and extended GSL in urban settings than their natural counterparts, particularly SOS shift. These findings suggested that the distinct urban phenological shifts would become increasingly important in offsetting the loss of vegetation productivity induced by urbanization.

AbstractCities are natural laboratories for studying vegetation responses to global environmental changes because of their climate, atmospheric, and biogeochemical conditions. However, few holistic studies have been conducted on the impact of urbanization on vegetation growth. We decomposed the overall impacts of urbanization on vegetation growth into direct (replacement of original land surfaces by impervious built‐up) and indirect (urban environments) components, using a conceptual framework and remotely sensed data for 377 metropolitan statistical areas (MSAs) in the conterminous United States (CONUS) in 2001, 2006, and 2011. Results showed that urban pixels are often greener than expected given the amount of paved surface they contain. The vegetation growth enhancement due to indirect effects occurred in 88.4%, 90.8%, and 92.9% of urban bins in 2001, 2006, and 2011, respectively. By defining offset value as the ratio of the absolute indirect and direct impact, we obtained that growth enhancement due to indirect effects compensated for about 29.2%, 29.5%, and 31.0% of the reduced productivity due to loss of vegetated surface area on average in 2001, 2006, and 2011, respectively. Vegetation growth responses to urbanization showed little temporal variation but large regional differences with higher offset value in the western CONUS than in the eastern CONUS. Our study highlights the prevalence of vegetation growth enhancement in urban environments and the necessity of differentiating various impacts of urbanization on vegetation growth, and calls for tailored field experiments to understand the relative contributions of various driving forces to vegetation growth and predict vegetation responses to future global change using cities as harbingers.

AbstractRising temperature shifts plant phenology. Chinese cities, experiencing extensive expansion and intensive warming, spanning a wide latitudinal range, might provide ideal experimental opportunities for observing and predicting phenological responses to warming temperature. Using the urban–rural gradient approach, we explored urbanization imprint on land surface phenology across the entire urbanization intensity (UI) gradient ranging from 0% to 100% in 343 Chinese cities using the VIIRS Land Surface Phenology along with MODIS Land Surface Temperature (LST) products. We found prevalent advancing and delaying trends for the start of the growing season (SOS) and the end of the growing season (EOS) with increasing UI across 343 Chinese cities, respectively. Overall, the phenology shifted earlier by 8.6 ± 0.54 days for SOS, later by 1.3 ± 0.51 days for EOS, and lengthened by 9.9 ± 0.77 days for the growing season length (GSL) in urban core areas (UI above 50%) relative to their rural counterparts (UI lower than 1%). The temperature sensitivity of SOS and EOS was 10.5 ± 0.25 days earlier and 2.9 ± 0.16 days later per 1°C LST increase in spring and autumn, respectively. Moreover, the northern cities witnessed higher temperature sensitivity for SOS and EOS than the southern ones. Both spring and autumn temperature sensitivity across these 343 cities would likely decrease with future urban warming, suggesting any projections of future phenological responses to continued warming must be approached with caution.

Abstract. Changes in carbon density (i.e., carbon stock per unit area) and land cover greatly affect carbon sequestration. Previous studies have shown that land cover change detection strongly depends on spatial scale. However, the influence of the spatial resolution of land cover change information on the estimated terrestrial carbon sequestration is not known. Here, we quantified and evaluated the impact of land cover change databases at various spatial resolutions (250 m, 500 m, 1 km, 2 km, and 4 km) on the magnitude and spatial patterns of regional carbon sequestration in the southeastern United States using the General Ensemble biogeochemical Modeling System (GEMS). Results indicated a threshold of 1 km in the land cover change databases and in the estimated regional terrestrial carbon sequestration. Beyond this threshold, significant biases occurred in the estimation of terrestrial carbon sequestration, its interannual variability, and spatial patterns. In addition, the overriding impact of interannual climate variability on the temporal change of regional carbon sequestration was unrealistically overshadowed by the impact of land cover change beyond the threshold. The implications of these findings directly challenge current continental- to global-scale carbon modeling efforts relying on information at coarse spatial resolution without incorporating fine-scale land cover dynamics.

La production primaire brute (ppm) détermine les quantités de carbone et d'énergie qui pénètrent dans les écosystèmes terrestres. Cependant, l'énorme incertitude du ppm entrave toujours la fiabilité des estimations du ppm et donc la compréhension du cycle mondial du carbone. Dans cette étude, à l'aide d'observations provenant des tours de flux de covariance de Foucault (CE) mondiale, nous avons évalué les performances de 22 modèles de ppm largement utilisés et la qualité des principales couches de données spatiales qui pilotent les modèles. Les résultats montrent que les produits de ppm mondiaux générés par les 22 modèles variaient considérablement dans les moyennes (de 92,7 à 178,9 Pg C an-1), les tendances (de -0,25 à 0,84 Pg C an-1) .Les structures de modèles (c'est-à-dire les modèles d'efficacité d'utilisation de la lumière, les modèles d'apprentissage automatique et les modèles biophysiques basés sur les processus) sont un aspect important contribuant à la grande incertitude. En outre, divers biais dans les ensembles de données spatiales actuellement disponibles ont été trouvés (par exemple, seulement 57% de la variation observée du rayonnement photosynthétiquement actif a été expliquée par l'ensemble de données spatiales), ce qui a grandement affecté les estimations mondiales de GPP. Notre analyse indique que le développement du modèle n'a pas convergé les simulations de GPP avec l'avancement de temps. À l'avenir, la recherche sur l'efficacité des structures de modèles et la précision des données d'entrée peut être plus importante que le développement de nouveaux modèles pour l'estimation mondiale des ppm. Déclaration d'importanceLa production primaire brute (GPP), la quantité de carbone fixée pendant la photosynthèse dans un laps de temps donné, est le carburant de la vie et l'un des composants clés du cycle mondial du carbone. Bien que de nombreux efforts aient été déployés pour estimer la GPP à l'échelle mondiale, son incertitude est restée élevée pendant des décennies. Il est nécessaire d'évaluer la performance des modèles pertinents et des ensembles de données à l'appui par rapport à des mesures sur le terrain de haute qualité pour déterminer où se trouvent les parties lâches, puis resserrer les extrémités lâches. Notre étude révèle que de grands biais existent dans certains des modèles et des produits de données spatiales, et la résolution de ces biais est une priorité élevée dans la modélisation de la GPP du site à l'échelle mondiale. La producción primaria bruta (GPP) determina las cantidades de carbono y energía que ingresan a los ecosistemas terrestres. Sin embargo, la tremenda incertidumbre de la GPP aún dificulta la confiabilidad de las estimaciones de GPP y, por lo tanto, la comprensión del ciclo global del carbono. En este estudio, utilizando observaciones de torres de flujo de covarianza de remolinos (EC) globales, evaluamos el rendimiento de 22 modelos de GPP ampliamente utilizados y la calidad de las principales capas de datos espaciales que impulsan los modelos. Los resultados muestran que los productos de GPP globales generados por los 22 modelos variaron mucho en los medios (de 92.7 a 178.9 Pg C año-1), tendencias (de -0.25 a 0.84 Pg C año-1). Las estructuras del modelo (es decir, modelos de eficiencia del uso de la luz, modelos de aprendizaje automático y modelos biofísicos basados en procesos) son un aspecto importante que contribuye a la gran incertidumbre. Además, se han encontrado varios sesgos en los conjuntos de datos espaciales actualmente disponibles (por ejemplo, solo el 57% de la variación observada en la radiación fotosintéticamente activa se explicó por el conjunto de datos espaciales), lo que contribuyó en gran medida a las estimaciones globales de GPP. Nuestro análisis indica que el desarrollo del modelo no convergió simulaciones de GPP con el avance de tiempo. En el futuro, la investigación sobre la eficacia de las estructuras de los modelos y la precisión de los datos de entrada puede ser más importante que el desarrollo de nuevos modelos para la estimación global de la GPP. Declaración de importanciaLa producción primaria bruta (GPP), la cantidad de carbono fijada durante la fotosíntesis en un período de tiempo determinado, es el combustible de la vida y uno de los componentes clave del ciclo global del carbono. Aunque se han realizado numerosos esfuerzos para estimar la GPP a escala global, su incertidumbre se ha mantenido alta durante décadas. Es necesario evaluar el rendimiento de los modelos relevantes y los conjuntos de datos de apoyo en comparación con las mediciones de campo de alta calidad para averiguar dónde están las partes sueltas y luego apretar los extremos sueltos. Nuestro estudio revela que existen grandes sesgos en algunos de los modelos y la conducción de los productos de datos espaciales, y abordar estos sesgos es una alta prioridad en el modelado de GPP desde el sitio hasta las escalas globales. Gross primary production (GPP) determines the amounts of carbon and energy that enter terrestrial ecosystems.However, the tremendous uncertainty of the GPP still hinders the reliability of the GPP estimates and therefore understanding of the global carbon cycle.In this study, using observations from global eddy covariance (EC) flux towers, we appraised the performance of 22 widely used GPP models and quality of major spatial data layers that drive the models.Results show that the global GPP products generated by the 22 models varied greatly in the means (from 92.7 to 178.9 Pg C yr-1), trends (from -0.25 to 0.84 Pg C yr-1).Model structures (i.e., light use efficiency models, machine learning models, and process-based biophysical models) are an important aspect contributing to the large uncertainty.In addition, various biases in currently available spatial datasets have found (e.g., only 57% of the observed variation in photosynthetically active radiation was explained by the spatial dataset), which contributed greatly affects global GPP estimates.Our analysis indicates that the model development did not converge GPP simulations with the advance of time.Moving forward, research into efficacy of model structures and the precision of input data may be more important than the development of new models for global GPP estimation. Significance StatementGross primary production (GPP), the amount of carbon fixed during photosynthesis in a given length of time, is the fuel of life and one of the key components of the global carbon cycle.Although numerous efforts have been made to estimate GPP at the global scale, its uncertainty has remained high for decades.It is necessary to evaluate the performance of relevant models and supporting datasets against high-quality field measurements to find out where the loose parts are, and then tighten the loose ends.Our study reveals that large biases exist in some of the models and driving spatial data products, and addressing these biases is a high priority in modeling GPP from site to global scales. يحدد إجمالي الإنتاج الأولي (GPP) كميات الكربون والطاقة التي تدخل النظم الإيكولوجية الأرضية. ومع ذلك، فإن عدم اليقين الهائل من GPP لا يزال يعيق موثوقية تقديرات GPP وبالتالي فهم دورة الكربون العالمية. في هذه الدراسة، باستخدام الملاحظات من أبراج تدفق الدوامة العالمية (EC)، قمنا بتقييم أداء 22 نموذجًا من نماذج GPP المستخدمة على نطاق واسع وجودة طبقات البيانات المكانية الرئيسية التي تحرك النماذج. تظهر النتائج أن منتجات GPP العالمية الناتجة عن 22 نموذجًا اختلفت اختلافًا كبيرًا في الوسائل (من 92.7 إلى 178.9 صفحة C yr -1)، والاتجاهات (من -0.25 إلى 0.84 صفحة C yr -1). تعد هياكل النماذج (أي نماذج كفاءة استخدام الضوء ونماذج التعلم الآلي والنماذج الفيزيائية الحيوية القائمة على العمليات) جانبًا مهمًا يساهم في عدم اليقين الكبير. بالإضافة إلى ذلك، تم العثور على تحيزات مختلفة في مجموعات البيانات المكانية المتاحة حاليًا (على سبيل المثال، تم تفسير 57 ٪ فقط من التباين الملحوظ في الإشعاع النشط الضوئي من خلال مجموعة البيانات المكانية)، مما ساهم بشكل كبير في تقديرات GPP العالمية. يشير تحليلنا إلى أن تطوير النموذج لم يتقارب مع محاكاة GPP مع التقدم للمضي قدمًا، قد يكون البحث في فعالية هياكل النماذج ودقة بيانات المدخلات أكثر أهمية من تطوير نماذج جديدة لتقدير GPP العالمي. بيان الأهميةالإنتاج الأولي الإجمالي (GPP)، كمية الكربون الثابتة أثناء عملية التمثيل الضوئي في فترة زمنية معينة، هو وقود الحياة وأحد المكونات الرئيسية لدورة الكربون العالمية. على الرغم من بذل العديد من الجهود لتقدير GPP على المستوى العالمي، إلا أن عدم اليقين ظل مرتفعًا لعقود. من الضروري تقييم أداء النماذج ذات الصلة ودعم مجموعات البيانات مقابل القياسات الميدانية عالية الجودة لمعرفة مكان الأجزاء السائبة، ثم تشديد النهايات السائبة. تكشف دراستنا عن وجود تحيزات كبيرة في بعض النماذج وقيادة منتجات البيانات المكانية، ومعالجة هذه التحيزات هي أولوية عالية في نمذجة GPP من الموقع إلى المقاييس العالمية.

AbstractGiven that already‐observed temperature increase within cities far exceeds the projected global temperature rise by the end of the century, urban environments often offer a unique opportunity for studying ecosystem response to future warming. However, the validity of thermal gradients in space serving as a substitute for those in time is rarely tested. Here, we investigated vegetation phenology dynamics in China's 343 cities and empirically test whether phenological responses to spatial temperature rise in urban settings can substitute for those to temporal temperature rise in their natural counterparts based on satellite‐derived vegetation phenology and land surface temperature from 2003 to 2018. We found prevalent advancing spring phenology with “high confidence” and delaying autumn phenology with “medium confidence” under the context of widespread urban warming. Furthermore, we showed that space cannot substitute for time in predicting phenological shifts under climate warming at the national scale and for most cities. The thresholds of ~11°C mean annual temperature and ~600 mm annual precipitation differentiated the magnitude of phenological sensitivity to temperature across space and through time. Below those thresholds, there existed stronger advanced spring phenology and delayed autumn phenology across the spatial urbanization gradients than through time, and vice versa. Despite the complex and diverse relationships between phenological sensitivities across space and through time, we found that the directions of the temperature changes across spatial gradients were converged (i.e., mostly increased), but divergent through temporal gradients (i.e., increased or decreased without a predominant direction). Similarly, vegetation phenology changes more uniformly over space than through time. These results suggested that the urban environments provide a real‐world condition to understand vegetation phenology response under future warming.

Abstract. Partial cutting, which removes some individual trees from a forest, is one of the major and widespread forest management practices that can significantly alter both forest structure and carbon (C) storage. Using 746 observations from 82 publications, we synthesized the impacts of partial cutting on three variables associated with forest structure (i.e. mean annual growth of diameter at breast height (DBH), basal area (BA), and volume) and four variables related to various C stock components (i.e. aboveground biomass C (AGBC), understory C, forest floor C, and mineral soil C). Results shows that the growth of DBH elevated by 112% after partial cutting, compared to the uncut control, while stand BA and volume reduced immediately by 34% and 29%, respectively. On average, partial cutting reduced AGBC by 43%, increased understory C storage by 392%, but did not show significant effects on C storages on forest floor and in mineral soil. All the effects on DBH growth, stand BA, volume, and AGBC intensified linearly with cutting intensity (CI) and decreased linearly with the number of recovery years (RY). In addition to the strong impacts of CI and RY, other factors such as climate zone and forest type also affected forest responses to partial cutting. The data assembled in this synthesis were not sufficient to determine how long it would take for a complete recovery after cutting because long-term experiments were rare. Future efforts should be tailored to increase the duration of the experiments and balance geographic locations of field studies.

AbstractUrban green spaces provide manifold environmental benefits and promote human well‐being. Unfortunately, these services are largely undervalued, and the potential of urban areas themselves to mitigate future climate change has received little attention. In this study, we quantified and mapped city‐wide aboveground carbon storage of urban green spaces in China's capital, Beijing, using field survey data of diameter at breast height (DBH) and tree height from 326 field survey plots, combined with satellite‐derived vegetation index at a fine resolution of 6 m. We estimated the total amount of carbon stored in the urban green spaces to be 956.3 Gg (1 Gg = 109 g) in 2014. There existed great spatial heterogeneity in vegetation carbon density varying from 0 to 68.1 Mg C ha‐1, with an average density of 7.8 Mg C ha−1. As expected, carbon density tended to decrease with urban development intensity (UDI). Likely being affected by vegetation cover proportion and configuration of green space patches, large differences were presented between the 95th and 5th quantile carbon density for each UDI bin, showing great potential for carbon sequestration. However, the interquartile range of carbon density narrowed drastically when UDI reached 60%, signifying a threshold for greatly reduced carbon sequestration potentials for higher UDI. These findings suggested that urban green spaces have great potential to make contribution to mitigating against future climate change if we plan and design urban green spaces following the trajectory of high carbon density, but we should be aware that such potential will be very limited when the urban development reaches certain intensity threshold.