Global Atmospheric Carbon Dioxide Concentration Simulation Grid Dataset (1992-2020)

Hou, Weiye; Jin, Jiaxin; Yan, Tao; Liu, Ying

doi:10.3974/geodb.2021.11.01.v1

Cited by 2 publications

(3 citation statements)

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“…The monthly LAI data were obtained from the GLASS dataset (http://www.glass.umd.edu/Download.html (accessed on 10 November 2023)), with a spatial resolution of 0.1 • × 0.1 • . The atmospheric CO 2 concentration was developed by Hou et al [30], with a spatial resolution of 2 • × 2.5 • and a temporal resolution of one month. Based on the University of Maryland (UMD) Land Cover Classification Scheme (LCCS), the Biogeochemical Cycle (BGC) classification scheme of the Food and Agriculture Organization of the United Nations (UN FAO), and related research results [34,35], we reclassified the land cover data of the study area and extracted more than 30 land cover types (Table 2).…”

Section: Data Sourcesmentioning

confidence: 99%

“…Nitrogen deposition: The carbon balance of most terrestrial ecosystems is affected by nitrogen deposition, and in the case of the tropics, tropical forests do not respond significantly to nitrogen deposition due to phosphorus limitation [28,29]. Land cover change: Hou et al showed that land cover change negatively affects GPP on a global scale [30]. In the tropics, land cover change has more pronounced negative impacts in southeastern South America, eastern and central Africa, and the Indian Peninsula, with slightly positive impacts observed in Southeast Asia.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics

Chen,

Zhang,

Guo

et al. 2024

Forests

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Gross primary productivity (GPP), representing organic carbon fixation through photosynthesis, is crucial for developing science-based strategies for sustainable development. Given that the tropical region harbors nearly half of all species, it plays a pivotal role in safeguarding the global environment against climate change and preserving global biodiversity. Thus, investigating changes in vegetation productivity within this region holds substantial practical importance for estimating global vegetation productivity. In this study, we employed an enhanced P model to estimate vegetation GPP in the tropical region from 2001 to 2020, based on which we quantified the spatiotemporal changes and associated mechanisms. The results reveal that the annual mean GPP in the tropical region ranged from 2603.9 to 2757.1 g·cm−2 a−1, demonstrating an overall apparent increasing trend. Inland areas were mainly influenced by precipitation, while coastal areas were primarily influenced by temperature. Land cover changes, especially conversion to cropland, significantly influence GPP, with deciduous—evergreen forest transitions causing notable decreases. Climate change emerges as the dominant factor affecting GPP, as indicated by the contribution rate analysis. This research interprets the spatiotemporal pattern and mechanisms of GPP in the tropics, offering valuable insights for sustainable ecosystem management.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics

Chen,

Zhang,

Guo

et al. 2024

Forests

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“…The temperature and precipitation data used in our study were sourced from the NOAA Physical Sciences Laboratory [36,37]. We obtained the CO 2 concentration data from a global dataset that represents atmospheric levels of carbon dioxide on a 2 × 2.5 • grid [38].…”

Section: Data Sourcesmentioning

confidence: 99%

Effects of Temperature, Precipitation, and CO2 on Plant Phenology in China: A Circular Regression Approach

Tang,

Zhou,

2023

Forests

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Leveraging circular regression, this study analyzed phenological data from China spanning the period 2003 to 2015, meticulously examining the effects of temperature, precipitation, and CO2 concentrations on the phenological patterns of woody and herbaceous plants. For woody plants, the results showed that rising temperatures and increased precipitation notably advanced early growth phases, such as budburst, leaf unfolding, and first flowering (p < 0.001). Specifically, CO2 concentrations had a pronounced impact on the leaf unfolding phase (p < 0.001). In contrast, autumnal events, particularly fruit maturity, autumn coloring, and leaf fall, were delayed by warmer temperatures and higher precipitation (p < 0.001), Of these events, only fruit maturity demonstrated sensitivity to CO2 concentration variations. In the realm of herbaceous plants, elevated temperatures and precipitation collectively hastened the budburst phase (p < 0.001), which is an effect further accentuated by high CO2 levels (p < 0.001). Moreover, rising temperatures and augmented precipitation were instrumental in advancing the flowering phase (p < 0.001). Conversely, warmer conditions slowed down the fruiting process (p < 0.001), with this delay somewhat mitigated by the effects of increased precipitation. Interestingly, while CO2 concentrations had negligible influence on the flowering and fruiting stages, they noticeably delayed seed dispersal and the initiation of senescence (p < 0.001). Overall, the prevailing trend suggests that plants, whether woody or herbaceous in nature, tend to prolong their growth season under warmer and more humid conditions. The influence of CO2 concentration, however, is contingent upon the specific phenological phase and plant type. Our findings emphasize the nuanced and stage-specific responses of plant phenology to temperature, precipitation, and CO2, highlighting the value of using circular regression in ecological studies.

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Global Atmospheric Carbon Dioxide Concentration Simulation Grid Dataset (1992-2020)

Cited by 2 publications

References 0 publications

Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics

Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics

Effects of Temperature, Precipitation, and CO2 on Plant Phenology in China: A Circular Regression Approach

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