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A global moderate resolution dataset of gross primary production of vegetation for 2000-2016.

ABSTRACT: Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500?m) and temporal (8-day) resolutions over the entire globe for 2000-2016. This GPP dataset is based on an improved light use efficiency theory and is driven by satellite data from MODIS and climate data from NCEP Reanalysis II. It also employs a state-of-the-art vegetation index (VI) gap-filling and smoothing algorithm and a separate treatment for C3/C4 photosynthesis pathways. All these improvements aim to solve several critical problems existing in current GPP products. With a satisfactory performance when validated against in situ GPP estimates, this dataset offers an alternative GPP estimate for regional to global carbon cycle studies.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5667571 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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A global moderate resolution dataset of gross primary production of vegetation for 2000-2016.

Zhang Yao Y Xiao Xiangming X Wu Xiaocui X Zhou Sha S Zhang Geli G Qin Yuanwei Y Dong Jinwei J

Scientific data 20171024

Accurate estimation of the gross primary production (GPP) of terrestrial vegetation is vital for understanding the global carbon cycle and predicting future climate change. Multiple GPP products are currently available based on different methods, but their performances vary substantially when validated against GPP estimates from eddy covariance data. This paper provides a new GPP dataset at moderate spatial (500 m) and temporal (8-day) resolutions over the entire globe for 2000-2016. This GPP da ...[more]

PMID: 29064464

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Project description:Carbon isotope discrimination (Δ13 C) in C3 woody plants is a key variable for the study of photosynthesis. Yet how Δ13 C varies at decadal scales, and across regions, and how it is related to gross primary production (GPP), are still incompletely understood. Here we address these questions by implementing a new Δ13 C modelling capability in the land-surface model JULES incorporating both photorespiratory and mesophyll-conductance fractionations. We test the ability of four leaf-internal CO2 concentration models embedded in JULES to reproduce leaf and tree-ring (TR) carbon isotopic data. We show that all the tested models tend to overestimate average Δ13 C values, and to underestimate interannual variability in Δ13 C. This is likely because they ignore the effects of soil water stress on stomatal behavior. Variations in post-photosynthetic isotopic fractionations across species, sites and years, may also partly explain the discrepancies between predicted and TR-derived Δ13 C values. Nonetheless, the "least-cost" (Prentice) model shows the lowest biases with the isotopic measurements, and lead to improved predictions of canopy-level carbon and water fluxes. Overall, modelled Δ13 C trends vary strongly between regions during the recent (1979-2016) historical period but stay nearly constant when averaged over the globe. Photorespiratory and mesophyll effects modulate the simulated global Δ13 C trend by 0.0015 ± 0.005‰ and -0.0006 ± 0.001‰ ppm-1 , respectively. These predictions contrast with previous findings based on atmospheric carbon isotope measurements. Predicted Δ13 C and GPP tend to be negatively correlated in wet-humid and cold regions, and in tropical African forests, but positively related elsewhere. The negative correlation between Δ13 C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ13 C in those forests. Our results demonstrate that the combined analysis of Δ13 C and GPP can help understand the drivers of photosynthesis changes in different climatic regions.

Dataset Information

A global moderate resolution dataset of gross primary production of vegetation for 2000-2016.

Publications

A global moderate resolution dataset of gross primary production of vegetation for 2000-2016.

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