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A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020)

Citation

Wenjun, Bi; Yanlian, Zhou (2022), A global 0.05° dataset for gross primary production of sunlit and shaded vegetation canopies (1992–2020), Dryad, Dataset, https://doi.org/10.5061/dryad.dfn2z352k

Abstract

Distinguishing gross primary production of sunlit and shaded leaves (GPPsun and GPPshade) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPPshade and GPPsun over 1992-2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPPsun, and GPPshade over 1992-2020 at 125.0±3.8 (mean ± std) Pg C a-1, 50.5±1.2 Pg C a-1, and 74.5±2.6 Pg C a-1, respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPPsun and GPPshade) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPPshade overwhelmingly outweighs GPPsun. This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.

Methods

The dataset used a revised two-leaf light use efficiency (TL-LUE) model with an added CO2 concentration regular scalar and the modified temperature scalar. These products are driven by the GLOBMAP leaf area index (GLOBMAP-LAI), Climatic Research Unit and Japanese reanalysis (CRUJRA 2.2) meteorological data, and European Space Agency Climate Change Initiative Land Cover (ESA-CCI) data.

Usage Notes

The units of three temporal resolutions (8-day, monthly, annual) are gC m-2 8day-1, gC m-2 month-1 and gC m-2 a-1, respectively. And the scale factor of the monthly data is 0.1, that of the 8-day data is 0.01. In the dataset, in order to ensure the authenticity, we did not delete or modify a small number of abnormally high values (caused by LAI). Therefore, when using this dataset, you can set thresholds to remove the anomalies.

Funding

National Key Research and Development Program of China, Award: 2019YFA0606604

National Natural Science Foundation of China, Award: 42077419

The Open Funding Project of the State Key Laboratory of Remote Sensing Science, Award: OFSLRSS202012