DOI: 10.57760/sciencedb.16141
title: A new long-term terrestrial gross primary productivity dataset based on improved P model (IP model)
title: A new long-term terrestrial gross primary productivity dataset based on improved P model (IP model)

description: Abstract:Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2 d−1, -0.14 g C m−2 d−1, 1.77 g C m−2 d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products. Data description:This is a new terrestrial gross primary productivity (GPP) dataset, called IPGPP. IPGPP is generated based on a improved P model (IP model). The temporal resolution of IPGPP is 1 month. The spatial resolution of IPGPP is about 0.1 deg (equal to the original resolution of ERA5-Land monthly dataset ). IPGPP currently covers the period 1981-2021. We will continue to update the IPGPP.Note:if the IPGPP is used in your research, please be sure to cite the literature below:Zhang Zhenyu, Ju Weimin, Li Xiaoyu, Cheng Xianfu, Zhou Yanlian, Xu Shuhao, Liu Chengyu, and Li Jing. "Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity." Agricultural and Forest Meteorology, 2024, 346: 109883.https://doi.org/10.1016/j.agrformet.2023.109883Note [2024-08-10]: We have recently updated the IPGPP dataset, with time coverage until the end of 2022, and spatial resolution strictly limited to 0.1 deg (1800*3600)
description: Abstract:Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2 d−1, -0.14 g C m−2 d−1, 1.77 g C m−2 d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products. Data description:This is a new terrestrial gross primary productivity (GPP) dataset, called IPGPP. IPGPP is generated based on a improved P model (IP model). The temporal resolution of IPGPP is 1 month. The spatial resolution of IPGPP is about 0.1 deg (equal to the original resolution of ERA5-Land monthly dataset ). IPGPP currently covers the period 1981-2021. We will continue to update the IPGPP.Note:if the IPGPP is used in your research, please be sure to cite the literature below:Zhang Zhenyu, Ju Weimin, Li Xiaoyu, Cheng Xianfu, Zhou Yanlian, Xu Shuhao, Liu Chengyu, and Li Jing. "Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity." Agricultural and Forest Meteorology, 2024, 346: 109883.https://doi.org/10.1016/j.agrformet.2023.109883Note [2024-08-10]: We have recently updated the IPGPP dataset, with time coverage until the end of 2022, and spatial resolution strictly limited to 0.1 deg (1800*3600)