Impacts of Triple La Niña Events on Forest Gross Primary Productivity in China from 2020 to 2022

Abstract. Based on GOSIF GPP data and climate data, this study systematically explored the impact mechanism of triple La Niña event on gross primary productivity (GPP) of forest ecosystem in China from 2020 to 2022 and its physiological and ecological driving process. The results show that GPP in China forest presents a significant dynamic response of "initial inhibition-gradual recovery" to La Niña event. The annual average GPP decreased from 1799.27 Tg C year−1 in the base period (2017–2018) to 1783.89 Tg C year−1, of which 2020 reached the lowest value of 1763.68 Tg C year−1 in the study period, but showed strong recovery ability in the following two years, rising to 1804.54 Tg C year−1. Spatially, the subtropical monsoon region realized "V" recovery through vegetation adaptation, while the temperate monsoon region was inhibited by soil moisture at root zone and phenological delay. The temperate region showed "early stage determines late stage" characteristics, and the subtropical region formed "spring and autumn compensation" pattern. These differences are mainly due to the latitudinal difference regulation of La Niña event on East Asian monsoon system. The temperate region is adjusted by mid-high latitude circulation while the subtropical region directly responds to the continuous drought caused by tropical sea surface temperature (SST) anomaly. This study clarified the response of China forest to triple La Niña event through multi-scale analysis, which provided important scientific basis for improving ecosystem model parameterization and predicting carbon sink function evolution.

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Publication Details

Journal
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Published
2025-11-26
DOI
https://doi.org/10.5194/isprs-archives-xlviii-4-w14-2025-227-2025
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
Field-Weighted Citation Impact
0.00

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article

Impacts of Triple La Niña Events on Forest Gross Primary Productivity in China from 2020 to 2022

Jie Jiang, Qiaoli Wu, Wensi Ma, Wei He
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Plant Water Relations and Carbon Dynamics
article

Impacts of Triple La Niña Events on Forest Gross Primary Productivity in China from 2020 to 2022

Jie Jiang, Qiaoli Wu, Wensi Ma, Wei He
article en

Abstract

Abstract. Based on GOSIF GPP data and climate data, this study systematically explored the impact mechanism of triple La Niña event on gross primary productivity (GPP) of forest ecosystem in China from 2020 to 2022 and its physiological and ecological driving process. The results show that GPP in China forest presents a significant dynamic response of "initial inhibition-gradual recovery" to La Niña event. The annual average GPP decreased from 1799.27 Tg C year−1 in the base period (2017–2018) to 1783.89 Tg C year−1, of which 2020 reached the lowest value of 1763.68 Tg C year−1 in the study period, but showed strong recovery ability in the following two years, rising to 1804.54 Tg C year−1. Spatially, the subtropical monsoon region realized "V" recovery through vegetation adaptation, while the temperate monsoon region was inhibited by soil moisture at root zone and phenological delay. The temperate region showed "early stage determines late stage" characteristics, and the subtropical region formed "spring and autumn compensation" pattern. These differences are mainly due to the latitudinal difference regulation of La Niña event on East Asian monsoon system. The temperate region is adjusted by mid-high latitude circulation while the subtropical region directly responds to the continuous drought caused by tropical sea surface temperature (SST) anomaly. This study clarified the response of China forest to triple La Niña event through multi-scale analysis, which provided important scientific basis for improving ecosystem model parameterization and predicting carbon sink function evolution.

˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesVol. XLVIII-4/W14-2025
Zhejiang University of Technology (CN), Beijing University of Civil Engineering and Architecture (CN), Nanjing University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 66%
Plant Water Relations and Carbon Dynamics
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