Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming: Simulated Results from IAP-DGVM in CAS-ESM2

Gao, Xiaofei; Zhu, Jiang; Zeng, Xiaodong; Zhang, Minghua; Dai, Yongjiu; Ji, Duoying; Zhang, He

doi:10.1007/s00376-021-1138-3

Cited by 16 publications

(4 citation statements)

References 91 publications

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“…Global environmental changes, particularly climate warming, are rapidly altering the growth condition of terrestrial vegetation (Yuan et al., 2019), disrupting the carbon balance of the pre‐existing terrestrial ecosystem and threatening global socio‐ecological systems sustainable development (Piao et al., 2020; Shi et al., 2021). Thus, the response of vegetated ecosystems to global warming has become an important research component with fruitful results (Alkama et al., 2022; X. F. Gao et al., 2021; Wu et al., 2017). However, the time scale of these studies is mainly focused on the last a few decades, it is still unclear about the global warming trend, changes in vegetation productivity and its sensitivity to warming under different CO 2 emission scenarios in the future.…”

Section: Discussionmentioning

confidence: 99%

Global Vegetation‐Temperature Sensitivity and Its Driving Forces in the 21st Century

Yuxi,

Li,

Yuemin

et al. 2024

Earth's Future

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It has been projected that climatic warming will contribute to vegetation productivity variability at the global scale. With a continued warming, to what extent and where the vegetation productivity is most affected by warming has still not been adequately quantified. Herein, based on 11 earth system model outputs, we predict the characteristics of vegetation‐temperature sensitivity (Svpt, defined as higher/lower temperature produce more/less vegetation) changes under different CO2 emission scenarios and various vegetation types, further assessing the relationship of the Svpt to socio‐ecosystems. At the end of the 21st century, the area proportion with the global temperature increases >2°C under the SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios are 3%, 40%, 97%, and 99%, respectively. The largest contribution to the global terrestrial gross primary productivity growth is at low latitudes. 33%–63% of global terrestrial ecosystems show a significantly negative trend in the Svpt, indicating the weakened promotion effect of warming on vegetation growth. In particular, in 2060, there will be a clear reversal of the trend from carbon sink to carbon source under the SSP3‐7.0 scenario, spatially distributed mainly in the Amazon rainforest, tropical Africa, and southern North America. Precipitation is also an important factor affecting the Svpt change, and there is an inverted U‐shaped relationship between them. Precipitation thresholds are higher under the high emission scenario when Svpt reaches its highest value. Moreover, socio‐demographic pressures in places like Central Africa and East Africa will offset the promotion of vegetation growth by warming; in the future, these countries should develop appropriate population and land management strategies to achieve socio‐ecosystems sustainable development.

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Section: Discussionmentioning

confidence: 99%

Global Vegetation‐Temperature Sensitivity and Its Driving Forces in the 21st Century

Yuxi,

Li,

Yuemin

et al. 2024

Earth's Future

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“…The studies of the CMIP5‐based (Gao et al, 2016, 2022; Piao et al, 2020; Zheng et al, 2017) indicate that global warming will lead to an increase in vegetation cover in mid‐ and high‐latitude regions and highland areas under RCPs (Representative Concentration Pathways), which are RCP4.5 and RCP8.5 scenarios. This is further confirmed by our findings.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Contributions of natural and anthropogenic factors to historical changes in vegetation cover and its future projections in the Yellow River basin, China

Yang,

Zhang

et al. 2023

Intl Journal of Climatology

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The fragile ecology of the Yellow River basin (YRB) is vulnerable to global warming and human activities. As the second largest river in China, the YRB covers the subhumid, semi‐arid, arid and alpine climate zones, with considerably spatial differences in vegetation cover (VC), thus demonstrating substantial variations in response to climate change. In this study, characteristics of VC variations in different climate zones of the YRB during 1982–2019 were analysed, impacts of climate change and human activities on VC variations were identified, and possible VC variations in the future were projected under different climate scenarios. Results show that NDVI in the YRB significantly increased at a rate of 2.3 × 10−3 year−1 from 1982 to 2019, especially in the middle reaches of the YRB. Temperature is the main driver of VC variations in the source region of the YRB (alpine zone), wherein the warming trend contributes to the greening. The internal variability of VC is also influenced by temperature, with a marginal effect of precipitation minus evaporation (P − E). In the middle and lower reaches of the YRB consisting of arid, semi‐arid, and subhumid zones, VC benefited only from the warming trend and had a less relationship with the internal variability of temperature, whereas the internal variability of VC was influenced by P − E and had a weaker relationship with the P − E trend. Contributions of climate change to VC variations in the source region and middle and lower reaches of the YRB are approximately 62%, 42%, and 27%, respectively. Human activities considerably impact the ecological environment but mostly play a positive role. In the future, VC will increase in the YRB, especially under the SSP5‐8.5 scenario, and VC reveals the most prominent changes in the lower reaches. This study suggests that climate change might be favourable to VC in YRB, and emphasizes that human activities should adapt to climate change in an orderly manner.

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“…On the other hand, GPP has been decreasing in tropical zones since 2005. This possibly happened due to a higher vapour pressure deficit in the warmer climate over the tropics, which could lead to a suppression of photosynthesis [39]. The comparison among different global carbon fluxes is discussed in Section 4.2.…”

Section: Multi-site Averaged Time Seriesmentioning

confidence: 99%

A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning

Shangguan

Xiong

Nourani

et al. 2023

Forests

Self Cite

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Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes dataset (named as GCFD) of gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) has been developed via a deep learning based convolutional neural network (CNN) model. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. Flux measurements were used as a training target while remote sensing of vegetation conditions and meteorological data were used as predictors. The results showed that CNN could outperform other commonly used machine learning methods such as random forest (RF) and artificial neural network (ANN) by leading to satisfactory performance with R2 values of the validation stage as 0.82, 0.72 and 0.62 for GPP, RECO, and NEE modelling, respectively. Thus, CNN trained using reanalysis meteorological data and remote sensing data was chosen to produce the global dataset. GCFD showed higher accuracy and more spatial details than some other global carbon flux datasets with reasonable spatial pattern and temporal variation. GCFD is also in accordance with vegetation conditions detected by remote sensing. Owing to the obtained results, GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required.

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Changes in Global Vegetation Distribution and Carbon Fluxes in Response to Global Warming: Simulated Results from IAP-DGVM in CAS-ESM2

Cited by 16 publications

References 91 publications

Global Vegetation‐Temperature Sensitivity and Its Driving Forces in the 21st Century

Global Vegetation‐Temperature Sensitivity and Its Driving Forces in the 21st Century

Contributions of natural and anthropogenic factors to historical changes in vegetation cover and its future projections in the Yellow River basin, China

A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning

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