Dominant regions and drivers of the variability of the global land carbon sink across timescales

Zhang, Xuanze; Wang, Ying‐Ping; Peng, Shushi; Rayner, P.J.W.; Ciais, Philippe; Silver, Jeremy D.; Piao, Shilong; Zhu, Zaichun; Lu, Xingjie; Zheng, Xunhua

doi:10.1111/gcb.14275

Cited by 35 publications

(49 citation statements)

References 70 publications

(149 reference statements)

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“…We find that climate‐internal variability dominates the variability in NBP on interannual to multidecadal timescales, which is consistent to previous studies (Bauska et al, ; Sitch et al, ; Zhang et al, ). The spectral analysis of NBP over 850‐1849 shows strong periodic peaks of around 4 years and 50 years.…”

Section: Discussionmentioning

confidence: 84%

“…The spectral analysis of NBP over 850‐1849 shows strong periodic peaks of around 4 years and 50 years. A similar result was found for interannual variability in another ensemble of land carbon models (TRENDY), which were forced by observational climate data over 1901‐2010, albeit over a much shorter period (Zhang et al, ). The spectrum for the TRENDY ensemble demonstrates periodic peaks at about 2.5, 4, and 30 years with much smaller peak values (e.g., about 6 (PgC) 2 /year ‐2 at the period of 4 years) compared to the CESM‐LME NBP spectrum (about 16 (Pg C) 2 /year ‐2 at period of 4 years).…”

Section: Discussionmentioning

confidence: 84%

“…It can be shown (Zhang et al, ; see Text S1 in the supporting information) that for a fixed timescale, the amplitude of temporal variability in NBP, NPP, and the sum RH and D can be related as follows:

A_{NBP} = \frac{ω}{\sqrt{ω^{2} + {k_{eco}}^{2}}} A_{NPP},

A_{RH + normalD} = \frac{k_{eco}}{\sqrt{ω^{2} + {k_{eco}}^{2}}} A_{NPP},

where A NPP , A RH+D , and A NBP denote the amplitude of NPP, RH plus D, and NBP, respectively, at a given frequency (units of year ‐1 , or angular frequency ω ). The turnover rate ( k eco ) is defined as the ratio of the output (RH + D) to the total C pool within an ecosystem ( C eco ):

k_{eco} = \frac{RH + normalD}{C_{eco}} .

…”

Section: Methodsmentioning

confidence: 99%

“…A is a transfer matrix, representing the fractions of carbon transferred from one pool to the others. Following Zhang et al (), we have

boldA = ((), \begin{array}{c} 0 & 0 & 0 \\ a_{21} & - a_{22} & 0 \\ 0 & a_{32} & - a_{33} \end{array}),

RH ((), t) = a_{22} c_{2} ((), t) + a_{33} c_{3} ((), t),

NBP ((), t) = \frac{d ((), c_{1} ((), t) + c_{2} ((), t) + c_{3} ((), t))}{dt} .

…”

Section: Methodsmentioning

confidence: 99%

“…From equations and , it can be seen that for a given timescale (or a time frequency; Zhang et al, ),

{A^{2}}_{NBP} = {A^{2}}_{NPP} - {A^{2}}_{RH + normalD} .

…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Greenhouse Gas Concentration and Volcanic Eruptions Controlled the Variability of Terrestrial Carbon Uptake Over the Last Millennium

Zhang

Peng

Ciais

et al. 2019

J Adv Model Earth Syst

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The terrestrial net biome production (NBP) is considered as one of the major drivers of interannual variation in atmospheric CO2 levels. However, the determinants of variability in NBP under the background climate (i.e., preindustrial conditions) remain poorly understood, especially on decadal‐to‐centennial timescales. We analyzed 1,000‐year simulations spanning 850‐1,849 from the Community Earth System Model (CESM) and found that the variability in NBP and heterotrophic respiration (RH) were largely driven by fluctuations in the net primary production (NPP) and carbon turnover rates in response to climate variability. On interannual to multidecadal timescales, variability in NBP was dominated by variation in NPP, while variability in RH was driven by variation in turnover rates. However, on centennial timescales (100‐1,000 years), the RH variability became more tightly coupled to that of NPP. The NBP variability on centennial timescales was low, due to the near cancellation of NPP and NPP‐driven RH changes arising from climate internal variability and external forcings: preindustrial greenhouse gases, volcanic eruptions, land use changes, orbital change, and solar activity. Factorial experiments showed that globally on centennial timescales, the forcing of changes in greenhouse gas concentrations were the largest contributor (51%) to variations in both NPP and RH, followed by volcanic eruptions impacting NPP (25%) and RH (31%). Our analysis of the carbon‐cycle suggests that geoengineering solutions by injection of stratospheric aerosols might be ineffective on longer timescales.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 84%

A_{NBP} = \frac{ω}{\sqrt{ω^{2} + {k_{eco}}^{2}}} A_{NPP},

A_{RH + normalD} = \frac{k_{eco}}{\sqrt{ω^{2} + {k_{eco}}^{2}}} A_{NPP},

k_{eco} = \frac{RH + normalD}{C_{eco}} .

…”

Section: Methodsmentioning

confidence: 99%

“…A is a transfer matrix, representing the fractions of carbon transferred from one pool to the others. Following Zhang et al (), we have

boldA = ((), \begin{array}{c} 0 & 0 & 0 \\ a_{21} & - a_{22} & 0 \\ 0 & a_{32} & - a_{33} \end{array}),

RH ((), t) = a_{22} c_{2} ((), t) + a_{33} c_{3} ((), t),

NBP ((), t) = \frac{d ((), c_{1} ((), t) + c_{2} ((), t) + c_{3} ((), t))}{dt} .

…”

Section: Methodsmentioning

confidence: 99%

“…From equations and , it can be seen that for a given timescale (or a time frequency; Zhang et al, ),

{A^{2}}_{NBP} = {A^{2}}_{NPP} - {A^{2}}_{RH + normalD} .

…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Greenhouse Gas Concentration and Volcanic Eruptions Controlled the Variability of Terrestrial Carbon Uptake Over the Last Millennium

Zhang

Peng

Ciais

et al. 2019

J Adv Model Earth Syst

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Climate and Vegetation Drivers of Terrestrial Carbon Fluxes: A Global Data Synthesis

Chen

Zou

et al. 2019

Adv. Atmos. Sci.

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Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

Peng

et al. 2021

Adv. Atmos. Sci.

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Gross primary production (GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems. A set of validated monthly GPP data from 1957 to 2010 in 0.5° × 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging (BMA). The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Niño years indicated that GPP response to El Niño varies with Pacific Decadal Oscillation (PDO) phases: when the PDO was in the cool phase, it was likely that GPP was greater in northern China (32°-38°N, 111°-122°E) and less in the Yangtze River valley (28°-32°N, 111°-122°E); in contrast, when PDO was in the warm phase, the GPP anomalies were usually reversed in these two regions. The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon. The previously published findings on how El Niño during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Niño in this study theoretically credible. This paper not only introduces an effective way to use BMA in grids that have mixed plant function types, but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Niño and PDO.

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Dominant regions and drivers of the variability of the global land carbon sink across timescales

Cited by 35 publications

References 70 publications

Greenhouse Gas Concentration and Volcanic Eruptions Controlled the Variability of Terrestrial Carbon Uptake Over the Last Millennium

Greenhouse Gas Concentration and Volcanic Eruptions Controlled the Variability of Terrestrial Carbon Uptake Over the Last Millennium

Climate and Vegetation Drivers of Terrestrial Carbon Fluxes: A Global Data Synthesis

Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

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