Global vegetation distribution driving factors in two Dynamic Global Vegetation Models of contrasting complexities

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This study compares the dynamic vegetation response in two Dynamic Global Vegetation Models (DGVMs) with contrasting processes complexity to changes in climate and atmospheric CO2. We consider observed pre-industrial climates and four different simulated climate change states relative to preindustrial conditions: the mid-Holocene (6 ka), the pre-industrial state with halved CO2 levels (140 ppm), doubled CO2 (560 ppm), and quadrupled CO2 (1120 ppm). The two DVGMs are LPJ-GUESS and VECODE. The input climate is derived from an earth system model of intermediate complexity (iLOVECLIM). We evaluate the sensitivities of these two DGVMs to changing climate and CO2 levels and assess the impact of their respective complexity on these sensitivities. Our results show that both DGVMs yield results consistent with the broad features of pre-industrial vegetation dynamics and changes in vegetation from mid-Holocene to pre-industrial. They also agree with the patterns of vegetation responses to the more extreme varying CO2 scenarios, yet with stronger magnitudes in LPJ-GUESS than in VECODE; in particular, large uncertainties are associated with the response of tropical vegetation to varying CO2 levels. LPJ-GUESS simulates stronger positive responses of global net primary production (NPP) to elevated CO2 levels than VECODE, particularly in tropical regions. The increase in global NPP differs by 8% between the two DGVMs under 2*CO2 scenarios. Also, LPJ-GUESS simulates tropical vegetation sensitivities, defined here as the changes in tree-cover per degree of temperature anomaly, varying from 0.5 (°C−1), 0.25 (°C−1) to 0.15 (°C−1) under ½*CO2, 2*CO2, and 4*CO2 scenarios. In VECODE these values are around 0.05 (°C−1) for all scenarios. The higher sensitivity of LPJ-GUESS to CO2 concentration levels is likely related to the inclusion of more detailed ecophysiological processes. The two DGVMs' different complexity of eco-physiological processes also impacts on vegetation requirements for rainfall due to the physiological effects that more efficient water use of vegetation is facilitated under elevated atmospheric CO2 concentration. The sensitivity of global Leaf Area Index (LAI) in the two DGVMs decreases with the increasing atmospheric CO2 from pre-industrial level to 4*CO2 scenario. The uncertainties of vegetation simulations are mainly contributed by the tropical vegetation response to climate and CO2 concentration due to the inclusion of ecosystem processes in both DGVMs and the scheme of vegetation classification. Based on our results, we recommend to use a standard set of vegetation types and to set up systematic simulations detecting the range of vegetation sensitivity to varying CO2 and climate forcing when inter-comparing different DGVMs.

Original languageEnglish
Pages (from-to)51-65
Number of pages15
JournalGlobal and Planetary Change
Volume180
DOIs
Publication statusPublished - 1 Sep 2019

Fingerprint

vegetation
climate
distribution
net primary production
vegetation dynamics
Holocene
vegetation classification
climate forcing
tropical region
leaf area index
temperature anomaly
vegetation type
water use
simulation
rainfall
climate change
ecosystem

Keywords

  • CO scenarios
  • DGVMs
  • Ecophysiological processes
  • Global vegetation responses
  • Mid-Holocene

Cite this

@article{a2d6d2790ecd42478a6ff1d2cb31a01d,
title = "Global vegetation distribution driving factors in two Dynamic Global Vegetation Models of contrasting complexities",
abstract = "This study compares the dynamic vegetation response in two Dynamic Global Vegetation Models (DGVMs) with contrasting processes complexity to changes in climate and atmospheric CO2. We consider observed pre-industrial climates and four different simulated climate change states relative to preindustrial conditions: the mid-Holocene (6 ka), the pre-industrial state with halved CO2 levels (140 ppm), doubled CO2 (560 ppm), and quadrupled CO2 (1120 ppm). The two DVGMs are LPJ-GUESS and VECODE. The input climate is derived from an earth system model of intermediate complexity (iLOVECLIM). We evaluate the sensitivities of these two DGVMs to changing climate and CO2 levels and assess the impact of their respective complexity on these sensitivities. Our results show that both DGVMs yield results consistent with the broad features of pre-industrial vegetation dynamics and changes in vegetation from mid-Holocene to pre-industrial. They also agree with the patterns of vegetation responses to the more extreme varying CO2 scenarios, yet with stronger magnitudes in LPJ-GUESS than in VECODE; in particular, large uncertainties are associated with the response of tropical vegetation to varying CO2 levels. LPJ-GUESS simulates stronger positive responses of global net primary production (NPP) to elevated CO2 levels than VECODE, particularly in tropical regions. The increase in global NPP differs by 8{\%} between the two DGVMs under 2*CO2 scenarios. Also, LPJ-GUESS simulates tropical vegetation sensitivities, defined here as the changes in tree-cover per degree of temperature anomaly, varying from 0.5 (°C−1), 0.25 (°C−1) to 0.15 (°C−1) under ½*CO2, 2*CO2, and 4*CO2 scenarios. In VECODE these values are around 0.05 (°C−1) for all scenarios. The higher sensitivity of LPJ-GUESS to CO2 concentration levels is likely related to the inclusion of more detailed ecophysiological processes. The two DGVMs' different complexity of eco-physiological processes also impacts on vegetation requirements for rainfall due to the physiological effects that more efficient water use of vegetation is facilitated under elevated atmospheric CO2 concentration. The sensitivity of global Leaf Area Index (LAI) in the two DGVMs decreases with the increasing atmospheric CO2 from pre-industrial level to 4*CO2 scenario. The uncertainties of vegetation simulations are mainly contributed by the tropical vegetation response to climate and CO2 concentration due to the inclusion of ecosystem processes in both DGVMs and the scheme of vegetation classification. Based on our results, we recommend to use a standard set of vegetation types and to set up systematic simulations detecting the range of vegetation sensitivity to varying CO2 and climate forcing when inter-comparing different DGVMs.",
keywords = "CO scenarios, DGVMs, Ecophysiological processes, Global vegetation responses, Mid-Holocene",
author = "Huan Li and Hans Renssen and Roche, {Didier M.}",
year = "2019",
month = "9",
day = "1",
doi = "10.1016/j.gloplacha.2019.05.009",
language = "English",
volume = "180",
pages = "51--65",
journal = "Global and Planetary Change",
issn = "0921-8181",
publisher = "Elsevier",

}

Global vegetation distribution driving factors in two Dynamic Global Vegetation Models of contrasting complexities. / Li, Huan; Renssen, Hans; Roche, Didier M.

In: Global and Planetary Change, Vol. 180, 01.09.2019, p. 51-65.

Research output: Contribution to JournalArticleAcademicpeer-review

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T1 - Global vegetation distribution driving factors in two Dynamic Global Vegetation Models of contrasting complexities

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AU - Renssen, Hans

AU - Roche, Didier M.

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N2 - This study compares the dynamic vegetation response in two Dynamic Global Vegetation Models (DGVMs) with contrasting processes complexity to changes in climate and atmospheric CO2. We consider observed pre-industrial climates and four different simulated climate change states relative to preindustrial conditions: the mid-Holocene (6 ka), the pre-industrial state with halved CO2 levels (140 ppm), doubled CO2 (560 ppm), and quadrupled CO2 (1120 ppm). The two DVGMs are LPJ-GUESS and VECODE. The input climate is derived from an earth system model of intermediate complexity (iLOVECLIM). We evaluate the sensitivities of these two DGVMs to changing climate and CO2 levels and assess the impact of their respective complexity on these sensitivities. Our results show that both DGVMs yield results consistent with the broad features of pre-industrial vegetation dynamics and changes in vegetation from mid-Holocene to pre-industrial. They also agree with the patterns of vegetation responses to the more extreme varying CO2 scenarios, yet with stronger magnitudes in LPJ-GUESS than in VECODE; in particular, large uncertainties are associated with the response of tropical vegetation to varying CO2 levels. LPJ-GUESS simulates stronger positive responses of global net primary production (NPP) to elevated CO2 levels than VECODE, particularly in tropical regions. The increase in global NPP differs by 8% between the two DGVMs under 2*CO2 scenarios. Also, LPJ-GUESS simulates tropical vegetation sensitivities, defined here as the changes in tree-cover per degree of temperature anomaly, varying from 0.5 (°C−1), 0.25 (°C−1) to 0.15 (°C−1) under ½*CO2, 2*CO2, and 4*CO2 scenarios. In VECODE these values are around 0.05 (°C−1) for all scenarios. The higher sensitivity of LPJ-GUESS to CO2 concentration levels is likely related to the inclusion of more detailed ecophysiological processes. The two DGVMs' different complexity of eco-physiological processes also impacts on vegetation requirements for rainfall due to the physiological effects that more efficient water use of vegetation is facilitated under elevated atmospheric CO2 concentration. The sensitivity of global Leaf Area Index (LAI) in the two DGVMs decreases with the increasing atmospheric CO2 from pre-industrial level to 4*CO2 scenario. The uncertainties of vegetation simulations are mainly contributed by the tropical vegetation response to climate and CO2 concentration due to the inclusion of ecosystem processes in both DGVMs and the scheme of vegetation classification. Based on our results, we recommend to use a standard set of vegetation types and to set up systematic simulations detecting the range of vegetation sensitivity to varying CO2 and climate forcing when inter-comparing different DGVMs.

AB - This study compares the dynamic vegetation response in two Dynamic Global Vegetation Models (DGVMs) with contrasting processes complexity to changes in climate and atmospheric CO2. We consider observed pre-industrial climates and four different simulated climate change states relative to preindustrial conditions: the mid-Holocene (6 ka), the pre-industrial state with halved CO2 levels (140 ppm), doubled CO2 (560 ppm), and quadrupled CO2 (1120 ppm). The two DVGMs are LPJ-GUESS and VECODE. The input climate is derived from an earth system model of intermediate complexity (iLOVECLIM). We evaluate the sensitivities of these two DGVMs to changing climate and CO2 levels and assess the impact of their respective complexity on these sensitivities. Our results show that both DGVMs yield results consistent with the broad features of pre-industrial vegetation dynamics and changes in vegetation from mid-Holocene to pre-industrial. They also agree with the patterns of vegetation responses to the more extreme varying CO2 scenarios, yet with stronger magnitudes in LPJ-GUESS than in VECODE; in particular, large uncertainties are associated with the response of tropical vegetation to varying CO2 levels. LPJ-GUESS simulates stronger positive responses of global net primary production (NPP) to elevated CO2 levels than VECODE, particularly in tropical regions. The increase in global NPP differs by 8% between the two DGVMs under 2*CO2 scenarios. Also, LPJ-GUESS simulates tropical vegetation sensitivities, defined here as the changes in tree-cover per degree of temperature anomaly, varying from 0.5 (°C−1), 0.25 (°C−1) to 0.15 (°C−1) under ½*CO2, 2*CO2, and 4*CO2 scenarios. In VECODE these values are around 0.05 (°C−1) for all scenarios. The higher sensitivity of LPJ-GUESS to CO2 concentration levels is likely related to the inclusion of more detailed ecophysiological processes. The two DGVMs' different complexity of eco-physiological processes also impacts on vegetation requirements for rainfall due to the physiological effects that more efficient water use of vegetation is facilitated under elevated atmospheric CO2 concentration. The sensitivity of global Leaf Area Index (LAI) in the two DGVMs decreases with the increasing atmospheric CO2 from pre-industrial level to 4*CO2 scenario. The uncertainties of vegetation simulations are mainly contributed by the tropical vegetation response to climate and CO2 concentration due to the inclusion of ecosystem processes in both DGVMs and the scheme of vegetation classification. Based on our results, we recommend to use a standard set of vegetation types and to set up systematic simulations detecting the range of vegetation sensitivity to varying CO2 and climate forcing when inter-comparing different DGVMs.

KW - CO scenarios

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