Toward dynamic global vegetation models for simulating vegetation–climate interactions and feedbacks: recent developments, limitations, and future challenges

2010 ◽  
Vol 18 (NA) ◽  
pp. 333-353 ◽  
Author(s):  
Anne Quillet ◽  
Changhui Peng ◽  
Michelle Garneau
Keyword(s):  
Land Use ◽  
Vegetation Dynamics ◽  
Climate Models ◽  
Global Climate ◽  
Evaluation Methodology ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Dynamics Models ◽  
Climate Studies

There is a lack in representation of biosphere–atmosphere interactions in current climate models. To fill this gap, one may introduce vegetation dynamics in surface transfer schemes or couple global climate models (GCMs) with vegetation dynamics models. As these vegetation dynamics models were not designed to be included in GCMs, how are the latest generation dynamic global vegetation models (DGVMs) suitable for use in global climate studies? This paper reviews the latest developments in DGVM modelling as well as the development of DGVM–GCM coupling in the framework of global climate studies. Limitations of DGVM and coupling are shown and the challenges of these methods are highlighted. During the last decade, DGVMs underwent major changes in the representation of physical and biogeochemical mechanisms such as photosynthesis and respiration processes as well as in the representation of regional properties of vegetation. However, several limitations such as carbon and nitrogen cycles, competition, land-use and land-use changes, and disturbances have been identified. In addition, recent advances in model coupling techniques allow the simulation of the vegetation–atmosphere interactions in GCMs with the help of DGVMs. Though DGVMs represent a good alternative to investigate vegetation–atmosphere interactions at a large scale, some weaknesses in evaluation methodology and model design need to be further investigated to improve the results.

scholarly journals Historical CO<sub>2</sub> emissions from land-use and land-cover change and their uncertainty

2020 ◽  
Author(s):  
Thomas Gasser ◽  
Léa Crepin ◽  
Yann Quilcaille ◽  
Richard A. Houghton ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Sink ◽  
Data Sets ◽  
Tropical Regions ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Global Carbon

Abstract. Emissions from land-use and land-cover change are a key component of the global carbon cycle. Models are required to disentangle these emissions and the land carbon sink, however, because only the sum of both can be physically observed. Their assessment within the yearly community-wide effort known as the Global Carbon Budget remains a major difficulty, because it combines two lines of evidence that are inherently inconsistent: bookkeeping models and dynamic global vegetation models. Here, we propose a unifying approach relying on a bookkeeping model that embeds processes and parameters calibrated on dynamic global vegetation models, and the use of an empirical constraint. We estimate global CO2 emissions from land-use and land-cover change were 1.36 ± 0.42 Pg C yr−1 (1-σ range) on average over 2009–2018, and 206 ± 57 Pg C cumulated over 1750–2018. We also estimate that land-cover change induced a global loss of additional sink capacity – that is, a foregone carbon removal, not part of the emissions – of 0.68 ± 0.57 Pg C yr−1 and 32 ± 23 Pg C over the same periods, respectively. Additionally, we provide a breakdown of our results' uncertainty following aspects that include the land-use and land-cover change data sets used as input, and the model's biogeochemical parameters. We find the biogeochemical uncertainty dominates our global and regional estimates, with the exception of tropical regions in which the input data dominates. Our analysis further identifies key sources of uncertainty, and suggests ways to strengthen the robustness of future Global Carbon Budgets.

RED DGVM: simple approach to modelling vegetation with novel implications.

2020 ◽  
Author(s):  
Arthur Argles ◽  
Jonathan Moore ◽  
Peter Cox
Keyword(s):  
Equilibrium State ◽  
Climate Models ◽  
Size Structure ◽  
Significant Proportion ◽  
Parameter Tuning ◽  
Equilibrium Solutions ◽  
Demographic Processes ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

&lt;p&gt;The modelled global vegetation for the end of the 21st century is currently is insufficiently constrained&lt;br&gt;by climate models. A significant proportion of that uncertainty has been attributed to the limitations&lt;br&gt;of current Dynamic Global Vegetation Models (DGVMs), and the misrepresentation of mortality, dis-&lt;br&gt;turbance and regrowth within forests. Improving the simulation of the underlying processes of de-&lt;br&gt;mographic change is of primary importance in the development of predictors of future climate.&lt;/p&gt;&lt;p&gt;Here we present the Robust Ecosystem Demography (RED), a new dynamical vegetation model which&lt;br&gt;simulates the size-structure of forests by partitioning the population of a Plant Functional Type (PFT)&lt;br&gt;into mass classes. Allometric scaling of mortality and growth across mass classes allows for a variety&lt;br&gt;of complex demographic processes to be captured, such as disturbances and regrowth. Competition&lt;br&gt;among PFTs is done purely through restricting the recruitment of new vegetation to unshaded space.&lt;br&gt;RED represents a reduction of complexity from more numerically unwieldy cohort DGVMs which&lt;br&gt;simulate both size and patch dimensions. The limited number of dimensions and simple competitive&lt;br&gt;regime allows the equilibrium state to be solved for analytically, providing two potential functions - (i)&lt;br&gt;Avoiding-spinning up by providing an equilibrium state for intilisation. (ii) Insights into the demog-&lt;br&gt;raphy of vegetated areas, arising from parameter tuning to fit observation, such as coverage or carbon&lt;br&gt;mass. When paired with a rate of mortality or carbon assimilate rate gives, respectfully, required as-&lt;br&gt;similate or mortality rates.&lt;br&gt;We demonstrate the model functionality using offline UKESM PFT carbon assimilate rates, paired&lt;br&gt;with observed vegetation cover from the ESA LC_CCI datasets for the 9 different PFTs. From this&lt;br&gt;dataset we calibrate a novel global equilibrium mortality map for each PFT and show the competitive&lt;br&gt;and successional behaviour of dynamical runs with convergence to the fitted equilibrium. Finally, we&lt;br&gt;explore underlying ecological questions that emerge from the equilibrium solutions.&lt;/p&gt;

scholarly journals Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations

2017 ◽  
Author(s):  
Wei Li ◽  
Philippe Ciais ◽  
Shushi Peng ◽  
Chao Yue ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Large Range ◽  
Regional Scale ◽  
Tropical Regions ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

Abstract. The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions, and for ecosystem processes affected by LULCC. One drawback of DGVMs however is their large uncertainty. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (EcLUC) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and EcLUC. This method is applicable at global and regional scale. Compared to the large range of EcLUC in the original ensemble (94 to 273 Pg C) during 1901–2012, current biomass observations allow us to derive a new best estimate of 155 ± 50 (1-σ Gaussian error) Pg C. The constrained LULCC emissions are higher than prior DGVM values in tropical regions, but significantly lower in North America. Our approach of constraining cumulative LULCC emissions based on biomass observations reduces the uncertainty of the historical carbon budget, and can also be applied to evaluate the impact of land-based mitigation activities.

scholarly journals Modelling Holocene peatland dynamics with an individual-based dynamic vegetation model

2017 ◽  
Vol 14 (10) ◽  
pp. 2571-2596 ◽  
Author(s):  
Nitin Chaudhary ◽  
Paul A. Miller ◽  
Benjamin Smith
Keyword(s):  
Vegetation Dynamics ◽  
Carbon Fluxes ◽  
Published Data ◽  
Climate Feedbacks ◽  
Climate Conditions ◽  
Fertilization Effect ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. Dynamic global vegetation models (DGVMs) are designed for the study of past, present and future vegetation patterns together with associated biogeochemical cycles and climate feedbacks. However, most DGVMs do not yet have detailed representations of permafrost and non-permafrost peatlands, which are an important store of carbon, particularly at high latitudes. We demonstrate a new implementation of peatland dynamics in a customized Arctic version of the LPJ-GUESS DGVM, simulating the long-term evolution of selected northern peatland ecosystems and assessing the effect of changing climate on peatland carbon balance. Our approach employs a dynamic multi-layer soil with representation of freeze–thaw processes and litter inputs from a dynamically varying mixture of the main peatland plant functional types: mosses, shrubs and graminoids. The model was calibrated and tested for a sub-Arctic mire in Stordalen, Sweden, and validated at a temperate bog site in Mer Bleue, Canada. A regional evaluation of simulated carbon fluxes, hydrology and vegetation dynamics encompassed additional locations spread across Scandinavia. Simulated peat accumulation was found to be generally consistent with published data and the model was able to capture reported long-term vegetation dynamics, water table position and carbon fluxes. A series of sensitivity experiments were carried out to investigate the vulnerability of high-latitude peatlands to climate change. We found that the Stordalen mire may be expected to sequester more carbon in the first half of the 21st century due to milder and wetter climate conditions, a longer growing season, and the CO2 fertilization effect, turning into a carbon source after mid-century because of higher decomposition rates in response to warming soils.

scholarly journals Historical CO<sub>2</sub> emissions from land use and land cover change and their uncertainty

2020 ◽  
Vol 17 (15) ◽  
pp. 4075-4101 ◽  
Author(s):  
Thomas Gasser ◽  
Léa Crepin ◽  
Yann Quilcaille ◽  
Richard A. Houghton ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Budget ◽  
Carbon Sink ◽  
Global Carbon Budget ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation ◽  
Global Carbon

Abstract. Emissions from land use and land cover change are a key component of the global carbon cycle. However, models are required to disentangle these emissions from the land carbon sink, as only the sum of both can be physically observed. Their assessment within the yearly community-wide effort known as the “Global Carbon Budget” remains a major difficulty, because it combines two lines of evidence that are inherently inconsistent: bookkeeping models and dynamic global vegetation models. Here, we propose a unifying approach that relies on a bookkeeping model, which embeds processes and parameters calibrated on dynamic global vegetation models, and the use of an empirical constraint. We estimate that the global CO2 emissions from land use and land cover change were 1.36±0.42 PgC yr−1 (1σ range) on average over the 2009–2018 period and reached a cumulative total of 206±57 PgC over the 1750–2018 period. We also estimate that land cover change induced a global loss of additional sink capacity – that is, a foregone carbon removal, not part of the emissions – of 0.68±0.57 PgC yr−1 and 32±23 PgC over the same periods, respectively. Additionally, we provide a breakdown of our results' uncertainty, including aspects such as the land use and land cover change data sets used as input and the model's biogeochemical parameters. We find that the biogeochemical uncertainty dominates our global and regional estimates with the exception of tropical regions in which the input data dominates. Our analysis further identifies key sources of uncertainty and suggests ways to strengthen the robustness of future Global Carbon Budget estimates.

scholarly journals Forests, savannas and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models

2014 ◽  
Vol 11 (6) ◽  
pp. 9471-9510 ◽  
Author(s):  
M. Baudena ◽  
S. C. Dekker ◽  
P. M. van Bodegom ◽  
B. Cuesta ◽  
S.I. Higgins ◽  
...  
Keyword(s):  
Global Climate ◽  
Predictive Ability ◽  
Tree Cover ◽  
Fuel Load ◽  
Mean Annual Precipitation ◽  
Knowledge Gap ◽  
Climate Conditions ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future, due to global climate change. Dynamic Global Vegetation Models (DGVMs) are very useful to understand vegetation dynamics under present climate, and to predict its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modelling. Model outcomes, obtained including different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. Through these comparisons, and by drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need an improved representation in the DGVMs. The first mechanism includes water limitation to tree growth, and tree-grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass-fire feedback, which maintains both forest and savanna occurrences in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant savanna trees, and fire-resistant and shade-intolerant forest trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.

Advancing representation of anthropogenic fire in dynamic global vegetation models

2021 ◽  
Author(s):  
James Millington ◽  
Oliver Perkins ◽  
Matthew Kasoar ◽  
Apostolos Voulgarakis
Keyword(s):  
Land Use ◽  
Fire Regimes ◽  
Economic Indicators ◽  
Burned Area ◽  
Agent Based ◽  
Anthropogenic Fire ◽  
Fire Use ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

&lt;div&gt; &lt;p&gt;It is now commonly-understood that improved understanding of global fire regimes demands better representation of anthropogenic fire in dynamic global vegetation models (DGVMs). However, currently there is no clear agreement on how human activity should be incorporated into fire-enabled DGVMs and existing models exhibit large differences in the sensitivities of socio-economic variables. Furthermore, existing approaches are limited to empirical statistical relations between fire regime variables and globally available socio-economic indicators such as population density or GDP. Although there has been some limited representation in global models of the contrasting ways in which different classes of actors use or manage fires, we argue that fruitful progress in advancing representation of anthropogenic fire in DGVMs will come by building on agent-based modelling approaches. Here, we report on our progress developing a global agent-based representation of anthropogenic fire and its coupling with the JULES-INFERNO fire-enabled DGVM.&amp;#160;&amp;#160;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;Our modelling of anthropogenic fire adopts an approach that classifies &amp;#8216;agent functional types&amp;#8217; (AFTs) to represent human fire activity based on land use/cover and Stephen Pyne&amp;#8217;s fire development stages. For example, the &amp;#8216;swidden&amp;#8217; AFT represents shifting cultivation farmers managing cropland and secondary vegetation in a pre-industrial development setting. This approach is based on the assumption that anthropogenic fire use and management is primarily a function of land use but influenced by socio-economic context, leading different AFTs to produce qualitatively distinct fire regimes. The literature empirically supports this assumption, however data on human fire interactions are fragmented across many academic fields (including anthropology, geography, land economics). Therefore, we developed a Database of Anthropogenic Fire Impacts (DAFI) containing 1798 case studies of fire use/management from 519 publications, covering more than 100 countries and all major biomes (except Arctic/Antarctic). We discuss DAFI development, patterns in the resulting data, and possible applications. Specifically, DAFI is used with ancillary data (e.g. biophysical, socio-economic indicators), classification and regression methods to test and refine our initial AFT classification, characterise AFT fire variables, and distribute AFTs spatially. Our model will then simulate AFT distributions for alternative scenarios of change (e.g. specified by the Shared Socioeconomic Pathways).&amp;#160;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;Coupling distinct models can be achieved in a variety of ways, but broadly we can distinguish between &amp;#8216;loose&amp;#8217; coupling in which information flow is uni-directional, and &amp;#8216;tight&amp;#8217; coupling in which information flows are integrated with feedbacks and dynamic updating. Our intention is to tightly couple our AFT model with JULES-INFERNO, such that fire use and suppression behaviours from the former influence simulated fire ignitions and burned area in the latter. Reciprocally, total burned area simulated by JULES-INFERNO will feedback to influence spatial distribution of AFTs in the next time step, modifying anthropogenic fire patterns for the next step of DGVM simulation. We discuss the potential for this tight model coupling to capture socio-ecological feedbacks in fire regimes, as well as&amp;#160; possible pitfalls and steps needed to test and verify model outputs. These are early steps in an important journey to improve representation of anthropogenic fire in DGVMs.&lt;/p&gt; &lt;/div&gt;

scholarly journals Forests, savannas, and grasslands: bridging the knowledge gap between ecology and Dynamic Global Vegetation Models

2015 ◽  
Vol 12 (6) ◽  
pp. 1833-1848 ◽  
Author(s):  
M. Baudena ◽  
S. C. Dekker ◽  
P. M. van Bodegom ◽  
B. Cuesta ◽  
S. I. Higgins ◽  
...  
Keyword(s):  
Global Climate ◽  
Predictive Ability ◽  
Tree Cover ◽  
Fuel Load ◽  
Mean Annual Precipitation ◽  
Knowledge Gap ◽  
Climate Conditions ◽  
Dynamic Global Vegetation Models ◽  
Vegetation Models ◽  
Global Vegetation

Abstract. The forest, savanna, and grassland biomes, and the transitions between them, are expected to undergo major changes in the future due to global climate change. Dynamic global vegetation models (DGVMs) are very useful for understanding vegetation dynamics under the present climate, and for predicting its changes under future conditions. However, several DGVMs display high uncertainty in predicting vegetation in tropical areas. Here we perform a comparative analysis of three different DGVMs (JSBACH, LPJ-GUESS-SPITFIRE and aDGVM) with regard to their representation of the ecological mechanisms and feedbacks that determine the forest, savanna, and grassland biomes, in an attempt to bridge the knowledge gap between ecology and global modeling. The outcomes of the models, which include different mechanisms, are compared to observed tree cover along a mean annual precipitation gradient in Africa. By drawing on the large number of recent studies that have delivered new insights into the ecology of tropical ecosystems in general, and of savannas in particular, we identify two main mechanisms that need improved representation in the examined DGVMs. The first mechanism includes water limitation to tree growth, and tree–grass competition for water, which are key factors in determining savanna presence in arid and semi-arid areas. The second is a grass–fire feedback, which maintains both forest and savanna presence in mesic areas. Grasses constitute the majority of the fuel load, and at the same time benefit from the openness of the landscape after fires, since they recover faster than trees. Additionally, these two mechanisms are better represented when the models also include tree life stages (adults and seedlings), and distinguish between fire-prone and shade-tolerant forest trees, and fire-resistant and shade-intolerant savanna trees. Including these basic elements could improve the predictive ability of the DGVMs, not only under current climate conditions but also and especially under future scenarios.

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