Hysteresis of terrestrial carbon cycle to CO2 ramp-up and -down forcing

Mapping Intimacies ◽

10.21203/rs.3.rs-1074581/v1 ◽

2021 ◽

Author(s):

Sowon Park ◽

Jong-Seong Kug

Keyword(s):

Carbon Cycle ◽

Boreal Forests ◽

Current Knowledge ◽

Arctic Region ◽

Terrestrial Carbon ◽

Initial State ◽

Terrestrial Biosphere ◽

Terrestrial Carbon Cycle ◽

Negative Emissions ◽

Long Time

Abstract To prevent excessive global warming, we have faced a situation to reduce net carbon dioxide (CO2) emissions. However, the behavior of Earth’s terrestrial biosphere under negative emissions is highly uncertain. Herein, we show strong hysteresis in the terrestrial carbon cycle in response to CO2 ramp-up and -down forcing. Owing to the strong hysteresis lag, the terrestrial biosphere stores more carbon at the end of simulations than at its initial state, lessening the burden on net-negative emissions. This hysteresis is latitudinally dependent, showing a longer timescale of reversibility in high latitudes. Particularly, carbon in boreal forests can be stored for a long time. However, the hysteresis of the carbon cycle in the pan-Arctic region depends on the presence of permafrost processes. That is, unexpected irreversible carbon emissions may occur in permafrost even after achieving net-zero emissions, indicating the importance of permafrost processes, which is highly uncertain based on our current knowledge.

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The climate dependence of the terrestrial carbon cycle; including parameter and structural uncertainties

Biogeosciences Discussions ◽

10.5194/bgd-9-13439-2012 ◽

2012 ◽

Vol 9 (10) ◽

pp. 13439-13496 ◽

Cited By ~ 9

Author(s):

M. J. Smith ◽

M. C. Vanderwel ◽

V. Lyutsarev ◽

S. Emmott ◽

D. W. Purves

Keyword(s):

Carbon Cycle ◽

Empirical Data ◽

Current Knowledge ◽

Empirical Support ◽

Data Sets ◽

Earth System ◽

Model Formulation ◽

Terrestrial Carbon ◽

Terrestrial Carbon Cycle ◽

Component Processes

Abstract. The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System over the coming decades and centuries. However Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Essential to achieving this goal will be assessing the empirical support for alternative models of component processes, identifying key uncertainties and inconsistencies, and ultimately identifying the models that are most consistent with empirical evidence. To begin meeting these requirements we data-constrained all parameters of all component processes within a global terrestrial carbon model. Our goals were to assess the climate dependencies obtained for different component processes when all parameters have been inferred from empirical data, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model, and to identify a methodology by which alternative component models could be compared within the same framework in future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies although it was not possible to data-constrain all parameters indicating some potentially redundant details. Similar climate dependencies were obtained for most processes whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models make better constrained projections.

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The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties

Biogeosciences ◽

10.5194/bg-10-583-2013 ◽

2013 ◽

Vol 10 (1) ◽

pp. 583-606 ◽

Cited By ~ 35

Author(s):

M. J. Smith ◽

D. W. Purves ◽

M. C. Vanderwel ◽

V. Lyutsarev ◽

S. Emmott

Keyword(s):

Carbon Cycle ◽

Empirical Data ◽

Dynamic Equilibrium ◽

Current Knowledge ◽

Data Sets ◽

Earth System ◽

Model Formulation ◽

Terrestrial Carbon ◽

Terrestrial Carbon Cycle ◽

Component Processes

Abstract. The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System (the thin layer that contains and supports life) over the coming decades and centuries. However, Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their terrestrial carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Utilising empirical data to constrain and assess component processes in terrestrial carbon cycle models will be essential to achieving this goal. We used a new model construction method to data-constrain all parameters of all component processes within a global terrestrial carbon model, employing as data constraints a collection of 12 empirical data sets characterising global patterns of carbon stocks and flows. Our goals were to assess the climate dependencies inferred for all component processes, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model and ultimately to identify a methodology by which alternative component models could be compared within the same framework in the future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies, although it was not possible to data-constrain all parameters, indicating some potentially redundant details. Similar climate dependencies were obtained for most processes, whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models to make better constrained projections.

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Vegetation distribution and terrestrial carbon cycle in a carbon-cycle configuration of JULES4.6 with new plant functional types

10.5194/gmd-2017-311 ◽

2018 ◽

Cited By ~ 1

Author(s):

Anna B. Harper ◽

Andrew J. Wiltshire ◽

Peter M. Cox ◽

Pierre Friedlingstein ◽

Chris D. Jones ◽

...

Keyword(s):

Carbon Cycle ◽

Soil Carbon ◽

Boreal Forests ◽

Carbon Sink ◽

Vegetation Distribution ◽

Terrestrial Carbon ◽

Terrestrial Carbon Cycle ◽

Vegetation Carbon ◽

Global Vegetation ◽

Global Carbon

Abstract. Dynamic global vegetation models (DGVMs) are used for studying historical and future changes to vegetation and the terrestrial carbon cycle. JULES (the Joint UK Land Environment Simulator) represents the land surface in the Hadley Centre climate models and in the UK Earth System Model. Recently the number of plant functional types (PFTs) in JULES were expanded from 5 to 9 to better represent functional diversity in global ecosystems. Here we introduce a more mechanistic representation of vegetation dynamics in TRIFFID, the dynamic vegetation component of JULES, that allows for any number of PFTs to compete based solely on their height, removing the previous hardwired dominance hierarchy where dominant types are assumed to outcompete subdominant types. With the new set of 9 PFTs, JULES is able to more accurately reproduce global vegetation distribution compared to the former 5 PFT version. Improvements include the coverage of trees within tropical and boreal forests, and a reduction in shrubs, which dominated at high latitudes. We show that JULES is able to realistically represent several aspects of the global carbon cycle. The simulated gross primary productivity (GPP) is within the range of observations, but simulated net primary productivity (NPP) is slightly too high. GPP in JULES from 1982–2011 was 133 PgC yr−1, compared to observation-based estimates between 123±8 (over the same time period) and 150–175 PgC yr−1. NPP from 2000–2013 was 72 PgC yr−1, compared to satellite-derived NPP of 55 PgC yr−1 over the same period and independent estimates of 56.2±14.3 PgC yr−1. The simulated carbon stored in vegetation is 542 PgC, compared to an observation-based range of 400–600 PgC. Soil carbon is much lower (1422 PgC) than estimates from measurements (>2400 PgC), with large underestimations of soil carbon in the tropical and boreal forests. We also examined some aspects of the historical terrestrial carbon sink as simulated by JULES. Between the 1900s and 2000s, increased atmospheric carbon dioxide levels enhanced vegetation productivity and litter inputs into the soils, while land-use change removed vegetation and reduced soil carbon. The result was a simulated increase in soil carbon of 57 PgC but a decrease in vegetation carbon by of PgC. JULES simulated a loss of soil and vegetation carbon of 14 and 124 PgC, respectively, due to land-use change from 1900–2009. The simulated land carbon sink was 2.0±1.0 PgC yr−1 from 2000–2009, in close agreement to estimates from the IPCC and Global Carbon Project.

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Optimal model complexity for terrestrial carbon cycle prediction

Biogeosciences ◽

10.5194/bg-18-2727-2021 ◽

2021 ◽

Vol 18 (8) ◽

pp. 2727-2754

Author(s):

Caroline A. Famiglietti ◽

T. Luke Smallman ◽

Paul A. Levine ◽

Sophie Flack-Prain ◽

Gregory R. Quetin ◽

...

Keyword(s):

Carbon Cycle ◽

Critical Role ◽

Structural Complexity ◽

Forecast Skill ◽

Model Complexity ◽

Terrestrial Carbon ◽

Terrestrial Biosphere ◽

Area Index ◽

Predictive Skill ◽

Terrestrial Carbon Cycle

Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.

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Optimal model complexity for terrestrial carbon cycle prediction

10.5194/bg-2020-478 ◽

2020 ◽

Author(s):

Caroline A. Famiglietti ◽

T. Luke Smallman ◽

Paul A. Levine ◽

Sophie Flack-Prain ◽

Gregory R. Quetin ◽

...

Keyword(s):

Carbon Cycle ◽

Critical Role ◽

Structural Complexity ◽

Forecast Skill ◽

Model Complexity ◽

Terrestrial Carbon ◽

Terrestrial Biosphere ◽

Area Index ◽

Predictive Skill ◽

Terrestrial Carbon Cycle

Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly-determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at 6 globally-distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, the complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust, observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.

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Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types

Geoscientific Model Development ◽

10.5194/gmd-11-2857-2018 ◽

2018 ◽

Vol 11 (7) ◽

pp. 2857-2873 ◽

Cited By ~ 12

Author(s):

Anna B. Harper ◽

Andrew J. Wiltshire ◽

Peter M. Cox ◽

Pierre Friedlingstein ◽

Chris D. Jones ◽

...

Keyword(s):

Carbon Cycle ◽

Soil Carbon ◽

Boreal Forests ◽

Carbon Sink ◽

Vegetation Distribution ◽

Terrestrial Carbon ◽

Terrestrial Carbon Cycle ◽

Vegetation Carbon ◽

Global Vegetation ◽

Global Carbon

Abstract. Dynamic global vegetation models (DGVMs) are used for studying historical and future changes to vegetation and the terrestrial carbon cycle. JULES (the Joint UK Land Environment Simulator) represents the land surface in the Hadley Centre climate models and in the UK Earth System Model. Recently the number of plant functional types (PFTs) in JULES was expanded from five to nine to better represent functional diversity in global ecosystems. Here we introduce a more mechanistic representation of vegetation dynamics in TRIFFID, the dynamic vegetation component of JULES, which allows for any number of PFTs to compete based solely on their height; therefore, the previous hardwired dominance hierarchy is removed. With the new set of nine PFTs, JULES is able to more accurately reproduce global vegetation distribution compared to the former five PFT version. Improvements include the coverage of trees within tropical and boreal forests and a reduction in shrubs, the latter of which dominated at high latitudes. We show that JULES is able to realistically represent several aspects of the global carbon (C) cycle. The simulated gross primary productivity (GPP) is within the range of observations, but simulated net primary productivity (NPP) is slightly too high. GPP in JULES from 1982 to 2011 is 133 Pg C yr−1, compared to observation-based estimates (over the same time period) between 123 ± 8 and 150–175 Pg C yr−1. NPP from 2000 to 2013 is 72 Pg C yr−1, compared to satellite-derived NPP of 55 Pg C yr−1 over the same period and independent estimates of 56.2 ± 14.3 Pg C yr−1. The simulated carbon stored in vegetation is 542 Pg C, compared to an observation-based range of 400–600 Pg C. Soil carbon is much lower (1422 Pg C) than estimates from measurements (> 2400 Pg C), with large underestimations of soil carbon in the tropical and boreal forests. We also examined some aspects of the historical terrestrial carbon sink as simulated by JULES. Between the 1900s and 2000s, increased atmospheric carbon dioxide levels enhanced vegetation productivity and litter inputs into the soils, while land use change removed vegetation and reduced soil carbon. The result is a simulated increase in soil carbon of 57 Pg C but a decrease in vegetation carbon of 98 Pg C. The total simulated loss of soil and vegetation carbon due to land use change is 138 Pg C from 1900 to 2009, compared to a recent observationally constrained estimate of 155 ± 50 Pg C from 1901 to 2012. The simulated land carbon sink is 2.0 ± 1.0 Pg C yr−1 from 2000 to 2009, in close agreement with estimates from the IPCC and Global Carbon Project.

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