scholarly journals Optimal model complexity for terrestrial carbon cycle prediction

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.

scholarly journals Optimal model complexity for terrestrial carbon cycle prediction

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.

Optimal model complexity for terrestrial carbon cycle prediction using data assimilation

2020 ◽  
Author(s):  
Caroline A. Famiglietti ◽  
T. Luke Smallman ◽  
Sophie Flack-Prain ◽  
Rong Ge ◽  
Victoria Meyer ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Carbon Balance ◽  
Prediction Accuracy ◽  
Structural Complexity ◽  
Model Complexity ◽  
Cycle Model ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle

<p>The future role of the terrestrial biosphere in the global carbon cycle is highly uncertain. Modeling and predicting the terrestrial net carbon balance is difficult due to the numerous processes driving variability of gross fluxes. Many approaches to reducing this model uncertainty have focused on model structure, namely by adding additional processes (<em>e.g., </em>nutrient dynamics or vegetation demography) and thus increasing complexity. While these developments seek to achieve greater structural realism by mirroring the complexity of the natural world, they often rely, by necessity, on poorly-determined or over-generalized parameters. Furthermore, increased structural complexity may increase the risk that parameters with compensating errors are found during model development, thereby reducing model accuracy in prediction. It is not clear whether or to what extent carbon cycle predictability scales with structural complexity, or whether an intermediate, optimum level of complexity exists that may balance the costs of a low (more biased) or high (more variant) complexity model. Here, we explore and define the relationship between carbon cycle model complexity and prediction accuracy. To do so, we leverage the CARbon Data MOdel fraMework (CARDAMOM), a Bayesian data assimilation system that retrieves terrestrial carbon cycle variables (including pools, fluxes, and static parameters) by combining multiple observations with a relatively simple ecosystem carbon balance model. CARDAMOM includes several ecological and dynamical constraints that can prevent ecologically unrealistic parameter combinations and reduce compensating errors between parameters (also known as equifinality). Furthermore, it is a flexible framework to which process representations, parameters, and constraints can easily be added and removed. We used CARDAMOM to develop a suite of model versions spanning a broad range of structural complexity, including the number of carbon pools and the allocation of carbon to the canopy. We assessed a model’s complexity based on its inherent dimensionality, determined via a principal component analysis that reduces the parameter space to its principal components. We tested and compared the training and forecast accuracies of net ecosystem exchange predictions using 14 increasingly complex versions of CARDAMOM, each with 48 different experimental designs (<em>i.e., </em>combinations of data constraints and error assumptions) at 5 globally-distributed eddy covariance sites representing a range of biomes and vegetation types across a total of 70 site-years. We also compared the model performance values against a range of machine learning approaches, which are assumed to represent the limit of infinite model complexity due to their large number of underlying parameters. In this presentation, we use this population to demonstrate and explain patterns in the mapping of model complexity and other assimilation choices to prediction accuracy, offering theoretical and empirical insights into the optimal structure of a carbon cycle model.</p>

scholarly journals Supplementary material to "Optimal model complexity for terrestrial carbon cycle prediction"

2020 ◽  
Author(s):  
Caroline A. Famiglietti ◽  
T. Luke Smallman ◽  
Paul A. Levine ◽  
Sophie Flack-Prain ◽  
Gregory R. Quetin ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Model Complexity ◽  
Optimal Model ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Supplementary Material

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

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.

scholarly journals Potential strong contribution of future anthropogenic land-use and land-cover change to the terrestrial carbon cycle

2018 ◽  
Vol 13 (6) ◽  
pp. 064023 ◽  
Author(s):  
Benjamin Quesada ◽  
Almut Arneth ◽  
Eddy Robertson ◽  
Nathalie de Noblet-Ducoudré
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Carbon Cycle ◽  
Land Cover Change ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle ◽  
Anthropogenic Land Use

scholarly journals Spatiotemporal patterns of terrestrial carbon cycle during the 20th century

2009 ◽  
Vol 23 (4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Shilong Piao ◽  
Philippe Ciais ◽  
Pierre Friedlingstein ◽  
Nathalie de Noblet-Ducoudré ◽  
Patricia Cadule ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
20Th Century ◽  
Spatiotemporal Patterns ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Cycle

scholarly journals Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

2017 ◽  
Author(s):  
Marko Scholze ◽  
Michael Buchwitz ◽  
Wouter Dorigo ◽  
Luis Guanter ◽  
Shaun Quegan
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Full Description ◽  
Model Data ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle ◽  
Uncertainty Estimates ◽  
Global Carbon

Abstract. The global carbon cycle is an important component of the Earth system and it interacts with the hydrological, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification 5 of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by model-data fusion or data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation, and systematic and 10 well error-characterized observations relevant to the carbon cycle. Relevant observations for assimilation include various in-situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model 15 benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current 20 observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al. (2005) emphasising the rapid advance in relevant space-based observations.

scholarly journals Examining the sensitivity of the terrestrial carbon cycle to the expression of El Niño

2020 ◽  
Author(s):  
Lina Teckentrup ◽  
Martin G. De Kauwe ◽  
Andrew J. Pitman ◽  
Benjamin Smith
Keyword(s):  
Carbon Cycle ◽  
El Niño ◽  
El Nino ◽  
Terrestrial Carbon ◽  
Near Surface ◽  
Terrestrial Carbon Cycle ◽  
El Niño Events ◽  
Ep El Niño ◽  
The Impact ◽  
El Nino Events

Abstract. The El Niño‐Southern Oscillation (ENSO) influences the global climate and the variability in the terrestrial carbon cycle on interannual timescales. Two different expressions of El Niño have recently been identified: (i) Central–Pacific (CP) and (ii) Eastern–Pacific (EP). Both types of El Nino are characterised by above average sea surface temperature anomalies in the respective locations. Studies exploring the impact of these expressions of El Niño on the carbon cycle have identified changes in the amplitude of the concentration of interannual atmospheric carbon dioxide (CO2) variability, as well as different lags in terrestrial CO2 release to the atmosphere following increased tropical near surface air temperature. We employ the dynamic global vegetation model LPJ–GUESS within a synthetic experimental framework to examine the sensitivity and potential long term impacts of these two expressions of El Niño on the terrestrial carbon cycle. We manipulated the occurrence of CP and EP events in two climate reanalysis datasets during the later half of the 20th and early 21st century by replacing all EP with CP and separately all CP with EP El Niño events. We found that the different expressions of El Niño affect interannual variability in the terrestrial carbon cycle. However, the effect on longer timescales was negligible for both climate reanalysis datasets. We conclude that capturing any future trends in the relative frequency of CP and EP El Niño events may not be critical for robust simulations of the terrestrial carbon cycle.

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