Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length

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 (e.g., 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&#8217;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 (i.e., 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.

Download Full-text

Terrestrial Carbon Cycle Dynamics under Recent and Future Climate Change

Journal of Climate ◽

10.1175/jcli3359.1 ◽

2005 ◽

Vol 18 (10) ◽

pp. 1609-1628 ◽

Cited By ~ 74

Author(s):

H. Damon Matthews ◽

Andrew J. Weaver ◽

Katrin J. Meissner

Keyword(s):

Climate Change ◽

Carbon Cycle ◽

Future Climate ◽

First Century ◽

Future Climate Change ◽

Cycle Model ◽

Terrestrial Carbon ◽

Carbon Cycle Model ◽

Terrestrial Carbon Cycle ◽

Twenty First Century

Abstract The behavior of the terrestrial carbon cycle under historical and future climate change is examined using the University of Victoria Earth System Climate Model, now coupled to a dynamic terrestrial vegetation and global carbon cycle model. When forced by historical emissions of CO2 from fossil fuels and land-use change, the coupled climate–carbon cycle model accurately reproduces historical atmospheric CO2 trends, as well as terrestrial and oceanic uptake for the past two decades. Under six twenty-first-century CO2 emissions scenarios, both terrestrial and oceanic carbon sinks continue to increase, though terrestrial uptake slows in the latter half of the century. Climate–carbon cycle feedbacks are isolated by comparing a coupled model run with a run where climate and the carbon cycle are uncoupled. The modeled positive feedback between the carbon cycle and climate is found to be relatively small, resulting in an increase in simulated CO2 of 60 ppmv at the year 2100. Including non-CO2 greenhouse gas forcing and increasing the model’s climate sensitivity increase the effect of this feedback to 140 ppmv. The UVic model does not, however, simulate a switch from a terrestrial carbon sink to a source during the twenty-first century, as earlier studies have suggested. This can be explained by a lack of substantial reductions in simulated vegetation productivity due to climate changes.

Download Full-text

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

10.5194/bg-2016-557 ◽

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.

Download Full-text

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

Biogeosciences ◽

10.5194/bg-14-3401-2017 ◽

2017 ◽

Vol 14 (14) ◽

pp. 3401-3429 ◽

Cited By ~ 22

Author(s):

Marko Scholze ◽

Michael Buchwitz ◽

Wouter Dorigo ◽

Luis Guanter ◽

Shaun Quegan

Keyword(s):

Data Assimilation ◽

Carbon Cycle ◽

Global Carbon Cycle ◽

Ecosystem Dynamics ◽

Full Description ◽

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 hydrology, 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 of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by 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 well error-characterised 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 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 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.

Download Full-text