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

2017 ◽  
Vol 14 (14) ◽  
pp. 3401-3429 ◽  
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.

scholarly journals A novel model–data fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2

2021 ◽  
Author(s):  
Hamze Dokoohaki ◽  
Bailey D. Morrison ◽  
Ann Raiho ◽  
Shawn P. Serbin ◽  
Michael Dietze
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Soil Carbon ◽  
Model Data ◽  
Terrestrial Carbon ◽  
State Data ◽  
Flux Tower ◽  
Terrestrial Carbon Cycle ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to robustly and coherently synthesize multiple streams of information that each provide partial information about different pools and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn model-data eco-informatics system, and its application for the development of a proof-of-concept carbon "reanalysis" product that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. We first calibrated this system against plant trait and flux tower Net Ecosystem Exchange (NEE) using a novel emulated hierarchical Bayesian approach. Next, we extended the Tobit-Wishart Ensemble Filter (TWEnF) State Data Assimilation (SDA) framework, a generalization of the common Ensemble Kalman Filter which accounts for censored data and provides a fully Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of ~500 points selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations of aboveground biomass (Landsat LandTrendr) and LAI (MODIS MOD15) were sequentially assimilated into the SIPNET model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble weights, also showed good agreement with eddy flux tower NEE and reduced RMSE compared to the calibrated forecast. Ultimately, PEcAn's carbon cycle reanalysis provides a scalable framework for harmonizing multiple data constraints and providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) and accelerating terrestrial carbon cycle research.

Terrestrial carbon cycle model-data fusion: Progress and challenges

2021 ◽  
Author(s):  
Xin Li ◽  
Hanqing Ma ◽  
Youhua Ran ◽  
Xufeng Wang ◽  
Gaofeng Zhu ◽  
...  
Keyword(s):  
Data Fusion ◽  
Carbon Cycle ◽  
Model Data ◽  
Cycle Model ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle

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 Comment on "Carbon farming in hot, dry coastal areas: an option for climate change mitigation" by Becker et al. (2013)

2013 ◽  
Vol 4 (2) ◽  
pp. 869-873
Author(s):  
M. Heimann
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Co2 Concentration ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
Reference Scenario ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Sink ◽  
Carbon Dioxide Co2 ◽  
Global Carbon

Abstract. Becker et al. (2013) argue that an afforestation of 0.73 109 ha with Jatropha curcas plants would generate an additional terrestrial carbon sink of 4.3 PgC yr−1, enough to stabilise the atmospheric mixing ratio of carbon dioxide (CO2) at current levels. However, this is not consistent with the dynamics of the global carbon cycle. Using a well established global carbon cycle model, the effect of adding such a hypothetical sink leads to a reduction of atmospheric CO2 levels in the year 2030 by 25 ppm compared to a reference scenario. However, the stabilisation of the atmospheric CO2 concentration requires a much larger additional sink or corresponding reduction of anthropogenic emissions.

Combined Simple Biosphere/Carnegie-Ames-Stanford Approach terrestrial carbon cycle model

2008 ◽  
Vol 113 (G3) ◽  
Author(s):  
Kevin Schaefer ◽  
G. James Collatz ◽  
Pieter Tans ◽  
A. Scott Denning ◽  
Ian Baker ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Cycle Model ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle

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

2018 ◽  
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.

scholarly journals Ignoring detailed fast-changing dynamics of land use overestimates regional terrestrial carbon sequestration

2009 ◽  
Vol 6 (2) ◽  
pp. 3215-3235 ◽  
Author(s):  
S. Zhao ◽  
S. Liu ◽  
Z. Li ◽  
T. L. Sohl
Keyword(s):  
Land Use ◽  
Carbon Sequestration ◽  
Land Use Change ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Forest Harvesting ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Sequestration ◽  
Data Frequency ◽  
Global Carbon

Abstract. Land use change is critical in determining the distribution, magnitude and mechanisms of terrestrial carbon budgets at the local to global scales. To date, almost all regional to global carbon cycle studies are driven by a static land use map or land use change statistics with decadal time intervals. The biases in quantifying carbon exchange between the terrestrial ecosystems and the atmosphere caused by using such land use change information have not been investigated. Here, we used the General Ensemble biogeochemical Modeling System (GEMS), along with consistent and spatially explicit land use change scenarios with different intervals (1 yr, 5 yrs, 10 yrs and static, respectively), to evaluate the impacts of land use change data frequency on estimating regional carbon sequestration in the southeastern United States. Our results indicate that ignoring the detailed fast-changing dynamics of land use can lead to a significant overestimation of carbon uptake by the terrestrial ecosystem. Regional carbon sequestration increased from 0.27 to 0.69, 0.80 and 0.97 Mg C ha−1 yr−1 when land use change data frequency shifting from 1 year to 5 years, 10 years interval and static land use information, respectively. Carbon removal by forest harvesting and prolonged cumulative impacts of historical land use change on carbon cycle accounted for the differences in carbon sequestration between static and dynamic land use change scenarios. The results suggest that it is critical to incorporate the detailed dynamics of land use change into local to global carbon cycle studies. Otherwise, it is impossible to accurately quantify the geographic distributions, magnitudes, and mechanisms of terrestrial carbon sequestration at local to global scales.

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