scholarly journals A semi-analytical solution to accelerate spin-up of a coupled carbon and nitrogen land model to steady state

2012 ◽  
Vol 5 (5) ◽  
pp. 1259-1271 ◽  
Author(s):  
J. Y. Xia ◽  
Y. Q. Luo ◽  
Y.-P. Wang ◽  
E. S. Weng ◽  
O. Hararuk
Keyword(s):  
Steady State ◽  
Analytical Solution ◽  
Carbon Storage ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Storage Capacity ◽  
Computational Time ◽  
Ecosystem Carbon ◽  
Biogeochemical Models ◽  
Pool Sizes

Abstract. The spin-up of land models to steady state of coupled carbon–nitrogen processes is computationally so costly that it becomes a bottleneck issue for global analysis. In this study, we introduced a semi-analytical solution (SAS) for the spin-up issue. SAS is fundamentally based on the analytic solution to a set of equations that describe carbon transfers within ecosystems over time. SAS is implemented by three steps: (1) having an initial spin-up with prior pool-size values until net primary productivity (NPP) reaches stabilization, (2) calculating quasi-steady-state pool sizes by letting fluxes of the equations equal zero, and (3) having a final spin-up to meet the criterion of steady state. Step 2 is enabled by averaged time-varying variables over one period of repeated driving forcings. SAS was applied to both site-level and global scale spin-up of the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model. For the carbon-cycle-only simulations, SAS saved 95.7% and 92.4% of computational time for site-level and global spin-up, respectively, in comparison with the traditional method (a long-term iterative simulation to achieve the steady states of variables). For the carbon–nitrogen coupled simulations, SAS reduced computational cost by 84.5% and 86.6% for site-level and global spin-up, respectively. The estimated steady-state pool sizes represent the ecosystem carbon storage capacity, which was 12.1 kg C m−2 with the coupled carbon–nitrogen global model, 14.6% lower than that with the carbon-only model. The nitrogen down-regulation in modeled carbon storage is partly due to the 4.6% decrease in carbon influx (i.e., net primary productivity) and partly due to the 10.5% reduction in residence times. This steady-state analysis accelerated by the SAS method can facilitate comparative studies of structural differences in determining the ecosystem carbon storage capacity among biogeochemical models. Overall, the computational efficiency of SAS potentially permits many global analyses that are impossible with the traditional spin-up methods, such as ensemble analysis of land models against parameter variations.

scholarly journals A semi-analytical solution to accelerate spin-up of a coupled carbon and nitrogen land model to steady state

2012 ◽  
Vol 5 (2) ◽  
pp. 803-836 ◽  
Author(s):  
J. Xia ◽  
Y. Luo ◽  
Y.-P. Wang ◽  
E. Weng ◽  
O. Hararuk
Keyword(s):  
Steady State ◽  
Analytical Solution ◽  
Carbon Storage ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Storage Capacity ◽  
Computational Time ◽  
Ecosystem Carbon ◽  
Biogeochemical Models ◽  
Pool Sizes

Abstract. The spin-up of land models to steady state of coupled carbon-nitrogen processes is computationally so costly that it becomes a~bottleneck issue for global analysis. In this study, we introduced a semi-analytical solution (SAS) for the spin-up issue. SAS is fundamentally based on the analytic solution to a set of equations that describe carbon transfers within ecosystems over time. SAS is implemented by three steps: (1) having an initial spin-up with prior pool-size values until net primary productivity (NPP) reaches steady state, (2) calculating quasi steady-state pool sizes by letting fluxes of the equations equal zero, and (3) having a final spin-up to meet the criterion of steady state. Step 2 is enabled by averaged time-varying variables over one period of repeated driving forcings. SAS was applied to both site-level and global scale spin-up of the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model. For the carbon-cycle-only simulations, SAS saved 95.7% and 92.4% of computational time for site-level and global spin-up, respectively, in comparison with the traditional method. For the carbon-nitrogen-coupled simulations, SAS reduced computational cost by 84.5% and 86.6% for site-level and global spin-up, respectively. The estimated steady-state pool sizes represent the ecosystem carbon storage capacity, which was 12.1 kg C m−2 with the coupled carbon-nitrogen global model, 14.6% lower than that with the carbon-only model. The nitrogen down-regulation in modeled carbon storage is partly due to the 4.6% decrease in carbon influx (i.e., net primary productivity) and partly due to the 10.5% reduction in residence times. This steady-state analysis accelerated by the SAS method can facilitate comparative studies of structural differences in determining the ecosystem carbon storage capacity among biogeochemical models. Overall, the computational efficiency of SAS potentially permits many global analyses that are impossible with the traditional spin-up methods, such as ensemble analysis of land models against parameter variations.

scholarly journals Divergent predictions of carbon storage between two global land models: attribution of the causes through traceability analysis

2016 ◽  
Vol 7 (3) ◽  
pp. 649-658 ◽  
Author(s):  
Rashid Rafique ◽  
Jianyang Xia ◽  
Oleksandra Hararuk ◽  
Ghassem R. Asrar ◽  
Guoyong Leng ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Carbon Storage ◽  
Residence Time ◽  
Net Primary Productivity ◽  
Storage Capacity ◽  
Woody Biomass ◽  
Residence Times ◽  
Ecosystem Carbon ◽  
Global Land ◽  
Traceability Analysis

Abstract. Representations of the terrestrial carbon cycle in land models are becoming increasingly complex. It is crucial to develop approaches for critical assessment of the complex model properties in order to understand key factors contributing to models' performance. In this study, we applied a traceability analysis which decomposes carbon cycle models into traceable components, for two global land models (CABLE and CLM-CASA′) to diagnose the causes of their differences in simulating ecosystem carbon storage capacity. Driven with similar forcing data, CLM-CASA′ predicted  ∼ 31 % larger carbon storage capacity than CABLE. Since ecosystem carbon storage capacity is a product of net primary productivity (NPP) and ecosystem residence time (τE), the predicted difference in the storage capacity between the two models results from differences in either NPP or τE or both. Our analysis showed that CLM-CASA′ simulated 37 % higher NPP than CABLE. On the other hand, τE, which was a function of the baseline carbon residence time (τ′E) and environmental effect on carbon residence time, was on average 11 years longer in CABLE than CLM-CASA′. This difference in τE was mainly caused by longer τ′E of woody biomass (23 vs. 14 years in CLM-CASA′), and higher proportion of NPP allocated to woody biomass (23 vs. 16 %). Differences in environmental effects on carbon residence times had smaller influences on differences in ecosystem carbon storage capacities compared to differences in NPP and τ′E. Overall, the traceability analysis showed that the major causes of different carbon storage estimations were found to be parameters setting related to carbon input and baseline carbon residence times between two models.

scholarly journals Divergent predictions of carbon storage between two global land models: attribution of the causes through traceability analysis

2015 ◽  
Vol 6 (2) ◽  
pp. 1579-1604
Author(s):  
R. Rafique ◽  
J. Xia ◽  
O. Hararuk ◽  
G. Asrar ◽  
Y. Wang ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Carbon Storage ◽  
Residence Time ◽  
Net Primary Productivity ◽  
Storage Capacity ◽  
Woody Biomass ◽  
Complex Model ◽  
Ecosystem Carbon ◽  
Global Land ◽  
Traceability Analysis

Abstract. Representations of the terrestrial carbon cycle in land models are becoming increasingly complex. It is crucial to develop approaches for critical assessment of the complex model properties in order to understand key factors contributing to models' performance. In this study, we applied a traceability analysis, which decomposes carbon cycle models into traceable components, to two global land models (CABLE and CLM-CASA') to diagnose the causes of their differences in simulating ecosystem carbon storage capacity. Driven with similar forcing data, the CLM-CASA' model predicted ~ 31 % larger carbon storage capacity than the CABLE model. Since ecosystem carbon storage capacity is a product of net primary productivity (NPP) and ecosystem residence time (τE), the predicted difference in the storage capacity between the two models results from differences in either NPP or τE or both. Our analysis showed that CLM-CASA' simulated 37 % higher NPP than CABLE due to higher rates of carboxylation (Vcmax) in CLM-CASA'. On the other hand, τE, which was a function the baseline carbon residence time (τ'E) and environmental effect on carbon residence time, was on average 11 years longer in CABLE than CLM-CASA'. The difference in τE was mainly found to be caused by longer τ'E in CABLE than CLM-CASA'. This difference in τE was mainly caused by longer τ'E of woody biomass (23 vs. 14 years in CLM-CASA') and higher proportion of NPP allocated to woody biomass (23 vs. 16 %). Differences in environmental effects on carbon residence times had smaller influences on differences in ecosystem carbon storage capacities compared to differences in NPP and τ'E. Overall; the traceability analysis is an effective method for identifying sources of variations between the two models.

Traceable components of terrestrial carbon storage capacity in biogeochemical models

2013 ◽  
Vol 19 (7) ◽  
pp. 2104-2116 ◽  
Author(s):  
Jianyang Xia ◽  
Yiqi Luo ◽  
Ying-Ping Wang ◽  
Oleksandra Hararuk
Keyword(s):  
Carbon Storage ◽  
Storage Capacity ◽  
Terrestrial Carbon ◽  
Biogeochemical Models

scholarly journals Divergent controls of soil organic carbon between observations and process-based models

2021 ◽  
Author(s):  
Katerina Georgiou ◽  
Avni Malhotra ◽  
William R. Wieder ◽  
Jacqueline H. Ennis ◽  
Melannie D. Hartman ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Global Scale ◽  
Mean Annual Temperature ◽  
Biogeochemical Models ◽  
Soc Stocks ◽  
Process Based Models

AbstractThe storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions.

scholarly journals Some aspects of ecophysiological and biogeochemical responses of tropical forests to atmospheric change

2004 ◽  
Vol 359 (1443) ◽  
pp. 463-476 ◽  
Author(s):  
Jeffrey Q. Chambers ◽  
Whendee L. Silver
Keyword(s):  
Environmental Factors ◽  
Carbon Storage ◽  
Tropical Forests ◽  
Land Surface ◽  
Net Primary Productivity ◽  
Amazon Forest ◽  
Old Growth ◽  
Individual Tree ◽  
Ecosystem Carbon ◽  
Sequestration Rate

Atmospheric changes that may affect physiological and biogeochemical processes in old–growth tropical forests include: (i) rising atmospheric CO 2 concentration; (ii) an increase in land surface temperature; (iii) changes in precipitation and ecosystem moisture status; and (iv) altered disturbance regimes. Elevated CO 2 is likely to directly influence numerous leaf–level physiological processes, but whether these changes are ultimately reflected in altered ecosystem carbon storage is unclear. The net primary productivity (NPP) response of old–growth tropical forests to elevated CO 2 is unknown, but unlikely to exceed the maximum experimentally measured 25% increase in NPP with a doubling of atmospheric CO 2 from pre–industrial levels. In addition, evolutionary constraints exhibited by tropical plants adapted to low CO 2 levels during most of the Late Pleistocene, may result in little response to increased carbon availability. To set a maximum potential response for a Central Amazon forest, using an individual–tree–based carbon cycling model, a modelling experiment was performed constituting a 25% increase in tree growth rate, linked to the known and expected increase in atmospheric CO 2 . Results demonstrated a maximum carbon sequestration rate of ca . 0.2 Mg C per hectare per year (ha −1 yr −1 , where 1 ha = 10 4 m 2 ), and a sequestration rate of only 0.05 Mg C ha −1 yr −1 for an interval centred on calendar years 1980–2020. This low rate results from slow growing trees and the long residence time of carbon in woody tissues. By contrast, changes in disturbance frequency, precipitation patterns and other environmental factors can cause marked and relatively rapid shifts in ecosystem carbon storage. It is our view that observed changes in tropical forest inventory plots over the past few decades is more probably being driven by changes in disturbance or other environmental factors, than by a response to elevated CO 2 . Whether these observed changes in tropical forests are the beginning of long–term permanent shifts or a transient response is uncertain and remains an important research priority.

Ecosystem carbon storage capacity as affected by disturbance regimes: A general theoretical model

2012 ◽  
Vol 117 (G3) ◽  
pp. n/a-n/a ◽  
Author(s):  
Ensheng Weng ◽  
Yiqi Luo ◽  
Weile Wang ◽  
Han Wang ◽  
Daniel J. Hayes ◽  
...  
Keyword(s):  
Theoretical Model ◽  
Carbon Storage ◽  
Storage Capacity ◽  
Disturbance Regimes ◽  
Ecosystem Carbon

scholarly journals Comprehensive description of the carbon cycle of an ancient temperate broadleaved woodland

2010 ◽  
Vol 7 (3) ◽  
pp. 3735-3763 ◽  
Author(s):  
K. Fenn ◽  
Y. Malhi ◽  
M. Morecroft ◽  
C. Lloyd ◽  
M. Thomas
Keyword(s):  
Carbon Cycle ◽  
Eddy Covariance ◽  
Primary Productivity ◽  
Net Primary Productivity ◽  
Ecosystem Respiration ◽  
Stem Respiration ◽  
Ecosystem Carbon ◽  
Deciduous Woodland ◽  
Below Ground ◽  
Eddy Covariance Measurements

Abstract. There exist very few comprehensive descriptions of the productivity and carbon cycling of forest ecosystems. Here we present a description of the components of annual Net Primary Productivity (NPP), Gross Primary Productivity (GPP), autotrophic and heterotrophic respiration, and ecosystem respiration (RECO) for a temperate mixed deciduous woodland at Wytham Woods in southern Britain, calculated using "bottom-up" biometric and chamber measurements (leaf and wood production and soil and stem respiration). These are compared with estimates of these parameters from eddy-covariance measurements made at the same site. NPP was estimated as 7.0±0.8 Mg C ha−1 yr−1, and GPP as 20.3+1.0 Mg C ha−1 yr−1, a value which closely matched to eddy covariance-derived GPP value of 21.1 Mg C ha−1 yr−1. Annual RECO was calculated as 18.9±1.7 Mg C ha−1 yr−1, close to the eddy covariance value of 19.8 Mg C ha−1 yr−1; the seasonal cycle of biometric and eddy covariance RECO estimates also closely matched. The consistency between eddy covariance and biometric measurements substantially strengthens the confidence we attach to each as alternative indicators of site carbon dynamics, and permits an integrated perspective of the ecosystem carbon cycle. 37% of NPP was allocated below ground, and the ecosystem carbon use efficiency (CUE, = NPP/GPP) calculated to be 0.35±0.05, lower than reported for many temperate broadleaved sites.

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