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 Level Carbon and Net Primary Productivity of an Old-Growth and a Regenerating Humid Tropical Forest of North-Eastern India

2015 ◽  
Vol 1 (01) ◽  
pp. 85-98
Author(s):  
Saroj Kanta Barik ◽  
Ratul Baishya
Keyword(s):  
Net Primary Productivity ◽  
Eastern India ◽  
Old Growth ◽  
Ground Biomass ◽  
Ecosystem Carbon ◽  
Old Growth Forest ◽  
North Eastern ◽  
Ecosystem Level ◽  
Below Ground ◽  
Humid Tropical Forest

Ecosystem level carbon and net primary productivity (NPP) estimates for old-growth and regenerating tropical forests of India are lacking. The study was conducted to estimate ecosystem level carbon contents and NPP, based on above and below ground biomass of trees, shrubs and herbs in an old growth and a regenerating humid tropical forest of Nongkhyllem Wildlife Sanctuary, Meghalaya in north-eastern India. Soil carbon contents were also estimated in both the forest types to estimate ecosystem level carbon. The tree above ground biomass values in old-growth and regenerating forests were 313.8 and 152.4 Mg ha-1 and the below ground values were 50.8 and 30.3 Mg ha-1, respectively. The corresponding total above ground biomass values including trees, litter, herb and shrub components were 323.7 and 159.3 Mg ha-1, respectively. Of the total ecosystem biomass values of 374.5 Mg ha-1 in the old-growth forest, 86% was in the above ground and 14% was in the below ground compartment. The corresponding proportions in the regenerating forest with total biomass of 189.6 Mg ha-1 were 84% and 16%, respectively. The total ecosystem carbon contents in old-growth and regenerating forests were 265.5 and 147.8 Mg C ha-1, of which soil organic carbon was 83.2 and 55.6 Mg C ha-1, respectively that contributed 31.3% and 37.6% to the total ecosystem carbon in the respective forests. However, ecosystem NPP in the regenerating forest (18.4 Mg ha-1 yr-1) was greater than the old growth forest(13.6 Mg ha-1 yr-1)

Carbon storage in Lake States aspen ecosystems

1992 ◽  
Vol 22 (8) ◽  
pp. 1107-1110 ◽  
Author(s):  
D.H. Alban ◽  
D.A. Perala
Keyword(s):  
Soil Carbon ◽  
Carbon Storage ◽  
Timber Harvesting ◽  
Old Growth ◽  
Standing Biomass ◽  
Ecosystem Carbon ◽  
Old Growth Forests ◽  
Lake States

Total ecosystem carbon in the soil and vegetation was measured for a range of aspen (Populustremuloides Michx.) ecosystems, including a chronosequence on the same soil ranging in age from 0 to 80 years. Soil carbon stayed relatively constant throughout the stand's life and was not affected by timber harvesting. Changes in ecosystem carbon closely paralleled the changes in standing biomass. Aspen grown on 40-year rotations on good soils will sequester several times as much carbon per year as old-growth forests.

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 Representation of phosphorus cycle in Joint UK Land Environment Simulator (vn5.5_JULES-CNP)

2021 ◽  
Author(s):  
Mahdi Nakhavali ◽  
Lina M. Mercado ◽  
Iain P. Hartley ◽  
Stephen Sitch ◽  
Fernanda V. Cunha ◽  
...  
Keyword(s):  
Elevated Co2 ◽  
Land Surface ◽  
Net Primary Productivity ◽  
Low Fertility ◽  
Amazon Forest ◽  
Phosphorus Cycle ◽  
Soil P ◽  
P Limitation ◽  
P Cycling ◽  
Ambient Co2

Abstract. Most Land Surface Models (LSMs), the land components of Earth system models (ESMs), include representation of N limitation on ecosystem productivity. However only few of these models have incorporated phosphorus (P) cycling. In tropical ecosystems, this is likely to be particularly important as N tends to be abundant but the availability of rock-derived elements, such as P, can be very low. Thus, without a representation of P cycling, tropical forest response in areas such as Amazonia to rising atmospheric CO2 conditions remains highly uncertain. In this study, we introduced P dynamics and its interactions with the N and carbon (C) cycles into the Joint UK Land Environment Simulator (JULES). The new model (JULES-CNP) includes the representation of P stocks in vegetation and soil pools, as well as key processes controlling fluxes between these pools. We evaluate JULES-CNP at the Amazon nutrient fertilization experiment (AFEX), a low fertility site, representative of about 60 % of Amazon soils. We apply the model under ambient CO2 and elevated CO2. The model is able to reproduce the observed plant and soil P pools and fluxes under ambient CO2. We estimate P to limit net primary productivity (NPP) by 24 % under current CO2 and by 46 % under elevated CO2. Under elevated CO2, biomass in simulations accounting for CNP increase by 10 % relative to at contemporary CO2, although it is 5 % lower compared with CN and C-only simulations. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low fertility soils.

The role of non-structural carbohydrates in simulations of ecosystem carbon fluxes.

2020 ◽  
Author(s):  
Simon Jones ◽  
Lucy Rowland ◽  
Peter Cox ◽  
Debbie Hemming ◽  
Andy Wiltshire ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Land Surface ◽  
Large Scale ◽  
Carbon Fluxes ◽  
Carbon Accumulation ◽  
Future Research ◽  
Labile Carbon ◽  
Ecosystem Carbon ◽  
Unrealistic Assumption ◽  
Plant Respiration

<p>Accurately representing the response of ecosystems to environmental change in land surface models (LSM) is crucial to making accurate predictions of future climate. Many LSMs do not correctly capture plant respiration and growth fluxes, particularly in response to extreme climatic events. This is in part due to the unrealistic assumption that total plant carbon expenditure (PCE) is always equal to gross carbon accumulation by photosynthesis. We present and evaluate a simple model of labile carbon storage and utilisation (SUGAR), designed to be integrated into an LSM, that allows simulated plant respiration and growth to vary independently of photosynthesis. SUGAR buffers simulated PCE against seasonal variation in photosynthesis, producing more constant (less variable) predictions of plant growth and respiration relative to an LSM that does not represent labile carbon storage. This allows the model to more accurately capture observed carbon fluxes at a large-scale drought experiment in a tropical moist forest in the Amazon, relative to the Joint UK Land Environment Simulator LSM (JULES). SUGAR is designed to improve the representation of carbon storage in LSMs and provides a simple framework that allows new processes to be integrated as the empirical understanding of carbon storage in plants improves. The study highlights the need for future research into carbon storage and allocation in plants, particularly in response to extreme climate events such as drought.</p>

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 Towards quantifying uncertainty in predictions of Amazon ‘dieback’

2008 ◽  
Vol 363 (1498) ◽  
pp. 1857-1864 ◽  
Author(s):  
Chris Huntingford ◽  
Rosie A Fisher ◽  
Lina Mercado ◽  
Ben B.B Booth ◽  
Stephen Sitch ◽  
...  
Keyword(s):  
Land Surface ◽  
General Circulation ◽  
Net Primary Productivity ◽  
Circulation Model ◽  
Light Interception ◽  
Rooting Depth ◽  
Amazon Forest ◽  
Carbon Cycle Model ◽  
Wide Range ◽  
Amazonian Rainforest

Simulations with the Hadley Centre general circulation model (HadCM3), including carbon cycle model and forced by a ‘business-as-usual’ emissions scenario, predict a rapid loss of Amazonian rainforest from the middle of this century onwards. The robustness of this projection to both uncertainty in physical climate drivers and the formulation of the land surface scheme is investigated. We analyse how the modelled vegetation cover in Amazonia responds to (i) uncertainty in the parameters specified in the atmosphere component of HadCM3 and their associated influence on predicted surface climate. We then enhance the land surface description and (ii) implement a multilayer canopy light interception model and compare with the simple ‘big-leaf’ approach used in the original simulations. Finally, (iii) we investigate the effect of changing the method of simulating vegetation dynamics from an area-based model (TRIFFID) to a more complex size- and age-structured approximation of an individual-based model (ecosystem demography). We find that the loss of Amazonian rainforest is robust across the climate uncertainty explored by perturbed physics simulations covering a wide range of global climate sensitivity. The introduction of the refined light interception model leads to an increase in simulated gross plant carbon uptake for the present day, but, with altered respiration, the net effect is a decrease in net primary productivity. However, this does not significantly affect the carbon loss from vegetation and soil as a consequence of future simulated depletion in soil moisture; the Amazon forest is still lost. The introduction of the more sophisticated dynamic vegetation model reduces but does not halt the rate of forest dieback. The potential for human-induced climate change to trigger the loss of Amazon rainforest appears robust within the context of the uncertainties explored in this paper. Some further uncertainties should be explored, particularly with respect to the representation of rooting depth.

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.

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.

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