scholarly journals The validity of floating chambers in quantifying CO2 flux from headwater streams

2020 ◽  
Author(s):  
M. J. Rawitch ◽  
G. L. Macpherson ◽  
A. E. Brookfield
Keyword(s):  
Carbon Cycle ◽  
Headwater Streams ◽  
Co2 Flux ◽  
Field Measurements ◽  
Global Carbon Cycle ◽  
Low Velocity ◽  
Flume Experiments ◽  
Moderate Range ◽  
Co2 Degassing ◽  
Global Carbon

Abstract The amount of CO2 exiting headwater streams through degassing plays an important role in the global carbon cycle, yet quantification of CO2 degassing remains challenging because of the morphology of headwater streams and because of uncertainty about whether floating or suspended chambers provide valid measurements in moving water. We show that experiments using large and small floating chambers in flowing water over a moderate range of water velocities (0.13–0.23 m s−1) in a laboratory flume resulted in similar k600s to published field measurements with similar water velocities. We confirmed the flume experiments with paired stirred-still beaker experiments, where resulting k600s fell within the extrapolated trend of the flume experiments. We propose that the floating chambers can provide good estimates of CO2 degassing, particularly in shallow, low-velocity, morphologically complex headwater streams, permitting quantification of this important contributor to the global carbon cycle.

scholarly journals Trivial improvements of predictive skill due to direct reconstruction of global carbon cycle

2021 ◽  
Author(s):  
Aaron Spring ◽  
István Dunkl ◽  
Hongmei Li ◽  
Victor Brovkin ◽  
Tatiana Ilyina
Keyword(s):  
Carbon Cycle ◽  
Initial Conditions ◽  
Co2 Flux ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
State Variables ◽  
Predictive Skill ◽  
Perfect Model ◽  
Prediction Systems ◽  
Global Carbon

Abstract. State-of-the-art carbon cycle prediction systems are initialized from reconstruction simulations in which state variables of the climate system are assimilated. While currently only the physical state variables are assimilated, biogeochemical state variables adjust to the state acquired through this assimilation indirectly instead of being assimilated themselves. In the absence of comprehensive biogeochemical reanalysis products, such approach is pragmatic. Here we evaluate a potential advantage of having perfect carbon cycle observational products to be used for direct carbon cycle reconstruction. Within an idealized perfect-model framework, we define 50 years of a control simulation under pre-industrial CO2 levels as our target representing observations. We nudge variables from this target onto arbitrary initial conditions 150 years later mimicking an assimilation simulation generating initial conditions for hindcast experiments of prediction systems. We investigate the tracking performance, i.e. bias, correlation and root-mean-square-error between the reconstruction and the target, when nudging an increasing set of atmospheric, oceanic and terrestrial variables with a focus on the global carbon cycle explaining variations in atmospheric CO2. We compare indirect versus direct carbon cycle reconstruction against a resampled threshold representing internal variability. Afterwards, we use these reconstructions to initialize ensembles to assess how well the target can be predicted after reconstruction. Interested in the ability to reconstruct global atmospheric CO2, we focus on the global carbon cycle reconstruction and predictive skill. We find that indirect carbon cycle reconstruction through physical fields reproduces the target variations on a global and regional scale much better than the resampled threshold. While reproducing the large scale variations, nudging introduces systematic regional biases in the physical state variables, on which biogeochemical cycles react very sensitively. Global annual surface oceanic pCO2 initial conditions are indirectly reconstructed with an anomaly correlation coefficient (ACC) of 0.8 and debiased root mean square error (RMSE) of 0.3 ppm. Direct reconstruction slightly improves initial conditions in ACC by +0.1 and debiased RMSE by −0.1 ppm. Indirect reconstruction of global terrestrial carbon cycle initial conditions for vegetation carbon pools track the target by ACC of 0.5 and debiased RMSE of 0.5 PgC. Direct reconstruction brings negligible improvements for air-land CO2 flux. Global atmospheric CO2 is indirectly tracked by ACC of 0.8 and debiased RMSE of 0.4 ppm. Direct reconstruction of the marine and terrestrial carbon cycles improves ACC by 0.1 and debiased RMSE by −0.1 ppm. We find improvements in global carbon cycle predictive skill from direct reconstruction compared to indirect reconstruction. After correcting for mean bias, indirect and direct reconstruction both predict the target similarly well and only moderately worse than perfect initialization after the first lead year. Our perfect-model study shows that indirect carbon cycle reconstruction yields satisfying initial conditions for global CO2 flux and atmospheric CO2. Direct carbon cycle reconstruction adds little improvements in the global carbon cycle, because imperfect reconstruction of the physical climate state impedes better biogeochemical reconstruction. These minor improvements in initial conditions yield little improvement in initialized perfect-model predictive skill. We label these minor improvements due to direct carbon cycle reconstruction trivial, as mean bias reduction yields similar improvements. As reconstruction biases in real-world prediction systems are even stronger, our results add confidence to the current practice of indirect reconstruction in carbon cycle prediction systems.

scholarly journals Trivial improvements in predictive skill due to direct reconstruction of the global carbon cycle

2021 ◽  
Vol 12 (4) ◽  
pp. 1139-1167
Author(s):  
Aaron Spring ◽  
István Dunkl ◽  
Hongmei Li ◽  
Victor Brovkin ◽  
Tatiana Ilyina
Keyword(s):  
Carbon Cycle ◽  
Initial Conditions ◽  
Co2 Flux ◽  
Global Carbon Cycle ◽  
Atmospheric Co2 ◽  
State Variables ◽  
Predictive Skill ◽  
Perfect Model ◽  
Prediction Systems ◽  
Global Carbon

Abstract. State-of-the art climate prediction systems have recently included a carbon component. While physical-state variables are assimilated in reconstruction simulations, land and ocean biogeochemical state variables adjust to the state acquired through this assimilation indirectly instead of being assimilated themselves. In the absence of comprehensive biogeochemical reanalysis products, such an approach is pragmatic. Here we evaluate a potential advantage of having perfect carbon cycle observational products to be used for direct carbon cycle reconstruction. Within an idealized perfect-model framework, we reconstruct a 50-year target period from a control simulation. We nudge variables from this target onto arbitrary initial conditions, mimicking an assimilation simulation generating initial conditions for hindcast experiments of prediction systems. Interested in the ability to reconstruct global atmospheric CO2, we focus on the global carbon cycle reconstruction performance and predictive skill. We find that indirect carbon cycle reconstruction through physical fields reproduces the target variations. While reproducing the large-scale variations, nudging introduces systematic regional biases in the physical-state variables to which biogeochemical cycles react very sensitively. Initial conditions in the oceanic carbon cycle are sufficiently well reconstructed indirectly. Direct reconstruction slightly improves initial conditions. Indirect reconstruction of global terrestrial carbon cycle initial conditions are also sufficiently well reconstructed by the physics reconstruction alone. Direct reconstruction negligibly improves air–land CO2 flux. Atmospheric CO2 is indirectly very well reconstructed. Direct reconstruction of the marine and terrestrial carbon cycles slightly improves reconstruction while establishing persistent biases. We find improvements in global carbon cycle predictive skill from direct reconstruction compared to indirect reconstruction. After correcting for mean bias, indirect and direct reconstruction both predict the target similarly well and only moderately worse than perfect initialization after the first lead year. Our perfect-model study shows that indirect carbon cycle reconstruction yields satisfying initial conditions for global CO2 flux and atmospheric CO2. Direct carbon cycle reconstruction adds little improvement to the global carbon cycle because imperfect reconstruction of the physical climate state impedes better biogeochemical reconstruction. These minor improvements in initial conditions yield little improvement in initialized perfect-model predictive skill. We label these minor improvements due to direct carbon cycle reconstruction “trivial”, as mean bias reduction yields similar improvements. As reconstruction biases in real-world prediction systems are likely stronger, our results add confidence to the current practice of indirect reconstruction in carbon cycle prediction systems.

scholarly journals On constraining the strength of the terrestrial CO<sub>2</sub> fertilization effect in an Earth system model

2016 ◽  
Author(s):  
V. K. Arora ◽  
J. F. Scinocca
Keyword(s):  
Carbon Cycle ◽  
Carbon Budget ◽  
Co2 Flux ◽  
Global Carbon Cycle ◽  
System Model ◽  
Historical Period ◽  
Earth System ◽  
Co2 Fertilization ◽  
Fertilization Effect ◽  
Global Carbon

Abstract. Earth system models (ESMs) explicitly simulate the interactions between the physical climate system components and biogeochemical cycles. Physical and biogeochemical aspects of ESMs are routinely compared against their observation-based counterparts to assess model performance and to evaluate how this performance is affected by ongoing model development. Here, we assess the performance of version 4.2 of the Canadian Earth system model against four, land carbon cycle focused, observation-based determinants of the global carbon cycle and the historical global carbon budget over the 1850–2005 period. Our objective is to constrain the strength of the terrestrial CO2 fertilization effect which is known to be the most uncertain of all carbon cycle feedbacks. The observation-based determinants include (1) globally-averaged atmospheric CO2 concentration, (2) cumulative atmosphere–land CO2 flux, (3) atmosphere–land CO2 flux for the decades of 1960s, 1970s, 1980s, 1990s and 2000s and (4) the amplitude of the globally-averaged annual CO2 cycle and its increase over the 1980 to 2005 period. The optimal simulation that satisfies constraints imposed by the first three determinants yields a net primary productivity (NPP) increase from ~ 58 Pg C yr−1 in 1850 to about ~ 74 Pg C yr−1 in 2005; an increase of ~ 27 % over the 1850–2005 period. The simulated loss in the global soil carbon amount due to anthropogenic land use change over the historical period is also broadly consistent with empirical estimates. Yet, it remains possible that these determinants of the global carbon cycle are insufficient to adequately constrain the historical carbon budget, and consequently the strength of terrestrial CO2 fertilization effect as it is represented in the model, given the large uncertainty associated with LUC emissions over the historical period.

scholarly journals Constraining the strength of the terrestrial CO<sub>2</sub> fertilization effect in the Canadian Earth system model version 4.2 (CanESM4.2)

2016 ◽  
Vol 9 (7) ◽  
pp. 2357-2376 ◽  
Author(s):  
Vivek K. Arora ◽  
John F. Scinocca
Keyword(s):  
Carbon Cycle ◽  
Carbon Budget ◽  
Co2 Flux ◽  
Global Carbon Cycle ◽  
System Model ◽  
Historical Period ◽  
Earth System ◽  
Co2 Fertilization ◽  
Fertilization Effect ◽  
Global Carbon

Abstract. Earth system models (ESMs) explicitly simulate the interactions between the physical climate system components and biogeochemical cycles. Physical and biogeochemical aspects of ESMs are routinely compared against their observation-based counterparts to assess model performance and to evaluate how this performance is affected by ongoing model development. Here, we assess the performance of version 4.2 of the Canadian Earth system model against four land carbon-cycle-focused, observation-based determinants of the global carbon cycle and the historical global carbon budget over the 1850–2005 period. Our objective is to constrain the strength of the terrestrial CO2 fertilization effect, which is known to be the most uncertain of all carbon-cycle feedbacks. The observation-based determinants include (1) globally averaged atmospheric CO2 concentration, (2) cumulative atmosphere–land CO2 flux, (3) atmosphere–land CO2 flux for the decades of 1960s, 1970s, 1980s, 1990s, and 2000s, and (4) the amplitude of the globally averaged annual CO2 cycle and its increase over the 1980 to 2005 period. The optimal simulation that satisfies constraints imposed by the first three determinants yields a net primary productivity (NPP) increase from  ∼  58 Pg C year−1 in 1850 to about  ∼  74 Pg C year−1 in 2005; an increase of  ∼  27 % over the 1850–2005 period. The simulated loss in the global soil carbon amount due to anthropogenic land use change (LUC) over the historical period is also broadly consistent with empirical estimates. Yet, it remains possible that these determinants of the global carbon cycle are insufficient to adequately constrain the historical carbon budget, and consequently the strength of terrestrial CO2 fertilization effect as it is represented in the model, given the large uncertainty associated with LUC emissions over the historical period.

Are response function representations of the global carbon cycle ever interpretable?

Tellus B ◽  
2009 ◽  
Vol 61 (2) ◽  
Author(s):  
Sile Li ◽  
Andrew J. Jarvis ◽  
David T. Leedal
Keyword(s):  
Carbon Cycle ◽  
Response Function ◽  
Global Carbon Cycle ◽  
Global Carbon

EPR-derived structures of flavin radical and iron-sulfur clusters from Methylosinus sporium 5 reductase

2021 ◽  
Author(s):  
Han Sol Jeong ◽  
Sugyeong Hong ◽  
Hee Seon Yoo ◽  
Jin Kim ◽  
Yujeong Kim ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Crucial Role ◽  
Methane Monooxygenase ◽  
Global Carbon Cycle ◽  
Ambient Conditions ◽  
Iron Sulfur Clusters ◽  
Iron Sulfur ◽  
Greenhouse Effects ◽  
Global Carbon ◽  
Significant Attention

Methane monooxygenase (MMO) has attracted significant attention owing to its crucial role in the global carbon cycle; it impedes greenhouse effects by converting methane to methanol under ambient conditions. The...

scholarly journals Amazonian Fire Events Disturbed the Global Carbon Cycle: A Study from 2019 Amazon Wildfire Using Google Earth Engine

2020 ◽  
Vol 3 (1) ◽  
pp. 43
Author(s):  
Subhajit Bandopadhyay ◽  
Dany A. Cotrina Sánchez
Keyword(s):  
Carbon Cycle ◽  
Carbon Sink ◽  
Brazilian Amazon ◽  
Global Carbon Cycle ◽  
Forest Productivity ◽  
Google Earth ◽  
Comparative Approach ◽  
Burned Area ◽  
Google Earth Engine ◽  
Global Carbon

An unprecedented number of wildfire events during 2019 throughout the Brazilian Amazon caught global attention, due to their massive extent and the associated loss in the Amazonian forest—an ecosystem on which the whole world depends. Such devastating wildfires in the Amazon has strongly hampered the global carbon cycle and significantly reduced forest productivity. In this study, we have quantified such loss of forest productivity in terms of gross primary productivity (GPP), applying a comparative approach using Google Earth Engine. A total of 12 wildfire spots have been identified based on the fire’s extension over the Brazilian Amazon, and we quantified the loss in productivity between 2018 and 2019. The Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and MODIS burned area satellite imageries, with a revisit time of 8 days and 30 days, respectively, have been used for this study. We have observed that compared to 2018, the number of wildfire events increased during 2019. But such wildfire events did not hamper the natural annual trend of GPP of the Amazonian ecosystem. However, a significant drop in forest productivity in terms of GPP has been observed. Among all 11 observational sites were recorded with GPP loss, ranging from −18.88 gC m−2 yr−1 to −120.11 gC m−2 yr−1, except site number 3. Such drastic loss in GPP indicates that during 2019 fire events, all of these sites acted as carbon sources rather than carbon sink sites, which may hamper the global carbon cycle and terrestrial CO2 fluxes. Therefore, it is assumed that these findings will also fit for the other Amazonian wildfire sites, as well as for the tropical forest ecosystem as a whole. We hope this study will provide a significant contribution to global carbon cycle research, terrestrial ecosystem studies, sustainable forest management, and climate change in contemporary environmental sciences.

Distribution of radiocarbon as a test of global carbon cycle models

1995 ◽  
Vol 9 (1) ◽  
pp. 153-166 ◽  
Author(s):  
Atul K. Jain ◽  
Haroon S. Kheshgi ◽  
Martin I. Hoffert ◽  
Donald J. Wuebbles
Keyword(s):  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Global Carbon
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