scholarly journals Reducing CO2 Flux by Decreasing Tillage in Ohio: Overcoming Conjecture with Data

2020 ◽  
Author(s):  
Deb O’Dell ◽  
Neal S Eash ◽  
Bruce B Hicks ◽  
Joel N Oetting ◽  
Thomas J Sauer ◽  
...  
Keyword(s):  
Co2 Flux

Effect of recent observations on Asian CO2 flux estimates by transport model inversions

Tellus B ◽  
2003 ◽  
Vol 55 (2) ◽  
pp. 522-529 ◽  
Author(s):  
Shamil Maksyutov ◽  
Toshinobu Machida ◽  
Hitoshi Mukai ◽  
Prabir K. Patra ◽  
Takakiyo Nakazawa ◽  
...  
Keyword(s):  
Transport Model ◽  
Co2 Flux

scholarly journals Variation in Soil Properties Regulate Greenhouse Gas Fluxes and Global Warming Potential in Three Land Use Types on Tropical Peat

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 465 ◽  
Author(s):  
Kiwamu Ishikura ◽  
Untung Darung ◽  
Takashi Inoue ◽  
Ryusuke Hatano
Keyword(s):  
Global Warming ◽  
Soil Moisture ◽  
Groundwater Level ◽  
Global Warming Potential ◽  
C:N Ratio ◽  
Co2 Flux ◽  
Nitrate Content ◽  
N2o Flux ◽  
Ch4 Flux ◽  
In The Beginning

This study investigated spatial factors controlling CO2, CH4, and N2O fluxes and compared global warming potential (GWP) among undrained forest (UDF), drained forest (DF), and drained burned land (DBL) on tropical peatland in Central Kalimantan, Indonesia. Sampling was performed once within two weeks in the beginning of dry season. CO2 flux was significantly promoted by lowering soil moisture and pH. The result suggests that oxidative peat decomposition was enhanced in drier position, and the decomposition acidify the peat soils. CH4 flux was significantly promoted by a rise in groundwater level, suggesting that methanogenesis was enhanced under anaerobic condition. N2O flux was promoted by increasing soil nitrate content in DF, suggesting that denitrification was promoted by substrate availability. On the other hand, N2O flux was promoted by lower soil C:N ratio and higher soil pH in DBL and UDF. CO2 flux was the highest in DF (241 mg C m−2 h−1) and was the lowest in DBL (94 mg C m−2 h−1), whereas CH4 flux was the highest in DBL (0.91 mg C m−2 h−1) and was the lowest in DF (0.01 mg C m−2 h−1), respectively. N2O flux was not significantly different among land uses. CO2 flux relatively contributed to 91–100% of GWP. In conclusion, it is necessary to decrease CO2 flux to mitigate GWP through a rise in groundwater level and soil moisture in the region.

scholarly journals CO<sub>2</sub> flux determination by closed-chamber methods can be seriously biased by inappropriate application of linear regression

2007 ◽  
Vol 4 (6) ◽  
pp. 1005-1025 ◽  
Author(s):  
L. Kutzbach ◽  
J. Schneider ◽  
T. Sachs ◽  
M. Giebels ◽  
H. Nykänen ◽  
...  
Keyword(s):  
Linear Regression ◽  
Nonlinear Regression ◽  
Co2 Flux ◽  
Exponential Model ◽  
Co2 Uptake ◽  
Co2 Fluxes ◽  
Closed Chamber ◽  
Exponential Regression ◽  
Closure Time ◽  
Over Time

Abstract. Closed (non-steady state) chambers are widely used for quantifying carbon dioxide (CO2) fluxes between soils or low-stature canopies and the atmosphere. It is well recognised that covering a soil or vegetation by a closed chamber inherently disturbs the natural CO2 fluxes by altering the concentration gradients between the soil, the vegetation and the overlying air. Thus, the driving factors of CO2 fluxes are not constant during the closed chamber experiment, and no linear increase or decrease of CO2 concentration over time within the chamber headspace can be expected. Nevertheless, linear regression has been applied for calculating CO2 fluxes in many recent, partly influential, studies. This approach has been justified by keeping the closure time short and assuming the concentration change over time to be in the linear range. Here, we test if the application of linear regression is really appropriate for estimating CO2 fluxes using closed chambers over short closure times and if the application of nonlinear regression is necessary. We developed a nonlinear exponential regression model from diffusion and photosynthesis theory. This exponential model was tested with four different datasets of CO2 flux measurements (total number: 1764) conducted at three peatlands sites in Finland and a tundra site in Siberia. Thorough analyses of residuals demonstrated that linear regression was frequently not appropriate for the determination of CO2 fluxes by closed-chamber methods, even if closure times were kept short. The developed exponential model was well suited for nonlinear regression of the concentration over time c(t) evolution in the chamber headspace and estimation of the initial CO2 fluxes at closure time for the majority of experiments. However, a rather large percentage of the exponential regression functions showed curvatures not consistent with the theoretical model which is considered to be caused by violations of the underlying model assumptions. Especially the effects of turbulence and pressure disturbances by the chamber deployment are suspected to have caused unexplainable curvatures. CO2 flux estimates by linear regression can be as low as 40% of the flux estimates of exponential regression for closure times of only two minutes. The degree of underestimation increased with increasing CO2 flux strength and was dependent on soil and vegetation conditions which can disturb not only the quantitative but also the qualitative evaluation of CO2 flux dynamics. The underestimation effect by linear regression was observed to be different for CO2 uptake and release situations which can lead to stronger bias in the daily, seasonal and annual CO2 balances than in the individual fluxes. To avoid serious bias of CO2 flux estimates based on closed chamber experiments, we suggest further tests using published datasets and recommend the use of nonlinear regression models for future closed chamber studies.

scholarly journals CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

2021 ◽  
Vol 13 (15) ◽  
pp. 2996
Author(s):  
Qinwei Zhang ◽  
Mingqi Li ◽  
Maohua Wang ◽  
Arthur Paul Mizzi ◽  
Yongjian Huang ◽  
...  
Keyword(s):  
United States ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Co2 Flux ◽  
Total Carbon ◽  
Mean Bias Error ◽  
Bias Error ◽  
Parallel Experiment ◽  
Concentration Measurements ◽  
Global Stocktake

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

Wildland fire ash enhances short-term CO2 flux from soil in a Southern African savannah

2021 ◽  
pp. 108334
Author(s):  
Carmen Sánchez-García ◽  
Cristina Santín ◽  
Stefan H. Doerr ◽  
Tercia Strydom ◽  
Emilia Urbanek
Keyword(s):  
Wildland Fire ◽  
Co2 Flux ◽  
Short Term

The use of CO2 flux time series for parameter and carbon stock estimation in carbon cycle research

2011 ◽  
Vol 18 (1) ◽  
pp. 179-193 ◽  
Author(s):  
Timothy Charles Hill ◽  
Edmund Ryan ◽  
Mathew Williams
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Carbon Stock ◽  
Co2 Flux ◽  
Stock Estimation

scholarly journals First experimental evidence for the CO<sub>2</sub>-driven origin of Stromboli's major explosions

2011 ◽  
Vol 3 (1) ◽  
pp. 411-430 ◽  
Author(s):  
A. Aiuppa ◽  
M. Burton ◽  
P. Allard ◽  
T. Caltabiano ◽  
G. Giudice ◽  
...  
Keyword(s):  
Experimental Evidence ◽  
Co2 Emission ◽  
Weight Ratio ◽  
Co2 Flux ◽  
Recent Model ◽  
Stromboli Volcano ◽  
Plumbing System ◽  
Gas Leakage ◽  
Basaltic Volcanoes ◽  
Monitoring Strategies

Abstract. We report on the first detection of CO2 flux precursors of the till now unforecastable larger than normal ("major") explosions that intermittently occur at Stromboli volcano (Italy). Automated survey of the crater plume emissions in the period 2006–2010, during which 12 such explosions happened, demonstrate that these events are systematically preceded by a brief phase of increasing CO2/SO2 weight ratio (up to >40) and CO2 flux (>1300 t/d) with respect to the time-averaged values of 3.7 and ~500 t/d typical for standard Stromboli's activity. These signals are best explained by the accumulation of CO2-rich gas at a discontinuity of the plumbing system (decreasing CO2 emission at the surface), followed by increasing gas leakage prior to the explosion. Our observations thus support the recent model of Allard (2010) for a CO2-rich gas trigger of recurrent major explosions at Stromboli, and demonstrate the possibility to forecast these events in advance from geochemical precursors. These observations and conclusions have clear implications for monitoring strategies at other open-vent basaltic volcanoes worldwide.

scholarly journals Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

2013 ◽  
Vol 13 (23) ◽  
pp. 11643-11660 ◽  
Author(s):  
A. Chatterjee ◽  
A. M. Michalak
Keyword(s):  
Kalman Filter ◽  
Inverse Modeling ◽  
Ensemble Kalman Filter ◽  
Co2 Flux ◽  
Estimation Problem ◽  
Square Root ◽  
Flux Estimation ◽  
Source Sink ◽  
Operational Constraints ◽  
Square Root Filter

Abstract. Data assimilation (DA) approaches, including variational and the ensemble Kalman filter methods, provide a computationally efficient framework for solving the CO2 source–sink estimation problem. Unlike DA applications for weather prediction and constituent assimilation, however, the advantages and disadvantages of DA approaches for CO2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA approaches (an ensemble square root filter and a variational technique) using a batch inverse modeling setup as a benchmark, within the context of a simple one-dimensional advection–diffusion prototypical inverse problem that has been designed to capture the nuances of a real CO2 flux estimation problem. Experiments are designed to identify the impact of the observational density, heterogeneity, and uncertainty, as well as operational constraints (i.e., ensemble size, number of descent iterations) on the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA approaches to keep the problem setup analogous to a typical real CO2 flux estimation problem. Results demonstrate that the performance of the DA approaches depends on a complex interplay between the measurement network and the operational constraints. Overall, the variational approach (contingent on the availability of an adjoint transport model) more reliably captures the large-scale source–sink patterns. Conversely, the ensemble square root filter provides more realistic uncertainty estimates. Selection of one approach over the other must therefore be guided by the carbon science questions being asked and the operational constraints under which the approaches are being applied.

Eddy covariance CO2 flux above a Gmelin larch forest on continuous permafrost in Central Siberia during a growing season

2008 ◽  
Vol 93 (3-4) ◽  
pp. 133-147 ◽  
Author(s):  
Y. Nakai ◽  
Y. Matsuura ◽  
T. Kajimoto ◽  
A. P. Abaimov ◽  
S. Yamamoto ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Co2 Flux ◽  
Growing Season ◽  
Larch Forest ◽  
Central Siberia ◽  
Continuous Permafrost
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