A comparison of atmospheric CO2 flux signals obtained from GEOS-Chem flux inversions constrained by in situ or GOSAT observations by Polavarapu et al.

2018 ◽  
Author(s):  
Anonymous
Keyword(s):  
Co2 Flux ◽  
Atmospheric Co2

scholarly journals Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne measurements of atmospheric CO2

2014 ◽  
Vol 41 (3) ◽  
pp. 1065-1070 ◽  
Author(s):  
Frédéric Chevallier ◽  
Paul I. Palmer ◽  
Liang Feng ◽  
Hartmut Boesch ◽  
Christopher W. O'Dell ◽  
...  
Keyword(s):  
Co2 Flux ◽  
Atmospheric Co2

scholarly journals Design and evaluation of CO<sub>2</sub> observation network to optimize surface CO<sub>2</sub> fluxes in Asia using observation system simulation experiments

2020 ◽  
Vol 20 (8) ◽  
pp. 5175-5195
Author(s):  
Jun Park ◽  
Hyun Mee Kim
Keyword(s):  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Observation System ◽  
In Situ Observation ◽  
Co2 Fluxes ◽  
Simulation Experiments ◽  
Influence Matrix ◽  
Observation Network ◽  
Simulated Surface

Abstract. Continuous efforts have been made to monitor atmospheric CO2 mole fractions as it is one of the most influential greenhouse gases in Earth's atmosphere. The atmospheric CO2 mole fractions are mostly determined by CO2 exchanges at the Earth's surface (i.e., surface CO2 flux). Inverse modeling, which is a method to estimate the CO2 exchanges at the Earth's surface, derives surface CO2 fluxes using modeled and observed atmospheric CO2 mole fraction data. Although observation data are crucial for successful modeling, comparatively fewer in situ observation sites are located in Asia compared to Europe or North America. Based on the importance of the terrestrial ecosystem of Asia for global carbon exchanges, more observation stations and an effective observation network design are required. In this paper, several observation network experiments were conducted to optimize the surface CO2 flux of Asia using CarbonTracker and observation system simulation experiments (OSSEs). The impacts of the redistribution of and additions to the existing observation network of Asia were evaluated using hypothetical in situ observation sites. In the case of the addition experiments, 10 observation stations, which is a practical number for real implementation, were added through three strategies: random addition, the influence matrix (i.e., self-sensitivity), and ecoregion information within the model. The simulated surface CO2 flux in Asia in summer can be improved by redistributing the existing observation network. The addition experiments revealed that considering both the distribution of normalized self-sensitivity and ecoregion information can yield better simulated surface CO2 fluxes compared to random addition, regardless of the season. This study provides a diagnosis of the existing observation network and useful information for future observation network design in Asia to estimate the surface CO2 flux and also suggests the use of an influence matrix for designing CO2 observation networks. Unlike other previous observation network studies with many numerical experiments for optimization, comparatively fewer experiments were required in this study. Thus, the methodology used in this study may be used for designing observation networks for monitoring greenhouse gases at both continental and global scales.

scholarly journals A comparison of posterior atmospheric CO<sub>2</sub> adjustments obtained from in situ and GOSAT constrained flux inversions

2018 ◽  
Vol 18 (16) ◽  
pp. 12011-12044 ◽  
Author(s):  
Saroja M. Polavarapu ◽  
Feng Deng ◽  
Brendan Byrne ◽  
Dylan B. A. Jones ◽  
Michael Neish
Keyword(s):  
Co2 Flux ◽  
Global Scale ◽  
Atmospheric Co2 ◽  
Boreal Summer ◽  
Spatial Structures ◽  
Dense Network ◽  
Independent Observations ◽  
Flux Measurements ◽  
The Tropics

Abstract. Posterior fluxes obtained from inverse modelling are difficult to verify because there is no dense network of flux measurements available to evaluate estimates against. Here we present a new diagnostic to evaluate structures in posterior fluxes. First, we simulate the change in atmospheric CO2 fields between posterior and prior fluxes, referred to as the posterior atmospheric adjustments due to updated fluxes (PAAFs). Second, we calculate the uncertainty in atmospheric CO2 fields due solely to uncertainty in the meteorological fields, referred to as the posterior atmospheric adjustments due to imperfect meteorology (PAAMs). We argue that PAAF can only be considered robust if it exceeds PAAM, that is, the changes in atmospheric CO2 between the posterior and prior fluxes should at least exceed atmospheric CO2 changes arising from imperfect meteorology. This diagnostic is applied to two CO2 flux inversions: one which assimilates observations from the in situ CO2 network and the other which assimilates observations from the Greenhouse Gases Observing SATellite (GOSAT). On the global scale, PAAF in the troposphere reflects northern extratropical fluxes, whereas stratospheric adjustments primarily reflect tropical fluxes. In general, larger spatiotemporal variations in PAAF are obtained for the GOSAT inversion than for the in situ inversion. Zonal standard deviations of the PAAF exceed the PAAM through most of the year when GOSAT observations are used, but the minimum value is exceeded only in boreal summer when in situ observations are used. Zonal spatial structures in GOSAT-based PAAF exceed PAAM throughout the year in the tropics and through most of the year in the northern extratropics, suggesting GOSAT flux inversions can constrain zonal asymmetries in fluxes. However, we cannot discount the possibility that these structures are influenced by biases in GOSAT retrievals. Verification of such spatial structures will require a dense network of independent observations. Because PAAF depends on the choice of prior fluxes, the comparison with PAAM is system dependent and thus can be used to monitor a given assimilation system's behaviour.

scholarly journals Measuring and modelling the isotopic composition of soil respiration: insights from a grassland tracer experiment

2011 ◽  
Vol 8 (5) ◽  
pp. 1333-1350 ◽  
Author(s):  
U. Gamnitzer ◽  
A. B. Moyes ◽  
D. R. Bowling ◽  
H. Schnyder
Keyword(s):  
Steady State ◽  
Isotopic Composition ◽  
Transport Model ◽  
Tracer Experiment ◽  
Atmospheric Co2 ◽  
Soil Co2 ◽  
Soil Air ◽  
Co2 Transport ◽  
Atmosphere System

Abstract. The carbon isotopic composition (δ13C) of CO2 efflux (δ13Cefflux) from soil is generally interpreted to represent the actual isotopic composition of the respiratory source (δ13CRs). However, soils contain a large CO2 pool in air-filled pores. This pool receives CO2 from belowground respiration and exchanges CO2 with the atmosphere (via diffusion and advection) and the soil liquid phase (via dissolution). Natural or artificial modification of δ13C of atmospheric CO2 (δ13Catm) or δ13CRs causes isotopic disequilibria in the soil-atmosphere system. Such disequilibria generate divergence of δ13Cefflux from δ13CRs (termed "disequilibrium effect"). Here, we use a soil CO2 transport model and data from a 13CO2/12CO2 tracer experiment to quantify the disequilibrium between δ13Cefflux and δ13CRs in ecosystem respiration. The model accounted for diffusion of CO2 in soil air, advection of soil air, dissolution of CO2 in soil water, and belowground and aboveground respiration of both 12CO2 and 13CO2 isotopologues. The tracer data were obtained in a grassland ecosystem exposed to a δ13Catm of −46.9 ‰ during daytime for 2 weeks. Nighttime δ13Cefflux from the ecosystem was estimated with three independent methods: a laboratory-based cuvette system, in-situ steady-state open chambers, and in-situ closed chambers. Earlier work has shown that the δ13Cefflux measurements of the laboratory-based and steady-state systems were consistent, and likely reflected δ13CRs. Conversely, the δ13Cefflux measured using the closed chamber technique differed from these by −11.2 ‰. Most of this disequilibrium effect (9.5 ‰) was predicted by the CO2 transport model. Isotopic disequilibria in the soil-chamber system were introduced by changing δ13Catm in the chamber headspace at the onset of the measurements. When dissolution was excluded, the simulated disequilibrium effect was only 3.6 ‰. Dissolution delayed the isotopic equilibration between soil CO2 and the atmosphere, as the storage capacity for labelled CO2 in water-filled soil pores was 18 times that of soil air. These mechanisms are potentially relevant for many studies of δ13CRs in soils and ecosystems, including FACE experiments and chamber studies in natural conditions. Isotopic disequilibria in the soil-atmosphere system may result from temporal variation in δ13CRs or diurnal changes in the mole fraction and δ13C of atmospheric CO2. Dissolution effects are most important under alkaline conditions.

scholarly journals The potential for regional-scale bias in top-down CO<sub>2</sub> flux estimates due to atmospheric transport errors

2014 ◽  
Vol 14 (16) ◽  
pp. 23681-23709
Author(s):  
S. M. Miller ◽  
I. Fung ◽  
J. Liu ◽  
M. N. Hayek ◽  
A. E. Andrews
Keyword(s):  
Boundary Layer ◽  
Atmospheric Transport ◽  
Regional Scale ◽  
Co2 Flux ◽  
Atmospheric Co2 ◽  
Co2 Fluxes ◽  
Atmospheric Measurements ◽  
Meteorological Model ◽  
Co2 Transport

Abstract. Estimates of CO2 fluxes that are based on atmospheric data rely upon a meteorological model to simulate atmospheric CO2 transport. These models provide a quantitative link between surface fluxes of CO2 and atmospheric measurements taken downwind. Therefore, any errors in the meteorological model can propagate into atmospheric CO2 transport and ultimately bias the estimated CO2 fluxes. These errors, however, have traditionally been difficult to characterize. To examine the effects of CO2 transport errors on estimated CO2 fluxes, we use a global meteorological model-data assimilation system known as "CAM–LETKF" to quantify two aspects of the transport errors: error variances (standard deviations) and temporal error correlations. Furthermore, we develop two case studies. In the first case study, we examine the extent to which CO2 transport uncertainties can bias CO2 flux estimates. In particular, we use a common flux estimate known as CarbonTracker to discover the minimum hypothetical bias that can be detected above the CO2 transport uncertainties. In the second case study, we then investigate which meteorological conditions may contribute to month-long biases in modeled atmospheric transport. We estimate 6 hourly CO2 transport uncertainties in the model surface layer that range from 0.15 to 9.6 ppm (standard deviation), depending on location, and we estimate an average error decorrelation time of ∼2.3 days at existing CO2 observation sites. As a consequence of these uncertainties, we find that CarbonTracker CO2 fluxes would need to be biased by at least 29%, on average, before that bias were detectable at existing non-marine atmospheric CO2 observation sites. Furthermore, we find that persistent, bias-type errors in atmospheric transport are associated with consistent low net radiation, low energy boundary layer conditions. The meteorological model is not necessarily more uncertain in these conditions. Rather, the extent to which meteorological uncertainties manifest as persistent atmospheric transport biases appears to depend, at least in part, on the energy and stability of the boundary layer. Existing CO2 flux studies may be more likely to estimate inaccurate regional fluxes under those conditions.

scholarly journals Analysis of temporal and spatial variability of atmospheric CO<sub>2</sub> concentration within Paris from the GreenLITE™ laser imaging experiment

2019 ◽  
Vol 19 (22) ◽  
pp. 13809-13825 ◽  
Author(s):  
Jinghui Lian ◽  
François-Marie Bréon ◽  
Grégoire Broquet ◽  
T. Scott Zaccheo ◽  
Jeremy Dobler ◽  
...  
Keyword(s):  
Urban Canopy ◽  
Temporal Variations ◽  
Horizontal Resolution ◽  
Atmospheric Co2 ◽  
Laser Imaging ◽  
Near Surface ◽  
Co2 Concentrations ◽  
Temporal And Spatial ◽  
The City

Abstract. In 2015, the Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) measurement system was deployed for a long-duration experiment in the center of Paris, France. The system measures near-surface atmospheric CO2 concentrations integrated along 30 horizontal chords ranging in length from 2.3 to 5.2 km and covering an area of 25 km2 over the complex urban environment. In this study, we use this observing system together with six conventional in situ point measurements and the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and two urban canopy schemes (Urban Canopy Model – UCM; Building Effect Parameterization – BEP) at a horizontal resolution of 1 km to analyze the temporal and spatial variations in CO2 concentrations within the city of Paris and its vicinity for the 1-year period spanning December 2015 to November 2016. Such an analysis aims at supporting the development of CO2 atmospheric inversion systems at the city scale. Results show that both urban canopy schemes in the WRF-Chem model are capable of reproducing the seasonal cycle and most of the synoptic variations in the atmospheric CO2 point measurements over the suburban areas as well as the general corresponding spatial differences in CO2 concentration that span the urban area. However, within the city, there are larger discrepancies between the observations and the model results with very distinct features during winter and summer. During winter, the GreenLITE™ measurements clearly demonstrate that one urban canopy scheme (BEP) provides a much better description of temporal variations and horizontal differences in CO2 concentrations than the other (UCM) does. During summer, much larger CO2 horizontal differences are indicated by the GreenLITE™ system than both the in situ measurements and the model results, with systematic east–west variations.

In situ sensing of atmospheric CO2 with laser diodes near 2.05 μm: a spectroscopic study

2004 ◽  
Vol 45 (3) ◽  
pp. 229-237 ◽  
Author(s):  
V. Zéninari ◽  
A. Vicet ◽  
B. Parvitte ◽  
L. Joly ◽  
G. Durry
Keyword(s):  
Spectroscopic Study ◽  
Laser Diodes ◽  
Atmospheric Co2 ◽  
In Situ Sensing
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