scholarly journals Modeling global atmospheric CO<sub>2</sub> with improved emission inventories and CO<sub>2</sub> production from the oxidation of other carbon species

2010 ◽  
Vol 3 (2) ◽  
pp. 689-716 ◽  
Author(s):  
R. Nassar ◽  
D. B. A. Jones ◽  
P. Suntharalingam ◽  
J. M. Chen ◽  
R. J. Andres ◽  
...  

Abstract. The use of global three-dimensional (3-D) models with satellite observations of CO2 in inverse modeling studies is an area of growing importance for understanding Earth's carbon cycle. Here we use the GEOS-Chem model (version 8-02-01) CO2 mode with multiple modifications in order to assess their impact on CO2 forward simulations. Modifications include CO2 surface emissions from shipping (~0.19 Pg C yr−1), 3-D spatially-distributed emissions from aviation (~0.16 Pg C yr−1), and 3-D chemical production of CO2 (~1.05 Pg C yr−1). Although CO2 chemical production from the oxidation of CO, CH4 and other carbon gases is recognized as an important contribution to global CO2, it is typically accounted for by conversion from its precursors at the surface rather than in the free troposphere. We base our model 3-D spatial distribution of CO2 chemical production on monthly-averaged loss rates of CO (a key precursor and intermediate in the oxidation of organic carbon) and apply an associated surface correction for inventories that have counted emissions of CO2 precursors as CO2. We also explore the benefit of assimilating satellite observations of CO into GEOS-Chem to obtain an observation-based estimate of the CO2 chemical source. The CO assimilation corrects for an underestimate of atmospheric CO abundances in the model, resulting in increases of as much as 24% in the chemical source during May–June 2006, and increasing the global annual estimate of CO2 chemical production from 1.05 to 1.18 Pg C. Comparisons of model CO2 with measurements are carried out in order to investigate the spatial and temporal distributions that result when these new sources are added. Inclusion of CO2 emissions from shipping and aviation are shown to increase the global CO2 latitudinal gradient by just over 0.10 ppm (~3%), while the inclusion of CO2 chemical production (and the surface correction) is shown to decrease the latitudinal gradient by about 0.40 ppm (~10%) with a complex spatial structure generally resulting in decreased CO2 over land and increased CO2 over the oceans. Since these CO2 emissions are omitted or misrepresented in most inverse modeling work to date, their implementation in forward simulations should lead to improved inverse modeling estimates of terrestrial biospheric fluxes.

2010 ◽  
Vol 3 (3) ◽  
pp. 889-948 ◽  
Author(s):  
R. Nassar ◽  
D. B. A. Jones ◽  
P. Suntharalingam ◽  
J. M. Chen ◽  
R. J. Andres ◽  
...  

Abstract. The use of global three-dimensional (3-D) models with satellite observations of CO2 in inverse modeling studies is an area of growing importance for understanding Earth's carbon cycle. Here we use the GEOS-Chem model (version 8-02-01) CO2 simulation with multiple modifications in order to assess their impact on CO2 forward simulations. Modifications include CO2 surface emissions from shipping (~0.19 Pg C/yr), 3-D spatially-distributed emissions from aviation (~0.16 Pg C/yr), and 3-D chemical production of CO2 (~1.05 Pg C/yr). Although CO2 chemical production from the oxidation of CO, CH4 and other carbon gases is recognized as an important contribution to global CO2, it is typically accounted for by conversion from its precursors at the surface rather than in the free troposphere. We base our model 3-D spatial distribution of CO2 chemical production on monthly-averaged loss rates of CO (a key precursor and intermediate in the oxidation of organic carbon) and apply an associated surface correction for inventories that have counted emissions of carbon precursor as CO2. We also explore the benefit of assimilating satellite observations of CO into GEOS-Chem to obtain an observation-based estimate of the CO2 chemical source. The CO assimilation corrects for an underestimate of atmospheric CO abundances in the model, resulting in increases of as much as 24% in the chemical source during May–June 2006, and increasing the global annual estimate of CO2 chemical production from 1.05 to 1.18 Pg C. Comparisons of model CO2 with measurements are carried out in order to investigate the spatial and temporal distributions that result when these new sources are added. Inclusion of CO2 emissions from shipping and aviation are shown to increase the global CO2 latitudinal gradient by just over 0.10 ppm (~3%), while the inclusion of CO2 chemical production (and the surface correction) is shown to decrease the latitudinal gradient by about 0.40 ppm (~10%) with a complex spatial structure generally resulting in decreased CO2 over land and increased CO2 over the oceans. Since these CO2 emissions are omitted or misrepresented in most inverse modeling work to date, their implementation in forward simulations should lead to improved inverse modeling estimates of terrestrial biospheric fluxes.


1996 ◽  
Vol 33 (4-5) ◽  
pp. 233-240 ◽  
Author(s):  
F. S. Goderya ◽  
M. F. Dahab ◽  
W. E. Woldt ◽  
I. Bogardi

A methodology for incorporation of spatial variability in modeling non-point source groundwater nitrate contamination is presented. The methodology combines geostatistical simulation and unsaturated zone modeling for estimating the amount of nitrate loading to groundwater. Three dimensional soil nitrogen variability and 2-dimensional crop yield variability are used in quantifying potential benefits of spatially distributed nitrogen input. This technique, in combination with physical and chemical measurements, is utilized as a means of illustrating how the spatial statistical properties of nitrate leaching can be obtained for different scenarios of fixed and variable rate nitrogen applications.


Author(s):  
Lingsheng Meng ◽  
Chi Yan ◽  
Wei Zhuang ◽  
Weiwei Zhang ◽  
Xiao‐Hai Yan

Author(s):  
Olga Petrenko ◽  
Mateu Sbert ◽  
Olivier Terraz ◽  
Djamchid Ghazanfarpour

Flowers belong to one of the natural phenomena that cannot be captured completely, as there is enormous variety of shapes both within and between individuals. The authors propose a procedural modeling of flowering plants using an extension of L-Systems – a model based on three-dimensional generalized maps. Conventionally, in order to build a model the user has to write the grammar, which consists of the description of 3Gmaps and all the production rules. The process of writing a grammar is usually quite laborious and tedious. In order to avoid this the authors propose new interface functionality: the inverse modeling by automatic generation of L-systems. The user describes the flower he wants to model, by assigning the properties of its organs. The algorithm uses this information as an input, which is then analyzed and coded as L-systems grammar.


2016 ◽  
Author(s):  
Dhanyalekshmi Pillai ◽  
Michael Buchwitz ◽  
Christoph Gerbig ◽  
Thomas Koch ◽  
Maximilian Reuter ◽  
...  

Abstract. Currently 52 % of the world's population resides in urban areas and as a consequence, approximately 70 % of fossil fuel emissions of CO2 arise from cities. This fact in combination with large uncertainties associated with quantifying urban emissions due to lack of appropriate measurements makes it crucial to obtain new measurements useful to identify and quantify urban emissions. This is required, for example, for the assessment of emission mitigation strategies and their effectiveness. Here we investigate the potential of a satellite mission like Carbon Monitoring Satellite (CarbonSat), proposed to the European Space Agency (ESA) – to retrieve the city emissions globally, taking into account a realistic description of the expected retrieval errors, the spatiotemporal distribution of CO2 fluxes, and atmospheric transport. To achieve this we use (i) a high-resolution modeling framework consisting of the Weather Research Forecasting model with a greenhouse gas module (WRF-GHG), which is used to simulate the atmospheric observations of column averaged CO2 dry air mole fractions (XCO2), and (ii) a Bayesian inversion method to derive anthropogenic CO2 emissions and their errors from the CarbonSat XCO2 observations. We focus our analysis on Berlin in Germany using CarbonSat's cloud-free overpasses for one reference year. The dense (wide swath) CarbonSat simulated observations with high-spatial resolution (approx. 2 km × 2 km) permits one to map the city CO2 emission plume with a peak enhancement of typically 0.8–1.35 ppm relative to the background. By performing a Bayesian inversion, it is shown that the random error (RE) of the retrieved Berlin CO2 emission for a single overpass is typically less than 8 to 10 MtCO2 yr−1 (about 15 to 20 % of the total city emission). The range of systematic errors (SE) of the retrieved fluxes due to various sources of error (measurement, modeling, and inventories) is also quantified. Depending on the assumptions made, the SE is less than about 6 to 10 MtCO2 yr−1 for most cases. We find that in particular systematic modeling-related errors can be quite high during the summer months due to substantial XCO2 variations caused by biogenic CO2 fluxes at and around the target region. When making the extreme worst-case assumption that biospheric XCO2 variations cannot be modeled at all (which is overly pessimistic), the SE of the retrieved emission is found to be larger than 10 MtCO2 yr−1 for about half of the sufficiently cloud-free overpasses, and for some of the overpasses we found that SE may even be on the order of magnitude of the anthropogenic emission. This indicates that biogenic XCO2 variations cannot be neglected but must be considered during forward and/or inverse modeling. Overall, we conclude that CarbonSat is well suited to obtain city-scale CO2 emissions as needed to enhance our current understanding of anthropogenic carbon fluxes and that CarbonSat or CarbonSat-like satellites should be an important component of a future global carbon emission monitoring system.


2009 ◽  
Vol 9 (19) ◽  
pp. 7313-7323 ◽  
Author(s):  
H. Wang ◽  
D. J. Jacob ◽  
M. Kopacz ◽  
D. B. A. Jones ◽  
P. Suntharalingam ◽  
...  

Abstract. Inverse modeling of CO2 satellite observations to better quantify carbon surface fluxes requires a chemical transport model (CTM) to relate the fluxes to the observed column concentrations. CTM transport error is a major source of uncertainty. We show that its effect can be reduced by using CO satellite observations as additional constraint in a joint CO2-CO inversion. CO is measured from space with high precision, is strongly correlated with CO2, and is more sensitive than CO2 to CTM transport errors on synoptic and smaller scales. Exploiting this constraint requires statistics for the CTM transport error correlation between CO2 and CO, which is significantly different from the correlation between the concentrations themselves. We estimate the error correlation globally and for different seasons by a paired-model method (comparing GEOS-Chem CTM simulations of CO2 and CO columns using different assimilated meteorological data sets for the same meteorological year) and a paired-forecast method (comparing 48- vs. 24-h GEOS-5 CTM forecasts of CO2 and CO columns for the same forecast time). We find strong error correlations (r2>0.5) between CO2 and CO columns over much of the extra-tropical Northern Hemisphere throughout the year, and strong consistency between different methods to estimate the error correlation. Application of the averaging kernels used in the retrieval for thermal IR CO measurements weakens the correlation coefficients by 15% on average (mostly due to variability in the averaging kernels) but preserves the large-scale correlation structure. We present a simple inverse modeling application to demonstrate that CO2-CO error correlations can indeed significantly reduce uncertainty on surface carbon fluxes in a joint CO2-CO inversion vs. a CO2-only inversion.


Author(s):  
Xiaocong Xu ◽  
Jinpei Ou ◽  
Penghua Liu ◽  
Xiaoping Liu ◽  
Honghui Zhang

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