scholarly journals Improved simulation of group averaged CO<sub>2</sub> surface concentrations using GEOS-Chem and fluxes from VEGAS

2013 ◽  
Vol 13 (1) ◽  
pp. 2243-2271
Author(s):  
Z. H. Chen ◽  
J. Zhu ◽  
N. Zeng
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Surface Characteristics ◽  
Co2 Concentration ◽  
Regional Model ◽  
Surface Fluxes ◽  
Persistent Problem ◽  
Model Simulations ◽  
The Difference ◽  
Grid Points

Abstract. CO2 measurements have been combined with simulated CO2 distributions from a transport model in order to produce the optimal estimates of CO2 surface fluxes in inverse modeling. However one persistent problem in using model-observation comparisons for this goal relates to the issue of compatibility. Observations at a single site reflect all underlying processes of various scales that usually cannot be fully resolved by model simulations at the grid points nearest the site due to lack of spatial or temporal resolution or missing processes in models. In this article we group site observations of multiple stations according to atmospheric mixing regimes and surface characteristics. The group averaged values of CO2 concentration from model simulations and observations are used to evaluate the regional model results. Using the group averaged measurements of CO2 reduces the noise of individual stations. The difference of group averaged values between observation and modeled results reflects the uncertainties of the large scale flux in the region where the grouped stations are. We compared the group averaged values between model results with two biospheric fluxes from the model Carnegie-Ames-Stanford-Approach (CASA) and VEgetation-Global-Atmosphere-Soil (VEGAS) and observations to evaluate the regional model results. Results show that the modeling group averaged values of CO2 concentrations in all regions with fluxes from VEGAS have significant improvements for most regions. There is still large difference between two model results and observations for grouped average values in North Atlantic, Indian Ocean, and South Pacific Tropics. This implies possible large uncertainties in the fluxes there.

scholarly journals Improved simulation of regional CO<sub>2</sub> surface concentrations using GEOS-Chem and fluxes from VEGAS

2013 ◽  
Vol 13 (15) ◽  
pp. 7607-7618 ◽  
Author(s):  
Z. H. Chen ◽  
J. Zhu ◽  
N. Zeng
Keyword(s):  
North American ◽  
Large Scale ◽  
Transport Model ◽  
Co2 Concentration ◽  
Regional Model ◽  
Surface Fluxes ◽  
Persistent Problem ◽  
Model Simulations ◽  
Co2 Concentrations ◽  
Single Station

Abstract. CO2 measurements have been combined with simulated CO2 distributions from a transport model in order to produce the optimal estimates of CO2 surface fluxes in inverse modeling. However, one persistent problem in using model–observation comparisons for this goal relates to the issue of compatibility. Observations at a single station reflect all underlying processes of various scales. These processes usually cannot be fully resolved by model simulations at the grid points nearest the station due to lack of spatial or temporal resolution or missing processes in the model. In this study the stations in one region were grouped based on the amplitude and phase of the seasonal cycle at each station. The regionally averaged CO2 at all stations in one region represents the regional CO2 concentration of this region. The regional CO2 concentrations from model simulations and observations were used to evaluate the regional model results. The difference of the regional CO2 concentration between observation and modeled results reflects the uncertainty of the large-scale flux in the region where the grouped stations are. We compared the regional CO2 concentrations between model results with biospheric fluxes from the Carnegie-Ames-Stanford Approach (CASA) and VEgetation-Global-Atmosphere-Soil (VEGAS) models, and used observations from GLOBALVIEW-CO2 to evaluate the regional model results. The results show the largest difference of the regionally averaged values between simulations with fluxes from VEGAS and observations is less than 5 ppm for North American boreal, North American temperate, Eurasian boreal, Eurasian temperate and Europe, which is smaller than the largest difference between CASA simulations and observations (more than 5 ppm). There is still a large difference between two model results and observations for the regional CO2 concentration in the North Atlantic, Indian Ocean, and South Pacific tropics. The regionally averaged CO2 concentrations will be helpful for comparing CO2 concentrations from modeled results and observations and evaluating regional surface fluxes from different methods.

scholarly journals Three-dimensional variations of atmospheric CO<sub>2</sub>: aircraft measurements and multi-transport model simulations

2011 ◽  
Vol 11 (24) ◽  
pp. 13359-13375 ◽  
Author(s):  
Y. Niwa ◽  
P. K. Patra ◽  
Y. Sawa ◽  
T. Machida ◽  
H. Matsueda ◽  
...  
Keyword(s):  
Transport Model ◽  
Three Dimensional ◽  
Co2 Flux ◽  
Surface Flux ◽  
Co2 Concentration ◽  
Surface Fluxes ◽  
Latitudinal Gradients ◽  
Dimensional Structure ◽  
Free Troposphere ◽  
Model Simulations

Abstract. Numerical simulation and validation of three-dimensional structure of atmospheric carbon dioxide (CO2) is necessary for quantification of transport model uncertainty and its role on surface flux estimation by inverse modeling. Simulations of atmospheric CO2 were performed using four transport models and two sets of surface fluxes compared with an aircraft measurement dataset of Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL), covering various latitudes, longitudes, and heights. Under this transport model intercomparison project, spatiotemporal variations of CO2 concentration for 2006–2007 were analyzed with a three-dimensional perspective. Results show that the models reasonably simulated vertical profiles and seasonal variations not only over northern latitude areas but also over the tropics and southern latitudes. From CONTRAIL measurements and model simulations, intrusion of northern CO2 in to the Southern Hemisphere, through the upper troposphere, was confirmed. Furthermore, models well simulated the vertical propagation of seasonal variation in the northern free troposphere. However, significant model-observation discrepancies were found in Asian regions, which are attributable to uncertainty of the surface CO2 flux data. In summer season, differences in latitudinal gradients by the fluxes are comparable to or greater than model-model differences even in the free troposphere. This result suggests that active summer vertical transport sufficiently ventilates flux signals up to the free troposphere and the models could use those for inferring surface CO2 fluxes.

scholarly journals Error correlation between CO<sub>2</sub> and CO as constraint for CO<sub>2</sub> flux inversions using satellite data

2009 ◽  
Vol 9 (19) ◽  
pp. 7313-7323 ◽  
Author(s):  
H. Wang ◽  
D. J. Jacob ◽  
M. Kopacz ◽  
D. B. A. Jones ◽  
P. Suntharalingam ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Large Scale ◽  
Transport Model ◽  
Meteorological Data ◽  
Correlation Coefficients ◽  
Surface Fluxes ◽  
Satellite Observations ◽  
Carbon Surface ◽  
Chemical Transport Model ◽  
Error Correlation

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.

scholarly journals Quantitative evaluation of the uncertainty sources for the modeling of atmospheric CO<sub>2</sub> concentration within and in the vicinity of Paris city

2020 ◽  
Author(s):  
Jinghui Lian ◽  
François-Marie Bréon ◽  
Grégoire Broquet ◽  
Bo Zheng ◽  
Michel Ramonet ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Co2 Emissions ◽  
Large Scale ◽  
Transport Model ◽  
Co2 Concentration ◽  
Inversion Method ◽  
Different Boundary Conditions ◽  
Paris Area ◽  
The City ◽  
Atmospheric Inversion

Abstract. The top-down atmospheric inversion method that couples atmospheric CO2 observations with an atmospheric transport model has been used extensively to quantify CO2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO2 that are of different origins than the targeted CO2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of one-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations and CO2 boundary conditions. The simulated CO2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime observation-model misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Our results suggest three selection criteria for the CO2 data to be assimilated for the inversion of CO2 emissions from Paris (i) discard data that appear as statistical outliers in the model-data misfits which are interpreted as model's deficiencies under complex meteorological conditions; (ii) use only afternoon urban measurements in winter and suburban ones in summer; (iii) test the influence of different boundary conditions in inversions. If possible, using additional observations to constrain the boundary inflow, or using CO2 gradients of upwind-downwind stations, rather than absolute CO2 concentration, as atmospheric inversion inputs.

scholarly journals Leveraging the signature of heterotrophic respiration on atmospheric CO<sub>2</sub> for model benchmarking

2020 ◽  
Vol 17 (5) ◽  
pp. 1293-1308 ◽  
Author(s):  
Samantha J. Basile ◽  
Xin Lin ◽  
William R. Wieder ◽  
Melannie D. Hartman ◽  
Gretchen Keppel-Aleks
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
Heterotrophic Respiration ◽  
Atmospheric Co2 ◽  
Scale Model ◽  
Future Research ◽  
Tracer Transport ◽  
Test Bed ◽  
Model Simulations ◽  
Soil Heterotrophic Respiration

Abstract. Spatial and temporal variations in atmospheric carbon dioxide (CO2) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange, but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS, and CORPSE) that simulate HR fluxes within a biogeochemical test bed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil test-bed models) on atmospheric CO2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger-than-observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO2, in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

scholarly journals Error correlation between CO<sub>2</sub> and CO as constraint for CO<sub>2</sub> flux inversions using satellite data

2009 ◽  
Vol 9 (3) ◽  
pp. 11783-11810
Author(s):  
H. Wang ◽  
D. J. Jacob ◽  
M. Kopacz ◽  
D. B. A. Jones ◽  
P. Suntharalingam ◽  
...  
Keyword(s):  
Inverse Modeling ◽  
Large Scale ◽  
Transport Model ◽  
Meteorological Data ◽  
Co2 Flux ◽  
Surface Fluxes ◽  
Satellite Observations ◽  
Carbon Surface ◽  
Chemical Transport Model ◽  
Error Correlation

Abstract. Inverse modeling of CO2 satellite observations to better quantify carbon surface fluxes requires a forward model such as a chemical transport model (CTM) to relate the fluxes to the observed column concentrations. Model transport error is an important source of observational error. We investigate the potential of using CO satellite observations as additional constraints in a joint CO2–CO inversion to improve CO2 flux estimates, by exploiting the CTM transport error correlations between CO2 and CO. We estimate the error correlation globally and for different seasons by a paired-model method (comparing 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 CTM forecasts of CO2 and CO columns for the same forecast time). We find strong positive and negative error correlations (r2>0.5) between CO2 and CO columns over much of the world 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. Results from a testbed inverse modeling application show 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.

scholarly journals Exploring the inorganic composition of the Asian Tropopause Aerosol Layer using medium-duration balloon flights

2021 ◽  
Author(s):  
Hazel Vernier ◽  
Neeraj Rastogi ◽  
Hongyu Liu ◽  
Amit Kumar Pandit ◽  
Kris Bedka ◽  
...  
Keyword(s):  
Particle Size ◽  
Large Scale ◽  
Transport Model ◽  
Large Fraction ◽  
Western China ◽  
Aerosol Layer ◽  
Fire Event ◽  
Air Mass ◽  
Air Masses ◽  
Model Simulations

Abstract. Satellite observations have revealed an enhanced aerosol layer near the tropopause over Asia during the summer monsoon, called the Asian Tropopause Aerosol Layer (ATAL). In this work, aerosol particles in the ATAL were collected with a balloon-borne impactor near the tropopause region over India, using extended duration balloon flights, in summer 2017 and winter 2018. Their chemical composition was further investigated by quantitative analysis using offline ion chromatography. Nitrate (NO3−) and nitrite (NO2−) were found to be the dominant ions in the collected aerosols with values ranging between 87–343 ng/m3 STP during the summer campaign. In contrast, sulfate (SO42−) levels were found above the detection limit (> 10 ng/m3 STP) only in winter. In addition, we determined the origin of the air masses sampled during the flights through analysis of back trajectories along with convective influence. The results obtained therein were put into a context of large-scale transport and aerosol distribution with GEOS-Chem chemical transport model simulations. The first flight of summer 2017 which sampled air mass within the Asian monsoon anticyclone (AMA), influenced by convection over Western China, was associated with particle size radius (0.05–2 μm). In contrast, the second flight sampled air mass at the edge of the AMA associated with larger particle size radius (> 2 μm) with higher nitrite concentration. The sampled air masses in winter 2018 were likely affected by smoke from the Pacific Northwest fire event in Canada, which occurred 7 months prior to our campaign, leading to concentration enhancements of SO42− and Ca2+. Overall, our results suggest that nitrogen-containing particles represent a large fraction of aerosols populating the ATAL, in agreement with the results from aircraft measurements during the StratoClim campaign. Furthermore, GEOS-Chem model simulations suggest that lightning NOx emissions had a significant impact on the production of nitrate aerosols sampled during the summer 2017.

scholarly journals Evolution of aerosol optical thickness over Europe during the August 2003 heat wave as seen from CHIMERE model simulations and POLDER data

2006 ◽  
Vol 6 (7) ◽  
pp. 1853-1864 ◽  
Author(s):  
A. Hodzic ◽  
R. Vautard ◽  
H. Chepfer ◽  
P. Goloub ◽  
L. Menut ◽  
...  
Keyword(s):  
Optical Thickness ◽  
Forest Fires ◽  
Atmospheric Aerosol ◽  
Large Scale ◽  
Transport Model ◽  
Anthropogenic Sources ◽  
Chemistry Transport Model ◽  
Model Simulations ◽  
High Particle ◽  
Sun Photometer

Abstract. This study describes the atmospheric aerosol load encountered during the large-scale pollution episode that occurred in August 2003, by means of the aerosol optical thicknesses (AOTs) measured at 865 nm by the Polarization and Directionality of the Earth's Reflectances (POLDER) sensor and the simulation by the CHIMERE chemistry-transport model. During this period many processes (stagnation, photochemistry, forest fires) led to unusually high particle concentrations and optical thicknesses. The observed/simulated AOT comparison helps understanding the ability of the model to reproduce most of the gross AOT features observed in satellite data, with a general agreement within a factor 2 and correlations in the 0.4–0.6 range. However some important aerosol features are missed when using regular anthropogenic sources. Additional simulations including emissions and high-altitude transport of smoke from wildfires that occurred in Portugal indicate that these processes could dominate the AOT signal in some areas. Our results also highlight the difficulties of comparing simulated and POLDER-derived AOTs due to large uncertainties in both cases. Observed AOT values are significantly lower than the simulated ones (30–50%). Their comparison with the ground-based Sun photometer Aerosol Robotic Network (AERONET) measurements suggests, for the European sites considered here, an underestimation of POLDER-derived aerosol levels with a factor between 1 and 2. AERONET AOTs compare better with simulations (no particular bias) than POLDER AOTs.

scholarly journals New concepts for the comparison of tropospheric NO<sub>2</sub> column densities derived from car-MAX-DOAS observations, OMI satellite observations and the regional model CHIMERE during two MEGAPOLI campaigns in Paris 2009/10

2015 ◽  
Vol 8 (3) ◽  
pp. 2437-2500
Author(s):  
R. Shaiganfar ◽  
S. Beirle ◽  
H. Petetin ◽  
Q. Zhang ◽  
M. Beekmann ◽  
...  
Keyword(s):  
Power Plants ◽  
Transport Model ◽  
Spatial Scales ◽  
Regional Model ◽  
Satellite Observations ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Ozone Monitoring Instrument ◽  
Model Simulations ◽  
Comparison Results

Abstract. We compare tropospheric column densities (vertically integrated concentrations) of NO2 from three data sets for the metropolitan area of Paris during two extensive measurement campaigns (25 days in summer 2009 and 29 days in winter 2010) within the European research project MEGAPOLI. The selected data sets comprise a regional chemical transport model (CHIMERE) as well as two observational data sets: ground based mobile Multi-AXis-Differential Optical Absorption Spectroscopy (car-MAX-DOAS) measurements and satellite measurements from the Ozone Monitoring Instrument (OMI). On most days, car-MAX-DOAS measurements were carried out along large circles (diameter ~35 km) around Paris. The car-MAX-DOAS results are compared to coincident data from CHIMERE and OMI. All three data sets have their specific strengths and weaknesses, especially with respect to their spatio-temporal resolution and coverage as well as their uncertainties. Thus we compare them in two different ways: first, we simply consider the original data sets. Second, we compare modified versions making synergistic use of the complementary information from different data sets. For example, profile information from the regional model is used to improve the satellite data, observations of the horizontal trace gas distribution are used to adjust the respective spatial patterns of the model simulations, or the model is used as a transfer tool to bridge the spatial scales between car-MAX-DOAS and satellite observations. Using the modified versions of the data sets, the comparison results substantially improve compared to the original versions. In general, good agreement between the data sets is found outside the emission plume, but inside the emission plumes the tropospheric NO2 VCDs are systematically underestimated by the CHIMERE model and the satellite observations (compared to the car-MAX-DOAS observations). One major result from our study is that for satellite validation close to strong emission sources (like power plants or megacities) detailed information about the intra-pixel heterogeneity is essential. Such information may be gained from simultaneous car-MAX-DOAS measurements using multiple instruments or by combining (car-) MAX-DOAS measurements with results from regional model simulations.

scholarly journals Leveraging the signature of heterotrophic respiration on atmospheric CO<sub>2</sub> for model benchmarking

2019 ◽  
Author(s):  
Samantha J. Basile ◽  
Xin Lin ◽  
William R. Wieder ◽  
Melannie D. Hartman ◽  
Gretchen Keppel-Aleks
Keyword(s):  
Large Scale ◽  
Net Primary Productivity ◽  
Transport Model ◽  
Heterotrophic Respiration ◽  
Atmospheric Co2 ◽  
Scale Model ◽  
Future Research ◽  
Tracer Transport ◽  
Model Simulations ◽  
Soil Heterotrophic Respiration

Abstract. Spatial and temporal variations in atmospheric carbon dioxide (CO2) reflect large-scale net carbon exchange between the atmosphere and terrestrial ecosystems. Soil heterotrophic respiration (HR) is one of the component fluxes that drive this net exchange but, given observational limitations, it is difficult to quantify this flux or to evaluate global-scale model simulations thereof. Here, we show that atmospheric CO2 can provide a useful constraint on large-scale patterns of soil heterotrophic respiration. We analyze three soil model configurations (CASA-CNP, MIMICS and CORPSE) that simulate HR fluxes within a biogeochemical testbed that provides each model with identical net primary productivity (NPP) and climate forcings. We subsequently quantify the effects of variation in simulated terrestrial carbon fluxes (NPP and HR from the three soil testbed models) on atmospheric CO2 distributions using a three-dimensional atmospheric tracer transport model. Our results show that atmospheric CO2 observations can be used to identify deficiencies in model simulations of the seasonal cycle and interannual variability in HR relative to NPP. In particular, the two models that explicitly simulated microbial processes (MIMICS and CORPSE) were more variable than observations at interannual timescales and showed a stronger than observed temperature sensitivity. Our results prompt future research directions to use atmospheric CO2, in combination with additional constraints on terrestrial productivity or soil carbon stocks, for evaluating HR fluxes.

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