CMAQ simulation of atmospheric CO2 concentration in East Asia: Comparison with GOSAT observations and ground measurements

Li, Rong; Zhang, Meigen; Chen, Liangfu; Kou, Xingxia; Skorokhod, A. I.

doi:10.1016/j.atmosenv.2017.03.056

Cited by 34 publications

(20 citation statements)

References 26 publications

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“…In this study, the optimised CO 2 fluxes from NOAA's Car-bonTracker version CT2016 (Peters et al, 2007, with updates documented at http://carbontracker.noaa.gov, last access: 14 January 2020) were used as surface CO 2 fluxes for CO 2 simulations. The temporal resolution of the surface flux is 3 h. Because of their ready availability and careful validation, many studies aimed at global to regional to urban scales have used optimised fluxes from CarbonTracker for forward CO 2 simulations (Houweling et al, 2010;Ballav et al, 2012;Díaz-Isaac et al, 2014;Polavarapu et al, 2016;Li et al, 2017;Wu et al, 2018).…”

Section: Surface Fluxmentioning

confidence: 99%

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

et al. 2020

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Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high-spatiotemporal-resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km×10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower-resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate the high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations based on comparisons to surface and aircraft observations. In addition, the bias and standard deviation of forecast error in boreal summer are reduced by the regional model. Better representation of model topography in the regional model results in improved simulation of the CO2 diurnal cycle compared to the global model at Walnut Grove, California. The new regional model will form the basis of a flux inversion system that estimates regional-scale fluxes of GHGs over Canada.

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Section: Surface Fluxmentioning

confidence: 99%

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

et al. 2020

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“…In this study, the optimised CO2 fluxes from NOAA's CarbonTracker, version CT2016 (Peters et al, 2007, with updates documented at http://carbontracker.noaa.gov) were used as surface CO2 fluxes for CO2 simulations. The temporal resolution of the surface flux is 3 h. Because of their ready availability and careful validation, many studies aimed at global to regional to urban scales have used optimized fluxes from CarbonTracker for forward CO2 simulations (Houweling et al, 2010;Ballav et al, 2012;Díaz-Isaac et al, 2014;Polavarapu et al, 2016;Li et al, 2017;Wu et al, 2018). 20…”

Section: Surface Flux 15mentioning

confidence: 99%

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM-MACH-GHG v.137-reg

Kim¹,

Polavarapu²,

Chan³

et al. 2019

Preprint

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Abstract. In this study, we present the development of a regional atmospheric transport model for greenhouse gas (GHG) simulation based on an operational weather forecast model and a chemical transport model at Environment and Climate Change Canada (ECCC), with the goal of improving our understanding of the high spatio-temporal resolution interaction between the atmosphere and surface GHG fluxes over Canada and the United States. The regional model uses 10 km × 10 km horizontal grid spacing and 80 vertical levels spanning the ground to 0.1 hPa. The lateral boundary conditions of meteorology and tracers are provided by the global transport model used for GHG simulation at ECCC. The performance of the regional model and added benefit of the regional model over our lower resolution global models is investigated in terms of modelled CO2 concentration and meteorological forecast quality for multiple seasons in 2015. We find that our regional model has the capability to simulate high spatial (horizontal and vertical) and temporal scales of atmospheric CO2 concentrations, based on comparisons to surface and aircraft observations. In addition, reduced bias and standard deviation of forecast error in boreal summer are obtained by the regional model. Better representation of model topography in the regional model reduces transport and representation errors significantly compared to the global model, especially in regions of complex topography, as revealed by the more precise and detailed structure of the CO2 diurnal cycle produced at observation sites and in model space. The new regional model will form the basis of a flux inversion system that estimates regional scale fluxes of GHGs over Canada.

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“…This phenomenon coincides with the seasonal drawdown of XCO 2 and therefore may be connected to the biospheric fluxes in the GEOS-Chem model. Li et al [18] stated that the uncertainty in the terrestrial biosphere has a strong effect on CO 2 simulations in summer. Additionally, the result from Table 2 indicates that monthly averaged biases (GEOS-Chem-GOSAT/ACOS) during March 2010-December 2011 ranging from −0.11 ppm to 1.23 ppm.…”

Section: The Seasonal Correlation Coefficients Of Xco2 Between Gosat/mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…To better estimate the uncertainty in CO 2 model inversion, several studies have also assessed the differences between observations and models [15,18,19]. Li et al [18] compared the differences of atmospheric CO 2 concentration in East Asia between the Community Multiscale Air Quality Modeling System (CMAQ) and GOSAT observations to evaluate the uncertainties in model simulation. Saito et al [20] reported that the latitude-time variations of XCO 2 , CH 4 and N 2 O and the transport processes in troposphere and stratosphere were well modeled by an atmospheric general circulation model (AGCM)-based chemistry-transport model (ACTM), which was confirmed through comparisons to TCCON (Total Carbon Column Observing Network).…”

Section: Introductionmentioning

confidence: 99%

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Global Atmospheric CO2 Concentrations Simulated by GEOS-Chem: Comparison with GOSAT, Carbon Tracker and Ground-Based Measurements

et al. 2018

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Accurate quantification of the distribution and variability of atmospheric CO 2 is crucial for a better understanding of global carbon cycle characteristics and climate change. Model simulation and observations are only two ways to globally estimate CO 2 concentrations and fluxes. However, large uncertainties still exist. Therefore, quantifying the differences between model and observations is rather helpful for reducing their uncertainties and further improving model estimations of global CO 2 sources and sinks. In this paper, the GEOS-Chem model was selected to simulate CO 2 concentration and then compared with the Greenhouse Gases Observing Satellite (GOSAT) observations, CarbonTracker (CT) and the Total Carbon Column Observing Network (TCCON) measurements during 2009-2011 for quantitatively evaluating the uncertainties of CO 2 simulation. The results revealed that the CO 2 simulated from GEOS-Chem is in good agreement with other CO 2 data sources, but some discrepancies exist including: (1) compared with GOSAT retrievals, modeled XCO 2 from GEOS-Chem is somewhat overestimated, with 0.78 ppm on average; (2) compared with CT, the simulated XCO 2 from GEOS-Chem is slightly underestimated at most regions, although their time series and correlation show pretty good consistency; (3) compared with the TCCON sites, modeled XCO 2 is also underestimated within 1 ppm at most sites, except at Garmisch, Karlsruhe, Sodankylä and Ny-Ålesund. Overall, the results demonstrate that the modeled XCO 2 is underestimated on average, however, obviously overestimated XCO 2 from GEOS-Chem were found at high latitudes of the Northern Hemisphere in summer. These results are helpful for understanding the model uncertainties as well as to further improve the CO 2 estimation.

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CMAQ simulation of atmospheric CO2 concentration in East Asia: Comparison with GOSAT observations and ground measurements

Cited by 34 publications

References 26 publications

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM–MACH–GHG v.137-reg

The Canadian atmospheric transport model for simulating greenhouse gas evolution on regional scales: GEM-MACH-GHG v.137-reg

Global Atmospheric CO2 Concentrations Simulated by GEOS-Chem: Comparison with GOSAT, Carbon Tracker and Ground-Based Measurements

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