Abstract
We examine the capability of the Global Modeling Initiative (GMI) chemistry and transport model to reproduce global mid-tropospheric (618hPa) O3-CO correlations determined by the measurements from Tropospheric Emission Spectrometer (TES) aboard NASA's Aura satellite during boreal summer (July-August). The model is driven by three meteorological data sets (fvGCM with sea surface temperature for 1995, GEOS4-DAS for 2005, and MERRA for 2005), allowing us to examine the sensitivity of model O3-CO correlations to input meteorological data. Model simulations of radionuclide tracers (222Rn, 210Pb, and 7Be) are used to illustrate the differences in transport-related processes among the meteorological data sets. Simulated O3 values are evaluated with climatological ozone profiles from ozonesonde measurements and satellite tropospheric O3 columns. Despite the fact that three simulations show significantly different global and regional distributions of O3 and CO concentrations, all simulations show similar patterns of O3-CO correlations on a global scale. These patterns are consistent with those derived from TES observations, except in the tropical easterly biomass burning outflow regions. Discrepancies in regional O3-CO correlation patterns in the three simulations may be attributed to differences in convective transport, stratospheric influence, and subsidence, among other processes. To understand how various emissions drive global O3-CO correlation patterns, we examine the sensitivity of GMI/MERRA model-calculated O3 and CO concentrations and their correlations to emission types (fossil fuel, biomass burning, biogenic, and lightning NOx emissions). Fossil fuel and biomass burning emissions are mainly responsible for the strong positive O3-CO correlations over continental outflow regions in both hemispheres. Biogenic emissions have a relatively smaller impact on O3-CO correlations than other emissions, but are largely responsible for the negative correlations over the tropical eastern Pacific, reflecting the fact that O3 is consumed and CO generated during the atmospheric oxidation process of isoprene under low NOx conditions. We find that lightning NOx emissions degrade both positive correlations at mid-/high- latitudes and negative correlations in the tropics because ozone production downwind of lightning NOx emissions is not directly related to the emission and transport of CO. Our study concludes that O3-CO correlations may be used effectively to constrain the sources of regional tropospheric O3 in global 3-D models, especially for those regions where convective transport of pollution plays an important role.
Highlights
Ozone (O3) is an important greenhouse gas in the troposphere and a pollutant at the surface
In order to understand how ozone–carbon monoxide (O3–carbon monoxide (CO)) correlation patterns are driven by emissions, we examine the sensitivity of O3–CO correlations to emission types in the Global Modeling Initiative (GMI) model driven by the Modern-Era Retrospective Analysis for Research and Applications (MERRA) meteorological fields, which represent the state of the art of GEOS DAS at the time of this study
We have examined the capability of the GMI chemistry and transport models (CTMs) to reproduce the global mid-tropospheric O3–CO correlations from the Tropospheric Emission Spectrometer (TES) instrument on board the NASA Aura satellite during boreal summer (July–August)
Summary
Ozone (O3) is an important greenhouse gas in the troposphere and a pollutant at the surface. Shim et al (2009) examined the Mexico City pollution outflow using O3, CO, and their correlations from TES as well as aircraft measurements obtained during the Megacity Initiative: Local and Global Research Observations (MILAGRO) and Intercontinental Chemical Transport Experiment (INTEX-B) campaigns. These investigations found that TES data are characterized by smaller O3–CO correlation coefficients but larger linear regression slopes than in situ observations at 618 hPa partly due to the lack of variability in TES CO. This paper is organized as follows: Sect. 2: descriptions of the GMI model, meteorological data sets, and observational data sets; Sect. 3: presentation of the model simulations of radionuclides, O3, and CO; Sect. 4: evaluation of GMI O3 and CO simulations with observations; Sect. 5: evaluation of GMI O3–CO correlations with satellite observations from TES; Sect. 6: analysis of the effects of various emission types on the model-simulated O3–CO correlations; and Sect. 7: summary and conclusions
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