Determining effective strategies for mitigating surface ozone (O3) pollution requires knowledge of the relative ambient concentrations of its precursors, NOx, and VOCs. The space‐based tropospheric column ratio of formaldehyde to NO2 (FNR) has been used as an indicator to identify NOx‐limited versus NOx‐saturated O3 formation regimes. Quantitative use of this indicator ratio is subject to three major uncertainties: (1) the split between NOx‐limited and NOx‐saturated conditions may shift in space and time, (2) the ratio of the vertically integrated column may not represent the near‐surface environment, and (3) satellite products contain errors. We use the GEOS‐Chem global chemical transport model to evaluate the quantitative utility of FNR observed from the Ozone Monitoring Instrument over three northern midlatitude source regions. We find that FNR in the model surface layer is a robust predictor of the simulated near‐surface O3 production regime. Extending this surface‐based predictor to a column‐based FNR requires accounting for differences in the HCHO and NO2 vertical profiles. We compare four combinations of two OMI HCHO and NO2 retrievals with modeled FNR. The spatial and temporal correlations between the modeled and satellite‐derived FNR vary with the choice of NO2 product, while the mean offset depends on the choice of HCHO product. Space‐based FNR indicates that the spring transition to NOx‐limited regimes has shifted at least a month earlier over major cities (e.g., New York, London, and Seoul) between 2005 and 2015. This increase in NOx sensitivity implies that NOx emission controls will improve O3 air quality more now than it would have a decade ago.

Abstract. Satellite remote sensing of the Earth's atmospheric composition usually samples irregularly in space and time, and many applications require spatially and temporally averaging the satellite observations (level 2) to a regular grid (level 3). When averaging level 2 data over a long period to a target level 3 grid that is significantly finer than the sizes of level 2 pixels, this process is referred to as “oversampling”. An agile, physics-based oversampling approach is developed to represent each satellite observation as a sensitivity distribution on the ground, instead of a point or a polygon as assumed in previous methods. This sensitivity distribution can be determined by the spatial response function of each satellite sensor. A generalized 2-D super Gaussian function is proposed to characterize the spatial response functions of both imaging grating spectrometers (e.g., OMI, OMPS, and TROPOMI) and scanning Fourier transform spectrometers (e.g., GOSAT, IASI, and CrIS). Synthetic OMI and IASI observations were generated to compare the errors due to simplifying satellite fields of view (FOVs) as polygons (tessellation error) and the errors due to discretizing the smooth spatial response function on a finite grid (discretization error). The balance between these two error sources depends on the target grid size, the ground size of the FOV, and the smoothness of spatial response functions. Explicit consideration of the spatial response function is favorable for fine-grid oversampling and smoother spatial response. For OMI, it is beneficial to oversample using the spatial response functions for grids finer than ∼16 km. The generalized 2-D super Gaussian function also enables smoothing of the level 3 results by decreasing the shape-determining exponents, which is useful for a high noise level or sparse satellite datasets. This physical oversampling approach is especially advantageous during smaller temporal windows and shows substantially improved visualization of trace gas distribution and local gradients when applied to OMI NO2 products and IASI NH3 products. There is no appreciable difference in the computational time when using the physical oversampling versus other oversampling methods.

Abstract. We present an algorithm for the retrieval of glyoxal from backscattered solar radiation, and apply it to spectra measured by the Ozone Monitoring Instrument (OMI). The algorithm is based on direct spectrum fitting, and adopts a two-step fitting routine to account for liquid water absorption. Previous studies have shown that glyoxal retrieval algorithms are highly sensitive to the position of the spectral fit window. This dependence was systematically tested on real and simulated OMI spectra. We find that a combination of errors resulting from uncertainties in reference cross sections and spectral features associated with the Ring effect are consistent with the fit-window dependence observed in real spectra. This implies an optimal fitting window of 435-461 nm, consistent with previous satellite glyoxal retrievals. The results from the retrieval of simulated spectra also support previous findings that have suggested that glyoxal is sensitive to NO 2 cross-section temperature. The retrieval window limits of the liquid water retrieval are also tested. A retrieval window 385-470 nm reduces interference with strong spectral features associated with sand. We show that cross-track dependent offsets (stripes) present in OMI can be corrected using offsets derived from retrieved slant columns over the Sahara, and apply the correction to OMI data. Average glyoxal columns are on average lower than those of previous studies likely owing to the choice of reference sector for offset correction. OMI VCDs (vertical column densities)are lower compared to other satellites over the tropics and Asia during the monsoon season, suggesting that the new retrieval is less sensitive to water vapour abundance. Consequently we do not see significant glyoxal enhancements over tropical oceans. OMI-derived glyoxal-to-formaldehyde ratios over biogenic and anthropogenic source regions are consistent with surface observations.

Abstract. To further our understanding of the effects of biomass burning emissions on atmospheric composition,

Detecting biosignatures, such as molecular oxygen in combination with a reducing gas, in the atmospheres of transiting exoplanets has been a major focus in the search for alien life. We point out that in addition to these generic indicators, anthropogenic pollution could be used as a novel biosignature for intelligent life. To this end, we identify pollutants in the Earth's atmosphere that have significant absorption features in the spectral range covered by the James Webb Space Telescope (JWST). We focus on tetrafluoromethane (CF 4 ) and trichlorofluoromethane (CCl 3 F), which are the easiest to detect chlorofluorocarbons (CFCs) produced by anthropogenic activity. We estimate that ∼ 1.2 days (∼ 1.7 days) of total integration time will be sufficient to detect or constrain the concentration of CCl 3 F (CF 4 ) to ∼ 10 times current terrestrial level.