Person:

Chance, Kelly

Space-borne formaldehyde (HCHO) column measurements from the Ozone Monitoring Instrument (OMI), with 13 × 24 km2 nadir footprint and daily global coverage, provide new constraints on the spatial distribution of biogenic isoprene emission from North America. OMI HCHO columns for June-August 2006 are consistent with measurements from the earlier GOME satellite sensor (1996–2001) but OMI is 2–14% lower. The spatial distribution of OMI HCHO columns follows that of isoprene emission; anthropogenic hydrocarbon emissions are undetectable except in Houston. We develop updated relationships between HCHO columns and isoprene emission from a chemical transport model (GEOS-Chem), and use these to infer top-down constraints on isoprene emissions from the OMI data. We compare the OMI-derived emissions to a state-of-science bottom-up isoprene emission inventory (MEGAN) driven by two land cover databases, and use the results to optimize the MEGAN emission factors (EFs) for broadleaf trees (the main isoprene source). The OMI-derived isoprene emissions in North America (June–August 2006) with 1° × 1° resolution are spatially consistent with MEGAN (R2 = 0.48–0.68) but are lower (by 4–25% on average). MEGAN overestimates emissions in the Ozarks and the Upper South. A better fit to OMI (R2 = 0.73) is obtained in MEGAN by using a uniform isoprene EF from broadleaf trees rather than variable EFs. Thus MEGAN may overestimate emissions in areas where it specifies particularly high EFs. Within-canopy isoprene oxidation may also lead to significant differences between the effective isoprene emission to the atmosphere seen by OMI and the actual isoprene emission determined by MEGAN.

We analyze the theoretical basis of three different methods to validate and intercompare satellite measurements of atmospheric composition, and apply them to tropospheric ozone retrievals from the Tropospheric Emission Spectrometer (TES) and the Ozone Monitoring Instrument (OMI). The first method (in situ method) uses in situ vertical profiles for absolute instrument validation; it is limited by the sparseness of in situ data. The second method (CTM method) uses a chemical transport model (CTM) as an intercomparison platform; it provides a globally complete intercomparison with relatively small noise from model error. The third method (averaging kernel smoothing method) involves smoothing the retrieved profile from one instrument with the averaging kernel matrix of the other; it also provides a global intercomparison but dampens the actual difference between instruments and adds noise from the a priori. We apply the three methods to a full year (2006) of TES and OMI data. Comparison with in situ data from ozonesondes shows mean positive biases of 5.3 parts per billion volume (ppbv) (10%) for TES and 2.8 ppbv (5%) for OMI at 500 hPa. We show that the CTM method (using the GEOS-Chem CTM) closely approximates results from the in situ method while providing global coverage. It reveals that differences between TES and OMI are generally less than 10 ppbv (18%), except at northern mid-latitudes in summer and over tropical continents. The CTM method further allows for CTM evaluation using both satellite observations. We thus find that GEOS-Chem underestimates tropospheric ozone in the tropics due to possible underestimates of biomass burning, soil, and lightning emissions. It overestimates ozone in the northern subtropics and southern mid-latitudes, likely because of excessive stratospheric influx of ozone.

The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission was recommended by the National Research Council's (NRC's) Earth Science Decadal Survey to measure tropospheric trace gases and aerosols and coastal ocean phytoplankton, water quality, and biogeochemistry from geostationary orbit, providing continuous observations within the field of view. To fulfill the mandate and address the challenge put forth by the NRC, two GEO-CAPE Science Working Groups (SWGs), representing the atmospheric composition and ocean color disciplines, have developed realistic science objectives using input drawn from several community workshops. The GEO-CAPE mission will take advantage of this revolutionary advance in temporal frequency for both of these disciplines. Multiple observations per day are required to explore the physical, chemical, and dynamical processes that determine tropospheric composition and air quality over spatial scales ranging from urban to continental, and over temporal scales ranging from diurnal to seasonal. Likewise, high-frequency satellite observations are critical to studying and quantifying biological, chemical, and physical processes within the coastal ocean. These observations are to be achieved from a vantage point near 95°–100°W, providing a complete view of North America as well as the adjacent oceans. The SWGs have also endorsed the concept of phased implementation using commercial satellites to reduce mission risk and cost. GEO-CAPE will join the global constellation of geostationary atmospheric chemistry and coastal ocean color sensors planned to be in orbit in the 2020 time frame.

We present a global data set of free tropospheric ozone–CO correlations with 2° × 2.5° spatial resolution from the Ozone Monitoring Instrument (OMI) and Atmospheric Infrared Sounder (AIRS) satellite instruments for each season of 2008. OMI and AIRS have near-daily global coverage of ozone and CO respectively and observe coincident scenes with similar vertical sensitivities. The resulting ozone–CO correlations are highly statistically significant (positive or negative) in most regions of the world, and are less noisy than previous satellite-based studies that used sparser data. Comparison with ozone–CO correlations and regression slopes ((dO_3/dCO)) from MOZAIC (Measurements of OZone, water vapour, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft) aircraft profiles shows good general agreement. We interpret the observed ozone–CO correlations with the GEOS (Goddard Earth Observing System)-Chem chemical transport model to infer constraints on ozone sources. Driving GEOS-Chem with different meteorological fields generally shows consistent ozone–CO correlation patterns, except in some tropical regions where the correlations are strongly sensitive to model transport error associated with deep convection. GEOS-Chem reproduces the general structure of the observed ozone–CO correlations and regression slopes, although there are some large regional discrepancies. We examine the model sensitivity of (dO_3/dCO) to different ozone sources (combustion, biosphere, stratosphere, and lightning (NO_x)) by correlating the ozone change from that source to CO from the standard simulation. The model reproduces the observed positive (dO_3/dCO) in the extratropical Northern Hemisphere in spring–summer, driven by combustion sources. Stratospheric influence there is also associated with a positive (dO_3/dCO) because of the interweaving of stratospheric downwelling with continental outflow. The well-known ozone maximum over the tropical South Atlantic is associated with negative (dO_3/dCO) in the observations; this feature is reproduced in GEOS-Chem and supports a dominant contribution from lightning to the ozone maximum. A major model discrepancy is found over the northeastern Pacific in summer–fall where (dO_3/dCO) is positive in the observations but negative in the model, for all ozone sources. We suggest that this reflects a model overestimate of lightning at northern midlatitudes combined with an underestimate of the East Asian CO source.

Future geostationary satellite observations of tropospheric ozone aim to improve monitoring of surface ozone air quality. However, ozone retrievals from space have limited sensitivity in the lower troposphere (boundary layer). Data assimilation in a chemical transport model can propagate the information from the satellite observations to provide useful constraints on surface ozone. This may be aided by correlated satellite observations of carbon monoxide (CO), for which boundary layer sensitivity is easier to achieve. We examine the potential of concurrent geostationary observations of ozone and CO to improve constraints on surface ozone air quality through exploitation of ozone–CO model error correlations in a joint data assimilation framework. The hypothesis is that model transport errors diagnosed for CO provide information on corresponding errors in ozone. A paired-model analysis of ozone–CO error correlations in the boundary layer over North America in summer indicates positive error correlations in continental outflow but negative regional-scale error correlations over land, the latter reflecting opposite sensitivities of ozone and CO to boundary layer depth. Aircraft observations from the ICARTT campaign are consistent with this pattern but also indicate strong positive error correlations in fine-scale pollution plumes. We develop a joint ozone–CO data assimilation system and apply it to a regional-scale Observing System Simulation Experiment (OSSE) of the planned NASA GEO-CAPE geostationary mission over North America. We find substantial benefit from joint ozone–CO data assimilation in informing US ozone air quality if the instrument sensitivity for CO in the boundary layer is greater than that for ozone. A high-quality geostationary measurement of CO could potentially relax the requirements for boundary layer sensitivity of the ozone measurement. This is contingent on accurate characterization of ozone–CO error correlations. A finer-resolution data assimilation system resolving the urban scale would need to account for the change in sign of the ozone–CO error correlations between urban pollution plumes and the regional atmosphere.

Satellite observations of formaldehyde (HCHO) columns provide top-down constraints on emissions of highly reactive volatile organic compounds (HRVOCs). This approach has been used previously in the US to estimate isoprene emissions from vegetation, but application to anthropogenic emissions has been stymied by lack of a discernable HCHO signal. Here we show that temporal oversampling of HCHO data from the Ozone Monitoring Instrument (OMI) for 2005–2008 enables detection of urban and industrial plumes in eastern Texas including Houston, Port Arthur, and Dallas/Fort Worth. By spatially integrating the HCHO enhancement in the Houston plume observed by OMI we estimate an anthropogenic HCHO source of 250 ± 140 kmol h−1. This implies that anthropogenic HRVOC emissions in Houston are 4.8 ± 2.7 times higher than reported by the US Environmental Protection Agency inventory, and is consistent with field studies identifying large ethene and propene emissions from petrochemical industrial sources.

High-ozone events, approaching or exceeding the National Ambient Air Quality Standard (NAAQS), are frequently observed in the US Intermountain West in association with subsiding air from the free troposphere. Monitoring and attribution of these events is problematic because of the sparsity of the current network of surface measurements and lack of vertical information. We present an Observing System Simulation Experiment (OSSE) to evaluate the ability of the future geostationary satellite instrument Tropospheric Emissions: Monitoring of Pollution (TEMPO), scheduled for launch in 2018–2019, to monitor and attribute high-ozone events in the Intermountain West through data assimilation. TEMPO will observe ozone in the ultraviolet (UV) and visible (Vis) bands to provide sensitivity in the lower troposphere. Our OSSE uses ozone data from the GFDL AM3 chemistry-climate model (CCM) as the "true" atmosphere and samples it for April–June 2010 with the current surface network (CASTNet –Clean Air Status and Trends Network– sites), a configuration designed to represent TEMPO, and a low Earth orbit (LEO) IR (infrared) satellite instrument. These synthetic data are then assimilated into the GEOS-Chem chemical transport model (CTM) using a Kalman filter. Error correlation length scales (500 km in horizontal, 1.7 km in vertical) extend the range of influence of observations. We show that assimilation of surface data alone does not adequately detect high-ozone events in the Intermountain West. Assimilation of TEMPO data greatly improves the monitoring capability, with little information added from the LEO instrument. The vertical information from TEMPO further enables the attribution of NAAQS exceedances to background ozone. This is illustrated with the case of a stratospheric intrusion.

We use tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument to derive top-down constraints on emissions of nitrogen oxides (NOx ≡ NO + NO2), and combine these with a priori information from a bottom-up emission inventory (with error weighting) to achieve an optimized a posteriori estimate of the global distribution of surface NOx emissions. Our GOME NO2 retrieval improves on previous work by accounting for scattering and absorption of radiation by aerosols; the effect on the air mass factor (AMF) ranges from +10 to −40% depending on the region. Our AMF also includes local information on relative vertical profiles (shape factors) of NO2 from a global 3-D chemical transport model (GEOS-CHEM); assumption of a globally uniform shape factor, as in most previous retrievals, would introduce regional biases of up to 40% over industrial regions and a factor of 2 over remote regions. We derive a top-down NOx emission inventory from the GOME data by using the local GEOS-CHEM relationship between NO2 columns and NOx emissions. The resulting NOx emissions for industrial regions are aseasonal, despite large seasonal variation in NO2 columns, providing confidence in the method. Top-down errors in monthly NOx emissions are comparable with bottom-up errors over source regions. Annual global a posteriori errors are half of a priori errors. Our global a posteriori estimate for annual land surface NOx emissions (37.7 Tg N yr−1) agrees closely with the GEIA-based a priori (36.4) and with the EDGAR 3.0 bottom-up inventory (36.6), but there are significant regional differences. A posteriori NOx emissions are higher by 50–100% in the Po Valley, Tehran, and Riyadh urban areas, and by 25–35% in Japan and South Africa. Biomass burning emissions from India, central Africa, and Brazil are lower by up to 50%; soil NOx emissions are appreciably higher in the western United States, the Sahel, and southern Europe.

We use a continuous 6‐year record (1996–2001) of GOME satellite measurements of formaldehyde (HCHO) columns over east and south Asia to improve regional emission estimates of reactive nonmethane volatile organic compounds (NMVOCs), including isoprene, alkenes, HCHO, and xylenes. Mean monthly HCHO observations are compared to simulated HCHO columns from the GEOS‐Chem chemical transport model using state‐of‐science, “bottom‐up” emission inventories from Streets et al. (2003a) for anthropogenic and biomass burning emissions and Guenther et al. (2006) for biogenic emissions (MEGAN). We find that wintertime GOME observations can diagnose anthropogenic reactive NMVOC emissions from China, leading to an estimate 25% higher than Streets et al. (2003a). We attribute the difference to vehicular emissions. The biomass burning source for east and south Asia is almost 5 times the estimate of Streets et al. (2003a). GOME reveals a large source from agricultural burning in the North China Plain in June missing from current inventories. This source may reflect a recent trend toward in‐field burning of crop residues as the need for biofuels diminishes. Biogenic isoprene emission in east and south Asia derived from GOME is 56 ± 30 Tg yr, similar to 52 Tg yr from MEGAN. We find, however, that MEGAN underestimates emissions in China and overestimates emissions in the tropics. The higher Chinese biogenic and biomass burning emissions revealed by GOME have important implications for ozone pollution. We find 5 to 20 ppb seasonal increases in surface ozone in GEOS‐Chem for central and northern China when using GOME‐derived versus bottom‐up emissions. Our methodology can be adapted for other regions of the world to provide top‐down constraints on NMVOC emissions where multiple emission source types overlap in space and time.

Formaldehyde (HCHO) columns measured from space by solar UV backscatter allow mapping of reactive hydrocarbon emissions. The principal contributor to these emissions during the growing season is the biogenic hydrocarbon isoprene, which is of great importance for driving regional and global tropospheric chemistry. We present seven years (1995–2001) of HCHO column data for North America from the Global Ozone Monitoring Experiment (GOME), and show that the general seasonal and interannual variability of these data is consistent with knowledge of isoprene emission. There are some significant regional discrepancies with the seasonal patterns predicted from current isoprene emission models, and we suggest that these may reflect flaws in the models. The interannual variability of HCHO columns observed by GOME appears to follow the interannual variability of surface temperature, as expected from current isoprene emission models.