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The vertical distribution of ozone plays an important role in atmospheric chemistry, climate change, air pollution, and human health. Over the 21st century, spaceborne remote-sensing methods and instrumentation have evolved to better determine this distribution. We quantify the ability of ozone retrievals to characterize this distribution through a sequential combination of thermal infrared (TIR) and ultraviolet (UV) spectral radiances, harnessing co-located TIR measurements from the Cross-track Infrared Sounder (CrIS) on board the Suomi National Polar-orbiting Partnership (NPP) and UV measurements from the TROPOspheric Monitoring Instrument (TROPOMI), which is on the Sentinel 5-Precursor (S5P) satellite. Using the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) algorithm, the sequential combination of TIR and UV measurements, which follows retrievals from each instrument separately, moderately improves the ability of satellites to characterize global ozone profiles over the use of each instrument/band individually. The CrIS retrievals enhanced by TROPOMI radiances in the Huggins band (325–335 nm) show good agreement with independent datasets both in the troposphere and in the stratosphere in spite of calibration issues in the TROPOMI UV. Improved performance is characterized in the stratosphere from CrIS-TROPOMI, firstly through a modest increase in the degrees of freedom for signal (DFS; often between 0.1–0.2) and secondly through comparisons with the Microwave Limb Sounder (MLS), where a global multi-month-long comparison shows a mean difference ∼×10 lower than either CrIS or TROPOMI individually and R2 values 3 % higher. In the troposphere, CrIS-TROPOMI and CrIS show similar degrees of freedom for signal, with about 2 globally, but these are higher in the tropics partitioned equally between the lower and upper troposphere. CrIS-TROPOMI validation with ozonesondes shows improved performance over CrIS-only validation, with a difference in the tropospheric-column bias of between 30 % and 200 % depending on the season. Cross-comparisons with satellite instruments and reanalysis datasets show similar performances in terms of correlations and biases. These results demonstrate that CrIS and CrIS-TROPOMI retrievals have the potential to improve global satellite ozone retrievals, especially with future developments. If spectral accuracy is improved in future TROPOMI calibration, the degrees of freedom for signal in the stratosphere could double when using bands 1 and 2 of TROPOMI (270–330 nm), while tropospheric degrees of freedom for signal could increase by 25 %.

We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution and 2∘ × 2.5∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in computational performance compared to the equivalent analytical inversion of TROPOMI observations.

Accurate accounting of emissions and removals of CO2 is critical for the planning and verification of emission reduction targets in support of the Paris Agreement. Here, we present a pilot dataset of country-specific net carbon exchange (NCE; fossil plus terrestrial ecosystem fluxes) and terrestrial carbon stock changes aimed at informing countries' carbon budgets. These estimates are based on “top-down” NCE outputs from the v10 Orbiting Carbon Observatory (OCO-2) modeling intercomparison project (MIP), wherein an ensemble of inverse modeling groups conducted standardized experiments assimilating OCO-2 column-averaged dry-air mole fraction (XCO2) retrievals (ACOS v10), in situ CO2 measurements or combinations of these data. The v10 OCO-2 MIP NCE estimates are combined with “bottom-up” estimates of fossil fuel emissions and lateral carbon fluxes to estimate changes in terrestrial carbon stocks, which are impacted by anthropogenic and natural drivers. These flux and stock change estimates are reported annually (2015–2020) as both a global 1∘ × 1∘ gridded dataset and a country-level dataset and are available for download from the Committee on Earth Observation Satellites' (CEOS) website: https://doi.org/10.48588/npf6-sw92 (Byrne et al., 2022). Across the v10 OCO-2 MIP experiments, we obtain increases in the ensemble median terrestrial carbon stocks of 3.29–4.58 Pg CO2 yr−1 (0.90–1.25 Pg C yr−1). This is a result of broad increases in terrestrial carbon stocks across the northern extratropics, while the tropics generally have stock losses but with considerable regional variability and differences between v10 OCO-2 MIP experiments. We discuss the state of the science for tracking emissions and removals using top-down methods, including current limitations and future developments towards top-down monitoring and verification systems.

Nitrogen dioxide (NO2) column density measurements from satellites have been widely used in constraining emissions of nitrogen oxides (NOx = NO + NO2). However, the utility of these measurements is impacted by reduced observational coverage due to cloud cover and their reduced sensitivity toward the surface. Combining the information from satellites with surface observations of NO2 will provide greater constraints on emission estimates of NOx. We have developed a deep-learning (DL) model to integrate satellite data and in situ observations of surface NO2 to estimate NOx emissions in China. A priori information for the DL model was obtained from satellite-derived emissions from the Tropospheric Chemistry Reanalysis (TCR-2). A two-stage training strategy was used to integrate in situ measurements from the China Ministry of Ecology and Environment (MEE) observation network with the TCR-2 data. The DL model is trained from 2005 to 2018 and evaluated for 2019 and 2020. The DL model estimated a source of 19.4 Tg NO for total Chinese NOx emissions in 2019, which is consistent with the TCR-2 estimate of 18.5 ± 3.9 Tg NO and the 20.9 Tg NO suggested by the Multi-resolution Emission Inventory for China (MEIC). Combining the MEE data with TCR-2, the DL model suggested higher NOx emissions in some of the less-densely populated provinces, such as Shaanxi and Sichuan, where the MEE data indicated higher surface NO2 concentrations than TCR-2. The DL model also suggested a faster recovery of NOx emissions than TCR-2 after the Chinese New Year (CNY) holiday in 2019, with a recovery time scale that is consistent with Baidu “Qianxi” mobility data. In 2020, the DL-based analysis estimated about a 30 % reduction in NOx emissions in eastern China during the COVID-19 lockdown period, relative to pre-lockdown levels. In particular, the maximum emission reductions were 42 % and 30 % for the Jing-Jin-Ji (JJJ) and the Yangtze River Delta (YRD) mega-regions, respectively. Our results illustrate the potential utility of the DL model as a complementary tool for conventional data-assimilation approaches for air quality applications.

Site-level observations have shown pervasive cold season CO2 release across Arctic and boreal ecosystems, impacting annual carbon budgets. Still, the seasonality of CO2 emissions are poorly quantified across much of the high latitudes due to the sparse coverage of site-level observations. Space-based observations provide the opportunity to fill some observational gaps for studying these high-latitude ecosystems, particularly across poorly sampled regions of Eurasia. Here, we show that data-driven net ecosystem exchange (NEE) from atmospheric CO2 observations implies strong summer uptake followed by strong autumn release of CO2 over the entire cold northeastern region of Eurasia during the 2015–2019 study period. Combining data-driven NEE with satellite-based estimates of gross primary production (GPP), we show that this seasonality implies less summer heterotrophic respiration (Rh) and greater autumn Rh than would be expected given an exponential relationship between respiration and surface temperature. Furthermore, we show that this seasonality of NEE and Rh over northeastern Eurasia is not captured by the TRENDY v8 ensemble of dynamic global vegetation models (DGVMs), which estimate that 47 %–57 % (interquartile range) of annual Rh occurs during August–April, while the data-driven estimates suggest 59 %–76 % of annual Rh occurs over this period. We explain this seasonal shift in Rh by respiration from soils at depth during the zero-curtain period, when sub-surface soils remain unfrozen up to several months after the surface has frozen. Additional impacts of physical processes related to freeze–thaw dynamics may contribute to the seasonality of Rh. This study confirms a significant and spatially extensive early cold season CO2 efflux in the permafrost-rich region of northeast Eurasia and suggests that autumn Rh from subsurface soils in the northern high latitudes is not well captured by current DGVMs.

The new single-pixel TROPESS (TRopospheric Ozone and its Precursors from Earth System Sounding) profile retrievals of carbon monoxide (CO) from the Cross-track Infrared Sounder (CrIS) are evaluated using vertical profiles of in situ observations from the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) aircraft program and from the Atmospheric Tomography Mission (ATom) campaigns. The TROPESS optimal estimation retrievals are produced using the MUSES (MUlti-SpEctra, MUlti-SpEcies, MUlti-Sensors) algorithm, which has heritage from retrieval algorithms developed for the EOS/Aura Tropospheric Emission Spectrometer (TES). TROPESS products provide retrieval diagnostics and error covariance matrices that propagate instrument noise as well as the uncertainties from sequential retrievals of parameters such as temperature and water vapor that are required to estimate the carbon monoxide profiles. The validation approach used here evaluates biases in column and profile values as well as the validity of the retrieval error estimates using the mean and variance of the compared satellite and aircraft observations. CrIS–NOAA GML comparisons had biases of 0.6 % for partial column average volume mixing ratios (VMRs) and (2.3, 0.9, −4.5) % for VMRs at (750, 511, 287) hPa vertical levels, respectively, with standard deviations from 9 % to 14 %. CrIS–ATom comparisons had biases of −0.04 % for partial column and (2.2, 0.5, −3.0) % for (750, 511, 287) hPa vertical levels, respectively, with standard deviations from 6 % to 10 %. The reported observational errors for TROPESS/CrIS CO profiles have the expected behavior with respect to the vertical pattern in standard deviation of the comparisons. These comparison results give us confidence in the use of TROPESS/CrIS CO profiles and error characterization for continuing the multi-decadal record of satellite CO observations.

Ozone (O3) dry deposition is a major O3 sink. As a follow-up study of Huang et al. (2021), we quantify the impact of satellite soil moisture (SM) on model representations of this process when different dry-deposition parameterizations are implemented, based on which the implications for interpreting O3 air pollution levels and assessing the O3 impacts on human and ecosystem health are provided. The SM data from NASA's Soil Moisture Active Passive mission are assimilated into the Noah-Multiparameterization (Noah-MP) land surface model within the NASA Land Information System framework, semicoupled with Weather Research and Forecasting model with online Chemistry (WRF-Chem) regional-scale simulations covering the southeastern US. Major changes in the modeling system used include enabling the dynamic vegetation option, adding the irrigation process, and updating the scheme for the surface exchange coefficient. Two dry-deposition schemes are implemented, i.e., the Wesely scheme and a “dynamic” scheme, in the latter of which dry-deposition parameterization is coupled with photosynthesis and vegetation dynamics. It is demonstrated that, when the dynamic scheme is applied, the simulated O3 dry-deposition velocities vd and their stomatal and cuticular portions, as well as the total O3 fluxes Ft, are larger overall; vd and Ft are 2–3 times more sensitive to the SM changes due to the data assimilation (DA). Further, through case studies at two forested sites with different soil types and hydrological regimes, we highlight that, applying the Community Land Model type of SM factor controlling stomatal resistance (i.e., β factor) scheme in replacement of the Noah-type β factor scheme reduced the vd sensitivity to SM changes by ∼75 % at one site, while it doubled this sensitivity at the other site. Referring to multiple evaluation datasets, which may be associated with variable extents of uncertainty, the model performance of vegetation, surface fluxes, weather, and surface O3 concentrations shows mixed responses to the DA, some of which display land cover dependency. Finally, using model-derived concentration- and flux-based policy-relevant O3 metrics as well as their matching exposure–response functions, the relative biomass/crop yield losses for several types of vegetation/crops are estimated to be within a wide range of 1 %–17 %. Their sensitivities to the model's dry-deposition scheme and the implementation of SM DA are discussed.

We present an overview of an optimal estimation algorithm to retrieve peroxyacetyl nitrate (PAN) from single-field-of-view Level 1B radiances measured by the Cross-Track Infrared Sounder (CrIS). CrIS PAN retrievals show peak sensitivity in the mid-troposphere, with degrees of freedom for signal less than or equal to 1.0. We show comparisons with two sets of aircraft measurements from the Atmospheric Tomography Mission (ATom), the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) and the Georgia Tech chemical ionization mass spectrometer (GT-CIMS). We find a systematic difference between the two aircraft datasets, with vertically averaged mid-tropospheric values from the GT-CIMS around 14 % lower than equivalent values from PANTHER. However, the two sets of aircraft measurements are strongly correlated (R2 value of 0.92) and do provide a consistent view of the large-scale variation of PAN. We demonstrate that the retrievals of PAN from CrIS show skill in measurement of these large-scale PAN distributions in the remote mid-troposphere compared to the retrieval prior. The standard deviation of individual CrIS–aircraft differences is 0.08 ppbv, which we take as an estimate of the uncertainty of the CrIS mid-tropospheric PAN for a single satellite field of view. The standard deviation of the CrIS–aircraft comparisons for averaged CrIS retrievals (median of 20 satellite coincidences with each aircraft profile) is lower at 0.05 ppbv. This would suggest that the retrieval error is reduced with averaging, although not with the square root of the number of observations. We find a negative bias of the order of 0.1 ppbv in the CrIS PAN results with respect to the aircraft measurements. This bias shows a dependence on column water vapor. We provide a water-vapor-dependent bias correction for use with the CrIS PAN data.

This study evaluates the impact of satellite soil moisture (SM) data assimilation (DA) on regional weather and ozone (O3) modeling over the southeastern US during the summer. Satellite SM data are assimilated into the Noah land surface model using an ensemble Kalman filter approach within National Aeronautics and Space Administration's Land Information System framework, which is semicoupled with the Weather Research and Forecasting model with online Chemistry (WRF-Chem; standard version 3.9.1.1). The DA impacts on the model performance of SM, weather states, and energy fluxes show strong spatiotemporal variability. Dense vegetation and water use from human activities unaccounted for in the modeling system are among the factors impacting the effectiveness of the DA. The daytime surface O3 responses to the DA can largely be explained by the temperature-driven changes in biogenic emissions of volatile organic compounds and soil nitric oxide, chemical reaction rates, and dry deposition velocities. On a near-biweekly timescale, the DA modified the mean daytime and daily maximum 8 h average surface O3 by up to 2–3 ppbv, with the maximum impacts occurring in areas where daytime surface air temperature most strongly (i.e., by ∼2 K) responded to the DA. The DA impacted WRF-Chem upper tropospheric O3 (e.g., for its daytime-mean, by up to 1–1.5 ppbv) partially via altering the transport of O3 and its precursors from other places as well as in situ chemical production of O3 from lightning and other emissions. Case studies during airborne field campaigns suggest that the DA improved the model treatment of convective transport and/or lightning production. In the cases that the DA improved the modeled SM, weather fields, and some O3-related processes, its influences on the model's O3 performance at various altitudes are not always as desirable. This is in part due to the uncertainty in the model's key chemical inputs, such as anthropogenic emissions, and the model representation of stratosphere–troposphere exchanges. This can also be attributable to shortcomings in model parameterizations (e.g., chemical mechanism, natural emission, photolysis and deposition schemes), including those related to representing water availability impacts. This study also shows that the WRF-Chem upper tropospheric O3 response to the DA has comparable magnitudes with its response to the estimated US anthropogenic emission changes within 2 years. As reductions in anthropogenic emissions in North America would benefit the mitigation of O3 pollution in its downwind regions, this analysis highlights the important role of SM in quantifying air pollutants' source–receptor relationships between the US and its downwind areas. It also emphasizes that using up-to-date anthropogenic emissions is necessary for accurately assessing the DA impacts on the model performance of O3 and other pollutants over a broad region. This work will be followed by a Noah-Multiparameterization (with dynamic vegetation)-based study over the southeastern US, in which selected processes including photosynthesis and O3 dry deposition will be the foci.

In response to the coronavirus disease of 2019 (COVID-19), California issued statewide stay-at-home orders, bringing about abrupt and dramatic reductions in air pollutant emissions. This crisis offers us an unprecedented opportunity to evaluate the effectiveness of emission reductions in terms of air quality. Here we use the Weather Research and Forecasting model with Chemistry (WRF-Chem) in combination with surface observations to study the impact of the COVID-19 lockdown measures on air quality in southern California. Based on activity level statistics and satellite observations, we estimate the sectoral emission changes during the lockdown. Due to the reduced emissions, the population-weighted concentrations of fine particulate matter (PM2.5) decrease by 15 % in southern California. The emission reductions contribute 68 % of the PM2.5 concentration decrease before and after the lockdown, while meteorology variations contribute the remaining 32 %. Among all chemical compositions, the PM2.5 concentration decrease due to emission reductions is dominated by nitrate and primary components. For O3 concentrations, the emission reductions cause a decrease in rural areas but an increase in urban areas; the increase can be offset by a 70 % emission reduction in anthropogenic volatile organic compounds (VOCs). These findings suggest that a strengthened control on primary PM2.5 emissions and a well-balanced control on nitrogen oxides and VOC emissions are needed to effectively and sustainably alleviate PM2.5 and O3 pollution in southern California.

We use 2010–2015 Greenhouse Gases Observing Satellite (GOSAT) observations of atmospheric methane columns over North America in a high-resolution inversion of methane emissions, including contributions from different sectors and their trends over the period. The inversion involves an analytical solution to the Bayesian optimization problem for a Gaussian mixture model (GMM) of the emission field with up to 0.5∘×0.625∘ resolution in concentrated source regions. The analytical solution provides a closed-form characterization of the information content from the inversion and facilitates the construction of a large ensemble of solutions exploring the effect of different uncertainties and assumptions in the inverse analysis. Prior estimates for the inversion include a gridded version of the Environmental Protection Agency (EPA) Inventory of US Greenhouse Gas Emissions and Sinks (GHGI) and the WetCHARTs model ensemble for wetlands. Our best estimate for mean 2010–2015 US anthropogenic emissions is 30.6 (range: 29.4–31.3) Tg a−1, slightly higher than the gridded EPA inventory (28.7 (26.4–36.2) Tg a−1). The main discrepancy is for the oil and gas production sectors, where we find higher emissions than the GHGI by 35 % and 22 %, respectively. The most recent version of the EPA GHGI revises downward its estimate of emissions from oil production, and we find that these are lower than our estimate by a factor of 2. Our best estimate of US wetland emissions is 10.2 (5.6–11.1) Tg a−1, on the low end of the prior WetCHARTs inventory uncertainty range (14.2 (3.3–32.4) Tg a−1), which calls for better understanding of these emissions. We find an increasing trend in US anthropogenic emissions over 2010–2015 of 0.4 % a−1, lower than previous GOSAT-based estimates but opposite to the decrease reported by the EPA GHGI. Most of this increase appears driven by unconventional oil and gas production in the eastern US. We also find that oil and gas production emissions in Mexico are higher than in the nationally reported inventory, though there is evidence for a 2010–2015 decrease in emissions from offshore oil production.

Inter-annual variations in the tropical land carbon (C) balance are a dominant component of the global atmospheric CO2 growth rate. Currently, the lack of quantitative knowledge on processes controlling net tropical ecosystem C balance on inter-annual timescales inhibits accurate understanding and projections of land–atmosphere C exchanges. In particular, uncertainty on the relative contribution of ecosystem C fluxes attributable to concurrent forcing anomalies (concurrent effects) and those attributable to the continuing influence of past phenomena (lagged effects) stifles efforts to explicitly understand the integrated sensitivity of a tropical ecosystem to climatic variability. Here we present a conceptual framework – applicable in principle to any land biosphere model – to explicitly quantify net biospheric exchange (NBE) as the sum of anomaly-induced concurrent changes and climatology-induced lagged changes to terrestrial ecosystem C states (NBE = NBECON+NBELAG). We apply this framework to an observation-constrained analysis of the 2001–2015 tropical C balance: we use a data–model integration approach (CARbon DAta-MOdel fraMework – CARDAMOM) to merge satellite-retrieved land-surface C observations (leaf area, biomass, solar-induced fluorescence), soil C inventory data and satellite-based atmospheric inversion estimates of CO2 and CO fluxes to produce a data-constrained analysis of the 2001–2015 tropical C cycle. We find that the inter-annual variability of both concurrent and lagged effects substantially contributes to the 2001–2015 NBE inter-annual variability throughout 2001–2015 across the tropics (NBECON IAV = 80 % of total NBE IAV, r = 0.76; NBELAG IAV = 64 % of NBE IAV, r = 0.61), and the prominence of NBELAG IAV persists across both wet and dry tropical ecosystems. The magnitude of lagged effect variations on NBE across the tropics is largely attributable to lagged effects on net primary productivity (NPP; NPPLAG IAV 113 % of NBELAG IAV, r = −0.93, p value 2O states. We conclude that concurrent and lagged effects need to be explicitly and jointly resolved to retrieve an accurate understanding of the processes regulating the present-day and future trajectory of the terrestrial land C sink.

We introduce a Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) framework that directly accounts for model error in transport and chemistry, and we integrate a portfolio of data assimilation analyses obtained using multiple forward chemical transport models in a state-of-the-art ensemble Kalman filter data assimilation system. The data assimilation simultaneously optimizes both concentrations and emissions of multiple species through ingestion of a suite of measurements (ozone, NO2, CO, HNO3) from multiple satellite sensors. In spite of substantial model differences, the observational density and accuracy was sufficient for the assimilation to reduce the multi-model spread by 20 %–85 % for ozone and annual mean bias by 39 %–97 % for ozone in the middle troposphere, while simultaneously reducing the tropospheric NO2 column biases by more than 40 % and the negative biases of surface CO in the Northern Hemisphere by 41 %–94 %. For tropospheric mean OH, the multi-model mean meridional hemispheric gradient was reduced from 1.32±0.03 to 1.19±0.03, while the multi-model spread was reduced by 24 %–58 % over polluted areas. The uncertainty ranges in the a posteriori emissions due to model errors were quantified in 4 %–31 % for NOx and 13 %–35 % for CO regional emissions. Harnessing assimilation increments in both NOx and ozone, we show that the sensitivity of ozone and NO2 surface concentrations to NOx emissions varied by a factor of 2 for end-member models, revealing fundamental differences in the representation of fast chemical and dynamical processes. A systematic investigation of model ozone response and analysis increment in MOMO-Chem could benefit evaluation of future prediction of the chemistry–climate system as a hierarchical emergent constraint.

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6 µm ozone band is a fundamental quantity for understanding chemistry–climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present-day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6 µm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) simulations. The principal culprits are tropical middle and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mW m−2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mW m−2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mW m−2). We found that the atmospheric component (AM3) of the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model and Canadian Middle Atmosphere Model (CMAM) have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to different processes. Overall, the multi-model ensemble mean bias is -133±98 mW m−2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation.

We use 2010–2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used, even when prior error standard deviation distributions are lognormal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil and gas emissions in the EDGAR v4.3.2 inventory but little error in the United States where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil and gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC), and our inversion is generally more consistent with the UNFCCC data. The observed 2010–2015 growth in atmospheric methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison. We find that the inversion provides strong independent constraints on global methane emissions (546 Tg a−1) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8±0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.

While heterotrophic respiration (Rh) makes up about a quarter of gross global terrestrial carbon fluxes, it remains among the least-observed carbon fluxes, particularly outside the midlatitudes. In situ measurements collected in the Soil Respiration Database (SRDB) number only a few hundred worldwide. Similarly, only a single data-driven wall-to-wall estimate of annual average heterotrophic respiration exists, based on bottom-up upscaling of SRDB measurements using an assumed functional form to account for climate variability. In this study, we exploit recent advances in remote sensing of terrestrial carbon fluxes to estimate global variations in heterotrophic respiration in a top-down fashion at monthly temporal resolution and 4∘×5∘ spatial resolution. We combine net ecosystem productivity estimates from atmospheric inversions of the NASA Carbon Monitoring System-Flux (CMS-Flux) with an optimally scaled gross primary productivity dataset based on satellite-observed solar-induced fluorescence variations to estimate total ecosystem respiration as a residual of the terrestrial carbon balance. The ecosystem respiration is then separated into autotrophic and heterotrophic components based on a spatially varying carbon use efficiency retrieved in a model–data fusion framework (the CARbon DAta MOdel fraMework, CARDAMOM). The resulting dataset is independent of any assumptions about how heterotrophic respiration responds to climate or substrate variations. It estimates an annual average global average heterotrophic respiration flux of 43.6±19.3 Pg C yr−1. Sensitivity and uncertainty analyses showed that the top-down Rh are more sensitive to the choice of input gross primary productivity (GPP) and net ecosystem productivity (NEP) datasets than to the assumption of a static carbon use efficiency (CUE) value, with the possible exception of the wet tropics. These top-down estimates are compared to bottom-up estimates of annual heterotrophic respiration, using new uncertainty estimates that partially account for sampling and model errors. Top-down heterotrophic respiration estimates are higher than those from bottom-up upscaling everywhere except at high latitudes and are 30 % greater overall (43.6 Pg C yr−1 vs. 33.4 Pg C yr−1). The uncertainty ranges of both methods are comparable, except poleward of 45∘ N, where bottom-up uncertainties are greater. The ratio of top-down heterotrophic to total ecosystem respiration varies seasonally by as much as 0.6 depending on season and climate, illustrating the importance of studying the drivers of autotrophic and heterotrophic respiration separately, and thus the importance of data-driven estimates of Rh such as those estimated here.

Single-pixel tropospheric retrievals of HDO and H2O concentrations are retrieved from Atmospheric Infrared Sounder (AIRS) radiances using the optimal estimation algorithm developed for the Aura Tropospheric Emission Spectrometer (TES) project. We evaluate the error characteristics and vertical sensitivity of AIRS measurements corresponding to 5 d of TES data (or five global surveys) during the Northern Hemisphere summers between 2006 and 2010 (∼600 co-located comparisons per day). We find that the retrieval characteristics of the AIRS deuterium content measurements have similar vertical resolution in the middle troposphere as TES but with slightly less sensitivity in the lowermost troposphere, with a typical degrees of freedom (DOFS) in the tropics of 1.5. The calculated measurement uncertainty is ∼30 ‰ (parts per thousand relative to the deuterium composition of ocean water) for a tropospheric average between 750 and 350 hPa, the altitude region where AIRS is most sensitive, compared to ∼15 ‰ for the TES data. Comparison with the TES data also indicates that the uncertainty of a single target AIRS HDO ∕ H2O measurement is ∼30 ‰. Comparison of AIRS and TES data between 30∘ S and 50∘ N indicates that the AIRS data are biased low by ∼-2.6 ‰ with a latitudinal variation of ∼7.8 ‰. This latitudinal variation is consistent with the accuracy of TES data compared to in situ measurements, suggesting that both AIRS and TES have similar accuracy.

Abstract Quantifying variability in the ocean carbon sink remains problematic due to sparse observations and spatiotemporal variability in surface ocean pCO2. To address this challenge, we have updated and improved ECCO‐Darwin, a global ocean biogeochemistry model that assimilates both physical and biogeochemical observations. The model consists of an adjoint‐based ocean circulation estimate from the Estimating the Circulation and Climate of the Ocean (ECCO) consortium and an ecosystem model developed by the Massachusetts Institute of Technology Darwin Project. In addition to the data‐constrained ECCO physics, a Green's function approach is used to optimize the biogeochemistry by adjusting initial conditions and six biogeochemical parameters. Over seasonal to multidecadal timescales (1995–2017), ECCO‐Darwin exhibits broad‐scale consistency with observed surface ocean pCO2 and air‐sea CO2 flux reconstructions in most biomes, particularly in the subtropical and equatorial regions. The largest differences between CO2 uptake occur in subpolar seasonally stratified biomes, where ECCO‐Darwin results in stronger winter uptake. Compared to the Global Carbon Project OBMs, ECCO‐Darwin has a time‐mean global ocean CO2 sink (2.47 ± 0.50 Pg C year−1) and interannual variability that are more consistent with interpolation‐based products. Compared to interpolation‐based methods, ECCO‐Darwin is less sensitive to sparse and irregularly sampled observations. Thus, ECCO‐Darwin provides a basis for identifying and predicting the consequences of natural and anthropogenic perturbations to the ocean carbon cycle, as well as the climate‐related sensitivity of marine ecosystems. Our study further highlights the importance of physically consistent, property‐conserving reconstructions, as are provided by ECCO, for ocean biogeochemistry studies.

The Tropospheric Emission Spectrometer (TES) on the A-Train Aura satellite was designed to profile tropospheric ozone and its precursors, taking measurements from 2004 to 2018. Starting in 2008, TES global sampling of tropospheric ozone was gradually reduced in latitude, with global coverage stopping in 2011. To extend the record of TES, this work presents a multispectral approach that will provide O3 data products with vertical resolution and measurement error similar to TES by combining the single-footprint thermal infrared (TIR) hyperspectral radiances from the Aqua Atmospheric Infrared Sounder (AIRS) instrument and the ultraviolet (UV) channels from the Aura Ozone Monitoring Instrument (OMI). The joint AIRS+OMI O3 retrievals are processed through the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) retrieval algorithm. Comparisons of collocated joint AIRS+OMI and TES to ozonesonde measurements show that both systems have similar errors, with mean and standard deviation of the differences well within the estimated measurement error. AIRS+OMI and TES have slightly different biases (within 5 parts per billion) vs. the sondes. Both AIRS and OMI have wide swath widths ( ∼ 1650 km for AIRS; ∼ 2600 km for OMI) across satellite ground tracks. Consequently, the joint AIRS+OMI measurements have the potential to maintain TES vertical sensitivity while increasing coverage by 2 orders of magnitude, thus providing an unprecedented new data set with which to quantify the evolution of tropospheric ozone.

The Aura Tropospheric Emission Spectrometer (TES) is collecting closely spaced observations over 19 megacities. The objective is to obtain measurements that will lead to better understanding of the processes affecting air quality in and around these cities, and to better estimates of the seasonal and interannual variability. We explore the TES measurements of ozone, ammonia, methanol and formic acid collected around the Mexico City metropolitan area (MCMA) and in the vicinity of Lagos (Nigeria). The TES data exhibit seasonal signals that are correlated with Atmospheric Infrared Sounder (AIRS) CO and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), with in situ measurements in the MCMA and with Goddard Earth Observing System (GEOS)-Chem model output in the Lagos area. TES was able to detect an extreme pollution event in the MCMA on 9 April 2013, which is also evident in the in situ data. TES data also show that biomass burning has a greater impact south of the city than in the caldera where Mexico City is located. TES measured enhanced values of the four species over the Gulf of Guinea south of Lagos. Since it observes many cities from the same platform with the same instrument and applies the same retrieval algorithms, TES data provide a very useful tool for easily comparing air quality measures of two or more cities. We compare the data from the MCMA and Lagos, and show that, while the MCMA has occasional extreme pollution events, Lagos consistently has higher levels of these trace gases.

Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH4) budget, largely due to poorly constrained process controls on CH4 production in waterlogged soils. Process-based estimates of global wetland CH4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH4 emission estimates. Here we construct a global wetland CH4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs version 1.0). Our 0.5° × 0.5° resolution model ensemble is based on satellite-derived surface water extent and precipitation reanalyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature dependence parameterizations for the period 2009–2010; an extended ensemble subset based solely on precipitation and the data-constrained terrestrial carbon cycle analysis is derived for the period 2001–2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare the model against surface measurements of atmospheric CH4; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH4 emissions scenarios. We find that uncertainties in carbon decomposition rates and the wetland extent together account for more than 80 % of the dominant uncertainty in the timing, magnitude and seasonal variability in wetland CH4 emissions, although uncertainty in the temperature CH4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for top-down estimates of wetland CH4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH4 emission uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain the biogeochemical process controls of global wetland CH4 emissions.

The recent update on the US National Ambient Air Quality Standards (NAAQS) of the ground-level ozone (O3) can benefit from a better understanding of its source contributions in different US regions during recent years. In the Hemispheric Transport of Air Pollution experiment phase 1 (HTAP1), various global models were used to determine the O3 source–receptor (SR) relationships among three continents in the Northern Hemisphere in 2001. In support of the HTAP phase 2 (HTAP2) experiment that studies more recent years and involves higher-resolution global models and regional models' participation, we conduct a number of regional-scale Sulfur Transport and dEposition Model (STEM) air quality base and sensitivity simulations over North America during May–June 2010. STEM's top and lateral chemical boundary conditions were downscaled from three global chemical transport models' (i.e., GEOS-Chem, RAQMS, and ECMWF C-IFS) base and sensitivity simulations in which the East Asian (EAS) anthropogenic emissions were reduced by 20 %. The mean differences between STEM surface O3 sensitivities to the emission changes and its corresponding boundary condition model's are smaller than those among its boundary condition models, in terms of the regional/period-mean (3 sensitivities to the size of the emission perturbation is spatially varying, and the full (i.e., based on a 100 % emission reduction) source contribution obtained from linearly scaling the North American mean O3 sensitivities to a 20 % reduction in the EAS anthropogenic emissions may be underestimated by at least 10 %. The three boundary condition models' mean O3 sensitivities to the 20 % EAS emission perturbations are ∼ 8 % (May–June 2010)/∼ 11 % (2010 annual) lower than those estimated by eight global models, and the multi-model ensemble estimates are higher than the HTAP1 reported 2001 conditions. GEOS-Chem sensitivities indicate that the EAS anthropogenic NOx emissions matter more than the other EAS O3 precursors to the North American O3, qualitatively consistent with previous adjoint sensitivity calculations. In addition to the analyses on large spatial–temporal scales relative to the HTAP1, we also show results on subcontinental and event scales that are more relevant to the US air quality management. The EAS pollution impacts are weaker during observed O3 exceedances than on all days in most US regions except over some high-terrain western US rural/remote areas. Satellite O3 (TES, JPL–IASI, and AIRS) and carbon monoxide (TES and AIRS) products, along with surface measurements and model calculations, show that during certain episodes stratospheric O3 intrusions and the transported EAS pollution influenced O3 in the western and the eastern US differently. Free-running (i.e., without chemical data assimilation) global models underpredicted the transported background O3 during these episodes, posing difficulties for STEM to accurately simulate the surface O3 and its source contribution. Although we effectively improved the modeled O3 by incorporating satellite O3 (OMI and MLS) and evaluated the quality of the HTAP2 emission inventory with the Royal Netherlands Meteorological Institute–Ozone Monitoring Instrument (KNMI–OMI) nitrogen dioxide, using observations to evaluate and improve O3 source attribution still remains to be further explored.

The Measurements of Pollution in the Troposphere (MOPITT) instrument is the only satellite-borne sensor in operation that uses both thermal (TIR) and near-infrared (NIR) channels to estimate CO profiles. With more than 15 years (2000 to present) of validated multispectral observations, MOPITT provides the unique capability to separate CO in the lowermost troposphere (LMT, surface to 3 km (∼ 700 hPa)) from the free-tropospheric abundance. To extend this record, a new, hyper-spectral approach is presented here that will provide CO data products exceeding the capabilities of MOPITT by combining the short-wavelength infrared (SWIR, equivalent to the MOPITT NIR) channels from the TROPOspheric Monitoring Instrument (TROPOMI) to be launched aboard the European Sentinel 5 Precursor (S5p) satellite in 2016 and the TIR channels from the Cross-track Infrared Sounder (CrIS) aboard the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. We apply the MUlti-SpEctra, MUlti-SpEcies, Multi-SEnsors (MUSES) retrieval algorithm to quantify the potential of this joint CO product. CO profiles are retrieved from a single-footprint, full-spectral-resolution CrIS transect over Africa on 27–28 August 2013 coincident with significant biomass burning. Comparisons of collocated CrIS and MOPITT CO observations for the LMT show a mean difference of 2.8 ± 24.9 ppb, which is well within the estimated measurement uncertainty of both sensors. The estimated degrees of freedom (DOF) for CO signals from synergistic CrIS–TROPOMI retrievals are approximately 0.9 in the LMT and 1.3 above the LMT, which indicates that the LMT CO can be distinguished from the free troposphere, similar to MOPITT multispectral observations (0.8 in the LMT, and 1.1 above the LMT). In addition to increased sensitivity, the combined retrievals reduce measurement uncertainty, with ∼ 15 % error reduction in the LMT. With a daily global coverage and a combined spatial footprint of 14 km, the joint CrIS–TROPOMI measurements have the potential to extend and improve upon the MOPITT multispectral CO data records for the coming decade.

Ozone is an important greenhouse gas in terms of anthropogenic radiative forcing (RF). RF calculations for ozone were until recently entirely model based, and significant discrepancies were reported due to different model characteristics. However, new instantaneous radiative kernels (IRKs) calculated from hyperspectral thermal IR satellites have been able to help adjudicate between different climate model RF calculations. IRKs are defined as the sensitivity of the outgoing longwave radiation (OLR) flux with respect to the ozone vertical distribution in the full 9.6 μm band. Previous methods applied to measurements from the Tropospheric Emission Spectrometer (TES) on Aura rely on an anisotropy approximation for the angular integration. In this paper, we present a more accurate but more computationally expensive method to calculate these kernels. The method of direct integration is based on similar principles to the anisotropy approximation, but it deals more precisely with the integration of the Jacobians. We describe both methods and highlight their differences with respect to the IRKs and the ozone longwave radiative effect (LWRE), i.e., the radiative impact in OLR due to absorption by ozone, for both tropospheric and total columns, from measurements of the Infrared Atmospheric Sounding Interferometer (IASI) onboard MetOp-A. Biases between the two methods vary from −25 to +20 % for the LWRE, depending on the viewing angle. These biases point to the inadequacy of the anisotropy method, especially at nadir, suggesting that the TES-derived LWREs are biased low by around 25 % and that chemistry–climate model OLR biases with respect to TES are underestimated. In this paper we also exploit the sampling performance of IASI to obtain first daily global distributions of the LWRE, for 12 days (the 15th of each month) in 2011, calculated with the direct integration method. We show that the temporal variation of global and latitudinal averages of the LWRE shows patterns which are controlled by changes in the surface temperature and ozone variation due to specific processes, such as the ozone hole in the polar regions and stratospheric intrusions into the troposphere.

The upper troposphere and lower stratosphere (UTLS) represents a transition region between the more dynamically active troposphere and more stably stratified stratosphere. The region is characterized by strong gradients in the distribution of long-lived tracers, whose representation in models is sensitive to discrepancies in transport. We evaluate the GEOS-Chem model in the UTLS using carbon dioxide (CO2) and ozone (O3) observations from the HIAPER (The High-Performance Instrumented Airborne Platform for Environmental Research) Pole-to-Pole Observations (HIPPO) campaign in March 2010. GEOS-Chem CO2/O3 correlation suggests that there is a discrepancy in mixing across the tropopause in the model, which results in an overestimate of CO2 and an underestimate of O3 in the Arctic lower stratosphere. We assimilate stratospheric O3 data from the Optical Spectrograph and InfraRed Imager System (OSIRIS) and use the assimilated O3 fields together with the HIPPO CO2/O3 correlations to obtain an adjustment to the modeled CO2 profile in the Arctic UTLS (primarily between the 320 and 360 K isentropic surfaces). The HIPPO-derived adjustment corresponds to a sink of 0.60 Pg C for March–August 2010 in the Arctic. Imposing this adjustment results in a reduction in the CO2 sinks inferred from GOSAT observations for temperate North America, Europe, and tropical Asia of 19, 13, and 49 %, respectively. Conversely, the inversion increased the source of CO2 from tropical South America by 23 %. We find that the model also underestimates CO2 in the upper tropical and subtropical troposphere. Correcting for the underestimate in the model relative to HIPPO in the tropical upper troposphere leads to a reduction in the source from tropical South America by 77 %, and produces an estimated sink for tropical Asia that is only 19 % larger than the standard inversion (without the imposed source and sink). Globally, the inversion with the Arctic and tropical adjustment produces a sink of −6.64 Pg C, which is consistent with the estimate of −6.65 Pg C in the standard inversion. However, the standard inversion produces a stronger northern land sink by 0.98 Pg C to account for the CO2 overestimate in the high-latitude UTLS, suggesting that this UTLS discrepancy can impact the latitudinal distribution of the inferred sources and sinks. We find that doubling the model resolution from 4° × 5° to 2° × 2.5° enhances the CO2 vertical gradient in the high-latitude UTLS, and reduces the overestimate in CO2 in the extratropical lower stratosphere. Our results illustrate that discrepancies in the CO2 distribution in the UTLS can affect CO2 flux inversions and suggest the need for more careful evaluation of model errors in the UTLS.

We conduct analyses to assess how characteristics of observations of ozone and its precursors affect air quality forecasting and research. To carry out this investigation, we use a photochemical box model and its adjoint integrated with a Lagrangian 4D-variational data assimilation system. Using this framework in conjunction with pseudo-observations, we perform an ozone precursor source inversion and estimate surface emissions. We then assess the resulting improvement in ozone air quality prediction. We use an analytical model to conduct uncertainty analyses. Using this analytical tool, we address some key questions regarding how the characteristics of observations affect ozone precursor emission inversion and in turn ozone prediction. These questions include what the effect is of choosing which species to observe, of varying amounts of observation noise, of changing the observing frequency and the observation time during the diurnal cycle, and of how these different scenarios interact with different photochemical regimes. In our investigation we use three observed species scenarios: CO and NO2; ozone, CO, and NO2; and HCHO, CO and NO2. The photochemical model was set up to simulate a range of summertime polluted environments spanning NOx-(NO and NO2)-limited to volatile organic compound (VOC)-limited conditions. We find that as the photochemical regime changes, here is a variation in the relative importance of trace gas observations to be able to constrain emission estimates and to improve the subsequent ozone forecasts. For example, adding ozone observations to an NO2 and CO observing system is found to decrease ozone prediction error under NOx- and VOC-limited regimes, and complementing the NO2 and CO system with HCHO observations would improve ozone prediction in the transitional regime and under VOC-limited conditions. We found that scenarios observing ozone and HCHO with a relative observing noise of lower than 33 % were able to achieve ozone prediction errors of lower than 5 ppbv (parts per billion by volume). Further, only observing intervals of 3 h or shorter were able to consistently achieve ozone prediction errors of 5 ppbv or lower across all photochemical regimes. Making observations closer to the prediction period and either in the morning or afternoon rush hour periods made greater improvements for ozone prediction: 0.2–0.3 ppbv for the morning rush hour and from 0.3 to 0.8 ppbv for the afternoon compared to only 0–0.1 ppbv for other times of the day. Finally, we made two complementary analyses that show that our conclusions are insensitive to the assumed diurnal emission cycle and to the choice of which VOC species emission to estimate using our framework. These questions will address how different types of observing platform, e.g. geostationary satellites or ground monitoring networks, could support future air quality research and forecasting.

We use 2009–2011 space-borne methane observations from the Greenhouse Gases Observing SATellite (GOSAT) to estimate global and North American methane emissions with 4° × 5° and up to 50 km × 50 km spatial resolution, respectively. GEOS-Chem and GOSAT data are first evaluated with atmospheric methane observations from surface and tower networks (NOAA/ESRL, TCCON) and aircraft (NOAA/ESRL, HIPPO), using the GEOS-Chem chemical transport model as a platform to facilitate comparison of GOSAT with in situ data. This identifies a high-latitude bias between the GOSAT data and GEOS-Chem that we correct via quadratic regression. Our global adjoint-based inversion yields a total methane source of 539 Tg a−1 with some important regional corrections to the EDGARv4.2 inventory used as a prior. Results serve as dynamic boundary conditions for an analytical inversion of North American methane emissions using radial basis functions to achieve high resolution of large sources and provide error characterization. We infer a US anthropogenic methane source of 40.2–42.7 Tg a−1, as compared to 24.9–27.0 Tg a−1 in the EDGAR and EPA bottom-up inventories, and 30.0–44.5 Tg a−1 in recent inverse studies. Our estimate is supported by independent surface and aircraft data and by previous inverse studies for California. We find that the emissions are highest in the southern–central US, the Central Valley of California, and Florida wetlands; large isolated point sources such as the US Four Corners also contribute. Using prior information on source locations, we attribute 29–44 % of US anthropogenic methane emissions to livestock, 22–31 % to oil/gas, 20 % to landfills/wastewater, and 11–15 % to coal. Wetlands contribute an additional 9.0–10.1 Tg a−1.

We have examined the utility of retrieved column-averaged, dry-air mole fractions of CO2 (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT) for quantifying monthly, regional flux estimates of CO2, using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system. We focused on assessing the potential impact of biases in the GOSAT CO2 data on the regional flux estimates. Using different screening and bias correction approaches, we selected three different subsets of the GOSAT XCO2 data for the 4D-Var inversion analyses, and found that the inferred global fluxes were consistent across the three XCO2 inversions. However, the GOSAT observational coverage was a challenge for the regional flux estimates. In the northern extratropics, the inversions were more sensitive to North American fluxes than to European and Asian fluxes due to the lack of observations over Eurasia in winter and over eastern and southern Asia in summer. The regional flux estimates were also sensitive to the treatment of the residual bias in the GOSAT XCO2 data. The largest differences obtained were for temperate North America and temperate South America, for which the largest spread between the inversions was 1.02 and 0.96 Pg C, respectively. In the case of temperate North America, one inversion suggested a strong source, whereas the second and third XCO2 inversions produced a weak and strong sink, respectively. Despite the discrepancies in the regional flux estimates between the three XCO2 inversions, the a posteriori CO2 distributions were in good agreement (with a mean difference between the three inversions of typically less than 0.5 ppm) with independent data from the Total Carbon Column Observing Network (TCCON), the surface flask network, and from the HIAPER Pole-to-Pole Observations (HIPPO) aircraft campaign. The discrepancy in the regional flux estimates from the different inversions, despite the agreement of the global flux estimates suggests the need for additional work to determine the minimum spatial scales at which we can reliably quantify the fluxes using GOSAT XCO2. The fact that the a posteriori CO2 from the different inversions were in good agreement with the independent data although the regional flux estimates differed significantly, suggests that innovative ways of exploiting existing data sets, and possibly additional observations, are needed to better evaluate the inferred regional flux estimates.

The version 6 cloud products of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) instrument suite are described. The cloud top temperature, pressure, and height and effective cloud fraction are now reported at the AIRS field-of-view (FOV) resolution. Significant improvements in cloud height assignment over version 5 are shown with FOV-scale comparisons to cloud vertical structure observed by the CloudSat 94 GHz radar and the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). Cloud thermodynamic phase (ice, liquid, and unknown phase), ice cloud effective diameter (De), and ice cloud optical thickness (τ) are derived using an optimal estimation methodology for AIRS FOVs, and global distributions for 2007 are presented. The largest values of τ are found in the storm tracks and near convection in the tropics, while De is largest on the equatorial side of the midlatitude storm tracks in both hemispheres, and lowest in tropical thin cirrus and the winter polar atmosphere. Over the Maritime Continent the diurnal variability of τ is significantly larger than for the total cloud fraction, ice cloud frequency, and De, and is anchored to the island archipelago morphology. Important differences are described between northern and southern hemispheric midlatitude cyclones using storm center composites. The infrared-based cloud retrievals of AIRS provide unique, decadal-scale and global observations of clouds over portions of the diurnal and annual cycles, and capture variability within the mesoscale and synoptic scales at all latitudes.

In this analysis, Tropospheric Emission Spectrometer (TES) V004 nadir ozone (O3) profiles are validated with more than 4400 coinciding ozonesonde measurements taken across the world from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) during the period 2005–2010. The TES observation operator was applied to the sonde data to ensure a consistent comparison between TES and ozonesonde data, i.e. without the influence of the a priori O3 profile needed to regulate the retrieval. Generally, TES V004 O3 retrievals are biased high by 2–7 ppbv (7–15%) in the troposphere, consistent with validation results from earlier studies. Because of two degrees of freedom for signal in the troposphere, we can distinguish between upper and lower troposphere mean biases, respectively ranging from −0.4 to +13.3 ppbv for the upper troposphere and +3.9 to +6.0 ppbv for the lower troposphere. Focusing on the 464 hPa retrieval level, broadly representative of the free tropospheric O3, we find differences in the TES biases for the tropics (+3 ppbv, +7%), sub-tropics (+5 ppbv, +11%), and northern (+7 ppbv, +13%) and southern mid-latitudes (+4 ppbv, +10%). The relatively long-term record (6 yr) of TES–ozonesonde comparisons allowed us to quantify temporal variations in TES biases at 464 hPa. We find that there are no discernable biases in each of these latitudinal bands; temporal variations in the bias are typically within the uncertainty of the difference between TES and ozonesondes. Establishing these bias patterns is important in order to make meaningful use of TES O3 data in applications such as model evaluation, trend analysis, or data assimilation.

We use simultaneous observations of tropospheric ozone and outgoing longwave radiation (OLR) sensitivity to tropospheric ozone from the Tropospheric Emission Spectrometer (TES) to evaluate model tropospheric ozone and its effect on OLR simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES ozone for 2005–2010. These ozone biases have a significant impact on the OLR. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean tropospheric ozone low bias leads up to 120 mW m−2 OLR high bias locally but zonally compensating errors reduce the global OLR high bias to 39 ± 41 m Wm−2 relative to TES data. We show that there is a correlation (R2 = 0.59) between the magnitude of the ACCMIP OLR bias and the deviation of the ACCMIP preindustrial to present day (1750–2010) ozone radiative forcing (RF) from the ensemble ozone RF mean. However, this correlation is driven primarily by models whose absolute OLR bias from tropospheric ozone exceeds 100 m Wm−2. Removing these models leads to a mean ozone radiative forcing of 394 ± 42 m Wm−2. The mean is about the same and the standard deviation is about 30% lower than an ensemble ozone RF of 384 ± 60 m Wm−2 derived from 14 of the 16 ACCMIP models reported in a companion ACCMIP study. These results point towards a profitable direction of combining satellite observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

We present satellite based ozone profile estimates derived by combining radiances measured at thermal infrared (TIR) wavelengths from the Aura Tropospheric Emission Spectrometer (TES) and ultraviolet (UV) wavelengths measured by the Aura Ozone Monitoring Instrument (OMI). The advantage of using these combined wavelengths and instruments for sounding ozone over either instrument alone is improved sensitivity near the surface as well as the capability to consistently resolve the lower troposphere, upper troposphere, and lower stratosphere for scenes with varying geophysical states. For example, the vertical resolution of ozone estimates from either TES or OMI varies strongly by surface albedo and temperature. Typically, TES provides 1.6 degrees of freedom for signal (DOFS) and OMI provides less than 1 DOFS in the troposphere. The combination provides 2 DOFS in the troposphere with approximately 0.4 DOFS for near surface ozone (surface to 700 hPa). We evaluated these new ozone profile estimates with ozonesonde measurements and found that calculated errors for the joint TES and OMI ozone profile estimates are in reasonable agreement with actual errors as derived by the root-mean-square (RMS) difference between the ozonesondes and the joint TES/OMI ozone estimates. We also used a common a priori profile in the retrievals in order to evaluate the capability of different retrieval approaches on capturing near-surface ozone variability. We found that the vertical resolution of the joint TES/OMI ozone profile estimates shows significant improvements on quantifying variations in near-surface ozone with RMS differences of 49.9% and correlation coefficient of R = 0.58 for the TES/OMI near-surface estimates as compared to 67.2% RMS difference and R = 0.33 for TES and 115.8% RMS difference and R = 0.09 for OMI. This comparison removes the impacts of using the climatological a priori in the retrievals. However, it results in artificially large sonde/retrieval differences. The TES/OMI ozone profiles from the production code of joint retrievals will use climatological a priori and therefore will have more realistic ozone estimates than those from using a common a priori volume mixing ratio profile.

Ozone (O3) from 17 atmospheric chemistry models taking part in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) has been used to calculate tropospheric ozone radiative forcings (RFs). All models applied a common set of anthropogenic emissions, which are better constrained for the present-day than the past. Future anthropogenic emissions follow the four Representative Concentration Pathway (RCP) scenarios, which define a relatively narrow range of possible air pollution emissions. We calculate a value for the pre-industrial (1750) to present-day (2010) tropospheric ozone RF of 410 mW m−2. The model range of pre-industrial to present-day changes in O3 produces a spread (±1 standard deviation) in RFs of ±17%. Three different radiation schemes were used – we find differences in RFs between schemes (for the same ozone fields) of ±10%. Applying two different tropopause definitions gives differences in RFs of ±3%. Given additional (unquantified) uncertainties associated with emissions, climate-chemistry interactions and land-use change, we estimate an overall uncertainty of ±30% for the tropospheric ozone RF. Experiments carried out by a subset of six models attribute tropospheric ozone RF to increased emissions of methane (44±12%), nitrogen oxides (31 ± 9%), carbon monoxide (15 ± 3%) and non-methane volatile organic compounds (9 ± 2%); earlier studies attributed more of the tropospheric ozone RF to methane and less to nitrogen oxides. Normalising RFs to changes in tropospheric column ozone, we find a global mean normalised RF of 42 mW m−2 DU−1, a value similar to previous work. Using normalised RFs and future tropospheric column ozone projections we calculate future tropospheric ozone RFs (mW m−2; relative to 1750) for the four future scenarios (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) of 350, 420, 370 and 460 (in 2030), and 200, 300, 280 and 600 (in 2100). Models show some coherent responses of ozone to climate change: decreases in the tropical lower troposphere, associated with increases in water vapour; and increases in the sub-tropical to mid-latitude upper troposphere, associated with increases in lightning and stratosphere-to-troposphere transport. Climate change has relatively small impacts on global mean tropospheric ozone RF.

Present day tropospheric ozone and its changes between 1850 and 2100 are considered, analysing 15 global models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean compares well against present day observations. The seasonal cycle correlates well, except for some locations in the tropical upper troposphere. Most (75 %) of the models are encompassed with a range of global mean tropospheric ozone column estimates from satellite data, but there is a suggestion of a high bias in the Northern Hemisphere and a low bias in the Southern Hemisphere, which could indicate deficiencies with the ozone precursor emissions. Compared to the present day ensemble mean tropospheric ozone burden of 337 ± 23 Tg, the ensemble mean burden for 1850 time slice is ~30% lower. Future changes were modelled using emissions and climate projections from four Representative Concentration Pathways (RCPs). Compared to 2000, the relative changes in the ensemble mean tropospheric ozone burden in 2030 (2100) for the different RCPs are: −4% (−16%) for RCP2.6, 2% (−7%) for RCP4.5, 1% (−9%) for RCP6.0, and 7% (18%) for RCP8.5. Model agreement on the magnitude of the change is greatest for larger changes. Reductions in most precursor emissions are common across the RCPs and drive ozone decreases in all but RCP8.5, where doubled methane and a 40–150% greater stratospheric influx (estimated from a subset of models) increase ozone. While models with a high ozone burden for the present day also have high ozone burdens for the other time slices, no model consistently predicts large or small ozone changes; i.e. the magnitudes of the burdens and burden changes do not appear to be related simply, and the models are sensitive to emissions and climate changes in different ways. Spatial patterns of ozone changes are well correlated across most models, but are notably different for models without time evolving stratospheric ozone concentrations. A unified approach to ozone budget specifications and a rigorous investigation of the factors that drive tropospheric ozone is recommended to help future studies attribute ozone changes and inter-model differences more clearly.

Atmospheric carbon monoxide (CO) distributions are controlled by anthropogenic emissions, biomass burning, transport and oxidation by reaction with the hydroxyl radical (OH). Quantifying trends in CO is therefore important for understanding changes related to all of these contributions. Here we present a comprehensive record of satellite observations from 2000 through 2011 of total column CO using the available measurements from nadir-viewing thermal infrared instruments: MOPITT, AIRS, TES and IASI. We examine trends for CO in the Northern and Southern Hemispheres along with regional trends for Eastern China, Eastern USA, Europe and India. We find that all the satellite observations are consistent with a modest decreasing trend ~ −1 % yr−1 in total column CO over the Northern Hemisphere for this time period and a less significant, but still decreasing trend in the Southern Hemisphere. Although decreasing trends in the United States and Europe have been observed from surface CO measurements, we also find a decrease in CO over E. China that, to our knowledge, has not been reported previously. Some of the interannual variability in the observations can be explained by global fire emissions, but the overall decrease needs further study to understand the implications for changes in anthropogenic emissions.

The impacts of transported background (TBG) pollutants on western US ozone (O3) distributions in summer 2008 are studied using the multi-scale Sulfur Transport and dEposition Modeling system. Forward sensitivity simulations show that TBG contributes ~30–35 ppb to the surface Monthly mean Daily maximum 8-h Average O3 (MDA8) over Pacific Southwest (US Environmental Protection Agency (EPA) Region 9, including California, Nevada and Arizona) and Pacific Northwest (EPA Region 10, including Washington, Oregon and Idaho), and ~10–17 ppm-h to the secondary standard metric "W126 monthly index" over EPA Region 9 and ~3–4 ppm-h over Region 10. The strongest TBG impacts on W126 occur over the grass/shrub-covered regions. Among TBG pollutants, O3 is the major contributor to surface O3, while peroxyacetyl nitrate is the most important O3 precursor species. W126 shows larger responses than MDA8 to perturbations in TBG and stronger non-linearity to the magnitude of perturbations. The TBG impacts on both metrics overall negatively correlate to model vertical resolution and positively correlate to the horizontal resolution. The mechanisms that determine TBG contributions and their variation are analyzed using trajectories and the receptor-based adjoint sensitivity analysis, which demonstrate the connection between the surface O3 and O3 aloft (at ~1–4 km) 1–2 days earlier. The probabilities of airmasses originating from Mt. Bachelor (2.7 km) and 2.5 km above Trinidad Head (THD) entraining into the boundary layer reach daily maxima of 66% and 34% at ~03:00 p.m. Pacific Daylight Time (PDT), respectively, and stay above 50% during 09:00 a.m.–04:00 p.m. PDT for those originating 1.5 km above California's South Coast. Assimilation of the surface in-situ measurements significantly reduced the errors in the modeled surface O3 during a long-range transport episode by ~5 ppb on average (up to ~17 ppb) and increased the estimated TBG contributions by ~3 ppb. Available O3 vertical profiles from Tropospheric Emission Spectrometer (TES), Ozone Monitoring Instrument (OMI) and THD sonde identified this transport event, but assimilation of these observations in this case did not efficiently improve the O3 distributions except near the sampling locations, due to their limited spatiotemporal resolution and/or possible uncertainties.

We have analysed the sensitivity of the tropospheric ozone distribution over North America and the North Atlantic to boreal biomass burning emissions during the summer of 2010 using the GEOS-Chem 3-D global tropospheric chemical transport model and observations from in situ and satellite instruments. We show that the model ozone distribution is consistent with observations from the Pico Mountain Observatory in the Azores, ozonesondes across Canada, and the Tropospheric Emission Spectrometer (TES) and Infrared Atmospheric Sounding Instrument (IASI) satellite instruments. Mean biases between the model and observed ozone mixing ratio in the free troposphere were less than 10 ppbv. We used the adjoint of GEOS-Chem to show the model ozone distribution in the free troposphere over Maritime Canada is largely sensitive to NOx emissions from biomass burning sources in Central Canada, lightning sources in the central US, and anthropogenic sources in the eastern US and south-eastern Canada. We also used the adjoint of GEOS-Chem to evaluate the Fire Locating And Monitoring of Burning Emissions (FLAMBE) inventory through assimilation of CO observations from the Measurements Of Pollution In The Troposphere (MOPITT) satellite instrument. The CO inversion showed that, on average, the FLAMBE emissions needed to be reduced to 89% of their original values, with scaling factors ranging from 12% to 102%, to fit the MOPITT observations in the boreal regions. Applying the CO scaling factors to all species emitted from boreal biomass burning sources led to a decrease of the model tropospheric distributions of CO, PAN, and NOx by as much as −20 ppbv, −50 pptv, and −20 pptv respectively. The modification of the biomass burning emission estimates reduced the model ozone distribution by approximately −3 ppbv (−8%) and on average improved the agreement of the model ozone distribution compared to the observations throughout the free troposphere, reducing the mean model bias from 5.5 to 4.0 ppbv for the Pico Mountain Observatory, 3.0 to 0.9 ppbv for ozonesondes, 2.0 to 0.9 ppbv for TES, and 2.8 to 1.4 ppbv for IASI.

Ensemble climate model simulations used for the Intergovernmental Panel on Climate Change (IPCC) assessments have become important tools for exploring the response of the Earth System to changes in anthropogenic and natural forcings. The systematic evaluation of these models through global satellite observations is a critical step in assessing the uncertainty of climate change projections. This paper presents the technical steps required for using nadir sun-synchronous infrared satellite observations for multi-model evaluation and the uncertainties associated with each step. This is motivated by need to use satellite observations to evaluate climate models. We quantified the implications of the effect of satellite orbit and spatial coverage, the effect of variations in vertical sensitivity as quantified by the observation operator and the impact of averaging the operators for use with monthly-mean model output. We calculated these biases in ozone, carbon monoxide, atmospheric temperature and water vapour by using the output from two global chemistry climate models (ECHAM5-MOZ and GISS-PUCCINI) and the observations from the Tropospheric Emission Spectrometer (TES) instrument on board the NASA-Aura satellite from January 2005 to December 2008. The results show that sampling and monthly averaging of the observation operators produce zonal-mean biases of less than ±3 % for ozone and carbon monoxide throughout the entire troposphere in both models. Water vapour sampling zonal-mean biases were also within the insignificant range of ±3 % (that is ±0.14 g kg−1) in both models. Sampling led to a temperature zonal-mean bias of ±0.3 K over the tropical and mid-latitudes in both models, and up to −1.4 K over the boundary layer in the higher latitudes. Using the monthly average of temperature and water vapour operators lead to large biases over the boundary layer in the southern-hemispheric higher latitudes and in the upper troposphere, respectively. Up to 8 % bias was calculated in the upper troposphere water vapour due to monthly-mean operators, which may impact the detection of water vapour feedback in response to global warming. Our results reveal the importance of using the averaging kernel and the a priori profiles to account for the limited vertical resolution and clouds of a nadir observation during model application. Neglecting the observation operators resulted in large biases, which are more than 60 % for ozone, ±30 % for carbon monoxide, and range between −1.5 K and 5 K for atmospheric temperature, and between −60 % and 100 % for water vapour.

We infer CO2 surface fluxes using satellite observations of mid-tropospheric CO2 from the Tropospheric Emission Spectrometer (TES) and measurements of CO2 from surface flasks in a time-independent inversion analysis based on the GEOS-Chem model. Using TES CO2 observations over oceans, spanning 40° S–40° N, we find that the horizontal and vertical coverage of the TES and flask data are complementary. This complementarity is demonstrated by combining the datasets in a joint inversion, which provides better constraints than from either dataset alone, when a posteriori CO2 distributions are evaluated against independent ship and aircraft CO2 data. In particular, the joint inversion offers improved constraints in the tropics where surface measurements are sparse, such as the tropical forests of South America. Aggregating the annual surface-to-atmosphere fluxes from the joint inversion for the year 2006 yields −1.13±0.21 Pg C for the global ocean, −2.77±0.20 Pg C for the global land biosphere and −3.90±0.29 Pg C for the total global natural flux (defined as the sum of all biospheric, oceanic, and biomass burning contributions but excluding CO2 emissions from fossil fuel combustion). These global ocean and global land fluxes are shown to be near the median of the broad range of values from other inversion results for 2006. To achieve these results, a bias in TES CO2 in the Southern Hemisphere was assessed and corrected using aircraft flask data, and we demonstrate that our results have low sensitivity to variations in the bias correction approach. Overall, this analysis suggests that future carbon data assimilation systems can benefit by integrating in situ and satellite observations of CO2 and that the vertical information provided by satellite observations of mid-tropospheric CO2 combined with measurements of surface CO2, provides an important additional constraint for flux inversions.

The correlation between measured tropospheric ozone (O3) and carbon monoxide (CO) has been used extensively in tropospheric chemistry studies to explore the photochemical characteristics of different regions and to evaluate the ability of models to capture these characteristics. Here, we present the first study that uses multi-year, global, vertically resolved, simultaneous and collocated O3 and CO satellite (Tropospheric Emission Spectrometer) measurements, to determine this correlation in the middle/lower free troposphere for two different seasons, and to evaluate two chemistry-climate models. We find results that are fairly robust across different years, altitudes and timescales considered, which indicates that the correlation maps presented here could be used in future model evaluations. The highest positive correlations (around 0.8) are found in the northern Pacific during summer, which is a common feature in the observations and the G-PUCCINI model. We make quantitative comparisons between the models using a single-figure metric (C), which we define as the correlation coefficient between the modeled and the observed O3-CO correlations for different regions of the globe. On a global scale, the G-PUCCINI model shows a good performance in the summer (C=0.71) and a satisfactory performance in the winter (C=0.52). It captures midlatitude features very well, especially in the summer, whereas the performance in regions like South America or Central Africa is weaker. The UKCA model (C=0.46/0.15 for July–August/December–January on a global scale) performs better in certain regions, such as the tropics in winter, and it captures some of the broad characteristics of summer extratropical correlations, but it systematically underestimates the O3-CO correlations over much of the globe. It is noteworthy that the correlations look very different in the two models, even though the ozone distributions are similar. This demonstrates that this technique provides a powerful global constraint for understanding modeled tropospheric chemical processes. We investigated the sources of the correlations by performing a series of sensitivity experiments. In these, the sign of the correlation is, in most cases, insensitive to removing different individual emissions, but its magnitude changes downwind of emission regions when applying such perturbations. Interestingly, we find that the O3-CO correlation does not solely reflect the strength of O3 photochemical production, as often assumed by earlier studies, but is more complicated and may reflect a mixture of different processes such as transport.

The use of global three-dimensional (3-D) models with satellite observations of CO2 in inverse modeling studies is an area of growing importance for understanding Earth's carbon cycle. Here we use the GEOS-Chem model (version 8-02-01) CO2 mode with multiple modifications in order to assess their impact on CO2 forward simulations. Modifications include CO2 surface emissions from shipping (~0.19 Pg C yr−1), 3-D spatially-distributed emissions from aviation (~0.16 Pg C yr−1), and 3-D chemical production of CO2 (~1.05 Pg C yr−1). Although CO2 chemical production from the oxidation of CO, CH4 and other carbon gases is recognized as an important contribution to global CO2, it is typically accounted for by conversion from its precursors at the surface rather than in the free troposphere. We base our model 3-D spatial distribution of CO2 chemical production on monthly-averaged loss rates of CO (a key precursor and intermediate in the oxidation of organic carbon) and apply an associated surface correction for inventories that have counted emissions of CO2 precursors as CO2. We also explore the benefit of assimilating satellite observations of CO into GEOS-Chem to obtain an observation-based estimate of the CO2 chemical source. The CO assimilation corrects for an underestimate of atmospheric CO abundances in the model, resulting in increases of as much as 24% in the chemical source during May–June 2006, and increasing the global annual estimate of CO2 chemical production from 1.05 to 1.18 Pg C. Comparisons of model CO2 with measurements are carried out in order to investigate the spatial and temporal distributions that result when these new sources are added. Inclusion of CO2 emissions from shipping and aviation are shown to increase the global CO2 latitudinal gradient by just over 0.10 ppm (~3%), while the inclusion of CO2 chemical production (and the surface correction) is shown to decrease the latitudinal gradient by about 0.40 ppm (~10%) with a complex spatial structure generally resulting in decreased CO2 over land and increased CO2 over the oceans. Since these CO2 emissions are omitted or misrepresented in most inverse modeling work to date, their implementation in forward simulations should lead to improved inverse modeling estimates of terrestrial biospheric fluxes.

We compare Tropospheric Emission Spectrometer (TES) versions 3 and 4, V003 and V004, respectively, nadir-stare ozone profiles with ozonesonde profiles from the Arctic Intensive Ozonesonde Network Study (ARCIONS, http://croc.gsfc.nasa.gov/arcions/ during the Arctic Research on the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field mission. The ozonesonde data are from launches timed to match Aura's overpass, where 11 coincidences spanned 44° N to 71° N from April to July 2008. Using the TES "stare" observation mode, 32 observations are taken over each coincidental ozonesonde launch. By effectively sampling the same air mass 32 times, comparisons are made between the empirically-calculated random errors to the expected random errors from measurement noise, temperature and interfering species, such as water. This study represents the first validation of high latitude (>70°) TES ozone. We find that the calculated errors are consistent with the actual errors with a similar vertical distribution that varies between 5% and 20% for V003 and V004 TES data. In general, TES ozone profiles are positively biased (by less than 15%) from the surface to the upper-troposphere (~1000 to 100 hPa) and negatively biased (by less than 20%) from the upper-troposphere to the lower-stratosphere (100 to 30 hPa) when compared to the ozonesonde data. Lastly, for V003 and V004 TES data between 44° N and 71° N there is variability in the mean biases (from −14 to +15%), mean theoretical errors (from 6 to 13%), and mean random errors (from 9 to 19%).

We present carbon dioxide (CO2) estimates from the Tropospheric Emission Spectrometer (TES) on the EOS-Aura satellite launched in 2004. For observations between 40° S and 45° N, we find about 1 degree of freedom with peak sensitivity at 511 hPa. The estimated error is ~10 ppm for a single target and 1.3–2.3 ppm for monthly averages on spatial scales of 20°×30°. Monthly spatially-averaged TES data from 2005–2008 processed with a uniform initial guess and prior are compared to CONTRAIL aircraft data over the Pacific ocean, aircraft data at the Southern Great Plains (SGP) ARM site in the southern US, and the Mauna Loa and Samoa surface stations. Comparisons to Mauna Loa data show a correlation of 0.92, a standard deviation of 1.3 ppm, a predicted error of 1.2 ppm, and a ~2% low bias, which is subsequently corrected. Comparisons to SGP aircraft data over land show a correlation of 0.67 and a standard deviation of 2.3 ppm. TES data between 40° S and 45° N for 2006–2007 are compared to surface flask data, GLOBALVIEW, the Atmospheric Infrared Sounder (AIRS), and CarbonTracker. Comparison to GLOBALVIEW-CO2 ocean surface sites shows a correlation of 0.60 which drops when TES is offset in latitude, longitude, or time. At these same locations, TES shows a 0.62 and 0.67 correlation to CarbonTracker at the surface and 5 km, respectively. We also conducted an observing system simulation experiment to assess the potential utility of the TES data for inverse modeling of CO2 fluxes. We find that if biases in the data and model are well characterized, the averaged data have the potential to provide sufficient information to significantly reduce uncertainty on annual estimates of regional CO2 sources and sinks. Averaged pseudo-data at 10°×10° reduced uncertainty in flux estimates by as much as 70% for some tropical regions.

We conduct an inverse modeling analysis of measurements of atmospheric CO from the TES and MOPITT satellite instruments using the GEOS-Chem global chemical transport model to quantify emissions of CO in the tropics in November 2004. We also assess the consistency of the information provided by TES and MOPITT on surface emissions of CO. We focus on the tropics in November 2004, during the biomass burning season, because TES observations of CO and O3 and MOPITT observations of CO reveal significantly greater abundances of these gases than simulated by the GEOS-Chem model during that period. We find that both datasets suggest substantially greater emissions of CO from sub-equatorial Africa and the Indonesian/Australian region than in the climatological emissions in the model. The a posteriori emissions from sub-equatorial Africa based on TES and MOPITT data were 173 Tg CO/yr and 184 Tg CO/yr, respectively, compared to the a priori of 95 Tg CO/yr. In the Indonesian/Australian region, the a posteriori emissions inferred from TES and MOPITT data were 155 Tg CO/yr and 185 Tg CO/yr, respectively, whereas the a priori was 69 Tg CO/yr. The differences between the a posteriori emission estimates obtained from the two datasets are generally less than 20%. The a posteriori emissions significantly improve the simulated distribution of CO, however, large regional residuals remain, and are likely due to systematic errors in the analysis. Reducing these residuals and improving the accuracy of top-down emission estimates will require better characterization of systematic errors in the observations and the model (chemistry and transport).

The impact of surface emissions on the zonal structure of tropical tropospheric ozone and carbon monoxide is investigated for November 2004 using satellite observations, in-situ measurements, and chemical transport models in conjunction with inverse-estimated surface emissions.Vertical ozone profiles from the Tropospheric Emission Spectrometer (TES) and ozone sonde measurements from the Southern Hemisphere Additional Ozonesondes (SHADOZ) network show elevated concentrations of ozone over Indonesia and Australia (60–70 ppb) in the lower troposphere against the backdrop of the well-known zonal "wave-one" pattern with ozone concentrations of (70–80 ppb) centered over the Atlantic . Observational evidence from TES CO vertical profiles and Ozone Monitoring Instrument (OMI) NO2 columns point to regional surface emissions as an important contributor to the elevated ozone over Indonesia. This contribution is investigated with the GEOS-Chem chemistry and transport model using surface emission estimates derived from an optimal inverse model, which was constrained by TES and Measurements Of Pollution In The Troposphere (MOPITT) CO profiles (Jones et al., 2009). These a posteriori estimates, which were over a factor of 2 greater than climatological emissions, reduced differences between GEOS-Chem and TES ozone observations by 30–40% over Indonesia. The response of the free tropospheric chemical state to the changes in these emissions is investigated for ozone, CO, NOx, and PAN. Model simulations indicate that ozone over Indonesian/Australian is sensitive to regional changes in surface emissions of NOx but relatively insensitive to lightning NOx. Over sub-equatorial Africa and South America, free tropospheric NOx was reduced in response to increased surface emissions potentially muting ozone production.

We use an ensemble of aircraft, satellite, sonde, and surface observations for April–May 2006 (NASA/INTEX-B aircraft campaign) to better understand the mechanisms for transpacific ozone pollution and its implications for North American air quality. The observations are interpreted with a global 3-D chemical transport model (GEOS-Chem). OMI NO2 satellite observations constrain Asian anthropogenic NOx emissions and indicate a factor of 2 increase from 2000 to 2006 in China. Satellite observations of CO from AIRS and TES indicate two major events of Asian transpacific pollution during INTEX-B. Correlation between TES CO and ozone observations shows evidence for transpacific ozone pollution. The semi-permanent Pacific High and Aleutian Low cause splitting of transpacific pollution plumes over the Northeast Pacific. The northern branch circulates around the Aleutian Low and has little impact on North America. The southern branch circulates around the Pacific High and some of that air impacts western North America. Both aircraft measurements and model results show sustained ozone production driven by peroxyacetylnitrate (PAN) decomposition in the southern branch, roughly doubling the transpacific influence from ozone produced in the Asian boundary layer. Model simulation of ozone observations at Mt. Bachelor Observatory in Oregon (2.7 km altitude) indicates a mean Asian ozone pollution contribution of 9±3 ppbv to the mean observed concentration of 54 ppbv, reflecting mostly an enhancement in background ozone rather than episodic Asian plumes. Asian pollution enhanced surface ozone concentrations by 5–7 ppbv over western North America in spring 2006. The 2000–2006 rise in Asian anthropogenic emissions increased this influence by 1–2 ppbv.

Non-linear maximum a posteriori (MAP) estimates of atmospheric profiles from the Tropospheric Emission Spectrometer (TES) contains a priori information that may vary geographically, which is a confounding factor in the analysis and physical interpretation of an ensemble of profiles. One mitigation strategy is to transform profile estimates to a common prior using a linear operation thereby facilitating the interpretation of profile variability. However, this operation is dependent on the assumption of not worse than moderate non-linearity near the solution of the non-linear estimate. The robustness of this assumption is tested by comparing atmospheric retrievals from the Tropospheric Emission Spectrometer processed with a uniform prior with those processed with a variable prior and converted to a uniform prior following the non-linear retrieval. Linearly converting the prior following a non-linear retrieval is shown to have a minor effect on the results as compared to a non-linear retrieval using a uniform prior when compared to the expected total error, with less than 10% of the change in the prior ending up as unbiased fluctuations in the profile estimate results.

No abstract available.