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The hydroxyl radical (OH) fuels atmospheric chemical cycling as the main sink for methane and a driver of the formation and loss of many air pollutants, but direct OH observations are sparse. We develop and evaluate an observation-based proxy for short-term, spatial variations in OH (ProxyOH) in the remote marine troposphere using comprehensive measurements from the NASA Atmospheric Tomography (ATom) airborne campaign. ProxyOH is a reduced form of the OH steady-state equation representing the dominant OH production and loss pathways in the remote marine troposphere, according to box model simulations of OH constrained with ATom observations. ProxyOH comprises only eight variables that are generally observed by routine ground- or satellite-based instruments. ProxyOH scales linearly with in situ [OH] spatial variations along the ATom flight tracks (median r2 = 0.90, interquartile range = 0.80 to 0.94 across 2-km altitude by 20° latitudinal regions). We deconstruct spatial variations in ProxyOH as a first-order approximation of the sensitivity of OH variations to individual terms. Two terms modulate within-region ProxyOH variations-water vapor (H2O) and, to a lesser extent, nitric oxide (NO). This implies that a limited set of observations could offer an avenue for observation-based mapping of OH spatial variations over much of the remote marine troposphere. Both H2O and NO are expected to change with climate, while NO also varies strongly with human activities. We also illustrate the utility of ProxyOH as a process-based approach for evaluating intermodel differences in remote marine tropospheric OH.

The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10s (2km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, and other organic nitrates), consisting of 146494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80%-90% of the total reactivity lies in the top 50% of the parcels and 25%-35% in the top 10%, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100 km) differ only slightly from the 2km ATom 10s data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find generally good agreement with the reactivity rates for O3 and CH4. Models distinctly underestimate O3 production below 2km relative to the mid-troposphere, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation. This paper presents a corrected version of the paper published under the same authors and title (sans "corrected") as 10.5194/acp-21-13729-2021.

The continued warming of the Arctic could release vast stores of carbon into the atmosphere from high-latitude ecosystems, especially from thawing permafrost. Increasing uptake of carbon dioxide (CO2) by vegetation during longer growing seasons may partially offset such release of carbon. However, evidence of significant net annual release of carbon from site-level observations and model simulations across tundra ecosystems has been inconclusive. To address this knowledge gap, we combined top-down observations of atmospheric CO2 concentration enhancements from aircraft and a tall tower, which integrate ecosystem exchange over large regions, with bottom-up observed CO2 fluxes from tundra environments and found that the Alaska North Slope is not a consistent net source nor net sink of CO2 to the atmosphere (ranging from -6 to +6TgCyr-1 for 2012-2017). Our analysis suggests that significant biogenic CO2 fluxes from unfrozen terrestrial soils, and likely inland waters, during the early cold season (September-December) are major factors in determining the net annual carbon balance of the North Slope, implying strong sensitivity to the rapidly warming freeze-up period. At the regional level, we find no evidence of the previously reported large late-cold-season (January-April) CO2 emissions to the atmosphere during the study period. Despite the importance of the cold-season CO2 emissions to the annual total, the interannual variability in the net CO2 flux is driven by the variability in growing season fluxes. During the growing season, the regional net CO2 flux is also highly sensitive to the distribution of tundra vegetation types throughout the North Slope. This study shows that quantification and characterization of year-round CO2 fluxes from the heterogeneous terrestrial and aquatic ecosystems in the Arctic using both site-level and atmospheric observations are important to accurately project the Earth system response to future warming.

Formic acid (HCOOH) is an important component of atmospheric acidity but its budget is poorly understood, with prior observations implying substantial missing sources. Here we combine pole-to-pole airborne observations from the Atmospheric Tomography Mission (ATom) with chemical transport model (GEOS-Chem CTM) and back trajectory analyses to provide the first global in-situ characterization of HCOOH in the remote atmosphere. ATom reveals sub-100 ppt HCOOH concentrations over most of the remote oceans, punctuated by large enhancements associated with continental outflow. Enhancements correlate with known combustion tracers and trajectory-based fire influences. The GEOS-Chem model underpredicts these in-plume HCOOH enhancements, but elsewhere we find no broad indication of a missing HCOOH source in the background free troposphere. We conclude that missing non-fire HCOOH precursors inferred previously are predominantly short-lived. We find indications of a wet scavenging underestimate in the model consistent with a positive HCOOH bias in the tropical upper troposphere. Observations reveal episodic evidence of ocean HCOOH uptake, which is well-captured by GEOS-Chem; however, despite its strong seawater undersaturation HCOOH is not consistently depleted in the remote marine boundary layer. Over fifty fire and mixed plumes were intercepted during ATom with widely varying transit times and source regions. HCOOH:CO normalized excess mixing ratios in these plumes range from 3.4 to >50 ppt/ppb CO and are often over an order of magnitude higher than expected primary emission ratios. HCOOH is thus a major reactive organic carbon reservoir in the aged plumes sampled during ATom, implying important missing pathways for in-plume HCOOH production.

The NASA Atmospheric Tomography (ATom) mission built a photochemical climatology of air parcels based on in situ measurements with the NASA DC-8 aircraft along objectively planned profiling transects through the middle of the Pacific and Atlantic oceans. In this paper we present and analyze a data set of 10s (2km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O3 and CH4 (O3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, other organic nitrates), consisting of 146494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O3 and CH4 photochemical tendencies from this modeling data stream for ATom 1. We find that 80%-90% of the total reactivity lies in the top 50% of the parcels and 25%-35% in the top 10%, supporting previous model-only studies that tropospheric chemistry is driven by a fraction of all the air. In other words, accurate simulation of the least reactive 50% of the troposphere is unimportant for global budgets. Surprisingly, the probability densities of species and reactivities averaged on a model scale (100km) differ only slightly from the 2km ATom data, indicating that much of the heterogeneity in tropospheric chemistry can be captured with current global chemistry models. Comparing the ATom reactivities over the tropical oceans with climatological statistics from six global chemistry models, we find excellent agreement with the loss of O3 and CH4 but sharp disagreement with production of O3. The models sharply underestimate O3 production below 4km in both Pacific and Atlantic basins, and this can be traced to lower NOx levels than observed. Attaching photochemical reactivities to measurements of chemical species allows for a richer, yet more constrained-to-what-matters, set of metrics for model evaluation.

The global oxidation capacity, defined as the tropospheric mean concentration of the hydroxyl radical (OH), controls the lifetime of reactive trace gases in the atmosphere such as methane and carbon monoxide (CO). Models tend to underestimate the methane lifetime and CO concentrations throughout the troposphere, which is consistent with excessive OH. Approximately half of the oxidation of methane and non-methane volatile organic compounds (VOCs) is thought to occur over the oceans where oxidant chemistry has received little validation due to a lack of observational constraints. We use observations from the first two deployments of the NASA ATom aircraft campaign during July-August 2016 and January-February 2017 to evaluate the oxidation capacity over the remote oceans and its representation by the GEOS-Chem chemical transport model. The model successfully simulates the magnitude and vertical profile of remote OH within the measurement uncertainties. Comparisons against the drivers of OH production (water vapor, ozone, and NO y concentrations, ozone photolysis frequencies) also show minimal bias, with the exception of wintertime NO y . The severe model overestimate of NO y during this period may indicate insufficient wet scavenging and/or missing loss on sea-salt aerosols. Large uncertainties in these processes require further study to improve simulated NO y partitioning and removal in the troposphere, but preliminary tests suggest that their overall impact could marginally reduce the model bias in tropospheric OH. During the ATom-1 deployment, OH reactivity (OHR) below 3 km is significantly enhanced, and this is not captured by the sum of its measured components (cOHRobs) or by the model (cOHRmod). This enhancement could suggest missing reactive VOCs but cannot be explained by a comprehensive simulation of both biotic and abiotic ocean sources of VOCs. Additional sources of VOC reactivity in this region are difficult to reconcile with the full suite of ATom measurement constraints. The model generally reproduces the magnitude and seasonality of cOHRobs but underestimates the contribution of oxygenated VOCs, mainly acetaldehyde, which is severely underestimated throughout the troposphere despite its calculated lifetime of less than a day. Missing model acetaldehyde in previous studies was attributed to measurement uncertainties that have been largely resolved. Observations of peroxyacetic acid (PAA) provide new support for remote levels of acetaldehyde. The underestimate in both model acetaldehyde and PAA is present throughout the year in both hemispheres and peaks during Northern Hemisphere summer. The addition of ocean sources of VOCs in the model increases cOHRmod by 3% to 9% and improves model-measurement agreement for acetaldehyde, particularly in winter, but cannot resolve the model summertime bias. Doing so would require 100 Tg yr-1 of a long-lived unknown precursor throughout the year with significant additional emissions in the Northern Hemisphere summer. Improving the model bias for remote acetaldehyde and PAA is unlikely to fully resolve previously reported model global biases in OH and methane lifetime, suggesting that future work should examine the sources and sinks of OH over land.

Long-term atmospheric CO2 mole fraction and δ13CO2 observations over North America document persistent responses to the El Niño-Southern Oscillation. We estimate these responses corresponded to 0.61 (0.45 to 0.79) PgC year-1 more North American carbon uptake during El Niño than during La Niña between 2007 and 2015, partially offsetting increases of net tropical biosphere-to-atmosphere carbon flux around El Niño. Anomalies in derived North American net ecosystem exchange (NEE) display strong but opposite correlations with surface air temperature between seasons, while their correlation with water availability was more constant throughout the year, such that water availability is the dominant control on annual NEE variability over North America. These results suggest that increased water availability and favorable temperature conditions (warmer spring and cooler summer) caused enhanced carbon uptake over North America near and during El Niño.

Recent studies show conflicting estimates of trends in methane (CH4) emissions from oil and natural gas (ONG) operations in the United States. We analyze atmospheric CH4 measurements from 20 North American sites in the National Oceanic and Atmospheric Administration Global Greenhouse Gas Reference Network and determined trends for 2006–2015. Using CH4 vertical gradients as an indicator of regional surface emissions, we find no significant increase in emissions at most sites and modest increases at three sites heavily influenced by ONG activities. Our estimated increases in North American ONG CH4 emissions (on average approximately 3.4 ± 1.4 %/year for 2006–2015, ±σ) are much smaller than estimates from some previous studies and below our detection threshold for total emissions increases at the east coast sites that are sensitive to U.S. outflows. We also find an increasing trend in ethane/methane emission ratios, which has resulted in major overestimation of oil and gas emissions trends in some previous studies.

Satellite retrievals of carbon monoxide (CO) are routinely assimilated in atmospheric chemistry models to improve air quality forecasts, produce reanalyzes and to estimate emissions. This study applies the quantile‐conserving ensemble filter framework, a novel assimilation algorithm that can deal with non‐Gaussian and modestly nonlinear distributions. Instead of assuming normal distributions like the Ensemble Adjustments Kalman Filter (EAKF), we now apply a bounded normal rank histogram (BNRH) distribution for the prior. The goal is to efficiently estimate bounded quantities such as CO atmospheric mixing ratios and emission fluxes while maintaining the good performance achieved by the EAKF. We contrast assimilating meteorological and MOPITT (Measurement of Pollution in the Troposphere) observations for May 2018. We evaluate the results with the fourth deployment of the NASA Atmospheric Tomography Mission (ATom‐4) airborne field campaign. We also compare simulations with CO tropospheric columns from the network for the detection of atmospheric composition change and surface in‐situ observations from NOAA carbon cycle greenhouse gases. While the differences remain small, the BNRH approach clearly works better than the EAKF in comparison to all observation data sets. Plain Language Summary: The MOPITT instrument on the NASA/Terra satellite can detect carbon monoxide (CO) pollution in the lower and mid‐tropospheric atmosphere but cannot accurately differentiate small changes in the altitude of pollution plumes. Such satellite observations are assimilated in numerical model predictions to improve the spatial and temporal distribution of CO in the atmosphere and to estimate emission fluxes. We present a novel method that does not require assumptions about the model and the observations, leading to a more efficient and accurate assimilation of the satellite observations. Key Points: A novel non Gaussian and nonlinear ensemble data assimilation (DA) framework is applied to MOPITT joint state/flux optimizationThe new method performs better than the Ensemble Adjustment Kalman Filter in comparison to independent observationsMOPITT observations indicate that CAMS‐GLOB‐ANT_v5.3 emission fluxes are underestimated across the mid‐latitudes in May 2018 [ABSTRACT FROM AUTHOR]

Tropical lands play an important role in the global carbon cycle yet their contribution remains uncertain owing to sparse observations. Satellite observations of atmospheric carbon dioxide (CO2) have greatly increased spatial coverage over tropical regions, providing the potential for improved estimates of terrestrial fluxes. Despite this advancement, the spread among satellite‐based and in‐situ atmospheric CO2 flux inversions over northern tropical Africa (NTA), spanning 0–24°N, remains large. Satellite‐based estimates of an annual source of 0.8–1.45 PgC yr−1 challenge our understanding of tropical and global carbon cycling. Here, we compare posterior mole fractions from the suite of inversions participating in the Orbiting Carbon Observatory 2 (OCO‐2) Version 10 Model Intercomparison Project (v10 MIP) with independent in‐situ airborne observations made over the tropical Atlantic Ocean by the National Aeronautics and Space Administration (NASA) Atmospheric Tomography (ATom) mission during four seasons. We develop emergent constraints on tropical African CO2 fluxes using flux‐concentration relationships defined by the model suite. We find an annual flux of 0.14 ± 0.39 PgC yr−1 (mean and standard deviation) for NTA, 2016–2018. The satellite‐based flux bias suggests a potential positive concentration bias in OCO‐2 B10 and earlier version retrievals over land in NTA during the dry season. Nevertheless, the OCO‐2 observations provide improved flux estimates relative to the in situ observing network at other times of year, indicating stronger uptake in NTA during the wet season than the in‐situ inversion estimates. Plain Language Summary: Satellite carbon dioxide (CO2) observations over land imply a major revision to our understanding of the global carbon cycle linked to large emissions from northern tropical Africa (NTA) during the dry season, from October to May. We use aircraft observations made over the Atlantic Ocean in four seasons to evaluate flux models driven by a range of ground and satellite observations. Our results show that models using satellite observations over land overestimate annual emissions from NTA by approximately 1 PgC yr−1, concentrated in the dry season. At other times of year, satellite CO2 observations provide improved estimates of NTA exchange, with a stronger CO2 uptake during the wet season. Key Points: Emergent constraints derived from aircraft carbon dioxide (CO2) measurements and inversions estimate a near neutral northern tropical African CO2 budgetInversions using satellite observations overestimate annual emissions from northern tropical Africa (NTA) by approximately 1 PgC yr−1Satellite CO2 observations imply a strong sink during the wet season over NTA [ABSTRACT FROM AUTHOR]

Multi-inverse modeling inter-comparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources such as atmospheric CO2 observations, transport models, and prior fluxes. However, accurately quantifying ensemble CO2 flux errors remains challenging, often relying on the ensemble spread as a surrogate. This study proposes a method to quantify the errors of regional terrestrial biosphere CO2 flux estimates from 10 inverse models within the Orbiting Carbon Observatory-2 (OCO-2) MIP by using independent airborne CO2 measurements for the period 2015-2017. We first calculate the root-mean-square error (RMSE) between the ensemble mean of posterior CO2 concentration estimates and airborne observations and then isolate the CO2 concentration error caused solely by the ensemble mean of posterior terrestrial biosphere CO2 flux estimates by subtracting the errors of observation and transport in seven regions. Our analysis reveals significant regional variations in the average monthly RMSE over three years, ranging from 0.90 to 2.04 ppm. The ensemble flux error projected into CO2 space is a major component that accounts for 58-84% of the mean RMSE. We further show that in five regions, the observation-based error estimates exceed the atmospheric CO2 errors computed from the ensemble spread of posterior CO2 flux estimates by 1.37-1.89 times, implying an underestimation of the actual ensemble flux error, while their magnitudes are comparable in two regions. By identifying the most sensitive areas to airborne measurements through adjoint sensitivity analysis, we find that the underestimation of flux errors is prominent in eastern parts of Australia and East Asia, western parts of Europe and Southeast Asia, and midlatitude North America, suggesting the presence of systematic biases related to anthropogenic CO2 emissions in inversion estimates. The regions with no underestimation were southeastern Alaska and northeastern South America. Our study emphasizes the value of independent airborne measurements not only for the overall evaluation of inversion performance but also for quantifying regional errors in ensemble terrestrial biosphere flux estimates. [ABSTRACT FROM AUTHOR]

Multi-inverse modeling inter-comparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources such as atmospheric CO2 observations, transport models, and prior fluxes. However, accurately quantifying ensemble CO2 flux errors remains challenging, often relying on the ensemble spread as a surrogate. This study proposes a method to quantify the errors of regional terrestrial biosphere CO2 flux estimates from 10 inverse models within the Orbiting Carbon Observatory-2 (OCO-2) MIP by using independent airborne CO2 measurements for the period 2015–2017. We first calculate the root-mean-square error (RMSE) between the ensemble mean of posterior CO2 concentration estimates and airborne observations and then isolate the CO2 concentration error caused solely by the ensemble mean of posterior terrestrial biosphere CO2 flux estimates by subtracting the errors of observation and transport in seven regions. Our analysis reveals significant regional variations in the average monthly RMSE over three years, ranging from 0.90 to 2.04 ppm. The ensemble flux error projected into CO2 space is a major component that accounts for 58–84 % of the mean RMSE. We further show that in five regions, the observation-based error estimates exceed the atmospheric CO2 errors computed from the ensemble spread of posterior CO2 flux estimates by 1.37–1.89 times, implying an underestimation of the actual ensemble flux error, while their magnitudes are comparable in two regions. By identifying the most sensitive areas to airborne measurements through adjoint sensitivity analysis, we find that the underestimation of flux errors is prominent in eastern parts of Australia and East Asia, western parts of Europe and Southeast Asia, and midlatitude North America, suggesting the presence of systematic biases related to anthropogenic CO2 emissions in inversion estimates. The regions with no underestimation were southeastern Alaska and northeastern South America. Our study emphasizes the value of independent airborne measurements not only for the overall evaluation of inversion performance but also for quantifying regional errors in ensemble terrestrial biosphere flux estimates. [ABSTRACT FROM AUTHOR]

We describe an approach for determining limited information about the vertical distribution of carbon monoxide (CO) and carbon dioxide (CO2) from total column ground-based Total Carbon Column Observation Network (TCCON) observations. For CO and CO2 , it has been difficult to retrieve information about their vertical distribution from spectral line shapes because of the errors in the spectroscopy and the atmospheric temperature profile that mask the effects of variations in their mixing ratio with altitude. For CO2 the challenge is especially difficult given that these variations are typically 2 % or less. Nevertheless, if sufficient accuracy can be obtained, such information would be highly valuable for evaluation of retrievals from satellites and more generally for improving the estimate of surface sources and sinks of these trace gases. We present here the Temporal Atmospheric Retrieval Determining Information from Secondary Scaling (TARDISS) retrieval algorithm. TARDISS uses several simultaneously obtained total column observations of the same gas from different absorption bands with distinctly different vertical averaging kernels. The different total column retrievals are combined in TARDISS using a Bayesian approach where the weights and temporal covariance applied to the different retrievals include additional constraints on the diurnal variation in the vertical distribution for these gases. We assume that the near-surface part of the column varies rapidly over the course of a day (from surface sources and sinks, for example) and that the upper part of the column has a larger temporal covariance over the course of a day. Using measurements from the five North American TCCON sites, we find that the retrieved lower partial column (between the surface and ∼ 800 hPa) of the CO and CO2 dry mole fractions (DMFs) have slopes of 0.999 ± 0.002 and 1.001 ± 0.003 with respect to lower column DMF from integrated in situ data measured directly from aircraft and in AirCores. The average error for our lower column CO retrieval is 1.51 ppb (∼ 2 %) while the average error for our CO2 retrieval is 5.09 ppm (∼ 1.25 %). Compared with classical line-shape-derived vertical profile retrievals, our algorithm reduces the influence of forward model errors such as imprecision in spectroscopy (line shapes and intensities) and in the instrument line shape. In addition, because TARDISS uses the existing retrieved column abundances from TCCON (which themselves are computationally much less intensive than profile retrieval algorithms), it is very fast and processes years of data in minutes. We anticipate that this approach will find broad application for use in carbon cycle science. [ABSTRACT FROM AUTHOR]

The Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) is a new community modeling infrastructure that enables the study of atmospheric composition and chemistry across all relevant scales. We develop a MUSICAv0 grid with Africa refinement (~28 km x 28 km over Africa). We evaluate the MUSICAv0 simulation for 2017 with in situ observations and compare the model results to satellite products over Africa. A simulation from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), a regional model that is widely used in Africa studies, is also included in the analyses as a reference. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Both models underestimate carbon monoxide (CO) compared to in situ observations and satellite CO column retrievals from the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument. MUSICAv0 tends to overestimate ozone (O3), likely due to overestimated stratosphere-to-troposphere flux of ozone. Both models significantly underestimate fine particulate matter (PM2.5) at two surface sites in East Africa. The MUSICAv0 simulation agrees better with aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and tropospheric nitrogen dioxide (NO2) column retrievals from the Ozone Monitoring Instrument (OMI) than WRF-Chem. MUSICAv0 has a consistently lower tropospheric formaldehyde (HCHO) column than OMI retrievals. Based on model-satellite discrepancies between MUSICAv0 and WRF-Chem and MOPITT CO, MODIS AOD, and OMI tropospheric NO2, we find that future field campaign(s) and more in situ observations in an East African region (30°E - 45°E, 5°S - 5°N) could substantially improve the predictive skill of atmospheric chemistry model(s). This suggested focus region exhibits the largest model-in situ observation discrepancies, as well as targets for high population density, land cover variability, and anthropogenic pollution sources. [ABSTRACT FROM AUTHOR]