Abstract

The Copernicus Atmosphere Monitoring Service (CAMS) has recently produced a greenhouse gas reanalysis (version egg4) that covers almost 2 decades from 2003 to 2020 and which will be extended in the future. This reanalysis dataset includes carbon dioxide (CO2) and methane (CH4). The reanalysis procedure combines model data with satellite data into a globally complete and consistent dataset using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS). This dataset has been carefully evaluated against independent observations to ensure validity and to point out deficiencies to the user. The greenhouse gas reanalysis can be used to examine the impact of atmospheric greenhouse gas concentrations on climate change (such as global and regional climate radiative forcing), assess intercontinental transport, and serve as boundary conditions for regional simulations, among other applications and scientific uses. The caveats associated with changes in assimilated observations and fixed underlying emissions are highlighted, as is their impact on the estimation of trends and annual growth rates of these long-lived greenhouse gases.

Abstract

Cosmic rays entering the Earth s atmosphere produce showers of secondary particles such as protons, neutrons, and muons. The interaction of these particles with oxygen-16 (O-16) in minerals such as ice and quartz can produce carbon-14 (C-14). In glacial ice, C-14 is also incorporated through trapping of C-14-containing atmospheric gases ((CO2)-C-14,(CO)-C- 14, and (CH4)-C-14). Understanding the production rates of in situ cosmogenic C-14 is important to deconvolve the in situ cosmogenic and atmospheric( 14)C signals in ice, both of which contain valuable paleoenvironmental information. Unfortunately, the in situ C-14 production rates by muons (which are the dominant production mechanism at depths of > 6 m solid ice equivalent) are uncertain. In this study, we use measurements of in situ C-14 in ancient ice (> 50 ka) from the Taylor Glacier, an ablation site in Antarctica, in combination with a 2D ice flow model to better constrain the compound-specific rates of C-14 production by muons and the partitioning of in situ( 14)C between CO2, CO, and CH4. Our measurements show that 33.7 \% (+/- 11.4\%; 95 \% confidence interval) of the produced cosmogenic C-14 forms (CO)-C-14 and 66.1 \% (+/- 11.5\%; 95 \% confidence interval) of the produced cosmogenic C-14 forms (CO2)-C-14. (CH4)-C-14 represents a very small fraction (< 0.3\%) of the total. Assuming that the majority of in situ muogenic 14C in ice forms (CO2)-C-14, (CO)-C-14, and (CH4)-C-14, we also calculated muogenic( 14)C production rates that are lower by factors of 5.7 (3.6-13.9; 95 \% confidence interval) and 3.7 (2.0-11.9; 95 \% confidence interval) for negative muon capture and fast muon interactions, respectively, when compared to values determined in quartz from laboratory studies (Heisinger et al., 2002a, b) and in a natural setting (Lupker et al., 2015). This apparent discrepancy in muogenic C-14 production rates in ice and quartz currently lacks a good explanation and requires further investigation.

Abstract

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 10 s (2 km) merged and gap-filled observations of the key reactive species driving the chemical budgets of O-3 and CH4 (O-3, CH4, CO, H2O, HCHO, H2O2, CH3OOH, C2H6, higher alkanes, alkenes, aromatics, NOx, HNO3, HNO4, peroxyacetyl nitrate, and other organic nitrates), consisting of 146 494 distinct air parcels from ATom deployments 1 through 4. Six models calculated the O-3 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 2 km ATom 10 s 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 O-3 and CH4. Models distinctly underestimate O-3 production below 2 km 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 .

Abstract

Sulfur hexafluoride (SF6) is the most potent greenhouse gas (GHG), and its atmospheric abundance, albeit small, has been increasing rapidly. Although SF6 is used to assess atmospheric transport modeling and its emissions influence the climate for millennia, SF6 emission magnitudes and distributions have substantial uncertainties. In this study, we used NOAA s ground-based and airborne measurements of SF6 to estimate SF6 emissions from the United States between 2007 and 2018. Our results suggest a substantial decline of US SF6 emissions, a trend also reported in the US Environmental Protection Agency s (EPA) national inventory submitted under the United Nations Framework Convention on Climate Change (UNFCCC), implying that US mitigation efforts have had some success. However, the magnitudes of annual emissions derived from atmospheric observations are 40 \%-250 \% higher than the EPA s national inventory and substantially lower than the Emissions Database for Global Atmospheric Research (EDGAR) inventory. The regional discrepancies between the atmosphere-based estimate and EPA s inventory suggest that emissions from electric power transmission and distribution (ETD) facilities and an SF6 production plant that did not or does not report to the EPA may be underestimated in the national inventory. Furthermore, the atmosphere-based estimates show higher emissions of SF6 in winter than in summer. These enhanced wintertime emissions may result from increased maintenance of ETD equipment in southern states and increased leakage through aging brittle seals in ETD in northern states during winter. The results of this study demonstrate the success of past US SF6 emission mitigations and suggest that substantial additional emission reductions might be achieved through efforts to minimize emissions during servicing or through improving sealing materials in ETD.

Abstract

PurpleAir sensors (PASs) are low-cost tools to measure fine particulate matter (PM) concentrations and are now widely used, especially in regions with few regulatory monitors. However, the raw PAS data have significant biases, so the sensors must be calibrated to generate accurate data. The U.S. EPA recently developed a national correction equation and has integrated corrected PAS data onto its AirNow website. This integration results in much better spatial coverage for PM2.5 (particulate matter with diameters less than 2.5 mu m) across the US. The goal of our study is to evaluate the EPA correction equation for three different types of aerosols: typical urban wintertime aerosol, smoke from biomass burning, and mineral dust. We identified 50 individual pollution events, each having a peak hourly PM2.5 concentration of at least 47 mu g m(-3) and a minimum of 3 h over 40 mu g m(-3) and characterized the primary aerosol type as either typical urban, smoke, or long-range transported dust. For each event, we paired a PAS sampling outside air with a nearby regulatory PM2.5 monitor to evaluate the agreement. All 50 events show statistically significant correlations (R values between 0.71-1.00) between the hourly PAS and regulatory data but with varying slopes. We then corrected the PAS data using either the correction equation from Barkjohn et al. (2021) or a new equation that is now being used by the U.S. EPA for the AirNow Fire and Smoke Map (U.S. EPA, 2022b). Both equations do a good job at correcting the data for smoke and typical pollution events but with some differences. Using the Barkjohn et al. (2021) equation, we find mean slopes of 1.00 and 0.99 for urban and smoke aerosol events, respectively, for the corrected data versus the regulatory data. For heavy smoke events, we find a small change in the slope at very high PM2.5 concentrations (> 600 mu g m(-3)), suggesting a similar to 20 \% underestimate in the corrected PAS data at these extremely high concentrations. Using the new EPA equation, we find slopes of 0.95 and 0.88 for urban and smoke events, respectively, indicating a slight underestimate in PM2.5 using this equation, especially for smoke events. For dust events, while the PAS and regulatory data still show significant correlations, the PAS data using either correction equation underestimate the true PM2.5 by a factor of 5-6. We also examined several years of co-located regulatory and PAS data from a site near Owens Lake, California (CA), which experiences high concentrations of PM2.5 due to both smoke and locally emitted dust. For this site, we find similar results as above; the corrected PAS data are accurate in smoke but are too low by a factor of 5-6 in dust. Using these data, we also find that the ratios of PAS-measured PM10 / PM1 mass and 0.3 mu m / 5 mu m particle counts are significantly different for dust compared to smoke. Using this difference, we propose a modified correction equation that improves the PAS data for some dust events, but further work is needed to improve this algorithm.

Abstract

Balloon-borne ozonesondes launched weekly from South Pole Station (1986-2021) measure high-vertical-resolution profiles of ozone and temperature from the surface to 30-35 km altitude. The launch frequency is increased in late winter before the onset of rapid stratospheric ozone loss in September. Ozone hole metrics show that the yearly total column ozone and 14-21 km partial column ozone minimum values and September loss rate trends have been improving (less severe) since 2001. The 36-year record also shows interannual variability, especially in recent years (2019-2021). Here we show additional details of these 3 years by comparing annual minimum profiles observed on the date when the lowest integrated total column ozone occurs. We also compare the July-December time series of the 14-21 km partial column ozone values to the 36-year median with percentile intervals. The 2019 anomalous vortex breakdown showed stratospheric temperatures began warming in early September followed by reduced ozone loss. The minimum total column ozone of 180 Dobson units (DU) was observed on 24 September. This was followed by two stable and cold polar vortex years during 2020 and 2021 with total column ozone minimums at 104 DU (1 October) and 102 DU (7 October), respectively. These years also showed broad near-zero-ozone (loss saturation) regions within the 14-21 km layer by the end of September which persisted into October.Validation of the ozonesonde observations is conducted through the ongoing comparison of total column ozone measurements with the South Pole ground-based Dobson spectrophotometer. The ozonesondes show a more positive bias of 2 +/- 3 \% (higher) than the Dobson following a thorough evaluation and homogenization of the long-term ozonesonde record completed in 2018.

Abstract

Optimal estimation retrievals of trace gas total columns require prior vertical profiles of the gases retrieved to drive the forward model and ensure the retrieval problem is mathematically well posed. For well-mixed gases, it is possible to derive accurate prior profiles using an algorithm that accounts for general patterns of atmospheric transport coupled with measured time series of the gases in questions. Here we describe the algorithm used to generate the prior profiles for GGG2020, a new version of the GGG retrieval that is used to analyze spectra from solar-viewing Fourier transform spectrometers, including the Total Carbon Column Observing Network (TCCON). A particular focus of this work is improving the accuracy of CO2, CH4, N2O, HF, and CO across the tropopause and into the lower stratosphere. We show that the revised priors agree well with independent in situ and space-based measurements and discuss the impact on the total column retrievals.

Abstract

Understanding terrestrial ecosystems and their response to anthropogenic climate change requires quantification of land-atmosphere carbon exchange. However, top-down and bottom-up estimates of large-scale land-atmosphere fluxes, including the northern extratropical growing season net flux (GSNF), show significant discrepancies. We developed a data-driven metric for the GSNF using atmospheric carbon dioxide concentration observations collected during the High-Performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations and Atmospheric Tomography Mission flight campaigns. This aircraft-derived metric is bias-corrected using three independent atmospheric inversion systems. We estimate the northern extratropical GSNF to be 5.7 +/- 0.3 Pg C and use it to evaluate net biosphere productivity from the Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. While the model-to-model spread in the GSNF has decreased in the CMIP6 models relative to that of the CMIP5 models, there is still disagreement on the magnitude and timing of seasonal carbon uptake with most models underestimating the GSNF and overestimating the length of the growing season relative to the observations. We also use an emergent constraint approach to estimate annual northern extratropical gross primary productivity to be 56 +/- 17 Pg C, heterotrophic respiration to be 25 +/- 13 Pg C, and net primary productivity to be 28 +/- 12 Pg C. The flux inferred from these aircraft observations provides an additional constraint on large-scale gross fluxes in prognostic Earth system models that may ultimately improve our ability to accurately predict carbon-climate feedbacks.Plain Language Summary The exchange of carbon between the land and atmosphere is an important part of the Earth s climate, and this exchange might change due to human-caused climate change. However, estimates of land-atmosphere carbon fluxes made using different techniques do not agree with each other. We use atmospheric carbon dioxide observations collected during two flight campaigns to show that 5.7 Pg C is exchanged between the atmosphere and the land in the northern hemisphere during the summer growing season. This estimate is used to evaluate the performance of two generations of climate prediction models. The newer generation of models show less spread than the older generation, but there is still significant disagreement on the magnitude and timing of land-atmosphere carbon exchange among models. Most models underestimate the growing season net flux and overestimate the length of the growing season. We also use our observational estimate to reduce the spread on component fluxes of carbon exchange, namely uptake by photosynthesis and release by respiration.

Abstract

EM27/SUN devices are portable solar-viewing Fourier transform spectrometers (FTSs) that are being widely used to constrain measurements of greenhouse gas emissions and validate satellite trace gas measurements. On a 6-week-long campaign in the summer of 2018, four EM27/SUN devices were taken to five Total Carbon Column Observing Network (TCCON) stations in North America, to measure side by side, to better understand their durability, the accuracy and precision of retrievals from their trace gas measurements, and to constrain site-to-site bias among TCCON sites. We developed new EM27/SUN data products using both previous and current versions of the retrieval algorithm (GGG2014 and GGG2020) and used coincident AirCore measurements to tie the gas retrievals to the World Meteorological Organization (WMO) trace gas standard scales. We also derived air-mass-dependent correction factors for the EM27/SUN devices. Pairs of column-averaged dry-air mole fractions (denoted with an X) measured by the EM27/SUN devices remained consistent compared to each other during the entire campaign, with a 10 min averaged precision of 0.3 ppm (parts per million) for XCO2, 1.7 ppb (parts per billion) for XCH4, and 2.5 ppb for XCO. The maximum biases between TCCON stations were reduced in GGG2020 relative to GGG2014 from 1.3 to 0.5 ppm for XCO2 and from 5.4 to 4.3 ppb for XCH4 but increased for XCO from 2.2 to 6.1 ppb. The increased XCO biases in GGG2020 are driven by measurements at sites influenced by urban emissions (Caltech and the Armstrong Flight Research Center) where the priors overestimate surface CO. In addition, in 2020, one EM27/SUN instrument was sent to the Canadian Arctic TCCON station at Eureka, and side-by-side measurements were performed in March-July. In contrast to the other TCCON stations that showed an improvement in the biases with the newer version of GGG, the biases between Eureka s TCCON measurements and those from the EM27/SUN degraded with GGG2020, but this degradation was found to be caused by a temperature dependence in the EM27/SUN oxygen retrievals that is not apparent in the GGG2014 retrievals.

Abstract

The indirect effect of aerosols on climate through aerosol-cloud-interactions is still highly uncertain and limits our ability to assess anthropogenic climate change. The foundation of this uncertainty is in the number of cloud condensation nuclei (CCN), which itself mainly stems from uncertainty in aerosol sources and how particles evolve to become effective CCN. We analyze particle number size distribution (PNSD) and CCN measurements from an urban site in a two-step method: (1) we use an unsupervised clustering model to classify the main aerosol categories and processes occurring in the urban atmosphere and (2) we explore the influence of the identified aerosol populations on the CCN properties. According to the physical properties of each cluster, its diurnal timing, and additional air quality parameters, the clusters are grouped into five main aerosol categories: nucleation, growth, traffic, aged traffic, and urban background. The results show that, despite aged traffic and urban background categories are those with lower total particle number concentrations (N_tot) these categories are the most efficient sources in terms of contribution to the overall CCN budget with activation fractions (AF) around 0.5 at 0.75 % supersaturation (SS). By contrast, road traffic is an important aerosol source with the highest frequency of occurrence (32 %) and relatively high N_tot, however, its impact in the CCN activity is very limited likely due to lower particle mean diameter and hydrophobic chemical composition. Similarly, nucleation and growth categories, associated to new particle formation (NPF) events, present large N_tot with large frequency of occurrence (22 % and 28 %, respectively) but the CCN concentration for these categories is about half of the CCN concentration observed for the aged traffic category, which is associated with their small size. Overall, our results show that direct influence of traffic emissions on the CCN budget is limited, however, when these particles undergo ageing processes, they have a significant influence on the CCN concentrations and may be an important CCN source. Thus, aged traffic particles could be transported to other environments where clouds form, triggering a plausible indirect effect of traffic emissions on aerosol-cloud interactions and consequently contributing to climate change.

Abstract

We present a comparison of atmospheric transport model (ATM) simulations for carbonyl sulfide (COS), within the framework of the atmospheric tracer transport model intercomparison project TransCom-COS. Seven ATMs participated in the experiment and provided simulations of COS mixing ratios over the years 2010-2018, using state-of-the-art surface fluxes for various components of the COS budget: biospheric sink, oceanic source, sources from fire and industry. The main goal of TransCom-COS is to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of tropospheric COS mixing ratios. A control case with seasonal surface fluxes of COS was constructed. The results indicate that the COS mixing ratios are underestimated by at least 50 parts per trillion (ppt) in the tropics, pointing to a missing tropical source. In summer, the mixing ratios are overestimated by at least 50 ppt above 40 \& DEG;N, pointing to a likely missing sink in the high northern latitudes. Regarding the latitudinal profile, the model spread is greater than 60 ppt above 40?degrees N in boreal summer. Regarding the seasonal amplitude, the model spread reaches 50 ppt at 6 out of 15 sites, compared to an observed seasonal amplitude of 100 ppt. All models simulated a too late minimum by at least 2-3 months at two high northern-latitude sites, likely owing to errors in the seasonal cycle in the ocean emissions. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer. In this study, we evaluate the state-of-the-art fluxes for various components of the carbonyl sulfide (COS) budget: biospheric sink, oceanic source, sources from fire and industry. A control case with seasonal surface fluxes of COS was constructed. Seven atmospheric transport models provided simulations of COS mixing ratios. Then, the simulated mixing ratios were evaluated against atmospheric measurements at several surface sites. Results show that all models fail to capture the observed latitudinal distribution and that the model spread is small compared to the model-observation mismatch. In summer, the overestimated mixing ratios above 40 degrees N point to a likely missing sink in the high northern latitudes. The underestimated mixing ratios in the tropics point to a missing tropical source. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer.

Abstract

As the third most important greenhouse gas (GHG) after carbon dioxide (CO2) and methane (CH4), tropospheric ozone (O-3) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O-3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O-3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O-3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O-3 depletion in the springtime; however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been previously reported, and we show that there continues to be no convergence among models or improved accuracy in simulating tropospheric O-3 and its precursor species. The MMM underestimates Arctic surface O-3 by 5 \% to 15 \% depending on the location. The vertical distribution of tropospheric O-3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O-3 within a range of +/- 50 \% depending on the model and the altitude. The MMM performs best, within +/- 8 \% for most locations and seasons. However, nearly all models overestimate O-3 near the tropopause (similar to 300 hPa or similar to 8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O-3 at high latitudes. For example, the MMM is biased high by about 20 \% at Eureka. Observed and simulated O-3 precursors (CO, NOx, and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NOx but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O-3 matches the observed O-3 reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever and have not increased in accuracy for representing Arctic tropospheric O-3.