WOMBAT v1.0: a fully Bayesian global flux-inversion framework

WOMBAT (the WOllongong Methodology for Bayesian Assimilation of Trace-gases) is a fully Bayesian hierarchical statistical framework for flux inversion of trace gases from flask, in situ, and remotely sensed data. WOMBAT extends the conventional Bayesian synthesis framework through the consideration of a correlated error term, the capacity for online bias correction, and the provision of uncertainty quantification on all unknowns that appear in the Bayesian statistical model. We show, in an observing system simulation experiment (OSSE), that these extensions are crucial when the data are indeed biased and have errors that are spatio-temporally correlated. Using the GEOS-Chem atmospheric transport model, we show that WOMBAT is able to obtain posterior means and variances on non-fossil-fuel CO2 fluxes from Orbiting Carbon Observatory-2 (OCO-2) data that are comparable to those from the Model Intercomparison Project (MIP) reported in Crowell et al. (2019). We also find that WOMBAT's predictions of out-of-sample retrievals obtained from the Total Column Carbon Observing Network (TCCON) are, for the most part, more accurate than those made by the MIP participants.

Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO₂ and CO

Carbon dioxide (CO2) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at regional to city scale. Very few studies have focused on burning efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for deriving spatially-resolved ERs from asynchronous satellite measurements including 1) variations in meteorological conditions induced by different overpass times, 2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), and 3) interferences from the biosphere and biomass burning. In this study, we extended an established emission estimate approach to arrive at spatially-resolved ERs based on retrieved column-averaged CO2 (XCO2) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influence of the confounding factors listed above and further explain the intra-urban variations in ERs, we leveraged a Lagrangian atmospheric transport model and an urban land cover classification dataset and reported ERCO from the sounding level to the overpass- and city- levels. We found that the difference in the overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially-resolved ERCO. Specifically, a time difference of > 3 hours typically led to dramatic changes in the wind direction and shape of urban plumes and thereby making the calculation of accurate sounding-specific ERCO difficult. After removing those cases from consideration and applying a simple plume shift method when necessary, we discovered significant contrasts in combustion efficiencies between 1) two megacities versus two industry-oriented cities and 2) different regions within a city, based on six to seven nearly-coincident overpasses per city. Results suggest that the combustion efficiency for heavy industry in Los Angeles is slightly lower than its overall city-wide value (< 10 ppb-CO / ppm-CO2). In contrast, ERs related to the heavy industry in Shanghai are found to be much higher than Shanghai’s city-mean and more aligned with city-means of the two industry-oriented Chinese cities (approaching 20 ppb-CO / ppm-CO2). Although investigations based on a larger number of satellite overpasses are needed, our first analysis provides guidance for estimating intra-city gradients in combustion efficiency from future missions, such as those that will map column CO2 and CO concentration simultaneously with high spatiotemporal resolutions.

Application of CCM SOCOL-AERv2-BE to cosmogenic beryllium isotopes: description and validation for polar regions

The short-living cosmogenic isotope 7Be, which is produced by cosmic rays in the atmosphere, is often used as a tracer for atmospheric dynamics, with precise and high-resolution measurements covering the recent decades. The long-living isotope 10Be, as measured in polar ice cores with an annual resolution, is a proxy for long-term cosmic-ray variability, whose signal can, however, be distorted by atmospheric transport and deposition that need to be properly modeled to be accounted for. While transport of 7Be can be modeled with high accuracy using the known meteorological fields, atmospheric transport of 10Be was typically modeled using case-study-specific simulations or simplified box models based on parameterizations. Thus, there is a need for a realistic model able to simulate atmospheric transport and deposition of beryllium with a focus on polar regions and (inter)annual timescales that is potentially able to operate in a self-consistent mode without the prescribed meteorology. Since measurements of 10Be are extremely laborious and hence scarce, it is difficult to compare model results directly with measurement data. On the other hand, the two beryllium isotopes are believed to have similar transport and deposition properties, being different only in production and lifetime, and thus the results of 7Be transport can be generally applied to 10Be. Here we present a new model, called CCM SOCOL-AERv2-BE, to trace isotopes of 7Be and 10Be in the atmosphere based on the chemistry–climate model (CCM) SOCOL (SOlar Climate Ozone Links), which has been improved by including modules for the production, deposition, and transport of 7Be and 10Be. Production of the isotopes was modeled for both galactic and solar cosmic rays by applying the CRAC (Cosmic Ray Atmospheric Cascade) model. Transport of 7Be was modeled without additional gravitational settling due to the submicron size of the background aerosol particles. An interactive deposition scheme was applied including both wet and dry deposition. Modeling was performed using a full nudging to the meteorological fields for the period of 2002–2008 with a spin-up period of 1996–2001. The modeled concentrations of 7Be in near-ground air were compared with the measured ones at a weekly time resolution in four nearly antipodal high-latitude locations: two in the Northern (Finland and Canada) and two in the Southern (Chile and the Kerguelen Islands) Hemisphere. The model results agree with the measurements in the absolute level within error bars, implying that the production, decay, and lateral deposition are correctly reproduced. The model also correctly reproduces the temporal variability of 7Be concentrations on annual and sub-annual scales, including the presence and absence of the annual cycle in the Northern and Southern Hemisphere, respectively. We also modeled the production and transport of 7Be for a major solar energetic particle event (SPE) on 20 January 2005, which appears insufficient to produce a measurable signal but may serve as a reference event for historically known extreme SPEs. Thus, a new full 3D time-dependent model, based on CCM SOCOL, of 7Be and 10Be atmospheric production, transport, and deposition has been developed. Comparison with real data on the 7Be concentration in the near-ground air validates the model and its accuracy.

Unraveling pathways of elevated ozone induced by the 2020 lockdown in Europe by an observationally constrained regional model using TROPOMI

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NOx and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO2 columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NOx emissions in March 2020 (14 %–31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %–51 %) in April. However, NOx emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO2. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO2 reductions occurring in polluted areas are described fairly well by the model (model: −21 ± 17 %, observation: −29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r=0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, +1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by −4.83 ppbv, while ozone production rates dampened by largely negative JNO2[NO2]-kNO+O3[NO][O3] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

Effects of point source emission heights in WRF–STILT: a step towards exploiting nocturnal observations in models

An appropriate representation of point source emissions in atmospheric transport models is very challenging. In the Stochastic Time Inverted Lagrangian Transport model (STILT), all point source emissions are typically released from the surface, meaning that the actual emission stack height plus subsequent plume rise is not considered. This can lead to erroneous predictions of trace gas concentrations, especially during nighttime when vertical atmospheric mixing is minimal. In this study we use two WRF–STILT model approaches to simulate fossil fuel CO2 (ffCO2) concentrations: (1) the standard “surface source influence (SSI)” approach, and (2) an alternative “volume source influence (VSI)” approach, where nearby point sources release CO2 according to their effective emission height profiles. The comparison with 14C-based measured ffCO2 data from two-week integrated afternoon and nighttime samples collected at Heidelberg, 30 m above ground level, shows that the root-mean-square deviation (RMSD) between modelled and measured ffCO2 is indeed almost twice as high during night (RMSD = 6.3 ppm) compared to the afternoon (RMSD = 3.7 ppm) when using the standard SSI approach. In contrast, the VSI approach leads to a much better performance at nighttime (RMSD = 3.4 ppm), which is similar to its performance during afternoon (RMSD = 3.7 ppm). Representing nearby point source emissions with the VSI approach could, thus, be a first step towards exploiting nocturnal observations in STILT. To further investigate the differences between these two approaches, we conducted a model experiment in which we simulated the ffCO2 contributions from 12 artificial power plants with typical annual emissions of one million tons of CO2 and with distances between 5 and 200 km from the Heidelberg observation site. We find that such a power plant must be more than 50 km away from the observation site in order for the mean modelled ffCO2 concentration difference between the SSI and VSI approach to fall below 0.1 ppm.

Global to local impacts on atmospheric CO2 from the COVID-19 lockdown, biosphere and weather variabilities

The world-wide lockdown in response to the COVID-19 pandemic in year 2020 led to economic slowdown and large reduction in fossil fuel CO2 emissions, but it is unclear how much it would slow the increasing trend of atmospheric CO2 concentration, the main driver of climate change, and whether this impact can be observed in light of large biosphere and weather variabilities. We used a state-of-the-art atmospheric transport model to simulate CO2, driven by a new daily fossil fuel emissions dataset and hourly biospheric fluxes from a carbon cycle model forced with observed climate variability. Our results show 0.21 ppm decrease in atmospheric column CO2 anomaly in the Northern Hemisphere latitude band 0-45°N (NH45) in March 2020, and an average of 0.14 ppm for the period of February-April 2020, the largest in the last 10 years. A similar decrease was observed by the carbon satellite GOSAT. Using model sensitivity experiments, we further found that COVID and weather variability are the major contributors of this CO2 drawdown, and the biosphere gave a small positive anomaly. Measurements at marine boundary layer stations such as Hawaii exhibits 1-2 ppm anomalies, mostly due to weather and the biosphere. At city scale, on-road CO2 enhancement measured in Beijing shows reduction of 20-30 ppm, consistent with drastically reduced traffic during COVID lockdown. A stepwise drop of 20 ppm at the city-wide lockdown was observed in the city of Chengdu. The ability of our current carbon monitoring systems in detecting the small and short-lasting COVID signal on the background of fossil fuel CO2 accumulated over the last two centuries is encouraging. The COVID-19 pandemic is an unintended experiment. Its impact suggests that to keep atmospheric CO2 at a climate-safe level will require sustained effort of similar magnitude and improved accuracy and expanded spatiotemporal coverage of our monitoring systems.

COSMOGENIC 14CO FOR ASSESSING THE OH-BASED SELF-CLEANING CAPACITY OF THE TROPOSPERE

ABSTRACT An application of radiocarbon (14C) in atmospheric chemistry is reviewed. 14C produced by cosmic neutrons immediately forms 14CO, which reacts with hydroxyl radicals (OH) to 14CO2. By this the distribution and seasonality (the lifetime of 14CO is ∼1 month) of the pivotal atmospheric oxidant OH can be established. 14CO measurement is a complex but unique application which benefitted enormously from the realization of AMS, bearing in mind that 14CO abundance is of the order of merely 10 molecules per cm3 not only provides 14CO an independent measure for the OH based self-cleansing capacity of the troposphere, but also enabled detection of 14C production due to high energy solar protons in 1989. Although its production takes place throughout the atmosphere and does not have the character of a point source, transport processes in the atmosphere affect the distribution of 14CO. Vertical mixing in the troposphere renders gradients in its production rate less critical, but considerable meridional gradients exist. One question has remained open, namely confirmation of calculated 14C production by direct measurement. A new sampling method is proposed. The conclusions are a guide to future work on 14CO in relation to OH and atmospheric transport.