Observationally constrained representation of brown carbon emissions from wildfires in a chemical transport model

The month of August 2015 featured extensive wildfires in the Northwestern U.S. and no significant fires in Alaska and Canada. With the majority of carbonaceous aerosols (CA), including black carbon...

Confined self-propulsion of an isotropic active colloid

To spontaneously break their intrinsic symmetry and self-propel at the micron scale, isotropic active colloidal particles and droplets exploit the nonlinear convective transport of chemical solutes emitted/consumed at their surface by the surface-driven fluid flows generated by these solutes. Significant progress was recently made to understand the onset of self-propulsion and nonlinear dynamics. Yet, most models ignore a fundamental experimental feature, namely the spatial confinement of the colloid, and its effect on propulsion. In this work the self-propulsion of an isotropic colloid inside a capillary tube is investigated numerically. A flexible computational framework is proposed based on a finite-volume approach on adaptative octree grids and embedded boundary methods. This method is able to account for complex geometric confinement, the nonlinear coupling of chemical transport and flow fields, and the precise resolution of the surface boundary conditions, that drive the system's dynamics. Somewhat counterintuitively, spatial confinement promotes the colloid's spontaneous motion by reducing the minimum advection-to-diffusion ratio or Péclet number, ${Pe}$ , required to self-propel; furthermore, self-propulsion velocities are significantly modified as the colloid-to-capillary size ratio $\kappa$ is increased, reaching a maximum at fixed ${Pe}$ for an optimal confinement $0<\kappa <1$ . These properties stem from a fundamental change in the dominant chemical transport mechanism with respect to the unbounded problem: with diffusion now restricted in most directions by the confining walls, the excess solute is predominantly convected away downstream from the colloid, enhancing front-back concentration contrasts. These results are confirmed quantitatively using conservation arguments and lubrication analysis of the tightly confined limit, $\kappa \rightarrow 1$ .

ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model

High-quality stratospheric ozone profile data sets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments provide stratospheric ozone profile measurements over typical mission durations of 5–15 years. Various methodologies have then been applied to merge and homogenise the different satellite data in order to create long-term observation-based ozone profile data sets with minimal data gaps. However, individual satellite instruments use different measurement methods, sampling patterns and retrieval algorithms which complicate the merging of these different data sets. In contrast, atmospheric chemical models can produce chemically consistent long-term ozone simulations based on specified changes in external forcings, but they are subject to the deficiencies associated with incomplete understanding of complex atmospheric processes and uncertain photochemical parameters. Here, we use chemically self-consistent output from the TOMCAT 3-D chemical transport model (CTM) and a random-forest (RF) ensemble learning method to create a merged 42-year (1979–2020) stratospheric ozone profile data set (ML-TOMCAT V1.0). The underlying CTM simulation was forced by meteorological reanalyses, specified trends in long-lived source gases, solar flux and aerosol variations. The RF is trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set over the time periods of the Microwave Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS) (1991–1998) and Aura (2005–2016) missions. We find that ML-TOMCAT shows excellent agreement with available independent satellite-based data sets which use pressure as a vertical coordinate (e.g. GOZCARDS, SWOOSH for non-MLS periods) but weaker agreement with the data sets which are altitude-based (e.g. SAGE-CCI-OMPS, SCIAMACHY-OMPS). We find that at almost all stratospheric levels ML-TOMCAT ozone concentrations are well within uncertainties of the observational data sets. The ML-TOMCAT (V1.0) data set is ideally suited for the evaluation of chemical model ozone profiles from the tropopause to 0.1 hPa and is freely available via https://doi.org/10.5281/zenodo.5651194 (Dhomse et al., 2021).

Total methane content in the atmosphere of Western Siberia in 2000–2020 according to the data of chemical transport model MOZART-4

The paper considers the behavior of total methane content in the atmosphere of Western Siberia obtained using the global chemical transport model MOZART-4 (Model for OZone And Related chemical Tracers, version 4). We discuss details of the model configuration for simulation of methane content until the end of the XXI century within the Representative Concentration Pathways (RCP4.5 and RCP8.5). Boundary conditions at the lower levels of the model (methane content in the surface air layer) was obtained using data from the Earth System Research Laboratories (ESRL) project and the results of the Atmospheric Chemistry-Transport Models (ACTM) for 2007–2010. The methane content in the stratosphere was also defining according to the ACTM results. The climate data used in MOZART-4 is based on the results of the Goddard Earth Observing System, Version 5 (GEOS-5). The modification of the boundary conditions carried out in the work made it possible to reproduce the summer and winter maximum in the annual course of CH4. These results are confirmed by satellite and aircraft observations made on the territory of Western Siberia. It was found that the total methane content in the atmosphere of the studied region (45–65 N, 60–90 E) in 2000–2020 increased with a trend of about 3.5 ± 0.2 ppb/year. In 2000–2006, there is virtually no trend. The increase of CH4 in 2007–2020 has a trend of ∼ 5.0 ± 0.2 ppb/year. The global data obtained as a result of the simulation can be used as initial and boundary conditions of the chemical version of the regional climate model RegCM-CHEM4.

A New Separation Methodology for the Maritime Sector Emissions over the Mediterranean and Black Sea Regions

The aim of this paper is to apply a new lane separation methodology for the maritime sector emissions attributed to the different vessel types and marine traffic loads in the Mediterranean and the Black Sea defined via the European Marine and Observation Data network (EMODnet), developed in 2016. This methodology is implemented for the first time on the Copernicus Atmospheric Monitoring Service Global Shipping (CAMS-GLOB-SHIP v2.1) nitrogen oxides (NOX) emissions inventory, on the Sentinel-5 Precursor Tropospheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) tropospheric vertical column densities, and on the LOTOS-EUROS (Long Term Ozone Simulation—European Operational Smog) CTM (chemical transport model) simulations. By applying this new EMODnet-based lane separation method to the CAMS-GLOB-SHIP v2.1 emission inventory, we find that cargo and tanker vessels account for approximately 80% of the total emissions in the Mediterranean, followed by fishing, passenger, and other vessel emissions with contributions of 8%, 7%, and 5%, respectively. Tropospheric NO2 vertical column densities sensed by TROPOMI for 2019 and simulated by the LOTOS-EUROS CTM have been successfully attributed to the major vessel activities in the Mediterranean; the mean annual NO2 load of the observations and the simulations reported for the entire maritime EMODnet-reported fleet of the Mediterranean is in satisfactory agreement, 1.26 ± 0.56 × 1015 molecules cm−2 and 0.98 ± 0.41 × 1015 molecules cm−2, respectively. The spatial correlation of the annual maritime NO2 loads of all vessel types between observation and simulation ranges between 0.93 and 0.98. On a seasonal basis, both observations and simulations show a common variability. The wintertime comparisons are in excellent agreement for the highest emitting sector, cargo vessels, with the observations reporting a mean load of 0.98 ± 0.54 and the simulations of 0.81 ± 0.45 × 1015 molecules cm−2 and correlation of 0.88. Similarly, the passenger sector reports 0.45 ± 0.49 and 0.39 ± 0.45 × 1015 molecules cm−2 respectively, with correlation of 0.95. In summertime, the simulations report a higher decrease in modelled tropospheric columns than the observations, however, still resulting in a high correlation between 0.85 and 0.94 for all sectors. These encouraging findings will permit us to proceed with creating a top-down inventory for NOx shipping emissions using S5P/TROPOMI satellite observations and a data assimilation technique based on the LOTOS-EUROS chemical transport model.

A New Separation Methodology for the Maritime Sector Emissions Over the Mediterranean and Black Sea Regions

The aim of this paper is to apply a new lane separation methodology for the maritime sector emissions attributed to the different vessel types and marine traffic loads in the Mediterranean and the Black Sea defined via the European Marine and Observation Data network (EMODnet), developed in 2016. This methodology is implemented for the first time on the Copernicus Atmospheric Monitoring Service Global Shipping (CAMS-GLOB-SHIP v2.1) nitrogen dioxide (NOX) emissions inventory, on the Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) tropospheric vertical column densities and on the LOTOS-EUROS (LOng Term Ozone Simulation &ndash; EURopean Operational Smog) chemical transport model simulations. By applying this new, EMODnet-based lane separation method to the CAMS-GLOB-SHIP v2.1 emission inventory, we find that cargo and tanker vessels account for approximately 80% of the total emissions in the Mediterranean, followed by fishing, passenger, and other vessel emissions with contributions of 8%, 7% and 5%, respectively. Tropospheric NO2 vertical column densities sensed by TROPOMI for 2019 and simulated by the LOTOS-EUROS CTM have been successfully attributed to the major vessel activities in the Mediterranean; the mean annual NO2 load of the observations and the simulations reported for the entire maritime EMODnet-reported fleet of the Mediterranean is in satisfactory agreement, 1.26 &plusmn; 0.56x1015 molecules cm-2 and 0.98 &plusmn; 0.41x1015 molecules cm-2, respectively. The spatial correlation of the annual maritime NO2 loads of all vessel types between observation and simulation ranges between 0.93 and 0.98. On seasonal basis, both observations and simulations show a common variability. The winter-time comparisons are in excellent agreement for the highest emitting sector, cargo vessels, with the observations reporting a mean load of 0.98 &plusmn; 0.54 and the simulations of 0.81 &plusmn; 0.45x1015 molecules cm-2 and correlation of 0.88. Similarly, the passenger sector reports 0.45 &plusmn; 0.49 and 0.39 &plusmn; 0.45x1015 molecules cm-2 respectively, with correlation of 0.95. In summertime, the simulations report a higher decrease in modelled tropospheric columns than the observations, however still resulting in a high correlation between 0.85 and 0.94 for all sectors. These encouraging findings will permit us to proceed with creating a top-down inventory for NOx shipping emissions using S5P/TROPOMI satellite observations and a data assimilation technique based on the LOTOS-EUROS chemical transport model.