M. J. Evans

The DACCIWA project: dynamics-aerosol-chemistry-cloud interactions in West Africa

Massive economic and population growth, and urbanization are expected to lead to a tripling of anthropogenic emissions in southern West Africa (SWA) between 2000 and 2030. However, the impacts of this on human health, ecosystems, food security, and the regional climate are largely unknown. An integrated assessment is challenging due to (a) a superposition of regional effects with global climate change, (b) a strong dependence on the variable West African monsoon, (c) incomplete scientific understanding of interactions between emissions, clouds, radiation, precipitation, and regional circulations, and (d) a lack of observations. This article provides an overview of the DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) project. DACCIWA will conduct extensive fieldwork in SWA to collect high-quality observations, spanning the entire process chain from surface-based natural and anthropogenic emissions to impacts on health, ecosystems, and climate. Combining the resulting benchmark dataset with a wide range of modeling activities will allow (a) assessment of relevant physical, chemical, and biological processes, (b) improvement of the monitoring of climate and atmospheric composition from space, and (c) development of the next generation of weather and climate models capable of representing coupled cloud-aerosol interactions. The latter will ultimately contribute to reduce uncertainties in climate predictions. DACCIWA collaborates closely with operational centers, international programs, policy-makers, and users to actively guide sustainable future planning for West Africa. It is hoped that some of DACCIWA’s scientific findings and technical developments will be applicable to other monsoon regions.

The Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa Field Campaign: Overview and Research Highlights

Unprecedented ground-based and aircraft measurements in June-July 2016 in southern West Africa characterize atmospheric composition and dynamics, low-level cloud properties, the diurnal cycle, and air pollution impacts on health. nThe EU-funded project DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) investigates the relationship between weather, climate, and air pollution in southern West Africa, an area with rapid population growth, urbanisation, and increase in anthropogenic aerosol emissions. The air over this region contains a unique mixture of natural and anthropogenic gases, liquid droplets and particles, emitted in an environment, in which multi-layer clouds frequently form. These exert a large influence on the local weather and climate, mainly due to their impact on radiation, the surface energy balance, and thus the diurnal cycle of the atmospheric boundary layer. nIn June and July 2016, DACCIWA organized a major international field campaign in Ivory Coast, Ghana, Togo, Benin, and Nigeria. Three supersites in Kumasi, Save, and Ile-Ife conducted permanent measurements and 15 Intensive observation periods. Three European aircraft together flew 50 research flights between 27 June and 16 July 2016 for a total of 155 hours. DACCIWA scientists launched weather balloons several times a day across the region (772 in total), measured urban emissions, and evaluated health data. The main objective was to build robust statistics of atmospheric composition, dynamics, and low-level cloud properties in various chemical landscapes to investigate their mutual interactions. nThis article presents an overview of the DACCIWA field campaign activities as well as some first research highlights. The rich data obtained during the campaign will be made available to the scientific community and help to advance scientific understanding, modeling, and monitoring the atmosphere over southern West Africa.

How to most effectively expand the global surface ozone observing network

Abstract. Surface ozone observations with modern instrumentation have been made around the world for more than 40xa0years. Some of these observations have been made as one-off activities with short-term, specific science objectives and some have been made as part of wider networks which have provided a foundational infrastructure of data collection, calibration, quality control, and dissemination. These observations provide a fundamental underpinning to our understanding of tropospheric chemistry, air quality policy, atmosphere–biosphere interactions, etc. brought together eight of these networks to provide a single data set of surface ozone observations. We investigate how representative this combined data set is of global surface ozone using the output from a global atmospheric chemistry model. We estimate that on an area basis, 25u202f% of the globe is observed (34u202f% land, 21u202f% ocean). Whereas Europe and North America have almost complete coverage, other continents, Africa, South America, Australia, and Asia (12–17u202f%) show significant gaps. Antarctica is surprisingly well observed (78u202f%). Little monitoring occurs over the oceans, with the tropical and southern oceans particularly poorly represented. The surface ozone over key biomes such as tropical forests and savanna is almost completely unmonitored. A chemical cluster analysis suggests that a significant number of observations are made of polluted air masses, but cleaner air masses whether over the land or ocean (especially again in the tropics) are significantly under-observed. The current network is unlikely to see the impact of the El Nino–Southern Oscillation (ENSO) but may be capable of detecting other planetary-scale signals. Model assessment and validation activities are hampered by a lack of observations in regions where the models differ substantially, as is the ability to monitor likely changes in surface ozone over the next century. Using our methodology we are able to suggest new sites which would help to close the gap in our ability to measure global surface ozone. An additional 20 surface ozone monitoring sites (a 20u202f% increase in the World Meteorological Organization Global Atmosphere Watch (WMO GAW) ozone sites or a 1u202f% increase in the total background network) located on 10 islands and in 10 continental regions would almost double the area observed. The cost of this addition to the network is small compared to other expenditure on atmospheric composition research infrastructure and would provide a significant long-term benefit to our understanding of the composition of the atmosphere, information which will also be available for consideration by air quality control managers and policy makers.

Global modeling of tropospheric iodine aerosol

Natural aerosols play a central role in the Earth system. The conversion of dimethyl sulfide to sulfuric acid is the dominant source of oceanic secondary aerosol. Ocean emitted iodine can also produce aerosol. Using a GEOS-Chem model we present a simulation of iodine aerosol. The simulation compares well with the limited observational dataset. Iodine aerosol concentrations are highest in the tropical marine-boundary layer (MBL) averaging 5.2 ng (I) mxa0−xa03 with monthly maximum concentrations of 90 ng (I) mxa0−xa03. These masses are small compared to sulfate (0.75% of MBL burden, up to 11% regionally) but are more significant compared to DMS sourced sulfate (3% of the MBL burden, up to 101% regionally). In the pre-industrial, iodine aerosol makes up 0.88 % of the MBL burden sulfate mass and regionally up to 21%. Iodine aerosol may be an important regional mechanism for ocean-atmosphere interaction.

Application of random forest regression to the calculation ofgas-phase chemistry within the GEOS-Chem chemistry model v10

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consists of one month (July 2013) of output of chemical 5 conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families 10 (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine learning driven GEOS-Chem model compares well to the standard simulation. For O3, errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalised mean bias (NMB), root mean square error (RMSE) and R are 4.2%, 35%, and 0.9, respectively; after 30 days the errors increase to 13%, 67%, and 0.75, 15 respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10% and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short lived nitrogen species errors become large, with NMB, RMSE and R reaching >2100% >400%, and <0.1, respectively. 20 This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations but optimisation and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations and its applicability to operational air quality activities.

Observed NO/NO2 Ratios in the Upper Troposphere Imply Errors in NO-NO2-O3 Cycling Kinetics or an Unaccounted NOx Reservoir

Observations from the SEAC^4RS aircraft campaign over the southeast United States in August–September 2013 show NO/NO_2 concentration ratios in the upper troposphere that are approximately half of photochemical equilibrium values computed from Jet Propulsion Laboratory (JPL) kinetic data. One possible explanation is the presence of labile NO_x reservoir species, presumably organic, decomposing thermally to NO_2 in the instrument. The NO_2 instrument corrects for this artifact from known labile HNO_4 and CH_3O_2NO_2 NO_x reservoirs. To bridge the gap between measured and simulated NO_2, additional unaccounted labile NO_x reservoir species would have to be present at a mean concentration of ~40 ppt for the SEAC^4RS conditions (compared with 197 ppt for NOx). An alternative explanation is error in the low‐temperature rate constant for the NO + O_3 reaction (30% 1‐σ uncertainty in JPL at 240 K) and/or in the spectroscopic data for NO_2 photolysis (20% 1‐σ uncertainty). Resolving this discrepancy is important for understanding global budgets of tropospheric oxidants and for interpreting satellite observations of tropospheric NO_2 columns.

Measurements of nitric oxide and ammonia soil fluxes from a wetsavanna ecosystem site in West Africa during the DACCIWA fieldcampaign

Biogenic fluxes from soil at a local and regional scale are crucial to study air pollution and climate. Here we present field measurements of soil fluxes of nitric oxide (NO) and ammonia (NH3) observed over four different land cover types, i.e. bare soil, grassland, maize field, and forest, at an inland rural site in Benin, West Africa, during the DACCIWA field campaign in June and July 2016. At the regional scale, urbanization and a massive growth in population in West Africa have been causing a strong increase in anthropogenic emissions. Anthropogenic pollutants are transported inland and northward from the megacities located on the coast, where the reaction with biogenic emissions may lead to enhanced ozone production outside urban areas, as well as secondary organic aerosol formation, with detrimental effects on humans, animals, natural vegetation, and crops. We observe NO fluxes up to 48.05 ngN m−2 s−1. NO fluxes averaged over all land cover types are 4.79± 5.59 ngN m−2 s−1, and maximum soil emissions of NO are recorded over bare soil. NH3 is dominated by deposition for all land cover types. NH3 fluxes range between −6.59 and 4.96 ngN m−2 s−1. NH3 fluxes averaged over all land cover types are−0.91±1.27 ngN m−2 s−1, and maximum NH3 deposition is measured over bare soil. The observations show high spatial variability even for the same soil type, same day, and same meteorological conditions. We compare point daytime average measurements of NO emissions recorded during the field campaign with those simulated by GEOS-Chem (Goddard Earth Observing System Chemistry Model) for the same site and find good agreement. In an attempt to quantify NO emissions at the regional and national scale, we also provide a tentative estimate of total NO emissions for the entire country of Benin for the month of July using two distinct methods: upscaling point measurements and using the GEOS-Chem model. The two methods give similar results: 1.17± 0.6 and 1.44 GgN month−1, respectively. Total NH3 deposition estimated by upscaling point measurements for the month of July is 0.21 GgN month−1.

The sensitivity of global nucleation, cloud condensation nuclei and climate to SO2 and Criegee-intermediate chemistry

Recent studies have shown that the reaction of stabilized Criegee intermediates (CIs, formed from the ozonolysis of alkenes) with SO2 may be an important source sulfuric acid vapor (H2SO4) in alkene-rich regions. We use a global atmospheric chemistry model, GEOS-Chem, with the online aerosol microphysics module, TOMAS, to investigate the impact of CI+SO2 chemistry on cloud condensation nuclei (CCN) concentrations. To get an upper bound for the effect of CI+SO2 chemistry, we use the fastest published rate constants in our simulations. The addition of CI+SO2 chemistry increases H2SO4 globally by 4%, but by over 100% in forested regions, particularly the tropics. However, changes in CCN due to CI+SO2 chemistry were significantly lower, less than 1% globally and no more than 15% in any region. The estimated change in the brightness aerosol indirect effect (AIE) due to the addition of CI+SO2 chemistry was-0.03 W m−2 globally, which is over an order-of-magnitude smaller than many other model uncertainties such ...