Measurement-based modeling of bromine chemistry in the boundary layer: 1. Bromine chemistry at the Dead Sea

International audienceThe Dead Sea is an excellent natural laboratory for the investigation of Reactive Bromine Species (RBS) chemistry, due to the high RBS levels observed in this area, combined with anthropogenic air pollutants up to several ppb. The present study investigated the basic chemical mechanism of RBS at the Dead Sea using a numerical one-dimensional chemical model. Simulations were based on data obtained from comprehensive measurements performed at sites along the Dead Sea. The simulations showed that the high BrO levels measured frequently at the Dead Sea could only partially be attributed to the highly concentrated Br? present in the Dead Sea water. Furthermore, the RBS activity at the Dead Sea cannot solely be explained by a pure gas phase mechanism. This paper presents a chemical mechanism which can account for the observed chemical activity at the Dead Sea, with the addition of only two heterogeneous processes: the "Bromine Explosion" mechanism and the heterogeneous decomposition of BrONO2. Ozone frequently dropped below a threshold value of ~1 to 2 ppbv at the Dead Sea evaporation ponds, and in such cases, O3 became a limiting factor for the production of BrOx (BrO+Br). The entrainment of O3 fluxes into the evaporation ponds was found to be essential for the continuation of RBS activity, and to be the main reason for the jagged diurnal pattern of BrO observed in the Dead Sea area, and for the positive correlation observed between BrO and O3 at low O3 concentrations. The present study has shown that the heterogeneous decomposition of BrONO2 has a great potential to affect the RBS activity in areas influenced by anthropogenic emissions, mainly due to the positive correlation between the rate of this process and the levels of NO2. Further investigation of the influence of the decomposition of BrONO2 may be especially important in understanding the RBS activity at mid-latitudes

Utility of Geostationary Lightning Mapper-derived lightning NO emission estimates in air quality modeling studies

Lightning is one of the primary natural sources of nitric oxide (NO), and the influence of lightning-induced NO (LNO) emission on air quality has been investigated in the past few decades. In the current study an LNO emissions model, which derives LNO emission estimates from satellite-observed lightning optical energy, is introduced. The estimated LNO emission is employed in an air quality modeling system to investigate the potential influence of LNO on tropospheric ozone. Results show that lightning produced 0.174 Tg N of nitrogen oxides (NOx = NO + NO2) over the contiguous US (CONUS) domain between June and September 2019, which accounts for 11.4 % of the total NOx emission. In August 2019, LNO emission increased ozone concentration within the troposphere by an average of 1 %–2 % (or 0.3–1.5 ppbv), depending on the altitude; the enhancement is maximum at ∼ 4 km above ground level and minimum near the surface. The southeastern US has the most significant ground-level ozone increase, with up to 1 ppbv (or 2 % of the mean observed value) difference for the maximum daily 8 h average (MDA8) ozone. These numbers are near the lower bound of the uncertainty range given in previous studies. The decreasing trend in anthropogenic NOx emissions over the past 2 decades increases the relative contribution of LNO emissions to total NOx emissions, suggesting that the LNO production rate used in this study may need to be increased. Corrections for the sensor flash detection efficiency may also be helpful. Moreover, the episodic impact of LNO on tropospheric ozone can be considerable. Performing backward trajectory analyses revealed two main reasons for significant ozone increases: long-distance chemical transport and lightning activity in the upwind direction shortly before the event.</p

Satellite Data Assimilation into Meteorological/Air Quality

A report directed at improving the specification of surface parameters such as insolation, soil moisture, and surface heat capacity

Relevance of Photolysis Frequencies Calculation Aspects to the Ozone Concentration Simulation

For the simulation of photochemically created pollutants like ozone it is essential to correctly consider reaction rates induced by short-wave radiation. In atmospheric chemistry transport models this is achieved by the use of either off- or online calculated photolysis frequencies. In this study the effect of different input parameters of a radiation model on the calculated photolysis frequencies have been investigated. In the second step an atmospheric chemistry transport model was used to assess the impact of changed photolysis frequencies on the simulation of ozone concentrations. The impact of changed radiation model input parameters on the calculated photolysis frequencies vary not only with regard to the changed parameter but also with regard to the to the species to be dissociated. Furthermore the impact of different sets of photolysis rates employed in a chemical transport simulation on the modelled concentrations is differed and likely to be less important than other aspects of the simulation like the resolution of the grid and the emissions used. Apart from major surface albedo changes (grass to snow) and extreme changes in total ozone column content for JO3 clouds are the dominating factor in modifying the photolysis frequencies especially as they feature a highly temporal and special variation. The results show that simulated maximum ozone concentrations in areas with clouds are reduced

Response and sensitivity of the nocturnal boundary layer over land to added longwave radiative forcing

One of the most significant signals in the thermometer-observed temperature record since 1900 is the decrease in the diurnal temperature range over land, largely due to rising of the minimum temperatures. Generally, climate models have not well replicated this change in diurnal temperature range. Thus, the cause for night-time warming in the observed temperatures has been attributed to a variety of external causes. We take an alternative approach to examine the role that the internal dynamics of the stable nocturnal boundary layer (SNBL) may play in affecting the response and sensitivity of minimum temperatures to added downward longwave forcing. As indicated by previous nonlinear analyses of a truncated two-layer equation system, the SNBL can be very sensitive to changes in greenhouse gas forcing, surface roughness, heat capacity, and wind speed. A new single-column model growing out of these nonlinear studies is used to examine the SNBL. Specifically, budget analyses of the model are provided that evaluate the response of the boundary layer to forcing and sensitivity to mixing formulations. Based on these model analyses, it is likely that part of the observed long-term increase in minimum temperature is reflecting a redistribution of heat by changes in turbulence and not by an accumulation of heat in the boundary layer. Because of the sensitivity of the shelter level temperature to parameters and forcing, especially to uncertain turbulence parameterization in the SNBL, there should be caution about the use of minimum temperatures as a diagnostic global warming metric in either observations or models

Influence of satellite-derived photolysis rates and NO_x emissions on Texas ozone modeling

Uncertain photolysis rates and emission inventory impair the accuracy of state-level ozone (O₃) regulatory modeling. Past studies have separately used satellite-observed clouds to correct the model-predicted photolysis rates, or satellite-constrained top-down NO_x emissions to identify and reduce uncertainties in bottom-up NO_x emissions. However, the joint application of multiple satellite-derived model inputs to improve O₃ state implementation plan (SIP) modeling has rarely been explored. In this study, Geostationary Operational Environmental Satellite (GOES) observations of clouds are applied to derive the photolysis rates, replacing those used in Texas SIP modeling. This changes modeled O₃ concentrations by up to 80 ppb and improves O₃ simulations by reducing modeled normalized mean bias (NMB) and normalized mean error (NME) by up to 0.1. A sector-based discrete Kalman filter (DKF) inversion approach is incorporated with the Comprehensive Air Quality Model with extensions (CAMx)–decoupled direct method (DDM) model to adjust Texas NO_x emissions using a high-resolution Ozone Monitoring Instrument (OMI) NO₂ product. The discrepancy between OMI and CAMx NO₂ vertical column densities (VCDs) is further reduced by increasing modeled NO_x lifetime and adding an artificial amount of NO₂ in the upper troposphere. The region-based DKF inversion suggests increasing NO_x emissions by 10–50% in most regions, deteriorating the model performance in predicting ground NO₂ and O₃, while the sector-based DKF inversion tends to scale down area and nonroad NO_x emissions by 50%, leading to a 2–5 ppb decrease in ground 8 h O₃ predictions. Model performance in simulating ground NO₂ and O₃ are improved using sector-based inversion-constrained NO_x emissions, with 0.25 and 0.04 reductions in NMBs and 0.13 and 0.04 reductions in NMEs, respectively. Using both GOES-derived photolysis rates and OMI-constrained NO_x emissions together reduces modeled NMB and NME by 0.05, increases the model correlation with ground measurement in O₃ simulations, and makes O₃ more sensitive to NO_x emissions in the O₃ non-attainment areas

Correcting photolysis rates on the basis of satellite observed clouds

Article Number D10302Clouds can significantly affect photochemical activities in the boundary layer by altering radiation intensity, and therefore their correct specification in the air quality models is of outmost importance. In this study we introduce a technique for using the satellite observed clouds to correct photolysis rates in photochemical models. This technique was implemented in EPA's Community Multiscale Air Quality modeling system (CMAQ) and was tested over a 10 day period in August 2000 that coincided with the Texas Air Quality Study (TexAQS). The simulations were performed at 4 and 12 km grid size domains over Texas, extending east to Mississippi, for the period of 24 to 31 August 2000. The results clearly indicate that inaccurate cloud prediction in the model can significantly alter the predicted atmospheric chemical composition within the boundary layer and exaggerate or underpredict ozone concentration. Cloud impact is acute and more pronounced over the emission source regions and can lead to large errors in the model predictions of ozone and its by-products. At some locations the errors in ozone concentration reached as high as 60 ppb which was mostly corrected by the use of our technique. Clouds also increased the lifetime of ozone precursors leading to their transport out of the source regions and causing further ozone production down-wind. Longer lifetime for nitrogen oxides (NOx = NO + NO2) and its transport over regions high in biogenic hydrocarbon emissions (in the eastern part of the domain) led to increased ozone production that was missing in the control simulation. Over Houston-Galveston Bay area, the presence of clouds altered the chemical composition of the atmosphere and reduced the net surface removal of reactive nitrogen compounds. Use of satellite observed clouds significantly improved model predictions in areas impacted by clouds. Errors arising from an inconsistency in the cloud fields can impact the performance of photochemical models used for case studies as well as for air quality forecasting. Air quality forecast models often use the model results from the previous forecast (or some adjusted form of it) for the initialization of the new forecast. Therefore such errors can propagate into the future forecasts, and the use of observed clouds in the preparation of initial concentrations for air quality forecasting could be beneficial.http://gbic.tamug.edu/request.ht