Time-dependent entrainment of smoke presents an observational challenge for assessing aerosol–cloud interactions over the southeast Atlantic Ocean

The colocation of clouds and smoke over the southeast Atlantic Ocean during the southern African biomass burning season has numerous radiative implications, including microphysical modulation of the clouds if smoke is entrained into the marine boundary layer. NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign is studying this system with aircraft in three field deployments between 2016 and 2018. Results from ORACLES-2016 show that the relationship between cloud droplet number concentration and smoke below cloud is consistent with previously reported values, whereas cloud droplet number concentration is only weakly associated with smoke immediately above cloud at the time of observation. By combining field observations, regional chemistry–climate modeling, and theoretical boundary layer aerosol budget equations, we show that the history of smoke entrainment (which has a characteristic mixing timescale on the order of days) helps explain variations in cloud properties for similar instantaneous above-cloud smoke environments. Precipitation processes can obscure the relationship between above-cloud smoke and cloud properties in parts of the southeast Atlantic, but marine boundary layer carbon monoxide concentrations for two case study flights suggest that smoke entrainment history drove the observed differences in cloud properties for those days. A Lagrangian framework following the clouds and accounting for the history of smoke entrainment and precipitation is likely necessary for quantitatively studying this system; an Eulerian framework (e.g., instantaneous correlation of A-train satellite observations) is unlikely to capture the true extent of smoke–cloud interaction in the southeast Atlantic.</p

Social assessment on an integrated intervention for the prevention of Zika and other Aedes-borne diseases in pregnant women and their families in Mexico

Results of the project show that pregnant women can be provided with free or low-cost integrated interventions with methods of known efficacy (topical repellent and insecticide-treated window screens) along with educative strategies, and that they are highly motivated to use these methods to enhance maternal-child health for Zika and other Aedes-borne diseases (ABD). This is a one-page assessment/synopsis of the integrated intervention

Impacts of physical parameterization on prediction of ethane concentrations for oil and gas emissions in WRF-Chem

Recent increases in natural gas (NG) production through hydraulic fracturing have called the climate benefit of switching from coal-fired to natural gas-fired power plants into question. Higher than expected levels of methane, non-methane hydrocarbons (NMHC), and NOx have been observed in areas close to oil and NG operation facilities. Large uncertainties in the oil and NG operation emission inventories reduce the confidence level in the impact assessment of such activities on regional air quality and climate, as well as in the development of effective mitigation policies. In this work, we used ethane as the indicator of oil and NG emissions and explored the sensitivity of ethane to different physical parameterizations and simulation setups in the Weather Research and Forecasting with Chemistry (WRF-Chem) model using the US EPA National Emission Inventory (NEI-2011). We evaluated the impact of the following configurations and parameterizations on predicted ethane concentrations: planetary boundary layer (PBL) parameterizations, daily re-initialization of meteorological variables, meteorological initial and boundary conditions, and horizontal resolution. We assessed the uncertainties around oil and NG emissions using measurements from the FRAPPÉ and DISCOVER-AQ campaigns over the northern Front Range metropolitan area (NFRMA) in summer 2014. The sensitivity analysis shows up to 57.3&thinsp;% variability in the normalized mean bias of the near-surface modeled ethane across the simulations, which highlights the important role of model configurations on the model performance and ultimately the assessment of emissions. Comparison between airborne measurements and the sensitivity simulations indicates that the model–measurement bias of ethane ranged from −14.9 to −8.2&thinsp;ppb (NMB ranged from −80.5&thinsp;% to −44&thinsp;%) in regions close to oil and NG activities. Underprediction of ethane concentration in all sensitivity runs suggests an actual underestimation of the oil and NG emissions in the NEI-2011. An increase of oil and NG emissions in the simulations partially improved the model performance in capturing ethane and lumped alkanes (HC3) concentrations but did not impact the model performance in capturing benzene, toluene, and xylene; this is due to very low emission rates of the latter species from the oil and NG sector in NEI-2011.</p

Use of lidar aerosol extinction and backscatter coefficients to estimate cloud condensation nuclei (CCN) concentrations in the southeast Atlantic

Accurately capturing cloud condensation nuclei (CCN) concentrations is key to understanding the aerosol–cloud interactions that continue to feature the highest uncertainty amongst numerous climate forcings. In situ CCN observations are sparse, and most non-polarimetric passive remote sensing techniques are limited to providing column-effective CCN proxies such as total aerosol optical depth (AOD). Lidar measurements, on the other hand, resolve profiles of aerosol extinction and/or backscatter coefficients that are better suited for constraining vertically resolved aerosol optical and microphysical properties. Here we present relationships between aerosol backscatter and extinction coefficients measured by the airborne High Spectral Resolution Lidar 2 (HSRL-2) and in situ measurements of CCN concentrations. The data were obtained during three deployments in the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project, which took place over the southeast Atlantic (SEA) during September 2016, August 2017, and September–October 2018. Our analysis of spatiotemporally collocated in situ CCN concentrations and HSRL-2 measurements indicates strong linear relationships between both data sets. The correlation is strongest for supersaturations (S) greater than 0.25 % and dry ambient conditions above the stratocumulus deck, where relative humidity (RH) is less than 50 %. We find CCN–HSRL-2 Pearson correlation coefficients between 0.95–0.97 for different parts of the seasonal burning cycle that suggest fundamental similarities in biomass burning aerosol (BBA) microphysical properties. We find that ORACLES campaign-average values of in situ CCN and in situ extinction coefficients are qualitatively similar to those from other regions and aerosol types, demonstrating overall representativeness of our data set. We compute CCN–backscatter and CCN–extinction regressions that can be used to resolve vertical CCN concentrations across entire above-cloud lidar curtains. These lidar-derived CCN concentrations can be used to evaluate model performance, which we illustrate using an example CCN concentration curtain from the Weather Research and Forecasting Model coupled with physics packages from the Community Atmosphere Model version 5 (WRF-CAM5). These results demonstrate the utility of deriving vertically resolved CCN concentrations from lidar observations to expand the spatiotemporal coverage of limited or unavailable in situ observations.</p

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM

Modeling organic aerosols in a megacity: comparison of simple and complex representations of the volatility basis set approach

The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is modified to include a volatility basis set (VBS) treatment of secondary organic aerosol formation. The VBS approach, coupled with SAPRC-99 gas-phase chemistry mechanism, is used to model gas-particle partitioning and multiple generations of gas-phase oxidation of organic vapors. In addition to the detailed 9-species VBS, a simplified mechanism using 2 volatility species (2-species VBS) is developed and tested for similarity to the 9-species VBS in terms of both mass and oxygen-to-carbon ratios of organic aerosols in the atmosphere. WRF-Chem results are evaluated against field measurements of organic aerosols collected during the MILAGRO 2006 campaign in the vicinity of Mexico City. The simplified 2-species mechanism reduces the computational cost by a factor of 2 as compared to 9-species VBS. Both ground site and aircraft measurements suggest that the 9-species and 2-species VBS predictions of total organic aerosol mass as well as individual organic aerosol components including primary, secondary, and biomass burning are comparable in magnitude. In addition, oxygen-to-carbon ratio predictions from both approaches agree within 25 %, providing evidence that the 2-species VBS is well suited to represent the complex evolution of organic aerosols. Model sensitivity to amount of anthropogenic semi-volatile and intermediate volatility (S/IVOC) precursor emissions is also examined by doubling the default emissions. Both the emission cases significantly under-predict primary organic aerosols in the city center and along aircraft flight transects. Secondary organic aerosols are predicted reasonably well along flight tracks surrounding the city, but are consistently over-predicted downwind of the city. Also, oxygen-to-carbon ratio predictions are significantly improved compared to prior studies by adding 15 % oxygen mass per generation of oxidation; however, all modeling cases still under-predict these ratios downwind as compared to measurements, suggesting a need to further improve chemistry parameterizations of secondary organic aerosol formation

Surface Dimming by the 2013 Rim Fire Simulated by a Sectional Aerosol Model

The Rim Fire of 2013, the third largest area burned by fire recorded in California history, is simulated by a climate model coupled with a size-resolved aerosol model. Modeled aerosol mass, number and particle size distribution are within variability of data obtained from multiple airborne in-situ measurements. Simulations suggest Rim Fire smoke may block 4-6 of sunlight energy reaching the surface, with a dimming efficiency around 120-150 W m(exp -2) per unit aerosol optical depth in the mid-visible at 13:00-15:00 local time. Underestimation of simulated smoke single scattering albedo at mid-visible by 0.04 suggests the model overestimates either the particle size or the absorption due to black carbon. This study shows that exceptional events like the 2013 Rim Fire can be simulated by a climate model with one-degree resolution with overall good skill, though that resolution is still not sufficient to resolve the smoke peak near the source region

Revealing important nocturnal and day-to-day variations in fire smoke emissions through a multiplatform inversion

We couple airborne, ground-based, and satellite observations; conduct regional simulations; and develop and apply an inversion technique to constrain hourly smoke emissions from the Rim Fire, the third largest observed in California, USA. Emissions constrainedwithmultiplatform data show notable nocturnal enhancements (sometimes over a factor of 20), correlate better with daily burned area data, and are a factor of 2–4 higher than a priori estimates, highlighting the need for improved characterization of diurnal profiles and day-to-day variability when modeling extreme fires. Constraining only with satellite data results in smaller enhancements mainly due to missing retrievals near the emissions source, suggesting that top-down emission estimates for these events could be underestimated and a multi-platform approach is required to resolve them. Predictions driven by emissions constrained with multi-platform data present significant variations in downwind air quality and in aerosol feedback on meteorology, emphasizing the need for improved emissions estimates during exceptional events

Revealing important nocturnal and day-to-day variations in fire smoke emissions through a multiplatform inversion

We couple airborne, ground-based, and satellite observations; conduct regional simulations; and develop and apply an inversion technique to constrain hourly smoke emissions from the Rim Fire, the third largest observed in California, USA. Emissions constrainedwithmultiplatform data show notable nocturnal enhancements (sometimes over a factor of 20), correlate better with daily burned area data, and are a factor of 2–4 higher than a priori estimates, highlighting the need for improved characterization of diurnal profiles and day-to-day variability when modeling extreme fires. Constraining only with satellite data results in smaller enhancements mainly due to missing retrievals near the emissions source, suggesting that top-down emission estimates for these events could be underestimated and a multi-platform approach is required to resolve them. Predictions driven by emissions constrained with multi-platform data present significant variations in downwind air quality and in aerosol feedback on meteorology, emphasizing the need for improved emissions estimates during exceptional events