skip to main content


Title: Modeled Atmospheric Optical and Thermodynamic Responses to an Exceptional Trans‐Atlantic Dust Outbreak
Abstract

Long‐range aerosol transport is an important physical mechanism for ecological, biological, and hydrological elements of the earth system. Regarding the latter, regional climate models have no way of assimilating future aerosol concentrations, so dust aerosol emissions must be parameterized using local landscape and meteorological conditions. The purpose of this study is to evaluate the accuracy of different dust emission settings within the Weather Research and Forecasting model coupled with chemistry (WRF‐Chem) to facilitate future dynamical downscaling work. This study performs nine WRF‐Chem hindcasts, each utilizing a different dust emission configuration, from 1 March to 31 May 2015, coinciding with a Saharan air layer (SAL) dust outbreak during the 2015 Caribbean drought. WRF‐Chem aerosol optical depth (AOD) and Gálvez‐Davison Index (GDI), a convective forecasting parameter, are validated against analogous MODIS, AERONET, and ERA5 products. In aggregate, the GOCART dust emission scheme with Air Force Weather Agency modifications (GOCART‐AFWA) achieved the best balance between AOD and GDI accuracy when employing the default tuning constant (1.00). As the schemes emitted dust more aggressively, WRF‐Chem produced warming at 500 hPa, reducing GDI over the central and eastern Atlantic near the modeled dust trajectory. Though AOD was generally too low over the southwest Atlantic, the eastern Caribbean occupies a transition zone between negative and positive AOD biases where this field was hindcast with relative accuracy. Meanwhile, areas with positive AOD biases were associated with negative GDI biases (and vice versa) indicating the covariability between SAL dust loadings and thermodynamic conditions in the tropical north Atlantic.

 
more » « less
Award ID(s):
1831952
NSF-PAR ID:
10449559
Author(s) / Creator(s):
 ;  ;  
Publisher / Repository:
DOI PREFIX: 10.1029
Date Published:
Journal Name:
Journal of Geophysical Research: Atmospheres
Volume:
126
Issue:
5
ISSN:
2169-897X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Improved characterization of the spatiotemporal extent, intensity, and causes of extreme aerosol optical depth events is critical to quantifying their regional climate forcing and the link to near‐surface air quality. An analysis of regional‐scale extreme aerosol events over the eastern United States is undertaken using output from the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA‐2) reanalysis and observations from the MODerate resolution Imaging Spectroradiometers (MODIS). Six extreme aerosol optical depth (AOD) events during 2003–2007, dominated by anthropogenic emissions and characterized by a regional scale extent, are identified and simulated using the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) applied at 12 km resolution. Statistical analyses show output from WRF‐Chem during these events is generally negatively biased in terms of the mean AOD and PM2.5, but WRF‐Chem exhibits skill in capturing the peak AOD. WRF‐Chem also exhibits fidelity in reproducing the spatiotemporal characteristics of the extreme AOD events in intensity, location of centroid, propagation, duration, and their spatial extension. Considerable event‐to‐event variability in model skill in simulating spatial patterns of extreme events is observed, with the highest spatial correlation with MERRA‐2 AOD noted for events centered in the Midwest. Mean fractional bias in modeled peak AOD is minimized for the most intense events and for events centered over the southeastern USA. WRF‐Chem output is also negatively biased in downwelling shortwave radiation at the ground and specific humidity consistent with a positive bias in simulated precipitation relative to MERRA‐2.

     
    more » « less
  2. Abstract

    This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) generated 72 hr fine particulate matter (PM2.5) forecasts in Delhi during the crop residue burning season of October‐November 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol‐radiation interactions. The assimilation significantly pushes the model AOD and PM2.5toward the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF‐Chem forecast with MODIS AOD assimilation, aerosol‐radiation feedback turned on, and real‐time fire emissions reduce the mean bias by 88–195 μg/m3(70–86%) with the largest improvement during the peak air pollution episode of 6–13 November 2017. Aerosol‐radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third days of PM2.5forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM2.5forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol‐radiation feedback extends the benefits of assimilating satellite AOD beyond PM2.5forecasts to surface temperature forecast with a reduction in the mean bias of 0.9–1.5°C (17–30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.

     
    more » « less
  3. Abstract. Wildfire smoke is one of the most significant concerns ofhuman and environmental health, associated with its substantial impacts onair quality, weather, and climate. However, biomass burning emissions andsmoke remain among the largest sources of uncertainties in air qualityforecasts. In this study, we evaluate the smoke emissions and plumeforecasts from 12 state-of-the-art air quality forecasting systemsduring the Williams Flats fire in Washington State, US, August 2019, whichwas intensively observed during the Fire Influence on Regional to GlobalEnvironments and Air Quality (FIREX-AQ) field campaign. Model forecasts withlead times within 1 d are intercompared under the same framework basedon observations from multiple platforms to reveal their performanceregarding fire emissions, aerosol optical depth (AOD), surface PM2.5,plume injection, and surface PM2.5 to AOD ratio. The comparison ofsmoke organic carbon (OC) emissions suggests a large range of daily totalsamong the models, with a factor of 20 to 50. Limited representations of thediurnal patterns and day-to-day variations of emissions highlight the needto incorporate new methodologies to predict the temporal evolution andreduce uncertainty of smoke emission estimates. The evaluation of smoke AOD(sAOD) forecasts suggests overall underpredictions in both the magnitude andsmoke plume area for nearly all models, although the high-resolution modelshave a better representation of the fine-scale structures of smoke plumes.The models driven by fire radiativepower (FRP)-based fire emissions or assimilating satellite AODdata generally outperform the others. Additionally, limitations of thepersistence assumption used when predicting smoke emissions are revealed bysubstantial underpredictions of sAOD on 8 August 2019, mainly over thetransported smoke plumes, owing to the underestimated emissions on7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecastsshow both positive and negative overall biases for these models, with mostmembers presenting more considerable diurnal variations of sPM2.5.Overpredictions of sPM2.5 are found for the models driven by FRP-basedemissions during nighttime, suggesting the necessity to improve verticalemission allocation within and above the planetary boundary layer (PBL).Smoke injection heights are further evaluated using the NASA LangleyResearch Center's Differential Absorption High Spectral Resolution Lidar(DIAL-HSRL) data collected during the flight observations. As the firebecame stronger over 3–8 August, the plume height became deeper, with aday-to-day range of about 2–9 km a.g.l. However, narrower ranges arefound for all models, with a tendency of overpredicting the plume heights forthe shallower injection transects and underpredicting for the days showingdeeper injections. The misrepresented plume injection heights lead toinaccurate vertical plume allocations along the transects corresponding totransported smoke that is 1 d old. Discrepancies in model performance forsurface PM2.5 and AOD are further suggested by the evaluation of theirratio, which cannot be compensated for by solely adjusting the smoke emissionsbut are more attributable to model representations of plume injections,besides other possible factors including the evolution of PBL depths andaerosol optical property assumptions. By consolidating multiple forecastsystems, these results provide strategic insight on pathways to improvesmoke forecasts. 
    more » « less
  4. null (Ed.)
    Abstract In groundwater-limited settings, such as Puerto Rico and other Caribbean islands, societal, ecological, and agricultural water needs depend on regular rainfall. Though long-range numerical weather predication models explicitly predict precipitation, such quantitative precipitation forecasts (QPF) critically failed to detect the historic 2015 Caribbean drought. Consequently, this work examines the feasibility of developing a drought early warning tool using the Gálvez–Davison index (GDI), a tropical convective potential index, derived from the Climate Forecast System, version 2 (CFSv2). Drought forecasts are focused on Puerto Rico’s early rainfall season (ERS; April–July), which is susceptible to intrusions of strongly stable Saharan air and represents the largest source of hydroclimatic variability for the island. A fully coupled atmosphere–ocean–land model, the CFSv2 can plausibly detect the transatlantic advection of low-GDI Saharan air with multimonth lead times. The mean ERS GDI is calculated from semidaily CFSv2 forecasts beginning 1 January of each year between 2012 and 2018 and monitored as the initialization approaches 1 April. The CFSv2 demonstrates a broad region of statistically significant correlations with observed GDI across the eastern Caribbean up to 30 days prior to the ERS. During 2015, the CFSv2 forecast a low-GDI tongue extending across the Atlantic toward the Caribbean with 60–90 days lead time and placed Puerto Rico’s 2015 ERS beneath the 15th percentile of all 1982–2018 ERS forecasts with up to 30 days lead time. A preliminary GDI-based QPF tool tested herein is a statistically significant improvement over climatology for the driest years. 
    more » « less
  5. Abstract

    This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducted using the Weather Research and Forecasting dust model and the Gridpoint Statistical Interpolation analysis system. Six of these experiments differ in whether or not AOD observations are assimilated and the DA method used, the latter of which includes the three‐dimensional variational (3D‐Var), ensemble square root filter (EnSRF), and hybrid methods. The seventh experiment, which allows us to assess the impact of assimilating deep blue AOD data, assimilates only dark target AOD data using the hybrid method. The assimilation of MODIS AOD data clearly improves AOD analyses and forecasts up to 48 hr in length. Results also show that assimilating deep blue data has a primarily positive effect on AOD analyses and forecasts over and downstream of the major North African source regions. Without assimilating deep blue data (assimilating dark target only), AOD assimilation only improves AOD forecasts for up to 30 hr. Of the three DA methods examined, the hybrid and EnSRF methods produce better AOD analyses and forecasts than the 3D‐Var method does. Despite the clear benefit of AOD assimilation for AOD analyses and forecasts, the lack of information regarding the vertical distribution of aerosols in AOD data means that AOD assimilation has very little positive effect on analyzed or forecasted vertical profiles of backscatter.

     
    more » « less