Atmospheric & Oceanic Science

Permanent URI for this communityhttp://hdl.handle.net/1903/2264

Formerly known as the Department of Meteorology.

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Subject: search.filters.subject.Atmospheric Chemistry

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    Identification and Quantification of Regional Aerosol Trends and Impact on Clouds over the North Atlantic Ocean
    (2017) Jongeward, Andrew; Li, Zhanqing; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Aerosols and clouds contribute to atmospheric variability and Earth’s radiative balance across local, regional, and global scales. Originating from both natural and anthropogenic sources, aerosols can cause adverse health effects and can interact directly with solar radiation as well as indirectly through complex interactions with clouds. Aerosol optical depth (AOD) has been observed from satellite platforms for over 30 years. During this time, regional changes in emissions, arising from air quality policies and socioeconomic factors, have been suggested as causes for some observed AOD trends. In the United States, the Clean Air Act and amendments have produced improvements in air quality. In this work the impacts of improved air quality on the aerosol loading and aerosol direct and indirect effects over the North Atlantic Ocean are explored using satellite, ground, and model datasets on the monthly timescale during 2002 to 2012. It is established that two trends exist in the total AOD observed by MODIS over the North Atlantic. A decreasing AOD trend between −0.02 and −0.04 per decade is observed over the mid-latitude region. Using the GOCART aerosol model it is shown that this trend results from decreases in anthropogenic species. Ground based aerosol networks (AERONET and IMPROVE) support a decreasing trend in AOD and further strengthen links to anthropogenic aerosol species, particularly sulfate species. This anthropogenic decrease occurs primarily during spring and summer. During the same time period, MODIS also observes an increasing AOD trend of 0.02 per decade located in the sub-tropical region. This trend is shown to occur during summer and is the result of natural dust aerosol. Changes in the North African environment seen in the MERRA reanalysis suggest an accelerated warming over the Saharan Desert leads to changes in the African Easterly Jet, related Easterly Waves, and baroclinicity playing a role in an increase and northward shift in African dust. Both the direct and indirect impacts of the aerosol trends are investigated. Using the SBDART radiative transfer model, estimates of the shortwave direct radiative forcing are calculated. The decrease in anthropogenic AOD produces an increase of 2.0 ± 0.3 W/m2 per decade in the Earth-system absorbance over the mid-latitude site (37.5ºN, −68.5ºE). The increase in natural AOD results in a decrease of −1.1 ± 0.2 W/m2 per decade in the Earth-system absorbance over the sub-tropical site (23.5ºN, −55.5ºE). Evaluation of the first indirect effect demonstrates agreement with Twomey theory when considering the North Atlantic domain on the whole. A regional analysis reveals the existence of counter-Twomey behavior along the U.S. Atlantic coast. Using a daily dataset during summertime with focus on warm, non-precipitating clouds, it is found that aerosol-cloud interaction in this coastal region is sensitive to vertical velocity and aerosol size. Cases experiencing updrafts (ω < 0 Pa/s) and cases of mainly coarse-mode aerosol demonstrate good agreement with Twomey theory. Additionally, cases with low specific humidity near the cloud base show non-Twomey behavior for clouds with low liquid water path.
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    An evaluation of a severe smog episode in the Eastern U.S. using regional modeling and satellite measurements
    (2011) Yegorova, Elena Andreyevna; Dickerson, Russell R; Allen, Dale J; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    An ensemble of regional chemical modeling (WRF/Chem with RADM2) simulations, satellite, ozonesonde, and surface observations during July 7-11, 2007 was used to examine the horizontal and vertical signature of one of the worst smog events in the eastern U.S. in the past decade. The general features of this event -- a broad area of high pressure, weak winds and heavy pollution, terminated by the passage of a cold front -- were well simulated by the model. Average 8-hr maximum O3 has a mean (±Σ) bias of 0.59 (±11.0) ppbv and a root mean square error of 11.0 ppbv. WRF/Chem performed the best on poor air quality days, simulating correctly the spatial pattern of surface O3. Yet the model underpredicted O3 maxima by 5-7 ppbv in the Northeast and overpredicted by 8-11 ppbv in the Southeast. High O3 biases in the Southeast are explained by overpredicted temperatures in the model (>1.5°C). Sensitivity simulations with 1) accelerated O3 dry deposition velocity and 2) suppressed multiphase nitric acid formation pushed the model closer to observations. Simulated O3 vertical profiles over Beltsville, MD showed good agreement with ozonesonde measurements, but the modeled boundary layer depth was overpredicted on July 9, contributing to the low bias over this region. During this severe smog episode, space-borne TES detected high total tropospheric column ozone (TCO) over the Western Atlantic Ocean off the coast near North and South Carolina. The standard product (OMI/MLS) missed the magnitude of these local maxima, but the level-2 ozone profile (OMI) confirmed the TES observations. HYSPLIT back trajectories from these O3 maxima intersected regions of strong convection over the Southeast and Great Lakes regions. When lightning NO emissions were implemented in WRF/Chem, the high concentrations of NOx and O3 off the coast were well reproduced, showing that the exported O3 was produced by a combination of natural NO and pollutants lofted from the lower atmosphere. Lastly, WINTER MONEX O3 data from 1978 are presented for the first time here in discussion of open cell convection over Indonesia.
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    Assimilation of trace gas retrievals with the Local Ensemble Transform Kalman Filter
    (2009) Kuhl, David Derieg; Kalnay, Eugenia; Szunyogh, Istvan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Over the 50 year history of Numerical Weather Prediction (NWP), the focus has been on the modeling and prediction of meteorological parameters such as surface pressure, temperature, wind, and precipitation. However, due to concerns over pollution and to recent advancements in satellite technologies, an increasing number of NWP systems have been upgraded to include capabilities to analyze and predict the concentration of trace gases. This dissertation explores some of the specific issues that have to be addressed for an efficient modeling of the concentration of the trace gases. These issues include modeling the effects of convective mixing on the concentration of the trace gases and the multivariate assimilation of space-based observations of the concentration of the trace gases. In this dissertation, we assimilate observations of the concentration of trace gases with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) NWP model. We use a modified version of the NCEP GFS model that was operational in 2004 at resolution T62/L28. We modify the model by adding parameterization for the process of convective mixing of the trace gases. We consider two specific trace gases: ozone (O3) and carbon monoxide (CO). We incorporate these gases into the model by using 3-dimensional time-dependent O3 and CO production-loss values from the Real-time Air Quality Modeling System (RAQMS) global chemical model. The O3 observations we assimilate are from the Solar Backscatter UltraViolet generation 2 (SBUV/2) satellite instrument (version 8) flown on the NOAA 16 and 17 satellites. The CO observations we assimilate are from the Measurements Of Pollution In The Troposphere (MOPITT) instrument (version 3) flown on the NASA TERRA satellite. We also develop a new observation operator for the assimilation of retrievals with the LETKF. We carry out numerical experiment for the period between 000UTC 1 July 2004 to 000UTC 15 August in the summer of 2004. The analysis and forecast impact of the assimilation of trace gas observations on the meteorological fields is assessed by comparing the analyses and forecasts to the high resolution operational NCEP GFS analyses and to radiosonde observations. The analysis and forecast impact on the trace gas fields is assessed by comparing the analyzed and predicted fields to observations collected during the Intercontinental Chemical Transport Experiment (INTEX-A) field mission. The INTEX-A field mission was conducted to characterize composition of pollution over North America, thus providing us with ozonesonde and aircraft based verification data. We find that adding the process of convective mixing to the parameterization package of the model and the assimilation of observations of the trace gases improves the analysis and forecast of the concentration of the trace gases. In particular, our system is more accurate in quantifying the concentration of O3 in the troposphere than the original NCEP GFS. Also, our system is competitive with the state-of-the-art RAQMS atmospheric chemical model in analyzing the concentration of O3 and CO throughout the full atmospheric model column. The assimilation of O3 and CO observations has a mixed impact on the analysis and forecast of the meteorological fields. We find that most of the negative impact on the meteorological fields can be eliminated, without much reduction to the positive impact on the trace gas fields, by inflating the prescribed variance of the trace gas observations. The appendices of this dissertation reproduces two papers on related research. The first paper covers the northward front movement and rising surface temperatures in the great planes. The second paper covers the assessment of predictability with a Local Ensemble Kalman Filter.