Engineering topics

Cooper, Owen R.

Publications and source records attributed to Cooper, Owen R..

6 records

DOE OSTI2024

MIXv2: a long-term mosaic emission inventory for Asia (2010–2017)

The MIXv2 Asian emission inventory is developed under the framework of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase IV and produced from a mosaic of up-to-date regional emission inventories. We estimated the emissions for anthropogenic and biomass burning sources covering 23 countries and regions in East, Southeast and South Asia and aggregated emissions to a uniform spatial and temporal resolution for seven sectors: power, industry, residential, transportation, agriculture, open biomass burning and shipping. Compared to MIXv1, we extended the dataset to 2010–2017, included emissions of open biomass burning and shipping, and provided model-ready emissions of SAPRC99, SAPRC07, and CB05. A series of unit-based point source information was incorporated covering power plants in China and India. A consistent speciation framework for non-methane volatile organic compounds (NMVOCs) was applied to develop emissions by three chemical mechanisms. The total Asian emissions for anthropogenic/open biomass sectors in 2017 are estimated as follows: 41.6/1.1 Tg NO x , 33.2/0.1 Tg SO 2 , 258.2/20.6 Tg CO, 61.8/8.2 Tg NMVOC, 28.3/0.3 Tg NH 3 , 24.0/2.6 Tg PM 10 , 16.7/2.0 Tg PM 2.5 , 2.7/0.1 Tg BC (black carbon), 5.3/0.9 Tg OC (organic carbon), and 18.0/0.4 Pg CO 2 . The contributions of India and Southeast Asia were emerging in Asia during 2010–2017, especially for SO 2 , NH 3 and particulate matter. Gridded emissions at a spatial resolution of 0.1° with monthly variations are now publicly available. This updated long-term emission mosaic inventory is ready to facilitate air quality and climate model simulations, as well as policymaking and associated analyses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Global Climate

The 2019 global lower tropospheric temperature (LTT), which encompasses the atmosphere from the surface to ~10 km, ranked second warmest in seven datasets and first or third in the remaining two (Fig. 2.7). These records extend back to 1958 using radiosonde (balloon-borne instrumentation) data and one reanalysis dataset (JRA55), which demonstrate reasonable agreement with the 40+ year satellite record (since late 1978) and two other reanalysis datasets (since 1979 and 1980, ERA5 and MERRA2, respectively). Aweak El Niño contributed to increased global temperatures as 2019 values were +0.44° to +0.68°C higher than the 1981–2010 average (depending on the dataset), being just slightly cooler (~0.07°C on average) than the record warm year of 2016. At least fourof the five globally complete datasets (ERA5, MERRA2, JRA55, RSS, UAH) recorded each of the four months—June, September, November, and December—as experiencing their warmest monthly global LTT.

58 GEOSCIENCES↗

NASA NTRS2016

Tropospheric Ozone Change from 1980 to 2010 Dominated by Equatorward Redistribution of Emissions

Ozone is an important air pollutant at the surface, and the third most important anthropogenic greenhouse gas in the troposphere. Since 1980, anthropogenic emissions of ozone precursors methane, non-methane volatile organic compounds, carbon monoxide and nitrogen oxides (NOx) have shifted from developed to developing regions. Emissions have thereby been redistributed equatorwards, where they are expected to have a stronger effect on the tropospheric ozone burden due to greater convection, reaction rates and NOx sensitivity. Here we use a global chemical transport model to simulate changes in tropospheric ozone concentrations from 1980 to 2010, and to separate the influences of changes in the spatial distribution of global anthropogenic emissions of short-lived pollutants, the magnitude of these emissions, and the global atmospheric methane concentration. We estimate that the increase in ozone burden due to the spatial distribution change slightly exceeds the combined influences of the increased emission magnitude and global methane. Emission increases in Southeast, East and South Asia may be most important for the ozone change, supported by an analysis of statistically significant increases in observed ozone above these regions. The spatial distribution of emissions dominates global tropospheric ozone, suggesting that the future ozone burden will be determined mainly by emissions from low latitudes.

Zhang, Yuqiang↗

NASA NTRS2014

Science Accomplishments from a Decade of Aura OMI/MLS Tropospheric Ozone Measurements

Measurements of tropospheric ozone from combined Aura OMI and MLS instruments have yielded a large number of new and important science discoveries over the last decade. These discoveries have generated a much greater understanding of biomass burning, lightning NO, and stratosphere-troposphere exchange sources of tropospheric ozone, ENSO dynamics and photochemistry, intra-seasonal variability-Madden-Julian Oscillation including convective transport, radiative forcing, measuring ozone pollution from space, improvements to ozone retrieval algorithms, and evaluation of chemical-transport and chemistry-climate models. The OMI-MLS measurements have been instrumental in giving us better understanding of the dynamics and chemistry involving tropospheric ozone and the many drivers affecting the troposphere in general. This discussion will provide an overview focusing on our main science results.

ENSO↗

NASA NTRS2007

Transport in the Subtropical Lowermost Stratosphere during CRYSTAL-FACE

We use in situ measurements of water vapor (H2O), ozone (O3), carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), and total reactive nitrogen (NO(y)) obtained during the CRYSTAL-FACE campaign in July 2002 to study summertime transport in the subtropical lowermost stratosphere. We use an objective methodology to distinguish the latitudinal origin of the sampled air masses despite the influence of convection, and we calculate backward trajectories to elucidate their recent geographical history. The methodology consists of exploring the statistical behavior of the data by performing multivariate clustering and agglomerative hierarchical clustering calculations, and projecting cluster groups onto principal component space to identify air masses of like composition and hence presumed origin. The statistically derived cluster groups are then examined in physical space using tracer-tracer correlation plots. Interpretation of the principal component analysis suggests that the variability in the data is accounted for primarily by the mean age of air in the stratosphere, followed by the age of the convective influence, and lastly by the extent of convective influence, potentially related to the latitude of convective injection [Dessler and Sherwuud, 2004]. We find that high-latitude stratospheric air is the dominant source region during the beginning of the campaign while tropical air is the dominant source region during the rest of the campaign. Influence of convection from both local and non-local events is frequently observed. The identification of air mass origin is confirmed with backward trajectories, and the behavior of the trajectories is associated with the North American monsoon circulation.

Pittman, Jasna V.↗

NASA NTRS2007

Transport in the Subtropical Lowermost Stratosphere during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment

We use in situ measurements of water vapor (H2O), ozone (O3), carbon dioxide (CO2), carbon monoxide (CO), nitric oxide (NO), and total reactive nitrogen (NOy) obtained during the CRYSTAL-FACE campaign in July 2002 to study summertime transport in the subtropical lowermost stratosphere. We use an objective methodology to distinguish the latitudinal origin of the sampled air masses despite the influence of convection, and we calculate backward trajectories to elucidate their recent geographical history. The methodology consists of exploring the statistical behavior of the data by performing multivariate clustering and agglomerative hierarchical clustering calculations and projecting cluster groups onto principal component space to identify air masses of like composition and hence presumed origin. The statistically derived cluster groups are then examined in physical space using tracer-tracer correlation plots. Interpretation of the principal component analysis suggests that the variability in the data is accounted for primarily by the mean age of air in the stratosphere, followed by the age of the convective influence, and last by the extent of convective influence, potentially related to the latitude of convective injection (Dessler and Sherwood, 2004). We find that high-latitude stratospheric air is the dominant source region during the beginning of the campaign while tropical air is the dominant source region during the rest of the campaign. Influence of convection from both local and nonlocal events is frequently observed. The identification of air mass origin is confirmed with backward trajectories, and the behavior of the trajectories is associated with the North American monsoon circulation.

Pittman, Jasna V.↗