Current Research Projects

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Last Updated: January 10, 2022

Our research focuses on better understanding the chemical composition of the atmosphere, its perturbation by human activity, and the implications for life on Earth. We use advanced models of atmospheric composition to interpret observations from satellites, aircraft, ground networks, and other platforms. We view our models as part of an integrated observing system to increase fundamental knowledge and address pressing environmental issues.

We have a large number of ongoing projects at any given time, addressing broad research themes and organized in five subgroups (scroll down for more information):

  • Air Quality - Leaders: Shixian Zhai and Jared Brewer
  • Chemistry - Leaders: Kelvin Bates and Viral Shah
  • Climate and Health - Leader: Loretta Mickley
  • Methane - Leaders: Zhen Qu and Daniel Varon
  • Model Development - Leader: Sebastian Eastham (joint with MIT)

In addition we have three subgroups focused on practices:

  • Machine Learning & Data Science - Leaders: Daniel Varon, Makoto Kelp, Drew Pendergrass
  • Diversity, Inclusion, and Belonging  - Leaders: Eimy Bonilla and Hannah Nesser
  • GEOS-Chem Support Team - Bob Yantosca, Melissa Sulprizio, Lizzie Lundgren, Lucas Estrada

Air Quality

subgroup leaders: Shixian Zhai and Jared Brewer

Air Quality in China

Background

Air quality in China is among the worst in the world and the Chinese government has declared a "war on air pollution" involving massive controls on emissions. Newly installed surface air quality networks together with satellite observations allow us to track the evolution of Chinese air quality and the responses to emission controls, to better understand the factors controlling particulate matter and ozone pollution, and to make recommendations for future emission control strategies.

Objectives

  • Understand the effects of anthropogenic emissions, chemical processes, and other factors in determining ozone and PM air quality and its trends in China;
  • Make recommendations for improving air quality.

Approach

  • Analyze and model data from Chinese air quality networks;
  • Use machine learning to relate satellite observations to air quality;
  • Conduct model simulations to project the responses to emission controls.

People

References

Support

  • JLAQC
  • Samsung

Collaborators

  • Hong Liao (NUIST)

Air Quality in Korea

BackImage result for Seoul air qualityground

Both particulate matter (PM) and ozone air quality in South Korea are very bad. PM is not improving and ozone is getting worse. This poor air quality and its trends are not well understood, including the relative contributions from domestic emissions and transboundary pollution transport. Geostationary observations of aerosol optical depth (AOD) and new geostationary chemical observations from the GEMS satellite instrument launched in 2020 should enable major advances in our understanding and help advise Korea air quality policy.

 

Objectives

  • Understand the factors controlling PM and ozone pollution in Korea through analysis of surface and aircraft observations;
  • Interpret GOCI geostationary satellite observations of AOD and use them to infer surface PM concentrations;
  • Validate and exploit the new geostationary observations from GEMS.

Approach

  • Use the GEOS-Chem model to interpret observations from the AirKorea surface measurement network and from the NASA KORUS-AQ aircraft campaign;
  • Apply machine learning to GOCI AOD observations to achieve fine-scale PM mapping;
  • Integrate information from satellites, aircraft, sondes, and surface network data to understand the factors controlling ozone over Korea;
  • Apply machine-learning methods to intercompare GEMS with other satellite observations;
  • Improve retrievals and interpretation of GEMS NO2 through GEOS-Chem simulations of aircraft observations

People

References

Support

  • Samsung
  • NASA ACCDAM

Collaborators

  • Rokjin Park (SNU)
  • Jhoon Kim and Ja-Ho Koo (Yonsei U.)
  • Soontae Kim (Ajou U.)

Background Influences on Air Quality

                                          Background

Satellites are increasingly powerful resources for monitoring air quality but are highly sensitive to background concentrations of pollutants above the surface.  This background is poorly understood; it may result from natural sources or from pollution transported on international/intercontinental scales. Better understanding of  background influences is critical for interpreting satellite observations and  for developing strategies to improve air quality.

 

Objectives

  • Better understand the sources and chemical processes contributing to background air pollution;
  • Quantify the background contributions to pollutant concentrations and their trends;
  • Separate background from local contributions in attributing satellite measurements of air quality.

Approach

  • Determine the concentrations and sources of background NO2 for improved retrievals of NO2 from space and improved inference of NOx emissions;
  • Determine the contribution of the free troposphere to the high concentrations of surface ozone observed in East Asia.

People

References

  • Vertical distribution and sources of NO2 in the free troposphere: Implications for interpretation of OMI and TROPOMI NO2 data , presented by Viral Shah at the 101st American Meteorological Society meeting (remote), January 11, 2021. [PDF]
  • Qu, Z., D.J. Jacob, R.F. Silvern, V. Shah, P.C. Campbell, L.C. Valin, and L.T. Murray, US COVID-19 shutdown demonstrates importance of background NO2 in inferring nitrogen oxide (NOx) emissions from satellite NO2 observations , Geophys. Res. Lett., 48, e2021GL092783, 2021. [PDF]

Support

  • NASA Aura
  • EPA
  • Samsung

Collaborators

  • Joanna Joiner
  • Nicholas Krotkov
  • Lol Lamsal
  • Songyeon Choi (NASA/GSFC)
  • Eloise Marais (University College London)
  • Folkert Boersma (KNMI)

Chemistry

subgroup leaders: Kelvin Bates and Viral Shah

Tropospheric Oxidants

Background

Tropospheric oxidants (ozone and OH) are of central importance for global atmospheric chemistry. The hydroxyl radical (OH) is the main atmospheric oxidant responsible for removal of atmospheric gases yet we have little understanding of the factors controlling it and the associated long-term trends. Ozone is a major greenhouse gas in the middle/upper troposphere, a toxic gas at the surface, and the primary source of OH, but the factors controlling its global distribution and long-term trends are poorly understood. Chemical production and loss of ozone and OH involve complex nonlinear mechanisms coupled to transport on all scales. Key questions relate to the sources and chemistry of nitrogen oxides (NOx), oxygenated volalile organic compounds (VOCs), and halogens. 

Objectives

  • Develop a new method for monitoring global OH;
  • Better understand the factors controlling tropospheric ozone precursors and the implications for ozone;
  • Better understand global ozone trends.

Approach

  • Develop capability to monitor tropospheric OH and its trend using satellite observations of atmospheric methane;
  • Develop a new conceptual model for the budget of tropospheric ozone;
  • Use aircraft and satellite observations to better understand the factors controlling NOx, oxygenated VOCs, and halogens;
  • Synthesize this information in an improved global 3-D model representation of tropospheric ozone and its trends.

People

Collaborators

  • Xiao Lu (Sun Yat-sen University)
  • Xuan Wang (City University of Hong Kong)

References

Support

  • NASA ACMAP
  • NASA CMS
  • NASA ACCDAM

Organic Chemistry

Background

The atmospheric chemistry of volatile organic compounds (VOCs) has major implications for oxidant concentrations and for formation of organic aerosol. VOCs are emitted from a range of biogenic and anthropogenic sources. They undergo various oxidation cascades in the atmosphere, producing increasingly substituted and cleaved compounds, terminating eventually in the formation of CO2, the formation of secondary organic aerosol (SOA) or the removal by deposition. Most of the species and reactions involved have never been measured and must be inferred indirectly. Multigenerational products of VOC oxidation may be responsible for an "organic soup" in the remote troposphere that is suggested by observations but not understood at all. Representing organic chemistry in models is a major challenge.

Objectives

  • Develop state-of-science chemical mechanisms for atmospheric organics that can be practically implemented in models;
  • Determine the implications of these mechanisms for the effects of different VOCs on ozone, OH, aerosols;
  • Improve understanding of the budgets of oxygenated organics.

Approach

  • Develop VOC oxidation mechanisms for atmospheric chemistry models, test them in box models and in GEOS-Chem;
  • Use GEOS-Chem to study the resulting impacts of VOC emissions for air quality and global atmospheric chemistry;
  • Interpret observations of oxygenated organics in the remote troposphere from the ATom aircraft campaign.

People

References

  • Bates, K.H., D.J. Jacob, K. Li, P. Ivatt, M.J. Evans, Y. Yan, and J. Lin, Development and evaluation of a new compact mechanism for aromatic oxidation in atmospheric models, Atmos. Chem. Phys., 21, 18351-18374, 2021. 
  • Bates, K.H., Jacob, D.J., Wang, S., Hornbrook, R.S., Apel, E.C., Kim, M.J., Millet, D.B., Wells, K.C., Chen, X., Brewer, J.F., Ray, E.A., Diskin, G.S., Commane, R., Daube, B.C. and Wofsy, S.C., The global budget of atmospheric methanol: new constraints on secondary, oceanic, and terrestrial source, J. Geophys. Res., 126,  e2020JD033439, 2021. [PDF]

Support

  • EPA
  • NASA ACCDAM

Climate and Health

subgroup leader: Loretta Mickley

Loretta Mickley: Research Page

See Loretta Mickley's research page. Students/postdocs working on chemistry/climate interactions, effects of fires on air quality, and connections to public health generally have Loretta Mickley as primary research advisor. (Eimy BonillaPengfei LiuTianjia (Tina) LiuJonathan Moch, Miah Caine, Kent Toshima)

Methane

subgroup leaders: Zhen Qu and Daniel Varon

Detecting Methane Point Sources from Satellites

Background

Anthropogenic emissions of methane come from a very large number of relatively small point sources including coal mine vents, oil/gas production and processing facilities, stockyards, landfills, etc. These sources have highly variable emissions, and can spike under abnormal conditions. Satellites provide a unique vantage point for global continuous monitoring of point sources. Several new instruments with high pixel resolution hold particular promise.

 

Objectives

  • Develop retrieval methods for mapping atmospheric methane plumes from fine-scale satellite data;
  • Infer point source emissions from the plume observations;
  • Use the new generation of hyperspectral and multispectral imagers to detect methane point sources.

Approach

  • Determine the ability of new satellite instruments to detect methane plumes and quantify point sources;
  • Use machine-learning methods to improve methane plume detection and source rate quantification from satellite data;
  • Relate satellite detections of point sources to regional emissions through formal inversions.

People

Collaborators

  • Jason McKeever, Dylan Jervis, David Gains, and Stephane Germain (GHGSat, Inc.)
  • Riley Duren and Dan Cusworth (U. Arizona)
  • Dan Cusworth (JPL)

Support

  • GHGSat, Inc.
  • UNEP
  • EDF

References

  • Varon, D.J., D. Jervis, J. McKeever, I. Spence, D. Gains, and D.J. Jacob, High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations, Atmos. Meas. Tech., 14, 2771, 2785, 2021. [PDF

Methane Emission Inventories

Background

Methane is the second most important anthropogenic greenhouse gas after CO2. It is particularly important for near-term (~20 years) climate change. Methane is emitted by a wide range of processes including oil/gas systems, livestock, landfills, wastewater, rice cultivation, and wetlands. The magnitudes of these sources, their spatial distributions, and their temporal trends are poorly understood. Improving the "bottom-up" inventories that relate emissions to activity levels is imperative for climate policy. This can be done by using state-of-science activity and emission factor data, evaluating the resulting inventories with atmospheric observations through "top-down"inverse analyses, and using the results of these inverse analyses to further improve the emission inventories. This partnership between bottom-up and top-down approaches is key to improving understanding of methane emissions in a way that can enable policy action.

Objectives

  • Develop policy-relevant, high-resolution global methane emission inventories from fuel exploitation and livestock;
  • Develop a bottom-up model of emissions from hydroelectric reservoirs.

Approach

  • Estimate emissions and their distributions using best available data for activities and emission factors;
  • Assist EPA in the US, ECCC in Canada, and INECC in Mexico to develop gridded versions of their inventories.

People

Collaborators

  • Bram Maasakkers (SRON)
  • Kyle Delwiche (Stanford)

References

Support

  • NASA CMS
  • NASA AIST

Inference of Regional/National Methane Emissions from Satellite Data

Bac www.nytimes.comkground

Atmospheric observations of methane from satellites and  surface sites can provide powerful top-down information for testing the national emission inventories used in climate policy and reported to the United Nations.  Exploiting this capability involves statistical inversion of chemical transport models that relate emissions to atmospheric concentrations. New satellite observations from the TROPOMI instrument with ~5-km resolution hold particular promise.

Objectives

  • Use satellite and surface observations to quantify and attribute methane emissions on regional and national scales;
  • Provide in this manner continuous monitoring and evaluation of national emission inventories reported to the United Nations in service of climate action;
  • Enable improvement of these inventories by relating emissions to the underlying processes;
  • Facillitate access to satellite data by stakeholders aiming to quantify methane emissions.

Approach

  • Use TROPOMI satellite observations for 2018-present, together with other atmospheric data, to quantify methane emissions at the scale of individual countries and source regions;
  • Develop new inverse methods to improve the power of atmospheric methane observations for quantifying methane emissions;
  • Develop a cloud-hosted integrated methane inversion (IMI) workflow to enable exploitation of  TROPOMI data by stakeholders for purpose of inferring methane emissions.

People

Collaborators

  • Xiao Lu (Sun Yat-sen U.)
  • Lu Shen (PKU)
  • Arlyn Andrews (NOAA)
  • Ritesh Gautam (EDF)
  • Cynthia Randles (Exxon-Mobil)
  • Alba Lorente (SRON)

References

Support

  • NASA CMS
  • NASA AIST
  • NOAA
  • Exxon-Mobil
  • UNEP
  • EDF

Explaining the Global Rise in Methane

Background

Atmospheric methane is the second most important anthropogenic greenhouse gas after CO2. It is emitted by a large number of sectors (wetlands, livestock, oil/gas operations, landfills, coal mines, wastewater treatment, rice paddies...) and is removed from the atmosphere by oxidation by the OH radical. Unlike CO2, methane concentrations have been rising in fits and starts over the past decades.  Methane concentrations were flat in the early 2000s, then started increasing again in 2006 and the pace of increase has been accelerating. We need to understand why if we are to develop effective climate action to curb the methane increase.

Objectives

  • Better understand the global budget of methane including the contributions from different source sectors;
  • Understand the methane trend over the past two decades including the recent acceleration;
  • Determine the role of OH concentrations in driving the methane trend. 

Approach

  • Use inversions of long-term GOSAT satellite observations to attribute global methane trends;
  • Ue GOSAT and TROPOMI satellite observations to explain the recent acceleration of the trend;
  • Develop methods to separate the influences of methane sources and sinks in global inversions of satellite data, and apply them to infer trends in OH concentrations.

People

Collaborators

  • Anthony Bloom (JPL)
  • John Worden (JPL)
  • Yuzhong Zhang (Westlake University)

References

  • Attribution of the surge of global methane in 2020 using inverse analysis of GOSAT observations, presented by Zhen Qu at the 2021 American Geophysical Union fall meeting, New Orleans, December 17, 2021.
  • Qu, Z. D.J. Jacob, L. Shen, X. Lu, Y. Zhang, T.R. Scarpelli, H.O. Nesser, M.P. Sulprizio, J.D. Maasakkers, A.A. Bloom, J.R. Worden, R.J. Parker, and A.L. Delgado, Global distribution of methane emissions: a comparative inverse analysis of observations from the TROPOMI and GOSAT satellite instruments, submitted to Atmos. Chem. Phys., 2021.
  • Zhang, Y., D.J. Jacob, X. Lu, J.D. Maasakkers, T.R. Scarpelli, J.-X. Sheng, L. Shen, Z. Qu, M.P. Sulprizio, J. Chang, A.A. Bloom, S. Ma, J. Worden, R.J. Parker, and H. Boesch, Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations, Atmos. Chem. Phys., 21, 3643-3666, 2021. [PDF
  • Lu, X., D.J. Jacob, Y. Zhang, J.D. Maasakkers, M.P. Sulprizio, L. Shen, Z. Qu, T.R. Scarpelli, H. Nesser, R.M. Yantosca, J. Sheng, A. Andrews, R.J. Parker, H. Boesch, A.A. Bloom, S. Ma, Global methane budget and trend, 2010-2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observationsAtmos. Chem. Phys. , 21, 4637-4657, 2021. [Article]

Support

  • NASA CMS

Model Development

group leader: Sebastian Eastham, joint with MIT

GEOS-Chem for weather/climate models and data assimilation

Background

Weather and climate models generally do not include detailed atmospheric chemistry because of the perceived complexity and computational cost. Yet atmospheric chemistry is key to climate forcing and feedbacks, air quality forecasting, and chemical data assimilation. The GEOS-Chem "off-line" chemical transport model developed and used by a large atmospheric chemistry community worldwide can be used as an "on-line" stand-alone module to handle chemistry in weather and climate models. This provides a state-of-science chemistry capability to these models that is referenceable, easily maintained, and has large community backing. GEOS-Chem includes a powerful emissions modeling component, HEMCO, that can be used to serve emissions in climate and weather models independently of the chemistry.

Objectives

  • Implement GEOS-Chem as a chemical module in a wide range of weather and climate models, using exactly the same scientific code base as in the off-line GEOS-Chem chemical transport model;
  • Exploit this capability in the NASA GEOS climate model for global composition forecasting, aerosol-weather intractions, and data assimilation;
  • Exploit this capability in the NCAR CESM model for study of chemistry-climate-ecosystems interactions.

Approach

  • Contribute to development of the GEOS composition forecasts (GEOS-CF) powered by GEOS-Chem;
  • Apply GEOS-Chem within the GEOS climate model to study feedbacks of air quality on weather;
  • Develop the GEOS-Chem interface with CESM, and compare GEOS-Chem and CAM-Chem atmospheric chemistry simulations within the same CESM framework;
  • Develop HEMCO as a stand-alone tool to serve emissions in the CESM and other climate models.

People

Collaborators

  • Seb Eastham and Thibaud Fritz (MIT)
  • Steven Pawson (NASA/GSFC)
  • Christoph Keller (NASA/GSFC)
  • Louisa Emmons (NCAR)

References

Support

  • NSF
  • NASA MAP

Adaptive Methods and Machine Learning for Chemical Solvers

Background

Chemical mechanisms for atmospheric chemistry include hundreds of species interacting by kinetic equations on time scales ranging from less than a second to many years. Integration of these equations with numerical solvers is extremely expensive. This limits the level of chemical detail that can be afforded in atmospheric chemistry models, the spatial resolution of these models, and the inclusion of atmospheric chemistry in Earth System Models.

 

Objective

  • Speed up the chemical solvers used in global atmospheric chemistry models.

Approach

  • Develop a new universal adaptive method to reduce chemical mechanisms locally while maintaining accuracy;
  • Integrate aerosol/cloud chemistry with gas-phase mechanisms for improved accuracy and combined kinetic speed-up;
  • Implement these developments in a new public release of the Kinetic Pre-Processor (KPP);
  • Use machine-learning algorithms to reduce computation costs by orders of magnitude;

People

Collaborators

  • Lu Shen (PKU)
  • Rolf Sander (MPI)

References

Support

  • EPA

GEOS-Chem Worldwide Community Development and Management

GEOS-Chem website

GEOS-Chem logoWe develop, maintain, and support the GEOS-Chem atmospheric chemistry model for a user community of hundreds of research groups worldwide. Click on the GEOS-Chem website link above for more information.

People