Current Research Projects

Jacob research group banner

Last Updated: July 20, 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 Xu Feng
  • Chemistry - Leaders: Kelvin Bates and Ruijun Dang
  • 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: Nadia Colombi and Drew Pendergrass
  • GEOS-Chem Support Team - Bob Yantosca, Melissa Sulprizio, Lizzie Lundgren

Air Quality

subgroup leaders: Shixian Zhai and Xu Feng

Air Quality in China and Korea

Background

China and Korea have a very severe particulate matter (PM) and ozone air pollution problem. Surface air quality networks together with satellite and aircraft allow us to track changes in air quality and the responses to emission controls, to better understand the chemical processes controlling PM and ozone formation, and to guide 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 and Korea;
  • Validate and exploit observations from the new GEMS geostationary satellite instrument;
  • Develop a satellite-based monitoring system for air pollution in China and Korea.

Approach

  • Analyze observations from air quality networks, field campaigns, and satellites to gain insight into the processes controlling PM and ozone;
  • Use machine learning applied to satellite observations to develop a monitoring system for air quality;
  • Improve the retrievals and interpretation of GEMS observations.

People

References

Support

  • JLAQC
  • Samsung
  • NSF Fellowships to Nadia Colombi, Drew Pendergrass, Laura Yang
  • NDSEG Fellowship to Nick Balasus

Collaborators

  • Hong Liao (NUIST)
  • Jhoon Kim (Yonsei)

Particulate nitrate

BackImage result for Seoul air qualityground

Nitrate is a major component of particulate matter (PM) pollution. It is produced from the oxidation of nitrogen oxides (NOx) emitted by combustion but also depends on the supply of ammonia emitted by agriculture as well as other species through complicated chemical interactions. Decreases in NOx emissions have often not been successful at decreasing particulate nitrate.  Better understanding the factors controlling particulate nitrate is critical for achieving PM air quality standards.

 

Objectives

  • Interpret and model nitrate observations from air quality networks and field campaigns to better understand the driving processes;'
  • Diagnose the response of nitrate to emission changes in different parts of the world.

Approach

  • Apply the GEOS-Chem model to simulate observations of nitrate and related species with focus on East Asia;
  • Use satellite observations of NO2 and NH3 to diagnose whether nitrate information at a particular location is limited by emissions of NOx or NH3;
  • Interpret long-term nitrate trends from air quality networks.

People

References

Support

  • JLAQC
  • Samsung

Collaborators

  • Pierre Coheur (ULB)

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

  • Quantify the sources of background NO2 over the US and the implications for interpretation of satellite NO2 retrievals;
  • Determine the contribution of the free troposphere to the high concentrations of surface ozone observed in East Asia;
  • Use the GEOS-Chem module within the NCAR CESM to intercompare different model representations of background ozone over East Asia.

People

References

  • Shah, V., D.J. Jacob, R. Dang, L.N. Lamsal, S.A. Strode, S.D. Steenrod, K.F. Boersma, S.D. Eastham, T.M. Fritz, C. Thompson, J. Peischl, I. Bourgeois, I.B. Pollack, B.A. Nault, R.C. Cohen, P. Campuzano-Jost, J.L. Jimenez, S.T. Andersen, L.J. Carpenter, T. Sherwen, and M.J. Evans, Nitrogen oxides in the free troposphere: Implications for tropospheric oxidants and the interpretation of satellite NO2 measurements, submitted to Atmos. Chem. Phys., 2022.
  • 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
  • NSF Fellowship to Nadia Colombi

Collaborators

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

Chemistry

subgroup leaders: Kelvin Bates and Ruijun Dang

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.  The role of peroxyacetylnitrate (PAN) as a reservoir for long-range transport of NOis critical.

Objectives

  • Develop a new method for monitoring global OH;
  • Better understand the factors controlling tropospheric ozone precursors and the implications for ozone;
  • Better understand the role of PAN;
  • 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 peroxyacetylnitrate (PAN);
  • Synthesize this information in an improved global 3-D model representation of tropospheric ozone and its trends.

People

Collaborators

  • Bruno Franco (ULB)
  • Xiao Lu (Sun Yat-sen University)

References

Support

  • 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 detecting the atmospheric plumes originating from large point sources and thus enable  action to shut down these emissions.

 

 

Objectives

  • Develop retrieval methods for mapping atmospheric methane plumes from fine-scale satellite data;
  • Infer point source emissions from the plume observations;
  • Quantify the contributions from large point sources to total methane emissions.

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, David Gains, Stephane Germain (GHGSat, Inc.)

Support

  • GHGSat, Inc.
  • Global Methane Hub

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;
  • 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 governmental agencies in developing gridded versions of their inventories.

People

Collaborators

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

References

Support

  • NASA CMS
  • UNEP

Testing national inventories with satellite data to support climate agreements

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.  The TROPOMI satellite instrument with global daily mapping of atmospheric methane concentrations at 5.5x7 km2 resolution is of particular interest. Exploiting this information involves statistical inversion of chemical transport models that relate emissions to atmospheric concentrations.  

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.

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;
  • Exploit this top-down information to improve the bottom-up national inventories submitted to climate agreements;
  • Develop new inverse methods to improve the power of atmospheric methane observations for quantifying methane emissions.

People

Collaborators

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

References

Support

  • NASA CMS
  • NOAA
  • Exxon-Mobil
  • UNEP
  • EDF
  • Global Methane Hub
  • Harvard Climate Change Solutions Fund
  • NSF Fellowship to Sarah Hancock

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 and the recent acceleration;
  • Correct artifacts in the TROPOMI data to enable its application to global inversions of the methane 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)

References

  • Qu, Z., D. J. Jacob, Y. Zhang, L. Shen, D. J. Varon, X. Lu, T. Scarpelli, A. Bloom, J. Worden, and R. J. Parker , Attribution of the 2020 surge in atmospheric methane by inverse analysis of GOSAT observations, submitted to Environ. Res. Lett., 2022. 
  • 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

Support

  • NASA CMS
  • NDSEG Fellowship to Nick Balasus

Integrated Methane Inversion (IMI)

IMI logoBackground

TROPOMI satellite observations provide high-density mapping of atmospheric methane, revealing hotspots and source regions. Government agencies and other stakeholders have considerable interest in using these data to quantify methane emissions in support of climate policy, but doing so normally requires expertise in satellite observations and inverse modeling, as well as large computational resources. The availability of both TROPOMI data and GEOS-Chem on the AWS cloud provides an opportunity to serve stakeholders with a user-friendly inversion tool bringing compute to data.

Objective

  • Use best inversion practices developed by our group to develop an Integrated Methane Inversion (IMI) on the AWS cloud in service to stakeholders.

Approach

  • Set up the IMI so that users can quantify methane emissions from TROPOMI data for any region and period of interest, with no need for particular expertise
  • Maintain and continually develop the IMI for the benefit of stakeholders and with open-source software for transparency.

People

Collaborators

  • Felipe Cardoso-Saldana (Exxon-Mobil)

References

Support

  • NASA CMS
  • Exxon-Mobil Corp.

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;
  • 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  (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 public-release version 3.0 of the Kinetic Pre-Processor (KPP) including an adaptive auto-reducing capability;
  • 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 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

Local Ensemble Transform Kalman Filter for GEOS-Chem (CHEEREIO)

observing systemBackground

Satellite observations of atmospheric chemistry provide massive amounts of data that need to be analyzed with formal tools to advance our understanding of the system. A particular demand is for tools that can quantify emissions in near real time and with high resolution, and can account for nonlinear relationships between emissions and observed concentrations. A Local Ensemble Transform Kalman Filter (LETKF) applied to a global 3-D atmospheric chemistry like GEOS-Chem can meet this demand but has been complicated for users to access.

Objective

  • Build the CHemistry and Emissions REanalysis Interface with Observations (CHEEREIO 1.0) as a general, open-access, user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of chemical species based on atmospheric observations from satellites and suborbital platforms.

Approach

  • Apply a LETKF algorithm to GEOS-Chem to determine the Bayesian optimal emissions and/or concentrations of a set of species based on observations and prior uncertainty specified by the user in an easy-to-modify configuration file.
  • Demonstrate the capability with inversions of satellite data for methane and NO2
  • Deliver the toolkit to users for their own applications including new satellite observations.

People

  • Drew Pendergrass

Collaborators

  • Kazuyuki Miyazaki and Kevin Bowman (JPL)

Support

  • NASA CMS
  • Samsung
  • NSF Fellowship to Drew Pendergrass