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Atmospheric Chemistry Modeling Group – Harvard University

Group Leaders Daniel J. Jacob and Loretta J. Mickley

Current Research Projects

Last Updated: May 18, 2024

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 chemistry 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 of atmospheric chemistry and address pressing environmental issues.

We have a large number of ongoing projects at any given time, organizaed by sub-groups :

  • Air Quality & Chemistry – Leaders: Ruijun Dang, Xu Feng, Yujin Oak
  • Climate & Health – Leader: Loretta Mickley
  • Methane – Leaders: James East, Xiaolin Wang
  • Model Development and Software Tools – Leaders: Lizzie Lundgren, Melissa Sulprizio, Bob Yantosca
  • Group life and community  – Leaders: Sarah Hancock, Laura Yang

Air Quality and Chemistry

subgroup leaders: Ruijun Dang, Xu Feng, Yujin Oak

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;
  • 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;
  • Review the knowledge of air quality and trends in Korea with new insights from satellite observations.

People

References

Support

  • JLAQC
  • Samsung
  • NSF Fellowships to Nadia Colombi, Drew Pendergrass, Laura Yang

Collaborators

  • Ke Li and Hong Liao (NUIST)
  • Jhoon Kim (Yonsei)
  • Soontae Kim (Ajou U.)
Tropospheric oxidant chemistry

Background

Tropospheric ozone and OH (the main atmospheric oxidant) are central species for atmospheric chemistry. Tropospheric ozone affects air quality, is a major greenhouse gas, and is the source of OH. It is produced by photochemical oxidation of CO and volatile organic compounds (VOCs) in the presence of NOx, and is also transported from the stratosphere. It is lost by deposition and by photochemical processes including halogen chemistry. These processes also affect OH, which is the main sink for methane and other gases. The chemistry interacts with transport on all scales, making for a very complicated problem that current models still struggle with. Tropospheric ozone is presently rising, and current models overestimate OH, but we dont understand why.

Objectives

  • Better understand the factors controlling tropospheric ozone and OH;
  • Understand the origin of the elevated ozone background and its trend over East Asia;
  • Examine the effects of unconventional chemistry.

Approach

  • Use GEOS-Chem simulations in different configurations to evaluate the ability to simulate tropospheric ozone and OH, related species, and their trends. to interpret tropospheric observations from sondes, aircraft, and satellite over East Asia, and their trends.

People

Collaborators

  • Viral Shah (NASA GSFC)

References

  • Colombi, N.K. D.J. Jacob, X. Ye, R.M. Yantosca, K.H. Bates, D.C. Pendergrass, L.H. Yang, K. Li, Large and increasing stratospheric contribution to tropospheric ozone over East Asia, submitted to Atmos. Chem. Phys., 2025. PDF
  • Yang, L.H., D.J. Jacob, H. Lin, R. Dang, K.H. Bates, J.D. East, K.R. Travis, D.C. Pendergrass, and L.T. Murray, Assessment of Hydrogen’s Climate Impact Is Affected by Model OH Biases, Geophys. Res. Lett., 52, e2024GL112445, https://doi.org/10.1029/2024GL112445, 2025.
  • Penn, E., D.J. Jacob, Z. Chen, J.D. East, M.P. Sulprizio, L. Bruhwiler, J.D. Maasakkers, H. Nesser, Z. Qu, Y. Zhang, and J. Worden, What can we learn about tropospheric OH from satellite observations of methane?, Atmos. Chem. Phys., 25, 2947–2965, https://doi.org/10.5194/acp-25-2947-2025, 2025.

Support

  • JLAQC
Geostationary satellite observations

Background

The new constellation of geostationary satellite instruments for atmospheric composition including GEMS over Asia, TEMPO over North America, and Sentinel-4 over Europe offers unique opportunities to better understand the factors controlling air quality and atmospheric chemistry through dense hourly observations of a suite of gases. We have contributed to early validation and analysis of the GEMS data, and are now focusing on the exploitation of TEMPO data.

Objectives

  • Validate TEMPO observations through intercomparisons with other satellite instruments;
  • Use TEMPO observations to better understand and model lightning NOx emissions.

Approach

  • Apply machine learning to interpret differences between TEMPO and other instruments using TEMPO retrieval parameters as predictor variables;
  • Use cloud slicing to retrieve free tropospheric NO2 from TEMPO and interpret the data in terms of free tropospheric sources and chemistry of NOx.

People

References

Support

  • JLAQC
  • NASA

Collaborators

  • Xiong Liu, Gonzalo Abad (Harvard-Smithsonian)

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 Bonilla, Pengfei Liu, Tianjia (Tina) Liu, Jonathan Moch, Miah Caine, Kent Toshima)

Methane

subgroup leaders: James East and Xiaolin Wang

Constructing methane emission inventories

Background

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, and provides the necessary support for inverse analyses of satellite observations to further improve the emission estimates.

Objectives

  • Develop policy-relevant bottom-up methane emission inventories with high spatial resolution that can be evaluated with top-down analyses.

Approach

  • Develop the Global Fuel Emission Inventory (GFEI) to spatially allocate the emissions reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC);
  • Integrate satellite observations of point sources into the bottom-up inventories;
  • Use Landsat observations to construct global spatially resolved inventories of emissions from rice and wetlands;
  • Use CarbonMapper observations to construct a new global bottom-up inventory for coal mines.

People

Collaborators

  • Tia Scarpelli (Carbon Mapper)
  • Mark Omara (EDF)

Support

  • NASA CMS
  • UNEP
  • GHGSat

References

  • Chen, Z., Lin, H., Balasus, N., Hardy,A., East. J.D., Zhang, Y., Runkle, B.R.K., Hancock, S.E., Taylor, C.A., Du, X., Sander, B.O., Jacob, D. J., Global Rice Paddy Inventory (GRPI):a high-resolution inventory of methane emissions from rice agriculture based on Landsat satellite inundation data, Earth’s Future,13, e2024EF005479. https://doi.org/10.1029/2024EF005479, 2025.
  • Scarpelli, T. R., Roy, E., Jacob, D. J., Sulprizio, M. P., Tate, R. D., and Cusworth, D. H., Using new geospatial data and 2020 fossil fuel methane emissions for the Global Fuel Exploitation Inventory (GFEI) v3, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-552, in review, 2025.
  • Penn, E., D.H. Cusworth, K. Howell, K. O’Neill, T.R. Scarpelli, Z. Chen, R.A. Field, C.Ö. Karacan, E. Roy, and D.J. Jacob, Remote sensing enables basin-scale inventories of coal mine methane, submitted to Sci. Adv., 2025.
Improving urban, regional, and national inventories using satellite data

Background

Atmospheric observations of methane from satellites can provide powerful top-down information for evaluating the national emission inventories used in climate policy and reported to the United Nations Framework Convention on Climate Change (UNFCCC) under the Paris Agreement.  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 urban, regional, and national scales;
  • Exploit this top-down information to improve bottom-up emission inventories

Approach

  • Use TROPOMI satellite observations 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;
  • Use a new 12-km resolution version of GEOS-Chem to optimize methane emissions at urban scales;
  • Apply a Kalman filter system to weekly update of emissions from source regions using TROPOMI observations.

People

Collaborators

  • Carrie Jenks (Harvard Law School)
  • John Worden (JPL)
  • Kevin Bowman (JPL)

Support

  • NASA CMS
  • UNEP
  • Harvard Methane Initiative
  • NDESG Fellowship to Nicholas Balasus

References

  • Wang, X., D. J. Jacob, H. Nesser, N. Balasus, L. Estrada, M. P. Sulprizio, D. H. Cusworth, T. R. Scarpelli, Z. Chen, J. D. East, and D. J. Varon: Quantifying urban and landfill methane emissions in the United States using TROPOMI satellite data, submitted to Sci. Adv.,
    https://doi.org/10.48550/arXiv.2505.10835    , 2025.
  • Balasus, N., D.J. Jacob, G. Maxemin, C. Jenks, H. Nesser, J.D. Maasakkers, D.H. Cusworth, T.R. Scarpelli, D.J. Varon, and X. Wang, Satellite monitoring of annual US landfill methane emissions and trends, Environ. Res. Lett., 20, 024007, https://doi.org/10.1088/1748-9326/ada2b1, 2025.
  • Hancock, S.E., D.J. Jacob, Z. Chen, H. Nesser, A. Davitt, D.J. Varon, M.P. Sulprizio, N. Balasus, L.A. Estrada, J.D. East, E. Penn, C.A. Randles, J. Worden, I. Aben, R. J. Parker, and J. D. Maasakkers, Satellite quantification of methane emissions from South American countries: A high-resolution inversion of TROPOMI and GOSAT observations, Atmos. Chem. Phys., 25, 797–817, https://doi.org/10.5194/acp-25-797-2025, 2025.
Explaining the global budget and the rise in methane

Background

The atmospheric methane concentration has tripled since pre-industrial time and this growth has accelerated over the past decade. The drivers of these trends are not understood.  Methane 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. All these factors could contribute to the methane trend.  

Objectives

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

Approach

  • Apply the stretched-grid high-performance GEOS-Chem (GCHP) to high-resolution global inversion of satellite observations of atmospheric methane;
  • Use inversions of long-term satellite observations to attribute global methane trends and the recent acceleration;
  • Apply a new TROPOMI data set to global methane inversions for better separation of source sectors and the role of OH;
  • Appply a Local Ensemble Transform Kalman Filter (LETKF) as global continuous inversion system for methane emissions.

People

Collaborators

  • John Worden (JPL)

Support

  • NASA CMS
  • AWS

References

  • He, M., D. J. Jacob, L. Estrada, D. J. Varon, M. Sulprizio, N. Balasus, J. D. East, E. Penn, D. C. Pendergrass, Z. Chen, T. A. Mooring, J. D. Maasakkers, P. G. Brodrick, C. Frankenberg, K. W. Bowman, L. Bruhwiler: Attributing 2019-2024 methane growth using TROPOMI satellite observations, submitted to Sci. Adv., https://doi.org/10.22541/essoar.174886142.25607118/v1, 2025.
  • Pendergrass, D. C., Jacob, D. J., Balasus, N., Estrada, L., Varon, D. J., East, J. D., He, M., Mooring, T. A., Penn, E., Nesser, H., and Worden, J. R.: Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-1554, 2025.
  • Penn, E., D.J. Jacob, Z. Chen, J.D. East, M.P. Sulprizio, L. Bruhwiler, J.D. Maasakkers, H. Nesser, Z. Qu, Y. Zhang, and J. Worden, What can we learn about tropospheric OH from satellite observations of methane?, Atmos. Chem. Phys., 25, 2947–2965, https://doi.org/10.5194/acp-25-2947-2025, 2025
Constructing a multi-satellite emission monitoring system

Background

Satellite observations provide information on methane emissions from the global scale down to point sources. Merging the different satellite data streams into a coherent inversion system can provide comprehensive near-real-time monitoring of methane emissions  to guide climate action. 

Objectives

  • Develop methods to merge data from area flux mappers (TROPOMI, GOSAT, MethaneSAT…) and point source imagers (GHGSat, Carbon Mapper…) in inversions of methane emissions.

Approach

  • Apply our analytical inversion methods to integrate MethaneSAT and TROPOMI data;
  • Develop methods to use point source information in regional inversions;
  • Integrate the GHGSat gridded data set of point source emissions into inversions of TROPOMI data.

People

Collaborators

  • Ritesh Gautam (EDF)
  • Dylan Jervis (GHGSat)
  • Daniel Varon (MIT)

Support

  • EDF
  • GHGSat
  • Exxon-Mobil

References

Model Development and Software Tools

subgroup leaders: Lizzie Lundgren, Melissa Sulprizio, and Bob Yantosca

GEOS-Chem for weather/climate models and data assimilation

Background

Simulation of atmospheric chemistry in weather and climate models has generally been rudimentary.  Using the  GEOS-Chem chemical module within these models to simulate chemical evolution (including emissions, chemistry, and depostion) provides a state-of-science atmospheric chemistry capability that is referenceable, easily maintained, and has large community backing. 

Objectives

  • Implement GEOS-Chem as a chemical module in 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 meteorological model for global atmospheric composition analysis and forecasting;
  • Exploit this capability in the NCAR CESM model for study of chemistry-climate-ecosystems interactions.

Approach

  • Contribute to development of the NASA 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;
  • Evaluate transport errors associated with off-line models through comparison of on-line and off-line transport tracer simulations.

People

Collaborators

  • Lee Murray  (U. Rochester)
  • Arlene Fiore (MIT)
  • Steven Pawson, Viral Shah (NASA/GSFC)

References

Support

  • NASA GMAO
GEOS-Chem modularization

Background

Atmospheric chemistry models include a large number of modules to describe the evolution of concentrations including emissions, transport, radiation, numerical integration of chemical mechanisms, and wet and dry deposition. Separating these modules to enable plug-and-play (i.e., mix-and-match) of modules from different models can advance the general capabilities of atmospheric chemistry models, both off-line and within the context of Earth system models. GEOS-Chem has powerful modules that can be shared with the community, and can also gain from other community modules. We have already developed stand-alone modules for GEOS-Chem emissions (HEMCO) and for numerical integration of chemical kinetics (KPP 3.0), and are presently developing modules for photolysis and aerosol thermodynamics.

Objective

  • Enable plug-and-play approach to atmospheric chemistry modeling through development of stand-alone GEOS-Chem modules

Approach

  • Modularize GEOS-Chem components to enable their inclusion in other models;
  • Contribute to the NCAR MUSICA next-generation representation of atmospheric chemistry in the NCAR CESM.

People

Collaborators

  • Michael Prather (UC Irvine)
  • Louisa Emmons, Matt Dawson (NCAR)

References

Support

  • NSF (terminated)
GEOS-Chem development and management

Background

We develop, maintain, and support the GEOS-Chem atmospheric chemistry model for a user community of hundreds of research groups worldwide and in collaboration with Randall Martin’s group at Washington University. Click on the GEOS-Chem logo for more information.

People

Support

  • NASA ACMAP
  • JLAQC

Collaborators

  • Randall Martin, Bill Zhuge (Washington U.)

References

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

Background

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) 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 ;
  • Deliver the toolkit to users for their own applications including new satellite observations.

People

Collaborators

  • Kazuyuki Miyazaki and Kevin Bowman (JPL)
  • Dylan Jones (U. Toronto)

Support

  • NASA CMS

References

  • Pendergrass, D. C., Jacob, D. J., Balasus, N., Estrada, L., Varon, D. J., East, J. D., He, M., Mooring, T. A., Penn, E., Nesser, H., and Worden, J. R.: Trends and seasonality of 2019–2023 global methane emissions inferred from a localized ensemble transform Kalman filter (CHEEREIO v1.3.1) applied to TROPOMI satellite observations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-1554, 2025.
Integrated Methane Inversion (IMI)

Background

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 requires expertise in satellite observations and inverse modeling, as well as large computational resources. Research tools require transparency for results to be usable by policymakers.

Objective

  • Use the best satellite inversion practices developed by our group to develop and manage an Integrated Methane Inversion (IMI) as a community tool for researchers and stakeholders;
  • Make this capability available on the AWS cloud;
  • Extend the IMI to CO2 inversions;
  • Develop the Integral Earth (IE) web user interface for easy access to the IMI and for interactive visualization of inversion results.

Approach

  • Set up the IMI so that users can quantify methane emissions from TROPOMI satellite data for any region and period of interest, with no need for particular expertise;
  • Integrate into the IMI information from other satellite instruments including point source imagers and MethaneSAT;
  • Enable near-real-time continuous monitoring of methane emissions;
  • Diagnose and minimize the sensitivity of inversion results to boundary conditions in regional inversions; 
  • Maintain and continually develop the IMI for the benefit of researchers and stakeholders and with open-source software for transparency;
  • Develop IE for ease of use and as a repository of past IMI runs.

People

Collaborators

  • Emily Reidy, Felipe Cardoso-Saldana (Exxon-Mobil)
  • Bram Maasakkers, Ilse Aben (SRON)
  • Hannah Nesser, Kevin Bowman (JPL)
  • Sabour Baray (ECCC Canada)
  • Zichong Chen (HKUST)

References

Support

  • NASA CMS 
  • Exxon-Mobil 
  • NASA EIS-GHG
  • Harvard Methane Initiative

Harvard John A. Paulson
School of Engineering
And Applied Sciences

Harvard University
Department of Earth and
Planetary Sciences

Harvard University
Center For The
Environment

Joint Laboratory for Air
Quality and Climate
(JLAQC)