TransCom-2024: Quantifying errors in inversions of satellite trace gas retrievals
TransCom-2024: Quantifying errors in inversions of satellite trace gas retrievals
Location: NSF National Center for Atmospheric Research (NSF NCAR) Mesa Lab in Boulder, Colorado.
Date: Tuesday 28 May 2024
Meeting summary:
Inversions of atmospheric trace gas measurements to infer surface sources and sinks have long been used to assess the global budgets of CO2, CH4, N2O and other species; pushing these estimates to regional spatial scales and sub-annual time scales can yield insight into the processes driving the fluxes. The TransCom project has tried to quantify the uncertainties in such flux estimates, focusing on those errors related to the transport models underlying the inversions.
Recently, this ‘top-down’ approach has been enlisted for use in the policy realm: country-scale CO2 fluxes have been estimated from dense satellite XCO2 measurements and, once lateral fluxes are accounted for, converted into carbon stock changes; these may be compared to similar ‘bottom-up’ estimates that form the basis of the Paris Agreement emissions reduction efforts, providing a largely-independent check on the process. This year’s TransCom meeting focuses on quantifying errors in satellite-based flux inversions such as these, especially errors due to transport, but also those due to measurement biases, coverage/sampling issues, inversion method assumptions, lateral flux characterization, etc., from global down to regional and country scales.
Large differences have been found between satellite- and ground-based measurements of column-averaged trace gas concentrations and similar values derived from atmospheric transport models, even when such models assimilate the available (mostly surface-based) in situ data as a constraint. These differences show up in terms of global averages, latitudinal distributions, strat/trop exchange, and land/ocean differences, as well as at regional scales. Some reflect the real flux signals of interest, but most are due to measurement and transport errors that must be removed first. The transport model errors include those related to convection, spatial and temporal resolution, the treatment of water vapor in the models, and mixing out of the PBL, between hemispheres and into the stratosphere.
We welcome presentations that focus on understanding such transport errors, especially those affecting the satellite inversion problem. Collaborative work on flux inversion intercomparison is also sought, especially from global research community projects such as, e.g., REgional Carbon Cycle Assessment and Processes-2 (RECCAP2), the Global Carbon Project, the Orbiting Carbon Observatory-2 (OCO-2) flux inversion model intercomparison project (MIP), and other TransCom initiatives.
Presentations:
- Britt Stephens: Introduction to TransCom-2024
- Zoé Lloret: Refining the Global Picture: the Impact of Increased Resolution on CO₂ Atmospheric Inversions using OCO-2 XCO₂ retrievals
- Frédéric Chevallier: Introducing a high-resolution atmospheric transport model intercomparison
- Vitus Benson: Exploring neural network emulators for atmospheric transport
- Gretchen Keppel-Aleks: Assessing transport errors using GEOS-Chem and TM5 in WOMBAT inversions of OCO-2 data
- Yuming Jin: Refining transport models: leveraging airborne tracer observations and cross-isentrope diabatic mixing metrics
- Chris O'Dell: Everything you need to know about recent XCO2 updates to OCO-2 and OCO-3 products
- Jeongmin Yun: Quantification of regional terrestrial biosphere CO2 flux errors in v10 OCO-2 MIP ensemble using airborne measurements.
- David Baker: Findings from the OCO-2 v10 MIP: inversion method/setup differences drive fine-scale fluxes, transport and retrieval errors drive broad-scale fluxes
- Britt Stephens: Evaluating OCO-2 v10 MIP CO2 fluxes against ATom airborne CO2 surveys
- John Miller: Seasonal Bias in OCO-2-based CO2 flux estimation over Amazonia revealed by long-term aircraft measurements
- Prabir K. Patra: Evaluation of prior model simulation key to the successful inversion intercomparison
- Nicholas Parazoo: Benefits of more frequent sampling at high latitudes
- Julia Marshall: How seasonal differences in effective surface height can bias XCO2 retrievals
- Sojung Sim: Inverse modeling of CO2 emissions using satellite observations from OCO-2 and OCO-3
- Nikhil Dadheech: High-resolution greenhouse gas flux inversions using a machine learning surrogate model
- Takashi Maki: Introduction of independent satellite bias correction on CO2 flux inversion
Jointly with the NSF NCAR Climate & Global Dynamics seminar series, Dr. David Crisp, former OCO-2 science lead at NASA/JPL, will present a talk on top-down carbon fluxes supporting regional-scale carbon cycle science and national-scale greenhouse gas inventories.