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Summer School for Inverse Modeling of Greenhouse Gases (SSIM-GHG) 2024

Motivation


Rising concentrations of CO2 and CH4 are the driver of climate change and reflect a complex mix of natural and human sources and sinks. For example, plants and oceans absorb about half of human CO2 emissions every year, slowing the progression of climate change. Tracking progress to combat climate change requires good quality understanding of sources and sinks and underlying processes, which come from a variety of complex modeling tools that help scientists calculate the exchanges of CO2 and CH4 at the Earth’s surface from atmospheric data. This workshop, supported by the newly-established US Greenhouse Gas Center, aims to develop a future workforce skilled at using existing tools as well as building their own tools to understand sources and sinks of greenhouse gases.

Workshop goals


The goal of the workshop is to present and provide guidance and instruction of the state of the art in atmospheric data assimilation techniques needed to support current and future GHG observing systems. This includes different flux estimation techniques for GHGs, and retrieval techniques for estimating atmospheric GHGs from space-based and surface-based remote sensing platforms.


Sponsors


Presentations

Note: Some videos are concatenated across multiple speakers***

Week One:

Background/Intro – Tuesday, June 11

Instructors: Scott Denning, Sean Crowell, Gretchen Keppel-Aleks, Andy Jacobson

SpeakerTitleVideo
Scott DenningCarbon CycleVideo
Andy Jacobson, Gretchen Keppel-AleksObservations: CO2 through different types of observationsVideo

Scott Denning

Atmospheric transport at different scalesVideo
Bayesian Matrix Methods – Wednesday, June 12

SpeakerTitleVideo
Michael BertolacciBayesian Statistics RefresherVideo
Andrew SchuhBatch Inversion MethodVideo

4DVAR/Variational Methods – Thursday, June 13

SpeakerTitleVideo
David Baker4DVAR/Variational MethodsVideo

Ensemble Kalman Filter Methods – Friday, June 14

SpeakerTitleVideo
Andy JacobsonFiltering TechniquesVideo

Week Two:

Introduction to CH4, plume methods, MMRV and Bayesian Hierarchical Models – Monday, June 17

SpeakerTitleVideo
Alex TurnerWelcome to CH4Video
Alana AyessePlume MethodsVideo

Kim Mueller, Hannah Nesser

MMRV and PolicyVideo
Michael BertolacciHierarchical Bayesian ModelsVideo

Trace Gas Retrievals – Tuesday, June 18

SpeakerTitleVideo
Chris O’DellTrace Gas Retrieval TheoryVideo

Field Trip to Niwot Ridge, CO (facilitated by NOAA-GML), Wednesday, June 19

LPDM Methods – Thursday, June 20
SpeakerTitleVideo
Arlyn AndrewsIntroduction to LPDM MethodsVideo
Hannah NesserHow to handle boundary conditionsVideo

Vineet Yadav

Hierarchy of Steps in Regional Flux InversionVideo
Kim MuellerEngineering a Regional Flux InversionVideo

State Reduction Techniques and Speed Talks – Friday, June 21

SpeakerTopic/SlidesVideo
Hannah NesserState Reduction TechniquesVideo
Instructor Speed TalksInstructor Speed Talk SlidesVideo

Student Speed Talks

Student Speed Talk SlidesVideo

Resources

References

Q&A
Q. Is there agreement in the community on what to set for the ‘sensitivity to surface’ height for LPDMs? In the literature I’ve seen 40m, 100m, boundary layer height, and in the slides here: half boundary layer height. – Daemon Kennett

In short, no.  Historically I believe I maybe used 100-150 meters in Schuh et al 2010, 2013.  Considering the PBL is reasonably well mixed, half the PBL height may not be terribly different than 100 meters. Plus it would track a collapsing boundary layer at night which might be appropriate. So, at least for STILT, I think it would probably be fine. However, it would be very interesting to see if 0.25 or 0.75 of the PBLH (as opposed to 0.5)  would give you any different results.

Q. For surface in situ sites we typically assimilate afternoon observations when the boundary layer is well mixed. Any thoughts on conditions under which night-time observations could be assimilated, to help constrain the full diurnal cycle of fluxes? Maybe high wind-speed conditions? – Daemon Kennett

Yes, agree.  I’d think like eddy covariance folks.  I’d say set a threshold, wind speed for example, and make sure both model and obs see that level of winds.  Turbulence at night should make the obs and model more robust.

Q. It was mentioned that LPDM should only be used for “long-lived” species. What are the limits on this and/or does it depend on your spatial resolution? – Betsy Farris 

No, I wouldn’t say “long-lived”.  I think the misconception is likely that folding multi-species complex chemistry could be tricky.  However, species don’t have to be long lived.  A Lot of this original work was based on radioactive species w/ varying half lives.  The key is probably “simple” chemistry, decay, or passive.

Toy Example Code

Code for the toy examples presented in class can be found at the following link: https://github.com/US-GHG-Center/ssim-ghg

Please be aware the code and data are preliminary.