Call for abstract at AGU24 (GC117)
Shuai Yang
Dear Colleagues,
We are convening an AGU session (GC117) focused on greenhouse gas dynamics in various landscapes under climate change in the coming AGU24 Fall Meeting in DC. If your recent research aligns with the scope of our session, we warmly invite you submit an abstract. The session aims to provide an opportunity to engage in interdisciplinary communication across various career stages, to spark innovative ideas, and to contribute to advancing our understanding of greenhouse gas dynamics under climate change. Please see the details below:
Session GC117--Observing, Modeling, and Predicting Greenhouse Gas Dynamics in Various Landscapes under Climate Change
Website: https://agu.confex.com/agu/agu24/prelim.cgi/Session/226487
Conveners: Shuai Yang (LBNL), Zhenzhong Zeng (Sustech), Chunmiao Zheng (Sustech), Jinyun Tang (LBNL)
Invited speakers: Avni Malhotra (PNNL) and Kyle Delwiche (UC Berkely)
Description: The intricate dynamics of greenhouse gases (GHGs) stands as the primary driver of anthropogenic global climate change. This session aims to explore recent advancements and challenges in observing and modeling GHG dynamics across different landscapes (e.g., wetland, grassland, cropland, forest, urban, and reservoirs) under climate change. We invite submissions that delve into the development and application of GHG-related observations and models. Topics of interest include but are not limited to: advancements in observing and modeling techniques for predicting GHG emissions; integration of observational data, remote sensing, and ground-based measurements into GHG models; assessments of uncertainties and sensitivity analyses in GHG observations and modeling; understanding feedback mechanisms between climate change and GHG dynamics; implications of predicted GHG dynamics on water-carbon-energy cycles and ecosystem processes under both natural and managed conditions. We encourage interdisciplinary contributions that utilize in-situ measurements, meta-analyses, remote sensing data, and innovative modeling approaches which include machine learning techniques.
We look forward to seeing you in DC!
Thank you and best regards,
Shuai
On behalf of the session conveners,
Zhenzhong Zeng, Chunmiao Zheng, Jinyun Tang
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Shuai Yang
Postdoc scholar
Climate and Ecosystem Sciences Division
Lawrence Berkeley National Laboratory
1 Cyclotron Rd, Berkeley, CA, USA 94720