AGU session on Earth system predictability and biogeochemistry (B041)

[Katie Dagon]

Dear Colleagues,

We are writing to call your attention to a special session titled, “Improving Earth System Predictability: New Mechanisms, Feedbacks, and Approaches for Predicting Global Biogeochemical Cycles in Earth System Models”, at the 2021 American Geophysical Union (AGU) Fall Meeting to be held in New Orleans, Louisiana, and Online Everywhere on 13-17 December 2021.

This session, co-sponsored by both the World Climate Research Programme (WCRP) and the Ecological Society of America (ESA), will highlight integrated understanding of biogeochemical mechanisms that impact Earth system predictability, model benchmarking, and novel applications of machine learning.

Confirmed invited presenters are Linnia Hawkins (Oregon State University) and Yassir Eddebbar (Scripps Institution of Oceanography, UC San Diego).

Please consider submitting a contributed abstract to this session from the AGU Fall Meeting website at https://www.agu.org/Fall-Meeting/Pages/Present/Abstracts by the deadline date of 4 August 2021 at 23:59 EDT.

Full details of the session are described here:

B041 - Improving Earth System Predictability: New Mechanisms, Feedbacks, and Approaches for Predicting Global Biogeochemical Cycles in Earth System Models

Conveners: Forrest M. Hoffman (ORNL), Min Xu (ORNL), Katherine Dagon (NCAR), and Precious Mongwe (CSIR)

Predictions of future atmospheric CO2 levels are influenced by global carbon and nutrient cycles, climate interactions, and feedbacks to the Earth system. Relevant processes operate at different spatial and temporal scales and vary across terrestrial, coastal, and marine ecosystems. Uncertain biogeochemical feedbacks may be altered by anthropogenic disturbance agents, including tropospheric O3, changes in nutrient and hydrological cycles, eutrophication, acidification, land cover/land use change, and potential climate intervention strategies. This session focuses on integrated understanding of feedback mechanisms that impact Earth system predictability, methods for evaluating and benchmarking process representations in Earth system models, approaches for constraining future climate projections (e.g., emergent constraints), and novel applications of artificial intelligence and machine learning to improve predictive understanding of global biogeochemical cycles.

URL: https://agu.confex.com/agu/fm21/prelim.cgi/Session/119176

Thank you for considering contributing an abstract to this session and participating in the AGU Fall Meeting either in person in New Orleans or remotely.

Forrest Hoffman, Katie Dagon, Min Xu, and Precious Mongwe

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Katie Dagon

Pronouns: she/her/hers
Project Scientist I
Climate and Global Dynamics
National Center for Atmospheric Research
Email: kda...@ucar.edu

katiedagon.github.io