AGU Fall 2021 "GC052 - Integrated investigations of hydroclimate variability and extremes across multiple scales: processes and implications over complex terrains"

Dear Colleagues,

You are welcome to submit your abstract to our session on hydroclimate variability and extremes at the 2021 AGU Fall Meeting.

Our session is intended to bring together studies and advances on hydroclimate variability and extreme events from a coupled climate system perspective and multi-scale processes. More detailed information can be found in the session description at the end of this email. We have two invited talks from Dr. Filippo Giorgi (ICTP) and Dr. Roy Rasmussen (NCAR).

The full session description and abstract submission portal can be found here: https://agu.confex.com/agu/fm21/prelim.cgi/Session/120729

Please share this message with others who would be interested in this session. We look forward to meeting you in person or virtually.

Best regards,

Conveners: Xiaodong Chen (PNNL); Xingying Huang (NCAR)

Session Abstract:

Complex terrains feature significant land-atmosphere interactions that modulate local to regional hydroclimate systems, with elevated vulnerabilities to the hydroclimate variability and intensified extremes. There is an ongoing need to better understand and assess the coupled earth system processes over these regions. Such integrated investigations require a synergy of new insights from observations and advanced dynamical and statistical modeling. This session welcomes the submissions on the theories, simulations, and applications of such knowledge, including but not limited to: 1) investigations of climate variability that highlight the connections and interactions within multiple processes of the earth system from global to local scales; 2) understanding of sequential or compound hydroclimate extremes (storm, flooding, snow, drought, heatwave, wildfires, etc.) from the view of land-atmosphere interactions; 3) modeling and projections of climate variability and extremes with numerical or statistical tools, such as high-resolution climate models and physics-informed machine learning approaches.