U009
Data Analytics and Machine Learning Innovation for Climate and Earth Surface Processes

Session ID#: 83451

Session Description:
Earth observations and climate model outputs are witnessing an unprecedented increase in data volume, opening up new opportunities to advance physical understanding, improve earth system modeling, and increase predictive ability of weather, climate and earth surface processes at a range of scales. Mining these data for new knowledge presents also new challenges and opportunities to the data science community instigating the development or adaptation of tools from mathematics, statistics, and computer science for the problems at hand.  Topics of special interest include identifying causal sources of predictability, quantifying and attributing climate variability and change, improving micro-scale parameterizations in climate models, and deciphering landscape response to change.  This session will bring together ocean and atmospheric scientists, hydrologists, geomorphologists, and data scientists to critically debate challenges and opportunities in the era of big data and the wave of machine learning for advancing climate and earth system modeling.
Co-Organized:
  • A - Atmospheric Sciences
  • EP - Earth and Planetary Surface Processes
  • GC - Global Environmental Change
  • H - Hydrology
Index Terms:

1824 Geomorphology: general [HYDROLOGY]
1916 Data and information discovery [INFORMATICS]
3305 Climate change and variability [ATMOSPHERIC PROCESSES]
3337 Global climate models [ATMOSPHERIC PROCESSES]

Primary Convener:  Efi Foufoula-Georgiou, University of California Irvine, Irvine, CA, United States
Conveners:  James Tremper Randerson, University of California Irvine, Department of Earth System Science, Irvine, CA, United States, Jean Braun, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany and Alejandro Tejedor, University of California Irvine, Department of Civil and Environmental Engineering, Irvine, CA, United States; Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
Primary Liaison:  Efi Foufoula-Georgiou, University of California Irvine, Irvine, CA, United States
 
Pattern learning approaches for stochastic downscaling in geosciences (Invited) (489721)
Grégoire Mariethoz, University of Lausanne, Faculty of Geosciences and Environment, Institute of Earth Surface Dynamics, Lausanne, Switzerland
 
Viewing Forced Climate Patterns through an AI Lens (Invited) (491639)
Elizabeth A Barnes, Colorado State University Fort Collins, Fort Collins, CO, UNITED STATES, Imme Ebert-Uphoff, Colorado State University, Fort Collins, CO, United States, Chuck Anderson, Colorado State University, Fort Collins, United States, James W Hurrell, Colorado Climate Center, Atmospheric Science, Fort Collins, CO, United States and David Anderson, Pattern Exploration LLC, Fort Collins, United States
 
Machine learning for a better understanding of the Earth System (Invited) (492916)
Markus Reichstein1, Fabian Gans1, Miguel D Mahecha1, Nuno Carvalhais1 and Martin Jung2, (1)Max Planck Institute for Biogeochemistry, Department of Biogeochemical Integration, Jena, Germany, (2)Max Planck Institute for Biogeochemistry, Jena, Germany
 
New Techniques for Machine Learning in Earth Science with Google Earth Engine (Invited) (512879)
Matthew Hancher, Google, Mountain View, CA, United States and The Google Earth Engine Team
 
Machine Learning Components in Coastal Morphodynamic Models (Invited) (548682)
Evan B Goldstein, University of North Carolina at Greensboro, Geography, Environment, and Sustainability, Greensboro, NC, United States, Tomas Beuzen, Water Research Laboratory, UNSW Sydney, School of Civil and Environmental Engineering, Sydney, NSW, Australia and Giovanni Coco, University of Auckland, Auckland, New Zealand
 
Advances in Climate Informatics: Machine Learning for the Study of Climate Change (Invited) (555993)
Claire Monteleoni, University of Colorado Boulder, Computer Science, Boulder, CO, United States
 
Machine learning based cloud parametrizations and causal discovery for climate models (Invited) (571731)
Veronika Eyring, German Aerospace Center (DLR), Oberpfaffenhofen, Germany, Pierre Gentine, Department of Earth and Environmental Engineering, Columbia University, New York, NY, United States, Gunnar Behrens, German Aerospace Center DLR Oberpfaffenhofen, Oberpfaffenhofen, Germany, Mike S Pritchard, University California Irvine, Department of Earth System Science, Irvine, CA, United States, Stephan Rasp, Technical University of Munich, Munich, Germany and Jakob Runge, German Aerospace Center, Jena, Germany
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