A33A:
A New Look at Climate Diagnosis and Modeling in the Era of Climate Informatics I Posters


Session ID#: 7924

Session Description:
The size and complexity of observational and model-simulated climate data have seen accelerated growth since the late 1970s. This increasing amount of data and our growing computational capacity create unprecedented opportunities for bringing innovative approaches of machine learning and data mining to climate data for interdisciplinary knowledge discovery, thus the birth of a new area “climate informatics”. This session seeks contributions from all application areas with the goal of improving process-level understanding and modeling of the Earth’s coupled climate system through advanced data mining and machine learning methods. These include but are not restricted to the development and implementation of new data mining methods for climate diagnosis and atmospheric process study, new ideas of data assimilation, stochastic climate and environment modeling, use of causal discovery and structure learning methods to understand large-scale dynamical processes, uncertainty quantification in climate simulation and projection, and data-driven approaches in weather forecasting and climate prediction.
Primary Convener:  Yi Deng, Georgia Institute of Technology Main Campus, Atlanta, GA, United States
Convener:  Imme Ebert-Uphoff, Colorado State University, Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, United States
Chairs:  Yi Deng, Georgia Institute of Technology Main Campus, Atlanta, GA, United States and Imme Ebert-Uphoff, Colorado State University, Fort Collins, CO, United States
OSPA Liaison:  Yi Deng, Georgia Institute of Technology Main Campus, Atlanta, GA, United States

Cross-Listed:
  • GC - Global Environmental Change
  • H - Hydrology
  • NH - Natural Hazards
  • OS - Ocean Sciences
Co-Sponsor(s):
  • AMS: American Meteorological Society -
Index Terms:

1914 Data mining [INFORMATICS]
1942 Machine learning [INFORMATICS]
3305 Climate change and variability [ATMOSPHERIC PROCESSES]
3337 Global climate models [ATMOSPHERIC PROCESSES]

Abstracts Submitted to this Session:

Contrasting the Eastern Pacific El Niño and the Central Pacific El Niño: Process-based Feedback Attribution (86161)
Xiaoming HU, Sun Yat-Sen University, Guangzhou, China, Song Yang, Sun Yat-sen University, School of Atmospheric Sciences, Zhuhai, China and Ming Cai, Florida State University, Tallahassee, FL, United States
Quantifying barotropic and baroclinic eddy feedbacks in the persistence of the Annular Modes (86380)
Yang Zhang1, Yu Nie1,2, Gang Chen3, Xiu-Qun Yang4 and David Alex Burrrows5, (1)Nanjing University, Nanjing, China, (2)Beijing Climate Center, China Meteorological Administration, Beijing, China, (3)University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, (4)Nanjing University, School of Atmospheric Sciences, Nanjing, China, (5)Cornell University, Ithaca, NY, United States
Enabling Efficient Climate Science Workflows in High Performance Computing Environments (86699)
Hari Krishnan1, Suren Byna1, Michael F Wehner1, Junmin Gu1, Travis Allen O'Brien1, Burlen Loring2, Dáithí A Stone1, William Drew Collins3, Mr Prabhat1, Yunjie Liu1, Jeffrey N Johnson1 and Christopher J Paciorek4, (1)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Computational Research Division, Berkeley, United States, (3)Lawrence Berkeley National Laboratory, Earth and Environmental Science, Berkeley, CA, United States, (4)University of California, Berkeley, CA, United States
Running climate model in the commercial cloud computing environment: A case study using Community Earth System Model (CESM) (68015)
Xiuhong Chen1, Xianglei Huang2, Chaoyi Jiao2, Mark Flanner3, Todd Raeker2 and Brock Palen2, (1)University of Michigan, Department of Climate and Space Sciences and Engineering, Ann Arbor, United States, (2)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (3)University of Michigan Ann Arbor, Climate and Space Sciences and Engineering, Ann Arbor, MI, United States
Classification and Feature Selection Algorithms for Modeling Ice Storm Climatology (68492)
Ranjini Swaminathan1, Mohan Sridharan1, Katharine Hayhoe2 and Gillian Dobbie1, (1)University of Auckland, Auckland, New Zealand, (2)Texas Tech University, Climate Center, Lubbock, TX, United States
Impact of Tropical Pacific Precipitation Anomaly on the East Asian Upper-tropospheric Westerly Jet during the Boreal Winter (71306)
Zhiping Wen, Fudan University, Shanghai, China and Yuanyuan Guo, Sun Yat-Sen University, Guangzhou, China
A Self-Organizing Map-Based Approach to Generating Reduced-Size, Statistically Similar Climate Datasets (82820)
Ryan Cabell, Luca Delle Monache, Stefano Alessandrini and Luna Marie Rodriguez, National Center for Atmospheric Research, Boulder, CO, United States
A Simple Algorithm to Identify and Track Trough and Ridge in Reanalysis Data (85042)
Clara Miho Narukawa Iwabe, UNESP Sao Paulo State University, Rio Claro, Brazil and Diogo Fusco, UNESP Sao Paulo State University, Bauru, Brazil
Percolation Features on Climate Network under Attacks of El Niño Events (63343)
Zhenghui Lu, Peking University, Beijing, China
Characterizing and Understanding Large-Scale Wave Propagation in the Atmosphere through Graphs of 3D Information Flow (63735)
Yi Deng, Georgia Institute of Technology, Atlanta, GA, United States, Imme Ebert-Uphoff, Colorado State University, Fort Collins, CO, United States and Junwen Chen, Georgia Institute of Technology, School of Earth and Atmospheric Sciences, Atlanta, GA, United States

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