GC063
Novel approaches to constrain spread and reduce uncertainties in Earth system model projections
Session ID#: 74498
Session Description:
Earth system models (ESMs) are used to describe the complex interactions between physical, chemical, and biological processes, and to project future climate. As the number and complexity level of ESMs increase, it has become impractical to use traditional approaches to analyze their outputs. This could hinder discovery of key knowledge, which is critical for developing timely mitigation and adaptation plans for future climate change. Despite our improved understanding in the underlying processes, spreads among projections of key environmental variables remain persistently large. It is also known that ESMs are not inherently independent from one another, which may contribute to consistent model-data biases in the ESMs subset. This session invites contributions that explore novel methodologies to (i) improve the efficiency of model-data evaluations; (ii) use observable mechanisms to constrain the projection spreads; and (iii) develop advanced model weighting strategies. These methodologies include, but not limited to, BigData analysis and Machine Learning.
Index Terms:
1620 Climate dynamics [GLOBAL CHANGE]
1622 Earth system modeling [GLOBAL CHANGE]
1916 Data and information discovery [INFORMATICS]
1942 Machine learning [INFORMATICS]
Primary Convener: Jerry Tjiputra, NORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Bergen, Norway
Conveners: Forrest M. Hoffman, University of California Irvine, Department of Earth System Science, Irvine, CA, United States and Hyungjun Kim, The University of Tokyo, Institute of Industrial Science, Tokyo, Japan
Primary Liaison: Forrest M. Hoffman, University of California Irvine, Department of Earth System Science, Irvine, CA, United States
A Novel Statistical Framework to Account for Model Dependence and to Test Non-Exclusive Hypotheses: Application to Arctic Sea Ice (497953)
Roman Olson1,2, Soon-Il An3, Yanan Fan4, Won Chang5, Jason Peter Evans4 and June-Yi Lee6, (1)Yonsei University, Atmospheric Sciences, Seoul, South Korea, (2)IBS Center for Climate Physics, Busan, South Korea, (3)Yonsei University, Atmospheric Sciences, Seoul, Korea, Republic of (South), (4)University of New South Wales, Sydney, Australia, (5)University of Cincinnati Main Campus, Cincinnati, OH, United States, (6)Pusan National University, Busan, South Korea
Application of a genetic algorithm to locally optimize emergent constraints of the future North Atlantic Carbon Uptake (533942)
Jerry Tjiputra, NORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Bergen, Norway, Nadine Goris, NORCE Norwegian Research Centre, Bjerknes Centre for Climate Research, Bergen, Norway and Klaus Johannsen, NORCE Norwegian Research Centre, Bergen, Norway
Constraining European climate projections with regression-based optimal detection and attribution methods (535140)
Sabine Undorf, University of Edinburgh, Edinburgh, United Kingdom, Nicolas Freychet, School of Geosciences, University of Edinburgh, Edinburgh, United Kingdom and Gabriele C Hegerl, University of Edinburgh, Edinburgh, EH9, United Kingdom
An improved empirical quantile mapping procedure for bias correction of climate change projections (539714)
Kyuhyun Byun, University of Notre Dame, Civil and Environmental Engineering and Earth Sciences, Notre Dame, IN, United States and Alan F Hamlet, University of Notre Dame, Department of Civil and Environmental Engineering & Earth Sciences, Notre Dame, IN, United States
Selecting Extreme Climate Change Scenarios for Climate Impact Studies Using a Novel Integrated Computational Geometry Algorithm (548467)
Babak Farjad, Government of Alberta, Calgary, AB, Canada, Anil Gupta, Alberta Environment and Parks, Calgary, AB, Canada, Hossein Sartipizadeh, University of Washington & The University of Texas at Austin, Washington, United States and Alex J. Cannon, Environment and Climate Change Canada, Climate Data and Analysis Section, Climate Research Division, Victoria, BC, Canada
Constraining spread of uncertainty in future climate projections with Bayesian Model Averaging (560246)
Elias Charbel Massoud, JPL/NASA/Caltech, Pasadena, CA, United States, Huikyo Lee, Jet Propulsion Laboratory, Caltech, Altadena, CA, United States, Peter Gibson, JPL, Pasadena, United States, Paul C Loikith, Portland State University, Portland, OR, United States, Bin Guan, University of California, Los Angeles, Joint Institute for Regional Earth System Science and Engineering, Los Angeles, CA, United States and Duane Edward Waliser, NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
Developing a method adjusting bias of long-term and multivariate outputs from ensemble experiments by Earth system models for the projection of hydrology under climate change (629845)
Satoshi Watanabe, University of Tokyo, Graduate School of Engineering, Bunkyo-ku, Japan, Hyungjun Kim, University of Tokyo, Bunkyo-ku, Japan and Nobuyuki Utsumi, Jet Propulsion Laboratory / The University of Tokyo, Pasadena, United States
