GC004
Advances in Approaches for Earth System Model Uncertainty Quantification: Integrating Models and Observations to Enhance Predictability
Session ID#: 248920
Session Description:
Reducing uncertainty in Earth system models requires integrating uncertainty quantification with optimal observational strategies. However, challenges remain in diagnosing uncertainty sources, calibrating model parameters in the presence of structural errors, and designing observational networks to maximize predictive skill. Advances in AI-based surrogates, large ensembles, Bayesian inference, and Observing System Simulation Experiments (OSSEs) offer new opportunities to enhance model-observation integration and predictions.This session invites contributions that advance uncertainty quantification and observational strategies in Earth system modeling. Topics include, but are not limited to: (1) AI-based surrogates for improved computational efficiency and predictive accuracy; (2) Parameter estimation via large ensembles, such as Perturbed Parameter Ensembles (PPEs); (3) Sensitivity analysis to diagnose dominant uncertainty sources; (4) Bayesian inference and optimization (e.g., MCMC) for model calibration; (5) OSSEs for optimal experimental design and observational network refinement. We welcome submissions from diverse Earth system domains that bridge models and observations to improve predictive capabilities.
Co-Sponsor(s):
Index Terms:
1622 Earth system modeling [GLOBAL CHANGE]
1626 Global climate models [GLOBAL CHANGE]
1627 Coupled models of the climate system [GLOBAL CHANGE]
1990 Uncertainty [INFORMATICS]
Primary Convener: Zhongjing Jiang, University of Illinois Urbana-Champaign, DOE Center for Advanced Bioenergy and Bioproducts Innovation, Urbana, United States
Conveners: Dié WANG, Brookhaven National Laboratory, Upton, NY, United States, Gregory Elsaesser, Columbia University/NASA GISS, New York, United States and Duncan Watson-Parris, University of California San Diego, La Jolla, CA, United States
Diffusion-Based Modeling of Extratropical Cyclone Wind and Storm Surge Hazards (1879069)
Sonia Cromp, University of Wisconsin Madison, Madison, WI, United States, Kellie O'Grady, Educator, Earth Sciences, New Rochelle, United States, Grace Kisslinger, University of Georgia, Atmospheric Sciences and Geographic Information Systems, Athens, United States and Allegra N. LeGrande, NASA GISS 568, New York, United States
Leveraging Machine Learning and MCMC to Optimize Community Land Model Parameters for Accurate Gross Primary Productivity Estimates (1882489)
Mr. Sudhanshu Kumar1, Linnia R Hawkins2, Katherine Dagon3, Daniel Kennedy4, Di Tian1, Isabella Lu5 and Sarah Ryu6, (1)Auburn University, Department of Crop, Soil & Environmental Sciences, Auburn, United States, (2)Columbia University, Department of Earth and Environmental Engineering, New York, United States, (3)NSF National Center for Atmospheric Research, Boulder, United States, (4)National Center for Atmospheric Research, Boulder, CO, United States, (5)Columbia University, San Francisco, California, UNITED STATES, (6)University of California Berkeley, Department of Environmental Science, Policy, and Management, Berkeley, United States
Improving Extreme Precipitation Representation with ML Based Warm Microphysics in CESM (1906769)
Addisu Semie1, Brian Medeiros2, Qingyuan Yang1, Gregory Elsaesser3 and Da Fan1, (1)Columbia University, Learning the Earth with Artificial Intelligence and Physics (LEAP), a National Science Foundation (NSF) Science and Technology Center, New York, United States, (2)National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, Boulder, United States, (3)Columbia University/NASA GISS, New York, United States
Assessment of two-source modeling approach to project potential evapotranspiration under elevated CO2 and VPD. (1907105)
Seung Cheol Lee1, Yeonggyun Jeong2, Daeha Kim1 and Minha Choi3, (1)Jeonbuk National University, Department of Civil Engineering, Jeonju-si, South Korea, (2)Jeonbuk National University, Department of Civil Engineering, Jeonju, South Korea, (3)Sungkyunkwan University, School of Civil, Architectural Engineering & Landscape Architecture, Suwon, Gyeonggi-do, South Korea
Towards Model–Observing System Co-Design: An OSSE-Based Bayesian Framework for Prioritizing Earth System Measurements (1925088)
Zhongjing Jiang1,2, Tamanna Subba1, Natalie M Isenberg3,4, Jessica Gasparik1, Nathan M Urban5, Chongai Kuang1 and Dr. Shawn Serbin, BA, MS, PhD1,6, (1)Brookhaven National Laboratory, Environmental Science and Technologies Department, Upton, United States, (2)University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, (3)Pacific Northwest National Laboratory, The Advanced Computing, Mathematics, and Data Division, Richland, United States, (4)Brookhaven National Laboratory, Computing and Data Sciences, Upton, United States, (5)Brookhaven National Laboratory, Applied Mathematics Department, Computing and Data Sciences Directorate, Upton, United States, (6)NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, United States
Assessing Alternate Topographic Boundary Condition Performance in a Calibrated Physics Ensemble (1927711)
Dervla Meegan Kumar, University of California Irvine, Department of Earth System Science, Irvine, CA, United States, Gregory Elsaesser, Columbia University/NASA GISS, New York, United States, Kaitlyn Loftus, Applied Physics Laboratory Johns Hopkins, Laurel, United States, Clara Orbe, NASA Goddard Institute for Space Studies, New York, NY, United States, Jingbo Wu, Columbia University, New York City, United States and Jane Wilson Baldwin, NOAA GFD Lab-Princeton University, Princeton, United States
Variations of soil thermal properties and heat fluxes inferred with uncertainty quantification framework (1934324)
Dongwoon Kang1, Wenbo Zhou, PhD1, Aleksey Y Sheshukov2, Jingfeng Wang3, Gerardo Celis4, Peter Ungar5, Dorothee Ehrich6, Mary Heskel7 and Valeriy Yu Ivanov8, (1)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (2)Kansas State University, Biological and Agricultural Engineering, Manhattan, United States, (3)GA Ins of Tech-Civil & Env Eng, Atlanta, United States, (4)University of Arkansas, Department of Anthropology, Fayetteville, United States, (5)University of Arkansas, Fayetteville, United States, (6)Department of Arctic and Marine Biology, UiT, The Arctic University of Norway, Norway, (7)Macalester College, Biology, St. Paul, United States, (8)University of Michigan, Civil and Environmental Engineering, Ann Arbor, United States
Improvement of Simulating GPP and ET in the Land Surface Model Based on Parameter Sensitivity Analysis and Calibration (1937863)
Hongmei Li1, Wenpeng Xie2, Xiaoyang Li3 and Kei Yoshimura1, (1)The University of Tokyo, Kashiwa, Japan, (2)The University of Tokyo, Institute of Industrial Science, Tokyo, Japan, (3)The University of Tokyo, Chiba, Japan
Improving Biogeochemical Model Reliability Through Bayesian Calibration and Uncertainty Analysis (1941397)
Mahnaz Valipour, Murray State University, Civil and Sustainability Engineering, Murray, KY, United States, Habibollah Fakhraei, Southern Illinois University Carbondale, Civil and Environmental Engineering, Carbondale, IL, United States and Karim Norouzi-Moghanjoghi, University of Bremen, Department of Geosciences, Bremen, Germany
Bayesian Calibration of the DayCent Ecosystem Model to Simulate Evapotranspiration and Reduce Model Uncertainty Using OpenET Products (1957322)
Wenguang Sun, Colorado State University Fort Collins, Beltsville, UNITED STATES, Stephen M Ogle, Colorado State University, Fort Collins, United States and Lauren Hoskovec, Colorado State University, Natural Resource Ecology Laboratory, Fort Collins, United States
Improving the Representation of Evapotranspiration in Process-Based Models: Insights From the Use of Single and Multi-Objective Emulator-Based Calibration on Latent and Sensible Heat (1958723)
Ignacio Aguirre1, Wouter Knoben2, Nicolas Vasquez1 and Martyn P Clark1, (1)University of Calgary, Department of Civil Engineering, Schulich School of Engineering, Calgary, AB, Canada, (2)University of Calgary, Department of Civil Engineering, Schulich School of Engineering, Calgary, Canada
Optimizing Objective Model Calibration approaches using Single Column Models (1960349)
Pappu Paul, University of Illinois Urbana Champaign, Department of Climate, Meteorology & Atmospheric Sciences, Urbana, United States and Cristian Proistosescu, University of Illinois Urbana‐Champaign, Department of Climate, Meteorology & Atmospheric Sciences, Champaign, United States
A quasi-Lagrangian LES-SCM case library of marine cold-air outbreaks for improved extratropical cloud feedbacks in GISS-ModelE3.1 (1968869)
Florian Tornow, Ann M Fridlind, NASA Goddard Institute for Space Studies, New York, NY, United States, Gregory V Cesana, Columbia University of New York, CCSR, New York, United States, George Tselioudis, NASA/GISS, New York, United States, Kaitlyn Loftus, Columbia University, Climate School, New York, United States, Gregory Elsaesser, Columbia University/NASA GISS, New York, United States and Marcus van Lier Walqui, Columbia University, Center for Climate Systems Research, New York, United States; NASA Goddard Institute for Space Studies, New York, United States
Structural error-aware climate model calibration with global, local and zonal climatologies using Perturbed Parameter Ensembles (PPEs) (1973654)
Qingyuan Yang1, Addisu Semie1, Gregory Elsaesser2, Brian Medeiros3 and Da Fan1, (1)Columbia University, Learning the Earth with Artificial Intelligence and Physics (LEAP), a National Science Foundation (NSF) Science and Technology Center, New York, United States, (2)Columbia University/NASA GISS, New York, United States, (3)National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, Boulder, United States
Lessons from a Perturbed Parameter Ensemble driven with an El Nino Southern Oscillation SST Pattern (1982825)
Rachel Tam1, Yi Qin2, Cristian Proistosescu1 and Mark Zelinka3, (1)University of Illinois at Urbana Champaign, Department of Climate, Meteorology & Atmospheric Sciences, Urbana, United States, (2)Pacific Northwest National Laboratory, Richland, United States, (3)Lawrence Livermore National Laboratory, Livermore, CA, United States
Exploring Structural Differences and Parameter Calibration for Microphysics in CESM with Latent Representations using Contrastive Learning Models (1989947)
Da Fan1, David John Gagne II2, Mike S Pritchard3, Brian Medeiros4, Gregory Elsaesser5, Addisu Semie1 and Qingyuan Yang1, (1)Columbia University, Learning the Earth with Artificial Intelligence and Physics (LEAP), a National Science Foundation (NSF) Science and Technology Center, New York, United States, (2)NSF National Center for Atmospheric Research, Computational and Information Systems Lab, Boulder, United States, (3)University California Irvine, Earth System Science, Irvine, United States, (4)National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, Boulder, United States, (5)Columbia University/NASA GISS, New York, United States
Spatial Warping Error Models for Addressing Structural Biases in Aerosol Climate Simulations (1992296)
Mikael Kuusela, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, PA, United States, Erik Bensen, Carnegie Mellon University, Department of Statistics and Data Science, Pittsburgh, United States, Victor Sanchez, Carnegie Mellon University, Chemical Engineering, Pittsburgh, PA, United States and Hamish Gordon, Carnegie Mellon University, Chemical Engineering, Pittsburgh, United States
The Next Generation of NASA Calibrated Physics Ensembles for Climate Projections and Mission Support (1995091)
Marcus van Lier Walqui1,2, Kaitlyn Loftus3,4, Gregory Elsaesser5, Florian Tornow6, Ann M Fridlind7, Susanne Bauer2, Jingbo Wu8, Gavin A Schmidt9 and Andrew S Ackerman2, (1)Columbia University, Center for Climate Systems Research, New York, United States, (2)NASA Goddard Institute for Space Studies, New York, United States, (3)Columbia University, Climate School, New York, United States, (4)Columbia University, New York, United States, (5)Columbia University/NASA GISS, New York, United States, (6)Columbia University of New York, Palisades, United States, (7)NASA Goddard Institute for Space Studies, New York, NY, United States, (8)Columbia University, New York City, United States, (9)NASA/GISS, New York, United States
Calibrated Probabilistic Wind Forecasting with Diffusion Models (1995193)
Qidong Yang1, Jonathan Giezendanner2, Daniel Salles Civitarese3, Johannes Jakubik4, Eric Schmitt5, Anirban Chandra5, Jeremy Vila6, Detlef Hohl5, Christopher N Hill7, Campbell Watson8 and Sherrie Wang2, (1)Massachusetts Institute of Technology, Cambridge, MA, United States, (2)Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Cambridge, United States, (3)IBM Research, Rio de Janeiro, Brazil, (4)IBM Research, Zurich, Switzerland, (5)Shell, Boston, United States, (6)Shell Houston, Houston, United States, (7)MIT, Cambridge, United States, (8)IBM Research, Yorktown Heights, United States
A framework to assess parameter identifiability and observation utility in earth system model calibration (1999245)
Julian Schmitt1, Ricardo Baptista2, Teja Reddy2, Costa Christopoulos3, Oliver Dunbar1, Akshay Sridhar3 and Tapio Schneider4, (1)California Institute of Technology, Pasadena, CA, United States, (2)California Institute of Technology, Pasadena, United States, (3)California Institute of Technology, Environmental Science and Engineering, Pasadena, CA, United States, (4)California Institute of Technology, Environmental Science and Engineering, Pasadena, United States
Deep Contrastive Learning for Microphysics Scheme Comparison in a Perturbed Initial Condition CESM Ensemble (2001023)
Akila Sampath, University of Maryland Baltimore County, Baltimore, MD, United States, Da Fan, Columbia University, Learning the Earth with Artificial Intelligence and Physics (LEAP), a National Science Foundation (NSF) Science and Technology Center, New York, United States and David John Gagne II, NCAR/MMM, Boulder, United States
Towards Adaptive Sensing: Identifying High-Utility Observations from GNSS-R Data with Bayesian Inference (2003366)
Allegra Farrar, Massachusetts Institute of Technology, Aeronautics and Astronautics, Cambridge, MA, United States, Rajeswari Balasubramaniam, University of Michigan Ann Arbor, Ann Arbor, MI, United States, Christopher S Ruf, University of Michigan Ann Arbor, Climate and Space, Ann Arbor, MI, United States, Danielle Wood, Massachusetts Institute of Technology, Department of Media Arts and Sciences; Department of Aeronautics and Astronautics, Cambridge, MA, United States and Andrew Kruczkiewicz, Columbia University of New York, Center for Sustainable Urban Development, New York, United States
Perturbed Parameter Ensemble (PPE) Efforts at NASA GISS (2003956)
Gregory Elsaesser1, Marcus van Lier Walqui2, Kaitlyn Loftus3, Florian Tornow4, Ann M Fridlind5, Susanne Bauer6, Andrew S Ackerman6, Jingbo Wu7 and Gavin A Schmidt8, (1)Columbia University/NASA GISS, New York, United States, (2)Columbia University, Center for Climate Systems Research, New York, United States, (3)Applied Physics Laboratory Johns Hopkins, Laurel, United States, (4)Columbia University of New York, Palisades, United States, (5)NASA Goddard Institute for Space Studies, New York, NY, United States, (6)NASA Goddard Institute for Space Studies, New York, United States, (7)Columbia University, New York City, United States, (8)NASA/GISS, New York, United States
