White Papers

White papers were solicited from the scientific community to focus on development and application of AI methods in areas relevant to EESSD research with an emphasis on quantifying and improving Earth system predictability, particularly related to the integrative water cycle and associated water cycle extremes. The white papers describe innovative approaches to improving the predictability of the Earth system. We expect that a novel framework, derived from white paper concepts and a series of workshops, will improve capabilities for knowledge capture and distillation that provide future computational constructs across the EESSD research enterprise. We anticipate that exascale computing, edge computing, 5G/6G, and use of quantum computing and quantum sensors will be further developed during this period. View the call for white papers (PDF)

Number Title Author DOI
AI4ESP1001 A Spatiotemporal Sequence Forecasting Platform to Advance the Predictionof Changing Spatiotemporal Patterns of CO2 Concentrationby Incorporating Human Activity and Hydrological Extremes Melissa R. Allen-Dumas 10.2172/1769653
AI4ESP1002 Robust data-driven uncertainty quantification in water cycle extreme predictions Gemma J. Anderson 10.2172/1769775
AI4ESP1003 A Multi-Scale Inference, Estimation, and Prediction Engine for Earth System Modeling Marian Anghel 10.2172/1769648
AI4ESP1004 Open-source AI-ready data for prediction of coastal water and carbon budgets under a changing climate Bhavna Arora 10.2172/1769758
AI4ESP1005 Event-scale predictions of water and nitrogen exports in coastal watersheds Bhavna Arora 10.2172/1769706
AI4ESP1006 Predictive Understanding of Compound and Cascading Extremes and Their Impacts Moetasim Ashfaq 10.2172/1769764
AI4ESP1007 Leveraging machine learning to improve understanding and predictability of weather/climate extremesand the resilience of human systems Karhik Balaguru 10.2172/1769737
AI4ESP1008 AI-enabled MODEX and edge-computing over 5G for improving the predictability of water cycle extremes Prasanna Balaprakash 10.2172/1769672
AI4ESP1010 Interpretable Deep Learning for the Earth System with Fractal Nets Carolyn Begeman 10.2172/1769730
AI4ESP1011 Characterization of Extreme Hydroclimate Events in Earth System Models using ML/AI Katrina E. Bennett 10.2172/1769685
AI4ESP1012 Past and Future Trends of Severe Storms Emily Bercos-Hickey 10.2172/1769759
AI4ESP1014 Enhanced prediction of terrestrial feedbacks to the coastal carbon cycle:using machine learning to improve sub-grid biogeochemical processes Nick Bouskill 10.2172/1769704
AI4ESP1015 EAM-HLR: Enhancing the low-resolution E3SM Atmosphere Modelwith an ML model of high-low-resolution residual in convective processes Andrew M. Bradley 10.2172/1769697
AI4ESP1016 AI-Improved Resolution Projections of Population Characteristics and Imperviousness Can Improve Resolution and Accuracy of Urban Flood Predictions Christa Brelsford 10.2172/1769673
AI4ESP1017 Transforming ESM Physical Parameterization Development Using Machine Learning Trained on Global Cloud-Resolving Models and Process Observations Christopher S. Bretherton 10.2172/1769790
AI4ESP1018 Elucidating and predicting the dynamic evolution of water and land systems due to natural and energy-related forcings Grant Bromhal 10.2172/1769701
AI4ESP1019 Learning from learning machines machinesimproving the predictive power of energy-water-land nexus modelswith insights from complex measured and simulated data James Bentley Brown 10.2172/1769736
AI4ESP1020 Climate Intervention Assessment andAttributionFebruary Diana Bull 10.2172/1769687
AI4ESP1021 AI-Assisted Parameter Tuning Will Speed Development and Clarify Uncertainty in E3SM Peter M. Caldwell 10.2172/1769663
AI4ESP1022 Building Intelligent Cyberinfrastructure to Learn Iteratively from both Observations and Models for Understanding Watershed Dynamics Xingyuan Chen 10.2172/1769684
AI4ESP1023 Upscaling cross-scale flow and respiration interactions at river sediment interface leveraging observation, numerical models, and machine learning Yunxiang Chen 10.2172/1769792
AI4ESP1024 Enhancing Resilience of Urban Systems Against Climate-Induced Floods Using Advanced Data-Driven and Computing Techniques: A Driver-Pressure-State-Impact-Response (DPSIR) Framework Gyan Chhipi-Shrestha 10.2172/1769705
AI4ESP1025 Integrating Models with Real-time Field Data for Extreme Events: From Field Sensors to Models and Back with AI in the Loop Shreyas Cholia 10.2172/1769727
AI4ESP1026 Model Hierarchy for Mountainous Hydrological Observatories (MH2O) William D. Collins 10.2172/1769748
AI4ESP1027 Tracking Extremes in Exascale Simulations Utilizing Exascale Platforms William D. Collins 10.2172/1769788
AI4ESP1028 Framework for an adaptive integrated observation system using a hierarchy of machine learning approaches Jennifer Comstock 10.2172/1769712
AI4ESP1029 An AI-Assisted Approach to Represent Human Influence on Surface and Subsurface Hydrology Ethan Coon 10.2172/1769674
AI4ESP1129 Machine-Learning-Assisted Hybrid Earth System Modelling Istvan Szunyogh 10.2172/1769745
AI4ESP1030 A library of AI-assisted FAIR water cycle and related disturbancedatasets to enable model training, parameterization and validation Robert Crystal-Ornelas 10.2172/1769646
AI4ESP1031 Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded AI Baptiste Dafflon 10.2172/1769774
AI4ESP1032 Machine learning to extend and understand the sources and limits of water cycle predictability on subseasonal-to-decadal timescales in the Earth system Katherine Dagon 10.2172/1769744
AI4ESP1033 AI-Driven Data Discovery to Improve Earth System Predictability Ranjeet Devarakonda 10.2172/1769671
AI4ESP1034 Semi-automated Design of Artificial Intelligence Earth Systems Models Philipe Dias 10.2172/1769777
AI4ESP1035 Using AI to build a hydrobiogeochemical soil model Beth A. Drewniak 10.2172/1769793
AI4ESP1036 AI for Extreme Volcanic Climate Forcing and Feedback Forecasting in the 21st century Manvendra Dubey 10.2172/1769659
AI4ESP1037 Knowledge-Guided Machine Learning (KGML) Platform to Predict Integrated Water Cycle and Associated extremes Dipankar Dwivedi 10.2172/1769733
AI4ESP1038 A Modular System for ​Increasing Predictiveness for Extreme Climate Predictions Christopher Rakauckas 10.2172/1769647
AI4ESP1039 Jaynesian Analysis of Environmental Chemistry: Systems Model Component Integration via the Arctic Aquatic Carbon Cycle Scott Elliott 10.2172/1769731
AI4ESP1040 Rapid assimilation and analysis of a suit of remote sensing data for predicting extreme events and their impact on ecological-human systems Nicola Falco 10.2172/1769770
AI4ESP1041 Develop a weather-aware climate model to understand and predict extremes and associated power outages and renewable energy shortageswith uncertainty-aware and physics-informed machine learning Jiwen Fan 10.2172/1769695
AI4ESP1042 On AI Prediction of Hydrological Processes Based on Integration of Retrospective and Forecasting ML Techniques Boris Faybishenko 10.2172/1769756
AI4ESP1043 Reliable modeling and prediction of precipitation & radiation for mountainous hydrology Daniel Feldman 10.2172/1769771
AI4ESP1044 Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks  Yan Feng 10.2172/1769686
AI4ESP1045 Land Surface Modeling 2.0 for agricultural climate change impact assessments James A. Franke 10.2172/1769734
AI4ESP1046 A Grand Challenge "Uncertainty Project" to Accelerate Advances in Earth System Predictability: AI-Enabled Concepts and Applications Ann Fridlind 10.2172/1769643
AI4ESP1047 Science-integrated Artificial-intelligence for Flooding and precipitation Extremes (SAFE) Auroop R. Ganguly 10.2172/1769776
AI4ESP1048 Deep Learning for Ensemble Forecasting Andrew Geiss 10.2172/1769692
AI4ESP1049 Toward Hybrid Physics -Machine Learning to improve Land Surface Model predictions Mangistu (Stu) Geza 10.2172/1769785
AI4ESP1050 Geophysical Retrievals in an Artificial Intelligence (AI) Framework for Illuminating Processes Controlling Water Cycle Virendra P. Ghate 10.2172/1769714
AI4ESP1051 AI Automated Discovery of New Climate Water System Knowledge from Models and Observations André Goncalves 10.2172/1769658
AI4ESP1052 Autonomousreinforcement learning agentsfor improvingpredictions and observations of extreme climate events André Goncalves 10.2172/1769680
AI4ESP1053 Data-Driven Exploration of Climate Attractor Manifolds For Long-Term Predictability Carlo Graziani 10.2172/1769691
AI4ESP1054 Feature Detection Yawen Guan 10.2172/1769711
AI4ESP1055 Modeling Noise: Paths toward AI-Enabled Stochastic Earth System Models and Parameterizations Samson Hagos 10.2172/1769749
AI4ESP1056 Making Atmospheric Convective Parameterizations Obsolete with Machine Learning Emulation Walter Michael Hannah 10.2172/1769746
AI4ESP1057 The Usage of Observing System Simulation Experiments and Reinforcement Learning to Optimize Experimental Design and Operation Joseph C. Hardin 10.2172/1769782
AI4ESP1058 Machine Learned Radiative Transport for Enhanced Resolution Earth System Modeling Benjamin Hillman 10.2172/1769738
AI4ESP1059 Integrating AI with physics-based hydrological models and observations for insightinto changing climate and anthropogenic impacts Ben R. Hodges 10.2172/1769725
AI4ESP1060 AI-Constrained Bottom-Up Ecohydrology and Improved Prediction of Seasonal, Interannual, and Decadal Flood and Drought Risks Forrest M. Hoffman 10.2172/1769668
AI4ESP1061 Deep learning techniques to disentangle water use efficiency, climate change, and carbon sequestration across ecosystem scales1 Jennifer A. Holm 10.2172/1769694
AI4ESP1062 Multi-scale Multi-physics Scientific Machine Learning for Water Cycle Extreme Events Identification, Labelling, Representation, and Characterization Zhangshuan (Jason) Hou 10.2172/1769751
AI4ESP1063 Subseasonal-to-seasonal Prediction of Atmospheric Rivers in the Western United States Huanping Huang 10.2172/1769780
AI4ESP1064 The use of soil moisture and Standardized Evaporative Stress Ratio (SESR) anomalies for increased lead time of the development flash drought and heat waves Eric Hunt 10.2172/1769783
AI4ESP1065 Towards Trustworthy and Interpretable Deep Learning-assisted Ecohydrological Models Peishi Jiang 10.2172/1769787
AI4ESP1066 Combining artificial intelligence, Earth observations, and climate models to improve predictability of ice-biogeochemistry interactions Grace E. Kim 10.2172/1769689
AI4ESP1068 Improving Short Term Predictability of Hydrologic Models with Deep Learning Ryan King 10.2172/1769722
AI4ESP1069 Advancing the Predictability of Water Cycle Phenomena via the Application of AI to Model Ensemble Simulations and Observations Stephen A. Klein 10.2172/1769656
AI4ESP1070 Multisensor Agile Adaptive Sampling of Convective Storms Driven by Real-time Analytics Pavlos Kollias 10.2172/1769753
AI4ESP1071 Modular hybrid modeling to increase efficiency, explore structural uncertainty, andallow multidimensional complexity scaling in land surface models. Charles Koven 10.2172/1769750
AI4ESP1072 End-to-End Differentiable Modeling and Management of the Environment Christopher Krapu 10.2172/1769703
AI4ESP1073 Representing the Unrepresented Impact of River Ice on Hydrology, Biogeochemistry, Vegetation, and Geomorphology: A Hybrid Physics-Machine Learning Approach Jitendra Kumar 10.2172/1769772
AI4ESP1074 In Situ Inference for Earth System Predictability Earl Lawrence 10.2172/1769723
AI4ESP1075 Toward the Development of New Parameterizations for Surface Fluxes Temple R. Lee 10.2172/1769786
AI4ESP1076 Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events Lai-Yung (Ruby) Leung 10.2172/1769761
AI4ESP1077 Deep Learning for Hydro-Biogeochemistry ProcessesFebruary Li Li 10.2172/1769693
AI4ESP1078 A Self-Evolution Data Fusion Platform for Large-Scale Water Models Xinya Li 10.2172/1769652
AI4ESP1079 Structurally flexible cloud microphysics, observationally constrained at all scales via ML-accelerated Bayesian inference Marcus Van Lier-Walqui 10.2172/1769779
AI4ESP1080 Building an AI-enhanced modeling framework to address multiscale predictability challenges Yangang Liu 10.2172/1769683
AI4ESP1081 A Bayesian Neural Network Ensemble Approach for Improving Large-Scale Streamflow Predictability Dan Lu 10.2172/1769641
AI4ESP1082 An AI-Enabled MODEX Framework for Improving Predictability of Subsurface Water Storage across Local and Continental Scales Dan Lu 10.2172/1769675
AI4ESP1083 Advancing Regional Climate Predictability through ML-enabled Dynamical System Approach Jian Lu 10.2172/1769654
AI4ESP1084 Machine Learning for Surrogate Modeling of the Upper Ocean and HeatExchange Between the Ocean and Atmosphere Nicholas Lutsko 10.2172/1769742
AI4ESP1085 Facilitating better and faster simulations of aerosol-cloud interactions in Earth system models Po-Lun Ma 10.2172/1769709
AI4ESP1086 Assessing Teleconnections-Induced Predictability of Regional Water Cycle on Seasonal to Decadal Timescales Using Machine Learning Approaches Salil Mahajan 10.2172/1769676
AI4ESP1087 Identifying precursors of daily to seasonal hydrological extremes over the USA using deep learning techniques and climate model ensembles Nicola Maher 10.2172/1769719
AI4ESP1088 Separating Climate Signals with Machine Learning Ankur Mahesh 10.2172/1769778
AI4ESP1089 AI-Based Integrated Modeling and Observational Framework for Improving Seasonal to Decadal Prediction of Terrestrial Ecohydrological Extremes Jiafu Mao 10.2172/1769666
AI4ESP1090 Surrogate multi-fidelity data and model fusion forscientific discovery and uncertainty quantification inEarth System Models Romit Maulik 10.2172/1769781
AI4ESP1091 Trustworthy AI for Extreme Event Prediction and Understanding Amy McGovern 10.2172/1769791
AI4ESP1092 Computationally Tractable High-Fidelity Representation of Global Hydrol-ogy in ESMs via Machine Learning Approaches to Scale-Bridging Richard Tran Mills 10.2172/1769690
AI4ESP1093 Rethink hydrologic modeling framework with AI integrating multi-processes across scales Eugene Yan 10.2172/1769773
AI4ESP1094 New Understanding of Cloud Processes via Unsupervised Cloud Classification in Satellite Images Elisabeth J. Moyer 10.2172/1769754
AI4ESP1095 EdgeAI: How to Use AI to Collect Reliable and Relevant Watershed Data Maruti Kumar Mudunuru 10.2172/1769700
AI4ESP1096 Machine Learning for Adaptive Model Refinement to Bridge Scales Juliane Mueller 10.2172/1769741
AI4ESP1097 Machine Learning to Enable Efficient Uncertainty Quantification, Data Assimilation, and Informed Data AcquisitionAuthorsJuliane Juliane Mueller 10.2172/1769743
AI4ESP1098 Co-Evolving Climate Models under Uncertainty to Improve Predictive Skill Balu T. Nadiga 10.2172/1769688
AI4ESP1099 A Fire Community Observatory: Interdisciplinary, AI-informed Post-Fire Rapid Response for Improved Water Cycle Science at Watershed Scale Michelle E. Newcomer 10.2172/1769642
AI4ESP1100 Physics-Informed Deep Learning for Multiscale Water Cycle Prediction Brenda Ng 10.2172/1769760
AI4ESP1101 AI-Directed Adaptive Multifidelity Modeling of Water Availability and Quality at River Basin Scales Scott L. Painter 10.2172/1769669
AI4ESP1102 Integration of AI/ML with Data Assimilation for Earth System Prediction2 Stephen G. Penny 10.2172/1769728
AI4ESP1103 Hybrid (PDE+ML) models in the context of land icemodeling Mauro Perego 10.2172/1769717
AI4ESP1104 Advancing Sea Ice Predictability in E3SM with Machine Learning Kara Peterson 10.2172/1769655
AI4ESP1105 FAIR data infrastructure and tools for AI-assisted streamflow prediction  Line Pouchard 10.2172/1769710
AI4ESP1106 Water Cycle-Driven Infectious Diseases as Multiscale, Reliable, Continuously Updating Water Cycle Sensors Amy Powell 10.2172/1769797
AI4ESP1107 AI-Based Upgrades to Observatories Enabling Data Interoperability Giri Prakash 10.2172/1769667
AI4ESP1108 Early detection and uncertainty quantification of rapid sea-level rise from Antarctica Stephen Price 10.2172/1769698
AI4ESP1109 Machine learning and artificial intelligence for wildfire prediction James Tremper Randerson 10.2172/1769739
AI4ESP1110 Probabilistic Machine Learning and Data Assimilation Vishwas Rao 10.2172/1769766
AI4ESP1111 AI-Based Approach for Advancing the Understanding of Spatiotemporal Drought Characteristics Deeksha Rastogi 10.2172/1769665
AI4ESP1112 Predictabilityand feedbacks of the ocean-soil-plant-atmosphere water cycle: deep learning water conductance in Earth System Model Alexandre A. Renchon 10.2172/1769763
AI4ESP1113 Title:Machine learning to generate gridded extreme precipitation data sets for global land areas with limited in situ measurements Mark Risser 10.2172/1769784
AI4ESP1114 Integrating Applied Energy and BER Smart Data Capabilities to Develop a DOE Data Fabric for Energy-Water R&D  Kelly Rose 10.2172/1769726
AI4ESP1115 GANpiler Barry Rountree 10.2172/1769713
AI4ESP1116 Transfer Operator Framework for Earth System Predictability and Water Cycle Extremes Adam Rupe 10.2172/1769789
AI4ESP1117 Earth System Model Improvement Pipeline via Uncertainty Attribution and Active Learning Khachik Sargsyan 10.2172/1769699
AI4ESP1118 A new era of observationally-infused E3SM: GANs for unifying imagery archives Jon Schwenk 10.2172/1769649
AI4ESP1119 AI to Automate ModEx for Optimal Predictive Improvement and Scientific Discovery Shawn P. Serbin 10.2172/1769662
AI4ESP1120 Integrative data-driven approaches for characterization & prediction of aerosol-cloud processes Lyndsay Shand 10.2172/1769729
AI4ESP1121 Integrated parameter and process learning for hydrologic and biogeochemical modules in Earth System Models Chaopeng Shen 10.2172/1769724
AI4ESP1122 Improved Understanding of Coupled Water and Carbon Cycle Processes through Machine Learning Approaches Debjani Sihi 10.2172/1769721
AI4ESP1123 AI predicted shifts in watershed hydrodynamics driven by extreme weather and fire Erica Siirila-Woodburn 10.2172/1769660
AI4ESP1124 Automated Discovery of DOminaNt physics Informed Surrogates (ADDONIS) Framework for Improving Water Cycling Predictability Kenneth (Chad) Sockwell 10.2172/1769678
AI4ESP1125 Preferential flow in subsurface hydrology: From a century of denial to a decade of addressing it via ML? Matthias Sprenger 10.2172/1769765
AI4ESP1126 On Demand Machine Learning for Multi-Fidelity Biogeochemistry in River Basins Impacted by Climate Extremes Carl I Steefel 10.2172/1769757
AI4ESP1127 Emergent Concepts from a Community Ideation on AI4ESP James Stegen 10.2172/1769702
AI4ESP1128 Bridging Multiscale Processes in Earth System Models with Physics-Guided Hierarchical Machine Learning Alexander Y. Sun 10.2172/1769682
AI4ESP1130 Using machine learning and artificial intelligence to improve model-data integrated earth system model predictions of water and carbon cycle extremes Jinyun Tang 10.2172/1769794
AI4ESP1131 Machine Learning for a-posteriori model-observed data fusion to enhance predictive value of ESM output Claudia Tebaldi 10.2172/1769740
AI4ESP1132 Learned implicit representations of aerosol chemistry and physics for enhancing the predictability of water cycle extreme events Christopher W. Tessum 10.2172/1769735
AI4ESP1133 Physics-Informed Machine Learning from Observations for Clouds, Convection, and Precipitation Parameterizations and Analysis Paul Ullrich 10.2172/1769762
AI4ESP1134 Black-Box Neural System Identificationand Differentiable Programmingto Improve Earth System Model PredictionsFebruary Nathan Urban 10.2172/1769681
AI4ESP1135 Using Machine Learning to Develop a Predictive Understanding of the Impacts of Extreme Water Cycle Perturbations on River Water Quality Charuleka Varadharajan 10.2172/1769795
AI4ESP1136 Observational Capabilities to Capture Water Cycle Event Dynamics and Impacts in the Age of AI Charuleka Varadharajan 10.2172/1769755
AI4ESP1137 Using machine learning to improve land use/cover characterization and projection for scenario-based global modeling Alan Di Vittorio 10.2172/1769796
AI4ESP1138 A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories Nathalie Voisin 10.2172/1769651
AI4ESP1139 AI-Driven Cross-Domain Knowledge Discoveryand Hypotheses Generation for Enhanced Earth System Predictability Svitlana Volkova 10.2172/1769670
AI4ESP1140 Automated Custom Calibration for E3SM Benjamin M. Wagman 10.2172/1769677
AI4ESP1141 Development of Explainable, Knowledge-Guided AI Models to Enhance the E3SM Land Model Development and Uncertainty Quantification Dali Wang 10.2172/1769696
AI4ESP1142 A Hybrid Climate Modeling System Using AI-assisted Process Emulators Jiali Wang 10.2172/1769645
AI4ESP1143 Exploring variability in seasonal average and extreme precipitation using unsupervised machine learning. Michael Wehner 10.2172/1769708
AI4ESP1144 High-Accuracy Module Emulators from Physically-Constrained AI Algorithms Anthony S. Wexler 10.2172/1769715
AI4ESP1145 Quality Data Essential for Modeling Water Cycles Effectively John Wu 10.2172/1769769
AI4ESP1146 How AI Predicts the Untrained and Unseen Yuxin Wu 10.2172/1769716
AI4ESP1147 Process-based Neural Network to Forecast Vegetation Dynamics Chonggang Xu 10.2172/1769768
AI4ESP1148 A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales Haowen Xu 10.2172/1769644
AI4ESP1149 Mapping hydrologic and biogeochemical information flows to improve predictive modelsand understand climate influence Zexuan Xu 10.2172/1769747
AI4ESP1150 Multiscale Reduced Order Modeling and Parameter Estimation for Climate Sciences Arvind Mohan 10.2172/1769752
AI4ESP1151 AI Scaling Laws for Extremes (AISLE) Da Yang 10.2172/1769661
AI4ESP1152 Process Discovery through Assimilation of Complex Biogeochemical Datasets Mavrik Zavarin 10.2172/1769767
AI4ESP1153 AI as a Bridge between ARM Observationsand E3SM for improving Clouds and Precipitation Yunyan Zhang 10.2172/1769657
AI4ESP1154 Improve wildfire predictability driven by extreme water cycle with interpretable physically-guided ML/AI Qing Zhu 10.2172/1769720
AI4ESP1155 Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)  Yufei Zou 10.2172/1769718
AI4ESP1013 AI-Automated Detection of Subgrid-scale Processes for Adaptivity Guidance Julie Bessac 10.2172/1769664
AI4ESP1156 Represent precipitation-induced geological hazards in Earth system models using artificial intelligence Zeli Tan 10.2172/1784543