OS002
AI for Ocean and Climate Change

Session ID#: 162361

Session Description:
World’ health, security, and economic well-being are closely linked to climate and weather. People are looking for information to help them understand climate and make decisions on how to manage climate-related risks and opportunities.

To meet this need, countries are accelerating dissemination of scientific data and information relevant to ocean and climate change science, adaptation, and mitigation. However, climate change is a complex scientific and multifaceted issue, amenable to Machine Learning (ML) and AI analysis, but in general, this has not yet occurred. Many ML algorithms have been available for decades, and possibly most notably neural networks. Until recently, constraints of computational architecture and power have restricted their application, and especially for issues as data-intensive as ocean and climate change. For example we can  identify Gulf Stream rings and mesoscale eddies fronts streamers and biophysical features using AI techniques and Lagrangian coherent structures (LCS) methods

Within this context, we invite contributions focusing on the skillful analysis and prediction of ocean and ecosystem related processes through ML and AI approaches. Contributions on oceans and ecosystem modeling approaches designed to improve forecasting across various temporal scales and understanding sources of uncertainty/error in the model predictions are also welcome. This session includes, but is not limited to, the following topics of interest on data-driven and ML algorithms; data analysis, explainability, and prediction methods and process-based modeling, integration, and fusion.

Index Terms:

0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]
1635 Oceans [GLOBAL CHANGE]
1637 Regional climate change [GLOBAL CHANGE]
4263 Ocean predictability and prediction [OCEANOGRAPHY: GENERAL]

Primary Convener:  Felimon Gayanilo, Texas A & M University Corpus Christi, Corpus Christi, TX, United States
Conveners:  Hassan Moustahfid, NOAA/NOS/US Integrated Ocean Observing System, Silver Spring, United States, Anass El Aouni, GeoStat team, INRIA Bordeaux Sud-Ouest, Talence, France and Avijit Gangopadhyay, Professor of Oceanography, School for Marine Science and Technology University of Massachusetts Dartmouth 836, S. Rodney French Blvd. New Bedford, MA, New Bedford, MA, United States
 
Large Scale Role of Rivers on Coastal Biogeochemistry in the Gulf of Mexico (1041859)
Bailey Armos, Texas A&M University College Station, Oceanography, College Station, TX, United States and Shuang Zhang, Texas A&M University, Department of Oceanography, College Station, United States
 
Using Deep Learning to Forecast Marine Fishery indicators in the North Pacific (1047215)
Giangiacomo Navarra, Georgia Institute of Technology Main Campus, Atlanta, GA, United States
 
Deep Neural Networks for the Prediction of Sediment Accumulation at the Seafloor. (1049959)
Naveenkumar Parameswaran1,2, Everardo Gonzalez1, Ewa Burwicz3, Klaus J G Wallmann1 and Malte Braack2, (1)GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, (2)University of Kiel, Kiel, Germany, (3)MARUM - University of Bremen, Bremen, Germany
 
Detecting Coastal Pumice From The Submarine Volcanic Eruption By Using High-Resolution Satellite Images. (1054071)
Tatsuyuki Sekine, Amanogi, Tokyo, Japan, Kenichi Sasaki, University of Colorado Boulder, Boulder, CO, United States and Yu Kudo, Amanogi Corp., Tokyo, Japan
 
Emerging Trends and Challenges in Data Science and Big Data Analytics for Mitigating Impacts of Ocean and Climate Change (1070578)
Somita Chaudhari, Vidyalankar Institute Of Technology, University of Mumbai, India, Mumbai, India, Pasquale De Toro, Director, Interdepartmental Research Center in Urban Planning ‘Alberto Calza Bini’, The University of Naples ‘Federico II’,Italy, Napoli, Italy, Maria Cerreta, The University of Naples ‘Federico II’,Italy, Professor of Environmental Assessment and Evaluation, Naples, Italy, Francesca Nocca, Researcher, Department of Architecture of the University of Naples Federico II,Italy, Department of Architecture, Naples, Italy and Enza Tersigni, Researcher in Architectural Technology, Università degli Studi di Napoli Federico II, Naples, Department of Architecture, Naples, Italy
 
Satellite Data-driven and Machine Learning-based Internal Wave Forecast Studies (1093106)
Xudong Zhang, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China and X. Li, CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
 
Physical Insights from the Prediction of Atlantic Multidecadal Variability using Explainable Deep Neural Networks (1111011)
Glenn Liu, MIT/WHOI Joint Program in Oceanography, Physical Oceanography Department, Woods Hole, United States, Peidong Wang, Massachusetts Institute of Technology, Cambridge, United States and Young-Oh Kwon, Woods Hole Oceanographic Institution, Woods Hole, United States
 
Sea-Level Projections via Spatiotemporal Deep Learning from Altimetry and CESM Large Ensembles (1119047)
Saumya Sinha, University of Colorado Boulder, Boulder, CO, UNITED STATES, Claire Monteleoni, University of Colorado Boulder, Computer Science, Boulder, CO, United States, John Fasullo, NCAR, Boulder, CO, United States and R Steven Nerem, Smead Aerospace Engineering Sciences, Colorado Center for Astrodynamics Research, University of Colorado Boulder, Boulder, CO, United States
 
Exploring the long term changes in the Indian Ocean storm surges using machine learning (1125013)
Sreeraj Puthiyadath, Indian Institute of Tropical Meteorology, Pune, India
 
Artificial Intelligence in Support of Coastal and Ocean Resilience to Climate Change (1134666)
Hassan Moustahfid, NOAA/NOS/US Integrated Ocean Observing System, Silver Spring, United States, Dr. Harriet Nash, NOAA/NOS/Coral Reef Conservation Program, Silver Spring, United States, Neil Weston, NOAA/NOS/Coast Survey Development Lab, Silver Spring, United States, Brandon Krumwiede, National Operational Hydrologic Remote Sensing Center, Chanhassen, United States, Lindsay Abrams, NOAA/NOS/CO-OPS/OD, Silver Spring, United States, Felipe Arzayus, NOAA/NOS/National Centers for Coastal Ocean Science, Silver Spring, United States, Kevin Ahlgren, NOAA's National Geodetic Survey, Silver Spring, MD, United States, Chen Zhang, NOAA Office of Coast Survey., Silver Spring, United States, Ryan Freedman, NOAA National Ocean Service, Channel Islands National Marine Sanctuary, Santa Barbara, CA, United States, Robb Wright, NOAA Office of Response and Restoration, Silver Spring, United States, Michael Schwatz, NOAA Coastal Change and Analysis Program at the Office for Coastal Management, Charleston, United States, Bill McMullen, NOAA NOS CTO, Silver spring, United States, Greg Dusek, NOAA Camp Springs, Camp Springs, MD, United States and James Spore, NOAA CO-OPS, Silver Spring, United States
 
Reconstruction of global daily Chlorophyll-a products from multisource information using a convolutional neural network (1135409)
Zhongkun Hong, Di Long and Xingdong Li, Tsinghua University, Department of Hydraulic Engineering, Beijing, China
 
Unsupervised clustering of Lagrangian trajectories in the Labrador Current (1141454)
Noémie Planat, McGill University, Montreal, QC, Canada, Mathilde Jutras, McGill University, Department of Earth and Planetary Sciences, Montreal, QC, Canada and Carolina O. Dufour, Princeton University, Program in Atmospheric and Oceanic Sciences, Princeton, United States
 
Estimation Of Chlorophyll Concentration Using Machine Learning Model (1145529)
Deepanshu Malik, Shouvik Dey, Rahul Deogharia and Sourav Sil, Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
 
Predicting Future Sea Level from Satellite Altimetry (1145601)
Mads Ehrhorn Kjær, Ole Baltazar Andersen and Carsten Ankjær Ludwigsen, DTU Space, Lyngby, Denmark
 
Random Forest-based Understanding of Earth System Model Predictions of Phytoplankton Diatom (1150996)
Sandupal Dutta, Johns Hopkins University, Baltimore, MD, United States, Anand Gnanadesikan, Johns Hopkins Univ-EPS, Baltimore, MD, United States and Christopher Holder, Johns Hopkins University, Department of Earth and Planetary Sciences, Baltimore, MD, United States
 
Predicting Ocean States at the North Atlantis II Seamounts with a Deep Learning Model (1153302)
Kerstin Cullen, US Naval Research Laboratory, Washington, DC, United States and Adam Rydbeck, U.S. Naval Research Laboratory, Stennis Space Center, United States
 
Applying Dynamical Adjustment Techniques to Quantify Contributions of Variability to North Atlantic Sea Surface Temperature (1156532)
Douglas Nedza and Timothy M DelSole, George Mason University Fairfax, Fairfax, VA, United States
 
Predictive Analysis of Climate Change Implications from Legacy ADCP Data using Ensemble Learning (1161957)
Soumyashree Kar1, Katharine Weathers2, Adrienne Copeland3, Vishwamithra Sunkara4, Jason McKenna4 and Philip L. Hoffman5, (1)University of Southern Mississippi, Ocean Enterprise, Stennis Space Center, MS, United States, (2)NOAA, Hancock County, United States, (3)NOAA Ocean Exploration, Silver Spring, MD, United States, (4)University of Southern Mississippi, Ocean Enterprise, Gulfport, United States, (5)NOAA Research, Office of Policy, Planning and Evaluation, Silver Spring, MD, United States
 
A quasi-circumpolar super gyre modulates the global overturning through upwelling in the Southern Ocean (1174862)
Maike Sonnewald, Princeton University, Princeton, NJ, United States
 
Using Deep Learning for Climate Tipping Point Discovery to Understand Atlantic Meridional Overturning Circulation (AMOC) Collapse (1179120)
Jennifer A. Sleeman1, David Chung2, Chace Ashcraft2, Anshu Saksena2, G. Jay Brett3, Marisa Hughes1, Anand Gnanadesikan4, Yannis Kevrekidis5, Marie-Aude Sabine Pradal6, Thomas W N Haine7 and Renske Gelderloos8, (1)Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (2)Johns Hopkins University Applied Physics Laboratory, Laurel, United States, (3)Applied Physics Laboratory Johns Hopkins, Laurel, United States, (4)Johns Hopkins Univ-EPS, Baltimore, MD, United States, (5)Johns Hopkins Univ, Chemical and Biomolecular Engineering, Baltimore, United States, (6)Johns Hopkins Univ, Baltimore, United States, (7)Johns Hopkins Univ, Baltimore, MD, United States, (8)Johns Hopkins University, Baltimore, MD, United States
 
EXAMINING EL NINO–SOUTHERN OSCILLATION (ENSO) IN AN ENSEMBLE VIEW ON THE ARAB NATIONS UTILIZING DIGITAL SIGNAL PROCESSING FROM 2010-2020 (1180591)
Sachithra Perera1, Mohamed Allali2, Hesham Mohamed El-Askary3, Rejoice Thomas4, Nikolay Todorov5 and Shahryar Fazli4, (1)Chapman University, Computational and Data Sciences Graduate Program, Schmid College of Science and Technology, Orange, United States, (2)Chapman University, Orange, CA, United States, (3)Chapman Univ, Orange, United States, (4)Chapman University, Schmid College of Science and Technology, Orange, CA, United States, (5)Chapman University, Computational and Data Sciences Graduate Program, Schmid College of Science and Technology, Chapman University, Orange, California 92866, USA, Orange, CA, United States
 
Automated Geospatial Model Development to find Coral Bleaching Spatial Vulnerability in Gulf of Mexico. (1187396)
Sudhanshu Sekhar Panda, University of North Georgia, Institute for Environmental Spatial Analysis, Oakwood, United States, Devendra Amatya, USDA Forest Service, Center for National Forested Wetlands Research, Cordesville, United States and Victoria H Oh, University of North Georgia, Biology, Oakwood, United States
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