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Understanding Climate Change from Data Fourth Workshop on Understanding Climate Change from Data The Fourth Annual Meeting of NSF Expeditions in Computing Award # 1029711 June 30- July 2, 2014 Workshop Venue: National Center for Atmospheric Research Mesa Labs Boulder, CO www.climatechange.cs.umn.edu 1 2 Understanding Climate Change from Data June 30 – July 2, 2014 Table of Contents Table of Contents pg 3 Final Program Schedule pg 5 Abstracts, June 30, 2014 Presenters pg 9 Abstracts, July 1, 2014 Presenters pg 13 Abstracts, July 2, 2014 Presenters pg 19 Panel Discussion: Data Science and Climate Science: Narrowing Gaps pg 20 Invited Participant Bios pg 21 Expeditions in Computing Team pg 31 Poster Session pg 41 Attendee Contact Information pg 57 3 4 Agenda: Fourth Workshop on Understanding Climate Change from Data Monday, June 30, 2014 pg 11:00 Registration opens, lunch begins- catered at Mesa Labs 12:20 Welcoming Remarks Session 1 Chair: Doug Nychka 9 12:30 James Hurrell, National Center for Atmospheric Research KEYNOTE: Climate Predictions and Projections in the Coming Decades: Uncertainty due to Natural Variability Vipin Kumar, University of Minnesota 1:05 Introduction to the NSF Expeditions in Computing on Understanding Climate Change: A Data Driven Approach 1:40 Auroop Ganguly, Northeastern University Informing Climate Adaptation with Big Data and Bigger Models 1:55 Break Session 2 Chair: William Hendrix 10 2:30 Noah Diffenbaugh, Stanford University Quantifying the influence of global warming on the likelihood of unprecedented extreme climate events 2:55 Arindam Banerjee, University of Minnesota Estimating High-Dimensional Dependencies: Applications to Multi-task Learning for Combining Climate Model Outputs 3:10 Timothy DelSole, George Mason University Using Climate Models to Constrain Learning Algorithms 3:35 Break Session 3 Chair: Raju Vatsavai 11 3:55 James Faghmous, University of Minnesota Monitoring Mesoscale Ocean Eddies From Space: A Theory-Guided Data Mining Perspective 4:10 Stefan Liess, University of Minnesota Different modes of variability over the Tasman Sea 4:25 Nagiza Samatova, North Carolina State University Modulatory Networks for Climate Extremes 4:40 Networking 5:00 Poster Session & Dinner, catered, at Mesa Labs 5 Tuesday, July 1, 2014 13 8:50 Registration opens Session 4 Chair: Auroop Ganguly 13 9:00 Warren Washington, National Center for Atmospheric Research KEYNOTE: Future Development of Climate and Earth System Models and Their Data Needs 9:35 Dimitris Giannakis, New York University Extraction and predictability of Madden-Julian oscillation signals in infrared brightness temperature data 10:00 Clara Deser, National Center for Atmospheric Research Unforced versus forced climate trends over North America 10:25 Break Session 5 Chair: Abdollah Homaifar 14 11:00 Ghassem R Asrar, University of Maryland The Role of Data in Integrated Human-Earth Systems Modeling and Assessment 11:25 Ramakrishna Nemani, NASA Ames Research Center NASA Earth Exchange (NEX): Collaborative computing for global change science 11:50 Forrest M. Hoffman, Oak Ridge National Laboratory and University of California - Irvine Representativeness-based Sampling Network Design for NGEE and Identifying Phenoregions for the Conterminous U.S. 12: 15 Lunch Break, NCAR Cafeteria Session 6 Chair: Sucharita Gopal 15 1:45 Nikunj C. Oza, NASA Ames Research Center Data Mining for Earth Science at NASA 2:10 Abdollah Homaifar, North Carolina Agricultural & Technical University Multiple linear trend analysis for non-stationary climatic time series 2:25 Alison Baker, National Center for Atmospheric Research Evaluating the Impact of Data Compression on Climate Simulation Data 2:50 Break Session 7 Chair: Scott Sellars 16 3:20 Bala Rajaratnam, Stanford University A Methodology for Robust Multiproxy Paleoclimate Reconstructions 3:45 Wei Ding, University of Massachusetts Boston A Data-Driven Machine Learning Framework for Long-Lead Flood Forecasting 4:10 Ansu Chatterjee, University of Minnesota A study of mixed-source variability and dependence in precipitation data over India 4:25 Richard (Ricky) Rood, University of Michigan Ann Arbor Climate Informatics: Human Experts and the End-to-End System 4:50 Reception, light refreshments 6 Wednesday, July 2, 2014 19 8:50 Registration opens Session 8 Chair: James Faghmous 19 Kevin Trenberth, National Center for Atmospheric Research 9:00 KEYNOTE: Climate change: It’s about the data isn’t it? Imme Ebert-Uphoff, Colorado State University 9:35 Weakening of atmospheric information flow in a warming climate - preliminary results 10:00 Break Panel Discussion: Data Science and Climate Science: Narrowing Gaps Moderator: Doug Nychka Panelists: Laurence Buja, National Center for Atmospheric Research 10:30 Alicia Karspeck, National Center for Atmospheric Research 20 Richard Loft, National Center for Atmospheric Research Linda Mearns, National Center for Atmospheric Research Srini Parthasarthy, Ohio State University Stephen Sain, National Center for Atmospheric Research Shashi Shekhar, University of Minnesota Claudia Tebaldi, National Center for Atmospheric Research 12:40 Closing remarks 7 8 Presentations, Session 1, June 30 James Hurrell – National Center for Atmospheric Research Keynote speaker, Title: Climate Predictions and Projections in the Coming Decades: Uncertainty due to Natural Variability A grand challenge problem is the prediction and projection of the consequences of natural and anthropogenic climate variability and change at regional and global scales. This includes understanding the impacts of climate change on the water cycle, water availability, weather extremes, and the health and functioning of marine and terrestrial ecosystems, the potential for abrupt changes in climate, and understanding the limits and options society has to respond to climate change. In this talk I will discuss several sources of uncertainty in predictions and projections of the future, highlighting that unpredictable, internally generated climate fluctuations make a substantial contribution to climate trends projected for the next 50 years. Results are based on large ensembles of climate change integrations with the Community Earth System Model (CESM). I also will show that the large-scale atmospheric circulation is responsible for much of the diversity in climate change projections across the individual ensemble members. Vipin Kumar – University of Minnesota Title: NSF Expeditions in Computing on Understanding Climate Change: A Data Driven Approach Climate change is the defining environmental challenge facing our planet, yet there is considerable uncertainty regarding the social and environmental impact due to the limited capabilities of existing physics- based models of the Earth system. Consequently, important questions relating to food security, water resources, biodiversity, and other socio-economic issues over relevant temporal and spatial scales remain unresolved. A new and transformative approach is required to understand the potential impact of climate change. Data driven approaches that have been highly successful in other scientific disciplines hold significant potential for application in environmental sciences. This Expeditions project addresses key challenges in the science of climate change by developing methods that take advantage of the wealth of climate and ecosystem data available from satellite and ground-based sensors, the observational record for atmospheric, oceanic, and terrestrial processes, and physics-based climate model simulations. These innovative approaches help provide an improved understanding of the complex nature of the Earth system and the mechanisms contributing to the adverse consequences of climate change, such as increased frequency and intensity of hurricanes, precipitation regime shifts, and the propensity for extreme weather events that result in environmental disasters. Methodologies developed as part of this project will be used to gain actionable insights and to inform policymakers. This presentation provides an overview of the challenges being addressed in this multi-disciplinary, multi-institutional project and includes highlights of some of the results obtained over the past year. Auroop Ganguly – Northeastern University Title: Informing Climate Adaptation with Big Data and Bigger Models Predictive insights on climate variability, extremes and uncertainty, especially at local to regional scales, may provide useful information for adaptation. However, while physics-based models may lose their ability to generalize with greater parameterization, especially when the parameters cannot be directly or indirectly estimated from data, insights obtained by mining massive volumes of observed and model- simulated data may have limited interpretability, particularly when explanations are attempted based 9 on simple physical intuition. The latter may even yield spurious results when complex dependence patterns are ignored. Another major set of challenges stem from the fact that the climate system is nonlinear and dynamical, even chaotic, but contaminated with random noise, which may include low- frequency, and even 1/f, components. While arguments and frameworks have been advanced to change the null hypothesis in adaptation decisions from stationary climate to warming environments, fundamental and achievable limits to predictability may need to be considered. This presentation discusses new findings and old perspectives, as well as prevailing beliefs in the scientific and adaptation communities, and suggests the need to consider both
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