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research:climatechange [2016/07/18 11:41]
127.0.0.1 external edit
research:climatechange [2017/07/11 08:21]
lhkellogg
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 One of the fundamental challenges in the study of climate change is how to combine models of past oceanographic circulation (as reconstructed from sparse geochemical data taken from deep sea sedimentary cores) with modern ocean circulation models (as constructed from modern oceanographic observations and computer simulations) to yield insight into the processes governing ocean circulation throughout the last glacial cycle from 150,000 years ago to the present. Similarly, a major challenge in computer science is how to extract information from sparse datasets, and how to effectively combine computational thinking with automated data analysis, to extract new knowledge about features and processes. Our multidisciplinary and multi-institutional project brings together computer scientists, oceanographers,​ paleoceanographers,​ and computational geophysicists to address these challenges as a team. We are developing an innovative suite of computational tools to explore changes in global ocean circulation. This project will merge analysis of ocean flow with 40 years of previously-collected data from deep sea sedimentary cores in order to gain new insights into past ocean circulation change. Our research will take advantage of the unique analytical resources and interdisciplinary collaborative environment provided by the UC Davis KeckCAVES (W.M. Keck Center for Active Visualization in the Earth Sciences). The KeckCAVES provides immersive interactive visualization technologies that help scientists identify meaningful patterns in complex datasets. In this unique collaborative environment,​ we are developing methods to improve data interpolation,​ to extract ocean flow patterns, and to detect changes in ocean flow over time, and along with interactive means of visualizing and interacting with those large and time-dependent datasets. The computational methods developed in this project are adaptable to a wide of range visualization and data analysis applications for analysis of flow fields. The scientific visualization methods we develop will be used to develop innovative, provocative,​ and intuitive instructional materials for undergraduate and graduate education; for making other researchers familiar with new scientific methodology (visually-aided analysis); for bringing science and scientific results to the public; and for bringing interactive scientific visualization technology to all the partner institutions in this collaborative project. One of the fundamental challenges in the study of climate change is how to combine models of past oceanographic circulation (as reconstructed from sparse geochemical data taken from deep sea sedimentary cores) with modern ocean circulation models (as constructed from modern oceanographic observations and computer simulations) to yield insight into the processes governing ocean circulation throughout the last glacial cycle from 150,000 years ago to the present. Similarly, a major challenge in computer science is how to extract information from sparse datasets, and how to effectively combine computational thinking with automated data analysis, to extract new knowledge about features and processes. Our multidisciplinary and multi-institutional project brings together computer scientists, oceanographers,​ paleoceanographers,​ and computational geophysicists to address these challenges as a team. We are developing an innovative suite of computational tools to explore changes in global ocean circulation. This project will merge analysis of ocean flow with 40 years of previously-collected data from deep sea sedimentary cores in order to gain new insights into past ocean circulation change. Our research will take advantage of the unique analytical resources and interdisciplinary collaborative environment provided by the UC Davis KeckCAVES (W.M. Keck Center for Active Visualization in the Earth Sciences). The KeckCAVES provides immersive interactive visualization technologies that help scientists identify meaningful patterns in complex datasets. In this unique collaborative environment,​ we are developing methods to improve data interpolation,​ to extract ocean flow patterns, and to detect changes in ocean flow over time, and along with interactive means of visualizing and interacting with those large and time-dependent datasets. The computational methods developed in this project are adaptable to a wide of range visualization and data analysis applications for analysis of flow fields. The scientific visualization methods we develop will be used to develop innovative, provocative,​ and intuitive instructional materials for undergraduate and graduate education; for making other researchers familiar with new scientific methodology (visually-aided analysis); for bringing science and scientific results to the public; and for bringing interactive scientific visualization technology to all the partner institutions in this collaborative project.
  
-Funded ​by NSF+ 
 +==== Selected Publications ==== 
 + 
 +Gebbie, G. "​Tracer transport timescales and the observed Atlantic-Pacific lag in the timing of the Last Termination,"​ Paleoceanography,​ v.27, 2012, p. 14. doi:​doi:​10.1029/​2011PA002273  
 + 
 +Peterson, C.D., L.E. Lisiecki, and J. V. Stern. "​Deglacial whole-ocean ?13C change estimated from 480 benthic foraminiferal records,"​ Paleoceanography,​ v.29, 2014, p. 549. doi:​10.1002/​2013PA002552  
 + 
 +Gebbie, G Streletz, G J Spero, H J. "How well would modern-day oceanic property distributions be known with paleoceanographic-like observations?,"​ Paleoceanography,​ v.31, 2016, p. 472. doi:​10.1002/​2015PA002917  
 + 
 +Gebbie, G., C. Peterson, L. Lisiecki, and H.J. Spero. Quaternary Science Reviews, v.125, 2015, p. 144. doi:​10.1016/​j.quascirev.2015.08.010  
 + 
 + 
 +==== Acknowledgements ==== 
 +This project is supported ​by the National Science Foundation'​s CDI program under award number OIA-1125422,​ titled "​Collaborative Research: CDI-Type II: 4 Dimensional Visualization of Past Ocean Circulation from Paleoceanographic Data" ​