Machine Learning Techniques for Data Reduction of Climate Applications
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals, such that data can be adaptively reduced without compromising the ...
Gong, Qian +5 more
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Rapid Northward Expansion of the Blacklegged Tick, Ixodes scapularis, in Response to Climate Change. [PDF]
Westcott JR +3 more
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Human perturbations to mercury in global rivers. [PDF]
Peng D +12 more
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Interactions Between Climate Mean and Variability Drive Future Agroecosystem Vulnerability. [PDF]
Sinha E +4 more
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Increasing flood hazard in the Lower Mississippi River due to extreme storm clustering. [PDF]
Liu Y +5 more
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Rising atmospheric moisture escalates the future impact of atmospheric rivers in the Antarctic climate system. [PDF]
Maclennan ML +6 more
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Multi-ignition fire complexes drive extreme fire years and impacts. [PDF]
Scholten RC +13 more
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Application-specific optimal model weighting of global climate models: A red tide example. [PDF]
Elshall A +6 more
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Evaluating CMIP6 simulations of historical Sahelian precipitation variability and potential links to future projections [PDF]
Prouse, Lauren J
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Climate change will reduce North American inland wetland areas and disrupt their seasonal regimes. [PDF]
Xu D +7 more
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