Atmosphere Ocean Science Colloquium

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Dimensionality reduction and network inference for climate data using 𝛿-MAPS

Speaker: Fabrizio Falasco, Courant, NYU

Location: Online

Date: Wednesday, October 20, 2021, 3:30 p.m.

Synopsis:

The exponential growth of climate data combined with advances in machine learning offers new opportunities to understand the climate system and its response to external forcings. In this talk, I will present 𝛿-MAPS, a recently developed complex network analysis method for climate fields.

Given a spatiotemporal climate field embedded on a grid, the framework first identifies patterns (domains) defined as spatially contiguous, homogeneous sets of time series. At a second step, it infers a weighted and direct network between domains. The resulting climate network allows to condense large spatiotemporal fields in few domains plus their connectivity patterns, therefore providing a simple new dimensionality reduction scheme for climate data. Applications ranging from climate models evaluation to the investigation of regime shifts at paleoclimate (~10³ years) scales are discussed.