Hosts: Maike Sonnewald and Aparna Radhakrishnan
The El Nino Southern Oscillation (ENSO) has a fundamental impact on climate. Using the machine learning (ML) framework in Sonnewald and Lguensat 2020 (SL2020), this project focuses on investigating ENSO dynamics in a climate model context. The ML framework identifies ocean dynamical regions (Sonnewald 2019) that reveal the underlying physics. The framework has been applied to the high latitude ocean, and the purpose of the project is to extend the application to investigate ENSO dynamics. Ultimately, the aim is to investigate ENSO variability in climate models from CMIP6 (Climate Model Intercomparison Project), with a potential focus on salinity effects that are a key consideration in conventional sampling strategy.
Specifically, the intern will look at the coherence between an index of ENSO and the concurrent ocean dynamical regimes, assessing these dynamics using ML. The dynamical regimes reveal several patterns (e.g.transport and associated distributions of water mass density, salinity barrier layers, etc) which may be linked to ENSO variability. The student will focus on identifying a pattern, and study the association between an ENSO index and the dynamical regimes using global climate model output from the CMIP6 archives, under various future climate forcing scenarios.