Climate projections are subject to three sources of uncertainties: initial condition, model and scenario. Our research is concerned with the first two kinds of errors. Specifically, how do the lack of ocean observations, the chaotic nature of the climate system and the poor representation of subgrid turbulent processes impact predictions on intra-seasonal to centennial timescales. Being able to quantify the predictability limits of the ocean is crucial for providing reliable climate predictions. We use theory, observations and numerical simulations to assess the uncertainties associated the ocean dynamics.

– Simple statistical models for prediction of seasonal to decadal sea surface temperatures in the Atlantic (e.g., Zanna 2012, Huddart et al. 2016) and Pacific (e.g., Dias et al. 2018), and sea level (e.g., Fraser et al. 2019)

– Quantification of uncertainty associated with initial conditions and model parameterization (e.g., Zanna et al. 2018)

Laure Zanna
Laure Zanna
Professor of Mathematics & Atmosphere/Ocean Science [she/her]

My research interests include climate dynamics, ocean modeling, and machine learning.