Probabilistic Climate Predictions

We have developed several approaches to think about climate dynamics and prediction in a probability way with various ensemble techniques:

– Representing uncertainty in initial conditions for ocean predictions (e.g., Tziperman et al. 2008, Zanna et al. 2011, Zanna et al. 2012)

– Representing model uncertainty for seasonal forecast (e.g., Andrejczuk et al. 2016), interannual (e.g., Juricke et al. 2018) and decadal forecast (e.g., Juricke et al. 2017)

– The role of ocean stochastic or chaotic processes on decadal variability (e.g., Juricke 2017, Zanna et al. 2018)

– The role of uncertainty from air-sea fluxes and model structure (e.g., Huber and Zanna, 2017, Zanna et al. 2018)

– Probabilistic representation and parameterization of ocean turbulence (e.g., Porta Mana & Zanna 2014, Zanna et al. 2017, David et al. 2017)

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

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