Complex network and dynamical system approaches to Climate Science

Fabrizio Falasca
Wednesday, March 31, 2021 - 9:00am
Dr. Annalisa Bracco (Advisor), Dr. Yi Deng, Dr. Emanuele Di Lorenzo, Dr. Predrag Cvitanović and Dr. Pascale Braconnot

The exponential growth of climate data combined with advances in machine learningoffers new opportunities to understand the climate system and its response to external forcings. This thesis explores and proposes data mining frameworks to reduce the complexityof spatiotemporal climate fields and facilitate analysis and interpretation.As complex as it appears, the dynamics of the climate system is dominated by spatiotemporal patterns and the identification of these patterns and their linkages offers a useful framework for dimensionality reduction. In the first part of this work, I leverage this observationand propose a framework for model evaluation. The approach allows to compare modesof climate variability and their interrelationships across datasets. I apply the proposedmethodology on observational sea surface temperature (SST) datasets and 30 members ofthe Community Earth System Model Large Ensemble (CESM-LE) that differ only slightlyin the initial conditions. I then compare the modelled and observed climate networks, identify biases, and distinguish between models errors and differences arising from the internalvariability of the system.In the second part of the thesis, I present a strategy for dimensionality reduction in paleoclimate simulations. Given two simulations of the last 6000 years, the search forabruptornon-abruptshifts in dynamics at spatiotemporal scales is undertaken. I show that, at least inthe modelled climate, multidecadal oscillations can rapidly emerge and fade due to the system’s internal dynamics over periods as short as 200–300 years. Moreover, we argue thatchanges in the global connectivity patterns are, on average, abrupt and chaotic. Finally, wefocus solely on the tropical Indian and Pacific oceans, were a slow and gradual change inmodes of variability and their linkages are identified. During the mid-Holocene, the IndianOcean (IO) basin hosted an energetic equatorial dipole mode, largely independent of the ElNiño Southern Oscillation (ENSO) and different from the IO dipole observed today. Also,mid-Holocene ENSO was much weaker. We quantified the causal relationship betweensuch changes and the evolution of the whole Indo-Pacific climate mean state. Changesin the Earth’s orbital configuration, slow and small compared to the ongoing changes inanthropogenic climate drivers, caused large changes in major modes of variability in theIndo-Pacific region.