Dimensionality Reduction and Network Inference for Climate Data Using δ-MAPS: Application to the CESM Large Ensemble Sea Surface Temperature

Abstract

A framework for analyzing and benchmarking climate model outputs is built upon δ-MAPS, a recently developed complex network analysis method. The framework allows for the possibility of highlighting quantifiable topological differences across data sets, capturing the magnitude of interactions including lagged relationships and quantifying the modeled internal variability, changes in domains properties and in their connections over space and time. A set of four metrics is proposed to assess and compare the modeled domains shapes, strengths, and connectivity patterns. δ-MAPS is applied to investigate the topological properties of sea surface temperature from observational data sets and in a subset of the Community Earth System Model (CESM) Large Ensemble focusing on the past 35 years and over the 20th and 21st centuries. Model ensemble members are mapped in a reduced metric space to quantify internal variability and average model error. It is found that network properties are on average robust whenever individual member or ensemble trends are removed. The assessment identifies biases in the CESM representation of the connectivity patterns that stem from too strong autocorrelations of domains signals and from the overestimation of the El Niño–Southern Oscillation amplitude and its thermodynamic feedback onto the tropical band in most members.