Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate

Author(s)

Feldhoff, Jan H. ^{[1, 2]} ; Lange, Stefan ^{[1, 2]} ; Volkholz, Jan ^[2] ; Donges, Jonathan F. ^{[2, 3]} ; Kurths, Juergen ^{[1, 2, 4]} ; Gerstengarbe, Friedrich-Wilhelm ^{[2, 5]}

Affiliation

^[1] Humboldt Univ, Dept Phys, D-12489 Berlin - Germany

^[2] Potsdam Inst Climate Impact Res, D-14412 Potsdam - Germany

^[3] Stockholm Univ, Stockholm Resilience Ctr, S-11419 Stockholm - Sweden

^[4] Univ Aberdeen, Inst Complex Syst & Math Biol, Aberdeen AB243UE - Scotland

^[5] Humboldt Univ, Dept Geog, D-12489 Berlin - Germany

• Document type: Journal article
• Source: Climate Dynamics; v. 44, n. 5-6, p. 1567-1581, MAR 2015.
• Web of Science Citations: 13

Abstract

In this study we introduce two new node-weighted difference measures on complex networks as a tool for climate model evaluation. The approach facilitates the quantification of a model's ability to reproduce the spatial covariability structure of climatological time series. We apply our methodology to compare the performance of a statistical and a dynamical regional climate model simulating the South American climate, as represented by the variables 2 m temperature, precipitation, sea level pressure, and geopotential height field at 500 hPa. For each variable, networks are constructed from the model outputs and evaluated against a reference network, derived from the ERA-Interim reanalysis, which also drives the models. We compare two network characteristics, the (linear) adjacency structure and the (nonlinear) clustering structure, and relate our findings to conventional methods of model evaluation. To set a benchmark, we construct different types of random networks and compare them alongside the climate model networks. Our main findings are: (1) The linear network structure is better reproduced by the statistical model statistical analogue resampling scheme (STARS) in summer and winter for all variables except the geopotential height field, where the dynamical model CCLM prevails. (2) For the nonlinear comparison, the seasonal differences are more pronounced and CCLM performs almost as well as STARS in summer (except for sea level pressure), while STARS performs better in winter for all variables. (AU)

• FAPESP's process: 11/50151-0 - Dynamical phenomena in complex networks: fundamentals and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support type: Research Projects - Thematic Grants