Evaluation of global teleconnections in CMIP6 climate projections using complex networks

Abstract

Abstract. In climatological research, the evaluation of climate models is one of the central research subjects. As an expression of large-scale dynamical processes, global teleconnections play a major role in interannual to decadal climate variability. Their realistic representation is an indispensable requirement for the simulation of climate change, both natural and anthropogenic. Therefore, the evaluation of global teleconnections is of utmost importance when assessing the physical plausibility of climate projections. We present an application of the graph-theoretical analysis tool δ-MAPS, which constructs complex networks on the basis of spatio-temporal gridded data sets, here sea surface temperature and geopotential height at 500 hPa. Complex networks complement more traditional methods in the analysis of climate variability, like the classification of circulation regimes or empirical orthogonal functions, assuming a new non-linear perspective. While doing so, a number of technical tools and metrics, borrowed from different fields of data science, are implemented into the δ-MAPS framework in order to overcome specific challenges posed by our target problem. Those are trend empirical orthogonal functions (EOFs), distance correlation and distance multicorrelation, and the structural similarity index. δ-MAPS is a two-stage algorithm. In the first place, it assembles grid cells with highly coherent temporal evolution into so-called domains. In a second step, the teleconnections between the domains are inferred by means of the non-linear distance correlation. We construct 2 unipartite and 1 bipartite network for 22 historical CMIP6 climate projections and 2 century-long coupled reanalyses (CERA-20C and 20CRv3). Potential non-stationarity is taken into account by the use of moving time windows. The networks derived from projection data are compared to those from reanalyses. Our results indicate that no single climate projection outperforms all others in every aspect of the evaluation. But there are indeed models which tend to perform better/worse in many aspects. Differences in model performance are generally low within the geopotential height unipartite networks but higher in sea surface temperature and most pronounced in the bipartite network representing the interaction between ocean and atmosphere.

Analysis of spatially coherent forecast error structures

“…Errors related to overestimation/underestimation of links in the ITCZ can also be seen in the degree difference between reanalysis and forecast networks of OLR (Supporting Information Figure S6d). However, as our purpose here is not the evaluation of climate interactions predicted by models (Steinhaeuser and Tsonis, 2014; Boers et al ., 2015; Gregory et al ., 2022; Dalelane et al ., 2023), we do not seek a detailed understanding of the differences between the reanalysis and forecast network connectivity structure. But from our aforementioned discussion, it is clear that the topological structure of the forecast error correlation network of the climate variable indeed highlights the primary source of structured error in that variable, which may not be revealed from the connectivity structure of the variable itself.…”

“…The climate network approach has been used to study patterns of climate variability in different climate variables, such as temperature, pressure, geopotential height, wind, and precipitation, at various scales (Tsonis and Roebber, 2004; Yamasaki et al ., 2008; Donges et al ., 2009a; Ludescher et al ., 2013; Radebach et al ., 2013; Runge et al ., 2015; Gelbrecht et al ., 2017; Boers et al ., 2019; Gupta et al ., 2021; Lu et al ., 2022; Gupta et al ., 2023). The methodology has also been used in previous studies for the purpose of model evaluation in order to identify the underestimation or overestimation of statistical links, and hence teleconnection patterns, by comparing the depiction of climate interactions in the reanalysis data with that in the forecasts (Steinhaeuser and Tsonis, 2014; Boers et al ., 2015; Feldhoff et al ., 2015; Di Capua et al ., 2022; Gregory et al ., 2022; Dalelane et al ., 2023).…”

Interactive Focus+Context Rendering for Hexahedral Mesh Inspection

“…For iso-contours in scalar ensemble fields, Ferstl et al [15] assess the spatial correlation of their occurrence at different locations in the domain. Global teleconnections are visualized by Delalene et al [16] to analyze in interannual to decadal climate variability. They use dependence measures to infer spatial functional networks between clustered subdomains, in combination with correlation matrices to visualize such dependencies.…”

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

“…The method has been applied to investigate the nonlinear relationship between air pollution and meteorological variables [45], gene-gene interactions [46], etc. Dalelane et al [47] evaluated the global teleconnections in CMIP6 climate projections using the distance correlation. However, the method has not yet been used to describe the nonlinear relationship between SST and EP.…”