Dominant patterns of interaction between the tropics and mid-latitudes in boreal summer: causal relationships and the role of timescales

Abstract

Abstract. Tropical convective activity represents a source of predictability for mid-latitude weather in the Northern Hemisphere. In winter, the El Niño–Southern Oscillation (ENSO) is the dominant source of predictability in the tropics and extratropics, but its role in summer is much less pronounced and the exact teleconnection pathways are not well understood. Here, we assess how tropical convection interacts with mid-latitude summer circulation at different intra-seasonal timescales and how ENSO affects these interactions. First, we apply maximum covariance analysis (MCA) between tropical convective activity and mid-latitude geopotential height fields to identify the dominant modes of interaction. The first MCA mode connects the South Asian monsoon with the mid-latitude circumglobal teleconnection pattern. The second MCA mode connects the western North Pacific summer monsoon in the tropics with a wave-5 pattern centred over the North Pacific High in the mid-latitudes. We show that the MCA patterns are fairly insensitive to the selected intra-seasonal timescale from weekly to 4-weekly data. To study the potential causal interdependencies between these modes and with other atmospheric fields, we apply the causal discovery method PCMCI at different timescales. PCMCI extends standard correlation analysis by removing the confounding effects of autocorrelation, indirect links and common drivers. In general, there is a two-way causal interaction between the tropics and mid-latitudes, but the strength and sometimes sign of the causal link are timescale dependent. We introduce causal maps that show the regionally specific causal effect from each MCA mode. Those maps confirm the dominant patterns of interaction and in addition highlight specific mid-latitude regions that are most strongly connected to tropical convection. In general, the identified causal teleconnection patterns are only mildly affected by ENSO and the tropical mid-latitude linkages remain similar. Still, La Niña strengthens the South Asian monsoon generating a stronger response in the mid-latitudes, while during El Niño years the Pacific pattern is reinforced. This study paves the way for process-based validation of boreal summer teleconnections in (sub-)seasonal forecast models and climate models and therefore works towards improved sub-seasonal predictions and climate projections.

Dynamic Bayesian Networks for Evaluation of Granger Causal Relationships in Climate Reanalyses

“…manuscript submitted to Journal of Advances in Modeling Earth Systems (JAMES) Di Capua et al, 2020;Harwood et al, 2021) and models (Deng & Ebert-Uphoff, 2014;Ebert-Uphoff & Deng, 2017), and, most recently, for model evaluation (Vázquez-Patiño et al, 2020;Nowack et al, 2020).…”

“…where a τ , b τ , and ν 2 i are prior hyperparameters. Similar linear models have previously been applied (Kretschmer et al, 2016(Kretschmer et al, , 2017Saggioro et al, 2020;Di Capua et al, 2020) to perform estimation of the interaction strengths after an initial stage of constraint-based structure learning. Using the underlying generative model, Eq.…”

“…where a τ , b τ , and  2 i are prior hyperparameters. Similar linear models have previously been applied (Di Capua et al, 2020;Kretschmer, Coumou, et al, 2016;Kretschmer, Runge, & Coumou, 2017 ;Saggioro et al, 2020) to perform estimation of the interaction strengths after an initial stage of constraint-based structure learning. Using the underlying generative model, Equation 11, posterior estimates for both the structural features and model parameters can be obtained within a single sampling scheme.…”

“…The local marginal likelihoods Ψ i ( D ; G ) and posterior distributions for the parameters of a given graph can be analytically evaluated provided that conjugate normal‐gamma priors are assumed for the conditional precision and coefficients , where a τ , b τ , and are prior hyperparameters. Similar linear models have previously been applied (Di Capua et al., 2020; Kretschmer, Coumou, et al., 2016; Kretschmer, Runge, & Coumou, 2017​; Saggioro et al., 2020) to perform estimation of the interaction strengths after an initial stage of constraint‐based structure learning. Using the underlying generative model, Equation , posterior estimates for both the structural features and model parameters can be obtained within a single sampling scheme.…”

“…As a result, graphical models based on DAGs provide a powerful tool for studying causal relationships (Pearl, 1995), and form a useful basis for other forms of causal inference (Greenland & Brumback, 2002). Bayesian, or belief, networks (BNs), as such models are usually called, have therefore received increasing attention from the climate science community (Ebert‐Uphoff & Deng, 2012a), having variously been used for forecasting and risk assessment based on expert systems (e.g., Abramson et al., 1996; Boneh et al., 2015; Catenacci & Giupponi, 2009, 2013; Leonard et al., 2014; Peter et al., 2009), for learning independence relationships and possible causal interactions in observations (Di Capua et al., 2020; Ebert‐Uphoff & Deng, 2012b; Harwood et al., 2021; Horenko et al., 2017; Kretschmer, Coumou, et al., 2016; Kretschmer, Runge, & Coumou, 2017; Li et al., 2018; Pfleiderer et al., 2020; Runge, 2015, 2018a, 2018b; Runge, Bathiany, et al., 2019; Runge, Nowack, et al., 2019; Runge, Petoukhov, Donges, et al., 2015; Runge, Petoukhov, & Kurths, 2014; Saggioro et al., 2020; Samarasinghe, Deng, & Ebert‐Uphoff, 2020; Samarasinghe, McGraw, et al., 2019) and models (Deng & Ebert‐Uphoff, 2014; Ebert‐Uphoff & Deng, 2017), and, most recently, for model evaluation (Nowack et al., 2020; Vázquez‐Patiño et al., 2020).…”