Due to recent advances in scanning-probe technology, the electronic structure of individual molecules can now also be investigated if they are immobilized by adsorption on nonconductive substrates. As a consequence, different molecular charge states are now experimentally accessible. Thus motivated, we investigate as an experimentally relevant example the electronic and structural properties of a NaCl(001) surface with and without pentacene adsorbed (neutral and charged) by employing density-functional theory. We estimate the polaronic reorganization energy to be E reorg 0.8 − 1.0 eV, consistent with experimental results obtained for molecules of similar size. To account for environmental effects on this estimate, different models for charge screening are compared. Finally, we calculate the density profile of one of the frontier orbitals for different occupations and confirm the experimentally observed localization of the charge density upon charging and relaxation of molecule-insulator interface from ab initio calculations.

The El Niño-Southern Oscillation (ENSO) is the most dominant interannual variation in the global climate system. It is a dynamical atmospheric and oceanic phenomenon characterized by anomalously warm (El Niño) or cold (La Niña) phases of sea surface temperatures (SSTs) in the Equatorial Pacific. Both phases are known to impact Earth's climate significantly on large spatial scales, typically referred to as teleconnections (Trenberth, 1997) and thus have been investigated in many studies over the past two decades (Capotondi et al., 2015(Capotondi et al., , 2020Timmermann et al., 2018).Yet, significant differences in the downstream impacts of El Niño events are reported (Shi et al., 2019), depending on the amplitude and spatial position of SST anomalies. These differences can be partly related to the type of El Niño. The diversity of El Niño events is typically characterized by two modes: The "canonical" or Eastern Pacific (EP) El Niño (Rasmusson & Carpenter, 1982) with peak SST anomalies in the eastern equatorial Pacific, and the "El Niño Modoki" (Ashok et al., 2007) or Central Pacific (CP) El Niño with peak SST anomalies in the Central Equatorial Pacific (Kao & Yu, 2009). Although the effect of both El Niño types on different locations of the Earth-such as the Indian Ocean (IO) (e.g., Klein et al., 1999), maritime continent (e.g., G. Wang and Hendon 2007), tropical Atlantic (e.g., Huang 2004), and Northern America (e.g., Yu et al., 2012)-has been studied thoroughly (see Okumura 2019 andTaschetto et al., 2020 for an overview), previous work has mainly focused on single teleconnections of the El Niño types.In comparison, little is known about differences in the spatial extent of global teleconnection patterns between EP and CP events. In this study, we address this issue by introducing a novel machine learning approach that

<p>Sea surface temperature anomalies (SSTA) associated with the El-Niño Southern Oscillation (ENSO) show strong event-to-event variability, known as ENSO diversity. El Niño and La Niña events are typically divided into Eastern Pacific (EP) and Central Pacific (CP) types based on the zonal location of peak SSTA. The separation of these types is usually based on temperature differences between pairs of predefined indices, such as averages over boxes in the Eastern and Central Pacific or the two leading Principal Components of tropical SSTA. <br />Using results from unsupervised learning of SSTA data, we argue that ENSO diversity is not well described by distinctly separate classes but rather forms a continuum with events grouping into "soft'' clusters. We apply a Gaussian mixture model (GMM) to a low-dimensional projection of tropical SSTA to describe the multi-modal distribution of ENSO events. We find that El-Niño events are best described by three overlapping clusters while La-Niña events only show two "soft'' clusters. The three El-Niño clusters are described by i) maximum SSTA in the CP, ii) maximum SSTA in the EP, and iii) strong basin-wide warming of SSTA which we refer to as the "super El-Niño'' cluster. The "soft'' clusters of La-Niña correspond to i) anomalous cool SST in the CP and ii) anomalously cool SST in the EP. We estimate the probability that a given ENSO event belongs to a chosen cluster and use these probabilities as weights for estimating averages of atmospheric variables corresponding to each cluster. These weighted composites show qualitatively similar patterns to the typically used averages over EP and CP events. However, the weighted composites show a higher signal-to-noise ratio in the mid-latitudes for the "super El-Niño'' events. <br />We further apply our approach to CESM2 model data and discuss the potential of GMM clustering for evaluating how well ENSO diversity is captured in Global Circulation models.</p>

<p>The El Niño Southern Oscillation (ENSO) exhibits a large diversity of events characterized by the location of extreme sea surface temperature anomalies either in the Eastern Pacific (EP) or Central Pacific (CP). While broadband stochastic wind forcing is one of the known drivers of ENSO, the relative influence of its high- and low-frequency component on ENSO diversity remains unclear. </p><p>We conduct a spectral analysis of westerly wind anomalies, yielding high- and low-frequency wind components for six different regions in the equatorial Pacific. The influence of these high- and low-frequency westerly wind anomalies, as well as their spatial location, is used for predicting ENSO diversity at different lead times. Using causal network discovery combined with multiple linear and non-linear regression analyses, we obtain different predictors for the different types of ENSO. Our results identify causally relevant, spatial and frequency-band restricted westerly wind anomalies at different lead times, which might improve early forecasting of El Niño events and improve our understanding of stochastic wind forcing energizing ENSO. </p>

<p>Representing spatio-temporal climate variables as complex networks allows uncovering nontrivial structure in the data. Although various tools for detecting communities in climate networks have been used to group nodes (spatial locations) with similar climatic conditions, we are often interested in identifying important links between communities. Of particular interest are methods to detect teleconnections, i.e. links over large spatial distances mitigated by atmospheric processes.</p><p>We propose to use a recently developed network measure based on Ricci-curvature to visualize teleconnections in climate networks. Ricci-curvature allows to distinguish between- and within-community links in networks. Applied to networks constructed from surface temperature anomalies we show that Ricci-curvature separates spatial scales. We use Ricci-curvature to study differences in global teleconnection patterns of different types of El Niño events, namely the Eastern Pacific (EP) and Central Pacific (CP) types. Our method reveals a global picture of teleconnection patterns, showing confinement of teleconnections to the tropics under EP conditions but showing teleconnections to the tropics, Northern and Southern Hemisphere under CP conditions. The obtained teleconnections corroborate previously reported impacts of EP and CP.<br>Our results suggest that Ricci-curvature is a promising visual-analytics-tool to study the topology of climate systems with potential applications across observational and model data.</p>