A multi-modal representation of El Ni\~no Southern Oscillation Diversity

The El Ni\~no-Southern Oscillation (ENSO) is characterized by alternating periods of warm (El Ni\~no) and cold (La Ni\~na) sea surface temperature anomalies (SSTA) in the equatorial Pacific. Although El Ni\~no and La Ni\~na are well-defined climate patterns, no two events are alike. To date, ENSO diversity has been described primarily in terms of the longitudinal location of peak SSTA, used to define a bimodal classification of events in Eastern Pacific (EP) and Central Pacific (CP) types. Here, we use low-dimensional representations of Pacific SSTAs to argue that binary categorical memberships are unsuitable to describe ENSO events. Using fuzzy unsupervised clustering, we recover the four known ENSO categories, along with a fifth category: an Extreme El Ni\~no. We show that Extreme El Ni\~nos differ both in their intensity and temporal evolution from canonical EP El Ni\~nos. We also find that CP La Ni\~nas, EP El Ni\~nos, and Extreme El Ni\~nos contribute the most to interdecadal ENSO variability

Detecting Concept Drift With Neural Network Model Uncertainty

Deployed machine learning models are confronted with the problem of changing data over time, a phenomenon also called concept drift. While existing approaches of concept drift detection already show convincing results, they require true labels as a prerequisite for successful drift detection. Especially in many real-world application scenarios-like the ones covered in this work-true labels are scarce, and their acquisition is expensive. Therefore, we introduce a new algorithm for drift detection, Uncertainty Drift Detection (UDD), which is able to detect drifts without access to true labels. Our approach is based on the uncertainty estimates provided by a deep neural network in combination with Monte Carlo Dropout. Structural changes over time are detected by applying the ADWIN technique on the uncertainty estimates, and detected drifts trigger a retraining of the prediction model. In contrast to input data-based drift detection, our approach considers the effects of the current input data on the properties of the prediction model rather than detecting change on the input data only (which can lead to unnecessary retrainings). We show that UDD outperforms other state-of-the-art strategies on two synthetic as well as ten real-world data sets for both regression and classification tasks

Reorganization energy and polaronic effects of pentacene on NaCl films

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 similar or equal to 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

Teleconnection patterns of different El Ni\~no types revealed by climate network curvature

The diversity of El Ni\~no events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) types. While the remote impacts, i.e. teleconnections, of EP and CP events have been studied for different regions individually, a global picture of their teleconnection patterns is still lacking. Here, we use Forman-Ricci curvature applied on climate networks constructed from 2-meter air temperature data to distinguish regional links from teleconnections. Our results confirm that teleconnection patterns are strongly influenced by the El Ni\~no type. EP events have primarily tropical teleconnections whereas CP events involve tropical-extratropical connections, particularly in the Pacific. Moreover, the central Pacific region does not have many teleconnections, even during CP events. It is mainly the eastern Pacific that mediates the remote influences for both El Ni\~no types.Comment: Preprin

Teleconnection Patterns of Different El Niño Types Revealed by Climate Network Curvature

The diversity of El Niño events is commonly described by two distinct flavors, the Eastern Pacific (EP) and Central Pacific (CP) type. While the remote impacts, that is, teleconnections, of EP and CP events have been studied for different regions individually, a global picture of their structure is still lacking. Here, we use Forman‐Ricci curvature applied on climate networks constructed from surface air temperature data to distinguish regional links from teleconnections. Our results confirm that both El Niño types influence the teleconnection patterns, however, with different spatial manifestations. Our analysis suggests that EP El Niños alter the general circulation which changes the teleconnection structure to primarily tropical teleconnections. In contrast, the teleconnection pattern of CP El Niños show only subtle changes to normal conditions. Moreover, this work identifies the dynamics of the Eastern Pacific as a proxy for the remote impact of both El Niño types.Plain Language Summary: El Niño events, characterized by anomalous sea surface temperatures (SSTs) in the Tropical Pacific, come in two flavors; Eastern Pacific (EP) and Central Pacific (CP) types, depending on the longitudinal location of the strongest SST anomalies. Their remote impacts, known as teleconnections, differ. Although there are many studies investigating teleconnections of EP and CP events for individual target regions, a global analysis of the spatial distribution of their teleconnections is still lacking. In this study, we use the theory of complex networks to study EP and CP El Niño teleconnections. We construct “climate networks” from global surface air temperature data and use the notion of “curvature” of a network link to uncover their spatial organization. We show that the most negatively curved links highlight important teleconnection patterns that differ depending on the El Niño type. EP events change the teleconnection structure to the tropics while CP and Normal year conditions reveal teleconnections to all latitudes. Interestingly, the Central Pacific does not show many teleconnections, even during CP El Niño events which we attribute to the varying location of warm water anomalies in the Central Pacific. The Eastern Pacific changes more consistently allowing identifying remote impacts of both El Niños types.Key Points: Ricci curvature of boreal winter climate networks reveals long‐range teleconnection structure. Eastern Pacific (EP) El Niños show primarily teleconnections in tropical while Central Pacific El Niños teleconnections on all latitudes. The EP contains robust teleconnections for both El Niño types.Deutsche Forschungsgemeinschaft, DFG http://dx.doi.org/10.13039/501100001659researc