ALBERT

Description: We used transition path theory (TPT) to infer “reactive” pathways of floating marine debris trajectories. The TPT analysis was applied on a pollution-aware time-homogeneous Markov chain model constructed from trajectories produced by satellite-tracked undrogued buoys from the National Oceanic and Atmospheric Administration's Global Drifter Program. The latter involved coping with the openness of the system in physical space, which further required an adaptation of the standard TPT setting. Directly connecting pollution sources along coastlines with garbage patches of varied strengths, the unveiled reactive pollution routes represent alternative targets for ocean cleanup efforts. Among our specific findings we highlight: constraining a highly probable pollution source for the Great Pacific garbage patch; characterizing the weakness of the Indian Ocean gyre as a trap for plastic waste; and unveiling a tendency of the subtropical gyres to export garbage toward the coastlines rather than to other gyres in the event of anomalously intense winds. Given a Markov chain, namely, a model describing the stochastic state transitions in which the transition probability of each state depends only on the state attained in the previous event, transition path theory (TPT) provides a rigorous approach to study the statistics of transitions from a set of states to another, possibly disconnected set of states. Envisioning the motion of floating debris as described by a Markov chain that accounts for the ability of coastal states to “pollute the oceans,” TPT is employed to unveil “reactive” pathways representing direct transitions from potential release locations along the shorelines to accumulation sites across the world ocean. These include the subtropical gyres, whose strength in this context is investigated

Description: Complex network theory provides an important tool for the analysis of complex systems such as the Earth’s climate. In this context, functional climate networks can be constructed using a spatiotemporal climate dataset and a suitable time series distance function. The resulting coarse-grained view on climate variability consists of representing distinct areas on the globe (i.e., grid cells) by nodes and connecting pairs of nodes that present similar time series. One fundamental concern when constructing such a functional climate network is the definition of a metric that captures the mutual similarity between time series. Here we study systematically the effect of 29 time series distance functions on functional climate network construction based on global temperature data. We observe that the distance functions previously used in the literature commonly generate very similar networks while alternative ones result in rather distinct network structures and reveal different long-distance connection patterns. These patterns are highly important for the study of climate dynamics since they generally represent pathways for the long-distance transportation of energy and can be used to forecast climate variability on subseasonal to interannual or even decadal scales. Therefore, we propose the measures studied here as alternatives for the analysis of climate variability and to further exploit their complementary capability of capturing different aspects of the underlying dynamics that may help gaining a more holistic empirical understanding of the global climate system.

Description: Julia code to calculate recurrence plots of the Rössler system: - calculated from the original continuous data (regular recurrence plot) and - from the events series representing the maxima of the x-component (edit distance recurrence plot).

Description: Deep-time paleoclimatic records document large-scale shifts and perturbations in Earth's climate; during the Cenozoic in particular transitions have been recorded on time scales of 10 thousand to 1 million years. Bifurcations in the leading dynamical modes could be a key element driving these events. Such bifurcation-induced critical transitions are typically preceded by characteristic early-warning signals, for example in terms of rising standard deviation and lag-one autocorrelation. These early-warning signals are generated by a widening of the underlying basin of attraction when approaching the bifurcation, a phenomenon dubbed critical slowing down. The associated dynamical transitions should therefore be preceded by characteristic signals that can be detected by statistical methods. Here, we reveal the presence of significant early-warning signals prior to several climate events within a paleoclimate record spanning the last 66 million years - the Cenozoic Era. We computed standard deviation and lag-one autocorrelation of the CENOzoic Global Reference benthic foraminifer carbon and oxygen Isotope Dataset (CENOGRID), comprising two time series of deep sea carbonate isotope variations of 18O and 13C. We find significant early-warning signals for five out of nine previously identified Cenozoic paleoclimatic events in at least one of the two records, which can be considered as viable candidates for bifurcation-induced transitions to be analysed in follow-up studies. Our results suggest that some of the major climate events of the last 66 Ma were triggered by bifurcations in leading modes of variability, indicating bifurcations could be a key component of Earth's climate system deep-time evolution.

Description: Complex network approaches have been recently emerging as novel and complementary concepts of nonlinear time series analysis that are able to unveil many features that are hidden to more traditional analysis methods. In this work, we focus on one particular approach: the application of ordinal pattern transition networks for characterizing time series data. More specifically, we generalize a traditional statistical complexity measure (SCM) based on permutation entropy by explicitly disclosing heterogeneous frequencies of ordinal pattern transitions. To demonstrate the usefulness of these generalized SCMs, we employ them to characterize different dynamical transitions in the logistic map as a paradigmatic model system, as well as real-world time series of fluid experiments and electrocardiogram recordings. The obtained results for both artificial and experimental data demonstrate that the consideration of transition frequencies between different ordinal patterns leads to dynamically meaningful estimates of SCMs, which provide prospective tools for the analysis of observational time series. In the past decade, the field of nonlinear time series analysis has been undergoing fast developments benefiting from concepts from complex network theory. Along this line of research, ordinal pattern transition networks have been expanding the established concept of ordinal time series analysis and provide new insights into the dynamical organization underlying time series data that complement existing methods like permutation entropy. Permutation based on ordinal patterns is a simple and easy to implement concept that naturally provides statistical complexity measures (SCMs), which in the case of permutation entropy relies on pattern frequencies only. Yet, much additional information can be exploited by including ordinal pattern transition frequencies into the definitions of SCMs—an idea that, however, has not been widely developed and applied so far. In this work, we generalize existing permutation based SCMs by means of ordinal pattern transition networks that take into account the pattern transition properties explicitly. The usefulness of our generalizations is demonstrated by using time series of both model and experimental data

Description: Little is known about the distribution of ice in the Antarctic Ice Sheet (AIS) during the Last Glacial Maximum (LGM). Whereas marine and terrestrial geological data indicate that the grounded ice advanced to a position close to the continental-shelf break, the total ice volume is unclear. Glacial boundary conditions are potentially important sources of uncertainty, in particular basal friction and climatic boundary conditions. Basal friction exerts a strong control on the large-scale dynamics of the ice sheet and thus affects its size and is not well constrained. Glacial climatic boundary conditions determine the net accumulation and ice temperature and are also poorly known. Here we explore the effect of the uncertainty in both features on the total simulated ice storage of the AIS at the LGM. For this purpose we use a hybrid ice sheet shelf model that is forced with different basal drag choices and glacial background climatic conditions obtained from the LGM ensemble climate simulations of the third phase of the Paleoclimate Modelling Intercomparison Project (PMIP3). Overall, we find that the spread in the simulated ice volume for the tested basal drag parameterizations is about the same range as for the different general circulation model (GCM) forcings (4 to 6 m sea level equivalent). For a wide range of plausible basal friction configurations, the simulated ice dynamics vary widely but all simulations produce fully extended ice sheets towards the continental-shelf break. More dynamically active ice sheets correspond to lower ice volumes, while they remain consistent with the available constraints on ice extent. Thus, this work points to the possibility of an AIS with very active ice streams during the LGM. In addition, we find that the surface boundary temperature field plays a crucial role in determining the ice extent through its effect on viscosity. For ice sheets of a similar extent and comparable dynamics, we find that the precipitation field determines the total AIS volume. However, precipitation is highly uncertain. Climatic fields simulated by climate models show more precipitation in coastal regions than a spatially uniform anomaly, which can lead to larger ice volumes. Our results strongly support using these paleoclimatic fields to simulate and study the LGM and potentially other time periods like the last interglacial. However, their accuracy must be assessed as well, as differences between climate model forcing lead to a large spread in the simulated ice volume and extension.

Description: A prominent feature of earthquakes is their empirical laws, including memory (clustering) in time and space. Several earthquake forecasting models, such as the epidemic-type aftershock sequence (ETAS) model, were developed based on these empirical laws. Yet, a recent study [1] showed that the ETAS model fails to reproduce the significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) discovered in the data. Our generalized ETAS model accurately reproduces the short- and long-term/distance memory observed in the Italian and Southern Californian earthquake catalogs. The revised ETAS model is also found to improve earthquake forecasting after large shocks.

Description: Coupling between networks is widely prevalent in real systems and has dramatic effects on their resilience and functional properties. However, current theoretical models tend to assume homogeneous coupling where all the various subcomponents interact with one another, whereas real-world systems tend to have various different coupling patterns. We develop two frameworks to explore the resilience of such modular networks, including specific deterministic coupling patterns and coupling patterns where specific subnetworks are connected randomly. We find both analytically and numerically that the location of the percolation phase transition varies nonmonotonically with the fraction of interconnected nodes when the total number of interconnecting links remains fixed. Furthermore, there exists an optimal fraction r∗ of interconnected nodes where the system becomes optimally resilient and is able to withstand more damage. Our results suggest that, although the exact location of the optimal r∗ varies based on the coupling patterns, for all coupling patterns, there exists such an optimal point. Our findings provide a deeper understanding of network resilience and show how networks can be optimized based on their specific coupling patterns.

Description: Rapid and continuous analysis of radiocarbon (14C) concentration in carbonate samples at spatial resolution down to 100 µm has been made possible with the new LA-AMS (laser ablation accelerator mass spectrometry) technique. This novel approach can provide radiocarbon data at a spatial resolution similar to that of stable carbon (C) isotope measurements by isotope ratio mass spectrometry of micromilled samples and, thus, can help to interpret δ13C signatures, which otherwise are difficult to understand due to numerous processes contributing to changes in the C-isotope ratio. In this work, we analyzed δ13C and 14C on the Holocene stalagmite SPA 127 from the high-alpine Spannagel Cave (Austria). Both proxies respond in a complex manner to climate variability. Combined stable carbon and radiocarbon profiles allow three growth periods characterized by different δ13C signatures to be identified: (i) the period 8.5 to 8.0 ka is characterized by relatively low δ13C values with small variability combined with a comparably high radiocarbon reservoir effect (expressed as dead carbon fraction, dcf) of around 60 %. This points towards C contributions of host rock dissolution and/or from an “old” organic matter (OM) reservoir in the karst potentially mobilized due to the warm climatic conditions of the early Holocene. (ii) Between 8 and 3.8 ka there was a strong variability in δ13C with values ranging from −8 ‰ to +1 ‰ and a generally lower dcf. The δ13C variability is most likely caused by changes in C exchange between cave air CO2 and dissolved inorganic carbon in drip water in the cave, which are induced by reduced drip rates as derived from reduced stalagmite growth rates. Additionally, the lower dcf indicates that the OM reservoir contributed less to stalagmite growth in this period possibly as a result of reduced meteoric precipitation or because it was exhausted. (iii) In the youngest section between 3.8 and 2.4 ka, comparably stable and low δ13C values, combined with an increasing dcf reaching up to 50 % again, hint towards a contribution of an aged OM reservoir in the karst. This study reveals the potential of combining high-resolution 14C profiles in speleothems with δ13C records in order to disentangle climate-related C dynamics in karst systems.Rapid and continuous analysis of radiocarbon (14C) concentration in carbonate samples at spatial resolution down to 100 µm has been made possible with the new LA-AMS (laser ablation accelerator mass spectrometry) technique. This novel approach can provide radiocarbon data at a spatial resolution similar to that of stable carbon (C) isotope measurements by isotope ratio mass spectrometry of micromilled samples and, thus, can help to interpret δ13C signatures, which otherwise are difficult to understand due to numerous processes contributing to changes in the C-isotope ratio. In this work, we analyzed δ13C and 14C on the Holocene stalagmite SPA 127 from the high-alpine Spannagel Cave (Austria). Both proxies respond in a complex manner to climate variability. Combined stable carbon and radiocarbon profiles allow three growth periods characterized by different δ13C signatures to be identified: (i) the period 8.5 to 8.0 ka is characterized by relatively low δ13C values with small variability combined with a comparably high radiocarbon reservoir effect (expressed as dead carbon fraction, dcf) of around 60 %. This points towards C contributions of host rock dissolution and/or from an “old” organic matter (OM) reservoir in the karst potentially mobilized due to the warm climatic conditions of the early Holocene. (ii) Between 8 and 3.8 ka there was a strong variability in δ13C with values ranging from −8 ‰ to +1 ‰ and a generally lower dcf. The δ13C variability is most likely caused by changes in C exchange between cave air CO2 and dissolved inorganic carbon in drip water in the cave, which are induced by reduced drip rates as derived from reduced stalagmite growth rates. Additionally, the lower dcf indicates that the OM reservoir contributed less to stalagmite growth in this period possibly as a result of reduced meteoric precipitation or because it was exhausted. (iii) In the youngest section between 3.8 and 2.4 ka, comparably stable and low δ13C values, combined with an increasing dcf reaching up to 50 % again, hint towards a contribution of an aged OM reservoir in the karst. This study reveals the potential of combining high-resolution 14C profiles in speleothems with δ13C records in order to disentangle climate-related C dynamics in karst systems.Rapid and continuous analysis of radiocarbon (14C) concentration in carbonate samples at spatial resolution down to 100 µm has been made possible with the new LA-AMS (laser ablation accelerator mass spectrometry) technique. This novel approach can provide radiocarbon data at a spatial resolution similar to that of stable carbon (C) isotope measurements by isotope ratio mass spectrometry of micromilled samples and, thus, can help to interpret δ13C signatures, which otherwise are difficult to understand due to numerous processes contributing to changes in the C-isotope ratio. In this work, we analyzed δ13C and 14C on the Holocene stalagmite SPA 127 from the high-alpine Spannagel Cave (Austria). Both proxies respond in a complex manner to climate variability. Combined stable carbon and radiocarbon profiles allow three growth periods characterized by different δ13C signatures to be identified: (i) the period 8.5 to 8.0 ka is characterized by relatively low δ13C values with small variability combined with a comparably high radiocarbon reservoir effect (expressed as dead carbon fraction, dcf) of around 60 %. This points towards C contributions of host rock dissolution and/or from an “old” organic matter (OM) reservoir in the karst potentially mobilized due to the warm climatic conditions of the early Holocene. (ii) Between 8 and 3.8 ka there was a strong variability in δ13C with values ranging from −8 ‰ to +1 ‰ and a generally lower dcf. The δ13C variability is most likely caused by changes in C exchange between cave air CO2 and dissolved inorganic carbon in drip water in the cave, which are induced by reduced drip rates as derived from reduced stalagmite growth rates. Additionally, the lower dcf indicates that the OM reservoir contributed less to stalagmite growth in this period possibly as a result of reduced meteoric precipitation or because it was exhausted. (iii) In the youngest section between 3.8 and 2.4 ka, comparably stable and low δ13C values, combined with an increasing dcf reaching up to 50 % again, hint towards a contribution of an aged OM reservoir in the karst. This study reveals the potential of combining high-resolution 14C profiles in speleothems with δ13C records in order to disentangle climate-related C dynamics in karst systems.

Description: In the past few decades, boreal summers have been characterized by an increasing number of extreme weather events in the Northern Hemisphere extratropics, including persistent heat waves, droughts and heavy rainfall events with significant social, economic, and environmental impacts. Many of these events have been associated with the presence of anomalous large-scale atmospheric circulation patterns, in particular, persistent blocking situations, i.e., nearly stationary spatial patterns of air pressure. To contribute to a better understanding of the emergence and dynamical properties of such situations, we construct complex networks representing the atmospheric circulation based on Lagrangian trajectory data of passive tracers advected within the atmospheric flow. For these Lagrangian flow networks, we study the spatial patterns of selected node properties prior to, during, and after different atmospheric blocking events in Northern Hemisphere summer. We highlight the specific network characteristics associated with the sequence of strong blocking episodes over Europe during summer 2010 as an illustrative example. Our results demonstrate the ability of the node degree, entropy, and harmonic closeness centrality based on outgoing links to trace important spatiotemporal characteristics of atmospheric blocking events. In particular, all three measures capture the effective separation of the stationary pressure cell forming the blocking high from the normal westerly flow and the deviation of the main atmospheric currents around it. Our results suggest the utility of further exploiting the Lagrangian flow network approach to atmospheric circulation in future targeted diagnostic and prognostic studies. As the frequency and severity of mid-latitude extreme weather events such as heat waves, droughts, and heavy rainfall events are projected to further increase with ongoing climate change, developing reliable forecasts of such events is becoming a gradually more pressing issue. However, while the quality of predictions has been improving considerably on short-term (up to 2 weeks lead time) and seasonal time scales (beyond 3 months), sub-seasonal forecasting (from 2 weeks to about 3 months) remains a challenging task. This results in part from a limited understanding and representation of phenomena that could potentially increase the predictability at these sub-seasonal scales. One type of such phenomena is atmospheric blocking events. These large-scale, nearly stationary, atmospheric pressure patterns can remain in place for several days or even weeks, disturbing the usual westerly driven circulation and the resulting succession of weather regimes over the mid-latitudes. Despite numerous studies, a comprehensive theory explaining the emergence of blocking-related circulation anomalies and allowing an early forecasting of incipient blocking situations remains to be found. In this work, we utilize a network-based approach, so-called Lagrangian flow networks, for studying the atmospheric circulation associated with blocking situations during Northern hemisphere summer. We discuss the ability of different network measures to detect and track important spatial characteristics of blocking events, suggesting the potential of complex network approaches to provide key elements for future diagnostic and prognostic studies of atmospheric blocking events