Articles | Volume 27, issue 2
27 Apr 2020
Research article | 27 Apr 2020
Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis
Jaqueline Lekscha and Reik V. Donner
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Giorgia Di Capua, Dim Coumou, Bart van der Hurk, Antje Weissheimer, Andrew G. Turner, and Reik V. Donner
Weather Clim. Dynam. Discuss.,
Revised manuscript under review for WCDShort summary
Heavy rainfall in tropical regions interacts with mid-latitude circulation patterns and this interaction can explain weather patterns in the Northern Hemisphere during summer. . In this analysis we detect these tropical – extratropical interaction pattern both in observational datasets and data obtained by atmospheric models and assess how well can atmospheric model reproduce the observed patterns. We find a good agreement although these relationships are too weak in model data.
Tommaso Alberti, Reik V. Donner, and Stéphane Vannitsem
Earth Syst. Dynam., 12, 837–855,Short summary
We provide a novel approach to diagnose the strength of the ocean–atmosphere coupling by using both a reduced order model and reanalysis data. Our findings suggest the ocean–atmosphere dynamics presents a rich variety of features, moving from a chaotic to a coherent coupled dynamics, mainly attributed to the atmosphere and only marginally to the ocean. Our observations suggest further investigations in characterizing the occurrence and spatial dependency of the ocean–atmosphere coupling.
Frederik Wolf, Aiko Voigt, and Reik V. Donner
Earth Syst. Dynam., 12, 353–366,Short summary
In our work, we employ complex networks to study the relation between the time mean position of the intertropical convergence zone (ITCZ) and sea surface temperature (SST) variability. We show that the information hidden in different spatial SST correlation patterns, which we access utilizing complex networks, is strongly correlated with the time mean position of the ITCZ. This research contributes to the ongoing discussion on drivers of the annual migration of the ITCZ.
Frederik Wolf, Ugur Ozturk, Kevin Cheung, and Reik V. Donner
Earth Syst. Dynam., 12, 295–312,Short summary
Motivated by a lacking onset prediction scheme, we examine the temporal evolution of synchronous heavy rainfall associated with the East Asian Monsoon System employing a network approach. We find, that the evolution of the Baiu front is associated with the formation of a spatially separated double band of synchronous rainfall. Furthermore, we identify the South Asian Anticyclone and the North Pacific Subtropical High as the main drivers, which have been assumed to be independent previously.
Giorgia Di Capua, Jakob Runge, Reik V. Donner, Bart van den Hurk, Andrew G. Turner, Ramesh Vellore, Raghavan Krishnan, and Dim Coumou
Weather Clim. Dynam., 1, 519–539,Short summary
We study the interactions between the tropical convective activity and the mid-latitude circulation in the Northern Hemisphere during boreal summer. We identify two circumglobal wave patterns with phase shifts corresponding to the South Asian and the western North Pacific monsoon systems at an intra-seasonal timescale. These patterns show two-way interactions in a causal framework at a weekly timescale and assess how El Niño affects these interactions.
Giorgia Di Capua, Marlene Kretschmer, Reik V. Donner, Bart van den Hurk, Ramesh Vellore, Raghavan Krishnan, and Dim Coumou
Earth Syst. Dynam., 11, 17–34,Short summary
Drivers from both the mid-latitudes and the tropical regions have been proposed to influence the Indian summer monsoon (ISM) subseasonal variability. To understand the relative importance of tropical and mid-latitude drivers, we apply recently developed causal discovery techniques to disentangle the causal relationships among these processes. Our results show that there is indeed a two-way interaction between the mid-latitude circulation and ISM rainfall over central India.
Giorgia Di Capua, Marlene Kretschmer, Reik V. Donner, Bart van den Hurk, Ramesh Vellore, Raghavan Krishnan, and Dim Coumou
Earth Syst. Dynam. Discuss.,
Manuscript not accepted for further reviewShort summary
Both drivers from the mid-latitudes and from the tropical regions have been proposed to influence the Indian summer monsoon (ISM) subseasonal variability. To understand the relative importance of tropical and mid-latitude drivers, we apply recently developed causal discovery techniques to disentangle the causal relationships among these processes. Our results show that there is indeed a two-way interaction between the mid-latitude circulation and ISM rainfall over central India.
Tim Kittel, Catrin Ciemer, Nastaran Lotfi, Thomas Peron, Francisco Rodrigues, Jürgen Kurths, and Reik V. Donner
Nonlin. Processes Geophys. Discuss.,
Revised manuscript not accepted
Jasper G. Franke, Johannes P. Werner, and Reik V. Donner
Clim. Past, 13, 1593–1608,Short summary
We apply evolving functional network analysis, a tool for studying temporal changes of the spatial co-variability structure, to a set of Late Holocene paleoclimate proxy records covering the last two millennia. The emerging patterns obtained by our analysis are related to long-term changes in the dominant mode of atmospheric circulation in the region, the North Atlantic Oscillation (NAO). We obtain a qualitative reconstruction of the NAO long-term variability over the entire Common Era.
Lukas Baumbach, Jonatan F. Siegmund, Magdalena Mittermeier, and Reik V. Donner
Biogeosciences, 14, 4891–4903,Short summary
Temperature extremes play a crucial role for vegetation growth and vitality in vast parts of the European continent. Here, we study the likelihood of simultaneous occurrences of extremes in daytime land surface temperatures and the normalized difference vegetation index (NDVI) for three main periods during the growing season. Our results reveal a particularly high vulnerability of croplands to temperature extremes, while other vegetation types are considerably less affected.
Jonatan F. Siegmund, Marc Wiedermann, Jonathan F. Donges, and Reik V. Donner
Biogeosciences, 13, 5541–5555,Short summary
In this study we systematically quantify simultaneities between meteorological extremes and the timing of flowering of four shrub species across Germany by using event coincidence analysis. Our study confirms previous findings of experimental studies, highlighting the impact of early spring temperatures on the flowering of the investigated plants. Additionally, the analysis reveals statistically significant indications of an influence of temperature extremes in the fall preceding the flowering.
J. F. Donges, R. V. Donner, N. Marwan, S. F. M. Breitenbach, K. Rehfeld, and J. Kurths
Clim. Past, 11, 709–741,Short summary
Paleoclimate records from cave deposits allow the reconstruction of Holocene dynamics of the Asian monsoon system, an important tipping element in Earth's climate. Employing recently developed techniques of nonlinear time series analysis reveals several robust and continental-scale regime shifts in the complexity of monsoonal variability. These regime shifts might have played an important role as drivers of migration, cultural change, and societal collapse during the past 10,000 years.
Y. Zou, R. V. Donner, N. Marwan, M. Small, and J. Kurths
Nonlin. Processes Geophys., 21, 1113–1126,Short summary
We use visibility graphs to characterize asymmetries in the dynamics of sunspot areas in both solar hemispheres. Our analysis provides deep insights into the potential and limitations of this method, revealing a complex interplay between effects due to statistical versus dynamical properties of the observed data. Temporal changes in the hemispheric predominance of the graph connectivity are found to lag those directly associated with the total hemispheric sunspot areas themselves.
R. V. Donner and G. Balasis
Nonlin. Processes Geophys., 20, 965–975,
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligenceIntegrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine deltaPredicting sea surface temperatures with coupled reservoir computersUsing neural networks to improve simulations in the gray zoneThe blessing of dimensionality for the analysis of climate dataProducing realistic climate data with generative adversarial networksIdentification of droughts and heatwaves in Germany with regional climate networksExtracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of MexicoEnsemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitationApplications of matrix factorization methods to climate dataRemember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Nonlin. Processes Geophys., 29, 301–315,Short summary
In this study, we successfully built and evaluated machine learning models for predicting water level dynamics as a proxy for compound flooding hazards in a data-scarce delta. The issues that we tackled here are data scarcity and low computational resources for building flood forecasting models. The proposed approach is suitable for use by local water management agencies in developing countries that encounter these issues.
Benjamin Walleshauser and Erik Bollt
Nonlin. Processes Geophys., 29, 255–264,Short summary
As sea surface temperature (SST) is vital for understanding the greater climate of the Earth and is also an important variable in weather prediction, we propose a model that effectively capitalizes on the reduced complexity of machine learning models while still being able to efficiently predict over a large spatial domain. We find that it is proficient at predicting the SST at specific locations as well as over the greater domain of the Earth’s oceans.
Raphael Kriegmair, Yvonne Ruckstuhl, Stephan Rasp, and George Craig
Nonlin. Processes Geophys., 29, 171–181,Short summary
Our regional numerical weather prediction models run at kilometer-scale resolutions. Processes that occur at smaller scales not yet resolved contribute significantly to the atmospheric flow. We use a neural network (NN) to represent the unresolved part of physical process such as cumulus clouds. We test this approach on a simplified, yet representative, 1D model and find that the NN corrections vastly improve the model forecast up to a couple of days.
Nonlin. Processes Geophys., 28, 409–422,Short summary
In geophysics we often need to analyse large samples of high-dimensional fields. Fortunately but counterintuitively, such high dimensionality can be a blessing, and we demonstrate how this allows simple analytical results to be derived. These results include estimates of correlations between sample members and how the sample mean depends on the sample size. We show that the properties of high dimensionality with success can be applied to climate fields, such as those from ensemble modelling.
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370,Short summary
This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
Gerd Schädler and Marcus Breil
Nonlin. Processes Geophys., 28, 231–245,Short summary
We used regional climate networks (RCNs) to identify past heatwaves and droughts in Germany. RCNs provide information for whole areas and can provide many details of extreme events. The RCNs were constructed on the grid of the E-OBS data set. Time series correlation was used to construct the networks. Network metrics were compared to standard extreme indices and differed considerably between normal and extreme years. The results show that RCNs can identify severe and moderate extremes.
Jonathan M. Lilly and Paula Pérez-Brunius
Nonlin. Processes Geophys., 28, 181–212,Short summary
Long-lived eddies are an important part of the ocean circulation. Here a dataset for studying eddies in the Gulf of Mexico is created through the analysis of trajectories of drifting instruments. The method involves the identification of quasi-periodic signals, characteristic of particles trapped in eddies, from the displacement records, followed by the creation of a measure of statistical significance. It is expected that this dataset will be of use to other authors studying this region.
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91,Short summary
An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.
Dylan Harries and Terence J. O'Kane
Nonlin. Processes Geophys., 27, 453–471,Short summary
Different dimension reduction methods may produce profoundly different low-dimensional representations of multiscale systems. We perform a set of case studies to investigate these differences. When a clear scale separation is present, similar bases are obtained using all methods, but when this is not the case some methods may produce representations that are poorly suited for describing features of interest, highlighting the importance of a careful choice of method when designing analyses.
Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis
Nonlin. Processes Geophys., 27, 23–34,Short summary
Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.
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