Articles | Volume 27, issue 2
https://doi.org/10.5194/npg-27-261-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-27-261-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Apr 2020
Research article |
| 27 Apr 2020
| 27 Apr 2020
Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis
Jaqueline Lekscha
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK) – Member of the Leibniz Association, 14473 Potsdam, Germany
Department of Physics, Humboldt University, 12489 Berlin, Germany
Reik V. Donner
Potsdam Institute for Climate Impact Research (PIK) – Member of the Leibniz Association, 14473 Potsdam, Germany
Department of Water, Environment, Construction and Safety, Magdeburg–Stendal University of Applied Sciences, 39114 Magdeburg, Germany
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David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024, https://doi.org/10.5194/npg-31-115-2024, 2024
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Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.
Giorgia Di Capua, Dim Coumou, Bart van den Hurk, Antje Weisheimer, Andrew G. Turner, and Reik V. Donner
Weather Clim. Dynam., 4, 701–723, https://doi.org/10.5194/wcd-4-701-2023, https://doi.org/10.5194/wcd-4-701-2023, 2023
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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 atmospheric models can reproduce the observed patterns. We find a good agreement although these relationships are weaker in model data.
Julianna Carvalho-Oliveira, Giorgia di Capua, Leonard Borchert, Reik Donner, and Johanna Baehr
EGUsphere, https://doi.org/10.5194/egusphere-2023-1412, https://doi.org/10.5194/egusphere-2023-1412, 2023
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We demonstrate with a causality analysis that an important recurrent summer atmospheric pattern, the so-called East Atlantic teleconnection, is influenced by the extratropical North Atlantic in spring during the second half of the 20th century. This causal link is, however, not well represented by our evaluated seasonal climate prediction system. We show that simulations able to reproduce this link show improved surface climate prediction credibility over those that do not.
Tommaso Alberti, Reik V. Donner, and Stéphane Vannitsem
Earth Syst. Dynam., 12, 837–855, https://doi.org/10.5194/esd-12-837-2021, https://doi.org/10.5194/esd-12-837-2021, 2021
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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, https://doi.org/10.5194/esd-12-353-2021, https://doi.org/10.5194/esd-12-353-2021, 2021
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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, https://doi.org/10.5194/esd-12-295-2021, https://doi.org/10.5194/esd-12-295-2021, 2021
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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, https://doi.org/10.5194/wcd-1-519-2020, https://doi.org/10.5194/wcd-1-519-2020, 2020
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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, https://doi.org/10.5194/esd-11-17-2020, https://doi.org/10.5194/esd-11-17-2020, 2020
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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., https://doi.org/10.5194/esd-2019-11, https://doi.org/10.5194/esd-2019-11, 2019
Manuscript not accepted for further review
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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., https://doi.org/10.5194/npg-2017-69, https://doi.org/10.5194/npg-2017-69, 2017
Revised manuscript not accepted
Jasper G. Franke, Johannes P. Werner, and Reik V. Donner
Clim. Past, 13, 1593–1608, https://doi.org/10.5194/cp-13-1593-2017, https://doi.org/10.5194/cp-13-1593-2017, 2017
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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, https://doi.org/10.5194/bg-14-4891-2017, https://doi.org/10.5194/bg-14-4891-2017, 2017
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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, https://doi.org/10.5194/bg-13-5541-2016, https://doi.org/10.5194/bg-13-5541-2016, 2016
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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, https://doi.org/10.5194/cp-11-709-2015, https://doi.org/10.5194/cp-11-709-2015, 2015
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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, https://doi.org/10.5194/npg-21-1113-2014, https://doi.org/10.5194/npg-21-1113-2014, 2014
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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, https://doi.org/10.5194/npg-20-965-2013, https://doi.org/10.5194/npg-20-965-2013, 2013
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 intelligence
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Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations
The sampling method for optimal precursors of El Niño–Southern Oscillation events
A comparison of two causal methods in the context of climate analyses
A two-fold deep-learning strategy to correct and downscale winds over mountains
Downscaling of surface wind forecasts using convolutional neural networks
Learning Extreme Vegetation Response to Climate Forcing: A Comparison of Recurrent Neural Network Architectures
Data-driven methods to estimate the committor function in conceptual ocean models
Exploring meteorological droughts' spatial patterns across Europe through complex network theory
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Predicting sea surface temperatures with coupled reservoir computers
Using neural networks to improve simulations in the gray zone
The blessing of dimensionality for the analysis of climate data
Producing realistic climate data with generative adversarial networks
Identification of droughts and heatwaves in Germany with regional climate networks
Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico
Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation
Applications of matrix factorization methods to climate data
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand
Nonlin. Processes Geophys., 31, 409–431, https://doi.org/10.5194/npg-31-409-2024, https://doi.org/10.5194/npg-31-409-2024, 2024
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We train neural networks as denoising diffusion models for state generation in the Lorenz 1963 system and demonstrate that they learn an internal representation of the system. We make use of this learned representation and the pre-trained model in two downstream tasks: surrogate modelling and ensemble generation. For both tasks, the diffusion model can outperform other more common approaches. Thus, we see a potential of representation learning with diffusion models for dynamical systems.
Susana Barbosa, Maria Eduarda Silva, and Denis-Didier Rousseau
Nonlin. Processes Geophys., 31, 433–447, https://doi.org/10.5194/npg-31-433-2024, https://doi.org/10.5194/npg-31-433-2024, 2024
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The characterisation of abrupt transitions in palaeoclimate records allows understanding of millennial climate variability and potential tipping points in the context of current climate change. In our study an algorithmic method, the matrix profile, is employed to characterise abrupt warmings designated as Dansgaard–Oeschger (DO) events and to identify the most similar transitions in the palaeoclimate time series.
John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad
Nonlin. Processes Geophys., 31, 247–257, https://doi.org/10.5194/npg-31-247-2024, https://doi.org/10.5194/npg-31-247-2024, 2024
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During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably.
Bin Shi and Junjie Ma
Nonlin. Processes Geophys., 31, 165–174, https://doi.org/10.5194/npg-31-165-2024, https://doi.org/10.5194/npg-31-165-2024, 2024
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Different from traditional deterministic optimization algorithms, we implement the sampling method to compute the conditional nonlinear optimal perturbations (CNOPs) in the realistic and predictive coupled ocean–atmosphere model, which reduces the first-order information to the zeroth-order one, avoiding the high-cost computation of the gradient. The numerical performance highlights the importance of stochastic optimization algorithms to compute CNOPs and capture initial optimal precursors.
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024, https://doi.org/10.5194/npg-31-115-2024, 2024
Short summary
Short summary
Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.
Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig
Nonlin. Processes Geophys., 31, 75–97, https://doi.org/10.5194/npg-31-75-2024, https://doi.org/10.5194/npg-31-75-2024, 2024
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Forecasting wind fields over mountains is of high importance for several applications and particularly for understanding how wind erodes and disperses snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large-scale wind forecasts in mountains and increase their spatial resolution.
Florian Dupuy, Pierre Durand, and Thierry Hedde
Nonlin. Processes Geophys., 30, 553–570, https://doi.org/10.5194/npg-30-553-2023, https://doi.org/10.5194/npg-30-553-2023, 2023
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Forecasting near-surface winds over complex terrain requires high-resolution numerical weather prediction models, which drastically increase the duration of simulations and hinder them in running on a routine basis. A faster alternative is statistical downscaling. We explore different ways of calculating near-surface wind speed and direction using artificial intelligence algorithms based on various convolutional neural networks in order to find the best approach for wind downscaling.
Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora
EGUsphere, https://doi.org/10.5194/egusphere-2023-2368, https://doi.org/10.5194/egusphere-2023-2368, 2023
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We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.
Valérian Jacques-Dumas, René M. van Westen, Freddy Bouchet, and Henk A. Dijkstra
Nonlin. Processes Geophys., 30, 195–216, https://doi.org/10.5194/npg-30-195-2023, https://doi.org/10.5194/npg-30-195-2023, 2023
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Computing the probability of occurrence of rare events is relevant because of their high impact but also difficult due to the lack of data. Rare event algorithms are designed for that task, but their efficiency relies on a score function that is hard to compute. We compare four methods that compute this function from data and measure their performance to assess which one would be best suited to be applied to a climate model. We find neural networks to be most robust and flexible for this task.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
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Despite being among the most severe climate extremes, it is still challenging to assess droughts’ features for specific regions. In this paper we study meteorological droughts in Europe using concepts derived from climate network theory. By exploring the synchronization in droughts occurrences across the continent we unveil regional clusters which are individually examined to identify droughts’ geographical propagation and source–sink systems, which could potentially support droughts’ forecast.
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Nonlin. Processes Geophys., 29, 301–315, https://doi.org/10.5194/npg-29-301-2022, https://doi.org/10.5194/npg-29-301-2022, 2022
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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, https://doi.org/10.5194/npg-29-255-2022, https://doi.org/10.5194/npg-29-255-2022, 2022
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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, https://doi.org/10.5194/npg-29-171-2022, https://doi.org/10.5194/npg-29-171-2022, 2022
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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.
Bo Christiansen
Nonlin. Processes Geophys., 28, 409–422, https://doi.org/10.5194/npg-28-409-2021, https://doi.org/10.5194/npg-28-409-2021, 2021
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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, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
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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, https://doi.org/10.5194/npg-28-231-2021, https://doi.org/10.5194/npg-28-231-2021, 2021
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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, https://doi.org/10.5194/npg-28-181-2021, https://doi.org/10.5194/npg-28-181-2021, 2021
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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, https://doi.org/10.5194/npg-28-61-2021, https://doi.org/10.5194/npg-28-61-2021, 2021
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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, https://doi.org/10.5194/npg-27-453-2020, https://doi.org/10.5194/npg-27-453-2020, 2020
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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, https://doi.org/10.5194/npg-27-23-2020, https://doi.org/10.5194/npg-27-23-2020, 2020
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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.
Cited articles
Abarbanel, H. D. I., Brown, R., Sidorowich, J. J., and Tsimring, L. S.: The
analysis of observed chaotic data in physical systems,
Rev. Mod. Phys., 65, 1331–1392, https://doi.org/10.1103/revmodphys.65.1331, 1993. a
Barrio, R. and Serrano, S.: A three-parametric study of the Lorenz model,
Physica D, 229, 43–51,
https://doi.org/10.1016/j.physd.2007.03.013, 2007. a
Bradley, R. S.: Paleoclimatology (Third Edition), Academic Press, third edition
edn., https://doi.org/10.1016/C2009-0-18310-1, 2015. a, b
Cohen, A.: Paleolimnology: The History and Evolution of Lake Systems, Oxford
University Press, 2003. a
Dee, S., Emile-Geay, J., Evans, M. N., Allam, A., Steig, E. J., and Thompson,
D.: PRYSM: An open-source framework for PRoxY System Modeling, with
applications to oxygen-isotope systems,
J. Adv. Model. Earth Sy., 7, 1220–1247, https://doi.org/10.1002/2015MS000447, 2015. a, b, c
Dee, S. G., Russell, J. M., Morrill, C., Chen, Z., and Neary, A.: PRYSM v2.0:
A Proxy System Model for Lacustrine Archives,
Paleoceanogr. Paleocl., 33, 1250–1269, https://doi.org/10.1029/2018PA003413, 2018. a, b, c
De Jonge, C., Hopmans, E. C., Zell, C. I., Kim, J.-H., Schouten, S., and
Damsté, J. S. S.: Occurrence and abundance of 6-methyl branched glycerol
dialkyl glycerol tetraethers in soils: Implications for palaeoclimate
reconstruction, Geochim. Cosmochim. Ac., 141, 97–112,
https://doi.org/10.1016/j.gca.2014.06.013, 2014. a
Donges, J. F., Donner, R. V., Rehfeld, K., Marwan, N., Trauth, M. H., and Kurths, J.: Identification of dynamical transitions in marine palaeoclimate records by recurrence network analysis, Nonlin. Processes Geophys., 18, 545–562, https://doi.org/10.5194/npg-18-545-2011, 2011a. a, b, c, d
Donges, J. F., Donner, R. V., Trauth, M. H., Marwan, N., Schellnhuber, H.-J.,
and Kurths, J.: Nonlinear detection of paleoclimate-variability transitions
possibly related to human evolution, P. Natl. Acad.
Sci. USA, 108, 20422–20427, https://doi.org/10.1073/pnas.1117052108,
2011b. a, b
Donges, J. F., Donner, R. V., Marwan, N., Breitenbach, S. F. M., Rehfeld, K., and Kurths, J.: Non-linear regime shifts in Holocene Asian monsoon variability: potential impacts on cultural change and migratory patterns, Clim. Past, 11, 709–741, https://doi.org/10.5194/cp-11-709-2015, 2015a. a, b
Donges, J. F., Heitzig, J., Beronov, B., Wiedermann, M., Runge, J., Feng, Q. Y., Tupikina, L., Stolbova, V., Donner, R. V.,
Marwan, N., Dijkstra, H. A., and Kurths, J.:
Unified functional network and nonlinear time series analysis for complex
systems science: The pyunicorn package, Chaos, 25, 113101,
https://doi.org/10.1063/1.4934554, 2015b. a, b
Donner, R. V., Zou, Y., Donges, J. F., Marwan, N., and Kurths, J.: Ambiguities
in recurrence-based complex network representations of time series, Phys.
Rev. E, 81, 015101, https://doi.org/10.1103/physreve.81.015101, 2010a. a
Donner, R. V., Zou, Y., Donges, J. F., Marwan, N., and Kurths, J.: Recurrence
networks – a novel paradigm for nonlinear time series analysis,
New J. Phys., 12, 033025, https://doi.org/10.1088/1367-2630/12/3/033025,
2010b. a
Donner, R. V., Heitzig, J., Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.:
The geometry of chaotic dynamics – a complex network perspective,
Eur. Phys. J. B, 84, 653–672, https://doi.org/10.1140/epjb/e2011-10899-1,
2011a. a, b
Donner, R. V., Small, M., Donges, J. F., Marwan, N., Zou, Y., Xiang, R., and
Kurths, J.: Recurrence-based time series analysis by means of complex network
methods, Int. J. Bifurcat. Chaos, 21, 1019–1046,
https://doi.org/10.1142/s0218127411029021, 2011b. a
Eroglu, D., McRobie, F. H., Ozken, I., Stemler, T., Wyrwoll, K.-H.,
Breitenbach, S. F. M., Marwan, N., and Kurths, J.: See-saw relationship of
the Holocene East Asian-Australian summer monsoon, Nat. Commun., 7,
12929, https://doi.org/10.1038/ncomms12929, 2016. a, b
Esper, J., Krusic, P. J., Ljungqvist, F. C., Luterbacher, J., Carrer, M., Cook,
E., Davi, N. K., Hartl-Meier, C., Kirdyanov, A., Konter, O., Myglan, V.,
Timonen, M., Treydte, K., Trouet, V., Villalba, R., Yang, B., and Büntgen,
U.: Ranking of tree-ring based temperature reconstructions of the past
millennium, Quaternary Sci. Rev., 145, 134–151,
https://doi.org/10.1016/j.quascirev.2016.05.009, 2016. a
Evans, M., Tolwinski-Ward, S., Thompson, D., and Anchukaitis, K.: Applications
of proxy system modeling in high resolution paleoclimatology, Quaternary Sci. Rev., 76, 16–28,
https://doi.org/10.1016/j.quascirev.2013.05.024, 2013. a, b
Franke, J. G. and Donner, R. V.: Dynamical anomalies in terrestrial proxies of
North Atlantic climate variability during the last 2 ka, Climatic Change,
143, 87–100, https://doi.org/10.1007/s10584-017-1979-z, 2017. a, b, c, d
Fraser, A. M. and Swinney, H. L.: Independent coordinates for strange
attractors from mutual information, Phys. Rev. A, 33, 1134–1140,
https://doi.org/10.1103/PhysRevA.33.1134, 1986. a
Fritts, H. C.: Tree rings and climate, Academic Press,
https://doi.org/10.1016/B978-0-12-268450-0.X5001-0, 1976. a
Gennaretti, F., Arseneault, D., Nicault, A., Perreault, L., and Bégin, Y.:
Volcano-induced regime shifts in millennial tree-ring chronologies from
northeastern North America, P. Natl. Acad. Sci. USA,
111, 10077–10082, https://doi.org/10.1073/pnas.1324220111, 2014. a, b, c, d
Goswami, B., Boers, N., Rheinwalt, A., Marwan, N., Heitzig, J., Breitenbach, S.
F. M., and Kurths, J.: Abrupt transitions in time series with uncertainties,
Nat. Commun., 9, 48, https://doi.org/10.1038/s41467-017-02456-6, 2018. a
Hakim, G. J., Emile-Geay, J., Steig, E. J., Noone, D., Anderson, D. M., Tardif,
R., Steiger, N., and Perkins, W. A.: The last millennium climate reanalysis
project: Framework and first results, J. Geophys. Res.-Atmos., 121, 6745–6764, https://doi.org/10.1002/2016JD024751, 2016. a, b, c, d
Herron, M. M. and Langway, C. C.: Firn Densification: An Empirical Model,
J. Glaciol., 25, 373–385, https://doi.org/10.3189/S0022143000015239, 1980. a
Huang, J., van den Dool, H. M., and Georgarakos, K. P.: Analysis of
Model-Calculated Soil Moisture over the United States (1931–1993) and
Applications to Long-Range Temperature Forecasts, J. Climate, 9,
1350–1362, https://doi.org/10.1175/1520-0442(1996)009<1350:AOMCSM>2.0.CO;2, 1996. a
Johnsen, S. J., Clausen, H. B., Cuffey, K. M., Hoffmann, G., Schwander, J., and
Creyts, T.: Diffusion of stable isotopes in polar firn and ice: the isotope
effect in firn diffusion, Physics of Ice Core Records, 159, 121–140,
https://doi.org/10.7916/D8KW5D4X, 2000. a
ouzel, J.: A brief history of ice core science over the last 50 yr, Clim. Past, 9, 2525–2547, https://doi.org/10.5194/cp-9-2525-2013, 2013. a
Kantz, H. and Schreiber, T.: Nonlinear time series analysis, Cambridge
University Press, 2 edn., 2004. a
Kennel, M. B., Brown, R., and Abarbanel, H. D. I.: Determining embedding
dimension for phase-space reconstruction using a geometrical construction,
Phys. Rev. A, 45, 3403–3411, https://doi.org/10.1103/PhysRevA.45.3403, 1992. a
Lekscha, J. and Donner, R. V.: Phase space reconstruction for non-uniformly
sampled noisy time series, Chaos, 28, 085702, https://doi.org/10.1063/1.5023860,
2018. a, b
Lekscha, J. and Donner, R. V.: Areawise significance tests for windowed
recurrence network analysis, P. Roy. Soc. A, 475,
20190161, https://doi.org/10.1098/rspa.2019.0161, 2019. a, b, c
Lorenz, E. N.: Deterministic Nonperiodic Flow, J. Atmos.
Sci., 20, 130–141, https://doi.org/10.1175/1520-0469(1963)020<0130:dnf>2.0.co;2,
1963. a, b
Maraun, D., Kurths, J., and Holschneider, M.: Nonstationary Gaussian processes
in wavelet domain: Synthesis, estimation, and significance testing, Phys.
Rev. E, 75, 016707, https://doi.org/10.1103/PhysRevE.75.016707, 2007. a
Marwan, N., Thiel, M., and Nowaczyk, N. R.: Cross recurrence plot based synchronization of time series, Nonlin. Processes Geophys., 9, 325–331, https://doi.org/10.5194/npg-9-325-2002, 2002. a
Marwan, N., Trauth, M. H., Vuille, M., and Kurths, J.: Comparing modern and
Pleistocene ENSO-like influences in NW Argentina using nonlinear time
series analysis methods, Clim. Dynam., 21, 317–326,
https://doi.org/10.1007/s00382-003-0335-3, 2003. a
Marwan, N., Donges, J. F., Zou, Y., Donner, R. V., and Kurths, J.: Complex
network approach for recurrence analysis of time series, Phys. Lett. A,
373, 4246–4254, https://doi.org/10.1016/j.physleta.2009.09.042, 2009. a
Packard, N. H., Crutchfield, J. P., Farmer, J. D., and Shaw, R. S.: Geometry
from a Time Series, Phys. Rev. Lett., 45, 712–716,
https://doi.org/10.1103/physrevlett.45.712, 1980. a
Partin, J. W., Quinn, T. M., Shen, C.-C., Emile-Geay, J., Taylor, F. W.,
Maupin, C. R., Lin, K., Jackson, C. S., Banner, J. L., Sinclair, D. J., and
Huh, C.-A.: Multidecadal rainfall variability in South Pacific Convergence
Zone as revealed by stalagmite geochemistry, Geology, 41, 1143–1146,
https://doi.org/10.1130/G34718.1, 2013. a
Portes, L. L., Benda, R. N., Ugrinowitsch, H., and Aguirre, L. A.: Impact of
the recorded variable on recurrence quantification analysis of flows, Phys.
Lett. A, 378, 2382–2388, https://doi.org/10.1016/j.physleta.2014.06.014, 2014. a
Portes, L. L., Montanari, A. N., Correa, D. C., Small, M., and Aguirre, L. A.:
The reliability of recurrence network analysis is influenced by the
observability properties of the recorded time series, Chaos, 29, 083101,
https://doi.org/10.1063/1.5093197, 2019. a
Rössler, O.: An equation for continuous chaos, Phys. Lett. A, 57, 397–398, https://doi.org/10.1016/0375-9601(76)90101-8, 1976. a
Russell, J. M., Hopmans, E. C., Loomis, S. E., Liang, J., and Damsté, J.
S. S.: Distributions of 5- and 6-methyl branched glycerol dialkyl glycerol
tetraethers (brGDGTs) in East African lake sediment: Effects of temperature,
pH, and new lacustrine paleotemperature calibrations, Org. Geochem.,
117, 56–69, https://doi.org/10.1016/j.orggeochem.2017.12.003, 2018. a, b
Schleussner, C.-F., Divine, D. V., Donges, J. F., Miettinen, A., and Donner,
R. V.: Indications for a North Atlantic ocean circulation regime shift at
the onset of the Little Ice Age, Clim. Dynam., 45, 3623–3633,
https://doi.org/10.1007/s00382-015-2561-x, 2015. a
Schreiber, T. and Schmitz, A.: Surrogate time series, Physica D, 142, 346–382,
https://doi.org/10.1016/S0167-2789(00)00043-9, 2000. a
St. George, S.: An overview of tree-ring width records across the Northern
Hemisphere, Quaternary Sci. Rev., 95, 132–150,
https://doi.org/10.1016/j.quascirev.2014.04.029, 2014. a
St. George, S. and Esper, J.: Concord and discord among Northern Hemisphere
paleotemperature reconstructions from tree rings, Quaternary Sci. Rev.,
203, 278–281, https://doi.org/10.1016/j.quascirev.2018.11.013, 2019. a
Takens, F.: Detecting strange attractors in turbulence, in: Lecture Notes in
Mathematics, 366–381, Springer Science and Business Media,
https://doi.org/10.1007/bfb0091924, 1980. a, b
Tardif, R., Hakim, G. J., Perkins, W. A., Horlick, K. A., Erb, M. P., Emile-Geay, J., Anderson, D. M., Steig, E. J., and Noone, D.: Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling, Clim. Past, 15, 1251–1273, https://doi.org/10.5194/cp-15-1251-2019, 2019. a, b, c, d
Thompson, L. G., Davis, M. E., Mosley-Thompson, E., Lin, P.-N., Henderson,
K. A., and Mashiotta, T. A.: Tropical ice core records: evidence for
asynchronous glaciation on Milankovitch timescales, J. Quaternary
Sci., 20, 723–733, https://doi.org/10.1002/jqs.972, 2005. a
Thompson, L. G., Mosley-Thompson, E., Davis, M. E., Zagorodnov, V. S., Howat,
I. M., Mikhalenko, V. N., and Lin, P.-N.: Annually Resolved Ice Core Records
of Tropical Climate Variability over the Past ∼1800 Years, Science, 340,
945–950, https://doi.org/10.1126/science.1234210, 2013. a, b
Tolwinski-Ward, S. E., Evans, M. N., Hughes, M. K., and Anchukaitis, K. J.: An
efficient forward model of the climate controls on interannual variation in
tree-ring width, Clim. Dynam., 36, 2419–2439,
https://doi.org/10.1007/s00382-010-0945-5, 2011. a, b, c
Tolwinski-Ward, S. E., Anchukaitis, K. J., and Evans, M. N.: Bayesian parameter estimation and interpretation for an intermediate model of tree-ring width, Clim. Past, 9, 1481–1493, https://doi.org/10.5194/cp-9-1481-2013, 2013. a
Trauth, M. H.: TURBO2: A MATLAB simulation to study the effects of
bioturbation on paleoceanographic time series, Comput. Geosci., 61,
1–10, https://doi.org/10.1016/j.cageo.2013.05.003, 2013. a
Trauth, M. H., Bookhagen, B., Marwan, N., and Strecker, M. R.: Multiple
landslide clusters record Quaternary climate changes in the northwestern
Argentine Andes, Palaeogeogr. Palaeocl., 194,
109–121, https://doi.org/10.1016/s0031-0182(03)00273-6, 2003. a
Vaganov, E. A., Hughes, M. K., and Shashkin, A. V.: Growth Dynamics of Conifer
Tree Rings, Springer-Verlag Berlin Heidelberg, 1 edn.,
https://doi.org/10.1007/3-540-31298-6, 2006.
a
Wackerbarth, A., Scholz, D., Fohlmeister, J., and Mangini, A.: Modelling the
δ18O value of cave drip water and speleothem calcite, Earth
Planet. Sc. Lett., 299, 387–397, https://doi.org/10.1016/j.epsl.2010.09.019,
2010. a
Wang, Y., Cheng, H., Edwards, R. L., He, Y., Kong, X., An, Z., Wu, J., Kelly,
M. J., Dykoski, C. A., and Li, X.: The Holocene Asian Monsoon: Links to
Solar Changes and North Atlantic Climate, Science, 308, 854–857,
https://doi.org/10.1126/science.1106296, 2005. a, b
Weijers, J. W., Schouten, S., van den Donker, J. C., Hopmans, E. C., and
Damsté, J. S. S.: Environmental controls on bacterial tetraether membrane
lipid distribution in soils, Geochim. Cosmochim. Ac., 71, 703–713,
https://doi.org/10.1016/j.gca.2006.10.003, 2007. a
Wong, C. I. and Breecker, D. O.: Advancements in the use of speleothems as
climate archives, Quaternary Sci. Rev., 127, 1–18,
https://doi.org/10.1016/j.quascirev.2015.07.019, 2015. a
Yarleque, C., Vuille, M., Hardy, D. R., Timm, O. E., De la Cruz, J., Ramos, H.,
and Rabatel, A.: Projections of the future disappearance of the Quelccaya Ice
Cap in the Central Andes, Sci. Rep.-UK, 8, 15564–15564,
https://doi.org/10.1038/s41598-018-33698-z, 2018. a, b
Zumbahlen, H.: CHAPTER 8 – Analog Filters, in: Linear Circuit Design Handbook,
edited by: Zumbahlen, H., Newnes, https://doi.org/10.1016/B978-0-7506-8703-4.00008-0,
2008. a
