scholarly journals Spatiotemporal model for benchmarking causal discovery algorithms

2020 ◽  
Author(s):  
Xavier-Andoni Tibau ◽  
Christian Reimers ◽  
Veronika Eyring ◽  
Joachim Denzler ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Time Series ◽  
Dimensionality Reduction ◽  
Climate Models ◽  
Model System ◽  
Causal Discovery ◽  
Emergent Properties ◽  
Widespread Application ◽  
Spatiotemporal Model ◽  
Climate Modes ◽  
Modes Of Variability

<p>We propose a spatiotemporal model system to evaluate methods of causal discovery. The use of causal discovery to improve our understanding of the spatiotemporal complex system Earth has become widespread in recent years (Runge et al., Nature Comm. 2019). A widespread application example are the complex teleconnections among major climate modes of variability. </p><p>The challenges in estimating such causal teleconnection networks are given by (1) the requirement to reconstruct the climate modes from gridded climate fields (dimensionality reduction) and (2) by general challenges for causal discovery, for instance, high dimensionality and nonlinearity. Both challenges are currently being tackled independently. Both dimensionality reduction methods and causal discovery have made strong progress in recent years, but the interaction between the two has not yet been much tackled so far. Thanks to projects like CMIP a vast amount of climate data is available. In climate models climate modes of variability emerge as macroscale features and it is challenging to objectively benchmark both dimension reduction and causal discovery methods since there is no ground truth for such emergent properties. </p><p>We propose a spatiotemporal model system that encodes causal relationships among well-defined modes of variability. The model can be thought of as an extension of vector-autoregressive models well-known in time series analysis. This model provides a framework for experimenting with causal discovery in large spatiotemporal models. For example, researchers can analyze how the performance of an algorithm is affected under different methods of dimensionality reduction and algorithms for causal discovery. Also challenging features such as non-stationarity and regime-dependence can be modelled and evaluated. Such a model will help the scientific community to improve methods of causal discovery for climate science.</p><p>Runge, J., S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, M. Kretschmer, M. D. Mahecha, J. Muñoz-Marı́, E. H. van Nes, J. Peters, R. Quax, M. Reichstein, M. Scheffer, B. Schölkopf, P. Spirtes, G. Sugihara, J. Sun, K. Zhang, and J. Zscheischler (2019). Inferring causation from time series in earth system sciences. Nature Communications 10 (1), 2553.</p>

Mapped-PCMCI: an algorithm for causal discovery at the grid level

2021 ◽  
Author(s):  
Xavier-Andoni Tibau Alberdi ◽  
Andreas Gerhardus ◽  
Veronika Eyring ◽  
Joachim Denzler ◽  
Jakob Runge
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Dimensional Space ◽  
Causal Discovery ◽  
Spatiotemporal Model ◽  
Vector Autoregressive ◽  
Complex Network Theory ◽  
Dependency Structure ◽  
Grid Level ◽  
Lower Dimensional

<p>We propose a novel causal discovery method for large-scale gridded time series datasets. Causal discovery has been applied to study a number of problems in climate research in recent years. Causal discovery can be conducted either among spatially aggregated variables (such as modes of climate variability) or by inferring a climate network where the associations among pairs of grid points are treated as a network. In the latter case, causal methods have to deal with several challenges arising from the high dimensionality of such datasets and the data's spatially and temporally redundant nature.</p><p>Our method, called Mapped-PCMCI, aims to overcome some of these challenges. The central idea is based on the assumption that there is a lower-dimensional representation of the causal dependencies among different locations. The method first reconstructs a lower-dimensional spatial representation of the data, then conducts causal discovery utilizing the PCMCI method (Runge. et al. 2019), in that lower-dimensional space, and finally maps causal relations back to the grid level. Using spatiotemporal data generated with the spatially aggregated vector-autoregressive (SAVAR) model (Tibau et al. 2020), we demonstrate that Mapped-PCMCI outperforms state-of-the-art methods in orders of magnitude by utilizing the assumption of a lower-dimensional dependency structure. Mapped-PCMCI can be used to better estimate climate networks and thereby help to understand the climate system from the perspective of complex network theory.</p><p> </p><p>J. Runge, P. Nowack, M. Kretschmer, S. Flaxman, D. Sejdinovic, Detecting and quantifying causal associations in large nonlinear time series datasets. Sci. Adv. 5, eaau4996 (2019).</p><p>Tibau, X.-A., Reimers, C., Eyring, V., Denzler, J., Reichstein, M., and Runge, J.: Spatiotemporal model for benchmarking causal discovery algorithms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9604, https://doi.org/10.5194/egusphere-egu2020-9604, 2020</p>

Recent progress and new methods for detecting causal relations in large nonlinear time series datasets

2020 ◽  
Author(s):  
Jakob Runge
Keyword(s):  
Time Series ◽  
Recent Progress ◽  
Nonlinear Time Series ◽  
Causal Discovery ◽  
Major Drawback ◽  
Causal Relations ◽  
Climate Research ◽  
Modes Of Variability ◽  
New Methods ◽  
Time Lagged

<p>Detecting causal relationships from observational time series datasets is a key problem in better understanding the complex dynamical system Earth. Recent methodological advances have addressed major challenges such as high-dimensionality and nonlinearity, e.g., PCMCI (Runge et al. Sci. Adv. 2019), but many more remain. In this talk I will give an overview of challenges and methods and present a novel algorithm to identify causal directions among contemporaneous (or instantaneous) relationships. Such contemporaneous relations frequently appear when time series are aggregated (e.g., at a monthly resolution). Then approaches such as Granger Causality and PCMCI fail because they currently only address time-lagged causal relations.<br>We present extensive numerical examples and results on the causal relations among major climate modes of variability. The work overcomes a major drawback of current causal discovery methods and opens up entirely new possibilities to discover causal relations from time series in climate research and other fields in geosciences.</p><p>Runge et al., Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances eeaau4996 (2019).</p>

scholarly journals Stationary and Non-Stationary Frameworks for Extreme Rainfall Time Series in Southern Italy

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1477 ◽  
Author(s):  
Davide De Luca ◽  
Luciano Galasso
Keyword(s):  
Time Series ◽  
Climate Models ◽  
Southern Italy ◽  
Regional Climate ◽  
Data Series ◽  
Rainfall Time Series ◽  
Annual Maximum ◽  
Data Set ◽  
Maximum Rainfall ◽  
Rainfall Events

This study tests stationary and non-stationary approaches for modelling data series of hydro-meteorological variables. Specifically, the authors considered annual maximum rainfall accumulations observed in the Calabria region (southern Italy), and attention was focused on time series characterized by heavy rainfall events which occurred from 1 January 2000 in the study area. This choice is justified by the need to check if the recent rainfall events in the new century can be considered as very different or not from the events occurred in the past. In detail, the whole data set of each considered time series (characterized by a sample size N > 40 data) was analyzed, in order to compare recent and past rainfall accumulations, which occurred in a specific site. All the proposed models were based on the Two-Component Extreme Value (TCEV) probability distribution, which is frequently applied for annual maximum time series in Calabria. The authors discussed the possible sources of uncertainty related to each framework and remarked on the crucial role played by ergodicity. In fact, if the process is assumed to be non-stationary, then ergodicity cannot hold, and thus possible trends should be derived from external sources, different from the time series of interest: in this work, Regional Climate Models’ (RCMs) outputs were considered in order to assess possible trends of TCEV parameters. From the obtained results, it does not seem essential to adopt non-stationary models, as significant trends do not appear from the observed data, due to a relevant number of heavy events which also occurred in the central part of the last century.

scholarly journals Improving runoff estimates from regional climate models: a performance analysis in Spain

2012 ◽  
Vol 16 (6) ◽  
pp. 1709-1723 ◽  
Author(s):  
D. González-Zeas ◽  
L. Garrote ◽  
A. Iglesias ◽  
A. Sordo-Ward
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Climate Models ◽  
Regional Climate ◽  
Aridity Index ◽  
Regional Climate Models ◽  
Hydrologic Model ◽  
Direct Runoff ◽  
Interpolation Methods ◽  
Basin Scale

Abstract. An important step to assess water availability is to have monthly time series representative of the current situation. In this context, a simple methodology is presented for application in large-scale studies in regions where a properly calibrated hydrologic model is not available, using the output variables simulated by regional climate models (RCMs) of the European project PRUDENCE under current climate conditions (period 1961–1990). The methodology compares different interpolation methods and alternatives to generate annual times series that minimise the bias with respect to observed values. The objective is to identify the best alternative to obtain bias-corrected, monthly runoff time series from the output of RCM simulations. This study uses information from 338 basins in Spain that cover the entire mainland territory and whose observed values of natural runoff have been estimated by the distributed hydrological model SIMPA. Four interpolation methods for downscaling runoff to the basin scale from 10 RCMs are compared with emphasis on the ability of each method to reproduce the observed behaviour of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index, defined as the ratio between potential evapotranspiration and precipitation. In addition, the comparison with respect to the global runoff reference of the UNH/GRDC dataset is evaluated, as a contrast of the "best estimator" of current runoff on a large scale. Results show that the bias is minimised using the direct original interpolation method and the best alternative for bias correction of the monthly direct runoff time series of RCMs is the UNH/GRDC dataset, although the formula proposed by Schreiber (1904) also gives good results.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines

2017 ◽  
pp. 317-339 ◽  
Author(s):  
Jochen Garcke ◽  
Rodrigo Iza-Teran ◽  
Marvin Marks ◽  
Mandar Pathare ◽  
Dirk Schollbach ◽  
...  
Keyword(s):  
Time Series ◽  
Dimensionality Reduction ◽  
Wind Turbines ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Of Time Series

scholarly journals A model intercomparison analysing the link between ozone and geopotential height anomalies in January

2007 ◽  
Vol 7 (6) ◽  
pp. 15409-15451 ◽  
Author(s):  
P. Braesicke ◽  
C. Brühl ◽  
M. Dameris ◽  
R. Deckert ◽  
V. Eyring ◽  
...  
Keyword(s):  
Geopotential Height ◽  
Climate Models ◽  
Horizontal Resolution ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Modes Of Variability ◽  
Statistical Framework ◽  
Column Ozone ◽  
Linear Correlations ◽  
Upper Boundary

Abstract. A statistical framework to evaluate the performance of chemistry-climate models with respect to the interaction between meteorology and ozone during northern hemisphere mid-winter, in particularly January, is used. Different statistical diagnostics from four chemistry-climate models (E39C, ME4C, UMUCAM, ULAQ) are compared with the ERA-40 re-analysis. First, we analyse vertical coherence in geopotential height anomalies as described by linear correlations between two different pressure levels (30 and 200 hPa) of the atmosphere. In addition, linear correlations between (partial) column ozone and geopotential height anomalies at 200 hPa are discussed to motivate a simple picture of the meteorological impacts on ozone on interannual timescales. Secondly, we discuss characteristic spatial structures in geopotential height and (partial) column ozone anomalies as given by their first two empirical orthogonal functions. Finally, we describe the covariance patterns between reconstructed anomalies of geopotential height and (partial) column ozone. In general we find good agreement between the models with higher horizontal resolution (E39C, ME4C, UMUCAM) and ERA-40. Some diagnostics seem to be capable of picking up model similarities (either that the models use the same dynamical core (E39C, ME4C), or that they have a high upper boundary (ME4C, UMUCAM)). The methodology allows to identify the leading modes of variability contributing to the overall ozone-geopotential height correlations and points to interesting differences between the chemistry-climate models and ERA-40. Those discrepancies have to be taken into account when providing confidence intervals for climate change integrations.

scholarly journals Arabidopsis sepals: A model system for the emergent process of morphogenesis

2021 ◽  
Vol 2 ◽  
Author(s):  
Adrienne H. K. Roeder
Keyword(s):  
Gene Expression ◽  
Arabidopsis Thaliana ◽  
Cell Division ◽  
Slow Growth ◽  
Expression Patterns ◽  
Giant Cells ◽  
Model System ◽  
Gene Expression Patterns ◽  
Emergent Properties ◽  
Correct Size

Abstract During development, Arabidopsis thaliana sepal primordium cells grow, divide and interact with their neighbours, giving rise to a sepal with the correct size, shape and form. Arabidopsis sepals have proven to be a good system for elucidating the emergent processes driving morphogenesis due to their simplicity, their accessibility for imaging and manipulation, and their reproducible development. Sepals undergo a basipetal gradient of growth, with cessation of cell division, slow growth and maturation starting at the tip of the sepal and progressing to the base. In this review, I discuss five recent examples of processes during sepal morphogenesis that yield emergent properties: robust size, tapered tip shape, laminar shape, scattered giant cells and complex gene expression patterns. In each case, experiments examining the dynamics of sepal development led to the hypotheses of local rules. In each example, a computational model was used to demonstrate that these local rules are sufficient to give rise to the emergent properties of morphogenesis.

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