369 research outputs found
Testing the detectability of spatio–temporal climate transitions from paleoclimate networks with the START model
A critical challenge in paleoclimate data analysis is the fact that the proxy data are
heterogeneously distributed in space, which affects statistical methods that
rely on spatial embedding of data. In the paleoclimate network approach nodes
represent paleoclimate proxy time series, and links in the network are given
by statistically significant similarities between them. Their location in
space, proxy and archive type is coded in the node attributes.
<br><br>
We develop a semi-empirical model for <b>S</b>patio-<b>T</b>emporally
<b>A</b>utoco<b>R</b>related <b>T</b>ime series, inspired by the
interplay of different Asian Summer Monsoon (ASM) systems. We use an ensemble
of transition runs of this START model to test whether and how
spatio–temporal climate transitions could be detectable from (paleo)climate
networks. We sample model time series both on a grid and at locations at
which paleoclimate data are available to investigate the effect of the
spatially heterogeneous availability of data. Node betweenness centrality,
averaged over the transition region, does not respond to the transition
displayed by the START model, neither in the grid-based nor in the scattered
sampling arrangement. The regionally defined measures of regional node degree
and cross link ratio, however, are indicative of the changes in both
scenarios, although the magnitude of the changes differs according to the
sampling.
<br><br>
We find that the START model is particularly suitable for pseudo-proxy
experiments to test the technical reconstruction limits of paleoclimate data
based on their location, and we conclude that (paleo)climate networks are
suitable for investigating spatio–temporal transitions in the dependence
structure of underlying climatic fields
Recommended from our members
Networks from Flows - From Dynamics to Topology
Complex network approaches have recently been applied to continuous spatial dynamical systems, like climate, successfully uncovering the system's interaction structure. However the relationship between the underlying atmospheric or oceanic flow's dynamics and the estimated network measures have remained largely unclear. We bridge this crucial gap in a bottom-up approach and define a continuous analytical analogue of Pearson correlation networks for advection-diffusion dynamics on a background flow. Analysing complex networks of prototypical flows and from time series data of the equatorial Pacific, we find that our analytical model reproduces the most salient features of these networks and thus provides a general foundation of climate networks. The relationships we obtain between velocity field and network measures show that line-like structures of high betweenness mark transition zones in the flow rather than, as previously thought, the propagation of dynamical information
Comparison of correlation analysis techniques for irregularly sampled time series
Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation) or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques. <br><br> All methods have comparable root mean square errors (RMSEs) for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF) for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF) the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods. <br><br> We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem &delta;<sup>18</sup>O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory) is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data
Recommended from our members
Comparison of correlation analysis techniques for irregularly sampled time series
Geoscientific measurements often provide time series with irregular time sampling, requiring either data reconstruction (interpolation) or sophisticated methods to handle irregular sampling. We compare the linear interpolation technique and different approaches for analyzing the correlation functions and persistence of irregularly sampled time series, as Lomb-Scargle Fourier transformation and kernel-based methods. In a thorough benchmark test we investigate the performance of these techniques. All methods have comparable root mean square errors (RMSEs) for low skewness of the inter-observation time distribution. For high skewness, very irregular data, interpolation bias and RMSE increase strongly. We find a 40 % lower RMSE for the lag-1 autocorrelation function (ACF) for the Gaussian kernel method vs. the linear interpolation scheme,in the analysis of highly irregular time series. For the cross correlation function (CCF) the RMSE is then lower by 60 %. The application of the Lomb-Scargle technique gave results comparable to the kernel methods for the univariate, but poorer results in the bivariate case. Especially the high-frequency components of the signal, where classical methods show a strong bias in ACF and CCF magnitude, are preserved when using the kernel methods. We illustrate the performances of interpolation vs. Gaussian kernel method by applying both to paleo-data from four locations, reflecting late Holocene Asian monsoon variability as derived from speleothem δ18O measurements. Cross correlation results are similar for both methods, which we attribute to the long time scales of the common variability. The persistence time (memory) is strongly overestimated when using the standard, interpolation-based, approach. Hence, the Gaussian kernel is a reliable and more robust estimator with significant advantages compared to other techniques and suitable for large scale application to paleo-data
'Word from the street' : when non-electoral representative claims meet electoral representation in the United Kingdom
Taking the specific case of street protests in the UK – the ‘word from the street’– this article examines recent (re)conceptualizations of political representation, most particularly Saward’s notion of ‘representative claim’. The specific example of nonelectoral claims articulated by protestors and demonstrators in the UK is used to illustrate: the processes of making, constituting, evaluating and accepting claims for and by constituencies and audiences; and the continuing distinctiveness of claims based upon electoral representation. Two basic questions structure the analysis: first, why would the political representative claims of elected representatives trump the nonelectoral claims of mass demonstrators and, second, in what ways does the ‘perceived legitimacy’ of the former differ from the latter
Recommended from our members
Characterizing the evolution of climate networks
Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, ErdA's-Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970-2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks
A Pilot Investigation of Critical Thinking in Undergraduate Students of Communication Sciences and Disorders
Speech-language pathologists use critical thinking on a daily basis to identify, evaluate, and implement evidence-based practices with their clients. Currently, however, there are minimal data describing the critical thinking of undergraduate students in the field of communication sciences and disorders. Without these data, it is unclear if and how students’ critical thinking differs at various points during their pre-service training. In the present study, we used the Cornell Critical Thinking Test – Level Z to describe the general critical thinking skills of 142 undergraduate students enrolled in two lower- (n = 95) and upper- (n = 47) level courses at a single university. We found no statistically significant differences between these two groups on the CCTT regarding their overall critical thinking performance (p = .068) or their skills of induction (p = .970), deduction (p = .160), observation (p = .384), assumptions (p = .342), or meaning interpretation (p = .155). Upper-level students, however, did consistently score slightly higher than their lower-level counterparts. Faculty should continue to develop undergraduate students’ critical thinking during their course of study. Although critical thinking appears to develop over the course of students’ undergraduate careers, formal instruction might be necessary to develop the skills necessary for successful practice as speech-language pathologists
Recommended from our members
Identification of dynamical transitions in marine palaeoclimate records by recurrence network analysis
The analysis of palaeoclimate time series is usually affected by severe methodological problems, resulting primarily from non-equidistant sampling and uncertain age models. As an alternative to existing methods of time series analysis, in this paper we argue that the statistical properties of recurrence networks - a recently developed approach - are promising candidates for characterising the system's nonlinear dynamics and quantifying structural changes in its reconstructed phase space as time evolves. In a first order approximation, the results of recurrence network analysis are invariant to changes in the age model and are not directly affected by non-equidistant sampling of the data. Specifically, we investigate the behaviour of recurrence network measures for both paradigmatic model systems with non-stationary parameters and four marine records of long-term palaeoclimate variations. We show that the obtained results are qualitatively robust under changes of the relevant parameters of our method, including detrending, size of the running window used for analysis, and embedding delay. We demonstrate that recurrence network analysis is able to detect relevant regime shifts in synthetic data as well as in problematic geoscientific time series. This suggests its application as a general exploratory tool of time series analysis complementing existing methods
- …