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

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. &lt;br&gt;&lt;br&gt; 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. &lt;br&gt;&lt;br&gt; 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 &amp;delta;&lt;sup&gt;18&lt;/sup&gt;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

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. 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

ESD Ideas: Photoelectrochemical carbon removal as negative emission technology

The pace of the transition to a low-carbon economy – especially in the fuels sector – is not high enough to achieve the 2&thinsp;°C target limit for global warming by only cutting emissions. Most political roadmaps to tackle global warming implicitly rely on the timely availability of mature negative emission technologies, which actively invest energy to remove CO2 from the atmosphere and store it permanently. The models used as a basis for decarbonization policies typically assume an implementation of such large-scale negative emission technologies starting around the year 2030, ramped up to cause net negative emissions in the second half of the century and balancing earlier CO2 release. On average, a contribution of −10&thinsp;Gt&thinsp;CO2&thinsp;yr−1 is expected by 2050 (Anderson and Peters, 2016). A viable approach for negative emissions should (i) rely on a scalable and sustainable source of energy (solar), (ii) result in a safely storable product, (iii) be highly efficient in terms of water and energy use, to reduce the required land area and competition with water and food demands of a growing world population, and (iv) feature large-scale feasibility and affordability.</p

Musical feedback system Jymmin leads to enhanced physical endurance in the elderly: A feasibility study

Background and objectives: Active music-making in combination with physical exercise has evoked several positive effects in users of different age groups. These include enhanced mood, muscular effectivity, pain threshold, and decreased perceived exertion. The present study tested the applicability of this musical feedback system, called Jymmin®, in combination with strength-endurance exercises in a population of healthy older adults. Research design and methods: Sixteen healthy, physically inactive older adults (5 males, 11 females) at the mean age of 70 years performed physical exercise in two conditions: A conventional work-out while listening passively music and a Jymmin® work-out, where musical sounds were created with one's work-out movements. According to the hypothesis that strength-endurance is increased during musical feedback exercise, parameters relating to strength-endurance were assessed, including exercise duration, number of repetitions, perceived exertion (RPE), and participants' mental state (Multidimensional Mood State Questionnaire; MDMQ). Results: Results show that participants exercised significantly longer while doing Jymmin® (Mdn = 248.75 s) as compared to the conventional work-out (Mdn = 182.73 s), (Z = 3.408, p = 0.001). The RPE did not differ between conventional work-out and the Jymmin® condition, even though participants worked out significantly longer during the Jymmin® condition (Mdn = 14.50; Z = −0.905; p = 0.366). The results of the MDMQ showed no significant differences between both conditions (Z = −1.037; p = 0.300). Discussion and implications: Results show that participants could work out longer while showing the same perceived exertion, relating to increased physical endurance. Music feedback work-out encouraged a greater degree of isometric contractions (muscle actively held at fixed length) and, therefore, less repetitions in this condition. In addition to the previously described effect on muscle effectivity, this non-stereotypic contraction pattern during music feedback training may have enhanced endurance in participants supporting them to better proportion energetic reserves during training (pacing)

On the influence of spatial sampling on climate networks

Peer reviewedPublisher PD

Synthetic and Enhanced Vision Systems for NextGen (SEVS) Simulation and Flight Test Performance Evaluation

The Synthetic and Enhanced Vision Systems for NextGen (SEVS) simulation and flight tests are jointly sponsored by NASA's Aviation Safety Program, Vehicle Systems Safety Technology project and the Federal Aviation Administration (FAA). The flight tests were conducted by a team of Honeywell, Gulfstream Aerospace Corporation and NASA personnel with the goal of obtaining pilot-in-the-loop test data for flight validation, verification, and demonstration of selected SEVS operational and system-level performance capabilities. Nine test flights (38 flight hours) were conducted over the summer and fall of 2011. The evaluations were flown in Gulfstream.s G450 flight test aircraft outfitted with the SEVS technology under very low visibility instrument meteorological conditions. Evaluation pilots flew 108 approaches in low visibility weather conditions (600 ft to 2400 ft visibility) into various airports from Louisiana to Maine. In-situ flight performance and subjective workload and acceptability data were collected in collaboration with ground simulation studies at LaRC.s Research Flight Deck simulator

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

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