Suchergebnisse

The socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden–Julian oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, and can promote or enhance extreme events in both, the tropics and the extratropics. Forecasting extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging due to a poor understanding of the phenomena that can increase predictability on this time scale. Here we show that two artificial neural networks (ANNs), a feed-forward neural network and a recurrent neural network, allow a very competitive MJO prediction. While our average prediction skill is about 26–27 days (which competes with that obtained with most computationally demanding stateof-the-art climate models), for some initial phases and seasons the ANNs have a prediction skill of 60 days or longer. Furthermore, we show that the ANNs have a good ability to predict the MJO phase, but the amplitude is underestimated. ; This work received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska Curie Grant Agreement No 8138444. C.M. also acknowledges funding by the Spanish Ministerio de Ciencia, Innovacion y Universidades (PGC2018-099443-B-I00), and the ICREA ACADEMIA program of Generalitat de Catalunya. We would like to thank Laura Ferranti, Linus Magnusson, and Nikolaos Mastrantonas for their expertise and useful discussions. Portions of this work are modifications based on work created and shared by Google and used according to terms described in the Creative Commons 4.0 Attribution License. ; Peer Reviewed ; Postprint (published version)

This study assesses the impact of low-frequency climate modes on Rossby Wave Packets (RWPs) during southern hemisphere summer. In particular, we focus on long-lived RWPs (lifespan above 8 days) and determine how El Niño-Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) influence their statistics, that is, their duration, frequency of occurrence, and activity areas. We used daily mean meridional winds at 300 hPa from December to March between 1979 and 2020 from the ERA5 and NCEP-DOE 2 reanalyses. We found that long-lived wave packets, which are a small percentage of the total number of wave packets, show large interannual variability; there are years in which these types of waves do not occur and years that present up to 9 wave packets. This suggests that large-scale circulation conditions set up by low-frequency climate modes can modulate their occurrence. Classifying years according to SAM phases reveals that the occurrence of long-lived RWPs is highest (lowest) during intense negative (positive) SAM events. ENSO influence, on the other hand, was found to be weak and not robust. Analysis of large scale circulation conditions shows that during negative SAM phases the jet shifts northward, strengthens in the Indian sector, and extends further into the Pacific basin, so that it acts as a better waveguide favoring the propagation of long-lived RWPs. Conversely, during positive phases of SAM, the jet shifts southward and an anticyclonic center develops to the southwest of Australia blocking the jet and the progression of the wave packets. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813844 (ITN CAFÉ). C.M acknowledges partial support from Span-ish Ministerio de Ciencia, Innovación y Universidades (PGC2018-099442-B-I00) and from ICREA ACADEMIA program of Generalitat de Catalunya. ; Postprint (published version)

We use statistical tools to characterize the response of an excitable system to periodic perturbations. The system is an optically injected semiconductor laser under pulsed perturbations of the phase of the injected field. We characterize the laser response by counting the number of pulses emitted by the laser, within a time interval, ¿T, that starts when a perturbation is applied. The success rate, SR(¿T), is then defined as the number of pulses emitted in the interval ¿T, relative to the number of perturbations. The analysis of the variation of SR with ¿T allows separating a constant lag of technical origin and a frequency-dependent lag of physical and dynamical origin. Once the lag is accounted for, the success rate clearly captures locked and unlocked regimes and the transitions between them. We anticipate that the success rate will be a practical tool for analyzing the output of periodically forced systems, particularly when very regular oscillations need to be generated via small periodic perturbations ; The work of B.G. and S.B. was conducted within the framework of the project OPTIMAL granted by the European Union by means of the Fond Européen de développement regional, FEDER. C.M. and J.T.-A. acknowledge support from the Spanish Ministerio de Ciencia, Innovacion y Universidades (PGC2018-099443-B-I00 ). C.M. also acknowledges partial support from ICREA ACADEMIA, Generalitat de Catalunya and a visiting professor fellowship of CNRS (France). J.T. also acknowledges partial support from the European Commission through the European Fund of Regional Development (FEDER), Convocatoria Excellencia KTT-UPC 2019 (Spain). ; Peer Reviewed ; Postprint (author's final draft)

Neurons encode and transmit information in spike sequences. However, despite the effort devoted to understand the encoding and trans-mission of information, the mechanisms underlying the neuronal encoding are not yet fully understood. Here, we use a nonlinear methodof time-series analysis (known as ordinal analysis) to compare the statistics of spike sequences generated by applying an input signal to theneuronal model of Morris–Lecar. In particular, we consider two different regimes for the neurons which lead to two classes of excitability:class I, where the frequency-current curve is continuous and class II, where the frequency-current curve is discontinuous. By applying ordinalanalysis to sequences of inter-spike-intervals (ISIs) our goals are (1) to investigate if different neuron types can generate spike sequences whichhave similar symbolic properties; (2) to get deeper understanding on the effects that electrical (diffusive) and excitatory chemical (i.e., exci-tatory synapse) couplings have; and (3) to compare, when a small-amplitude periodic signal is applied to one of the neurons, how the signalfeatures (amplitude and frequency) are encoded and transmitted in the generated ISI sequences for both class I and class II type neurons andelectrical or chemical couplings. We find that depending on the frequency, specific combinations of neuron/class and coupling-type allow amore effective encoding, or a more effective transmission of the signal. ; C.E. and C.R.M. acknowledge the Spanish State ResearchAgency, through the María de Maeztu Program for Units of Excel-lence in R & D (No. MDM-2017-0711). C.E. is funded by theConselleria d'Innovació, Recerca i Turisme of the Government ofthe Balearic Islands and the European Social Fund with Grant CodeNo. FPI/1900/2016. M.M. and C.M. acknowledge support from theSpanish Ministerio de Ciencia, Innovación y Universidades (No.PGC2018-099443-B-I00) and ICREA ACADEMIA, Generalitat deCatalunya. The authors also acknowledge the Ibersinc networkfunded by Spanish MINECO grant FIS2015-71929-RED ; Peer Reviewed ; Postprint (author's final draft)