skip to main content

Title: Observed El Niño‐La Niña Asymmetry in a Linear Model

Previous studies indicate an asymmetry in the amplitude and persistence of El Niño (EN) and La Niña (LN) events. We show that this observed EN‐LN asymmetry can be captured with a linear model driven by correlated additive and multiplicative (CAM) noise, without resorting to a deterministic nonlinear model. The model is derived from 1‐month lag statistics taken from monthly sea surface temperature (SST) data sets spanning the twentieth century, in an extension of an empirical‐dynamical technique called Linear Inverse Modeling. Our results suggest that noise amplitudes tend to be stronger for EN compared to LN events, which is sufficient to generate asymmetry in amplitude and also produces more persistent LN events on average. These results establish a null hypothesis for EN‐LN asymmetry and suggest that strong EN events may not be more predictable that what can be accounted for by a multivariate linear system driven by CAM noise.

more » « less
Author(s) / Creator(s):
 ;  ;  ;  ;  
Publisher / Repository:
DOI PREFIX: 10.1029
Date Published:
Journal Name:
Geophysical Research Letters
Page Range / eLocation ID:
p. 9909-9919
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. El Niño–Southern Oscillation (ENSO) events tend to peak at the end of the calendar year, a phenomenon called ENSO phase locking. This phase locking is a fundamental ENSO property that is determined by its basic dynamics. The conceptual ENSO recharge oscillator (RO) model is adopted to examine the ENSO phase-locking behavior in terms of its peak time, strength of phase locking, and asymmetry between El Niño and La Niña events. The RO model reproduces the main phase-locking characteristics found in observations, and the results show that the phase locking of ENSO is mainly dominated by the seasonal modulation of ENSO growth/decay rate. In addition, the linear/nonlinear mechanism of ENSO phase preference/phase locking is investigated using RO model. The difference between the nonlinear phase-locking mechanism and linear phase-preference mechanism is largely smoothed out in the presence of noise forcing. Further, the impact on ENSO phase locking from annual cycle modulation of the growth/decay rate, stochastic forcing, nonlinearity, and linear frequency are examined in the RO model. The preferred month of ENSO peak time depends critically on the phase and strength of the seasonal modulation of the ENSO growth/decay rate. Furthermore, the strength of phase locking is mainly controlled by the linear growth/decay rate, the amplitude of seasonal modulation of growth/decay rate, the amplitude of noise, the SST-dependent factor of multiplicative noise, and the linear frequency. The asymmetry of the sharpness of ENSO phase locking is induced by the asymmetric effect of state-dependent noise forcing in El Niño and La Niña events.

    more » « less

    At fixed galaxy stellar mass, there is a clear observational connection between structural asymmetry and offset from the star-forming main sequence, ΔSFMS. Herein, we use the TNG50 simulation to investigate the relative roles of major mergers (stellar mass ratios μ ≥ 0.25), minor (0.1 ≤ μ < 0.25), and mini mergers (0.01 ≤ μ < 0.1) in driving this connection amongst star-forming galaxies (SFGs). We use dust radiative transfer post-processing with SKIRT to make a large, public collection of synthetic Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) images of simulated IllustrisTNG (TNG) galaxies over 0.1 ≤ z ≤ 0.7 with log (M⋆/M⊙) ≥ 9 (∼750 k images). Using their instantaneous star formation rates (SFRs), known merger histories/forecasts, and HSC-SSP asymmetries, we show (1) that TNG50 SFGs qualitatively reproduce the observed trend between ΔSFMS and asymmetry and (2) a strikingly similar trend emerges between ΔSFMS and the time-to-coalescence for mini mergers. Controlling for redshift, stellar mass, environment, and gas fraction, we show that individual mini merger events yield small enhancements in SFRs and asymmetries that are sustained on long time-scales (at least ∼3 Gyr after coalescence, on average) – in contrast to major/minor merger remnants which peak at much greater amplitudes but are consistent with controls only ∼1 Gyr after coalescence. Integrating the boosts in SFRs and asymmetries driven by μ ≥ 0.01 mergers since z = 0.7 in TNG50 SFGs, we show that mini mergers are responsible for (i) 55 per cent of all merger-driven star formation and (ii) 70 per cent of merger-driven asymmetric structure. Due to their relative frequency and prolonged boost time-scales, mini mergers dominate over their minor and major counterparts in driving star formation and asymmetry in SFGs.

    more » « less
  3. Abstract

    Observations of relative paleointensity reveal several forms of asymmetry in the time dependence of the virtual axial dipole moment (VADM). Slow decline of the VADM into a reversal is often followed by a more rapid rise back to a quasi‐steady state. Asymmetry is also observed in trends of VADM during times of stable polarity. Trends of increasing VADM over time intervals of a few 10s of kyr are more intense and less frequent than decreasing trends. We examine the origin of this behavior using stochastic models. The usual (Langevin) model can account for asymmetries during reversals, but it cannot reproduce the observed asymmetry in trends during stable polarity. Better agreement is achieved with a different class of stochastic models in which the dipole is generated by a series of impulsive events in time. The timing of each event occurs randomly as a Poisson process and the amplitude is also randomly distributed. Predicted trends replicate the observed asymmetry when the generation events are large and the recurrence time is long (typically longer than 3 kyr). Large and infrequent generation events argue against dipole generation by small‐scale turbulent flow. Instead, the observations favor a mechanism that relies on expulsion of poloidal magnetic field from the core.

    more » « less
  4. Abstract

    Precipitation clusters are contiguous raining regions characterized by a precipitation threshold, size, and the total rainfall contained within—termed the cluster power. Tropical observations suggest that the probability distributions of both cluster size and power contain a power-law range (with slope ~ −1.5) bounded by a large-event “cutoff.” Events with values beyond the cutoff signify large, powerful clusters and represent extreme events. A two-dimensional stochastic model is introduced to reproduce the observed cluster distributions, including the slope and the cutoff. The model is equipped with coupled moisture and weak temperature gradient (WTG) energy equations, empirically motivated precipitation parameterization, temporally persistent noise, and lateral mixing processes, all of which collectively shape the model cluster distributions. Moisture–radiative feedbacks aid clustering, but excessively strong feedbacks push the model into a self-aggregating regime. The power-law slope is stable in a realistic parameter range. The cutoff is sensitive to multiple model parameters including the stochastic forcing amplitude, the threshold moisture value that triggers precipitation, and the lateral mixing efficiency. Among the candidates for simple analogs of precipitation clustering, percolation models are ruled out as unsatisfactory, but the stochastic branching process proves useful in formulating a neighbor probability metric. This metric measures the average number of nearest neighbors that a precipitating entity can spawn per time interval and captures the cutoff parameter sensitivity for both cluster size and power. The results here suggest that the clustering tendency and the horizontal scale limiting large tropical precipitating systems arise from aggregate effects of multiple moist processes, which are encapsulated in the neighbor probability metric.

    more » « less

    Accurate synthetic seismic wavefields can now be computed in 3-D earth models using the spectral element method (SEM), which helps improve resolution in full waveform global tomography. However, computational costs are still a challenge. These costs can be reduced by implementing a source stacking method, in which multiple earthquake sources are simultaneously triggered in only one teleseismic SEM simulation. One drawback of this approach is the perceived loss of resolution at depth, in particular because high-amplitude fundamental mode surface waves dominate the summed waveforms, without the possibility of windowing and weighting as in conventional waveform tomography.

    This can be addressed by redefining the cost-function and computing the cross-correlation wavefield between pairs of stations before each inversion iteration. While the Green’s function between the two stations is not reconstructed as well as in the case of ambient noise tomography, where sources are distributed more uniformly around the globe, this is not a drawback, since the same processing is applied to the 3-D synthetics and to the data, and the source parameters are known to a good approximation. By doing so, we can separate time windows with large energy arrivals corresponding to fundamental mode surface waves. This opens the possibility of designing a weighting scheme to bring out the contribution of overtones and body waves. It also makes it possible to balance the contributions of frequently sampled paths versus rarely sampled ones, as in more conventional tomography.

    Here we present the results of proof of concept testing of such an approach for a synthetic 3-component long period waveform data set (periods longer than 60 s), computed for 273 globally distributed events in a simple toy 3-D radially anisotropic upper mantle model which contains shear wave anomalies at different scales. We compare the results of inversion of 10 000 s long stacked time-series, starting from a 1-D model, using source stacked waveforms and station-pair cross-correlations of these stacked waveforms in the definition of the cost function. We compute the gradient and the Hessian using normal mode perturbation theory, which avoids the problem of cross-talk encountered when forming the gradient using an adjoint approach. We perform inversions with and without realistic noise added and show that the model can be recovered equally well using one or the other cost function.

    The proposed approach is computationally very efficient. While application to more realistic synthetic data sets is beyond the scope of this paper, as well as to real data, since that requires additional steps to account for such issues as missing data, we illustrate how this methodology can help inform first order questions such as model resolution in the presence of noise, and trade-offs between different physical parameters (anisotropy, attenuation, crustal structure, etc.) that would be computationally very costly to address adequately, when using conventional full waveform tomography based on single-event wavefield computations.

    more » « less