Precursory signals of the major El Niño Southern Oscillation events

Natural Time Analysis of Global Seismicity

Natural time analysis enables the introduction of an order parameter for seismicity, which is just the variance of natural time χ, κ1=⟨χ2⟩−⟨χ⟩2. During the last years, there has been significant progress in the natural time analysis of seismicity. Milestones in this progress are the identification of clearly distiguishable minima of the fluctuations of the order parameter κ1 of seismicity both in the regional and global scale, the emergence of an interrelation between the time correlations of the earthquake (EQ) magnitude time series and these minima, and the introduction by Turcotte, Rundle and coworkers of EQ nowcasting. Here, we apply all these recent advances in the global seismicity by employing the Global Centroid Moment Tensor (GCMT) catalog. We show that the combination of the above three milestones may provide useful precursory information for the time of occurrence and epicenter location of strong EQs with M≥8.5 in GCMT. This can be achieved with high statistical significance (p-values of the order of 10−5), while the epicentral areas lie within a region covering only 4% of that investigated.

Solar Cycle Signal in Climate and Artificial Neural Networks Forecasting

Natural climate variability is partially attributed to solar radiative forcing. The purpose of this study is to contribute to a better understanding of the influence of solar variability on the Earth’s climate system. The object of this work is the estimation of the variation of multiple climatic parameters (temperature, zonal wind, relative and specific humidity, sensible and latent surface heat flux, cloud cover and precipitable water) in response to solar cycle forcing. An additional goal is to estimate the response of the climate system’s parameters to short-term solar variability in multiple forecasting horizons and to evaluate the behavior of the climate system in shorter time scales. The solar cycle is represented by the 10.7 cm solar flux, a measurement collected by terrestrial radio telescopes, and is provided by NOAA/NCEI/STP, whereas the climatic data are provided by the NCEP/NCAR reanalysis 1 project. The adopted methodology includes the development of a linear regression statistical model in order to calculate the climatic parameters’ feedback to the 11-year solar cycle on a monthly scale. Artificial Neural Networks (ANNs) have been employed to forecast the solar indicator time series for up to 6 months in advance. The climate system’s response is further forecasted using the ANN’s estimated values and the regression equations. The results show that the variation of the climatic parameters can be partially attributed to solar variability. The solar-induced variation of each of the selected parameters, averaged globally, was of an order of magnitude of 10−1–10−3, and the corresponding correlation coefficients (Pearson’s r) were relatively low (−0.5–0.5). Statistically significant areas with relatively high solar cycle signals were found at multiple pressure levels and geographical areas, which can be attributed to various mechanisms.

Estimating the Epicenter of a Future Strong Earthquake in Southern California, Mexico, and Central America by Means of Natural Time Analysis and Earthquake Nowcasting

It has recently been shown in the Eastern Mediterranean that by combining natural time analysis of seismicity with earthquake networks based on similar activity patterns and earthquake nowcasting, an estimate of the epicenter location of a future strong earthquake can be obtained. This is based on the construction of average earthquake potential score maps. Here, we propose a method of obtaining such estimates for a highly seismically active area that includes Southern California, Mexico and part of Central America, i.e., the area N1035W80120. The study includes 28 strong earthquakes of magnitude M ≥7.0 that occurred during the time period from 1989 to 2020. The results indicate that there is a strong correlation between the epicenter of a future strong earthquake and the average earthquake potential score maps. Moreover, the method is also applied to the very recent 7 September 2021 Guerrero, Mexico, M7 earthquake as well as to the 22 September 2021 Jiquilillo, Nicaragua, M6.5 earthquake with successful results. We also show that in 28 out of the 29 strong M ≥7.0 EQs studied, their epicenters lie close to an estimated zone covering only 8.5% of the total area.

Estimating the Epicenter of an Impending Strong Earthquake by Combining the Seismicity Order Parameter Variability Analysis with Earthquake Networks and Nowcasting: Application in the Eastern Mediterranean

The variance κ1 of the natural time analysis of earthquake catalogs was proposed in 2005 as an order parameter for seismicity, whose fluctuations proved, in 2011, to be minimized a few months before the strongest mainshock when studying the earthquakes in a given area. After the introduction of earthquake networks based on similar activity patterns, in 2012, the study of their higher order cores revealed, in 2019, the selection of appropriate areas in which the precursory minima βmin of the fluctuations β of the seismicity order parameter κ1 could be observed up to six months before all strong earthquakes above a certain threshold. The eastern Mediterranean region was studied in 2019, where all earthquakes of magnitude M≥7.1 were found to be preceded by βmin without any false alarm. Combining these results with the method of nowcasting earthquakes, introduced in 2016, for seismic risk estimation, here, we show that the epicenter of an impending strong earthquake can be estimated. This is achieved by employing—at the time of observing the βmin—nowcasting earthquakes in a square lattice grid in the study area and by averaging, self-consistently, the results obtained for the earthquake potential score. This is understood in the following context: The minimum βmin is ascertained to almost coincide with the onset of Seismic Electric Signals activity, which is accompanied by the development of long range correlations between earthquake magnitudes in the area that is a candidate for a mainshock.

Effects of ENSO on Temperature, Precipitation, and Potential Evapotranspiration of North India’s Monsoon: An Analysis of Trend and Entropy

The influence of El Niño Southern Oscillation (ENSO) on the north Indian temperature, precipitation, and potential evapotranspiration (PET) change patterns were evaluated during the monsoon season across the last century. Trends and shifts in 146 districts were assessed using nonparametric statistical tests. To quantify their temporal variation, the concept of apportionment entropy was applied to both the annual and seasonal scales. Results suggest that the El Niño years played a greater role in causing hydro-climatological changes compared to the La Niña or neutral years. El Niño was more influential in causing shifts compared to trends. For certain districts, a phase change in ENSO reversed the trend/shift direction. The century-wide analysis suggested that the vast majority of the districts experienced significant decreasing trends/shifts in temperature and PET. However, precipitation experienced both increasing and decreasing trends/shifts based on the location of the districts. Entropy results suggested a lower apportionment of precipitation compared to the other variables, indicating an intermittent deviation of precipitation pattern from the generic trend. The findings may help understand the effects of ENSO on the hydro-climatological variables during the monsoon season. Practitioners may find the results useful as monsoon is the most important season for India causing climate extremes.

On the progress of the 2015–2016 El Niño event

Abstract. It has been recently reported that the current 2015–2016 El Niño could become "one of the strongest on record". To further explore this claim, we performed the new analysis described in detail in Varotsos et al. (2015) that allows the detection of precursory signals of the strong El Niño events by using a recently developed non-linear dynamics tool. In this context, the analysis of the Southern Oscillation Index time series for the period 1876–2015 shows that the running 2015–2016 El Niño would be rather a "moderate to strong" or even a "strong" event and not “one of the strongest on record", as that of 1997–1998.

Scaling regimes and linear/nonlinear responses of last millennium climate to volcanic and solar forcings

Abstract. At scales much longer than the deterministic predictability limits (about 10 days), the statistics of the atmosphere undergoes a drastic transition, the high-frequency weather acts as a random forcing on the lower-frequency macroweather. In addition, up to decadal and centennial scales the equivalent radiative forcings of solar, volcanic and anthropogenic perturbations are small compared to the mean incoming solar flux. This justifies the common practice of reducing forcings to radiative equivalents (which are assumed to combine linearly), as well as the development of linear stochastic models, including for forecasting at monthly to decadal scales. In order to clarify the validity of the linearity assumption and determine its scale range, we use last millennium simulations, with both the simplified Zebiak–Cane (ZC) model and the NASA GISS E2-R fully coupled GCM. We systematically compare the statistical properties of solar-only, volcanic-only and combined solar and volcanic forcings over the range of timescales from 1 to 1000 years. We also compare the statistics to multiproxy temperature reconstructions. The main findings are (a) that the variability in the ZC and GCM models is too weak at centennial and longer scales; (b) for longer than  ≈  50 years, the solar and volcanic forcings combine subadditively (nonlinearly) compounding the weakness of the response; and (c) the models display another nonlinear effect at shorter timescales: their sensitivities are much higher for weak forcing than for strong forcing (their intermittencies are different) and we quantify this with statistical scaling exponents.

Technical note: On the progress of the 2015–2016 El Niño event.. [DOI: 10.5194/acpd-15-35787-2015]

Abstract. It has been recently reported that the current 2015–2016 El Niño could become "one of the strongest on record". To further explore this claim, we performed the new analysis described in detail in Varotsos et al. (2015) that allows the detection of precursory signals of the strong El Niño events by using a recently developed non-linear dynamics tool. In this context, the analysis of the Southern Oscillation Index time series for the period 1876–2015 shows that the running 2015–2016 El Niño would be rather a "moderate to strong" or even a "strong" event and not "one of the strongest on record", as that of 1997–1998.