A waveform skewness index for measuring time series nonlinearity and its applications to the ENSO–Indian monsoon relationship

Many geophysical time series possess nonlinear characteristics that reflect the underlying physics of the phenomena the time series describe. The nonlinear character of times series can change with time, so it is important to quantify time series nonlinearity without assuming stationarity. A common way of quantifying the time evolution of time series nonlinearity is to compute sliding skewness time series, but it is shown here that such an approach can be misleading when time series contain periodicities. To remedy this deficiency of skewness, a new waveform skewness index is proposed for quantifying local nonlinearities embedded in time series. A waveform skewness spectrum is proposed for determining the frequency components that are contributing to time series waveform skewness. The new methods are applied to the El Niño–Southern Oscillation (ENSO) and the Indian monsoon to test a recently proposed hypothesis that states that changes in the ENSO–Indian monsoon relationship are related to ENSO nonlinearity. We show that the ENSO–Indian rainfall relationship weakens during time periods of high ENSO waveform skewness. The results from two different analyses suggest that the breakdown of the ENSO–Indian monsoon relationship during time periods of high ENSO waveform skewness is related to the more frequent occurrence of strong central Pacific El Niño events, supporting arguments that changes in the ENSO–Indian rainfall relationship are not solely related to noise.

Statistical Seasonal Forecasting of Tropical Cyclone Landfall on South China Utilizing Preseason Predictors

Predicting tropical cyclone (TC) activities has been a topic of great interest and research. Many existing seasonal forecasting models of TC predict the numbers of TC geneses and landfalls based on the environmental factors in the peak TC season. Here, we utilize the mainstream reanalysis datasets in 1979–2005 and propose a statistical seasonal forecasting model, namely the SYSU model, for predicting the number of TC landfalls on South China based on the preseason environmental factors. The multiple linear regression analysis shows that the April sea level pressure over the tropical central Pacific, the March-April mean sea surface temperature southwest to Australia, the March 850-hPa zonal wind east to Japan, and the April 500-hPa zonal wind over Bay of Bengal are the significant predictors. The model is validated by the leave-one-out cross validation and recent 15-year observations (2006–2020). The correlation coefficient between the modeled results and observations reaches 0.87 (p < 0.01). The SYSU model exhibits 90% hit rate (38 out of 42) in 1979–2020. The Antarctic Oscillation, and the variations of the western North Pacific subtropical high and Intertropical Convergence Zone could be the possible physical linkages or mechanisms. The model demonstrates an operational potential in the seasonal forecasting of TC landfall on South China.

Presence and potential distribution of malaria-infected New World primates of Costa Rica

Background In South and Central America, Plasmodium malariae/Plasmodium brasilianum, Plasmodium vivax, Plasmodium simium, and Plasmodium falciparum has been reported in New World primates (NWP). Specifically in Costa Rica, the presence of monkeys positive to P. malariae/P brasilianum has been identified in both captivity and in the wild. The aim of the present study was to determine the presence of P. brasilianum, P. falciparum, and P. vivax, and the potential distribution of these parasites-infecting NWP from Costa Rica. Methods The locations with PCR (Polymerase Chain Reaction) positive results and bioclimatic predictors were used to construct ecological niche models based on a modelling environment that uses the Maxent algorithm, named kuenm, capable to manage diverse settings to better estimate the potential distributions and uncertainty indices of the potential distribution. Results PCR analysis for the Plasmodium presence was conducted in 384 samples of four primates (Howler monkey [n = 130], White-face monkey [n = 132], Squirrel monkey [n = 50], and red spider monkey [n = 72]), from across Costa Rica. Three Plasmodium species were detected in all primate species (P. falciparum, P. malariae/P. brasilianum, and P. vivax). Overall, the infection prevalence was 8.9%, but each Plasmodium species ranged 2.1–3.4%. The niche model approach showed that the Pacific and the Atlantic coastal regions of Costa Rica presented suitable climatic conditions for parasite infections. However, the central pacific coast has a more trustable prediction for malaria in primates. Conclusions The results indicate that the regions with higher suitability for Plasmodium transmission in NWP coincide with regions where most human cases have been reported. These regions were also previously identified as areas with high suitability for vector species, suggesting that enzootic and epizootic cycles occur.

Impact of Eastern and Central Pacific El Niño on Lower Tropospheric Ozone in China

Tropospheric ozone is an essential atmospheric component as it plays a significant role in influencing radiation equilibrium and ecological health. It is affected not only by anthropogenic activities but also by natural climate variabilities. Here we examine the tropospheric ozone change in China associated with the Eastern Pacific (EP) and Central Pacific (CP) El Niño using satellite observations from 2007 to 2017 and GEOS-Chem simulations from 1980 to 2017. GEOS-Chem simulations reasonably reproduce the satellite-retrieved lower tropospheric ozone (LTO) changes despite a slight underestimation. Results show that El Niño generally exerts negative impacts on LTO concentration in China, except for southeastern China during the pre-CP El Niño autumn and post-EP El Niño summer. The budget analysis further indicates that for both events, LTO changes are dominated by the transport process controlled by circulation patterns and the chemical process influenced by local meteorological anomalies associated with El Niño, especially the solar radiation and relative humidity changes. The differences between EP and CP-induced LTO changes mostly lie in southern China. The different strengths, positions, and duration of western North Pacific anomalous anticyclone (WNPAC) induced by tropical warming are likely responsible for the different EP and CP LTO changes. During the post-EP El Niño summer, the Indian ocean capacitor also plays an important role in controlling LTO changes over southern China.

Evidence that Pacific tuna mercury levels are driven by marine methylmercury production and anthropogenic inputs

Pacific Ocean tuna is among the most-consumed seafood products but contains relatively high levels of the neurotoxin methylmercury. Limited observations suggest tuna mercury levels vary in space and time, yet the drivers are not well understood. Here, we map mercury concentrations in skipjack tuna across the Pacific Ocean and build generalized additive models to quantify the anthropogenic, ecological, and biogeochemical drivers. Skipjack mercury levels display a fivefold spatial gradient, with maximum concentrations in the northwest near Asia, intermediate values in the east, and the lowest levels in the west, southwest, and central Pacific. Large spatial differences can be explained by the depth of the seawater methylmercury peak near low-oxygen zones, leading to enhanced tuna mercury concentrations in regions where oxygen depletion is shallow. Despite this natural biogeochemical control, the mercury hotspot in tuna caught near Asia is explained by elevated atmospheric mercury concentrations and/or mercury river inputs to the coastal shelf. While we cannot ignore the legacy mercury contribution from other regions to the Pacific Ocean (e.g., North America and Europe), our results suggest that recent anthropogenic mercury release, which is currently largest in Asia, contributes directly to present-day human mercury exposure.

Simulation, Precursor Analysis and Targeted Observation Sensitive Area Identification for Two Types of ENSO using ENSO-MC v1.0

The global impact of an El Niño-Southern Oscillation (ENSO) event can differ greatly depending on whether it is an Eastern-Pacific-type (EP-type) event or a Central-Pacific-type (CP-type) event. Reliable predictions of the two types of ENSO are therefore of critical importance. Here we construct a deep neural network with multichannel structure for ENSO (named ENSO-MC) to simulate the spatial evolution of sea surface temperature (SST) anomalies for the two types of events. We select SST, heat content, and wind stress (i.e., three key ingredients of Bjerknes feedback) to represent coupled ocean-atmosphere dynamics that underpins ENSO, achieving skillful forecasts for the spatial patterns of SST anomalies out to one year ahead. Furthermore, it is of great significance to analyze the precursors of EP-type or CP-type events and identify targeted observation sensitive area for the understanding and prediction of ENSO. Precursors analysis is to determine what type of initial perturbations will develop into EP-type or CP-type events. Sensitive area identification is to determine the regions where initial states tend to have greatest impacts on evolution of ENSO. We use saliency map method to investigate the subsurface precursors and identify the sensitive areas of ENSO. The results show that there are pronounced signals in the equatorial subsurface before EP events, while the precursory signals of CP events are located in the North Pacific. It indicates that the subtropical precursors seem to favor the generation of the CP-type El Niño and the EP-type El Niño is more related to the tropical thermocline dynamics. And the saliency maps show that the sensitive areas of the surface and the subsurface are located in the equatorial central Pacific and the equatorial western Pacific, respectively. The sensitivity experiments imply that additional observations in the identified sensitive areas can improve forecasting skills. Our results of precursors and sensitive areas are consistent with the previous theories of ENSO, demonstrating the potential usage and advantages of the ENSO-MC model in improving the simulation, understanding and observations of two ENSO types.

Bridging Legends and Science: Field Evidence of a Large Tsunami that Affected the Kingdom of Tonga in the 15th Century

The pre-colonial history (i.e. before the 16th century) of Tonga and West Polynesia still suffers from major gaps despite significant scientific advances in recent years, particularly in the field of archaeology. By the 14th century, the powerful Tu’i Tonga kingdom united the islands of the Tongan archipelago under a centralised authority and, according to tradition, extended its influence to neighbouring island groups in the Central Pacific. However, some periods of deep crisis were identified, e.g. in the mid- 15th century, marked by an abrupt cessation of inter-archipelago migration on the deep seas in the Pacific, significant cultural changes, and a decrease in accessible natural resources. The origins of these disturbances are still debated, and they are usually assigned to internal political problems or loss of external influence vis-à-vis neighboring chiefdoms. However, the hypothesis of a major natural disaster was rarely suggested up to now, while field evidence points to the occurrence of a very large tsunami in the past, including the presence of numerous megablocks that were deposited by a “red wave” (or peau kula, which also mean tsunami in the Tongan language) according to a local myth. Drawing on a body of new evidence from sedimentary signatures and radiocarbon dating of charcoal and marine bioclasts, geomorphology, and sedimentology, in support of previously published archaeological data, we argue that a large tsunami inundated large areas of Tongatapu island in the mid-15th century with runup heights up to 30 m, and that the Tu’i Tonga kingdom was severely impacted by this event. We also discuss the likely sources of this tsunami.

The Lesser Pacific Striped Octopus, Octopus chierchiae: An Emerging Laboratory Model

Cephalopods have the potential to become useful experimental models in various fields of science, particularly in neuroscience, physiology, and behavior. Their complex nervous systems, intricate color- and texture-changing body patterns, and problem-solving abilities have attracted the attention of the biological research community, while the high growth rates and short life cycles of some species render them suitable for laboratory culture. Octopus chierchiae is a small octopus native to the central Pacific coast of North America whose predictable reproduction, short time to maturity, small adult size, and ability to lay multiple egg clutches (iteroparity) make this species ideally suited to laboratory culture. Here we describe novel methods for multigenerational culture of O. chierchiae, with emphasis on enclosure designs, feeding regimes, and breeding management. O. chierchiae bred in the laboratory grow from a 3.5 mm mantle length at hatching to an adult mantle length of approximately 20–30 mm in 250–300 days, with 15 and 14% survivorship to over 400 days of age in first and second generations, respectively. O. chierchiae sexually matures at around 6 months of age and, unlike most octopus species, can lay multiple clutches of large, direct-developing eggs every ∼30–90 days. Based on these results, we propose that O. chierchiae possesses both the practical and biological features needed for a model octopus that can be cultured repeatedly to address a wide range of biological questions.