Between-year fluctuations in schizophrenia birth rate and associations with the Southern Oscillation Index, cloud cover and sunshine

Our group have recently proposed that low prenatal vitamin D may be a risk-modifying factor for schizophrenia. Climate variability impacts on vitamin D levels in a population via fluctuations in the amount of available UV radiation. In order to explore this hypothesis, we examined fluctuations in the birthrates for people with schizophrenia born between 1920 and 1967 and three sets of variables strongly associated with UV radiation. These included: (a) the Southern Oscillation Index (SOI), a marker of El Nino which is the most prominent meteorological factor that influences Queensland weather: (b) measures of cloud cover and (c) measures of sunshine. Schizophrenia births were extracted from the Queensland Mental Health register and corrected for background population birth rates. Schizophrenia birth rates had several apparently non-random features in common with the SO1. The prominent SO1 fluctuation event that occurred between 1937 and 1943 is congruent with the most prominent fluctuation in schizophrenia birth rates. The relatively flat profile of SOI activity between 1927 and 1936 also corresponds to the flattest period in the schizophrenia time series. Both time series have prominent oscillations in the 3 ~, year range between 1946 and 1960. Significant associations between schizophrenia birth rates and measures of both sunshine and cloud cover were identified,and all three time series shared periodicity in the 3-4 year range. The analyses suggest that the risk of schizophrenia is higher for those born during times of increased cloud cover,reduced sunshine and positive SO1. These ecological analyses provide initial support for the vitamin D hypothesis, however alternative non-genetic candidate exposures also need to be considered. Other sites with year-to-year fluctuations in cloud cover and sunshine should examine patterns of association between these climate variables and schizophrenia birth rates. The Stanley Foundation supported this project.

Long range forecasts of the numbers of Helicoverpa punctigera and H-armigera (Lepidoptera : Noctuidae) in Australia using the Southern Oscillation Index and the Sea Surface Temperature

The use of long-term forecasts of pest pressure is central to better pest management. We relate the Southern Oscillation Index (SOI) and the Sea Surface Temperature (SST) to long-term light-trap catches of the two key moth pests of Australian agriculture, Helicoverpa punctigera (Wallengren) and H. armigera (Hubner), at Narrabri, New South Wales over 11 years, and for H. punctigera only at Turretfield, South Australia over 22 years. At Narrabri, the size of the first spring generation of both species was significantly correlated with the SOI in certain months, sometimes up to 15 months before the date of trapping. Differences in the SOI and SST between significant months were used to build composite variables in multiple regressions which gave fitted values of the trap catches to less than 25% of the observed values. The regressions suggested that useful forecasts of both species could be made 6-15 months ahead. The influence of the two weather variables on trap catches of H. punctigera at Turretfield were not as strong as at Narrabri, probably because the SOI was not as strongly related to rainfall in southern Australia as it is in eastern Australia. The best fits were again given by multiple regressions with SOI plus SST variables, to within 40% of the observed values. The reliability of both variables as predictors of moth numbers may be limited by the lack of stability in the SOI-rainfall correlation over the historical record. As no other data set is available to test the regressions, they can only be tested by future use. The use of long-term forecasts in pest management is discussed, and preliminary analyses of other long sets of insect numbers suggest that the Southern Oscillation Index may be a useful predictor of insect numbers in other parts of the world.

Power Oscillation index - A tool for investigation into unbalance phenomena in the distribution Networks

Unbalance and harmonics are two major distortions in the three-phase distribution systems. In this paper an investigation into unbalance phenomena in the distribution networks using instantaneous space vector theory, is presented. Power oscillation index (POI) and effective power factor (PFe) are calculated in the network nodes for several unbalance loading conditions. For system analysis a general power flow algorithm for three-phase four-wire radial distribution networks, based on backward-forward technique, is applied. Results obtained from several case studies using medium and low voltage test feeder with unbalanced load, are presented and discussed. © 2010 Praise Worthy Prize S.r.l. - All rights reserved.

Monthly values of the North Atlantic Oscillation Index from 1821 to 2000

Early instrumental pressure measurements from Gibraltar and the Reykjavik area of Iceland have been used to extend to 1821 the homogeneous pressure series at the two locations. In winter the two sites are located close to the centres of action that comprise the North Atlantic Oscillation (NAO). The extended 'winter half-year' record of the NAO enables recent changes in the record to be placed in the context of the period 1823-1996. The period since the early 1970s is the most prolonged positive phase of the oscillation and the late 1980s and early 1990s is the period with the highest values (strongest westerlies). The winter of 1995-1996 marked a dramatic switch in the index, with the change from 1994-1995 being the greatest change recorded from one year to the next since the series began in 1823. (The extended Gibraltar and Reykjavik monthly pressures and the NAO series can be found on the Climatic Research Unit home page, http://www.cru.uea.ac.uk/).

Geographical Distribution Of The Association Between El Niño South Oscillation And Dengue Fever In The Americas: A Continental Analysis Using Geographical Information System-based Techniques.

El Niño South Oscillation (ENSO) is one climatic phenomenon related to the inter-annual variability of global meteorological patterns influencing sea surface temperature and rainfall variability. It influences human health indirectly through extreme temperature and moisture conditions that may accelerate the spread of some vector-borne viral diseases, like dengue fever (DF). This work examines the spatial distribution of association between ENSO and DF in the countries of the Americas during 1995-2004, which includes the 1997-1998 El Niño, one of the most important climatic events of 20(th) century. Data regarding the South Oscillation index (SOI), indicating El Niño-La Niña activity, were obtained from Australian Bureau of Meteorology. The annual DF incidence (AIy) by country was computed using Pan-American Health Association data. SOI and AIy values were standardised as deviations from the mean and plotted in bars-line graphics. The regression coefficient values between SOI and AIy (rSOI,AI) were calculated and spatially interpolated by an inverse distance weighted algorithm. The results indicate that among the five years registering high number of cases (1998, 2002, 2001, 2003 and 1997), four had El Niño activity. In the southern hemisphere, the annual spatial weighted mean centre of epidemics moved southward, from 6° 31' S in 1995 to 21° 12' S in 1999 and the rSOI,AI values were negative in Cuba, Belize, Guyana and Costa Rica, indicating a synchrony between higher DF incidence rates and a higher El Niño activity. The rSOI,AI map allows visualisation of a graded surface with higher values of ENSO-DF associations for Mexico, Central America, northern Caribbean islands and the extreme north-northwest of South America.

The Effects of Joint ENSO-Antarctic Oscillation Forcing on the McMurdo Dry Valleys, Antarctica

Stable oxygen analyses and snow accumulation rates from snow pits sampled in the McMurdo Dry Valleys have been used to reconstruct variations in summer temperature and moisture availability over the last four decades. The temperature data show a common interannual variability, with strong regional warmings occurring especially in 1984/85, 1995/96 and 1990/91 and profound coolings during 1977/78, 1983/84, 1988/89, 1993/94, and 1996/97. Annual snow accumulation shows a larger variance between sites, but the early 1970s, 1984, 1997, and to a lesser degree 1990/91 are characterized overall by wetter conditions, while the early and late 1980s show low snow accumulation values. Comparison of the reconstructed and measured summer temperatures with the Southern Oscillation Index (SOI) and the Antarctic Oscillation (AAO) yield statistically significant correlations, which improve when phaserelationships are considered. A distinct change in the phase relationship of the correlation is observed, with the SOI-AAO leading over the temperature records by one year before, and lagging by one year after 1988. These results suggest that over the last two decades summer temperatures are influenced by opposing El Niho Southern Oscillation and AAO forcings and support previous studies that identified a change in the Tropical-Antarctic teleconnection between the 1980s and 1990s.

El Niño Southern Oscillation and Ross River Virus Outbreaks in Australia

Various authors have suggested a general predictive value of climatic indices of El Nino/Southem Oscillation events as indicators of outbreaks of arbovirus disease, particularly Ross River virus in Australia. By analyzing over 100 years of historical outbreak data on Ross River virus disease, our data indicate that, although high Southern Oscillation Index and La Nina conditions are potentially important predictors for the Murray Darling River region, this is not the case for the other four ecological zones in Australia. Our study, therefore, cautions against overgeneralization and suggests that, since climate and weather exert different influences and have different biological implications for the multiplicity of vectors involved, it is logical that predictors should be heterogeneous.

El Nino and arboviral disease prediction

Recent El Nino events have stimulated interest in the development of modeling techniques to forecast extremes of climate and related health events. Previous studies have documented associations between specific climate variables (particularly temperature and rainfall) and outbreaks of arboviral disease. In some countries, such diseases are sensitive to Fl Nino. Here we describe a climate-based model for the prediction of Ross River virus epidemics in Australia. From a literature search and data on case notifications, we determined in which years there were epidemics of Ross River virus in southern Australia between 1928 and 1998. Predictor variables were monthly Southern Oscillation index values for the year of an epidemic or lagged by 1 year. We found that in southeastern states, epidemic years were well predicted by monthly Southern Oscillation index values in January and September in the previous year. The model forecasts that there is a high probability of epidemic Ross River virus in the southern states of Australia in 1999. We conclude that epidemics of arboviral disease can, at least in principle, be predicted on the basis of climate relationships.

Deconvolving the delta O-18 seawater component from subseasonal coral delta O-18 and Sr/Ca at Rarotonga in the southwestern subtropical Pacific for the period 1726 to 1997

To reconstruct oceanographic variations in the subtropical South Pacific, 271-year long subseasonal time series of Sr/Ca and delta(18)O were generated from a coral growing at Rarotonga (21.5degreesS, 159.5degreesW). In this case, coral Sr/Ca appears to be an excellent proxy for sea surface temperature (SST) and coral delta(18)O is a function of both SST and seawater delta(18)O composition (delta(18)O(sw)). Here, we focus on extracting the delta(18)O(sw) signal from these proxy records. A method is presented assuming that coral Sr/Ca is solely a function of SST and that coral delta(18)O is a function of both SST and delta(18)O(sw). This method separates the effects of delta(18)O(sw) from SST by breaking the instantaneous changes of coral delta(18)O into separate contributions by instantaneous SST and delta(18)O(sw) changes, respectively. The results show that on average delta(18)O(sw) at Rarotonga explains similar to39% of the variance in delta(18)O and that variations in SST explains the remaining similar to61% of delta(18)O variance. Reconstructed delta(18)O(sw) shows systematic increases in summer months (December-February) consistent with the regional pattern of variations in precipitation and evaporation. The delta(18)O(sw) also shows a positive linear correlation with satellite-derived estimated salinity for the period 1980 to 1997 (r = 0.72). This linear correlation between reconstructed delta(18)O(sw) and salinity makes it possible to use the reconstructed delta(18)O(sw) to estimate the past interannual and decadal salinity changes in this region. Comparisons of coral delta(18)O and delta(18)O(sw) at Rarotonga with the Pacific decadal oscillation index suggest that the decadal and interdecadal salinity and SST variability at Rarotonga appears to be related to basin-scale decadal variability in the Pacific. Copyright (C) 2002 Elsevier Science Ltd.

Climate and acute/subacute paracoccidioidomycosis in a hyper-endemic area in Brazil

Methods Stepwise regression of annual data was applied to model incidence, calculated based on 91 cases, from lagged variables: antecedent precipitation, air temperature, soil water storage, absolute and relative air humidity, and Southern Oscillation Index (SOI). Results Multiple regression analyses resulted in a model, which explains 49% of the incidence variance, taking into account the absolute air humidity in the year of exposure, soil water storage and SOI of the previous 2 years. Conclusions The correlations may reflect enhanced fungal growth after increase in soil water storage in the longer term and greater spore release with increase in absolute air humidity in the short term.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.

Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO

Spatial and temporal variability in wheat production in Australia is dominated by rainfall occurrence. The length of historical production records is inadequate, however, to analyse spatial and temporal patterns conclusively. In this study we used modelling and simulation to identify key spatial patterns in Australian wheat yield, identify groups of years in the historical record in which spatial patterns were similar, and examine association of those wheat yield year groups with indicators of the El Nino Southern Oscillation (ENSO). A simple stress index model was trained on 19 years of Australian Bureau of Statistics shire yield data (1975-93). The model was then used to simulate shire yield from 1901 to 1999 for all wheat-producing shires. Principal components analysis was used to determine the dominating spatial relationships in wheat yield among shires. Six major components of spatial variability were found. Five of these represented near spatially independent zones across the Australian wheatbelt that demonstrated coherent temporal (annual) variability in wheat yield. A second orthogonal component was required to explain the temporal variation in New South Wales. The principal component scores were used to identify high- and low-yielding years in each zone. Year type groupings identified in this way were tested for association with indicators of ENSO. Significant associations were found for all zones in the Australian wheatbelt. Associations were as strong or stronger when ENSO indicators preceding the wheat season (April-May phases of the Southern Oscillation Index) were used rather than indicators based on classification during the wheat season. Although this association suggests an obvious role for seasonal climate forecasting in national wheat crop forecasting, the discriminatory power of the ENSO indicators, although significant, was not strong. By examining the historical years forming the wheat yield analog sets within each zone, it may be possible to identify novel climate system or ocean-atmosphere features that may be causal and, hence, most useful in improving seasonal forecasting schemes.

Relación entre anchoveta y otros componentes del ecosistema

Se hipotetiza que la mortalidad por pesca ocasiona efectos ecosistémicos, no sólo sobre la anchoveta, sino también sobre otros componentes del ecosistema, tales como los depredadores tope. El objetivo es realizar simulaciones con un modelo ecotrófico multiespecífico cubriendo el decenio de los años 2000, cambiando la mortalidad por pesca y analizando las variaciones en la biomasa de anchoveta, aves guaneras y lobos marinos. Se usó el Índice de Oscilación Peruano (IOP) para incluir una mediación que afecte la vulnerabilidad de las presas de la anchoveta. Se comparó el ajuste de los datos observados, usando dos tipos de dieta para anchoveta (fitoplanctófaga y zooplanctófaga). Se realizaron proyecciones de la biomasa, cambiando la mortalidad por pesca de 0,0 a 1,4 año-1. Las simulaciones con la dieta zooplanctófaga, que eleva el nivel trófico de la anchoveta de 2,35 a 3,36, produjo un menor ajuste entre los datos observados y simulados. La relación inversa entre la mortalidad por pesca y la biomasa desovante de la anchoveta, mostró que mortalidades por pesca entre 0,8 y 1,4 año-1 estarían asociadas a una biomasa desovante mínima de anchoveta, tomando en consideración sus relaciones multiespecíficas. También se encontró una relación inversa entre la mortalidad por pesca y las poblaciones de aves guaneras y lobos marinos.

Torrential rainfall in northeast of the Iberian Peninsula: synoptic patterns and WeMO influence

This study presents a catalogue of synoptic patterns of torrential rainfall in northeast of the Iberian Peninsula (IP). These circulation patterns were obtained by applying a T-mode Principal Component Analysis (PCA) to a daily data grid (NCEP/NCAR reanalysis) at sea level pressure (SLP). The analysis made use of 304 days which recorded >100 mm in one or more stations in provinces of Barcelona, Girona and Tarragona (coastland area of Catalonia) throughout the 1950-2005 period. The catalogue comprises 7 circulation patterns showing a great variety of atmospheric conditions and seasonal or monthly distribution. Likewise, we computed the mean index value of the Western Mediterranean Oscillation index (WeMOi) for the synoptic patterns obtained by averaging all days grouped in each pattern. The results showed a clear association between the negative values of this teleconnection index and torrential rainfall in northeast of the IP. We therefore put forward the WeMO as an essential tool for forecasting heavy rainfall in northeast of Spain