Modeling and projecting the occurrence of bivariate extreme heat events using a non-homogeneous common Poisson shock process

A joint model is proposed for analyzing and predicting the occurrence of extreme heat events in two temperature series, these being daily maximum and minimum temperatures. Extreme heat events are defined using a threshold approach and the suggested model, a non-homogeneous common Poisson shock process, accounts for the mutual dependence between the extreme events in the two series. This model is used to study the time evolution of the occurrence of extreme events and its relationship with temperature predictors. A wide range of tools for validating the model is provided, including influence analysis. The main application of this model is to obtain medium-term local projections of the occurrence of extreme heat events in a climate change scenario. Future temperature trajectories from general circulation models, conveniently downscaled, are used as predictors of the model. These trajectories show a generalized increase in temperatures, which may lead to extrapolation errors when the model is used to obtain projections. Various solutions for dealing with this problem are suggested. The results of the fitted model for the temperature series in Barcelona in 1951–2005 and future projections of extreme heat events for the period 2031–2060 are discussed, using three global circulation model trajectories under the SRES A1B scenario

NHPoisson: An R package for fitting and validating nonhomogeneous Poisson processes

NHPoisson is an R package for the modeling of nonhomogeneous Poisson processes in one dimension. It includes functions for data preparation, maximum likelihood estimation, covariate selection and inference based on asymptotic distributions and simulation methods. It also provides specific methods for the estimation of Poisson processes resulting from a peak over threshold approach. In addition, the package supports a wide range of model validation tools and functions for generating nonhomogenous Poisson process trajectories. This paper is a description of the package and aims to help those interested in modeling data using nonhomogeneous Poisson processes

Dynamic regression model for hourly river level forecasting under risk situations: An application to the Ebro River

This work proposes a new statistical modelling approach to forecast the hourly river level at a gauging station, under potential flood risk situations and over a medium-term prediction horizon (around three days). For that aim we introduce a new model, the switching regression model with ARMA errors, which takes into account the serial correlation structure of the hourly level series, and the changing time delay between them. A whole modelling approach is developed, including a two-step estimation, which improves the medium-term prediction performance of the model, and uncertainty measures of the predictions. The proposed model not only provides predictions for longer periods than other statistical models, but also helps to understand the physics of the river, by characterizing the relationship between the river level in a gauging station and its influential factors. This approach is applied to forecast the Ebro River level at Zaragoza (Spain), using as input the series at Tudela. The approach has shown to be useful and the resulting model provides satisfactory hourly predictions, which can be fast and easily updated, together with their confidence intervals. The fitted model outperforms the predictions from other statistical and numerical models, specially in long prediction horizons

Modeling and forecasting extreme hot events in the central Ebro Valley, a continental-mediterranean area

This work has three objectives, first, to analyze the observed change in the summer maximum daily temperature during the period 1951–2004, in the centre of the Ebro river basin, a region situated in the NE of the Iberian Peninsula. Secondly, to characterize the extreme hot event behaviour by means of a statistical model consisting of a non-homogeneous Poisson process, to represent the occurrence, and three regression models, each with an adequate non-Normal error distribution, to model its severity. The model parameters are allowed to depend on temperature covariates, to take into account the influence of global warming in hot event generating process. Finally, using the fitted model and different outputs from a GCM, we obtain a medium term projection, up to 2050, of the expected behaviour of these extreme events

Trend analysis of water quality series based on regression models with correlated errors

This work proposes a methodology for characterizing the time evolution of water quality time series taking into consideration the inherent problems that often appear in this type of data such as non-linear trends, series having missing data, outliers, irregular measurement patterns, seasonal behavior, and serial correlation. The suggested approach, based on regression models with a Gaussian autoregressive moving average (ARMA) error, provides a framework where those problems can be dealt with simultaneously. Also the model takes into account the effect of influential factors, such as river flows, water temperature, and rainfall. The proposed approach is general and can be applied to different types of water quality series. We applied the modeling framework to four monthly conductivity series recorded at the Ebro river basin (Spain). The results show that the model fits the data reasonably well, that time evolution of the conductivity series is non-homogeneous over the year and, in some cases, non-monotonic. In addition, the results compared favorably over those obtained using simple linear regression, pre-whitening, and trend-free-pre-whitening techniques

Understanding enhanced rotational dynamics of active probes in rod suspensions

Active Brownian particles (APs) have recently been shown to exhibit enhanced rotational diffusion (ERD) in complex fluids. Here, we experimentally observe ERD and numerically corroborate its microscopic origin for a quasi-two-dimensional suspension of colloidal rods. At high density, the rods form small rafts, wherein they perform small-amplitude, high-frequency longitudinal displacements. Activity couples AP-rod contacts to reorientation, with the variance therein leading to ERD. This is captured by a local, rather than a global relaxation time, as used in previous phenomenological modeling. Our result should prove relevant to the microrheological characterization of complex fluids and furthering our understanding of the dynamics of microorganisms in such media

Acute social isolation alters neurogenomic state in songbird forebrain

Prolonged social isolation has negative effects on brain and behavior in humans and other social organisms, but neural mechanisms leading to these effects are not understood. Here we tested the hypothesis that even brief periods of social isolation can alter gene expression and DNA methylation in higher cognitive centers of the brain, focusing on the auditory/associative forebrain of the highly social zebra finch. Using RNA sequencing, we first identified genes that individually increase or decrease expression after isolation and observed general repression of gene sets annotated for neurotrophin pathways and axonal guidance functions. We then pursued 4 genes of large effect size: EGR1 and BDNF (decreased by isolation) and FKBP5 and UTS2B (increased). By in situ hybridization, each gene responded in different cell subsets, arguing against a single cellular mechanism. To test whether effects were specific to the social component of the isolation experience, we compared gene expression in birds isolated either alone or with a single familiar partner. Partner inclusion ameliorated the effect of solo isolation on EGR1 and BDNF, but not on FKBP5 and UTS2B nor on circulating corticosterone. By bisulfite sequencing analysis of auditory forebrain DNA, isolation caused changes in methylation of a subset of differentially expressed genes, including BDNF. Thus, social isolation has rapid consequences on gene activity in a higher integrative center of the brain, triggering epigenetic mechanisms that may influence processing of ongoing experience.Peer reviewe

On the use of reanalysis data for downscaling

In this study, a worldwide overview on the expected sensitivity of downscaling studies to reanalysis choice is provided. To this end, the similarity of middle-tropospheric variables—which are important for the development of both dynamical and statistical downscaling schemes—from 40-yr European Centre for Medium- Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and NCEP–NCAR reanalysis data on a daily time scale is assessed. For estimating the distributional similarity, two comparable scores are used: the twosample Kolmogorov–Smirnov statistic and the probability density function (PDF) score. In addition, the similarity of the day-to-day sequences is evaluated with the Pearson correlation coefficient. As the most important results demonstrated, the PDF score is found to be inappropriate if the underlying data follow a mixed distribution. By providing global similarity maps for each variable under study, regions where reanalysis data should not assumed to be ‘‘perfect’’ are detected. In contrast to the geopotential and temperature, significant distributional dissimilarities for specific humidity are found in almost every region of the world. Moreover, for the latter these differences not only occur in the mean, but also in higher-order moments. However, when considering standardized anomalies, distributional and serial dissimilarities are negligible overmost extratropical land areas. Since transformed reanalysis data are not appropriate for regional climate models—in opposition to statistical approaches—their results are expected to be more sensitive to reanalysis choice

Preliminary spatiotemporal analysis of the association between socio-environmental factors and suicide

<p>Abstract</p> <p>Background</p> <p>The seasonality of suicide has long been recognised. However, little is known about the relative importance of socio-environmental factors in the occurrence of suicide in different geographical areas. This study examined the association of climate, socioeconomic and demographic factors with suicide in Queensland, Australia, using a spatiotemporal approach.</p> <p>Methods</p> <p>Seasonal data on suicide, demographic variables and socioeconomic indexes for areas in each Local Government Area (LGA) between 1999 and 2003 were acquired from the Australian Bureau of Statistics. Climate data were supplied by the Australian Bureau of Meteorology. A multivariable generalized estimating equation model was used to examine the impact of socio-environmental factors on suicide.</p> <p>Results</p> <p>The preliminary data analyses show that far north Queensland had the highest suicide incidence (e.g., Cook and Mornington Shires), while the south-western areas had the lowest incidence (e.g., Barcoo and Bauhinia Shires) in all the seasons. Maximum temperature, unemployment rate, the proportion of Indigenous population and the proportion of population with low individual income were statistically significantly and positively associated with suicide. There were weaker but not significant associations for other variables.</p> <p>Conclusion</p> <p>Maximum temperature, the proportion of Indigenous population and unemployment rate appeared to be major determinants of suicide at a LGA level in Queensland.</p

Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.This study was partially supported by the SPECS and EUPORIAS projects, funded by the European Commission through the Seventh Framework Programme for Research under grant agreements 308378 and 308291, respectively. JMG acknowledges partial support from the project MULTI-SDM (CGL2015-66583-R, MINECO/FEDER)