Bayesian analysis of the species-specific lengthening of the growing season in two European countries and the influence of an insect pest

A recent lengthening of the growing season in mid and higher latitudes of the northern hemisphere is reported as a clear indicator for climate change impacts. Using data from Germany (1951–2003) and Slovenia (1961–2004), we study whether changes in the start, end, and length of the growing season differ among four deciduous broad-leaved tree species and countries, how the changes are related to temperature changes, and what might be the confounding effects of an insect attack. The functional behaviour of the phenological and climatological time series and their trends are not analysed by linear regression, but by a new Bayesian approach taking into account different models for the functional description (one change-point, linear, constant models). We find advanced leaf unfolding in both countries with the same species order (oak > horse chestnut, beech, and birch). However, this advance is non linear over time and more apparent in Germany with clear change-points in the late 1970s, followed by marked advances (on average 3.67 days decade−1 in the 2000s). In Slovenia, we find a more gradual advance of onset dates (on average 0.8 days decade−1 in the 2000s). Leaf colouring of birch, beech, and oak has been slightly delayed in the last 3 decades, especially in Germany, however with no clear functional behaviour. Abrupt changes in leaf colouring dates of horse chestnut with recent advancing onset dates can be linked across countries to damage by a newly emerging pest, the horse chestnut leaf-miner (Cameraria ohridella). The lengthening of the growing season, more distinct in Germany than in Slovenia (on average 4.2 and 1.0 days decade−1 in the 2000s, respectively), exhibits the same species order in both countries (oak > birch > beech). Damage by horse chestnut leaf-miner leads to reduced lengthening (Germany) and drastic shortening (Slovenia) of the horse chestnut growing season (-12 days decade−1 in the 2000s). Advanced spring leaf unfolding and lengthening of the growing season of oak, beech and birch are highly significantly related to increasing March temperatures in both countries. Only beech and oak leaf unfolding in Germany, which is generally observed later in the year than that of the other two species, is more closely correlated with April temperatures, which comparably exhibit marked change-points at the end of the 1970s

Analysis of rare-event time series with application to Caribbean hurricane data

A Bayesian analysis of rare-event time series is presented. The model space for the description of the time series of Caribbean hurricanes includes a constant Poisson rate, a rate varying linearly in time and a rate described by a change point function. Bayesian model probabilities for the tropical systems of the Bermudas, Bahamas, and East and West Caribbean regions are calculated. For the Bahamas data also model probabilities for the three different hurricane intensity classes $(h_{3} + h_{4} + h_{5})$, $(h_{1} + h_{2})$ and tropical storms are obtained. All results show that inclusion of a nonlinear not necessarily monotonic model function is mandatory for proper data representation. For the Bahamas we calculate also predictive distributions of the Poisson rate from which the probabilities of H events in the years 2008 and 2015 are deduced. These data find application in future risk assessment in the insurance industry

Bayesian Analysis of Climate Change Impacts in Phenology

The identification of changes in observational data relating to the climate change hypothesis remains a topic of paramount importance. In particular, scientifically sound and rigorous methods for detecting changes are urgently needed. In this paper, we develop a Bayesian approach to nonparametric function estimation. The method is applied to blossom time series of Prunus avium L., Galanthus nivalis L. and Tilia platyphyllos SCOP. The functional behavior of these series is represented by three different models: the constant model, the linear model and the one change point model

Mixture Modeling for Background and Sources Separation in x-ray Astronomical Images

A probabilistic technique for the joint estimation of background and sources in high-energy astrophysics is described. Bayesian probability theory is applied to gain insight into the coexistence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. The present analysis is applied to ROSAT PSPC data (0.1-2.4 keV) in Survey Mode. A background map is modelled using a Thin-Plate spline. Source probability maps are obtained for each pixel (45 arcsec) independently and for larger correlation lengths, revealing faint and extended sources. We will demonstrate that the described probabilistic method allows for detection improvement of faint extended celestial sources compared to the Standard Analysis Software System (SASS) used for the production of the ROSAT All-Sky Survey (RASS) catalogues