Friedrich-Wilhelm Gerstengarbe

Daily mean sea level pressure reconstructions for the European-North Atlantic region for the period 1850-2003

Abstract The development of a daily historical European–North Atlantic mean sea level pressure dataset (EMSLP) for 1850–2003 on a 5° latitude by longitude grid is described. This product was produced using 86 continental and island stations distributed over the region 25°–70°N, 70°W–50°E blended with marine data from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS). The EMSLP fields for 1850–80 are based purely on the land station data and ship observations. From 1881, the blended land and marine fields are combined with already available daily Northern Hemisphere fields. Complete coverage is obtained by employing reduced space optimal interpolation. Squared correlations (r 2) indicate that EMSLP generally captures 80%–90% of daily variability represented in an existing historical mean sea level pressure product and over 90% in modern 40-yr European Centre for Medium-Range Weather Forecasts Re-Analyses (ERA-40) over most of the region. A lack of sufficient observations over Greenland and...

RECENT CLIMATE CHANGE IN THE NORTH ATLANTIC:EUROPEAN SECTOR

The climate variability of the North Atlantic:European sector is characterized by large-scale circulation patterns measured in terms of a time series of two binary variables: the occurrence of a Grosswetter state and of a cluster set of meridional sea level pressure (SLP) gradients (characterizing the westerlies). An outlier test of the decadal behaviour of the residence time of these states identifies the decade 1981‐1990 to be the first outlier. The climatological embedding and a possible stochastic:dynamical interpretation are presented. Copyright

What Makes Ticks Tick? Climate Change, Ticks, and Tick‐Borne Diseases

Over the past decades, tick-borne encephalitis (TBE), Lyme Borreliosis (LB), and other tick-borne diseases (TBDs) have become a growing public health problem in Europe and other parts of the world. In Europe, 90% to 95% of all tick bite incidences in humans are caused by Ixodes (I) ricinus (in Eastern Europe by persulcatus). Neglecting the large number of unreported cases, 100,000 to 150,000 of these incidences become manifest as LB and between 10,000 and 15,000 as TBE. TBE virus (TBEV) is endemic in regions of 27 European countries and the number of risk areas has increased every year.2 TBEV is also endemic in some parts of Japan, China, and Mongolia. Hence, there are severe health problems for inhabitants and travelers. TBE is predominantly reported in humans and seldom in dogs and horses. Recently, a clinical case of TBE was described in a monkey after natural exposition. The reason for this development is not quite clear, but it can be stated for certain that global warming causes some of these dramatic changes.4 Recent stringent data were discussed in this article. However, there are additional factors to be considered, such as social and political changes in agricultural production and in leisure time and an increase in traveling, which in turn leads to a higher exposition rate.

Climate shifts during the last century

Fluctuations of the land surface areas covered by Koeppen climates are analysed for the 1901 to 1995 period using trends and outliers as indicators of climate shift. Only the extreme climate zones of the global Tropics and of the Tundra (with the highly correlated northern hemisphere temperature) realise statistically significant shifts and outliers. There are nosignificant trends and outliers in the fluctuating ocean-atmosphere patterns (Pacific Decadal and North Atlantic Oscillations) and the highly correlated intermediate climate zones (dry, subtropical and boreal) of the surrounding continents.

Multiscale detection of abrupt climate changes: application to River Nile flood levels

The historical flood-level time series of the River Nile (AD 622–1470) is chosen to identify abrupt climate changes by applying global and local analysis techniques: the Mann–Kendall test and a non-hierarchical cluster analysis method to improve the Mann–Kendall test; a multiscale moving t-test with correction to the degree of freedom and an antisymmetric wavelet transform. The global estimates show three distinct epochs, AD 622–1078, 1079–1325 and 1326–1470, coinciding with larger scale climate changes: a relatively cool age, the Little Climatic Optimum of the Middle Ages, and an interim period before the Little Ice Age. The local estimates reveal the following results. The reference time of abrupt changes can be clearly identified, the associated time-scale coincides with the persistent anomaly period, and the maximum absolute t-value is statistically significant. There are about eight almost synchronous abrupt changes in the minimum and maximum River Nile flood levels, many of them are associated with 35–45 year persistence time-scales. An association of these short time-scales with those of interdecadal variability reported for the mid- and high-latitude sea-surface temperature of the North Atlantic is suggested, although information on phase coherence is not available. ©1997 by the Royal Meteorological Society. Int. J Climatol., 17: 1301–1315 (1997) (No. of Figures: 9 No. of Tables: 1 No. of References: 40)

A method to estimate the statistical confidence of cluster separation

SummaryCluster analysis contains several multivariate methods for the separation of patterns (clusters). The definition of the optimum or universally best cluster analysis is an unresolved issue. Three methods are of special importance: 1. The statistical confidence of cluster separation. 2. The definition of the optimal number of clusters. 3. The description of the internal cluster structure. Two new methods addressing these problems are presented. On the basis of nonhierarchical minimum-distance cluster analysis a new method is described that allows a separation of clusters in a statistically well-founded way. This method solves problems one and two. Using a newly developed special rank-sum analysis, a solution to the third problem is possible. An example shows the practicability of the proposed procedures.

Regional impact analysis of climate change on natural and managed forests in the Federal State of Brandenburg, Germany

A methodology for regional application of forest simulation models has been developed as part of an assessment of possible climate change impacts in the Federal state of Brandenburg (Germany). Here we report on the application of a forest gap model to analyse the impacts of climate change on species composition and productivity of natural and managed forests in Brandenburg using a statistical method for the development of climate scenarios. The forest model was linked to a GIS that includes soil and groundwater table maps, as well as gridded climate data with a resolution of 10 × 10 km and simulated a steady-state species composition which was classified into forest types based on the biomass distribution between species. Different climate scenarios were used to assess the sensitivity of species composition to climate change. The simulated forest distribution patterns for current climate were compared with a map of Potential Natural Vegetation (PNV) of Brandenburg.In order to analyse the possible consequences of climate change on forest management, we used forest inventory data to initialize the model with representative forest stands. Simulation experiments with two different management strategies indicated how forest management could respond to the projected impacts of climate change. The combination of regional analysis of natural forest dynamics under climate change with simulation experiments for managed forests outlines possible trends for the forest resources. The implications of the results are discussed, emphasizing the regional differences in environmental risks and the adaptation potentials of forestry in Brandenburg.

Boundary effects in network measures of spatially embedded networks

In studies of spatially confined networks, network measures can lead to false conclusions since most measures are boundary affected. This is especially the case if boundaries are artificial and not inherent in the underlying system of interest (e.g., borders of countries). An analytical estimation of boundary effects is not trivial due to the complexity of measures. The straightforward approach we propose here is to use surrogate networks that provide estimates of boundary effects in graph statistics. This is achieved by using spatially embedded random networks as surrogates that have approximately the same link probability as a function of spatial link lengths. The potential of our approach is demonstrated for an analysis of spatial patterns in characteristics of regional climate networks. As an example networks derived from daily rainfall data and restricted to the region of Germany are considered.

Climatological drivers of changes in flood hazard in Germany

Since several destructive floods have occurred in Germany in the last decades, it is of considerable interest and relevance (e.g., when undertaking flood defense design) to take a closer look at the climatic factors driving the changes in flood hazard in Germany. Even if there also exist non-climatic factors controlling the flood hazard, the present paper demonstrates that climate change is one main driver responsible for the increasing number of floods. Increasing trends in temperature have been found to be ubiquitous in Germany, with impact on air humidity and changes in (intense) precipitation. Growing trends in flood prone circulation pattern and heavy precipitation are significant in many regions of Germany over a multi-decade interval and this can be translated into the rise of flood hazard and flood risk.

Non-linear time series analysis of precipitation events using regional climate networks for Germany

AbstractSynchronous occurrences of heavy rainfall events and the study of their relation in time and space are of large socio-economical relevance, for instance for the agricultural and insurance sectors, but also for the general well-being of the population. In this study, the spatial synchronization structure is analyzed as a regional climate network constructed from precipitation event series. The similarity between event series is determined by the number of synchronous occurrences. We propose a novel standardization of this number that results in synchronization scores which are not biased by the number of events in the respective time series. Additionally, we introduce a new version of the network measure directionality that measures the spatial directionality of weighted links by also taking account of the effects of the spatial embedding of the network. This measure provides an estimate of heavy precipitation isochrones by pointing out directions along which rainfall events synchronize. We propose a climatological interpretation of this measure in terms of propagating fronts or event traces and confirm it for Germany by comparing our results to known atmospheric circulation patterns.