Changing Seasonal Rainfall Distribution With Climate Directs Contrasting Impacts at Evapotranspiration and Water Yield in the Western Mediterranean Region

Over the past century, climate change has been reflected in altered precipitation regimes worldwide. Because evapotranspiration is sensitive to both water availability and atmospheric demand for water vapor, it is essential to assess the likely consequences of future changes of these climate variables to evapotranspiration and, thus, runoff. We propose a simplified approach for annual evapotranspiration predictions, based on seasonal evapotranspiration estimates, accounting for the strong seasonality of meteorological conditions typical of Mediterranean climate, still holding the steady state assumption of basin water balance at mean annual scale. Sardinian runoff decreased over the 1975-2010 period by more than 40% compared to the preceding 1922-1974 period. Most of annual runoff in Sardinian basins is produced by winter precipitation, a wet season with relatively high evaporation rates. We derived linear seasonal evapotranspiration responses to seasonal precipitation, and, in turn, a relationship between the parameters of the linear functions and the seasonal vapor pressure deficit (D), accounting for residuals with basin properties. We then used these relationships to predict evapotranspiration and runoff using future Intergovernmental Panel on Climate Change climate scenarios, considering changing precipitation and D seasonality. We show that evapotranspiration is insensitive to D scenario changes. Although both evapotranspiration and runoff are sensitive to precipitation seasonality, future changes in runoff are related only to changes of winter precipitation, while evapotranspiration changes are related to those of spring and summer precipitation. Future scenario predicting further runoff decline is particularly alarming for the Sardinian water resources system, requiring new strategies and designs in water resources planning and management.Peer reviewe

Building long homogeneous temperature series across Europe: a new approach for the blending of neighboring series

Long and homogeneous series are a necessary requirement for reliable climate analysis. Relocation of measuring equipment from one station to another, such as from the city center to a rural area or a nearby airport, is one of the causes of discontinuities in these long series which may affect trend estimates. In this paper an updated procedure for the composition of long series, by combining data from nearby stations, is introduced. It couples an evolution of the blending procedure already implemented within the European Climate Assessment and Dataset (which combines data from stations no more than 12.5 km apart from each other) with a duplicate removal, alongside the quantile matching homogenization procedure. The ECA&D contains approximately 3000 homogenized series for each temperature variable prior to the blending procedure, around 820 of these are longer than 60 years; the process of blending increases the number of long series to more than 900. Three case studies illustrate the effects of the homogenization on single blended series, showing the effectiveness of separate adjustments on extreme and mean values (Geneva), on cases where blending is complex (Rheinstetten) and on series which are completed by adding relevant portions of GTS synoptic data (Siauliai). Finally, a trend assessment on the whole European continent reveals the removal of negative and very large trends, demonstrating a stronger spatial consistency. The new blended and homogenized data-set will allow a more reliable use of temperature series for indices calculation and for the calculation of gridded data-sets, and will be available for users on www.ecad.eu

Reassessing changes in diurnal temperature range: Intercomparison and evaluation of existing global data set estimates

Changes in diurnal temperature range (DTR) over global land areas are compared from a broad range of independent data sets. All data sets agree that global-mean DTR has decreased significantly since 1950, with most of that decrease occurring over 1960–1980. The since-1979 trends are not significant, with inter-data set disagreement even over the sign of global changes. Inter-data set spread becomes greater regionally and in particular at the grid box level. Despite this, there is general agreement that DTR decreased in North America, Europe, and Australia since 1951, with this decrease being partially reversed over Australia and Europe since the early 1980s. There is substantive disagreement between data sets prior to the middle of the twentieth century, particularly over Europe, which precludes making any meaningful conclusions about DTR changes prior to 1950, either globally or regionally. Several variants that undertake a broad range of approaches to postprocessing steps of gridding and interpolation were analyzed for two of the data sets. These choices have a substantial influence in data sparse regions or periods. The potential of further insights is therefore inextricably linked with the efficacy of data rescue and digitization for maximum and minimum temperature series prior to 1950 everywhere and in data sparse regions throughout the period of record. Over North America, station selection and homogeneity assessment is the primary determinant. Over Europe, where the basic station data are similar, the postprocessing choices are dominant. We assess that globally averaged DTR has decreased since the middle twentieth century but that this decrease has not been linear

On tail trend detection: modeling relative risk

The climate change dispute is about changes over time of environmental characteristics (such as rainfall). Some people say that a possible change is not so much in the mean but rather in the extreme phenomena (that is, the average rainfall may not change much but heavy storms may become more or less frequent). The paper studies changes over time in the probability that some high threshold is exceeded. The model is such that the threshold does not need to be specified, the results hold for any high threshold. For simplicity a certain linear trend is studied depending on one real parameter. Estimation and testing procedures (is there a trend?) are developed. Simulation results are presented. The method is applied to trends in heavy rainfall at 18 gauging stations across Germany and The Netherlands. A tentative conclusion is that the trend seems to depend on whether or not a station is close to the sea.Comment: 38 page

An ecological time-series study of heat-related mortality in three European cities

BACKGROUND: Europe has experienced warmer summers in the past two decades and there is a need to describe the determinants of heat-related mortality to better inform public health activities during hot weather. We investigated the effect of high temperatures on daily mortality in three cities in Europe (Budapest, London, and Milan), using a standard approach. METHODS: An ecological time-series study of daily mortality was conducted in three cities using Poisson generalized linear models allowing for over-dispersion. Secular trends in mortality and seasonal confounding factors were controlled for using cubic smoothing splines of time. Heat exposure was modelled using average values of the temperature measure on the same day as death (lag 0) and the day before (lag 1). The heat effect was quantified assuming a linear increase in risk above a cut-point for each city. Socio-economic status indicators and census data were linked with mortality data for stratified analyses. RESULTS: The risk of heat-related death increased with age, and females had a greater risk than males in age groups > or =65 years in London and Milan. The relative risks of mortality (per degrees C) above the heat cut-point by gender and age were: (i) Male 1.10 (95%CI: 1.07-1.12) and Female 1.07 (1.05-1.10) for 75-84 years, (ii) M 1.10 (1.06-1.14) and F 1.08 (1.06-1.11) for > or = or =85 years in Budapest (> or =24 degrees C); (i) M 1.03 (1.01-1.04) and F 1.07 (1.05-1.09), (ii) M 1.05 (1.03-1.07) and F 1.08 (1.07-1.10) in London (> or =20 degrees C); and (i) M 1.08 (1.03-1.14) and F 1.20 (1.15-1.26), (ii) M 1.18 (1.11-1.26) and F 1.19 (1.15-1.24) in Milan (> or =26 degrees C). Mortality from external causes increases at higher temperatures as well as that from respiratory and cardiovascular disease. There was no clear evidence of effect modification by socio-economic status in either Budapest or London, but there was a seemingly higher risk for affluent non-elderly adults in Milan. CONCLUSION: We found broadly consistent determinants (age, gender, and cause of death) of heat related mortality in three European cities using a standard approach. Our results are consistent with previous evidence for individual determinants, and also confirm the lack of a strong socio-economic gradient in heat health effects currently in Europe

Economic downturn results in tick-borne disease upsurge

<p>Abstract</p> <p>Background</p> <p>The emergence of zoonoses is due both to changes in human activities and to changes in their natural wildlife cycles. One of the most significant vector-borne zoonoses in Europe, tick-borne encephalitis (TBE), doubled in incidence in 1993, largely as a consequence of the socio-economic transition from communism to capitalism and associated environmental changes.</p> <p>Methods</p> <p>To test the effect of the current economic recession, unemployment in 2009 and various socio-economic indices were compared to weather indices (derived from principal component analyses) as predictors for the change in TBE case numbers in 2009 relative to 2004-08, for 14 European countries.</p> <p>Results</p> <p>Greatest increases in TBE incidence occurred in Latvia, Lithuania and Poland (91, 79 and 45%, respectively). The weather was rejected as an explanatory variable. Indicators of high background levels of poverty, e.g. percent of household expenditure on food, were significant predictors. The increase in unemployment in 2009 relative to 2008 together with 'in-work risk of poverty' is the only case in which a multivariate model has a second significant term.</p> <p>Conclusion</p> <p>Background socio-economic conditions determine susceptibility to risk of TBE, while increased unemployment triggered a sudden increase in risk. Mechanisms behind this result may include reduced resistance to infection through stress; reduced uptake of costly vaccination; and more exposure of people to infected ticks in their forest habitat as they make greater use of wild forest foods, especially in those countries, Lithuania and Poland, with major marketing opportunities in such products. Recognition of these risk factors could allow more effective protection through education and a vaccination programme targeted at the economically most vulnerable.</p

On conditional skewness with applications to environmental data

The statistical literature contains many univariate and multivariate skewness measures that allow two datasets to be compared, some of which are defined in terms of quantile values. In most situations, the comparison between two random vectors focuses on univariate comparisons of conditional random variables truncated in quantiles; this kind of comparison is of particular interest in the environmental sciences. In this work, we describe a new approach to comparing skewness in terms of the univariate convex transform ordering proposed by van Zwet (Convex transformations of random variables. Mathematical Centre Tracts, Amsterdam, 1964), associated with skewness as well as concentration. The key to these comparisons is the underlying dependence structure of the random vectors. Below we describe graphical tools and use several examples to illustrate these comparisons.The research of Félix Belzunce, Julio Mulero and José María Ruíz is partially funded by the Ministerio de Economía y Competitividad (Spain) under Grant MTM2012-34023-FEDER. Alfonso Suárez-Llorens acknowledges support received from the Ministerio de Economía y Competitividad (Spain) under Grant MTM2014-57559-P

Blocking representation in the ERA-Interim driven EURO-CORDEX RCMs

While Regional Climate Models (RCMs) have been shown to yield improved simulations compared to General Circulation Model (GCM), their representation of large-scale phenomena like atmospheric blocking has been hardly addressed. Here, we evaluate the ability of RCMs to simulate blocking situations present in their reanalysis driving data and analyse the associated impacts on anomalies and biases of European 2-m air temperature (TAS) and precipitation rate (PR). Five RCM runs stem from the EURO-CORDEX ensemble while three RCMs are WRF models with different nudging realizations, all of them driven by ERA-Interim for the period 1981?2010. The detected blocking systems are allocated to three sectors of the Euro-Atlantic region, allowing for a characterization of distinctive blocking-related TAS and PR anomalies. Our results indicate some misrepresentation of atmospheric blocking over the EURO-CORDEX domain, as compared to the driving reanalysis. Most of the RCMs showed fewer blocks than the driving data, while the blocking misdetection was negligible for RCMs strongly conditioned to the driving data. A higher resolution of the RCMs did not improve the representation of atmospheric blocking. However, all RCMs are able to reproduce the basic anomaly structure of TAS and PR connected to blocking. Moreover, the associated anomalies do not change substantially after correcting for the misrepresentation of blocking in RCMs. The overall model bias is mainly determined by pattern biases in the representations of surface parameters during non-blocking situations. Biases in blocking detections tend to have a secondary influence in the overall bias due to compensatory effects of missed blockings and non-blockings. However, they can lead to measurable effects in the presence of a strong blocking underestimation.This work was funded by the Austrian Science Fund (FWF) under the project: Understanding Contrasts in high Mountain hydrology in Asia (UNCOMUN: I 1295-N29). This research was supported by the Faculty of Environmental, Regional and Educational Sciences (URBI), University of Graz, as well as the Federal Ministry of Science, Research and Economy (BMWFW) by funding the OeAD Grant Marietta Blau. This work was partially supported (JMG and SH) by the project MULTI-SDM (CGL2015-66583- R, MINECO/FEDER). DB was supported by the PALEOSTRAT (CGL2015-69699-R) project funded by the Spanish Ministry of Economy and Competitiveness (MINECO)