Data reconstruction and homogenization for reducing uncertainties in high-resolution climate analysis in Alpine regions

Analysis of climatic series needs pre-processing to attain spatial- and time-consistent homogeneity. The latter, in high-resolution investigations, can rely on the strong correlations among series, which in turn requires a strict fulfilment of the quality standard in terms of completeness. Fifty-nine daily precipitation and temperature series of 50 years from Trentino, northern Italy, were pre-processed for climatic analysis. This study describes: (1) the preliminary gap-filling protocol for daily series, based on geostatistical correlations on both horizontal and vertical domains; (2) an algorithm to reduce inhomogeneity owing to the systematic snowfall underestimation of rain gauges; and (3) the processing protocol to take into account any source of undocumented inhomogeneity in series. This was performed by application of the t test and F-test of R code RHtestV2. This pre-processing shows straightforward results; correction of snowfall measurements re-evaluates attribution of patterns of altitudinal trends in time trends; homogenization increases the strength of the climatic signal and reduces the scattering of time trends, assessed over a few decades, of a factor of 2

Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models

International audienceModel Output Statistics (MOS) refers to a method of post-processing the direct outputs of numerical weather prediction (NWP) models in order to reduce the biases introduced by a coarse horizontal resolution. This technique is especially useful in orographically complex regions, where large differences can be found between the NWP elevation model and the true orography. This study carries out a comparison of linear and non-linear MOS methods, aimed at the prediction of minimum temperatures in a fruit-growing region of the Italian Alps, based on the output of two different NWPs (ECMWF T511?L60 and LAMI-3). Temperature, of course, is a particularly important NWP output; among other roles it drives the local frost forecast, which is of great interest to agriculture. The mechanisms of cold air drainage, a distinctive aspect of mountain environments, are often unsatisfactorily captured by global circulation models. The simplest post-processing technique applied in this work was a correction for the mean bias, assessed at individual model grid points. We also implemented a multivariate linear regression on the output at the grid points surrounding the target area, and two non-linear models based on machine learning techniques: Neural Networks and Random Forest. We compare the performance of all these techniques on four different NWP data sets. Downscaling the temperatures clearly improved the temperature forecasts with respect to the raw NWP output, and also with respect to the basic mean bias correction. Multivariate methods generally yielded better results, but the advantage of using non-linear algorithms was small if not negligible. RF, the best performing method, was implemented on ECMWF prognostic output at 06:00 UTC over the 9 grid points surrounding the target area. Mean absolute errors in the prediction of 2 m temperature at 06:00 UTC were approximately 1.2°C, close to the natural variability inside the area itself

Italian winegrowers’ and wine makers’ attitudes toward climate hazards and their strategy of adaptation to the change

This study reports the results of a survey disseminated to Italian winegrowers and wine makers to understand their attitude toward the main climate risk factors on grape and wine productions and their willingness to proactively act in facing the related consequences. A general noticeable concern about the future effects of climate change and variability emerged, even with some differences between stakeholders operating in different geographic and climatic areas. Current signals of adaptation mostly emerged at technological level, but they also included the varietal choice, with evidence to a switch from traditional varieties to others showing better pest and drought tolerance. In addition, some climate-smart cultural practices are considered ranging from water-saving irrigation methods to sustainable energy managemen

Climatic Factors Driving Invasion of the Tiger Mosquito (Aedes albopictus) into New Areas of Trentino, Northern Italy

Background:The tiger mosquito (Aedes albopictus), vector of several emerging diseases, is expanding into more northerly latitudes as well as into higher altitudes in northern Italy. Changes in the pattern of distribution of the tiger mosquito may affect the potential spread of infectious diseases transmitted by this species in Europe. Therefore, predicting suitable areas of future establishment and spread is essential for planning early prevention and control strategies.Methodology/Principal Findings:To identify the areas currently most suitable for the occurrence of the tiger mosquito in the Province of Trento, we combined field entomological observations with analyses of satellite temperature data (MODIS Land Surface Temperature: LST) and human population data. We determine threshold conditions for the survival of overwintering eggs and for adult survival using both January mean temperatures and annual mean temperatures. We show that the 0°C LST threshold for January mean temperatures and the 11°C threshold for annual mean temperatures provide the best predictors for identifying the areas that could potentially support populations of this mosquito. In fact, human population density and distance to human settlements appear to be less important variables affecting mosquito distribution in this area. Finally, we evaluated the future establishment and spread of this species in relation to predicted climate warming by considering the A2 scenario for 2050 statistically downscaled at regional level in which winter and annual temperatures increase by 1.5 and 1°C, respectively.Conclusions/Significance:MODIS satellite LST data are useful for accurately predicting potential areas of tiger mosquito distribution and for revealing the range limits of this species in mountainous areas, predictions which could be extended to an European scale. We show that the observed trend of increasing temperatures due to climate change could facilitate further invasion of Ae. albopictus into new areas. © 2011 Roiz et al.Peer Reviewe

Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review

Infectious diseases attributable to unsafe water supply, sanitation and hygiene (e.g. Cholera, Leptospirosis, Giardiasis) remain an important cause of morbidity and mortality, especially in low-income countries. Climate and weather factors are known to affect the transmission and distribution of infectious diseases and statistical and mathematical modelling are continuously developing to investigate the impact of weather and climate on water-associated diseases. There have been little critical analyses of the methodological approaches. Our objective is to review and summarize statistical and modelling methods used to investigate the effects of weather and climate on infectious diseases associated with water, in order to identify limitations and knowledge gaps in developing of new methods. We conducted a systematic review of English-language papers published from 2000 to 2015. Search terms included concepts related to water-associated diseases, weather and climate, statistical, epidemiological and modelling methods. We found 102 full text papers that met our criteria and were included in the analysis. The most commonly used methods were grouped in two clusters: process-based models (PBM) and time series and spatial epidemiology (TS-SE). In general, PBM methods were employed when the bio-physical mechanism of the pathogen under study was relatively well known (e.g. Vibrio cholerae); TS-SE tended to be used when the specific environmental mechanisms were unclear (e.g. Campylobacter). Important data and methodological challenges emerged, with implications for surveillance and control of water-associated infections. The most common limitations comprised: non-inclusion of key factors (e.g. biological mechanism, demographic heterogeneity, human behavior), reporting bias, poor data quality, and collinearity in exposures. Furthermore, the methods often did not distinguish among the multiple sources of time-lags (e.g. patient physiology, reporting bias, healthcare access) between environmental drivers/exposures and disease detection. Key areas of future research include: disentangling the complex effects of weather/climate on each exposure-health outcome pathway (e.g. person-to-person vs environment-to-person), and linking weather data to individual cases longitudinally

RMAWGEN: A software project for a daily Multi-SiteWeather Generator with R

The modeling in in climate change applications for agricultural or hydrological purposes often requires daily time-series of precipitation and temperature. This is the case of downscaled series from monthly or seasonal predictions of Global Climate Models (GCMs). This poster presents a software project, the R package RMAWGEN (R Multi-Sites Auto-regressive Weather GENerator), to generate daily temperature and precipitation time series in several sites by using the theory of vectorial auto-regressive models (VAR). The VAR model is used because it is able to maintain the temporal and spatial correlations among the several series. In particular, observed time series of daily maximum and minimum temperature and precipitation are used to calibrate the parameters of a VAR model (saved as “GPCAvarest2” or “varest2” classes, which inherit the "varest" S3 class defined in the package vars [Pfaff, 2008]). Therefore the VAR model, coupled with monthly mean weather variables downscaled by GCM predictions, allows to generate several stochastic daily scenarios. The structure of the package consists in functions that transform precipitation and temperature time series into Gaussian-distributed random variables through deseasonalization and Principal Component Analysis. Then a VAR model is calibrated on transformed time series. The time series generated by VAR are then inversely re-transformed into precipitation and/or temperature series. An application is included in the software package as an example; it is presented by using a dataset with daily weather time series recorded in 59 different sites of Trentino (Italy) and its neighborhoods for the period 1958-2007. The software is distributed as a Free Software with General Public License (GPL) and is available on CRAN website (http://cran.r-project.org/web/packages/RMAWGEN/index.html

Idroclima: a climatologic mapping of soil water content for Trentino

Project IdroClima (co-funded by the Climate change Fund of the Autonomous province of Trento) had the aim of studying the high-resolution (500 m) water content of Trentino soils. The water budget model GEOtop was applied to a 23-year period (1990-2012), corresponding to the hourly coverage of meteorological data. GEOtop accepts a thorough meteorological input, including several atmospheric variables. Pedology was described with a uniform soil, with 7 layers, owing to the lack of gridded information. Soil use has been input according to the CORINE Land Cover classes. A qualitative validation of the model was done with pre-existent probe measurements (2006-2008). The possible processing of results are multiple: the main results are analyzed and discussed in this wor