BackgroundThere is an extensive literature describing temperature-mortality associations in developed regions, but research from developing countries, and Africa in particular, is limited.MethodsWe conducted a time-series analysis using daily temperature data and a national dataset of all 8.8 million recorded deaths in South Africa between 1997 and 2013. Mortality and temperature data were linked at the district municipality level and relationships were estimated with a distributed lag non-linear model with 21 days of lag, and pooled in a multivariate meta-analysis.ResultsWe found an association between daily maximum temperature and mortality. The relative risk for all-age all-cause mortality on very cold and hot days (1st and 99th percentile of the temperature distribution) was 1.14 (1.10,1.17) and 1.06 (1.03,1.09), respectively, when compared to the minimum mortality temperature. This “U” shaped relationship was evident for every age and cause group investigated, except among 25–44 year olds. The strongest associations were in the youngest (< 5) and oldest (> 64) age groups and for cardiorespiratory causes. Heat effects occurred immediately after exposure but diminished quickly whereas cold effects were delayed but persistent. Overall, 3.4% of deaths (~ 290,000) in South Africa were attributable to non-optimum temperatures over the study period. We also present results for the 52 district municipalities individually.ConclusionsAn assessment of the largest-ever dataset for analyzing temperature-mortality associations in (South) Africa indicates mortality burdens associated with cold and heat, and identifies the young and elderly as particularly vulnerable.

The aim of this study was to identify any possible temperature changes within the last 50 years in the North-Western Italian Alps by examining data from 16 high-altitude weather stations in the period 1961-2010. The daily temperature time series were collected, digitized, subjected to an historical research to individuate discontinuities and retrieve metadata. We also carried out the data quality control and the homogenization which allowed the climatic indices trend detection. The analysis of the temperature values showed an increase in temperature, particularly at high altitudes sites. In fact, the stations located above 1600 m asl revealed a rise in temperatures and a decrease in the number of cold periods. For the maximum temperatures have been observed greater increases in spring and winter, for minimum temperatures in the summer. These trends confirm that climate change is occurring in an environment particularly sensitive to temperature changes, especially during the season of snow accumulation and vegetative growth. These results may be important for policy makers to define the best adaptation strategies in order to protect one of the most sensitive environments such as mountains.

A good climatic analysis requires accurate and homogeneous daily precipitation series; unluckily, inhomogeneity is frequently found and have to be considered, especially when it is due to non-climatic parameters. CoRain is a free and open source software written in R language that could greatly help analyzing inhomogeneity caused by rainfall measuring instruments. CoRain compares two parallel rain series (with an overlapping period) and tries to highlight overestimations and underestimations due to rain gauges in a specific condition, so that the user can consider it for future analysis. CoRain offers many information on the two analyzed series, starting with cleaning input data, comparing them and classifying rainy days by severity. CoRain is a cross-platform software, easily adaptable to different needs, that takes in input a single text file with daily information of the two rain series and outputs tables (in CSV format) and plots (as PNG images) that help in the interpretation of the data. Use of the program is very simple: the execution can be either interactive or non-interactive. CoRain code has been tested on different rain series in the Piedmont region (northwestern Italy), showing its importance in identifying climate variations and instrumentation errors.

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In this article, we study the historical temperature and precipitation series for Turin and investigate the origins and effects of the city's urban heat island. The data under study are part of some of the longest meteorological data series for the whole of Italy. The findings detailed here are the result of historical research and the retrieval of data regarding climate parameters and associated metadata. We performed quality control assessments and used a homogenisation process to remove possible errors which may have occurred in the recording of the data. We also analysed changes and trends in climate and climatic index data for a 147‐year period (1870–2016), and compared thermometric data from four rural stations with the Turin series to evaluate the urban heat island effect. Finally, we assessed a number of empirical models based on the number of inhabitants, the extent of urban growth and the increase in private and public transport usage in Turin to identify the urban heat island's evolution.