Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results

Time series homogenization for 299 of the available precipitation records for the island of Ireland (IENet) was performed. Four modern relative homogenization methods, that is, HOMER, ACMANT, CLIMATOL and AHOPS were applied to this network of station series where contiguous intact monthly records range from 30 to 70 years within the base period 1941-2010. Break detection results are compared between homogenization methods, and coincidences with available documentary information (metadata) were analysed. The lowest (highest) number of breaks were detected with HOMER (ACMANT). Large differences of break frequency were found, namely ACMANT and AHOPS detected 8 times as many breaks than HOMER, while the break frequency with CLIMATOL was intermediate. Also, the ratio of series classified to be homogeneous varies widely between the methods. It is 85% with HOMER, 60% with CLIMATOL, 31% with AHOPS, while only 22% with ACMANT. In a further experiment, all the available time series for Ireland and Northern Ireland, (910 series) were used with ACMANT and CLIMATOL to explore the stability of break frequency for the same 299 series examined in the base experiment. While overall break frequency slightly increased (by 6-13%), the break positions notably changed for individual time series. The number of breaks changed for 59% (23%) of the series with ACMANT (CLIMATOL). For the breaks detected coincidentally by at least three methods including ACMANT and CLIMATOL in the base experiment, the second experiment confirmed the break positions in 86-87% of the breaks. The consequences of these results in relation to the reliability of statistical homogenization are discussed. Sometimes markedly different step functions provide comparable good approaches. However, the accuracy of homogenized time series cannot be related directly to the instability of break detection results.

One hundred years of climate change in Mexico

Spatial assessments of historical climate change provide information that can be used by scientists to analyze climate variation over time and evaluate, for example, its effects on biodiversity, in order to focus their research and conservation efforts. Despite the fact that there are global climatic databases available at high spatial resolution, they represent a short temporal window that impedes evaluating historical changes of climate and their impacts on biodiversity. To fill this gap, we developed climate gridded surfaces for Mexico for three periods that cover most of the 20^th and early 21^st centuries: t₁-1940 (1910–1949), t₂-1970 (1950–1979) and t₃-2000 (1980–2009), and used these interpolated surfaces to describe how climate has changed over time, both countrywide and in its 19 biogeographic provinces. Results from our characterization of climate change indicate that the mean annual temperature has increased by nearly 0.2°C on average across the whole country from t₂-1970 to t₃-2000. However, changes have not been spatially uniform: Nearctic provinces in the north have suffered higher temperature increases than southern tropical regions. Central and southern provinces cooled at the beginning of the 20^th century but warmed consistently since the 1970s. Precipitation increased between t₁-1940 and t₂-1970 across the country, more notably in the northern provinces, and it decreased between t₂-1970 and t₃-2000 in most of the country. Results on the historical climate conditions in Mexico may be useful for climate change analyses for both environmental and social sciences. Nonetheless, our climatology was based on information from climate stations for which 9.4–36.2% presented inhomogeneities over time probably owing to non-climatic factors, and climate station density changed over time. Therefore, the estimated changes observed in our analysis need to be interpreted cautiously.

Effects of thermopeaking on the thermal response of alpine river systems to heatwaves

Within the past 30years there have been two major heatwave events (in 2003 and 2006) that broke 500-year-old temperature records in Europe. Owing to the growing concern of rising temperatures, we analyzed the potential response in a number of river sections that are subject to hydropeaking and thermopeaking through the intermittent release of water from hydropower stations. Thermopeaking in alpine streams is known to intermittently cool down the river water in summer and to warm it up in winter. We analyzed the response of river water temperature to air temperature during heatwaves at 19 gauging stations across Switzerland, using a 30-yr dataset at a 10-min resolution. Stations were either classified into "unpeaked" or "peaked" groups according to four statistical indicators related to hydropeaking and thermopeaking pressure. Peaked stations were exposed to reduced temporal variability in river water temperature, and it was determined that correlations between river water and air temperature were weaker for peaked stations compared with unpeaked stations. Similarly, peaked stations showed a much weaker response to heatwaves compared with unpeaked stations. It is important to note that this "cooling effect" created by hydro-thermopeaking was most pronounced during the two major heatwave events that took place in 2003 and 2006. Furthermore, results from thermal stress events on the growth of a typical cold eurythermic fish species (brown trout) increased continuously in rivers subject to peaked station water release during heatwaves. While hydropower operations that take place high up on mountains releasing hypolimnetic water may mitigate the adverse effects of heatwaves on downstream alpine river ecosystems locally, our results show the complexity of an artificial physical template associated with flow regime regulation in alpine streams.

Efficiencies of Inhomogeneity-Detection Algorithms: Comparison of Different Detection Methods and Efficiency Measures

Efficiency evaluations for change point Detection methods used in nine major Objective Homogenization Methods (DOHMs) are presented. The evaluations are conducted using ten different simulated datasets and four efficiency measures: detection skill, skill of linear trend estimation, sum of squared error, and a combined efficiency measure. Test datasets applied have a diverse set of inhomogeneity (IH) characteristics and include one dataset that is similar to the monthly benchmark temperature dataset of the European benchmarking effort known by the acronym COST HOME. The performance of DOHMs is highly dependent on the characteristics of test datasets and efficiency measures. Measures of skills differ markedly according to the frequency and mean duration of inhomogeneities and vary with the ratio of IH-magnitudes and background noise. The study focuses on cases when high quality relative time series (i.e., the difference between a candidate and reference series) can be created, but the frequency and intensity of inhomogeneities are high. Results show that in these cases the Caussinus-Mestre method is the most effective, although appreciably good results can also be achieved by the use of several other DOHMs, such as the Multiple Analysis of Series for Homogenisation, Bayes method, Multiple Linear Regression, and the Standard Normal Homogeneity Test.

Climate indicators for Italy: calculation and dissemination

Abstract. In Italy, meteorological data necessary and useful for climate studies are collected, processed and archived by a wide range of national and regional institutions. As a result, the density of the stations, the length and frequency of the observations, the quality control procedures and the database structure vary from one dataset to another. In order to maximize the use of those data for climate knowledge and climate change assessments, a computerized system for the collection, quality control, calculation, regular update and rapid dissemination of climate indicators was developed. The products publicly available through a dedicated web site are described, as well as an example of climate trends estimates over Italy, based on the application of statistical models on climate indicators from quality-checked and homogenised time series.