Analysis of maximum and minimum daily temperatures recorded at Fabra Observatory (Barcelona, NE Spain) in the period 1917–1998

Abstract

Daily maximum and minimum temperatures recorded without interruption at Fabra Observatory (Barcelona) from 1917 to 1998 are analysed studying their homogeneity, randomness, possible trends and their statistical significance, and time irregularities detected by means of concepts of entropy and spectral power analysis. The homogeneity of the series is tested on a monthly scale using the adaptive Kolmogorov-Zurbenko filter. With respect to the randomness of the time series, the von Neumann ratio test is applied to standardized values of extreme temperatures in four different time-scales (daily, monthly, seasonal and annual). The statistical significance of trends is quantified by applying the Spearman and Mann-Kendall tests to daily, monthly and seasonal data. The Mann-Kendall sequential test also leads to the detection of sharp changes in the time series when monthly data is analysed. The quantification of irregularities through entropy is investigated for standardized temperatures on daily, monthly and seasonal scales, based on the concept of mathematical information theory. Periodicities derived from spectral power analyses are checked with the hypothesis of white-noise and Markov red-noise stochastic processes. The most notable features, common to maximum and minimum temperatures, are the lack of randomness of the series for all the time-scales considered and the different trends obtained for the periods 1917-1980 and 1917-1998, which are confirmed by the Spearman and sequential Mann-Kendall tests. Nevertheless, the maximum and minimum temperature series show quite a different behaviour from the point of view of results concerning time irregularities in terms of entropy and periodicities. The main features of the results are discussed by comparing them with those obtained for other areas of the Mediterranean domain.

Recent warming in the Balearic Sea and Spanish Mediterranean coast. Towards an earlier and longer summer

“…Most studies agree that there has been an increase in temperature from the 1970s onward (Esteban-Parra et al, 1995Serra et al, 2001;Morales et al, 2005;Salat and Pascual, 2006;Brunet et al, 2007;Homar et al, 2010;Del Río et al, 2011;El Kenawy et al;. Amongst them, we highlight those that explore the monthly, seasonal and annual variability of the warming trends.…”

Patterns of monthly rainfall shortage and excess in terms of the standardized precipitation index for Catalonia (NE Spain)

“…To assess the homogeneity of the time series only from meta-data files, when they were available, became very doubtful in our case and, consequently, three homogeneity tests were considered. First, the adaptive Kolmogorov-Zurbenko filter (Zurbenko et al, 1996), recently applied for instance to extreme temperature series (Serra et al, 2001), was discarded due to the relatively short recording period of our series. This fact leads to very short filtered signals to decide about their homogeneity.…”

“…Second, the Alexandersson (1986) homogeneity test was also discarded, given that it was difficult to obtain homogeneous series of reference well correlated with neighbouring recording sites, because of the spatial variability of climatic conditions in Catalonia. Finally, the evolution in time of the Von Neumann ratio (Mitchell et al, 1966;Rodríguez et al, 1999;Serra et al, 2001) was studied for the 99 rain gauges, assuming that the residuals of rainfall amounts were distributed quite similar to a Gaussian distribution function. Residuals were defined as the difference between each monthly rainfall and the corresponding monthly averaged amount.…”

A Method of Homogenizing the Extremes and Mean of Daily Temperature Measurements

“…Australian climate data are certainly not the only data that suffer from homogeneity problems. There are many studies that have either assessed the homogeneity (e.g., Manton et al 2001;Serra et al 2001;Wijngaard et al 2003) or have homogenized climate series for use in long-term climate change assessment (e.g., Böhm et al 2001;Tuomenvirta 2001;Camuffo 2002;Della-Marta et al 2004;Auer et al 2005;Begert et al 2005). Many of these studies have focused on homogenizing monthly and annual average series, mainly because data are mostly available at that resolution and are readily accessible in digital form.…”