1. Thessaloniki | Greece| 19-21 September 2016
Homogenization of long-term time-series of temperature records in Cyprus Kezoudi Maria1,2*, Filippos Tymvios3,4 1University of Reading 2Cyprus University 3Cyprus Department of Meteorology 4The Cyprus Institute * corresponding author (University of Reading, MSc: Applied Meteorology with Climate Management)
Abstract
Climate research necessitates a methodical and detailed examination of long-term time-series of meteorological observations. The existence of quality of homogenous datasets is of paramount importance for such studies. The climatological database of the Cyprus Department of Meteorology consists of observations from a big number of stations, several of which contain records for some parameters, extending from the beginning of the twentieth century, albeit for varying time periods. Special care is taken for the quality of the observational data registered in the database without any effort made so far for the homogenization of the database. The aim of this work is the homogenization of the maximum temperature from 31 main meteorological stations of Cyprus during the period of , as well as the correction and analysis of the results over that period. Moreover, this study contains the detection and correction of the possible invalid data, the replacement of the time-series' missed values and the calculation of the correlation coefficient between the stations under investigation.
INTRODUCTION
The area of Eastern Mediterranean and the Middle East is likely to be affected by climate change, associated with increases in the frequency and intensity of droughts and hot weather conditions (Lelieveld et al., 2012). The distribution of the annual temperature has changed considerably, exhibiting a large variation under extremely dry and wet years (Miclaelides et al., 2009; Tymvios and Michaelides, 2002). In order to assess the true value of climate projections, a good knowledge of the past and current climatic conditions constitutes a necessity, thus the highest possible quality of the long­-term climate measurements is required. The use of raw climatic data is not recommended for regional climate studies, since such data can lead to serious complications or misleading results.
Homogenization is the procedure that aims to remove the aforementioned contamination (break­point) in the dataset, in order to provide a solid and consistent time­series able to provide the observational basis for climate research. Homogenized time­series are free from non­statistical noise, have less non­climatic biases and exhibit coherent signals with strong regional patterns, such as smoothed gradients (Moisselin and Mestre, ).
The main source of disruption in a non-­homogenized time­series is the relocation of the station. Long-­term datasets originating from single­location stations are extremely rare. Before the importance of a coherent database was scientifically postulated, in most archives of climatological records, data originating from stations with different locations used to be concaternated in an effort to “create” long and complete datasets with no missing records, data from stations that were considered to have “similar” characteristics were merged on a subjective basis. In addition, some time­series incorporate changes in the measuring device or noticeable changes in the surroundings of the station. Finally, all of the time­series that consist of data obtained with manual measuring methods embrace a subjective component too.
Metadata information -that must always accompany the records- is sometimes either missing or is incomplete. As a result, records of historical time­series are, as a rule, not absolutely or precisely known. Therefore, the raw climatological data that are available for research contain the climatological signal and the contamination of the station and observation effects that have to be removed.
Figure 1 The geographical distribution of the studied met stations.
DATA AND METHODOLOGY
The data of the current project have been provided from the Cyprus Department of Meteorology . 31 time­series from the main stations in Cyprus over the period of 1959 –2013 have been used. Nevertheless, limited information is provided about the historical evolution of the stations and changes in the instrumentation or the location of the installation. The geographical distribution of the observing stations is illustrated in Figure 1.
For the homogenization of the monthly maximum temperature time­series, the ACMANT2 software suite was utilized (Domonkos, 2011 ). In verifications with the benchmark dataset of the European project COST ES0601 ( the ACMANT automated homogenization method showed an advanced performance among other homogenization methods. ACMANT2 is the improved version of ACMANT, and it comprises automatic programs for homogenizing temperature and precipitation data, both on monthly and daily scales. The ACMANT method creates for each time-series a correlation table against the other datasets and builds composite a reference series from the weighted squared correlation of all the stations with correlation coefficient above 0.4. The time-series under investigation is then compared against the reference time period and necessary adjustments are conducted. The theoretical basis that the software is based on is discussed by Domonkos (2011, 2013).
Figure 2 Raw records and homogenised results of the seasonal temperatures for the station of Pano Panagia.
Arrows: Show the break points that have been investigated during the homogenisation process.
RESULTS
Comparison between raw and homogenized data
Several stations have some break-points in their recorded timeseries. In particular, many of the mountainous stations have at least one break-point in the time-series. The stations with the highest altitude preserve an even more disrupted time-series. These stations are prone to human errors and that behaviour can be attributed due to the fact that the observers were not professionals but volunteers.
The station of Pano Panagia is a characteristic example which contains contaminated points and trend. As it can be seen from the figure 2, there were four particular missed recorded data (break-points) over the period that the station has been in operation. During the homogenisation process, the pre-mentioned break points are replaced with homogenised data. In addition, from the start of the period (1961) until 1980, the homogenised data has slightly lower temperatures than the raw records. Consequently, taking into account the pre-mentioned results, the time-series of the station of Pano Panagia cannot be defined as a reference timeseries.
Figure 3 Correlation coefficient between of the station of Nicosia and the altitude difference of the other under-studied stations
Correlation between the stations - Homogenization results
A correlation table was created for all the stations used in the present analysis. An example of the correlation table created for the station of Nicosia is presented in Table 1. As can be seen from the Table 1, the correlation of the station of Nicosia in accordance with the rest of the under-studied stations fluctuates between and 0.99.
Figure 3 depicts the correlation of the station of Nicosia in accordance with its altitude difference of the other studied stations. As can be seen, the station of Nicosia is highly correlated (0.99) - almost perfectly correlation - with the station of Athalassa. The station with the lowest correlation in accordance with the station of Nicosia is the station of Pafos Airport, which is located 150 m lower than the station of Nicosia. In contrast to Nicosia's station, the station of Pafos Airport is coastal, which means that it can be affected by local winds' phenomena (e.g. sea breeze). Moreover, the mountainous stations have lower correlation than Nicosia's station, due to their higher altitude.
CONCLUSIONS
Monthly values of temperature from the 31 main stations of the Cyprus Department of Meteorology’s database were checked for homogeneity using the ACMANT software. Breaks were identified in several stations, especially over the early years of the time­series and they have been replaced with homogenised data, during the homogenisation process.
The current analysis showed that altitude has a key role in the correlation factor. For instance, stations with similar altitude are highly correlated. It is worth noting that the stations which have shown high correlation, under a suitable process can significantly contribute to fill the timeseries between of them.
REMARKS
The mountain observation stations seems to be more prone to human error. According to metadata, this is probably due to the fact that the measurements were taken from non-professionals and the maintenance of the instruments was not frequent.
The stations of Airports of Paphos and Larnaca and Athallasa’s could be considered as reference stations, because during the homogenization process, the differences between the raw and homogenised data were not significant (no break-points, a few points were differed). Looking at the metadata of the stations, it can be proved that due to the demands of these specific stations, their instruments have the higher level of accuracy and consistency available. Additionally, they have been frequently maintained by specialized professional staff .
The correlation between the mountainous weather stations seems to be strong, and the fluctuations between the average of their summer maximum temperature over the studied period (1961 – 2013) has been similar. At the same time, the correlation coefficient between the coastal areas of Cyprus has been high, while the coastal area of Paphos seems to have the lowest maximum temperatures as it is exposed to the westerly seasonal flow during winter and spring and the sea breezes during late spring, summer and early autumn.
REFERENCES
Domonkos P. (2011) Int J Geosci 2, pp. 293–309.
Domonkos P. (2013) Research article efficiencies of inhomogeneity­detection algorithms: comparison of different detection methods and efficiency measures. J Climatol 2013, pp. 1–15.
Lelieveld J, Hadjinicolaou P , Kostopoulou E, Chenoweth J, El Maayar M, Giannakopoulos C,
Hannides C, Lange MA, T anarhte M, T yrlis E, Xoplaki E (2012) Climate change and impacts in the
Eastern Mediterranean and the Middle East. Clim Change 114:667–687. doi: /s10584­012­
0418­4.
Miclaelides S, T ymvios F , Michaelidou T (2009) Spatial and temporal characteristics of the annual rainfall frequency distribution in Cyprus. Atmos Res 94, pp. 606–615. doi: /j.atmosres
T ymvios F , Michaelides S (2002) Analysis of spatial and temporal changes of the extreme rainfall
events in Cyprus. In: Proceedings of 6th Panhellenic conference on meteorology , climatology and
atmospheric physics, Ioannina, Greece, 25–28 Sept, pp. 476–483.