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Your search keyword '"Climate Time Series"' showing total 2 results
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2 results on '"Climate Time Series"'

1. Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960.

Author

Argiriou, Athanassios A., Li, Zhen, Armaos, Vasileios, Mamara, Anna, Shi, Yingling, and Yan, Zhongwei

Subjects
*TIME series analysis, *DATA libraries, *SPRING, *AUTUMN, *TEMPERATURE
Abstract

In this paper, we describe and analyze two datasets entitled "Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021" and "Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010". These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Benchmarking homogenization algorithms for monthly data

Author

Elke Rustemeier, T. Brandsma, J. Viarre, G. Müller-Westermeier, Peter Domonkos, Claude N. Williams, G. Vertacnik, D. Rasol, T. Likso, O. Mestre, Petr Stepanek, C. Gruber, Matthew J. Menne, M. Prohom Duran, Ralf Lindau, Sorin Cheval, Michele Brunetti, Simona Fratianni, Ingeborg Auer, Victor Venema, L. Andresen, M. Klancar, K. Kolokythas, Fiorella Acquaotta, Mónika Lakatos, Enric Aguilar, Tania Marinova, Tamás Szentimrey, Pavel Zahradníček, José Antonio Guijarro, and Pere Esteban

Subjects
Validation study, Climate Time Series, Computer science, lcsh:Environmental protection, Stratigraphy, Homogenization (climate), COST Action, Precipitation, lcsh:Environmental pollution, monthly homogenization algorithms, real inhomeogeneities, inserted inhomogeneities, blind testing, Temperature records, lcsh:TD169-171.8, Surface climate network, Precipitation records, lcsh:Environmental sciences, lcsh:GE1-350, Homogenization, Global and Planetary Change, Multiplicative function, Temperature, Paleontology, Benchmarking, Integrated approach, Missing data, Nonlinear system, lcsh:TD172-193.5, Outlier, Instrumental climate records, Algorithm
Abstract

The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

Published
2012

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