Homogenization of the Long Instrumental Daily-Temperature Series in Padua, Italy (1725–2023)

IF 3 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Climate Pub Date : 2024-06-07 DOI:10.3390/cli12060086
Claudio Stefanini, F. Becherini, Antonio della Valle, Dario Camuffo
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Abstract

The Padua temperature series is one of the longest in the world, as daily observations started in 1725 and have continued almost unbroken to the present. Previous works recovered readings from the original logs, and digitalized and corrected observations from errors due to instruments, calibrations, sampling times and exposure. However, the series underwent some changes (location, elevation, observing protocols, and different averaging methods) that affected the homogeneity between sub-series. The aim of this work is to produce a homogenized temperature series for Padua, starting from the results of previous works, and connecting all the periods available. The homogenization of the observations has been carried out with respect to the modern era. A newly released paleo-reanalysis dataset, ModE-RA, is exploited to connect the most ancient data to the recent ones. In particular, the following has been carried out: the 1774–2023 daily mean temperature has been homogenized to the modern data; for the first time, the daily values of 1765–1773 have been merged and homogenized; and the daily observations of the 1725–1764 period have been connected and homogenized to the rest of the series. Snowfall observations, extracted from the same logs from which the temperatures were retrieved, help to verify the robustness of the homogenization procedure by looking at the temperature frequency distribution on snowy days, before and after the correction. The possibility of adding new measurements with no need to apply transformations or homogenization procedures makes it very easy to update the time series and make it immediately available for climate change analysis.
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意大利帕多瓦长期仪器日温度序列的同质化(1725-2023 年)
帕多瓦温度系列是世界上最长的温度系列之一,每日观测始于 1725 年,几乎从未间断。之前的工作从原始日志中恢复了读数,并对因仪器、校准、采样时间和曝光造成的误差进行了数字化和修正。然而,该系列发生了一些变化(位置、海拔、观测规程和不同的平均方法),影响了子系列之间的同质性。这项工作的目的是,从以前的工作成果出发,连接所有可用的时段,为帕多瓦制作一个同质化的温度序列。观测数据的同质化是针对现代进行的。利用新发布的古分析数据集 ModE-RA,将最古老的数据与最近的数据连接起来。特别是进行了以下工作:将 1774-2023 年的日平均气温与现代数据同质化;首次将 1765-1773 年的日数值合并并同质化;将 1725-1764 年的日观测数据与其他系列数据连接并同质化。降雪量观测数据提取自与温度提取相同的日志,通过观察修正前后降雪日的温度频率分布,有助于验证均质化程序的稳健性。由于可以添加新的测量数据,无需应用转换或同质化程序,因此更新时间序列非常容易,可立即用于气候变化分析。
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来源期刊
Climate
Climate Earth and Planetary Sciences-Atmospheric Science
CiteScore
5.50
自引率
5.40%
发文量
172
审稿时长
11 weeks
期刊介绍: Climate is an independent, international and multi-disciplinary open access journal focusing on climate processes of the earth, covering all scales and involving modelling and observation methods. The scope of Climate includes: Global climate Regional climate Urban climate Multiscale climate Polar climate Tropical climate Climate downscaling Climate process and sensitivity studies Climate dynamics Climate variability (Interseasonal, interannual to decadal) Feedbacks between local, regional, and global climate change Anthropogenic climate change Climate and monsoon Cloud and precipitation predictions Past, present, and projected climate change Hydroclimate.
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