Effects of undetected data quality issues on climatological analyses

Hunziker, Stefan; Brönnimann, Stefan; Calle, Juan Marcos; Moreno, Isabel; Andrade, Marcos; Ticona, Laura; Huerta, A.; Lavado‐Casimiro, Waldo

doi:10.5194/cp-2017-64

Cited by 11 publications

(18 citation statements)

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“…Long‐term observations are fundamental for monitoring ongoing climate changes, as well as their impacts on human and natural systems. The analysis of climate evolution and its variability is particularly critical in countries characterized by geographically complex territories and a variety of climatic conditions (Hunziker et al ., ). According to the Köppen–Geiger classification system (Köppen, ), Italy's climate ranges from warm temperate with dry summer to polar and it is heavily influenced by the presence of two mountain chains (Alps and Apennines), which control wind circulation and precipitation patterns (Marchetti et al ., ).…”

Section: Introductionmentioning

confidence: 99%

A new homogenized daily data set for temperature variability assessment in Italy

Fioravanti

Piervitali

Desiato

2019

Intl Journal of Climatology

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We present a new data set of quality‐controlled and homogenized daily maximum (Tmax) and minimum (Tmin) temperature for Italy. The data set includes 144 Tmax and 139 Tmin long‐term series, covering the period 1961–2017. First, the paper provides a description of data sources and quality controls implemented for the detection of erroneous daily observations. Next, the temperature records used for this work are introduced. Following strict data continuity and completeness requirements, we identified more than 500 time series with at least 20 years of valid data (raw data set), which were spatially partitioned using a hierarchical clustering approach. For each cluster, the time series homogeneity was assessed using two different statistical automatic approaches: ACMANT and Climatol. The results of the homogenization process are illustrated only for the long‐term series subset. Both homogenization methods revealed the presence of non‐climatic discontinuities in most of the temperature series. Although Climatol detected a slightly lower number of breakpoints than ACMANT, the two methods are in good agreement with respect to the statistics which describe the number and timing of the breakpoints. Since no metadata are available, the plausibility of the homogenized time series was evaluated using different statistical measures: RMSE, Spearman correlation coefficient and trends estimation. Our results show that the homogenized data sets are more spatially coherent than the raw time series. In particular, the analysis of the annual temperature trends shows more realistic and reliable climatic patterns when the homogenized data sets are considered. For our data, the homogenization process only marginally changes the annual and seasonal warming trend values found for the area‐averaged anomaly raw series. The Tmax and Tmin homogenized data set will be regularly updated and is intended to be used for a variety of climate studies that require data at daily resolution, as the analysis of climate extremes.

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Section: Introductionmentioning

confidence: 99%

A new homogenized daily data set for temperature variability assessment in Italy

Fioravanti

Piervitali

Desiato

2019

Intl Journal of Climatology

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“…The second example (Progreso, Peru) is for a station on the Andean Plateau. This region has a particularly low spatial correlation for T min (Hunziker et al ., ), which negatively affects the detectability index for that variable. Nevertheless, the main breakpoint in T min (2003) is correctly detected.…”

Section: Dataset Usementioning

confidence: 99%

“…Hunziker et al . () found that data quality in that region is particularly poor and estimated one breakpoint per decade on average using a semi‐automatic detection software.…”

Section: Dataset Usementioning

confidence: 99%

The EUSTACE global land station daily air temperature dataset

Brugnara

Good

Squintu

et al. 2019

Geoscience Data Journal

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We describe a global dataset of quality‐controlled in situ daily air temperature observations covering the period 1850–2015, developed in the framework of the EUSTACE (EU Surface Temperature for All Corners of Earth) project (http://www.eustaceproject.org). The dataset includes a total of 35,364 daily series of maximum and minimum temperature obtained from seven different collections. About 97% of the series are publicly available in a common format, while the remaining 3% can be obtained from the original data providers. Unlike other similar products, duplicates have been removed without blending of series, which simplifies data traceability and improves the temporal homogeneity of the individual series at the cost of a smaller average length. Residual artificial signals (breakpoints) in the series caused by station relocations, changes in instrumentation, etc., have been detected by means of the combination of four breakpoint detection tests, four variables and three temporal aggregations. The combined results give not only the most probable position of the breakpoints, but also a measure of their likelihood. The reliability of the detection was estimated for each year of each target series, based on the number of reference series and on their correlation with the target series. Moreover, its general performance was evaluated through a benchmark of synthetic series. This product will be combined with datasets of marine and ice in situ air temperature observations and with measurements from satellite to produce the first complete global statistical reconstruction of daily near‐surface air temperature.

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“…However, Hunziker et al . () showed that the error of mean temperature trends depends strongly on whether the data are homogenized or not, whereas the kind of applied quality control is less important. As the aim is to derive trend errors of the raw data neither homogenization nor specific quality control is applied.…”

Section: Introductionmentioning

confidence: 97%

Random trend errors in climate station data due to inhomogeneities

Lindau

Venema

2019

Intl Journal of Climatology

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Inhomogeneities in station series are a large part of the uncertainty budget of long‐term temperature trend estimates. This article introduces two analytical equations for the dependence of the station trend uncertainty on the statistical properties of the inhomogeneities. One equation is for inhomogeneities that act as random deviations from a fixed baseline, where the deviation levels are random and independent. The second equation is for inhomogeneities, which behave like Brownian Motion (BM), where not the levels themselves but the jumps between them are independent. It shows that BM‐type breaks introduce much larger trend errors, growing linearly with the number of breaks. Using the information about type, strength, and frequency of the breaks for the United States and Germany, the random trend errors for these two countries are calculated for the period 1901–2000. An alternative and independent estimate is obtained by an empirical approach, exploiting the distance dependence of the trend variability for neighbouring station pairs. Both methods (empirical and analytical) find that the station trend uncertainty is larger in the United States (0.71 and 0.82°C per century, respectively) than in Germany (0.50 and 0.58°C per century, respectively). The good agreement of the analytical and the empirical estimate gives confidence in the methods to assess trend uncertainties, as well as in the method to determine the statistical properties of the break inhomogeneities.

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Effects of undetected data quality issues on climatological analyses

Cited by 11 publications

References 0 publications

A new homogenized daily data set for temperature variability assessment in Italy

A new homogenized daily data set for temperature variability assessment in Italy

The EUSTACE global land station daily air temperature dataset

Random trend errors in climate station data due to inhomogeneities

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