CPClimate of the PastCPClim. Past1814-9332Copernicus PublicationsGöttingen, Germany10.5194/cp-14-1-2018Effects of undetected data quality issues on climatological analysesHunzikerStefanstefan.hunziker@giub.unibe.chBrönnimannStefanCalleJuanMorenoIsabelhttps://orcid.org/0000-0001-9353-2388AndradeMarcoshttps://orcid.org/0000-0002-9736-493XTiconaLauraHuertaAdrianLavado-CasimiroWaldoInstitute of Geography, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandLaboratorio de Física de la Atmósfera, Instituto de Investigaciones Físicas, Universidad Mayor de San Andrés, La Paz, BoliviaServicio Nacional de Meteorología e Hidrología del Perú (SENAMHI), Lima, PeruStefan Hunziker (stefan.hunziker@giub.unibe.ch)3January201814112026April201716May201729September201710November2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://cp.copernicus.org/articles/14/1/2018/cp-14-1-2018.htmlThe full text article is available as a PDF file from https://cp.copernicus.org/articles/14/1/2018/cp-14-1-2018.pdf
Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of
observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such
undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations
of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are
minimum and maximum temperature and precipitation. About 40 % of the observations are inappropriate for the calculation of
monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard
quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs,
deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias
regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used
observational datasets and hence may affect a high number of climatological studies. It is of utmost importance to apply
comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and
large uncertainties.
Introduction
Records of in situ weather observations are essential for climatological analyses. Although automatic stations are now often in
use, many national station networks have been based completely on manned station observations, and many still depend largely or partly
on this type of observation. Various authors have demonstrated the possible errors in data records of manned stations (e.g. Rhines et al.,
2015; Trewin, 2010; Viney and Bates, 2004). In order to detect and remove such errors, observational time series should be quality
controlled before they are analysed (WMO, 2011, 2008). However, data quality issues are not always detected by common quality control
(QC) methods (Hunziker et al., 2017). The overall impact of such undetected errors on climatological analyses is largely unknown.
In order to detect and remove non-climatic signals such as station relocations from observational data, station records should be
homogenized (Aguilar et al., 2003; Brönnimann, 2015). For the success of the widely applied relative homogenization method, highly
correlated time series are required (Cao and Yan, 2012; Gubler et al., 2017; Plummer et al., 2003; Trewin, 2013; Venema et al.,
2012). Similarly, the important spatial consistency test in the QC process depends on suitable neighbouring stations (Durre et al.,
2010; Plummer et al., 2003). Usually, the correlation between station pairs decreases with increasing distance. In some regions of the
world, correlations are clearly lower or lose significance after shorter distances than in others (Gubler et al., 2017; New et al.,
2000). According to Gubler et al. (2017), not only climatological factors may be responsible for such differences, but also factors
related to the quality of the observations, such as station siting and observation practices. Besides potentially reducing the
correlation between station pairs, data quality issues may also induce inhomogeneities in time series (WMO, 2008). As a result, the
performance of statistical data homogenization methods is reduced due to the higher number of break points (Domonkos, 2013). To the
authors' knowledge, the impact of data quality problems on station correlations and statistical data homogenization has not been
thoroughly studied so far.
Trend magnitudes and signs in station records may strongly differ among neighbouring stations. This was observed in many parts of the
world and for various climate variables and indices, such as minimum temperature (López-Moreno et al., 2016), precipitation (Rosas
et al., 2016; Vuille et al., 2003), diurnal temperature range (Jaswal et al., 2016; New et al., 2006), and extremes indices (Skansi
et al., 2013; You et al., 2013). Certain trend differences may be expected even on short spatial scales due to factors such as
topography and feedback processes (You et al., 2010). However, errors in observations may also affect individual station trends and
hence increase the trend spread within a region. Furthermore, regional trends may deviate from observations in comparable areas. For
instance, studies have found stronger positive trends in maximum than minimum temperatures since the middle of the 20th century in the
Bolivian and Peruvian Altiplano (e.g. López-Moreno et al., 2016), and Alexander et al. (2006) detected a decrease in the number of
warm nights in the same region. These findings are not in accordance with the globally observed and expected increase in night-time
temperatures and the decrease in the diurnal temperature range (Alexander et al., 2006; Donat et al., 2013b; IPCC, 2013; Morak et al.,
2011; New et al., 2006; Quintana-Gomez, 1999; Vincent et al., 2005). Therefore, the question arises of whether non-climatic factors may cause
systematic trend biases in entire regions.
The present study addresses the aforementioned research questions by applying two different QC approaches on the same observational
dataset and comparing the results of relevant climatological analyses afterwards. As the standard QC approach, we used the method that
is applied to the GHCN-Daily dataset (Menne et al., 2012). As the enhanced approach, we applied the QC tests suggested by Hunziker
et al. (2017) that focus on the detection of systematically occurring data quality issues. Since this is not a self-contained method,
the GHCN-Daily QC was additionally applied afterwards.
The dataset used in the present study consists of manned station observations from the Central Andean region. This area is highly
suitable for investigating the impacts of undetected data quality issues for two main reasons: first, all the uncertainties
discussed in the previous paragraphs are found in Central Andean station data, and second, data quality issues that may not be detected
by standard QC methods are well studied (Hunziker et al., 2017). Furthermore, the topography in the area is complex, and station
density is sparse, making QC and data homogenization difficult. The dataset used contains the climatological key variables maximum
temperature (TX), minimum temperature (TN), and precipitation (PRCP).
In this article, we first describe the data (Sect. 2) and explain the methods (Sect. 3). Next, we present the results (Sect. 4), in
which we describe the frequency of the data quality issues (Sect. 4.1) and focus on their effects on the correlation of station pairs
(Sect. 4.2), data homogenization (Sect. 4.3), and trends (Sect. 4.4).We discuss the results (Sect. 5), and finally draw the conclusions
of our findings (Sect. 6).
Stations of the DECADE dataset with ≥20 years of valid observations for maximum temperature (TX), minimum
temperature (TN), and precipitation (PRCP). Solid lines represent country borders, and the dashed line is the border of the Peruvian
department of Puno. Circles and pluses indicate stations with ≥80 % of valid measurements from 1981 to 2010 in the datasets
quality controlled with a standard method (DATAQC-S) and with an enhanced approach (DATAQC-E),
respectively. White crosses mark stations with <80 % of valid observations from 1981 to 2010 in both datasets. Colours classify
stations regarding their elevation in lowlands (≤600ma.s.l.), valleys (601 to 3499 ma.s.l.), and Altiplano
(≥3500ma.s.l.). The grey background shading indicates the elevation in m a.s.l.
Data
The dataset used for the present study includes observational records from Bolivia (Servicio Nacional de Meteorología e
Hidrología de Bolivia, and the Bolivian civil airport administration), the Peruvian department of Puno (Servicio Nacional de
Meteorología e Hidrologí a del Perú), and selected Chilean and Paraguayan stations located near the Bolivian border
(Dirección Meteorológica de Chile, Dirección de Meteorología e Hidrología – Paraguay; Fig. 1). The dataset was
created within the framework of the project “Data on climate and Extreme weather for the Central AnDEs” (DECADE) and includes daily
TX, TN, and PRCP measurements ((http://www.geography.unibe.ch/research/climatology_group/research_projects/decade/index_eng.html)).
All records in the DECADE dataset originate from manned weather stations. This reflects the conditions
of weather observation networks in the Central Andean area where only a few automatic weather stations are in service (Hunziker
et al., 2017). The first records in the DECADE dataset date back to 1917, and the most recent observations were taken in 2015. For more
details on weather observations in the Central Andean region, see Hunziker et al. (2017).
The altitude of the stations in the study area ranges between 98 and 4667 ma.s.l. Stations at elevations
≤ 600 ma.s.l. group in the east (henceforward referred to as “lowland stations”), while stations at elevations
≥ 3500 ma.s.l. are located in the west (henceforward referred to as “Altiplano stations”; see Fig. 1). Stations at
altitudes between the lowlands and the Altiplano are grouped along the eastern slopes of the Central Andes and are henceforward referred to as
“valley stations”.
A large fraction of the 341 TX, 339 TN, and 698 PRCP time series in the original dataset cover only short observation periods or
contain large gaps. Therefore, all records with a sum of measurements <20 years (i.e. <7300 valid daily observations) were
excluded. This nearly divided the number of time series in half, resulting in 180 remaining TX and TN and 378 PRCP records. This
dataset containing the raw data (i.e. not quality controlled or homogenized) is called “DATARAW” henceforward.
For the present study, all time series of DATARAW were quality controlled and homogenized. However, for the
subsequent analyses (i.e. error frequency, correlation, and trend analyses), only the period 1981 to 2010 was analysed. During this
30-year standard period, the highest number of station records is available (104 TX, 106 TN, and 220 PRCP time series with ≥80 % of valid observations), and data quality is usually higher than earlier in time.
MethodsQuality control
DATARAW was quality controlled with two different approaches. The first approach represents an established standard QC
method. Such methods mostly focus on the detection of single suspicious values (Hunziker et al., 2017). The second approach
additionally takes systematically occurring data quality issues into account that may remain undetected with standard QC. It is
therefore considered as enhanced QC.
Standard approach
The Global Historical Climatology Network GHCN-Daily was developed for a wide range of applications, including studies of extreme
events (Menne et al., 2012), and it is the premier source of daily TX, TN, and PRCP observations from various regions of the globe
(Donat et al., 2013a). The GHCN-Daily data are quality controlled with a comprehensive set of 19 QC tests, including spatial
consistency tests (Durre et al., 2010). It is a fully automatic QC approach that was particularly developed to run unsupervised (Menne
et al., 2012). Evaluations of the performance showed that the method is effective at detecting gross errors and more subtle
inconsistencies with a low false-positive rate (Durre et al., 2010). This QC method was applied to DATARAW
(http://www.geography.unibe.ch/research/climatology_group/research_projects/decade/index_eng.html).
However, the detections (i.e. the flags for failing certain tests) of the GHCN-Daily QC had to be slightly adapted in order to be more
appropriate for weather observations in the Central Andean region. One of the internal consistency tests detects cases in which TX is
lower than TN of the previous day. This test should guarantee the physical consistency of TX and TN measurements that are
representative of a 24 h period. However, in various Bolivian stations (particularly stations at airports), TX is representative
of
the afternoon hours only (observations start at noon and end in the evening). This measurement practice should avoid problems in
attributing the observation to a specific calendar day. Usually, daily temperature maxima occur in the afternoon indeed. Nevertheless,
during certain weather events (particularly the frequent cold surges in the lowlands; e.g. Espinoza et al., 2013; Garreaud, 2001; Vera
and Vigliarolo, 2000), the temperature in the afternoon does not exceed the TN value measured in the morning. As a result, a high
number of observations in the lowlands was flagged. To the authors' knowledge, this measurement practice has been applied to TX but not
to TN observations, and no large-scale changes in this practice in the Central Andean area are known. Therefore, this practice (even
though not ideal) does not introduce any error or bias as long as it remains unchanged. As a consequence, internal consistency flags
that were set because of this particular QC test were regarded as invalid.
Furthermore, the GHCN-Daily QC did not flag a few extreme outliers. This may happen if a reported value exceeds the
maximum of five
places in tens of degrees Celsius or millimetres allowed in the GHCN-Daily data format (e.g. values ≤-10 000). In order to remove such
erroneous numbers, we added an additional flag to all unflagged temperature values >70∘C and
<-70∘C and
to all unflagged negative PRCP values.
In total, about 0.35 % (temperature) and 0.15 % (PRCP) of all measurements were flagged. This is similar to the overall
fraction of 0.24 % flagged observations in the GHCN-Daily dataset (Durre et al., 2010). In DATARAW, about two-thirds of
the flagged temperature and the great majority of the flagged PRCP observations are monthly or yearly duplicate data. For any further
analyses, all flagged values were removed. The dataset quality controlled with this standard QC approach will be called
“DATAQC-S” henceforward.
Description of systematic data quality issues and their frequencies
in the DECADE database (station records with ≥ 20 years of
observations) between 1981 and 2010. If not specified, the frequencies of
data quality issues apply to daily observations and monthly aggregations.
Frequencies of the data quality issues in maximum (TX) and minimum (TN)
temperature and precipitation (PRCP) observations are shown for the
different regions (Altiplano, valleys, and lowlands; see Fig. 1). Thresholds
leading to the exclusion of data were chosen so that data quality issues
should not affect the subsequent climatological analyses of the daily and
monthly aggregated data. For other analyses, these thresholds may not be
adequate and consequently the frequencies of relevant data quality issues may differ.
Tests were done in parallel, and time series segments may therefore be
affected by several data quality issues simultaneously. For a detailed
description of frequent data quality issues, see Hunziker et al. (2017).
Data quality issueDescriptionThreshold leading toexclusionFrequency [%] AltiplanoValleysLowlandsMissing temperature intervalsObservations within a temperature interval are missing or occur with a clearly reduced frequencyInterval of missing temperature observations >1∘CTX: 1.0TN: 7.4TX: 0.3TN: 6.9TX: 0.3TN: 0.0Rounding errorsRounding errors in the conversion from degrees Fahrenheit to degrees Celsius (may also indicate further errors in the data)Any error in the roundingTX: 0.0TN: 0.0TX: 4.3TN: 2.4TX: 1.9TN: 1.9Asymmetric rounding patternsNumbers in the decimal places are not equally distributed and occur in an asymmetric formAsymmetry in rounding pattern is strongTX: 8.8TN: 8.2PRCP: 10.0TX: 9.6TN: 11.0PRCP: 13.7TX: 5.1TN: 4.2PRCP: 6.7Low measurement resolutionThe reported resolution of the measurements is lowReported measurement resolution >1∘C and >1mmTX: 0.0TN: 0.0PRCP: 0.1TX: 4.1TN: 3.4PRCP: 0.0TX: 1.4TN: 1.4PRCP: 0.0Irregularities in the data patternObviously erroneous patterns in the data that cannot be classified as another data quality issue (e.g. all values in a very narrow range, randomly and strongly varying variance, truncation of negative temperatures)Irregularities in the data pattern are moderate or strongTX: 31.6TN: 28.5PRCP: 37.7TX: 42.3TN: 42.2PRCP: 42.9TX: 10.9TN: 15.8PRCP: 21.4Obvious inhomogeneitiesInhomogeneities that are large enough to be clearly identified visually as non-climatic and that occur frequently within a time series segment (i.e. inhomogeneities that are hardly correctable with data homogenization methods)Inhomogeneities are large and occur frequentlyTX: 7.7TN: 2.5PRCP: 2.3TX: 13.5TN: 12.6PRCP: 1.9TX: 0.8TN: 0.8PRCP: 3.2Heavy PRCP truncationsObservations of heavy PRCP events are truncated or their frequency is clearly reduced above a certain thresholdHeavy PRCP events are partially or completely truncatedPRCP: 13.3PRCP: 12.5PRCP: 5.2Small PRCP gapsSmall PRCP events are not reported, leading to a gap or a frequency reduction in values below a certain thresholdPartial and complete small PRCP gaps >5mm (monthly) and >2mm (daily)PRCP: 3.0 (monthly)PRCP: 15.2 (daily)PRCP: 7.5 (monthly)PRCP: 29.9(daily)PRCP: 9.5(monthly)PRCP: 21.9(daily)Weekly PRCP cyclesThe occurrence of PRCP events (wet days) significantly differs between the days of the weekWeekly PRCP cycles are strong (relaxation for monthly aggregated data if cycle pattern indicates regularly missed observations followed by accumulation the next day)PRCP: 0.0 (monthly)PRCP: 1.3 (daily)PRCP: 1.8(monthly)PRCP: 2.1 (daily)PRCP: 2.6(monthly)PRCP: 3.2(daily)Enhanced approach
Following the suggestions by Hunziker et al. (2017), DATARAW was carefully checked for systematically occurring data
quality issues. An extensive set of tests (11 for TX and TN, 15 for PRCP) was applied, and flags were set for each test on an annual
timescale (http://www.geography.unibe.ch/research/climatology_group/research_projects/decade/index_eng.html).
Thanks to flagging each quality issue individually in the database, specific time series segments can subsequently be
selected that are adequate for the intended purpose. Furthermore, for a segment of one station record (TN of Progreso in Peru; see
Hunziker et al., 2017), daily corrections were calculated, since the origin of the correctable error was unambiguously identified.
Time series segments affected by data quality issues that disturb the calculation of monthly means (temperature) and sums (PRCP) were
removed from further analyses, which reduced the number of valid measurements by about 40 %. Table 1 briefly describes the data
quality issues and related thresholds that led to the exclusion of time series segments. Thresholds were chosen so that quality
problems that may significantly affect the subsequent climatological analyses are excluded, whereas data containing minor problems
still remain in the dataset. Note that the QC tests were applied in parallel, and therefore time series segments may be affected by
several data quality issues simultaneously. If suspicious data patterns could not clearly be attributed to a specific data quality
issue, they were classified as “irregularities in the data pattern”. For details on most of the data quality issues included in the
present study, see Hunziker et al. (2017).
Some data quality issues may significantly affect daily observations, but they may lose their significance by monthly aggregation. This
particularly applies to observations affected by multi-day PRCP accumulations. Such data may still be adequate to calculate monthly
totals (WMO, 2011) but cannot be used on a daily timescale (Viney and Bates, 2004). Therefore, more rigorous thresholds were used for
data quality issues that cause multi-day PRCP accumulations (i.e. “small PRCP gaps” and “weekly PRCP cycles”) if the data were
later analysed on a daily timescale (Table 1). In the present study, daily data are used to analyse the correlation on a daily scale
(Sect. 3.3) and the climate change indices (Sect. 3.5).
The QC tests suggested by Hunziker et al. (2017) detect data quality issues that occur systematically during longer time periods
(months to years). Therefore, they are not a self-contained QC approach and should be combined with other tests. That is why the
GHCN-Daily QC was additionally applied (see Sect. 3.1.1) after removing time series segments of insufficient quality for monthly
aggregation. The GHCN-Daily QC added flags to approximately 0.26 % (temperature) and 0.10 % (PRCP) of the remaining
observations.
This QC procedure may be considered as an enhancement of applying the GHCN-Daily QC only. Hence, the resulting dataset will be named
“DATAQC-E” henceforward.
Note that Hunziker et al. (2017) further suggest the inclusion of additional information derived from metadata into the QC
process. This allows for the removal of station records that were generated under inappropriate conditions, such as poor station siting or
severe lack of station maintenance. The present study, however, only considers quality issues and errors that are directly detectable
in the measurement data. Hence, time series of questionable quality that could be removed by including metadata in the QC process
remain in the dataset.
Calculation of monthly and yearly means and sum
According to WMO (2011), monthly means can be calculated for continuous variables such as temperature if ≤ 10 daily measurements
are missing. However, we used the stricter approach suggested for the calculation of monthly 30-year standard normals (WMO, 1989) that
allows ≤ 5 missing observations (3 if in succession). For cumulative variables such as rainfall, values should be calculated only
if all daily observations are available or if unrecorded PRCP amounts are incorporated in the next measurement (WMO, 2011). At
various Bolivian stations, measurements are not taken on one day a week (usually Sundays; Hunziker et al., 2017). This particularly affects
weather stations at secondary airports that do not operate on Sundays. PRCP on these days is usually incorporated in
the measurement of the next operation day. Therefore, monthly PRCP sums were calculated if ≤5 daily observations were missing and
if no missing observations occurred in succession. Annual means (temperature) and sums (PRCP) were calculated based on monthly values,
and yearly values were calculated only if 12 valid months were available (WMO, 2011).
For many datasets and studies, gaps in time series are filled (e.g. Auer et al., 2007; Kizza et al., 2012; Vuille et al., 2000). There
are many techniques for data estimation (e.g. WMO, 2011) that may increase the time series completeness and hence the data
availability. However, data estimation is difficult to apply to Central Andean station records due to complex topography, sparse
station networks, and mostly few observed atmospheric variables. Furthermore, the input data for ACMANT3 (homogenization method used in
the present study; see Sect. 3.4.2) should not include estimated data (Domonkos and Coll, 2017). Hence, in order to avoid
introducing uncertainty by filling gaps, no data were estimated for the present study.
Correlation analysis
Before calculating the correlation coefficient of station pairs, time series were standardized by subtracting the mean and dividing by
the standard deviation (SD). In order to remove the influence of trends and inhomogeneities, the differences between one observation and the next were
calculated. From these time series of the first differences, Spearman rank correlations were computed for the period 1981 to 2010.
For correlations on the monthly timescale, daily observations were aggregated as described in Sect. 3.2. Only time series containing
≥80 % of valid monthly values in the 30-year period of interest were considered. Removing the flagged observations and
time series without sufficient data resulted in 98 (TX), 99 (TN), and 218 (PRCP) valid monthly station records for
DATAQC-S and in 56 (TX), 54 (TN), and 105 (PRCP) valid monthly time series for DATAQC-E.
To standardize measurement values on a daily timescale, daily means and SDs were calculated based on the linear interpolation of monthly
means and SDs. If equal values occurred in succession in the original observations, the first differences of the standardized values
were set to zero in order to not bias correlation coefficients by the seasonality of the standardization.
Because unreported shifting of dates occurs frequently in the Central Andean observation networks (Hunziker et al., 2017), temporal
dislocation in daily time series pairs must be considered. For example, a high correlation of two Central Andean time series of the
first differences often becomes slightly negative if one of the two time series is shifted by 1 day. Therefore, shifts of -2 to
+2 days were applied to one time series of each station pair, and the highest correlation value was expected to be the real
correlation coefficient. This method may artificially increase correlations that are close to zero or negative in reality. However,
such low correlations are not of interest in the present study. Furthermore, we use the median to quantify the effect of data quality
issues on correlations, which eliminates the potential bias introduced to low correlations. Time series with <80 % of daily
observations in the period 1981 to 2010 were removed from the daily correlation analysis. This resulted in 104 (TX), 106 (TN), and 220
(PRCP) valid daily station records available for DATAQC-S and in 59 (TX), 58 (TN), and 90 (PRCP) records for
DATAQC-E.
Data homogenizationClustering
In order to build station groups that share a similar background climate, we applied agglomerative hierarchical clustering with
complete linkage on the monthly station correlation matrices (see Sect. 3.3). Time series that did not share ≥120 common valid
months with ≥10 neighbours were removed from the data homogenization process. For the break detection and adjustment method used
in this study (Sect. 3.4.2), the optimal cluster size is usually around 20 to 30 stations, but the optimal number of stations can be
much higher if record lengths and data completeness differ between the time series (Domonkos and Coll, 2017). This strongly applies to
the Central Andean data. Therefore, we selected three clusters for TX and TN with a median size of 60 (DATAQC-S) and 40
(DATAQC-E) stations. For PRCP, 6 (DATAQC-S) and 5
(DATAQC-E) clusters were selected with
a median cluster size of 65 (DATAQC-S) and 42 (DATAQC-E). The minimum and maximum cluster size is 11 and 94
stations, respectively.
The spatial structure of the clusters is similar for DATAQC-S and DATAQC-E. For temperature, two main clusters
were detected, representing the lowlands and the Altiplano. Stations of the third cluster are located mostly along the eastern Andean
slopes. Spatial illustrations of the clusters are shown in Fig. S1 in the Supplement.
Break-point detection and adjustment
There are various established homogenization approaches (e.g. Aguilar et al., 2003; Ribeiro et al., 2016; Venema et al., 2012). For the
present study, the method ACMANT was chosen. ACMANT is a fully automatic method that does not incorporate metadata. Hence, the approach
is objective in contrast to semi-automatic approaches such as HOMER (Mestre et al., 2013) that require various subjective
decisions. This subjectivity may influence the results of the homogenization process (Vertačnik et al., 2015). For the aim of the
present study to evaluate the effects of undetected data quality issues, it is important to avoid such disturbances. ACMANT is
a state-of-the-art homogenization method with one of the best performances (Ribeiro et al., 2016; Venema et al., 2012). Recently, a new
version of the approach (ACMANT3) was published (Domonkos and Coll, 2017). Compared to previous versions (Domonkos, 2011, 2015), the
performance of the method was further improved and the range of use increased (Domonkos and Coll, 2017).
ACMANT3 includes a recommended function for detecting monthly outliers that was applied before detecting and correcting
break points. About twice as many monthly outliers were detected in DATAQC-S than in DATAQC-E. The highest
frequency of monthly outliers was found in TN of DATAQC-S with 0.16 outliers per decade. All monthly outliers were removed
from DATAQC-S and DATAQC-E.
ETCCDI climate change indices analysed in the present study. Note
that all indices were calculated on an annual timescale. Index units in
percentage were converted to days per year in the following analyses.
IDIndex nameIndex definitionUnitDTRDaily temperature rangeMonthly mean difference between TX and TN∘CTX10pCool daysPercentage of days when TX <10th percentile%TN10pCool nightsPercentage of days when TN <10th percentile%TX90pWarm daysPercentage of days when TX >90th percentile%TN90pWarm nightsPercentage of days when TN >90th percentile%FDFrost daysAnnual count of days when TN <0∘CdaysR95pTOTAnnual contribution from very wet daysAnnual total of daily PRCP when PRCP >95th percentilemmSDIISimple precipitation intensity indexPRCP sum on wet days (PRCP ≥1mm) divided bymm day-1the number of wet daysTrend calculation
Trends of annual values and climate change indices were analysed for the entire study area in the 30-year time period 1981 to
2010. However, trend signals differ between the varied climate zones covered by the DECADE dataset. Therefore, we decided to focus
particularly on the Altiplano region for trend analyses. Time series from the Altiplano that satisfy the completeness requirements
originate nearly exclusively from stations located in the north-western Bolivian department of La Paz and the adjacent Peruvian
department of Puno. In this spatially limited region, the station network is dense compared to the rest of the study area
(Fig. 1). Therefore, relatively homogeneous trend signals may be expected.
The magnitudes of linear trends were calculated with the Theil–Sen estimator, which is the median of the slopes of all data
pairs of a time series (Sen, 1968; Theil, 1950). The method is more insensitive to outliers and more robust than other trend estimators
such as ordinary least squares. For individual station records, the significance of trends is not of major interest in the present
study and was therefore not tested. Furthermore, taking serial correlation into account in trend tests would cause large uncertainties
due to the missing values in the time series. However, for the Altiplano stations, trends of spatially averaged anomalies were tested
with the Mann–Kendall test at the 5 % significance level. Before applying the Mann–Kendall test (Mann, 1945; Kendall, 1948), the time series were
pre-whitened (Wang and Swail, 2001; Zhang and Zwiers, 2004) in order to remove the influence of serial correlation.
Trends of annual means (temperature) and sums (PRCP) were analysed based on yearly aggregated data (see Sect. 3.2). Time series with <80 % of valid yearly values from 1981 to 2010 were removed previously. This resulted in 54 (TX), 48 (TN), and 105 (PRCP) valid annual
station records for DATAQC-S and in 40 (TX), 29 (TN), and 48 (PRCP) annual time series for DATAQC-E.
In order to investigate the effect of undetected data quality issues on extremes, we computed the frequently used climate change
indices defined by the CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI;
http://etccdi.pacificclimate.org/list_27_indices.shtml) for 1981 to 2010. For the calculation of the indices, we used the
software tool RClimDex (Zhang and Yang, 2004) that is often applied in climatological studies (e.g. Kioutsioukis et al., 2010; Kruger
and Sekele, 2013; New et al., 2006). RClimDex calculates monthly (yearly) index values if ≤3 (≤15) observations are missing
(Zhang and Yang, 2004). The indices discussed in the present study are namely the diurnal temperature range (DTR), cool days (TX10p),
cool nights (TN10p), warm days (TX90p), warm nights (TN90p), frost days (FD), annual contribution from very wet days (R95pTOT), and the
simple daily intensity index (SDII; Table 2). Note that all indices were calculated on an annual scale. For indices based on
percentiles, the baseline period was calculated from the 30-year period 1981 to 2010. Indices units in percentage were converted to
days per year.
The ETCCDI climate change indices describe moderate to very moderate extreme events that occur usually many times per year. Therefore,
they are particularly suitable for application on short time series. For the index calculation of the homogenized datasets, daily
measurements were corrected by adding monthly adjustment values (temperature) and by multiplying with monthly adjustment factors (PRCP)
that were computed with ACMANT3. Applying monthly corrections on a time series does not guarantee homogeneity on a daily timescale
(Brönnimann, 2015; Costa and Soares, 2009; Trewin, 2013). However, since the present study aims to compare the effects of different
QC methods, potential deficits in adjusting daily observations with monthly factors do not bias the results. Considering the large and
frequent inhomogeneities detected in the Central Andean time series (Sect. 4.3), the homogeneity of the ETCCDI climate change indices
will most likely be increased strongly by correcting the daily time series with the monthly adjustment values.
Annual frequency of the data quality issues that cause the exclusion of the affected time series segments for maximum and
minimum temperature (TX and TN, respectively) and precipitation (PRCP). If not specified, the frequencies apply to daily
observations and monthly aggregations. Note that tests for systematic data quality issues were done in parallel, and time series
segments may therefore be affected by several quality issues simultaneously.
Trends of the ETCCDI climate change indices were only calculated for time series with ≥80 % of valid yearly index values in
the period 1981 to 2010. For the analyses of the climate change indices, about 50 (DATAQC-S) and 30 (DATAQC-E)
valid time series for the temperature-derived indices (TX10P, TX90P, TN10P, TN90P, and FD) were available. For DTR, which depends on both
TX and TN observations, 41 (DATAQC-S) and 22 (DATAQC-E) time series could be analysed. For the PRCP-derived
indices SDII and R95pTOT, 106 (DATAQC-S) and 38 (DATAQC-E) indices time series were available.
ResultsFrequency of data quality issues
The frequency of systematic data quality issues clearly varies between the different regions (Table 1). Overall, data quality issues
occur least frequently in the lowlands. Many weather stations in this area are located at airports and are operated by the Bolivian
civil airport administration (Hunziker et al., 2017). The personnel at the airports are generally better trained in taking observations
than the laypersons running most of the other weather stations in the Central Andean area. In contrast, data quality issues occur most
frequently in the valleys. Many of these stations are located in rather remote regions, and they generally receive less attention from the
network operators than other stations in the network.
Some systematic data quality issues are relevant in one region, but not in another. For instance, the “missing temperature intervals”
are important in TN observations in the Altiplano and the valleys, but barely occur in the lowlands. This problem usually occurs in
measurements around 0 ∘C. Temperatures in the lowlands rarely drop to the freezing point, and hence this issue is largely
absent. In contrast, “weekly PRCP cycles” occur particularly often in the lowlands where the fraction of observations at airports is
large (secondary airports are usually out of service on Sundays).
The data quality issue “irregularities in the data pattern” reaches the threshold for exclusion of time series segments more often
than the other quality problems. This error classification combines all suspicious data patterns that cannot be clearly classified as
another quality issue. In contrast to other data quality issues, irregularities in the data pattern occur in all regions. Time series
segments of low quality are often affected by several problems simultaneously, which usually includes rather unspecific
irregularities in the data pattern.
Monthly and daily median correlation coefficients of station pairs
within a 300 km radius (100 km for daily PRCP) for maximum temperature (TX),
minimum temperature (TN), and precipitation (PRCP).
TX TN PRCP DATAQC-SDATAQC-EDATAQC-SDATAQC-EDATAQC-SDATAQC-EMonthlyAll stations (≤300km)0.530.680.390.630.340.45Altiplano stations (≤300km)0.680.720.570.640.450.50Valley stations (≤300km)0.460.600.340.610.310.35Lowland stations (≤300km)0.760.790.720.750.330.36DailyAll stations (≤300km for tempera-ture, ≤100km for PRCP)0.250.350.140.270.130.19Altiplano stations (≤300km fortemperature, ≤100km for PRCP)0.260.310.240.280.140.18Valley stations (≤300km for tem-perature, ≤100km for PRCP)0.260.450.120.220.120.24Lowland stations (≤300km fortemperature, ≤100km for PRCP)0.550.590.400.440.280.32
Correlation coefficients of station pairs as a function of station distance for maximum temperature (TX), minimum temperature
(TN), and precipitation (PRCP). This figure shows the example of monthly correlations in the Altiplano (≥3500ma.s.l.). Black circles indicate equal correlation coefficients in DATAQC-S and DATAQC-E
(absolute difference ≤0.01), grey circles indicate correlation coefficients of station combinations of DATAQC-S that
do not occur in DATAQC-E (or the absolute difference to the equivalent in DATAQC-E is >0.01), green
triangles show correlation coefficients of DATAQC-E that are higher than in DATAQC-S (difference >+0.01),
and red triangles show correlation coefficients of DATAQC-E that are lower than DATAQC-S (difference <-0.01).
Overall, the quality of the TX, TN, and particularly PRCP observations has slightly increased in the last decades (Fig. 2). However, the
frequency of some data quality issues has increased, such as strong “asymmetric rounding patterns” in TX and TN observations,
and
“missing temperature intervals” in TN time series. There is no strong or abrupt change in the frequency of the data quality issues
between 1981 and 2010. The same applies to the temporal development of data quality issues in the single regions Altiplano, valleys,
and lowlands (not shown).
Correlation analysis
Detecting and removing erroneous measurement values and time series segments affects the correlation of station pairs in two ways. On
the one hand, time series may no longer fulfil the completeness requirements in the time period of interest. This occurs more often when
applying the enhanced than the standard QC approach. While highly correlated station records remain in DATAQC-E, the
enhanced QC largely removes the low correlation coefficients found in DATAQC-S (Fig. 3). Hence, data quality issues that
are undetected by the standard QC method result in low correlation coefficients of station pairs. On the other hand, the correlations
of station pairs may change if rather short time series segments are removed due to data quality problems. Usually, this results in an
increase in the correlation coefficients (Fig. 3), which may reach up to 0.07 (TX) and 0.09 (PRCP) on a monthly and daily timescale. For TN, maximum correlation improvements are 0.10 and 0.05 on a monthly and a daily timescale, respectively. Since this study
only includes time series with ≥80 % of valid values, each time series pair shares ≥60 % of common observations
between 1981 and 2010 (i.e. ≥18 years).
Monthly median correlation coefficients within a 49 km running window for maximum temperature (TX), minimum
temperature (TN), and precipitation (PRCP). The median correlation coefficient is not shown if there are less than three station
pairs within the running window. Colours mark the medians for all regions combined, the lowlands (≤600ma.s.l.), the
valleys (601 to 3499 ma.s.l.), and the Altiplano (≥3500ma.s.l.). Light and dark colours indicate
correlation coefficients derived from DATAQC-S and DATAQC-E, respectively.
Same as Fig. 4 but for daily data.
The resulting median differences of correlation coefficients between DATAQC-S and DATAQC-E are relatively constant
up to station distances of approximately 300 km (Figs. 4 and 5). The overall differences between DATAQC-E and
DATAQC-S are 0.15 (TX), 0.24 (TN), and 0.11 (PRCP) on a monthly timescale and 0.10 (TX) and 0.13 (TN) on a daily timescale
(Table 3). For daily PRCP, median correlation coefficients converge quickly to zero with increasing station distance, and therefore
stations within a 100 km radius were analysed. The resulting median correlation difference for daily PRCP between
DATAQC-E and DATAQC-S is 0.06.
However, the effect of undetected data quality issues on station correlations varies strongly between the different regions. While it
is small in the lowlands, it is very pronounced in the valleys. This can be partly explained by the high fraction of station records
affected by severe data quality issues in the valleys. Lowland stations, in contrast, are often located at airports where data quality
problems occur less frequently.
There are remarkable differences between the median correlation coefficients of station pairs in the lowlands, the valleys, and the
Altiplano (Figs. 4 and 5, Table 3). This is primarily explained by the varied topography. While the lowlands are largely flat, the
topography of the Altiplano and the valleys is moderately and highly complex, respectively. Therefore, the median correlations are
overall highest in the lowlands and lowest in the valleys.
Break-point frequencies and break sizes. For minimum and maximum temperature (TX and TN, respectively), absolute break size values in ∘C are shown. For precipitation (PRCP), the factors of the break sizes are indicated.
Kernel density of the adjustments calculated with ACMANT3 for all regions and the complete time series (a) and for
the Altiplano stations from 1981 to 2010 (b). For maximum and minimum temperature (TX and TN, respectively), inhomogeneous
time series segments are corrected by adding the adjustment values, whereas for precipitation (PRCP), inhomogeneous segments are
corrected by multiplication with the adjustment factors.
However, spatial correlations are further modulated by regional weather and climate characteristics. For instance, PRCP correlation
coefficients in the Altiplano are higher than in the lowlands on a monthly timescale, whereas the opposite applies to correlations on
a daily timescale (Figs. 4 and 5, Table 3). On the one hand, the Altiplano receives precipitation from deep convective storms during
austral summer (Garreaud, 2009), and wet periods tend to cluster in episodes of about a week, interrupted by dry spells of similar
duration (Garreaud, 1999). On the other hand, cold surges in the lowlands occur with a periodicity of approximately 1 to 2 weeks
(Garreaud, 2000) and usually last 2 or 3 days (Espinoza et al., 2013). In summertime, these events cause synoptic-scale bands of
enhanced and suppressed deep convection that structure temporal PRCP occurrence (Garreaud, 2000). Hence, rain events in the Altiplano
cluster on clearly larger timescales than in the lowlands. This favours high correlation of monthly PRCP sums in the Altiplano and
high correlation coefficients of daily observations in the lowlands. Note, however, that the correlation differences between the
regions are more pronounced within DATAQC-S than within DATAQC-E.
Data homogenization
One out of three TN station clusters of DATAQC-S (34 station records) could not be homogenized because of too-low
spatial–temporal coherence. Most of the time series in this cluster are affected by systematic data quality issues that were not
detected with the standard QC approach. Since these station records could not be homogenized, they were excluded from all further
analyses.
ACMANT3 detected a high number of break points in the station records. For temperature, about one break point per decade was detected on
average, with a slightly higher break-point frequency in DATAQC-S than in DATAQC-E (Table 4). For PRCP, 0.3
break points per decade were found in DATAQC-S and 0.2 in DATAQC-E. Median, mean, and maximum break-point sizes
are clearly larger in DATAQC-S than in DATAQC-E for all climate variables (Table 4).
Adjustments values (temperature) and factors (PRCP) close to zero (temperature) and one (PRCP) are more frequent for
DATAQC-E than for DATAQC-S (Fig. 6). Furthermore, maxima of the absolute adjustments are higher for
DATAQC-S than DATAQC-E, reaching up to 10.2 ∘C (temperature) and 3.5 (PRCP). However, there are not
only differences in the SD, but also in the symmetry of the adjustment distributions. For example, the adjustment factors for PRCP of
DATAQC-E indicate a density peak at around 1.3, which is not found for DATAQC-S (Fig. 6). For TN of the
Altiplano stations from 1981 to 2010, there is a high density of adjustment values around -1 ∘C. This peak is more
pronounced in DATAQC-E than in DATAQC-S. As a result, the median adjustment in DATAQC-E is
-0.5 ∘C, whereas it is +0.2 ∘C in DATAQC-S. The same median adjustments are calculated for complete
record lengths of the Altiplano stations (not shown), indicating the detection of an overall warm bias in earlier TN observations of
DATAQC-E but not of DATAQC-S.
Trends of individual station records for maximum temperature (TX) (a), minimum temperature (TN) (b), and
precipitation (PRCP) (c) from 1981 to 2010. The first column shows the results for the unhomogenized dataset quality controlled
with the standard approach (DATAQC-S), the second column shows the homogenized dataset quality controlled with the standard
approach (DATAQC-S_H), the third column shows the unhomogenized dataset quality controlled with the enhanced approach
(DATAQC-E), and the fourth column shows the homogenized dataset quality controlled with the enhanced approach
(DATAQC-E_H). For temperature, trends are indicated in ∘C per decade. For PRCP, the relative magnitudes of the
trend changes from 1981 to 2010 are shown. They are calculated from the difference between the fitted value at the end and the beginning of
the time series, which is divided by the mean of the fit. A relative trend increase by 1 is equal to an increase by 200.0 %, and
a relative decrease by 1 is equal to a decrease by 66.7 %.
Henceforward, the homogenized datasets DATAQC-S and DATAQC-E are named “DATAQC-S_H” and
“DATAQC-E_H”, respectively. Note that some time series segments could not be homogenized due to lacking reference
stations with the required correlation. For the trend analyses, all time series segments that remained unhomogenized were also excluded
from the unhomogenized datasets (i.e. DATAQC-S and DATAQC-E) in order to maintain comparability between
unhomogenized and homogenized datasets.
TrendsAnnual temperature averages
Overall, there is a clear positive TX trend in the entire study area (Fig. 7). The few negative TX trends in the unhomogenized station
records disappear due to data homogenization. In the Altiplano, the trend of the spatially averaged anomalies is significant and varies
between +0.40 (DATAQC-E_H) and +0.44 ∘C (DATAQC-E) per decade (Table 5). TN trends, however, are
more ambiguous. Spatial trend patterns are unclear, except for DATAQC-E_H in which a clear warming is found in the
north-eastern Altiplano and slight cooling in the south and the lowlands. This pattern is spatially coherent and substantially
diverges from the spatial trend patterns derived from the other datasets. As a result, TN trends of spatially averaged anomalies
calculated from DATAQC-E_H in the Altiplano are significant with +0.22 ∘C per decade, whereas they are close to
zero and insignificant if calculated from the other datasets (Table 5). This may be at least partly ascribed to the results of the data
homogenization process, which suggest a clear overall warm bias in earlier TN observations in the Altiplano in DATAQC-E,
but not in DATAQC-S (Sect. 4.3).
Trends of spatially averaged anomalies in the Altiplano (≥3500ma.s.l.) in the period 1981 to 2010. Trends are shown for the annual means,
for the 10th and 90th percentile of maximum temperature (TX) and
minimum temperature (TN; i.e. TX10p, TN10p, TX90p, TN90p), and for the
number of frost days (FD). Bold numbers denote significance at the 5 %
level.
Trends of individual station records for maximum temperature (TX), minimum temperature (TN), and precipitation (PRCP) in the
period 1981 to 2010. Trend box plots for the complete study area and for the Altiplano (≥3500ma.s.l.) are
shown. Colours indicate the different datasets that are unhomogenized and quality controlled with the standard approach
(DATAQC-S), homogenized and quality controlled with the standard approach (DATAQC-S_H), unhomogenized and
quality controlled with the enhanced approach (DATAQC-E), and homogenized and quality controlled with the enhanced
approach (DATAQC-E_H). For temperature, trends are specified in ∘C per decade. For PRCP, relative trends
from
1980 to 2010 are shown (see the caption of Fig. 7 for details). The box plots show the median, the 25th and 75th percentile, and the
1.5× IQR (whiskers).
The spread of individual station trends is slightly lower in DATAQC-E than in DATAQC-S (Fig. 8). However, the
spread of trends is much more reduced by data homogenization than by enhancing the QC approach. For TX, the trend spreads derived from
the homogenized datasets DATAQC-S_H and DATAQC-E_H are similar, whereas they strongly differ for TN. The TN
trend spread of the entire study area derived from DATAQC-S_H is small and ranges between +0.02 and +0.09 ∘C
per decade within the 25th and 75th percentile. In contrast, the data homogenization of DATAQC-E does not cause such
a pronounced decrease in the trend spread.
Annual precipitation sums
PRCP trends are negative for most station records (Fig. 7). The spatial pattern of trend magnitudes is more coherent if trends are
calculated from DATAQC-E_H than from the other datasets. Despite the previous homogenization of the time series in
DATAQC-S_H, there are strong positive and negative trends of stations within a short distance. For all regions (lowlands,
valleys, and Altiplano), trends of the spatially averaged anomalies are negative, particularly if derived from DATAQC-E_H
(not shown). However, these trends are barely significant due to the high interannual variability of PRCP.
The trend spread and frequency of very strong trends is lower in DATAQC-E than in DATAQC-S (Fig. 8). Data
homogenization reduces the trend spread of the PRCP time series, but considerably less than for temperature data. Overall, the spread
of PRCP trends of individual station records is relatively large in all datasets.
Trends of individual station records of the complete study area for the climate change indices daily temperature range (DTR),
number of cool days (TX10p), number of warm days (TX90p), number of cool nights (TN10p), and number of warm nights (TN90p) in the
period 1981 to 2010. Colours indicate the different datasets that are unhomogenized and quality controlled with the standard
approach (DATAQC-S), homogenized and quality controlled with the standard approach (DATAQC-S_H),
unhomogenized and quality controlled with the enhanced approach (DATAQC-E), and homogenized and quality controlled with
the enhanced approach (DATAQC-E_H). For the DTR, trends are specified in ∘C per decade and for the other
indices in days per decade. The box plots show the median, the 25th and 75th percentile, and the 1.5× IQR (whiskers).
Same as Fig. 9 but for the Altiplano stations (≥3500ma.s.l.). Additionally, trends of frost days (FD) are
shown.
Climate change indices
Trends of the median diurnal temperature range (DTR) of all datasets are positive (Fig. 9). The spread of trends calculated from the
unhomogenized datasets is large, ranging from -1.21 to +2.17 ∘C per decade. It is lower for DATAQC-E than for
DATAQC-S, particularly on a regional scale such as in the Altiplano (Fig. 10). However, data homogenization is most relevant
for increasing the coherency of DTR trends. This is particularly remarkable for DATAQC-E_H in the Altiplano, where
individual station trends of the DTR are reduced to a range between +0.10 and +0.29 ∘C per decade (Fig. 10). Besides this high
DTR trend coherency derived from DATAQC-E_H in the Altiplano, trend magnitudes are clearly lower than those derived from
the other datasets. This manifests in an insignificant trend of the spatially averaged anomalies of +0.23 ∘C per decade for
DATAQC-E_H, whereas the trends calculated from the other datasets are all significant and range between +0.39
(DATAQC-S_H) and +0.54 ∘C per decade (DATAQC-S).
The overall trend signal of the TX-based percentile indices TX10p and TX90p is relatively uniform among the different datasets,
indicating a reduction in cool days and an increase in warm days (Figs. 9 and 10). The trends of the spatially averaged anomalies in
the Altiplano calculated from the different datasets are all significant and range between -11.9 (DATAQC-E_H) and
-14.4 (DATAQC-S_H) cool days per decade and between +8.7 (DATAQC-S) and +11.0 (DATAQC-S_H)
warm days per decade (Table 5). For both indices, the trend magnitudes are more pronounced for DATAQC-S_H than for
DATAQC-E_H.
The median trend magnitudes of TN-based percentile indices (TN10p, TN90p) are smaller than those based on TX, and the spreads of individual
station trends are larger, particularly in the Altiplano (Figs. 9 and 10). In this region, trend magnitudes derived from
DATAQC-E_H differ substantially from the other datasets (Fig. 10) by indicating a clear warming trend in all indices
(i.e. decrease in cool nights and frost days, increase in warm nights). This is confirmed by the trends of the spatially averaged
anomalies that indicate a significant decrease in cool nights (-5.8 days per decade) and a significant increase in warm nights
(+8.8 days per decade; Table 5). In contrast, the trends calculated from the other datasets are all insignificant and have lower
trend magnitudes. The same pattern is found for trends of the frequency of frost days (FD). Trends calculated from
DATAQC-E_H indicate a significant decrease in FD (-6.5 days per decade), whereas the trends of the other datasets are
insignificant and close to zero (Table 5).
The PRCP-based climate change indices indicate a slight decrease in the annual contribution of very wet days (R95pTOT) and a decreasing
intensity of precipitation events (SDII), particularly in the Altiplano. This signal is more pronounced for the datasets quality
controlled with the standard method than for the datasets quality controlled with the enhanced approach. Trends of the spatially
averaged anomalies are not significant, except for the trends of SDII derived from the dataset quality controlled with the standard
approach in the Altiplano, i.e. -3.1 mmday-1 (DATAQC-S) and -2.1 mmday-1
(DATAQC-S_H) per decade. In contrast to the indices derived from temperature data, applying the enhanced QC approach
reduces the trend spread of the PRCP-based indices more than statistical data homogenization. Compared to DATAQC-S, the
SDs
of the relative trends in the complete study area calculated from DATAQC-S_H, DATAQC-E, and
DATAQC-E_H are 20, 40, and 50 % lower, respectively.
Discussion
Systematically occurring data quality issues affect a large fraction of Central Andean station records, making about 40 %
inadequate for the calculation of monthly means (temperature) and sums (PRCP). The frequency of such problems may vary strongly in
space and time. Systematic data quality issues remain largely undetected when applying standard data quality control methods such as
the one used for the GHCN-Daily database. Hence, important data sources may be substantially affected by such undetected data quality issues
(abbreviated as UDQIs henceforward). Including tests to specifically detect such erroneous patterns could significantly increase the
quality of many datasets.
On a monthly timescale and up to 300 km of station distance, UDQIs
cause a reduction of median correlation coefficients by 0.15
(TX), 0.24 (TN), and 0.11 (PRCP) compared to unaffected data. On a daily timescale, this reduction is 0.10 (TX) and 0.13 (TN) for
station pairs within 300 km of distance and 0.06 (PRCP) for stations within 100 km of distance. These findings confirm the
assumption by Gubler et al. (2017) that the strong differences in correlation coefficients between station networks of the Peruvian
Andes and Switzerland may not be explained by unequal climate regimes alone. Hypothesizing that UDQIs occur more frequently in the station
networks of developing than developed countries, a higher frequency of such errors can be expected in tropical areas than in
mid-latitudes. Making this assumption, UDQIs may partly explain the particularly low correlation decay distances in the tropics
described by New et al. (2000). Using relatively high minimum correlation thresholds in climatological analyses (e.g. data
homogenization) may reduce the amount of station records affected by UDQIs. As a more advanced approach, weighting correlation
coefficients with station distances (i.e. more weight to station pairs further away from each other for equal correlation coefficients)
could particularly take UDQIs into account.
UDQIs induce additional inhomogeneities in observational records. The resulting decrease in the signal-to-noise ratio may decrease the
performance of statistical data homogenization methods (Domonkos, 2013). This is particularly problematic in sparse observational
networks in which a high number of break points may result in adjustments that deteriorate the temporal consistency of station records
(Gubler et al., 2017). In the Central Andean region, UDQIs increase the number of statistically detected break points by about 15 %
for TX and TN and by 50 % for PRCP. They also increase the median break-point size by 35 to 40 % (TX and TN) and 60 %
(RPCP) and increase break size maxima by up to 100 % (temperature) and 70 % (PRCP). Since UDQIs have larger relative effects on
break sizes than on the number of detected break points, they apparently deteriorate the detectability of small non-climatic
inhomogeneities.
The effect of UDQIs also manifests in the adjustment values (temperature) and factors (PRCP) resulting from the data homogenization
process. UDQIs cause a reduction in the frequency of small adjustments and an increase in large adjustments. They also may induce an
adjustment bias. For instance, the median adjustment value for TN station records in the Altiplano is -0.5 ∘C. If the same
dataset contains UDQIs, the resulting median adjustment is +0.2 ∘C. This difference in the adjustment could be caused in two
ways. First, UDQIs may introduce a systematic bias (a cold bias in earlier observations in this case). This would require the occurrence
of certain types of UDQIs in many station records of a dataset, which would cause a systematic bias and strongly change their
frequency in time. For the Central Andean area, however, there is no clear indication that UDQIs meet these requirements in the period
1981 to 2010. Second, UDQIs may not introduce a bias by themselves, but they impede the detection of an existing bias (warm bias in
earlier observations in the case of TN in the Altiplano) by introducing artificial noise. Such a warm bias could have been introduced, for
example, by location changes of weather stations to systematically different sites (e.g. further away from buildings). The second
possibility seems to be the more likely cause of the observed adjustment differences of TN records in the Altiplano. Hence, UDQIs may
impede the adjustments of systematic biases introduced by inhomogeneities. In summary, UDQIs may substantially decrease the performance
of statistical data homogenization methods.
Between 1981 and 2010, a pronounced and relatively uniform increase in global mean temperatures was observed (IPCC, 2013). Similarly,
clear overall warming trends in the same period were reported from analyses of extremes indices (Donat et al., 2013b). Hence, 1981 to
2010 is a suitable period for analysing linear temperature trends, and clear trend signals may be expected in the Central Andean area
too.
UDQIs increase the overall spread of individual station trends. Statistical data homogenization may largely reduce or eliminate this
effect, but only at the cost of more and larger break points, which lowers the performance of data homogenization methods. For instance,
the trend spread of homogenized TN time series quality controlled with the standard approach (DATAQC-S_H) is extremely
small (Fig. 8). This clearly deviates from the trend spreads observed for TX and from the trend spread derived from
DATAQC-E_H. Hence, station trends computed from DATAQC-S_H seem rather implausible and may indicate an
over-homogenization. In contrast, the low trend spread of the diurnal temperature range (DTR) derived from DATAQC-E_H
(Fig. 10) suggests that the independent data homogenizations of TX and TN observations are consistent with each other. Furthermore,
trends calculated from DATAQC-E_H are spatially more coherent than those derived from DATAQC-S_H,
particularly for TN and PRCP (Fig. 7).
If datasets contain UDQIs and/or are unhomogenized, TN trends of averaged anomalies in the Altiplano are close to zero and
insignificant, and trends of the diurnal temperature range (DTR) are strongly positive and significant at the 5 % level
(Table 5). In contrast, TN trends derived from DATAQC-E_H are significantly positive, and trends of the DTR are
insignificant. Hence, mean temperature trends in the Altiplano are more in accordance with the global observations (IPCC, 2013) if
systematically occurring data quality issues are removed from the dataset. Nevertheless, the influence of UDQIs in station records from
the Altiplano explains roughly half of the trend difference between TX and TN. Hence, there must be other factors (climatological or
non-climatological) that cause a stronger increase in TX than TN in the Altiplano. An indication of a climatological explanation
for the positive DTR trend is the simultaneously observed negative PRCP trend.
Several authors have described a negative correlation
between DTR and PRCP trends (Dittus et al., 2014; Jaswal et al., 2016; Zhou et al., 2009). Hence, the observations in the Altiplano
would be in accordance with these findings.
Since systematically occurring data quality issues especially affect extremes (Hunziker et al., 2017), UDQIs have a stronger effect on
ETCCDI climate change indices than on trends of annual means (temperature) and sums (PRCP). Particularly in the Altiplano, the spread
of individual station trends is usually more coherent if trends are calculated from DATAQC-E_H than from the other
datasets. For the climate change indices derived from PRCP analysed in the present study (R95pTOT and SDII), most very strong trends of
individual time series are caused by UDQIs and cannot be adjusted by statistical data homogenization. Hence, highly incoherent spatial
trend signals and strong differences in trend magnitudes between neighbouring stations as detected in many studies (e.g. Skansi et al.,
2013; Vuille et al., 2003; You et al., 2011) may potentially be ascribed to UDQIs.
The overall temperature trend signals derived from DATAQC-E_H in the Altiplano are highly coherent, indicating significant
warming throughout all indices. This trend pattern of moderate extreme events is more in accordance with the global observations
(e.g. IPCC, 2013) than the trend patterns derived from the other datasets. Consequently, UDQIs may at least partly explain the
discrepancies of trends detected in the Altiplano compared to most other world regions.
According to Donat et al. (2013b), gridding observations minimizes the impact of data quality issues at individual stations due to
averaging. This, however, may not be true if UDQIs cause systematic biases. We calculated trends of the relevant climate change indices
derived from the two 2.5∘×3.75∘ grid cells of the HadEX2 dataset (Donat et al., 2013b) that are most
representative for the Altiplano. Overall, these trends in the period 1981 to 2010 are most similar to the trends derived from
DATAQC-S_H. However, the trends of a few climate change indices derived from HadEx2 have extreme magnitudes, such as
a detected increase of +14.0 frost days per decade in one of the grid cells. Furthermore, virtually all of these trends are
insignificant due to large variabilities in the annual index time series. Even though these findings do not allow
clear
conclusions to be drawn, they suggest that UDQIs affect the dataset and influence the trend calculations.
The quantifications of the effects of UDQIs presented in this study are rather an estimate of the lower limit, since the enhanced QC
method applied here may still not have detected and removed all relevant data quality issues. Furthermore, metadata were not accessed
as an information source for QC, which may help to detect and remove additional time series segments of inadequate quality (Hunziker
et al., 2017). The quantifications presented in this article cannot be generalized to global datasets. The frequencies and
characteristics of data quality issues occurring in manned station networks depend on various factors, such as observing practices,
the capabilities of the personnel, and data transcription procedures. Furthermore, the wide range of QC approaches applied in
national weather services will detect different fractions of errors and data quality issues. The effects of UDQIs on climatological
analyses also depend on the climate regime. For instance, missed measurements of small precipitation events of up to a few millimetres
may only have a negligible effect on monthly sums in wet regions (e.g. Amazonian lowlands), whereas they may significantly bias monthly
PRCP sums in rather dry areas (e.g. Altiplano) due to low overall PRCP and evaporation losses. As demonstrated in this work, UDQIs have
stronger effects on climatological analyses derived from TN than TX observations. On the one hand, TN is generally more variable and
spatially heterogeneous than TX (Luhunga et al., 2014; Mahmood et al., 2006; New et al., 1999). On the other hand, measurement errors
may occur more frequently in TN than TX. For example, TN falls naturally more often below freezing temperature than TX, and temperature
values around and below 0 ∘C often trigger measurement errors by observers and data transcription errors (Hunziker
et al., 2017). Hence, the frequency and effects of UDQIs also vary spatially and temporally.
Removing a relatively large fraction of observations (about 40 % in the present study) from a dataset may affect the results of
climatological analyses. Reducing the spatial density of available data normally decreases the quality of the results such as for data
homogenization (Caussinus and Mestre, 2004; Domonkos, 2013; Gubler et al., 2017). With the present study, however, we have demonstrated
that removing time series segments affected by UDQIs increases the overall quality of the dataset, and the results of climatological analyses
are consequently more coherent and reliable. The disadvantage of fewer available observations is outperformed by the quality increase
in
the dataset.
Conclusions
Systematically occurring data quality issues may affect large fractions of time series in observational datasets. In the Central Andean
area, about 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation
sums. These problems remain largely undetected by standard quality control methods. In the present study, we applied a standard and an
enhanced quality control approach on the same dataset. The enhanced approach should particularly detect systematically occurring data
quality issues. We subsequently compared the results of various climatological analyses derived from the dataset quality controlled with the
two different methods.
Undetected data quality issues (UDQIs) substantially lower the correlation coefficients of station pairs. This directly affects various
methods such as clustering or data homogenization.
The performance of data homogenization approaches deteriorates if time series contain UDQIs. Since UDQIs induce inhomogeneities in time
series, they increase the number and average size of break points in the data. As a result of the increased noise in the station
records, the skill of statistical data homogenization methods to detect and correct smaller inhomogeneities is reduced. Furthermore,
data homogenization approaches may fail to detect and correct systematic biases caused by inhomogeneities due to UDQIs. In the
Altiplano,
for instance, a median adjustment value of -0.5 ∘C for minimum temperature observations was detected for time series free of
UDQIs, whereas a median adjustment of +0.2 ∘C was computed for the station records affected by UDQIs. This warm bias in
earlier TN observations may affect previous studies using station records from the Altiplano. Hence, data homogenization methods rely
on data that are largely free of UDQIs in order to perform satisfactorily.
Removing UDQIs from a dataset increases the spatial coherence and reduces the spread of individual stations trends. Furthermore,
UDQIs
may systematically bias trends. For instance, regional minimum temperature trends in the Altiplano are insignificant and close to zero
if calculated from station records affected by UDQIs, whereas trends are significant and clearly positive if derived from time series
free of UDQIs.
Since UDQIs especially affect extremes, they are particularly problematic for analysing trends of rare events such as for the ETCCDI
climate change indices. In the Altiplano, trends of various indices based on minimum temperature differ significantly if derived from
a dataset affected or unaffected by UDQIs. For some climate change indices based on precipitation, extreme trend magnitudes at
individual stations can be corrected by previously removing UDQIs from the dataset, but not by statistical data homogenization.
Most likely, the results of various studies are affected by UDQIs. If quality control approaches are enhanced and UDQIs removed, the results
of climatological analyses may become more coherent and reliable. Note that an enhanced and comprehensive quality control
cannot
substitute for appropriate data homogenization and vice versa.
The DECADE database including all quality control flags is
available under http://www.geography.unibe.ch/research/climatology_group/research_projects/decade/index_eng.html.
The Supplement related to this article is available online at https://doi.org/10.5194/cp-14-1-2018-supplement.
The authors declare that they have no conflict of interest.
Acknowledgements
This work is part of the project “Data on climate and Extreme weather for
the Central AnDEs” (DECADE), no. IZ01Z0_147320, which is
financed by the Swiss Programme for Research on Global Issues for Development
(r4d). It was also supported by the EU Horizon 2020 EUSTACE project (grant
agreement 640171). We thank Peter Domonkos for the support on ACMANT3 and
Xuebin Zhang and Yang Feng for providing the newest version of RClimDex. We
also thank the two anonymous reviewers for their helpful comments and
suggestions.
Edited by: Volker Rath
Reviewed by: two anonymous referees
References Aguilar, E., Auer, I., Brunet, M., Peterson, T. C., and Wieringa, J.: Guidlines on Climate Metadata and Homogenization. in:
WCDMP No. 53, WMO/TD No. 1186, World Meteorological Organization, Geneva, Switzerland, 2003.Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A. M. G., Haylock, M., Collins, D.,
Trewin, B., Rahimzadeh, F., Tagipour, A., Rupa Kumar, K., Revadekar, J., Griffiths, G., Vincent, L., Stephenson, D. B., Burn, J.,
Aguilar, E., Brunet, M., Taylor, M., New, M., Zhai, P., Rusticucci, M., and Vazquez-Aguirre, J. L.: Global observed changes in daily
climate extremes of temperature and precipitation, J. Geophys. Res.-Atmos., 111, D05109, 10.1029/2005JD006290, 2006.Auer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C.,
Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J.-M., Begert, M.,
Müller-Westermeier, G., Kveton, V., Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar, T., Dolinar, M.,
Gajic-Capka, M., Zaninovic, K., Majstorovic, Z., and Nieplova, E.: HISTALP – historical instrumental climatological surface time
series of the Greater Alpine Region, Int. J. Climatol., 27, 17–46, 10.1002/joc.1377, 2007.Brönnimann, S.: Climatic Changes Since 1700, Springer International Publishing, Cham, Switzerland,
10.1007/978-3-319-19042-6_4, 2015.Cao, L.-J., and Yan, Z.-W.: Progress in research on homogenization of climate data, Adv. Climate Change
Res., 3, 59–67, 10.3724/SP.J.1248.2012.00059, 2012.Caussinus, H. and Mestre, O.: Detection and correction of artificial shifts in climate series, J. R. Stat. Soc. C,
53, 405–425, 10.1111/j.1467-9876.2004.05155.x, 2004.Costa, A. and Soares, A.: Homogenization of climate data: review and new perspectives using geostatistics, Math. Geosci.,
41, 291–305, 10.1007/s11004-008-9203-3, 2009.Dittus, A., Karoly, D., C Lewis, S., and Alexander, L.: An investigation of some unexpected frost day increases in Southern
Australia, Aust. Meteorol. Ocean., 64, 261–271, 10.22499/2.6404.002, 2014.Domonkos, P.: Adapted Caussinus–Mestre Algorithm for Networks of Temperature Series (ACMANT), Int. J. Geosci., 2,
293–309, 10.4236/ijg.2011.23032, 2011.Domonkos, P.: Homogenization of precipitation time series with ACMANT, Theor. Appl. Climatol., 122, 303–314,
10.1007/s00704-014-1298-5, 2015. Domonkos, P.: Measuring performances of homogenization methods, Q. J. Hungar. Meteorol. Serv., 117, 91–112, 2013.Domonkos, P. and Coll, J.: Homogenisation of temperature and precipitation time series with ACMANT3: method description
and efficiency tests, Int. J. Climatol., 37, 1910–1921, 10.1002/joc.4822, 2017.Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R., and Caesar, J.: Global land-based datasets for monitoring
climatic extremes, B. Am. Meteorol. Soc., 94, 997–1006, 10.1175/bams-d-12-00109.1, 2013a.Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R., Dunn, R. J. H., Willett, K. M., Aguilar, E., Brunet, M.,
Caesar, J., Hewitson, B., Jack, C., Klein Tank, A. M. G., Kruger, A. C., Marengo, J., Peterson, T. C., Renom, M., Oria Rojas, C.,
Rusticucci, M., Salinger, J., Elrayah, A. S., Sekele, S. S., Srivastava, A. K., Trewin, B., Villarroel, C., Vincent, L. A., Zhai, P.,
Zhang, X., and Kitching, S.: Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth
century: the HadEX2 dataset, J. Geophys. Res.-Atmos., 118, 2098–2118, 10.1002/jgrd.50150, 2013b.Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G., and Vose, R. S.: Comprehensive automated quality assurance of
daily surface observations, J. Appl. Meteorol. Clim., 49, 1615–1633, 10.1175/2010JAMC2375.1, 2010.Espinoza, J., Ronchail, J., Lengaigne, M., Quispe, N., Silva, Y., Bettolli, M., Avalos, G., and Llacza, A.: Revisiting
wintertime cold air intrusions at the east of the Andes: propagating features from subtropical Argentina to Peruvian Amazon and
relationship with large-scale circulation patterns, Clim. Dynam., 41, 1983–2002, 10.1007/s00382-012-1639-y, 2013.Garreaud, R. D.: Multiscale analysis of the summertime precipitation over the Central Andes, Mon. Weather Rev., 127,
901–921, 10.1175/1520-0493(1999)127<0901:maotsp>2.0.co;2, 1999.Garreaud, R. D.: Cold air incursions over subtropical South America: mean structure and dynamics, Mon. Weather Rev., 128,
2544–2559, 10.1175/1520-0493(2000)128<2544:CAIOSS>2.0.CO;2, 2000.Garreaud, R. D.: Subtropical cold surges: regional aspects and global distribution, Int. J. Climatol., 21, 1181–1197,
10.1002/joc.687, 2001.Garreaud, R. D.: The Andes climate and weather, Adv. Geosci., 22, 3–11, 10.5194/adgeo-22-3-2009, 2009.Gubler, S., Hunziker, S., Begert, M., Croci-Maspoli, M., Konzelmann, T., Brönnimann, S., Schwierz, C., Oria, C., and
Rosas, G.: The influence of station density on climate data homogenization, Int. J. Climatol., 37, 4670–4683,
10.1002/joc.5114, 2017.Hunziker, S., Gubler, S., Calle, J., Moreno, I., Andrade, M., Velarde, F., Ticona, L., Carrasco, G., Castellón, Y.,
Oria, C., Croci-Maspoli, M., Konzelmann, T., Rohrer, M., and Brönnimann, S.: Identifying, attributing, and overcoming common data
quality issues of manned station observations, Int. J. Climatol., 37, 4131–4145, 10.1002/joc.5037, 2017.IPCC: Climate Change 2013: The Physical Science Basis, in: Contribution of Working Group I to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA,
10.1029/2000JD000115, 2013.Jaswal, A. K., Kore, P. A., and Singh, V.: Trends in diurnal temperature range over India (1961–2010) and their
relationship with low cloud cover and rainy days, J. Clim. Change, 2, 35–55, 10.3233/JCC-160016, 2016.
Kendall, M. G.: Rank correlation methods, Griffin, London, 1948.Kioutsioukis, I., Melas, D., and Zerefos, C.: Statistical assessment of changes in climate extremes over Greece
(1955–2002), Int. J. Climatol., 30, 1723–1737, 10.1002/joc.2030, 2010.Kizza, M., Westerberg, I., Rodhe, A., and Ntale, H. K.: Estimating areal rainfall over Lake Victoria and its basin using
ground-based and satellite data, J. Hydrol., 464–465, 401–411, 10.1016/j.jhydrol.2012.07.024, 2012.Kruger, A. C. and Sekele, S. S.: Trends in extreme temperature indices in South Africa: 1962–2009, Int. J. Climatol., 33,
661–676, 10.1002/joc.3455, 2013.López-Moreno, J. I., Morán-Tejeda, E., Vicente-Serrano, S. M., Bazo, J., Azorin-Molina, C., Revuelto, J.,
Sánchez-Lorenzo, A., Navarro-Serrano, F., Aguilar, E., and Chura, O.: Recent temperature variability and change in the Altiplano
of Bolivia and Peru, Int. J. Climatol., 36, 1773–1796, 10.1002/joc.4459, 2016.Luhunga, P. M., Mutayoba, E., and Ng'ongolo, H. K.: Homogeneity of monthly mean air temperature of the United Republic of
Tanzania with HOMER, Atmos. Clim. Sci., 4, 70–77, 10.4236/acs.2014.41010, 2014.Mahmood, R., Foster, S. A., and Logan, D.: The GeoProfile metadata, exposure of instruments, and measurement bias in
climatic record revisited, Int. J. Climatol., 26, 1091–1124, 10.1002/joc.1298, 2006.Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, 10.2307/1907187, 1945.Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An overview of the global historical climatology
network-daily database, J. Atmos. Ocean. Tech., 29, 897–910, 10.1175/JTECH-D-11-00103.1, 2012. Mestre, O., Domonkos, P., Picard, F., Auer, I., Robin, S., Lebarbier, E., Böhm, R., Aguilar, E., Guijarro, J.,
Vertachnik, G., Klancar, M., Dubuisson, B., and Stepanek, P.: HOMER: a homogenization software – methods and applications,
Q. J. Hungar. Meteorol. Serv., 117, 47–67, 2013.Morak, S., Hegerl, G. C., and Kenyon, J.: Detectable regional changes in the number of warm nights, Geophys. Res. Lett.,
38, L17703, 10.1029/2011GL048531, 2011.New, M., Hulme, M., and Jones, P.: Representing twentieth-century space–time climate variability. Part I: Development of
a 1961–90 mean monthly terrestrial climatology, J. Climate, 12, 829–856, 10.1175/1520-0442(1999)012<0829:RTCSTC>2.0.CO;2, 1999.New, M., Hulme, M., and Jones, P.: Representing twentieth-century space–time climate variability. Part II: Development of
1901–96 monthly grids of terrestrial surface climate, J. Climate, 13, 2217–2238, 10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2, 2000.New, M., Hewitson, B., Stephenson, D. B., Tsiga, A., Kruger, A., Manhique, A., Gomez, B., Coelho, C. A. S., Masisi, D. N.,
Kululanga, E., Mbambalala, E., Adesina, F., Saleh, H., Kanyanga, J., Adosi, J., Bulane, L., Fortunata, L., Mdoka, M. L., and
Lajoie, R.: Evidence of trends in daily climate extremes over southern and west Africa, J. Geophys. Res.-Atmos., 111, D14102,
10.1029/2005JD006289, 2006. Plummer, N., Allsopp, T., and Lopez, J. A.: Guidelines on Climate Observations Networks and Systems, in: WCDMP No. 53,
WMO/TD No. 1186, World Meteorological Organization, Geneva, Switzerland, 2003.Quintana-Gomez, R. A.: Trends of maximum and minimum temperatures in northern South America, J. Climate, 12, 2104–2112,
10.1175/1520-0442(1999)012<2104:TOMAMT>2.0.CO;2, 1999.Rhines, A., Tingley, M. P., McKinnon, K. A., and Huybers, P.: Decoding the precision of historical temperature
observations, Q. J. Roy. Meteorol. Soc., 141, 2923–2933, 10.1002/qj.2612, 2015.Ribeiro, S., Caineta, J., and Costa, A. C.: Review and discussion of homogenisation methods for climate data,
Phys. Chem. Earth, 94, 167–179, 10.1016/j.pce.2015.08.007, 2016.Rosas, G., Gubler, S., Oria, C., Acuña, D., Avalos, G., Begert, M., Castillo, E., Croci-Maspoli, M., Cubas, F.,
Dapozzo, M., Díaz, A., van Geijtenbeek, D., Jacques, M., Konzelmann, T., Lavado, W., Matos, A., Mauchle, F., Rohrer, M.,
Rossa, A., Scherrer, S. C., Valdez, M., Valverde, M., Villar, G., and Villegas, E.: Towards implementing climate services in
Peru – the project CLIMANDES, Climate Services, 4, 30–41, 10.1016/j.cliser.2016.10.001, 2016.Sen, P. K.: Estimates of the regression coefficient based on Kendall's Tau, J. Am. Stat. Assoc., 63, 1379–1389,
10.2307/2285891, 1968.Skansi, M. d. l. M., Brunet, M., Sigró, J., Aguilar, E., Arevalo Groening, J. A., Bentancur, O. J., Castellón
Geier, Y. R., Correa Amaya, R. L., Jácome, H., Malheiros Ramos, A., Oria Rojas, C., Pasten, A. M., Sallons Mitro, S., Villaroel
Jiménez, C., Martínez, R., Alexander, L. V., and Jones, P. D.: Warming and wetting signals emerging from analysis of changes
in climate extreme indices over South America, Global Planet. Change, 100, 295–307, 10.1016/j.gloplacha.2012.11.004, 2013. Theil, H.: A rank-invariant method of linear and polynomial regression analysis, Indag. Math, 12, 85–91, 1950.Trewin, B.: Exposure, instrumentation, and observing practice effects on land temperature measurements, WIRES
Clim. Change, 1, 490–506, 10.1002/wcc.46, 2010.Trewin, B.: A daily homogenized temperature data set for Australia, Int. J. Climatol., 33, 1510–1529,
10.1002/joc.3530, 2013.Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T.,
Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol,
D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M.,
Gruber, C., Prohom Duran, M., Likso, T., Esteban, P., and Brandsma, T.: Benchmarking homogenization algorithms for monthly data,
Clim. Past, 8, 89–115, 10.5194/cp-8-89-2012, 2012.Vera, C. S. and Vigliarolo, P. K.: A diagnostic study of cold-air outbreaks over South America, Mon. Weather Rev., 128,
3–24, 10.1175/1520-0493(2000)128<0003:adsoca>2.0.co;2, 2000.Vertačnik, G., Dolinar, M., Bertalanič, R., Klančar, M., Dvoršek, D., and Nadbath, M.: Ensemble
homogenization of Slovenian monthly air temperature series, Int. J. Climatol., 35, 4015–4026, 10.1002/joc.4265, 2015.Vincent, L. A., Peterson, T. C., Barros, V. R., Marino, M. B., Rusticucci, M., Carrasco, G., Ramirez, E., Alves, L. M.,
Ambrizzi, T., Berlato, M. A., Grimm, A. M., Marengo, J. A., Molion, L., Moncunill, D. F., Rebello, E., Anunciação, Y. M. T.,
Quintana, J., Santos, J. L., Baez, J., Coronel, G., Garcia, J., Trebejo, I., Bidegain, M., Haylock, M. R., and Karoly, D.: Observed
trends in indices of daily temperature extremes in South America 1960–2000, J. Climate, 18, 5011–5023, 10.1175/JCLI3589.1, 2005.Viney, N. R. and Bates, B. C.: It never rains on Sunday: the prevalence and implications of untagged multi-day rainfall
accumulations in the Australian high quality data set, Int. J. Climatol., 24, 1171–1192, 10.1002/joc.1053, 2004.Vuille, M., Bradley, R. S., and Keimig, F.: Mean annual temperature trends and their vertical structure in the tropical
Andes, Geophys. Res. Lett., 27, 3885–3888, 10.1029/2000GL011871, 2000.Vuille, M., Bradley, R., Werner, M., and Keimig, F.: 20th century climate change in the tropical Andes: observations and
model results, in: Climate Variability and Change in High Elevation Regions: Past, Present & Future, Advances
in Global Change Research, edited by: Diaz, H., Springer, Dordrecht, 10.1007/978-94-015-1252-7_5, 2003.Wang, X. L. and Swail, V. R.: Changes of Extreme Wave Heights in Northern Hemisphere Oceans and Related Atmospheric Circulation
Regimes, J. Climate, 14, 2204–2221, 10.1175/1520-0442(2001)014<2204:coewhi>2.0.co;2, 2001.WMO: Calculation of Monthly and Annual 30-Year Standard Normals, WCDP No. 10, WMO/TD No. 341, World Meteorological
Organization, Geneva, Switzerland, 1989.
WMO: Guide to Meteorological Instruments and Methods of Observation, 7th Edn., WMO No. 8, World Meteorological
Organization, Geneva, Switzerland, 2008. WMO: Guide to Climatological Practices, 3rd Edn., WMO No. 100, World Meteorological Organization, Geneva, Switzerland, 2011.You, Q., Kang, S., Pepin, N., Flügel, W.-A., Yan, Y., Behrawan, H., and Huang, J.: Relationship between temperature
trend magnitude, elevation and mean temperature in the Tibetan Plateau from homogenized surface stations and reanalysis data, Global
Planet. Change, 71, 124–133, 10.1016/j.gloplacha.2010.01.020, 2010.You, Q., Kang, S., Aguilar, E., Pepin, N., Flügel, W.-A., Yan, Y., Xu, Y., Zhang, Y., and Huang, J.: Changes in daily
climate extremes in China and their connection to the large scale atmospheric circulation during 1961–2003, Clim. Dynam., 36,
2399–2417, 10.1007/s00382-009-0735-0, 2011.You, Q., Fraedrich, K., Ren, G., Pepin, N., and Kang, S.: Variability of temperature in the Tibetan Plateau based on
homogenized surface stations and reanalysis data, Int. J. Climatol., 33, 1337–1347, 10.1002/joc.3512, 2013. Zhang, X. and Yang, F.: RClimDex (1.0) User Guide, Climate Research Branch Environment Canada, Downsview, Ontario, Canada, 2004.Zhang, X. and Zwiers, F. W.: Comment on “Applicability of prewhitening to eliminate the influence of serial correlation on the Mann–Kendall
test” by Sheng Yue and Chun Yuan Wang, Water Resour. Res., 40, W03805, 10.1029/2003WR002073, 2004.Zhou, L., Dai, A., Dai, Y., Vose, R. S., Zou, C.-Z., Tian, Y., and Chen, H.: Spatial dependence of diurnal temperature
range trends on precipitation from 1950 to 2004, Clim. Dynam., 32, 429–440, 10.1007/s00382-008-0387-5, 2009.