Investigation of methods for hydroclimatic data
E. Steirou and D. Koutsoyiannis
Department of Water Resources and Environmental Engineering,
National Technical University of Athens, Greece
Presentation available online: itia.ntua.gr/1212
22 ‐ 27 April 2012
Climate, Hydrology and
Temperature increase during the last century
● The dominant view concerning the climate change is summarised by the IPCC
(Intergovernmental Panel on Climate Change) Assessment Reports.
● Fourth Assessment Report (2007): a non-uniform but clear temperature
increase of 0.6 - 0.7οC is estimated during the last hundred years
► Estimations are not
based on raw data but on
data adjusted in order to
Different estimates of global
temperature changes (IPCC, 2007)
1840 1860 1880 1900 1920 1940 1960 1980 2000
● Historical and contemporary climatic time series contain inhomogeneities –
errors introduced by changes of instruments, location etc.
● The homogenization of climatic time series is made with mainly statistical
methods of identification and correction of recorded and non-recorded
inhomogeneities and is a subject of debate.
De Bilt station – The Netherlands
GHCN-Monthly Version 2
(aggregated to annual)
The difference between the
trends of the raw and the
homogenized data is often
Aim of our work
1. To classify and evaluate the observed inhomogeneities in historical and modern
time series, as well as their adjustment methods.
2. To investigate if and how the homogenization procedure affected temperature
3. To investigate the behaviour of common homogenization methods, when applied
to synthetic time series with specified statistical characteristics.
In this presentation we focus on points 2 and 3.
● Different types (shifts, trends, outliers)
● Different causes (thermometer/recording errors,
changes in measurement conditions, differences in
observational hours and in the methods used to
calculate the mean temperature)
Changes of instruments –
shelters in the USA in the
Α: initial wooden Cotton
Β: modern plastic shelters
Discontinuities in the
series at the National
Observatory of Athens:
– instrument change
in June 1995
– calibration of the
new thermometer in
The homogenization procedure usually consists of three basic steps:
1. Removal of outliers – usually values out of a range of ±(3σ to 5σ) are rejected
2. Corrections to account for different data/methods to estimate mean daily
temperatures and corrections for recorded changes of measurement conditions
3. Application of statistical methods to remove shifts or false trends identified in a
single time series (absolute methods) or in comparison of a “candidate” time
series to one or more “reference” time series (relative methods, more common)
► Common assumption of homogenization methods is that temperature data
(and generally hydroclimatic data) are independent and normally distributed.
► Relative methods: they require high statistical correlation between candidate
and reference series.
Discussion on the homogenization-1
► Homogenization results are usually not supported by metadata or experiments (a
known exception in literature is the experiment at the Kremsmünster Monastery,
Example: change of thermometers–shelters in the USA in the 1980s (Quayle et al., 1991)
● No single case of an old and a new observation station running for some time
together for testing of results is available!
● On the contrary, comparison and correction were made using statistics of remote
(statistically correlated) stations.
Two neighbouring stations are corrected based on two groups of
reference stations located at distances of hundreds of km.
● candidate stations ○ reference stations
(Quayle et al., 1991)
Discussion on the homogenization-2
► Homogenization methods do not take into consideration some characteristics of
hydroclimatic data (long-term persistence, microclimatic changes, time lags).
► Some inhomogeneities detected are statistically non-significant and they can
lead to false corrections.
► Inhomogeneities not reflecting systematic instrumentation changes in a specific
period are expected to have a random character, not introducing a consistent bias
in long time series that needs to be corrected.
+0.03 ±0.38 οC
Example: Adjustments of daily summer
maximum temperatures in the Greater
Mediterranean Region (Kuglitch et al., 2009)
► Corrections may introduce bigger errors than the errors they try to remove.
Evaluation of homogenization results
In the USA, due to the large number
of stations satisfying the criteria, we
divided the region into 7 sections
and selected a number of stations in
proportion to their area.
From the total number of stations of the database
GHCN-Monthly Version 2 we examined 163 stations
worldwide satisfying certain criteria:
● They have both raw and adjusted data.
● Each time series contains ≥ 100 years of data.
● Each time series contains ≤ 4 successive missing
● In each time series the percentage of missing
years does not exceed 10%.
● Time series end at or later than 1990.
● We calculated annual values from monthly values (a year with more than 4
missing months in total or 3 consecutive missing months was considered
● We calculated trends for both raw and adjusted data.
● We calculated the Hurst coefficient in two cases of stations with a big
difference between the trends of raw and adjusted data.
1860 1880 1900 1920 1940 1960 1980 2000
Sulina station – Romania
GHCN-Monthly Version 2
► The Hurst coefficient
increased due to the trend
increase of the time series.
Trend difference due to homogenization
Homogenization has amplified the estimation of
global temperature increase.
In 2/3 of the stations
negative trends or
trends to positive.
Global Temperature Increase
(from the examined series)
► The expected
proportion would be 1/2.
Time series Q with an
Test statistic Τa: offset
Evaluation of the SNHT performance
● A time series Q is formed as a function
of the candidate (tested) time series Y and
a number of reference time series Xj.
● The time series Q is normalised to time
● The test creates a test statistic Τa which
at the point of a shift takes its maximum
► Standard Normal Homogeneity Test
(SNHT) for single shifts is one of the most
common homogenization methods (GHCN -
Version 3) for temperature data. A version of
the method is used for precipitation data.
SNHT for single shifts
We created two time series X, Y each one containing 100 elements and time series W
as a linear function of X, Y.
Time series X, Y: μ=0 and σ=1
Data with long-term persistence: H=0.85, SMA model (Koutsoyiannis, 2000)
The coefficients κ, λ were calculated so that ρWY=0.9 and σW=1.
W: candidate series
Y: reference series
The method was applied in three different cases of synthetic time series:
1. independent data normally distributed with a shift
2. homogeneous data with long-term persistence
3. data with long-term persistence and a shift
1. Independent data normally distributed with a shift
► SNHT seems to be satisfactory when applied
to independent data normally distributed.
SNHT located and corrected
the shift of 0.5οC.
The original trend of the time
series was recovered.
We induced a shift of 0.5 οC to
the candidate time series.
The time series is considered homogeneous
2. Homogeneous data with long-term
The method detected two false (non
existing) inhomogeneities. The time
series was corrected in two steps even
if it was already homogeneous.
W (1st correction)
W (2nd correction)
The observed increase of the Hurst
coefficient is caused by the increase of
the trend of the time series.
► The homogenization changed
the trend of the time series.
3. Data with long-term persistence and shift
● We induced a shift of 0.5οC after time 40.
● We applied the homogenization method
until a homogenous time series was derived.
1st step – false inhomogeneity
2nd step – real inhomogeneity
3rd step – false inhomogeneity
4th step – false inhomogeneity
● The homogenization changed the trend of
the time series. Statistical characteristics
similar to the homogenized time series of
the previous example.
Time series considered
► SNHT does not seem to have a
satisfactory behaviour when applied
to data with long-term persistence.
1. Homogenization is necessary to remove errors introduced in climatic time
2. Homogenization practices used until today are mainly statistical, not well
justified by experiments and are rarely supported by metadata. It can be
argued that they often lead to false results: natural features of hydroclimatic
time series are regarded errors and are adjusted.
3. While homogenization is expected to increase or decrease the existing
multiyear trends in equal proportions, the fact is that in 2/3 of the cases the
trends increased after homogenization.
4. The above results cast some doubts in the use of homogenization procedures
and tend to indicate that the global temperature increase during the
last century is smaller than 0.7-0.8°C.
5. A new approach of the homogenization procedure is needed, based on
experiments, metadata and better comprehension of the stochastic
characteristics of hydroclimatic time series.
Alexandersson H, Moberg A. (1997) ‘Homogenization of Swedish temperature data Part I: homogeneity
test for linear trends’, Int J Climatol 17:25–34.
Founda, D., Kambezidis, H.D., Petrakis, M., Zanis, P., Zerefos, C. (2009) ‘A correction of the recent air-
temperature record at the historical meteorological station of the National Observatory of Athens
(NOA) due to instrument change’, Theoretical and Applied Climatology, 97 (3-4), pp. 385-389.
IPCC (2007) Summary for Policymakers, Climate Change 2007: The Physical Science Basis. Contribution
of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change, Cambridge: Cambridge University.
Koutsoyiannis, D. (2000) ‘A generalized mathematical framework for stochastic simulation and forecast
of hydrologic time series’, Water Resources Research, 36 (6), 1519–1533.
Kuglitsch, F.G., Toreti, A., Xoplaki, E., Della-Marta, P.M., Luterbacher, J., Wanner, H. (2009)
‘Homogenization of daily maximum temperature series in the Mediterranean’, Journal of Geophysical
Research D: Atmospheres, 114 (15), art. no. D15108 2.
Quayle, R. G., D. R. Easterling, T. R. Karl, and P. Y. Hughes (1991) ‘Effects of recent thermometer
changes in the Cooperative Station Network” Bull. Amer. Meteor. Soc., 72, 1718–1723.