TRAINING SESSION ON HOMOGENISATION METHOD

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TRAINING SESSION ON HOMOGENISATION METHOD
What should be corrected? How to correct? And
some examples of homogenisation results
Bologna, 17th-18th May 2005
Maurizio Maugeri, University of Milan
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• Basic problem what has to be corrected?

• Our methodology
• When the signal is not so clear our philosophy
  is to homogenize the data
• only in the following cases
• when there is some information in the metadata
• when more reference series give coherent
  adjustment estimates and their scattering around
  the mean value is lower
• than the break amount
• In our opinion, only in these cases the
  corrections really improve the data quality,
  whereas in other cases there is a high risk of
  introducing corrections whose associated errors
  are higher than the corrections.
• The point concerning the fundamental question of
  when actually perform correction is an important
  open issue of any research concerning the
  reconstruction of the past climate.

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• Basic problem how to correct?

Once we decide to correct one break, the series
used to estimate the adjustments are chosen among
the reference series that result homogeneous in a
sufficiently long sub-period centred on the break
year, and that well correlate with the candidate
one. We chose to use several series to estimate
the adjustments to be sure about their stability
and to avoid unidentified outliers in the
reference series from producing bad corrections.
Moreover, it often happens that homogeneous
sub-intervals between two detected breaks are so
short that the signal-to-noise ratios of the
adjustments obtained with only one reference
series are very low. So, the use of more series
allows us to correct a great number of short
sub-periods that would have to be left unchanged
otherwise. The adjustments from each reference
series are calculated on a monthly basis, and
then they are fitted with a trigonometric
function in order to smooth the noise and to
extract only the physical signal (the adjustments
often follow a yearly cycle). The benefits of
using smoothed adjustments instead of the rough
ones are well described in Auer et al. (2005).
The final set of monthly adjustments is then
calculated by averaging all the yearly cycles,
excluding from the computation those stations
whose set of adjustments shows an incoherent
behaviour compared with the others. When a clear
yearly cycle is not evident, the adjustments used
to correct the monthly data are chosen as
constant through the year and are calculated as
the average among the monthly values for
temperature, and as the weighted average for
precipitation, where the weights are the ratios
between monthly mean precipitation and total
annual precipitation.
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The ZAMG approach the seven principles of
homogenisation

• 1. Ignore any previous homogeneity work
  undertaken for any of the series (i.e. start from
  the beginning assuming all series contain
  potential breaks)
• 2. Test in small, well correlated sub-regions (a
  maximum of ten series tested against each other
  results in a 1010 decision matrix, which enables
  most detected breaks to be assigned to a most
  likely candidate series)
• 3. Choose the most appropriate reference series
  with a non-affected subinterval for the
  adjustment of each detected break (i.e. different
  reference series can be used for each detected
  break in a candidate series). Open question at
  what extent can we use homogenized series in the
  procedure?
• 4. Avoid erratic monthly adjustments by smoothing
  the annual course of adjustment factors
• 5. Detect outliers using spatial comparisons (by
  mapping precipitation values both in absolute and
  relative units) for each month of the study
  period
• 6. Attempt to determine support for homogeneity
  adjustments when little metadata available (i.e.
  contact data providers for more information in
  difficult cases)
• 7. Give preference to good metadata rather than
  mathematical methods in all cases, especially
  where adjustment factors can be directly
  calculated from sufficiently-long series of
  parallel measurements

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Why is homogenisation so difficult?
QUALITY IMPROVED LONG-TERM DATABASE FOR AIR
TEMPERATURE (BASED ON MONTHLY MEANS) IN THE
GREATER ALPINE REGION
The HISTALP temperature network (release 2004-07)
dots lt1500m, triangles gt1500m, blue  old
series only updated, red new early parts,
yellow new series
Updated from Böhm, R., I.Auer, M.Brunetti,
M.Maugeri, T.Nanni and W.Schöner 2001 Regional
temperature variability in the European Alps
1760-1998 from homogenized instrumental time
series. Int. J. Climatol. 21 1779-1801.
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Why is homogenisation so difficult?
Some examples of the results of systematic
homogenisation activity
Updated from Böhm, R., I.Auer, M.Brunetti,
M.Maugeri, T.Nanni and W.Schöner 2001 Regional
temperature variability in the European Alps
1760-1998 from homogenized instrumental time
series. Int. J. Climatol. 21 1779-1801.
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TEMPERATURE
TEMPERATURE
Temporal evolution of the HISTALP-temperature
dataset comparison of the versions 2001 and
2004 number of stations (LEFT) and mean network
density (RIGHT)
Updated from Böhm, R., I.Auer, M.Brunetti,
M.Maugeri, T.Nanni and W.Schöner 2001 Regional
temperature variability in the European Alps
1760-1998 from homogenized instrumental time
series. Int. J. Climatol. 21 1779-1801.
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Why is homogenisation so difficult?
Graphs correlation-distance
Brunetti, M., Maugeri, M., Monti, F., Nanni, T.
Temperature and precipitation variability in
Italy in the last two centuries from homogenized
instrumental time series, Int. J. Climatol., in
press.
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TEMPERATURE
HISTALP_TEMPERATURE
Updated from Böhm, R., I.Auer, M.Brunetti,
M.Maugeri, T.Nanni and W.Schöner 2001 Regional
temperature variability in the European Alps
1760-1998 from homogenized instrumental time
series. Int. J. Climatol. 21 1779-1801.
10
TEMPERATURE
HISTALP_TEMPERATURE
Frequency distribution of outlier magnitudes
(LEFT) and time series of the outlier-rate (real
outliers) per year (RIGHT)
Updated from Böhm, R., I.Auer, M.Brunetti,
M.Maugeri, T.Nanni and W.Schöner 2001 Regional
temperature variability in the European Alps
1760-1998 from homogenized instrumental time
series. Int. J. Climatol. 21 1779-1801.
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TEMPERATURE
HISTALP_TEMPERATURE
Böhm, R., I.Auer, M.Brunetti, M.Maugeri, T.Nanni
and W.Schöner 2001 Regional temperature
variability in the European Alps 1760-1998 from
homogenized instrumental time series. Int. J.
Climatol. 21 1779-1801.
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PRECIPITATION
HISTALP_PRECIPITATION
Auer I, Böhm R, Jurkovic A, Orlik A, Potzmann R,
Schöner W, Ungersböck M, Brunetti M, Nanni T,
Maugeri M, Briffa K, Jones P, Efthymiadis D,
Mestre O, Moisselin JM, Begert M, Brazdil R,
Bochnicek O, Cegnar T, Gajic-Capka M, Zaninivic
K, Majstorovic Z, Szalai S, Szentimrey T, 2004 A
new instrumental precipitation dataset in the
greater alpine region for the period 1800-2002.
accepted for Int. J. Climatol. 24.
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PRECIPITATION
HISTALP_PRECIPITATION
Auer I, Böhm R, Jurkovic A, Orlik A, Potzmann R,
Schöner W, Ungersböck M, Brunetti M, Nanni T,
Maugeri M, Briffa K, Jones P, Efthymiadis D,
Mestre O, Moisselin JM, Begert M, Brazdil R,
Bochnicek O, Cegnar T, Gajic-Capka M, Zaninivic
K, Majstorovic Z, Szalai S, Szentimrey T, 2004 A
new instrumental precipitation dataset in the
greater alpine region for the period 1800-2002.
accepted for Int. J. Climatol. 24.
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PRECIPITATION
HISTALP_PRECIPITATION
Auer I, Böhm R, Jurkovic A, Orlik A, Potzmann R,
Schöner W, Ungersböck M, Brunetti M, Nanni T,
Maugeri M, Briffa K, Jones P, Efthymiadis D,
Mestre O, Moisselin JM, Begert M, Brazdil R,
Bochnicek O, Cegnar T, Gajic-Capka M, Zaninivic
K, Majstorovic Z, Szalai S, Szentimrey T, 2004 A
new instrumental precipitation dataset in the
greater alpine region for the period 1800-2002.
accepted for Int. J. Climatol. 24
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HISTALP_PRECIPITATION
Auer I, Böhm R, Jurkovic A, Orlik A, Potzmann R,
Schöner W, Ungersböck M, Brunetti M, Nanni T,
Maugeri M, Briffa K, Jones P, Efthymiadis D,
Mestre O, Moisselin JM, Begert M, Brazdil R,
Bochnicek O, Cegnar T, Gajic-Capka M, Zaninivic
K, Majstorovic Z, Szalai S, Szentimrey T, 2004 A
new instrumental precipitation dataset in the
greater alpine region for the period 1800-2002.
accepted for Int. J. Climatol. 24
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PRECIPITATION
HISTALP_PRECIPITATION
Auer I, Böhm R, Jurkovic A, Orlik A, Potzmann R,
Schöner W, Ungersböck M, Brunetti M, Nanni T,
Maugeri M, Briffa K, Jones P, Efthymiadis D,
Mestre O, Moisselin JM, Begert M, Brazdil R,
Bochnicek O, Cegnar T, Gajic-Capka M, Zaninivic
K, Majstorovic Z, Szalai S, Szentimrey T, 2004 A
new instrumental precipitation dataset in the
greater alpine region for the period 1800-2002.
accepted for Int. J. Climatol. 24
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PRECIPITATION
HISTALP_PRECIPITATION
Auer I, Böhm R, Jurkovic A, Orlik A, Potzmann R,
Schöner W, Ungersböck M, Brunetti M, Nanni T,
Maugeri M, Briffa K, Jones P, Efthymiadis D,
Mestre O, Moisselin JM, Begert M, Brazdil R,
Bochnicek O, Cegnar T, Gajic-Capka M, Zaninivic
K, Majstorovic Z, Szalai S, Szentimrey T, 2004 A
new instrumental precipitation dataset in the
greater alpine region for the period 1800-2002.
accepted for Int. J. Climatol. 24
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Homogenisation number of detected breaks (ITALY)
Number of detected breaks per year. From a) to d)
absolute values (for mean temperature, maximum
temperature, minimum temperature and
precipitation respectively) form e) to h) in
relation to the available series (for mean
temperature, maximum temperature, minimum
temperature and precipitation respectively)
Brunetti, M., Maugeri, M., Monti, F., Nanni, T.
Temperature and precipitation variability in
Italy in the last two centuries from homogenized
instrumental time series, Int. J. Climatol., in
press.
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Homogenisation mean annual adjusting series
(ITALY)
Mean annual adjusting series obtained by
calculating the yearly average differences
(ratios) between the homogenised and the original
temperature (precipitation) series. a) Mean
temperature, b) Maximum temperature, c) Minimum
temperature, and d) Precipitation. Standard
deviations (thin lines) and total correction
ranges (dotted lines) are indicated too. The
standard deviations were not calculated before
1803 for mean temperature and before 1814 for
minimum and maximum temperature, due to a sample
size less than 5 stations
Brunetti, M., Maugeri, M., Monti, F., Nanni, T.
Temperature and precipitation variability in
Italy in the last two centuries from homogenized
instrumental time series, Int. J. Climatol., in
press.
20
Homogenisation (Italy) statistics of the breaks
Brunetti, M., Maugeri, M., Monti, F., Nanni, T.
Temperature and precipitation variability in
Italy in the last two centuries from homogenized
instrumental time series, Int. J. Climatol., in
press.
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Homogenisation trends in C/100 years (1865-2003)
Brunetti, M., Maugeri, M., Monti, F., Nanni, T.
Temperature and precipitation variability in
Italy in the last two centuries from homogenized
instrumental time series, Int. J. Climatol., in
press.
22
Homogenisation and trend estimates
Brunetti M., Buffoni, L., Maugeri, M., Nanni, T.,
2000 Trends of minimum and maximum daily
temperatures in Italy from 1865 to 1996. Theor.
Appl. Climatol., 66, 49-60.
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Homogenisation a last note
Homogenisation has not only benefits one of the
limits is that it reduces the actual
dimensionality of the data-set, sometimes making
it more difficult to locate the climatic signal
at local scale. This can be a not trivial
question in the context of impact studies for
further details, see