Different approaches for the homogenisation of the Spanish Daily Temperature Series (SDATS )

SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

Different approaches for the homogenisation of the Spanish

Daily Temperature Series (SDATS)

Aguilar, E., Brunet, M., Sigró, J.Climate Change Research Group,

Universitat Rovira i Virgili, Tarragona, Spain


MOTIVATION

• SDATS dataset included only the “longest and most reliable series”, leading to a low density network

• CCRG is involved in a coordinated project (EXPICA) that wants to relate temperature and precipitation extrems to circulation patterns over the Iberian Peninsula

• Can our current homogenization procedure for daily data feed temperatures to EXPICA?

• Can we apply other procedures with the current network? (i.e. HOM)

• Do we have to expand it?• CAFIDEXPI subproject re-homogenization on a daily

bases of SDATS and calculation of extreme indices


Spanish Daily Temperature Series

-22 Stations

-Unevenly distributed across Spain


HOMOGENIZATION STEPS

QCd daily data of TMax and TMin

Screen Bias Minimisation over monthly series of TMax and TMin

SDTS

Calculation of Monthly Values of TMax and TMin

Blind break-point detection over annual, seasonal TMax, Tmin, Tmean with automated SNHT (1997)

Breakpoint validation (metadata, plot checks, …)

Generation of correction pattern

Application to monthly Tmax and Tmin (As described in Aguilar et al, 2002)

Monthly, Seasonal, Annual Tmax, Tmin, DTR, TMean Series (STS)

Interpolation to daily data (Vincent et al., 2002)

Validation of daily corrected values


SCREEN BIAS MINIMIZATION

CCRG’s SCREEN project (CICYT) 2 replicas of Montsouris Screen, on operation since 2003

Large effect on TMax

Much smaller effect on TMin


SCREEN BIAS MINIMIZATION

10,00 20,00 30,00 40,00

Montsouris TMax

0,00

1,00

2,00

3,00

Mon

tsou

ris-S

teve

nson

10,00 20,00 30,00 40,00

Tmaxmont mur

10,0

20,0

30,0

40,0

Tmax

stev

mur

New Estimation (Murcia): TMaxStev = -0.508 + TMaxMont*0.975


The homogenization methods. SNHT

Reference Series

q-series (data-reference)

z-series (standarized q-series)

Original Data

k

jj

k

jjjj

r

YXyr

1

2

1

2 ))((

Reference Series

q-series (data-reference)

z-series (standarized q-series)

Original Data

k

jj

k

jjjj

r

YXyr

1

2

1

2 ))((

Most Probable Breakpoint: Max of 22

21 )( zjnzjT j

Correction Factor:12 qqf

Most Probable Breakpoint: Max of 22

21 )( zjnzjT j

Correction Factor:12 qqf

SESION 1

Series A1Series B1Series C1Series D1Series E1

...Series X1

INITIAL PHASE

SESION 2


...Series X2

SESION n

Series AnSeries BnSeries CnSeries DnSeries En

...Series Xn

Iterationuntil no

more breakpoints

are found

SESION 1


...Series X1

INITIAL PHASE

SESION 2


...Series X2

SESION n


...Series Xn

Iterationuntil no

more breakpoints

are found

SESION 1


...Series X1

FINAL PHASE

An ... Xnare used as references

HOMOGENEOUS SERIESSeries AhSeries BhSeries ChSeries DhSeries Eh

...Series Xh

SESION 1


...Series X1

FINAL PHASE



...Series Xh

SESION 1


...Series X1

INITIAL PHASE

SESION 2


...Series X2

SESION n


...Series Xn

Iterationuntil no

more breakpoints

are found

SESION 1


...Series X1

INITIAL PHASE

SESION 2


...Series X2

SESION n


...Series Xn

Iterationuntil no

more breakpoints

are found

SESION 1


...Series X1

FINAL PHASE



...Series Xh

SESION 1


...Series X1

FINAL PHASE



...Series Xh

Automated Software by Enric Aguilar. Available under request


INTERPOLATION TO DAILY DATA

Spline through monthly temperature adjustments

• Easy to implement

• No assumptions about changes in variance

• Integrated daily adjustments = monthly adjustments


THE HOM METHOD CONCEPT• 1) DEFINE HSPs for the candidates and reference stations• 2) Identify highly correlated ref station that overlaps HSP1 and HSP2 of the

reference• 3) Model (LOESS) the relations in HSP1• 4) Predict the temperature at the candidate in HSP2 using observations

from the reference series in HSP2• 5) Create a paired difference between predicted and observed temperatures

in HSP2• 6) Find the probability distribution (L-Moments, 6 distributions) of the

candidate in HSP1 and HSP2• 7) Bin each difference in 5) according to the associated predicted

temperature according the distribution of HSP1• 8) Fit a smoothly varying function between the binned differences to obtain

adjustments for each percentile• 9) Using the probability distribution of the candidate in HSP2 , determine the

percentile of each observation and adjust accordingly to the value obtained in 8)


The HOM method concept


PRELIMINARY APPLICATION OF HOM METHOD TO LA CORUÑA, MADRID, MURCIA

-We compare the results obtained with CCRG procedure with the HOM method

- HOM is applied to raw data (with no screen adjustments) using the breakpoints detected through the CCRG’s procedure.

-We use 3 series: Madrid, Murcia and La Coruña, analyzing the impacts of the different approaches over annual trends in TMIN and TMAX and on four extreme indices: warm days (TX90p); cold days (TX10p), warm nights (TN90p) and cold nights (TN10p


LA CORUÑA

• The method cannot be applied to this station with the current dataset

• Correlations with other series are too low• Best candidates do not have overlapping

HSPs. For example, San Sebastian• Introduction of new stations (Gijón, Oviedo,

shorter Galician stations) should improve this situation


MADRID

• Changes in screen around 1893 can HOM capture this kind of problems?

• Artificial trend (urban) between 1893 and 1960 this can be a problem for HOM, as we’re modelling HSPs and 1893-1960 won’t be exactly an HSP. To try to tackle this we are using to schemes for Madrid– 1893,1960– -1893, 1920,1940 (understanding the urban trend as

a succession of same sign shifts)• Jump in 1960


Black = raw; Red CCRG; Blue HOM-1break; Green HOM-3breaks


Model and CDF. Inhomogeneity in 1893. HOM-1break. TMAX. August.

Larger values are evident in HSP2 (pre-1893) represented by dashed lines. The adjustments capture this jump


Model and CDF. Inhomogeneity in 1893. HOM-1break. TMAX. April

Change in variance and in mean. Lower percentiles need more correction than upper percentiles. Is this what we should expect from the source of inhomogeneity we know (i.e. change in screen)?


SOMETHING I’VE HIDDING FROM YOU!

• Reference chosen among the available stations with a reasonable number of pairs and a reasonable correlation:– Reference for April is Badajoz– Reference for August is Cádiz (!)

• This is far from optimum; there is little chance to find closer neighbors for this part of the record…


Trends for annual TMAX

compared to trends from CCRG original approach (bold italic, different sign of point estimate; bold different sign in the confidence interval)

FIRST YEAR SERIES LTV TES UTV

1854 MADRID_CCRG_ANNUAL_MAX 0.005 0.010 0.016

1854 MADRID_HOMA_ANNUAL_MAX 0.000 0.004 0.009

1854 MADRID_HOMB_ANNUAL_MAX 0.002 0.007 0.011

1854 MADRID_ORIG_ANNUAL_MAX -0.017 -0.008 0.001




1894 MADRID_ORIG_ANNUAL_MAX 0.006 0.012 0.017







1942 MADRID_HOMB_ANNUAL_MAX -0.001 0.014 0.029



Same for TX90pFIRST YEAR SERIES LTV TES UTV

1854 MADRID_CCRG_TX90p 0.000 0.029 0.055

1854 MADRID_HOMA_TX90p -0.041 -0.011 0.021

1854 MADRID_HOMB_TX90p -0.006 0.018 0.043

1854 MADRID_ORIG_TX90p -0.140 -0.069 -0.001

1894 MADRID_CCRG_TX90p 0.067 0.089 0.115

1894 MADRID_HOMA_TX90p -0.038 0.000 0.038

1894 MADRID_HOMB_TX90p 0.027 0.056 0.083

1894 MADRID_ORIG_TX90p 0.023 0.052 0.084

1919 MADRID_CCRG_TX90p 0.079 0.116 0.149

1919 MADRID_HOMA_TX90p -0.057 0.007 0.067

1919 MADRID_HOMB_TX90p 0.020 0.068 0.115

1919 MADRID_ORIG_TX90p 0.039 0.088 0.131

1942 MADRID_CCRG_TX90p 0.108 0.158 0.213

1942 MADRID_HOMA_TX90p -0.030 0.064 0.156

1942 MADRID_HOMB_TX90p 0.006 0.084 0.167

1942 MADRID_ORIG_TX90p 0.080 0.147 0.202


Same for TX10pFIRST YEAR SERIES LTV TES UTV

1854 MADRID_CCRG_TX10p -0.111 -0.087 -0.062

1854 MADRID_HOMA_TX10p -0.082 -0.059 -0.037

1854 MADRID_HOMB_TX10p -0.068 -0.047 -0.027

1854 MADRID_ORIG_TX10p -0.019 0.007 0.031

1894 MADRID_CCRG_TX10p -0.157 -0.126 -0.095

1894 MADRID_HOMA_TX10p -0.096 -0.068 -0.036

1894 MADRID_HOMB_TX10p -0.081 -0.049 -0.021

1894 MADRID_ORIG_TX10p -0.086 -0.058 -0.027

1919 MADRID_CCRG_TX10p -0.150 -0.111 -0.067

1919 MADRID_HOMA_TX10p -0.127 -0.086 -0.042

1919 MADRID_HOMB_TX10p -0.118 -0.070 -0.029

1919 MADRID_ORIG_TX10p -0.114 -0.067 -0.025

1942 MADRID_CCRG_TX10p -0.199 -0.119 -0.051

1942 MADRID_HOMA_TX10p -0.193 -0.115 -0.049

1942 MADRID_HOMB_TX10p -0.159 -0.075 -0.006

1942 MADRID_ORIG_TX10p -0.183 -0.100 -0.035


MURCIA

• Murcia presents a change in SCREEN around 1912

• And relocations – 1939– 1954– 1984


Annual values derived from daily homogenized data. Black lines: original data; red lines: CCRG procedure (correcting change of screen in 1912 and relocations in 1939, 1954 and 1984); green lines HOM adjustments using 1863-1912; 1913-1939; 1940-1954 and 1955-2006 as HSPs. Notice the excellent agreement between methods in the highlithed area of the plot


ADJUSTMENTS FOR MURCIA. Break 1984. May

(USING ALICANTE, now this is good!!)

Wide range of adjustments; from slightly negative to about +1ºC in the higher percentiles


Histograms of differences between CCRG adjustments and ORIGinal data (left); HOM adjustments and ORIginal data (center) and CCRG and HOM adjustments (right) for different months (rows). Due the nature of the two sets of adjustments, notice a largest gamma of adjustment values when HOM is implied in the differencing. The pairs of series, show significant changes in variance.


CONCLUSIONS AND FUTURE WORK• There is a strong consensus about the need of improving the

homogenization of climatological time series, specially on daily and sub-daily scales The CCRG has been homogenizing daily values using an effective combination of an adapted version of SNHT + interpolation of monthly factors to daily values

• The HOM method provides a powerful tool to adjust daily datasets accounting for Higher Order Moments inhomogeneities

• Although HOM method and CCRG procedures can show very similar adjustments when annual values are re-computed from homogenized daily values, in some ocasions adjustments can show large differences. This differences – enlarged when seasonal or monthly series are analyzed, can be partially attributed to the lack of good references to produces overlapping HSPs or – in other cases – to non identified breakpoints. But they could also derive from the larger range of corrections applied to daily values for each month

• In the near future, several projects by the CCRG – specially the CAFIDEXPI (Changes in Frequency Intensity and Duration of EXtremes in the Iberian Peninsula) and CLICAL - will introduce new series to SDATS for the compilation of a new version of. The application HOM method – when applicable – will continue to be explored.

Documents

Different approaches for the homogenisation of the Spanish Daily Temperature Series (SDATS )