north sea - caspian pattern (ncp) and its influence on the hydroclimate of turkey

NORTH SEA - CASPIAN PATTERN NORTH SEA - CASPIAN PATTERN (NCP)(NCP)

and its influence on the hydroclimate of and its influence on the hydroclimate of TurkeyTurkey

OZAN MERT GÖKTÜRKOZAN MERT GÖKTÜRKİİTÜ EURASIA INSTITUTE OF EARTH TÜ EURASIA INSTITUTE OF EARTH SCSCIENCESIENCES

ContentsContents

A reA review of NCPview of NCP Data sets and methodologyData sets and methodology

Results: effects of NCPResults: effects of NCP

Problems and discussionProblems and discussion

NNorth Sea – orth Sea – CCaspian aspian PPattern (attern (NCPNCP) ) (Kutiel ve Benaroch, 2002)(Kutiel ve Benaroch, 2002)

NCPINCPI = gpm(= gpm(500hP500hPa) (0°, 55° N ; 10° E, 55° N)a) (0°, 55° N ; 10° E, 55° N) – –gpm(gpm(500hPa500hPa) (50° E, 45° N ; 60° E, 45° N)) (50° E, 45° N ; 60° E, 45° N)

σ/)( NCPINCPIz ii −=

→≥ 5.0iz

→−≤ 5.0iz

NCP(+)NCP(+)

NCP(NCP(--))

Progress of NCPI through the year Progress of NCPI through the year (Kutiel ve Benaroch, 2002)(Kutiel ve Benaroch, 2002)

NCP(NCP(--)) Kutiel ve Benaroch (2002)Kutiel ve Benaroch (2002)

NCP(NCP(++)) Kutiel ve Benaroch (2002)Kutiel ve Benaroch (2002)

HypothesisHypothesis

There should be a significant relation There should be a significant relation between NCP and Turkey’s between NCP and Turkey’s precipitation/streamflow regimes.precipitation/streamflow regimes.

This relation can be investigated by This relation can be investigated by

Pearson’s correPearson’s correlation coefficient and also lation coefficient and also with the Canonical Correlation Analysiswith the Canonical Correlation Analysis. .

Data setsData sets

PredictorsPredictors

- Large-scale 500 hPa geopotential - Large-scale 500 hPa geopotential height fieldheight field

- NCPI- NCPI PredictandsPredictands

- Monthly precipitation series of - Monthly precipitation series of TurkeyTurkey

- Month- Monthly streamflow series of Turkeyly streamflow series of Turkey

MonthMonthly mean ly mean 500 hPa 500 hPa geopotential geopotential height fieldheight field

1010°°W - 60W - 60°°EE3030°°N - 70N - 70°°NN2.52.5°°x2.5x2.5°° grid grid493 grid points493 grid points

1958 – 20031958 – 2003 NCEP-NCAR NCEP-NCAR

ReanalysisReanalysis

PredictorsPredictors

Predictands: Predictands: streamflowstreamflow

Monthly, 110 Monthly, 110 stationsstations

1958-20031958-2003

MonthMonthly, ly, 260 stations260 stations 1958-20031958-2003

Predictands: Predictands: precipitationprecipitation

Data pre-processingData pre-processing

““De De trending” : to include only the trending” : to include only the variations. variations.

Outlier trimming: to avoid the distortion Outlier trimming: to avoid the distortion of the analysis by the extreme values of the analysis by the extreme values

IQRqPout 375.0 +=

Data homogenizationData homogenization

Alexandersson (1986) Alexandersson (1986) Based on comparison with a reference Based on comparison with a reference

regional time seriesregional time series

MethodologyMethodology

Pearson’s correlation (well-known)Pearson’s correlation (well-known)

∑∑∑ −−−−=i

ii

ii

ii yyxxyyxxr 22 )()(/))((

Correlate Correlate NCPINCPI with with monthly streamflows monthly streamflows and precipitations…and precipitations…

MethodologyMethodology

Canonical Correlation Analysis (CCA)Canonical Correlation Analysis (CCA)

Pearson korelasyonu -> Pearson korelasyonu -> univariate univariate time time seriesseries

CCACCA -> -> multivariatemultivariate time series time series

CCA CCA

Any correlation between these Any correlation between these two???two???

1. 1. Write the time series as anomalies,Write the time series as anomalies,

′

μrrr

−=′ tt XX μrrr

−=′ tt YY

Time

Space

tXr

tYr

2.2. These anomalies are composed of These anomalies are composed of independent independent spatial patternsspatial patterns and their and their time coefficientstime coefficients… …

(von Storch ve Zwiers, 1999)(von Storch ve Zwiers, 1999)

∑=

=′k

i

itit eX

1,

rrα

Time coefficients

Spatial patterns

X X

+

X+ +

…

=

∑=

=′k

iitit eX

1,

rrα

CCACCA

3. Find such spatial patterns that 3. Find such spatial patterns that correlation between their time correlation between their time coefficients are the greatestcoefficients are the greatest. That is , . That is , maximizemaximize

)().(

),(YX

YX

VarVar

Cov

ααααρ =

CCACCA

4. ...4. ... finally finally,,

YY

XYXX

T

XYYY

XXT

XYYYXYXX

ee

eerr

rr

ζη

ζη

4

411

11

=

=

∑∑∑∑∑∑∑∑

−−

−−

Xie

rCanonical predictor patterns

Canonical predictand patterns

Yie

r

CCACCA

5.5.

--CCanonical anonical CCorrelation orrelation CCoefficient (CCC)oefficient (CCC)

)().(

),(Yi

Xi

Yi

Xi

iVarVar

Cov

αα

ααρ =

Variance Variance representedrepresented

…….%.%

Corr.with NCPI =Corr.with NCPI =

Anomalies - predictandsAnomalies - predictandsAnomalies - predictorsAnomalies - predictors

Variance Variance representedrepresented

% ....% ....

CCC = …..CCC = …..

January January – – streamflowstreamflow

Pearson’s correlations with NCPIPearson’s correlations with NCPI

FebruaryFebruary – streamflow – streamflow


NCP(NCP(--))

JanuaryJanuary

StreamflowStreamflow

11. CCA pair. CCA pairCCC = CCC = 0.920.92

r.Var.r.Var.

%37%37

r.Var.r.Var.

%16%16

NCPI cor.NCPI cor.

0.460.46

JanuaryJanuary – – precipitationprecipitation


JanuaryJanuary

Precip.Precip.11. CCA pair. CCA pairCCC = CCC = 0.980.98

r.Var.r.Var.

%43%43

r.Var.r.Var.

%23%23

NCPI cor.NCPI cor.

0.720.72

January vs February (precip)January vs February (precip)

MarchMarch – – streamflowstreamflow


MarchMarch – precipitation – precipitation


AprilApril – – precipitationprecipitation

Pearson’s correPearson’s correlations with NCPIlations with NCPI

MayMay – – streamflowstreamflow

Pearson’s correlations with NCPI Pearson’s correlations with NCPI

• Streamflow... No significant relation Streamflow... No significant relation for the rest of the yearfor the rest of the year

• Precipitation... No significant relation Precipitation... No significant relation with NCP for May and June…with NCP for May and June…

JulyJuly – – precipitationprecipitation


AugustAugust – – precipitationprecipitation


September September – – precipitationprecipitation


October - precipitationOctober - precipitation


NovemberNovember - precipitationprecipitation


NovemberNovember

Precip.Precip.11. CCA pair. CCA pairCCC = CCC = 0.950.95

t.Var.t.Var.

%17%17

r.Var.r.Var.

%26%26

NCPI ileNCPI ile

0.680.68

DecemberDecember – – precipitationprecipitation


DecemberDecember

Precip.Precip.1. CCA pair1. CCA pairCCC = CCC = 0.980.98

r.Var.r.Var.

%21%21

r.Var.r.Var.

%17%17

Corr Corr

with NCPI with NCPI

0.610.61

Winter (DJF)Winter (DJF) – – precipitationprecipitation


Winter (DJF)Winter (DJF) – – streamflowstreamflow


Spring (MAM)Spring (MAM) – – streamflowstreamflow

NCPI ile Pearson korelasyonlarıNCPI ile Pearson korelasyonları

Summer (JJA)Summer (JJA) – – precipitationprecipitation


Fall (SON)Fall (SON) – – precipitationprecipitation


SummarySummary

NCPNCP effective mostly in winter effective mostly in winter NCP(+) enhances precip at Black Sea NCP(+) enhances precip at Black Sea

shorelineshoreline NCP(-) enhances precip at western NCP(-) enhances precip at western

provincesprovinces February, NCP(+), (subtropical jet)February, NCP(+), (subtropical jet) Some peculiar locations (e.g. Artvin, Some peculiar locations (e.g. Artvin,

Sinop)Sinop) NCP effective also in summer NCP effective also in summer

Future studies?Future studies?

• NCP and NAO??? A combined index?NCP and NAO??? A combined index?

• Is NCP predictable???Is NCP predictable???

THANKSTHANKS

north sea - caspian pattern (ncp) and its influence on the hydroclimate of turkey

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