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NORTH SEA - CASPIAN PATTERN (NCP) and its influence on the hydroclimate of Turkey. OZAN MERT GÖKTÜRK İ TÜ EURASIA INSTITUTE OF EARTH SC IENCES. Contents. A re view of NCP Data sets and methodology Results: effects of NCP Problems and discussion. - PowerPoint PPT Presentation
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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
Pearson’s correlations with NCPIPearson’s correlations with NCPI
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
Pearson’s correlations with NCPIPearson’s correlations with NCPI
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
Pearson’s correlations with NCPIPearson’s correlations with NCPI
MarchMarch – precipitation – precipitation
Pearson’s correlations with NCPIPearson’s correlations with NCPI
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
Pearson’s correlations with NCPIPearson’s correlations with NCPI
AugustAugust – – precipitationprecipitation
Pearson’s correlations with NCPIPearson’s correlations with NCPI
September September – – precipitationprecipitation
Pearson’s correlations with NCPIPearson’s correlations with NCPI
October - precipitationOctober - precipitation
Pearson’s correlations with NCPIPearson’s correlations with NCPI
NovemberNovember - precipitation- precipitation
Pearson’s correlations with NCPIPearson’s correlations with NCPI
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
Pearson’s correlations with NCPIPearson’s correlations with NCPI
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
Pearson’s correlations with NCPIPearson’s correlations with NCPI
Winter (DJF)Winter (DJF) – – streamflowstreamflow
Pearson’s correlations with NCPIPearson’s correlations with NCPI
Spring (MAM)Spring (MAM) – – streamflowstreamflow
NCPI ile Pearson korelasyonlarıNCPI ile Pearson korelasyonları
Summer (JJA)Summer (JJA) – – precipitationprecipitation
Pearson’s correlations with NCPIPearson’s correlations with NCPI
Fall (SON)Fall (SON) – – precipitationprecipitation
Pearson’s correlations with NCPIPearson’s correlations with NCPI
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