Influence of the atmospheric blocking on the hydrometeorological variables from the Danube basin and...

Influence of the atmospheric blocking on the hydrometeorological variables from the Danube basin and possible response to the solar/geomagnetic activity

Ileana Mares1, Venera Dobrica1, C. Demetrescu1, C. Mares2 1 Institute of Geodynamics, Romanian Academy, Bucharest, Romania

2 National Institute of Hydrology and Water Management, Bucharest, Romania

Abstract 1-2

METHODS

Solar/Geomagnetic Signal

Large/regional scale

Conclusion

DATA

Main goal: To test the signal of atmospheric circulation at the large scale (Atlantico European region) on the hydroclimatic variables in the Danube basin and to see if exist some links between solar/geomagnetic activity and the atmopheric variables.

Data analysis is achieved at:At large/regional scale. Four blocking indices were considered for the regions: Greenland

(GBI), Atlantic-European (AEBI), Atlantic (ABI) and Europe (EBI). In addition, an index for Greenland-Balkan Oscillation (GBOI) was introduced. Climate variables at the regional, defined at 15 stations : precipitation, temperature, drought indices quantified by four indices of Palmer type, and a simple drought index calculated only from precipitation and temperature.

Local scale. For the Danube lower basin (DLB) discharge, we have considered the Orsova station as representative for this basin. Orsova is situated at the Danube entrance into the DLB. The Orsova discharge is an integrator of the discharges from Danube upper and middle basin (DUMB).

Solar activity was represented by Wolf numbers and 10.7cm solar flux and the geomagnetic activity by the aa index.

The analysis was achieved for two periods:1901-2000 and 1948-2000, separately for each season.

Methods : The time series of temperatures and precipitation were represented by the first principal component (PC1) of the development in empirical orthogonal functions (EOFs) and the four Palmer indices were analyzed by the PC1 of the development in multivariate EOFs (MEOFs). Cross correlations, low pass filter, power spectra and composite maps were performed.

5 10 15 20 25 3042

43

44

45

46

47

48

49

50

51

52

1-Augsburg

2-Innsbruk

3-Regensburg

4-Sonnblick

5-Salzburg

6-Kredarika

7-Ljubljana

8-Graz

9-Zagreb

10-Wien

11-Sarajevo

12-Osijek13-Novi-Sad

14-Beograd

15-Arad

* Orsova

Longitude

Lat

itud

e

1313

1/4

15 stations upstream of Orsova hydrological stationGBOI


Ileana Mares1, Venera Dobrica1, C. Demetrescu1, C. Mares 2

1 Institute of Geodynamics, Romanian Academy, Bucharest, Romania2 National Institute of Hydrology and Water Management, Bucharest, Romania

Abstract 1-2

METHODS



Conclusion

DATA

We have demonstrated that Greenland Balkan Oscillation Index (GBOI) is a better predictor than North Atlantic Oscillation index (NAOI), with the strong relationship with winter precipitation for analyzed area. From how GBO and NAO indices are defined, they have opposite signs. From the detailed analysed on the stations revealed that the GBOI signal is stronger than NAO signal, except for the first stations located in the upper basin of the Danube. Also in winter, GBOI has a statistically significant signal in the time series of PC1-MEOF, i .e. the state of drought or moisture in the upper and middle Danube basin. Spatial distribution from SLP composites for the extremes clearly show that winter GBOI, is a good predictor for the extremes states of the moisture in the Danube basin.

5 10 15 20 25 3042

43

44

45

46

47

48

49

50

51

52

1-Augsburg

2-Innsbruk

3-Regensburg

4-Sonnblick

5-Salzburg

6-Kredarika

7-Ljubljana

8-Graz

9-Zagreb

10-Wien

11-Sarajevo


14-Beograd

15-Arad

* Orsova

Longitude

Lati

tud

e

1313

2/4

http://thumbs.dreamstime.com/z/flaming-sun-12127799.jpg


Ileana Mares 1, Venera Dobrica 1, C. Demetrescu 1, C. Mares 2 1 Institute of Geodynamics, Romanian Academy, Bucharest, Romania


Abstract 1-2

METHODS



Conclusion

DATA

By eliminating other causes such as ENSO, i.e. by applying a low pass filter to eliminate periods of less than 8 years, the significant signals of the 10.7cm solar flux were found in the time series of AEBI (winter), of Orsova discharge (spring ) with a lag = - 3, and in GBOI (summer) with a lag = - 2. In these analyzes, negative lags correspond to variables terrestrial taken before the solar or geomagnetic indices. From the cross-correlation analysis with a lag of 5-years, between the hydro-atmospheric variables and the geomagnetic or solar activity, were obtained very different results, depending on the season and variables analyzed.

.

5 10 15 20 25 3042

43

44

45

46

47

48

49

50

51

52

1-Augsburg

2-Innsbruk

3-Regensburg

4-Sonnblick

5-Salzburg

6-Kredarika

7-Ljubljana

8-Graz

9-Zagreb

10-Wien

11-Sarajevo


14-Beograd

15-Arad

* Orsova

Longitude

La

titu

de

1313

3/4

-5 -4 -3 -2 -1 0 1 2 3 4 5-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Lag (AEBI - WIN ; Flux 10.7cm - WIN)

Corre

latio

n Co

effic

ient

unfiltered

filtered

99%

99%

95%

95%

0 5 10 15 20 25 30 35 40 45 50-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Time

Am

plitude

FLUX 10.7cm-WIN-STD (1951-2000)

AEBI-WIN-F8 (1948-1997)





Abstract 1-2

METHODS



Conclusion

DATA

By MTM procedure, the power spectra have highlighted both quasi-periodicities related to solar activity and the other oscillations such as QBO. For example in the time series of AEBI (spring), the most significant periodicity is related to QBO (2.2 years) and with an approximately 90% confidence level there is a peak at 10 years, which may be linked to 11-year solar cycle.

For example in the time series of AEBI (spring), the most significant periodicity is related to QBO (2.2 years) and with an approximately 90% confidence level there is a peak at 10 years, which may be linked to 11-year solar cycle.

5 10 15 20 25 3042

43

44

45

46

47

48

49

50

51

52

1-Augsburg

2-Innsbruk

3-Regensburg

4-Sonnblick

5-Salzburg

6-Kredarika

7-Ljubljana

8-Graz

9-Zagreb

10-Wien

11-Sarajevo


14-Beograd

15-Arad

* Orsova

Longitude

Lati

tud

e

1313

4/4

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

1

2

3

4

5

6

99%

95%

90%

Frequency (cycles / year)

Po

we

r sp

ectr

al d

en

sity

Periodogram

3.8 y

10.0 y

2.2 y2.2 y2.2 y2.2 y

0 0.1 0.2 0.3 0.4 0.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

99%

95%

90%


Coh

ere

nce

0 0.1 0.2 0.3 0.4 0.5

-100

-80

-60

-40

-20

0

20

40

60

80

100

Pha

se(D

eg

)





Data (1901-2000) regional scaleSince the Danube discharge estimation has great importance for the economic sector of Romania, in the present investigation we focused on predictors for Danube lower basin discharge.The lower basin Danube discharge was evidenced by Orsova station, located at the entrance of the Danube in Romania and representing an integrator of the upper and middle basin. The precipitation and temperature fields represented by 15 stations upstream of Orsova and located for the most part in the middle Danube basin. For the 1901-2000, were used series of precipitation and average monthly air temperature provided by the Climatic Research Unit (CRU) TS3.10.01 (http://climexp.knmi.nl). Datasets are calculated on high-resolution (0.5 x 0.5 degree) grids by the CRU.The four Palmer indices or_PDSI, or_PHDI, or_WPLM, or_ZIND (“or” stands for “original”) were calculated on a monthly basis using a routine developed by Nathan Wells from the National Agricultural Decision Support System (http://nadss.unl.edu). The routine takes as inputs the precipitation, temperature and available water capacity (AWC). The AWC values were extracted from the Harmonised World Soil Database (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012) for each of the 15 stations in our study. Also, for each station was calculated a simple drought index (TPPI), which depend only on precipitation and temperature.All analyses were achieved using the seasonal averages for all variables considered in this study.

DATA

Iteration 2Iteration 2

http://climexp.knmi.nl/

http://nadss.unl.edu/


Ileana Mares1, Venera Dobrica1, C. Demetrescu 1, C. Mares2


Data (1901-2000) large scaleIn order to see the influence of large-scale atmospheric circulation on the variables at the regional scale, we considered the seasonal mean values of sea level pressure field (SLP) on the sector (50°W-40°E, 30°–65°N). We first attempted extracting SLP values from ERA-40, which is a re-analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) in collaboration with many institutions (Uppala et al. 2005). As the ERA-40 data starts from 1958, we had to extract SLP data for the remaining period from the National Center for Atmospheric Research (NCAR), (http://rda.ucar.edu/datasets/ds010.1). As mentioned in the associated documentation, this dataset contains the longest continuous time series of monthly gridded Northern Hemisphere sea-level pressure data in the DSS archive. The 5-degree latitude/longitude grids, computed from the daily grids in ds010.0, begin in 1899 and cover the Northern Hemisphere from 15°N to the North Pole. The accuracy and quality of this data is discussed in Trenberth and Paolino (1980).We found a new index started from tests achieved using correlative analysis between the first principal component of EOF development of the precipitation field defined at 15 stations from Danube basin and each grid point where SLP is defined. By determining the centers of inverse correlation nuclei (positive and negative) and by considering the normalized differences between SLP at Nuuk and Novi Sad, we obtained this index, which we called Greenland-Balkan-Oscillation index (GBOI). This index was applied in (Mares et al. 2013,2014).For this 100 year period the solar/geomagnetic activities were quantified by Wolf number and aa index.

Iteration 3Iteration 3Iteration 1Iteration 1

http://rda.ucar.edu/datasets/ds010.1

http://dss.ucar.edu/datasets/ds010.0/




Data (1948-2000)

For 1948-2000 period beside of precipitation, temperature, discharge in the Danube lower basin and atmospheric index (GBOI), we introduced and blocking indices. For the geopotential at 500 hPa (1948-2000) provided by British Atmospheric Data Centre (BADC) three sectors were taken into account: Atlantic-European (AE) on the domain (50°W- 40°E; 35°N - 65°N), Atlantic (A) defined in (50°W - 0°, 35°N - 65°N) and European (E) in the region (0° - 40°E; 35°N - 65°N). The geopotential field was filtered by blocking index (IB) as is described in Lejenas and Okland (1983). A measure of blocking over Greenland (GBI) obtained from 500 mb geopotential height area averaged 60°- 80°N, 280°- 340°E (NCEP/NCAR Reanalysis) was extracted from http://www.esrl.noaa.gov/psd/data/correlation/gbi.ncep.dayFor the period 1948-2000, solar forcing is quantified by the 10.7 cm solar flux in stead of Wolf number.Since the 10.7cm flux is a more objective measurement, and always measured on the same instruments, this proxy "sunspot number" should have a similar behaviour but smaller intrinsic scatter than the true sunspot number. ftp://ftp.ngdc.noaa.gov/STP/SOLAR_DATA/

Iteration 2Iteration 2 X

http://www.esrl.noaa.gov/psd/data/correlation/gbi.ncep.day

ftp://ftp.ngdc.noaa.gov/STP/SOLAR_DATA/




Method (General Methods) The time series of temperature and precipitation were filtered by the first principal component (PC1) of empirical orthogonal functions (EOFs)

development and four indices Palmer were represented by their overall characteristics, i.e. PC1 development in multivariate EOFs (MEOFs).In Mares et al. (2009, 2014), the efficiency of the MEOFs approach when considering more than two climate fields has been demonstrated.

The 500 hPa geopotential field (Φ ) was filtered by blocking index (IB) as is described in Lejenas and Okland (1983), as a mean for λ longitudes:IB (λ) = Φ (λ, 57.50 N) - Φ (λ, 37.50 N)Cross correlations, power spectra, composite maps and filters were also performed. Low pass filters were applied to eliminate oscillations due to other factors (eg ENSO) than the possible influence of solar/ geomagnetic activities. The

Mann filter (Mann, 2004, 2008) was applied with three variants that eliminate frequencies corresponding the periods lower than 8, 10 and 20 years. The analysis revealed that from the three variants, time series cutoff 8 and 10 years responded best to variations in solar / geomagnetic activities.

As is known in the literature the response of climate variables to the solar/geomagnetic activity is evidenced not only simultaneously but also certain differences, we performed cross - correlation with a lag of 5 years. Explanation of the physical mechanism of correlations with certain lags between solar activity and climate variables is found in Gray et al. (2013) and Scaife et al. (2013).

METHODS





Methods (background spectra)

Testing the statistical significance of the peaks obtained from an analysis of a time series by power spectra is usually done by building a reference spectrum (background) and comparing the amplitude spectrum analyzed time series based spectrum amplitudes. This spectrum is a series based on white noise or most often a red noise series (Ghil et al. 2002, Torrence and Campo, 1998). At first all amplitudes above the background noise amplitudes are considered significant. But to test how significant are these peaks are testing their statistical significance compared with different levels of significance desired.

A significance test requires null hypothesis significance. For spectral analysis, the null hypothesis is that the time series has no significant peak and spectral estimation differs from the noise spectrum (background). Rejection of the null hypothesis means accepting peaks of the spectrum series of observations that exceed a certain level of significance.

As shown in Mann and Less (1996) theoretical justifications exist for considering red noise as noise reference (background) for climate and hydrological time series.

According to Torrence and Compo (1998) a simple red noise model is an autoregressive process to lag - 1 (AR (1) or Markov) as:

(1)

where α is the lag-1 autocorrelation, x0 =0, si zn is considered Gaussian white noise.

nnn zxx 1





According to Gilman et al. (1963), the power spectrum of the series (1) after normalization is

(2)where k=0, ..., N/2 is the frequency index, N is the length of the series of observations. For α = 0 in (2), we obtain the spectrum of white noise.In applications (Torrence and Compo, 1998), in the spectrum (2) α is estimated as a average between the autocorrelation at lag-1 and lag-2 as follows: (3)If the power spectrum is obtained by a single estimate using classical simple method, i.e. using a fast Fourier transform (FFT), and assuming that the power spectrum of the series is normally distributed, then its square has a distribution with two degrees of freedom. To determine the level of significance, multiply the power spectrum of the background series given by (2) with the percentiles (eg. 95%) corresponding to the two degrees of freedom.


)/2cos(21

12

2

NkPk

2/)( 21




Methods (Significance of power spectra peaks)

The power spectra achieved in this study were estimated by multitaper method (MTM) (Ghil et al., 2002, Mann and Less (1996)). MTM method is a nonparametric technique that does not require a priori a model for the generation of time series analysis, while harmonic spectral analysis assumes that the data generation process include components purely periodic and white noise which are overlapped (Ghil et al., 2002).

In the analysis by MTM, the parameter representing bandwidth that refers to the frequency-time is defined by nw = p. The number of tapers K = 2p-1.The power spectrum of the time series analyzed by MTM is assumed according to Mann and Less (1996) that is distributed as Hi 2 with ν degrees of freedom, where ν is the number of degrees of freedom in spectral estimation. In the analysis by MTM number of degrees of freedom is about twice the adaptive multitaper's used (K).

Reference spectrum (2) is assumed to have a distribution Hi 2 /ν and it is compared with the values tabulated to determine the significance of analyzed time series peaks. In the choice of the number of tapers (K), in agreement with Ghil et al. (2002) and Mann and Less (1996), for the instrumental climate observations, K = 3 is a good compromise between frequency resolution required and highlighting distinct climatic signals.

Iteration 4Iteration 4Iteration 2Iteration 2

http://en.wiktionary.org/wiki/%CE%BD






Methods ( Applications –Examples)

Spectra were estimated by pmtm routine from MATLAB, which is based on the work of Thomson (1982). For example, we will present some of the power spectra and different confidence levels chosen 80%, 90%, 95% and 99%.Power spectrum (Fig. E1) for annual values of Orsova discharge (1880-2007) was estimate by MTM with K=3. From initial series linear trend was removed. Solid red curve represents the red noise reference, for which the confidence levels (80%, 90% and 95%) were calculated. The significance periods for geomagnetic index aa (1901-2000) for fall and spring are given in Fig. E2 (a,b). Estimations are obtained for nfft =128.

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

0.5

1

1.5

2

2.5

3

3.5

4

4.5

95%

90%

80%

3.7y21.3y 14.2y4.9y

2.4y

Pow

er spectra

Frequency (cycles /year)

Fig. E1. Power spectrum (black curve) for annual discharge values Orsova and background spectrum (solid red).





0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

1

2

3

4

5

6

7

8

9

10

99%

95%

90%


Po

we

r sp

ectr

al d

en

sity

Periodogram

16.7y10 y

4.2 y3.2 y 2.8y 2.5y

a) FALL

Methods ( Applications –Example)




0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

1

2

3

4

5

6

7

8

9

10

99%

95%

90%


Po

we

r sp

ectr

al d

en

sity

Periodogram

11 y

9 y

5.9 y 4.8 y

3.7y 2.7y

b) SPRING

Fig. E2. Power spectra for aa (1901-2000).Iteration 5Iteration 5





Methods (Spectral Coherence- Phase)

Magnitude-squared coherence Cxy is a function of frequency with values between 0 and 1.In practice, according with Ghil et al. (2002) Cxy has to be computed by using some kind of time-frequency ensemble averaging. Multitaper method with K tapers provides a practical way to compute Cxy by using the individual DFT estimates Xk and Yk of data x and y tapered with k-th taper:

The confidence limits of estimated coherence at a given level can be calculated as:

In our case K=3 and confidence levels (CL) have the values:CL(99%)=0.900CL(99%)= 0.7764CL(99%)= 0.6838ExampleThe Mann filter (low pass) applied to Blocking Index, with a cutoff corresponding to 8 year, led to a good coherence between solar flux and Blocking index over Atlantic European region during spring ( Fig. E3). R=0.36 (99% CL)

K

kkK

K

kkk

K

kkkK

XY

fYfX

fYfX

fC

1

2

1

2

2

1

*

)(.)(

)().(

)(

)1/(11 K





0 0.1 0.2 0.3 0.4 0.50

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

99%

95%

90%


Coh

ere

nce

0 0.1 0.2 0.3 0.4 0.5

-100

-80

-60

-40

-20

0

20

40

60

80

100

Phase(D

eg)

Fig. E3. Coherence and Phase for Solar flux and Atlantic European Blocking Index during SPRING (1948-2000).

X





Solar activity was represented by Wolf numbers for the period 1901-2000 and by 10.7-cm solar flux for the period 1948-2000. Although the solar flux is closely correlated with Wolf numbers, these values are not identical, the correlation coefficient varying with the season (0.98-0.99). The geomagnetic activity was quantified by aa index for the two periods analyzed (1901-2000 and 1948-2000). Regarding the link between solar activity and geomagnetic, details are found in Demetrescu and Dobrica (2008).Solar/geomagnetic signal was tested by: correlative analyses (simultaneous and cross correlation), composite maps and spectral analyses.The most significant results were obtained for the filtered terrestrial variables, taken with some lags related to solar or geomagnetic activity.The AEBI time series is filtered, cutoff to the frequencies corresponding to the period lower than 8 years. The correlation coefficient R= - 0.62 for Lag=- 3, namely the correlation is maximum when AEBI (Fig. S1) is taken with three years before the solar flux (Fig.S1 b). For GBOI (summer) R=0.69 and Lag= - 2 (Fig. S2), and for Orsova discharge (spring) R=0.59 at Lag= - 3 (Fig. S3).

SOLAR/GEOMAGNETIC SIGNAL




-5 -4 -3 -2 -1 0 1 2 3 4 5-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Lag (AEBI - WIN ; Flux 10.7cm - WIN)

Corr

ela

tio

n C

oe

ffic

ien

t

unfiltered

filtered

99%

99%

95%

95%

0 5 10 15 20 25 30 35 40 45 50-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Time

Am

plitude

FLUX 10.7cm-WIN-STD (1951-2000)

AEBI-WIN-F8 (1948-1997)

a)

b)

Fig. S1. Cross correlation between solar flux 10.7cm and Atlantic European blocking index (AEBI) during winter.




-5 -4 -3 -2 -1 0 1 2 3 4 5-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Lag (GBOI -SUM ; Flux 10.7-SUM)

Corr

ela

tio

n C

oe

ffic

ien

t

95%99%

95%

99%

unfiltered

filtered

0 5 10 15 20 25 30 35 40 45 50-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Time

Am

plitude

FLUX 10.7cm-SUM-STD (1948-2000)

GBOI-SLP-SUM-F8 (1946-1998)

b)Fig. S2. Cross correlation between solar flux 10.7cm and GBOI during summer.

a)




-5 -4 -3 -2 -1 0 1 2 3 4 5-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Lag (Q-Ors-SPR; Flux 10.7-SPR)

Cor

rela

tio

n C

oeff

icie

nt

unfiltered

filtered

95%

95%

99%

99%

0 5 10 15 20 25 30 35 40 45 50-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Time

Am

plitude

FLUX 10.7cm-SPR-STD (1951-2000)

ORS-SPR-STD-F8 (1948-1997)

b)

Fig. S3. Cross correlation between solar flux 10.7cm and Orsova discharge during spring.

a)




By MTM procedure, the power spectra have highlighted both quasi-periodicities related to solar activity and the other oscillations such as QBO.For example in the time series of AEBI (spring), the most significant periodicity is related to QBO (2.2 years) and with an approximately 90% confidence level is a peak at 10 years, which may be linked to 11-year solar cycle.

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

1

2

3

4

5

6

99%

95%

90%


Po

we

r sp

ectr

al d

en

sity Periodogram

3.8 y

10.0 y

2.2 y2.2 y2.2 y2.2 y

Fig. S4.

Power spectrum for spring AEBI (1948-2000).




Fig. S5. Composite maps for winter SLP anomalies, associated with different solar activities from period 1901-2000.

X




We have demonstrated that Greenland Balkan Oscillation Index (GBOI) is a better predictor than North Atlantic Oscillation index (NAOI), with the strong relationship with winter precipitation for analyzed area. Correlations between GBOI and precipitation are stable over time (Table 1, Fig.1). The details on the stations are given in Fig.2. From how GBO and NAO indices are defined, they have opposite signs. It is clear that the GBOI signal is stronger than NAO signal, except for the first stations located in the upper basin of the Danube. Also in winter, GBOI has a statistically significant signal in the time series of PC1-MEOF, i.e. the state of drought or moisture in the upper and middle Danube basin (Fig. 3). Spatial distribution from SLP composites for the extremes (Fig.4) clearly show that winter GBOI, is a good predictor for the extremes states of the moisture in the Danube basin.Since the Danube discharge estimation in spring season has great importance for the economic sector of Romania, the best predictors with one season anticipation (winter) were revealed, with high confidence level (> 99%): precipitation (Fig.5), GBOI and European blocking index (EBI) (Fig.6).

Table 1. Correlation coefficient between first principal component (PC1) for the precipitation and atmospheric indices NAO and GBO, during winter

Period NAOI GBOI

1916-1957 -0.36 0.75

1958-1999 -0.43 0.84

LARGE / REGIONAL SCALE




-5

-4

-3

-2

-1

0

1

2

3

4

5

1958

1959

1960

1961

1962

1963

1964

1965

1966

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Year

Am

plitu

de

GBOI_ERA40 PC1_PP_OBS

Fig.1. Winter precipitation PC1 versus winter GBOI for 1958-1999 (R=0.84).




Fig.2. Correlation coefficients between winter precipitation at 15 stations and NAOI and GBOI for two periods: a) 1916-1957; b) 1958-1999.The correlations between PC1-PP and two indices are marked by horizontal lines.




-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

1901

1904

1907

1910

1913

1916

1919

1922

1925

1928

1931

1934

1937

1940

1943

1946

1949

1952

1955

1958

1961

1964

1967

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

Year

Am

plitu

de

PC1-MEOF-WIN GBOI-WIN5 per. Mov. Avg. (GBOI-WIN) 5 per. Mov. Avg. (PC1-MEOF-WIN)

Fig. 3. GBOI winter values in comparison with PC1-MEOF. The trends are estimated by the moving average with the span of 5. (R=0.55).




a)




FIG. 4 . Composite map of winter SLP (departures from average) corresponding to the extreme states of PC1-MEOF: a) State 1 (extreme droughts); b) State 5 (extremely wet)

b)




Fig. 5. Scatter plot of Orsova spring discharge versus inter PC1 – PP in the 1958-1999.




Fig.6. Spring Orsova discharge versus winter European blocking index (R= - 0.54) and winter GBOI (R=0.53) for the period 1948-2000.

X




CONCLUSIONSWe have demonstrated that Greenland Balkan Oscillation Index (GBOI) is a better predictor than North Atlantic Oscillation index

(NAOI), with the strong relationship with winter precipitation for analyzed area. From how GBO and NAO indices are defined, they have opposite signs. From the detailed analysed on the stations revealed that the GBOI signal is stronger than NAO signal, except for the first stations located in the upper basin of the Danube. Also in winter, GBOI has a statistically significant signal in the time series of PC1-MEOF, ie the state of drought or moisture in the upper and middle Danube basin (Fig. 3).

Spatial distribution from SLP composites for the extremes (Fig.4) clearly show that winter GBOI, is a good predictor for the extremes states of the moisture in the Danube basin.

Since the Danube discharge estimation in spring season has great importance for the economic sector of Romania, the best predictors with a season anticipation (winter) were revealed, with high confidence level (> 99%): precipitation (Fig.5), GBOI and European blocking index (EBI) (Fig.6).

From the analysis of cross correlation with a lag of five years between atmospheric variables considered in this study and solar/geomagnetic activity, we obtained very different results, depending on the season and on the considered variables. By eliminating other causes such as ENSO, i.e. by applying a low pass filter to eliminate periods of less than 8 years, the significant signals os the 10.7cm solar flux were found in the time series of AEBI (winter), of Orsova discharge (spring ) with a lag = -3, and in GBOI (summer) with a lag = -2. In these analyzes, negative lags correspond to variables terrestrial taken before the solar or geomagnetic indices.

By MTM procedure, the power spectra have highlighted both quasi-periodicities related to solar activity and the other oscillations such as QBO.In the time series of AEBI (spring), the most significant periodicity is related to QBO (2.2 years) and with an approximately 90% confidence level is a peak at 10 years, which may be linked to 11-year solar cycle.

X

Influence of the atmospheric blocking on the hydrometeorological variables from the Danube basin and...

Documents

Transcript of Influence of the atmospheric blocking on the hydrometeorological variables from the Danube basin and...