Multi-Annual Variability of Discharges in LPB and its Relationship With Climatic Indices
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Multi-Annual Variability of Discharges in LPB and its Relationship With Climatic Indices Fernanda Maciel, Alvaro Díaz, Rafael Terra Instituto de Mecánica de los Fluidos e Ingeniería Ambiental (IMFIA), Facultad de Ingeniería, Universidad de la República, Uruguay [email protected], [email protected], [email protected] 1. DATA & METHODS 2. RESULTS 3. CONCLUSIONS Data Monthly discharges of: Negro River (at Rincón del Bonete dam) 1908-2007 Uruguay River (at Salto Grande dam) 1909-2007 Paraná River (at Posadas) 1901-1999 Global climatic indices (annual series): PDO: Pacific decadal oscillation SAM: Southern Annular Mode) NAO: North Atlantic Oscillation AMO: Atlantic Multidecadal Oscillation Niño 3.4 Methods SSA: Singular Spectrum Analysis (Vautard et al., 1992; Ghil et al., 2002). Decompose the time series in data adaptive additive components. This components can be low frequency components (LFC) , oscillatory patterns (not necessarily harmonic) and noise. A Monte Carlo (MC) test is used to analyze the significance level of each pattern. MEM: Maximum Entropy Method (Penland et al., 1991; Vautard et al., 1992). Gives the power spectrum and is more efficient with narrow band time series (i.e. SSA reconstructed components – RCs). MTM: Multi Taper Method (Vautard et al., 1992; Ghil et al., 2002). Gives the power spectrum of a time series. The results for annual series are presented in the following figure, where red markers indicate that the pseudo-period are captured with the three methods, black markers with two methods and the blue ones indicate that it appears in only one method. The low frequency component (LFC) detected is related, in this work, with variability associated to periods larger than 30 years. The temporal bands where both rivers and indices present significant patterns are: 2.2-2.6 years 3.2-3.8 years About 6 years 8-9 years LFC (>30 years) We next compute the reconstructions (RCs obtained with SSA) for a common frequency band. 3.2-3.8 and about 6 years 8-9 years Correlations NAO NAO Lagged AMO (Aug- Jul) Negro River (annual) 0.17 0.55 -0.42 Trimester MJJ of Negro River 0.34 0.73 -0.57 Trimester JJA of Negro River 0.35 0.51 -0.28 Filtered Uruguay River (annual) 0.58 0.74 -0.72 Filtered Paraná River (annual) 0.80 0.39 -0.60 N egro N egro U ruguay U ruguay P araná N iño 3.4 PDO NAO SAM AMO AMO 0 2.5 5 7.5 10 12.5 15 17.5 20 22.5 25 CBF Pseudo-períodos (años) Calendar Calendar Aug- Jul Aug-Jul Calendar Calendar Calendar Aug- Jul Aug-Jul Aug-Jul Sept-Aug Pseudo-periods (years) LFC 3.2-3.8 years In all tables, bold values indicate that the correlations are 90% significant using a Student test of two tails. No significant correlations are obtained between rivers and SAM or PDO RCs in this frequency band. For river annual series, significant correlations are obtained with NAO, AMO and N3.4. For the latter, higher values are obtained for Uruguay and Paraná rivers (~ 0.7). In this frequency band rivers and N3.4 RCs explain between 6% and 11% of their original time series variance. AMO and NAO RCs explain only 3%. Uruguay and Paraná rivers do not present significant components between 8-9 years. Only after removing ENSO components (3.2-3.8 and about 6 years) from the original series, significant patterns of 8-9 years appear when applying SSA. Correlations with NAO and AMO (Aug-Jul) have opposite signs (their raw time series are negatively correlated) and higher values are obtained with ENSO- filtered series of Uruguay and Paraná rivers. The results obtained for Paraná River are consistent with previous results of Robertson and Mechoso (1998). If NAO RC is shifted a year forward, correlations are higher, except for filtered Paraná River RC. This could indicate the existence of interannual “predictability” for the components at this temporal band. The explained variance of the original annual time series in this frequency band is about 8-10% for both rivers and indices RCs, except for filtered Paraná River RC, which explains 15.1%. Low Frequency Variability Correlations NAO SAM AMO Negro River (annual) 0.51 - 0.17 - 0.12 Uruguay River (annual) 0.67 0.03 - 0.21 Paraná River (annual) 0.64 0.03 - 0.37 It is found that the AMO LFC is in phase with a tropical Atlantic and AMO indices used by Seager et al (2010), and with an oscillation in the global climate system obtained by Schlesinger and Ramankutty (1994), which is mainly related to anomalies in the Atlantic SST (left figure). In addition, the AMO RC explains 50.8% of the original time series variance. NAO and SAM LFCs explain the 5.3% (NAO) and 7.9% (SAM) of their original time series variance. For the rivers annual series LFCs, the explained variance is: 19.5% (Paraná), 10.9% (Uruguay) and 6.8% (Negro). Correlations with NAO and AMO LFCs are significant, being positive for the former and negative for the latter. The higher values are achieved for Uruguay and Paraná rivers. “An oscillation in the global climate system of period 65- 70 years.” Schlesinger and Ramankutty, 1994. Annual AMO LFC. Tropical Atlantic SST index vs. AMO index, Seager et al, 2010 RC of AMO index associated with LFC Correlations RCs of seasonal series NAO AMO N3.4 DJF of Negro River . 0.16 0.48 FMA of Negro River -0.43 0.70 -0.43 JJA of Negro River -0.35 0.47 -0.36 JAS of Negro River -0.49 0.64 -0.53 ASO of Negro River -0.20 0.46 -0.20 SON of Uruguay River 0.43 -0.33 0.89 JAS of Paraná River 0.39 -0.27 0.69 ASO of Paraná River 0.33 -0.29 0.76 SON of Paraná River 0.21 -0.17 0.81 OND of Paraná River 0.13 -0.13 0.81 NDJ of Paraná River 0.04 -0.12 0.78 RC of N3.4 0.27 -0.41 Correlations between indices RCs and seasonal river RCs are presented in the table above. Generally, the three indices present significant correlations in the same period of the year (from late winter into spring). Correlations with N3.4 and NAO RCs have the same sign, being opposite to that obtained with AMO RC. About 6 years Although the 6-year pseudo-period is one of the most robust results for Uruguay river annual series, no significant correlations were obtained with PDO or N3.4 RCs. Both indices present significant correlations with Negro River seasonal RCs in the same season (spring and early summer). Correlations RCs of seasonal series PDO N3.4 Trimester JJA of Negro River -0.08 0.22 Trimester OND of Negro River 0.23 0.57 Trimester NDJ of Negro River 0.33 0.78 N3.4 0.63 4. REFERENCES In ENSO temporal band (3.2-3.8 and about 6 years) very higher correlations (around 0.80) are found between N3.4 and river streamflow RCs for spring and early summer. High correlations between both NAO and AMO RCs and river RCs in the 8-9 year band suggest that North Atlantic and LPB streamflow variabilities are connected in the decadal timescale. Multi-decadal variability of annual AMO index is well represented by its LFC, which explains 50.8% of the original time series variance. No apparent links were found between rivers discharges and PDO or SAM indices. Ghil, M., M. R. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, M. E. Mann, A. W. Robertson, A. Saunders, Y. Tian, F. Varadi and P. Yiou (2002). Advanced spectral methods for climatic time series. Reviews of Geophysics, 40(1), doi: 10.1029/2001RG000092. Penland, C., M. Ghil and K. M. Weickmann (1991). Adaptive filtering and maximum entropy spectra with application to changes in atmospheric anglar momentum. Journal of Geophysical, Vol. 96. Robertson, A. W. and C. R. Mechoso (1998). Interannual and Decadal Cycles in River Flows of Southeastern South America . Journal of Climate, Vol. 11. Schlesinger, M.E. and N. Ramankutty (1994). An Oscillation in the Global Climate System of Period 65-70 Years. Nature, Vol. 367. Seager, R., N. Naik, W. Baethgen, A. Robertson, Y. Kushnir, J. Nakamura and S. Jurburg (2010). Tropical oceanic causes of interannual to multidecadal precipitation variability in southeast South America over the past century. Journal of Climate, revised. Vautard, R., P. Yiou and M. Ghil (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals Physica D, Vol. 1920-1921 1940-1941 1960-1961 1980-1981 2000-2001 -3 -2 -1 0 1 2 3 Years S tandardized R C s associated w ith pseudo-periods betw een 8 and 9 years NAO R C s is lagged a year"forw ard" R C ofNAO R C ofU ruguay R iver annual filtered series Correlation: 0.74 1900 1920 1940 1960 1980 2000 -1 -0.5 0 0.5 1 1.5 2 S tandardized LFC s LFC ofNAO LFC ofN egro R iver LFC ofU ruguay R iver LFC P araná R iver 1900 1920 1940 1960 1980 2000 -1 -0.5 0 0.5 1 1.5 2 S tandardize LFC s LFC ofAM O LFC ofN egro R iver LFC ofU ruguay R iver LFC ofParaná R iver
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RC of AMO index associated with LFC. Multi-Annual Variability of Discharges in LPB and its Relationship With Climatic Indices. Fernanda Maciel, Alvaro Díaz, Rafael Terra - PowerPoint PPT Presentation
Transcript of Multi-Annual Variability of Discharges in LPB and its Relationship With Climatic Indices
Multi-Annual Variability of Discharges in LPB and its Relationship With Climatic Indices
R e d T e m á t i c a d e M e d i o A m b i e n t e
U n i v e r s i d a d d e l a R e p ú b l i c a
Fernanda Maciel, Alvaro Díaz, Rafael TerraInstituto de Mecánica de los Fluidos e Ingeniería Ambiental (IMFIA), Facultad de Ingeniería, Universidad de la República, Uruguay
[email protected], [email protected], [email protected]
1. DATA & METHODS 2. RESULTS
3. CONCLUSIONS
Data
Monthly discharges of:Negro River (at Rincón del Bonete dam) 1908-2007Uruguay River (at Salto Grande dam) 1909-2007Paraná River (at Posadas) 1901-1999
Global climatic indices (annual series): PDO: Pacific decadal oscillationSAM: Southern Annular Mode)NAO: North Atlantic OscillationAMO: Atlantic Multidecadal OscillationNiño 3.4
Methods
SSA: Singular Spectrum Analysis (Vautard et al., 1992; Ghil et al., 2002). Decompose the time series in data adaptive additive components. This components can be low frequency components (LFC) , oscillatory patterns (not necessarily harmonic) and noise. A Monte Carlo (MC) test is used to analyze the significance level of each pattern.
MEM: Maximum Entropy Method (Penland et al., 1991; Vautard et al., 1992). Gives the power spectrum and is more efficient with narrow band time series (i.e. SSA reconstructed components – RCs).
MTM: Multi Taper Method (Vautard et al., 1992; Ghil et al., 2002). Gives the power spectrum of a time series.
The results for annual series are presented in the following figure, where red markers indicate that the pseudo-period are captured with the three methods, black markers with two methods and the blue ones indicate that it appears in only one method. The low frequency component (LFC) detected is related, in this work, with variability associated to periods larger than 30 years.
The temporal bands where both rivers and indices present significant patterns are:
2.2-2.6 years3.2-3.8 yearsAbout 6 years8-9 yearsLFC (>30 years)
We next compute the correlations between rivers and indices reconstructions (RCs obtained with SSA) for a common frequency band.
3.2-3.8 and about 6 years
8-9 years Correlations NAO NAO
Lagged
AMO
(Aug-Jul)
Negro River
(annual) 0.17 0.55 -0.42
Trimester MJJ
of Negro River 0.34 0.73 -0.57
Trimester JJA
of Negro River 0.35 0.51 -0.28
Filtered Uruguay River
(annual) 0.58 0.74 -0.72
Filtered Paraná River
(annual) 0.80 0.39 -0.60
Negro Negro Uruguay Uruguay Paraná Niño 3.4 PDO NAO SAM AMO AMO0
2.5
5
7.5
10
12.5
15
17.5
20
22.5
25
CBF
Pse
udo-
perí
odos
(año
s)
Calendar Calendar Aug-Jul Aug-Jul Calendar Calendar Calendar Aug-Jul Aug-Jul Aug-JulSept-Aug
Pse
ud
o-p
erio
ds
(yea
rs)
LFC
3.2-3.8 years
In all tables, bold values indicate that the correlations are 90% significant using a Student test of two tails.
No significant correlations are obtained between rivers and SAM or PDO RCs in this frequency band.
For river annual series, significant correlations are obtained with NAO, AMO and N3.4. For the latter, higher values are obtained for Uruguay and Paraná rivers (~ 0.7).
In this frequency band rivers and N3.4 RCs explain between 6% and 11% of their original time series variance. AMO and NAO RCs explain only 3%.
Uruguay and Paraná rivers do not present significant components between 8-9 years. Only after removing ENSO components (3.2-3.8 and about 6 years) from the original series, significant patterns of 8-9 years appear when applying SSA.
Correlations with NAO and AMO (Aug-Jul) have opposite signs (their raw time series are negatively correlated) and higher values are obtained with ENSO-filtered series of Uruguay and Paraná rivers. The results obtained for Paraná River are consistent with previous results of Robertson and Mechoso (1998).
If NAO RC is shifted a year forward, correlations are higher, except for filtered Paraná River RC. This could indicate the existence of interannual “predictability” for the components at this temporal band.
The explained variance of the original annual time series in this frequency band is about 8-10% for both rivers and indices RCs, except for filtered Paraná River RC, which explains 15.1%.
Low Frequency Variability
Correlations NAO SAM AMO
Negro River (annual) 0.51 -0.17 -0.12
Uruguay River (annual) 0.67 0.03 -0.21
Paraná River (annual) 0.64 0.03 -0.37
It is found that the AMO LFC is in phase with a tropical Atlantic and AMO indices used by Seager et al (2010), and with an oscillation in the global climate system obtained by Schlesinger and Ramankutty (1994), which is mainly related to anomalies in the Atlantic SST (left figure). In addition, the AMO RC explains 50.8% of the original time series variance.
NAO and SAM LFCs explain the 5.3% (NAO) and 7.9% (SAM) of their original time series variance. For the rivers annual series LFCs, the explained variance is: 19.5% (Paraná), 10.9% (Uruguay) and 6.8% (Negro).
Correlations with NAO and AMO LFCs are significant, being positive for the former and negative for the latter. The higher values are achieved for Uruguay and Paraná rivers.
“An oscillation in the global climate system of period 65-70 years.” Schlesinger
and Ramankutty, 1994.
Annual AMO LFC.
Tropical Atlantic SST index vs. AMO index, Seager et al, 2010
RC of AMO index associated with LFC
Correlations
RCs of seasonal series
NAO AMO N3.4
DJF of Negro River . 0.16 0.48
FMA of Negro River -0.43 0.70 -0.43
JJA of Negro River -0.35 0.47 -0.36
JAS of Negro River -0.49 0.64 -0.53
ASO of Negro River -0.20 0.46 -0.20
SON of Uruguay River 0.43 -0.33 0.89
JAS of Paraná River 0.39 -0.27 0.69
ASO of Paraná River 0.33 -0.29 0.76
SON of Paraná River 0.21 -0.17 0.81
OND of Paraná River 0.13 -0.13 0.81
NDJ of Paraná River 0.04 -0.12 0.78
RC of N3.4 0.27 -0.41
Correlations between indices RCs and seasonal river RCs are presented in the table above. Generally, the three indices present significant correlations in the same period of the year (from late winter into spring).
Correlations with N3.4 and NAO RCs have the same sign, being opposite to that obtained with AMO RC.
About 6 years
Although the 6-year pseudo-period is one of the most robust results for Uruguay river annual series, no significant correlations were obtained with PDO or N3.4 RCs.
Both indices present significant correlations with Negro River seasonal RCs in the same season (spring and early summer).
Correlations
RCs of seasonal series
PDO N3.4
Trimester JJA of Negro River -0.08 0.22
Trimester OND of Negro River 0.23 0.57
Trimester NDJ of Negro River 0.33 0.78
N3.4 0.63
4. REFERENCES
In ENSO temporal band (3.2-3.8 and about 6 years) very higher correlations (around 0.80) are found between N3.4 and river streamflow RCs for spring and early summer.
High correlations between both NAO and AMO RCs and river RCs in the 8-9 year band suggest that North Atlantic and LPB streamflow variabilities are connected in the decadal timescale.
Multi-decadal variability of annual AMO index is well represented by its LFC, which explains 50.8% of the original time series variance.
No apparent links were found between rivers discharges and PDO or SAM indices.
Ghil, M., M. R. Allen, M. D. Dettinger, K. Ide, D. Kondrashov, M. E. Mann, A. W. Robertson, A. Saunders, Y. Tian, F. Varadi and P. Yiou (2002). Advanced spectral methods for climatic time series. Reviews of Geophysics, 40(1), doi: 10.1029/2001RG000092.Penland, C., M. Ghil and K. M. Weickmann (1991). Adaptive filtering and maximum entropy spectra with application to changes in atmospheric anglar momentum. Journal of Geophysical, Vol. 96.Robertson, A. W. and C. R. Mechoso (1998). Interannual and Decadal Cycles in River Flows of Southeastern South America . Journal of Climate, Vol. 11.Schlesinger, M.E. and N. Ramankutty (1994). An Oscillation in the Global Climate System
of Period 65-70 Years. Nature, Vol. 367. Seager, R., N. Naik, W. Baethgen, A. Robertson, Y. Kushnir, J. Nakamura and S. Jurburg (2010). Tropical oceanic causes of interannual to multidecadal precipitation variability in southeast South America over the past century. Journal of Climate, revised. Vautard, R., P. Yiou and M. Ghil (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals Physica D, Vol. 58.
1920-1921 1940-1941 1960-1961 1980-1981 2000-2001-3
-2
-1
0
1
2
3
Years
Standardized RCs associated with pseudo-periods between 8 and 9 yearsNAO RCs is lagged a year "forward"
RC of NAORC of Uruguay Riverannual filtered series
Correlation: 0.74
1900 1920 1940 1960 1980 2000
-1
-0.5
0
0.5
1
1.5
2
Standardized LFCs
LFC of NAOLFC of Negro RiverLFC of Uruguay RiverLFC Paraná River
1900 1920 1940 1960 1980 2000
-1
-0.5
0
0.5
1
1.5
2
Standardize LFCs
LFC of AMOLFC of Negro River
LFC of Uruguay RiverLFC of Paraná River
