Multi-Annual Variability of Discharges in LPB and its Relationship With Climatic Indices

1. 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 Instituto de Mecánica de los Fluidos e Ingeniería Ambiental (IMFIA), Facultad de Ingeniería, Universidad de la República, Uruguay fmaciel@fing.edu.uy, adiaz@fing.edu.uy, rterra@fing.edu.uy 1. DATA & METHODS 2. RESULTS • 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 correlations between rivers and indices reconstructions (RCs obtained with SSA) for a common frequency band. • 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 additivecomponents. 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. LFC 3.2-3.8 and about 6 years 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%. Pseudo-periods (years) 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 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. 8-9 years Calendar Aug-Jul Calendar Aug-Jul Sept-Aug Aug-Jul Aug-Jul Calendar Calendar Calendar Aug-Jul 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%. Correlation: 0.74 Low Frequency Variability “An oscillation in the global climate system of period 65-70 years.” Schlesinger and Ramankutty, 1994. 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. Annual AMO LFC. Tropical Atlantic SST index vs. AMO index, Seager et al, 2010 4. REFERENCES 3. CONCLUSIONS • 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. • 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.