A non-uniqueness problem in the identification of power-law spectral scaling for hydroclimatic time series

Fleming, Sean W.

doi:10.1080/02626667.2013.851384

Cited by 9 publications

(9 citation statements)

References 56 publications

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“…The PDO appears to undergo rapid transitions between extended periods of opposite phase every few decades or so (e.g., Ebbesmeyer et al 1991;Graham 1994;Mantua et al 1997;Minobe 1997;Fleming 2009;Minobe 1999), as denoted by the green lines in Fig. 6h.…”

Section: A Empirical Autoregressive Modelsmentioning

confidence: 99%

“…10b). Also, how well each extended AR1 model 1901-2009, CMIP3 over 1900-99, CMIP5 over 1901-2004, and LENS over 1920-2005 For seasonal correlations, positive lags indicate that the Niño-3.4 or PNA index leads the seasonal PDO index, and the label along the abscissa indicates the season for which the PDO is defined.…”

Section: B Pdo Representation By Coupled Climate Modelsmentioning

confidence: 99%

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The Pacific Decadal Oscillation, Revisited

et al. 2016

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The Pacific decadal oscillation (PDO), the dominant year-round pattern of monthly North Pacific sea surface temperature (SST) variability, is an important target of ongoing research within the meteorological and climate dynamics communities and is central to the work of many geologists, ecologists, natural resource managers, and social scientists. Research over the last 15 years has led to an emerging consensus: the PDO is not a single phenomenon, but is instead the result of a combination of different physical processes, including both remote tropical forcing and local North Pacific atmosphere-ocean interactions, which operate on different time scales to drive similar PDO-like SST anomaly patterns. How these processes combine to generate the observed PDO evolution, including apparent regime shifts, is shown using simple autoregressive models of increasing spatial complexity. Simulations of recent climate in coupled GCMs are able to capture many aspects of the PDO, but do so based on a balance of processes often more independent of the tropics than is observed. Finally, it is suggested that the assessment of PDO-related regional climate impacts, reconstruction of PDO-related variability into the past with proxy records, and diagnosis of Pacific variability within coupled GCMs should all account for the effects of these different processes, which only partly represent the direct forcing of the atmosphere by North Pacific Ocean SSTs.

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Section: A Empirical Autoregressive Modelsmentioning

confidence: 99%

Section: B Pdo Representation By Coupled Climate Modelsmentioning

confidence: 99%

The Pacific Decadal Oscillation, Revisited

et al. 2016

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“…Mudelsee (2007) demonstrated via observations and modeling that the Hurst phenomenon arises progressively downstream because of the aggregation of discharge variations from separate tributaries. Fleming (2014), using annual observations of Thames discharge for 1883-2011, showed there were insufficient data to either demonstrate or rule out a power law. We invoke the explanation of the Hurst phenomenon by Mudelsee (2007) and interpret the lack of a power law in Fig.…”

Section: A Observed Discharge Versus Observed Precipitationmentioning

confidence: 99%

“…However, many studies have inferred a power-law character from the power spectra of monthly and annual discharge data from large basins (e.g., Pelletier and Turcotte 1997). Such an interpretation implies long-term memory associated with the Hurst phenomenon (Hurst 1951;Mesa and Poveda 1993;Heneghan and McDarby 2000;Schepers et al 1992;Bryce and Sprague 2012;Fleming 2014).…”

Section: A Observed Discharge Versus Observed Precipitationmentioning

confidence: 99%

Evaluating the Performance of Hydrological Models via Cross-Spectral Analysis: Case Study of the Thames Basin, United Kingdom

Weedon

Prudhomme

Crooks

et al. 2015

Journal of Hydrometeorology

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Nine distributed hydrological models, forced with common meteorological inputs, simulated naturalized daily discharge from the Thames basin for . While model-dependent evaporative losses are critical for modeling mean discharge, multiple physical processes at many time scales influence the variability and timing of discharge. Here the use of cross-spectral analysis is advocated to measure how the average amplitude-and independently, the average phase-of modeled discharge differ from observed discharge at daily to decadal time scales. Simulation of the spectral properties of the model discharge via numerical manipulation of precipitation confirms that modeled transformation involves runoff generation and routing that amplify the annual cycle, while subsurface storage and routing of runoff between grid boxes introduces most of the autocorrelation and delays. Too much or too little modeled evaporation affects discharge variability, as do the capacity and time constants of modeled stores. Additionally, the performance of specific models would improve if four issues were tackled: 1) nonsinusoidal annual variations in model discharge (prolonged low base flow and shortened high base flow; three models), 2) excessive attenuation of highfrequency variability (three models), 3) excessive short-term variability in winter half years but too little variability in summer half years (two models), and 4) introduction of phase delays at the annual scale only during runoff generation (three models) or only during routing (one model). Cross-spectral analysis reveals how reruns of one model using alternative methods of runoff generation-designed to improve performance at the weekly to monthly time scales-degraded performance at the annual scale. The cross-spectral approach facilitates hydrological model diagnoses and development.

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“…These are: (i) Identification of leading-lagging relations between two cyclic time series that impact each other, and (ii) the identification of common cycles for the paired series. The method is different from the ordinary cross correlation techniques (e.g., as applied to the Atlantic Meridional Overturning Circulation (AMOC) in Escudier et al [1] or to PDO in Fleming [38]). The method does not require the series to be stationary, not even over a short interval.…”

Section: Methodsmentioning

confidence: 99%

Atmospheric and Ocean Dynamics May Explain Cycles in Oceanic Oscillations

Seip

Grøn

2019

Climate

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What causes cycles in oceanic oscillations, and is there a change in the characteristics of oscillations in around 1950? Characteristics of oceanic cycles and their sources are important for climate predictability. We here compare cycles generated in a simple model with observed oceanic cycles in the great oceans: The North Atlantic Oscillation (NAO), El Niño, the Southern Oscillation Index (SOI), and the Pacific Decadal Oscillation (PDO). In the model, we let a stochastic movement in one oceanic oscillation cause a similar but lagging movement in another oceanic oscillation. The two interacting oscillations show distinct cycle lengths depending upon how strongly one oscillation creates lagging cycles in the other. The model and observations both show cycles around two to six, 13 to 16, 22 to 23, and 31 to 32 years. The ultimate cause for the distinct cycles is atmospheric and oceanic “bridges” that connect the ocean basins, but the distinct pattern in cycle lengths is determined by properties of statistical distributions. We found no differences in the leading or lagging strength between well separated basins (the North Atlantic and the Pacific) and overlapping ocean basins (both in the Pacific). The cyclic pattern before 1950 appears to be different from the cyclic pattern after 1950.

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A non-uniqueness problem in the identification of power-law spectral scaling for hydroclimatic time series

Cited by 9 publications

References 56 publications

The Pacific Decadal Oscillation, Revisited

The Pacific Decadal Oscillation, Revisited

Evaluating the Performance of Hydrological Models via Cross-Spectral Analysis: Case Study of the Thames Basin, United Kingdom

Atmospheric and Ocean Dynamics May Explain Cycles in Oceanic Oscillations

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