The complex dynamics of the seasonal component of USA's surface temperature

Vecchio, A.; Capparelli, V.; Carbone, V.

doi:10.5194/acp-10-9657-2010

Cited by 21 publications

(12 citation statements)

References 44 publications

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“…An alternative method to unveil the characteristic timescales of nonstationary signals is the empirical mode decomposition (EMD) technique, introduced by Huang et al [] [see also Wu and Huang , ], as a preconditioning method for the application of the Hilbert transform. EMD is an adaptive method based on the local characteristics of the data, useful to analyze natural signals [ Vecchio et al , , , ; Alberti et al , ; Vecchio et al , ], also including geomagnetic time series [ De Michelis et al , ; De Michelis and Consolini , ]. Particularly, the EMD does not require to have any “a priori” assumption on the functional form of the basis of the decomposition.…”

Section: Methodsmentioning

confidence: 99%

Timescale separation in the solar wind‐magnetosphere coupling during St. Patrick's Day storms in 2013 and 2015

et al. 2017

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In this work, we present a case study of the relevant timescales responsible for coupling between the changes of the solar wind and interplanetary magnetic field (IMF) conditions and the magnetospheric dynamics during the St. Patrick's Day Geomagnetic Storms in 2013 and 2015. We investigate the behavior of the interplanetary magnetic field (IMF) component Bz, the Perreault‐Akasofu coupling function and the AE, AL, AU, SYM‐H, and ASY‐H geomagnetic indices at different timescales by using the empirical mode decomposition (EMD) method and the delayed mutual information (DMI). The EMD, indeed, allows to extract the intrinsic oscillations (modes) present into the different data sets, while the DMI, which provides a measure of the total amount of the linear and nonlinear shared information (correlation degree), allows to investigate the relevance of the different timescales in the solar wind‐magnetosphere coupling. The results clearly indicate the existence of a relevant timescale separation in the solar wind‐magnetosphere coupling. Indeed, while fluctuations at long timescales (τ > 200 min) show a large degree of correlation between solar wind parameters and magnetospheric dynamics proxies, at short timescales (τ < 200 min) this direct link is missing. This result suggests that fluctuations at timescales lower than 200 min, although triggered by changes of the interplanetary conditions, are mainly dominated by internal processes and are not directly driven by solar wind/IMF. Conversely, the magnetospheric dynamics in response to the solar wind/IMF driver at timescales longer than 200 min resembles the changes observed in the solar wind/IMF features. Finally, these results can be useful for Space Weather forecasting.

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Section: Methodsmentioning

confidence: 99%

Timescale separation in the solar wind‐magnetosphere coupling during St. Patrick's Day storms in 2013 and 2015

et al. 2017

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“…The trends are identified through the empirical mode decomposition (EMD) technique, developed to process nonlinear and nonstationary data [ Huang et al , ; Vecchio and Carbone , ], and successfully applied in many different fields [ Loh et al , ; Echeverria et al , ; Coughlin et al , ; Vecchio et al , ; Laurenza et al , ; Capparelli et al , ; Lee and Ouarda , , ]. EMD decomposes a time series into a finite number of intrinsic mode functions (IMFs) and a residual by using an adaptive basis derived from the time series through a so‐called “sifting” process, namely,

T (t) = \sum_{j = 0}^{m - 1} θ_{j} (t) + r_{normalm} (t),

where T denotes the temperature time series and each IMF θ j ( t ) and residual r m ( t ) are time‐dependent.…”

Section: Methodsmentioning

confidence: 99%

“…In a temperature record, the main modulations are the daily oscillation (due to the difference of temperature from day to night) and the annual oscillation (due to seasonality). If we apply the EMD decomposition on temperature time series with a sampling rate less than 1 year, then a single IMF mode will turn out to be dominant compared to the rest of the IMF modes [ Vecchio et al , ], making the iteration to identify the EMD residual very sensitive to the sifting conditions. For this reason, we decided to use the annual mean of the temperature time series for each station.…”

Section: Methodsmentioning

confidence: 99%

A spatiotemporal analysis of U.S. station temperature trends over the last century

et al. 2013

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[1] This study presents a nonlinear spatiotemporal analysis of 1167 station temperature records from the United States Historical Climatology Network covering the period from 1898 through 2008. We use the empirical mode decomposition method to extract the generally nonlinear trends of each station. The statistical significance of each trend is assessed against three null models of the background climate variability, represented by stochastic processes of increasing temporal correlation length. We find strong evidence that more than 50% of all stations experienced a significant trend over the last century with respect to all three null models. A spatiotemporal analysis reveals a significant cooling trend in the South-East and significant warming trends in the rest of the contiguous U.S. It also shows that the warming trend appears to have migrated equatorward. This shows the complex spatiotemporal evolution of climate change at local scales.

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“…Qian et al (2011) note that such a-priori defined seasonal structures might underestimate nonlinear climatic variations and propose the usage of adaptive and temporally local methods such as empirical mode decomposition (EMD) and ensemble EMD. In an analysis of seasonal components of temperature records, Vecchio et al (2010) showed that there has been a good agreement of the estimated phase shift of temperature using EMD and the estimate of Stine et al (2009). Because of the simplicity of the method of Stine et al (2009) and good agreement with more complex methods, this method has been used to estimate the annual phases of temperature and the runoff ratio in this analysis.…”

Section: Annual Periodic Signal Extractionmentioning

confidence: 99%

Long term variability of the annual hydrological regime and sensitivity to temperature phase shifts in Saxony/Germany

Renner

Bernhofer

2011

Hydrol. Earth Syst. Sci.

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Abstract.Recently, climatological studies report observational evidence of changes in the timing of the seasons, such as earlier timing of the annual cycle of surface temperature, earlier snow melt and earlier onset of the phenological spring season. Also hydrological studies report earlier timing and changes in monthly streamflows. From a water resources management perspective, there is a need to quantitatively describe the variability in the timing of hydrological regimes and to understand how climatic changes control the seasonal water budget of river basins.Here, the timing of hydrological regimes from 1930-2009 was investigated in a network of 27 river gauges in Saxony/Germany through a timing measure derived by harmonic function approximation of annual periods of runoff ratio series. The timing measure proofed to be robust and equally applicable to both mainly pluvial river basins and snow melt dominated regimes.We found that the timing of runoff ratio is highly variable, but markedly coherent across the basins analysed. Differences in average timing are largely explained by basin elevation. Also the magnitude of low frequent changes in the seasonal timing of streamflow and the sensitivity to the changes in the timing of temperature increase with basin elevation. This sensitivity is in turn related to snow storage and release, whereby snow cover dynamics in late winter explain a large part of the low-and high-frequency variability.A trend analysis based on cumulative anomalies revealed a common structural break around the year 1988. While the timing of temperature shifted earlier by 4 days, accompanied by a temperature increase of 1 K, the timing of runoff ratio Correspondence to: M. Renner (maik.renner@mailbox.tu-dresden.de) within higher basins shifted towards occurring earlier about 1 to 3 weeks. This accelerated and distinct change indicates, that impacts of climate change on the water cycle may be strongest in higher, snow melt dominated basins.

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The complex dynamics of the seasonal component of USA's surface temperature

Cited by 21 publications

References 44 publications

Timescale separation in the solar wind‐magnetosphere coupling during St. Patrick's Day storms in 2013 and 2015

Timescale separation in the solar wind‐magnetosphere coupling during St. Patrick's Day storms in 2013 and 2015

A spatiotemporal analysis of U.S. station temperature trends over the last century

Long term variability of the annual hydrological regime and sensitivity to temperature phase shifts in Saxony/Germany

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