Introductory Overview

Author(s):  
Douglas G. Goodin

Timescale is the organizing framework of this volume. In various sections, we consider the effects of climate variability on ecosystems at timescales ranging from weeks or months to centuries. In part III, we turn our attention to interdecadal-scale events. The timescales we consider are not absolutely defined, but for our purposes we define the interdecadal scale to encompass effects occurring with recurring cycles generally ranging from 10 to 50 years. A recurring theme in many of the chapters in this section is the effect on ecosystem response of teleconnection patterns associated with recognized quasi-periodic atmospheric circulation modes. These circulation modes include the well-known El Niño– Southern Oscillation (ENSO) phenomenon, which is generally thought to recur at shorter, interdecadal timescales but also includes some longer-term periodicities. Several other climate variability modes, including the Pacific North American index (PNA), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and North Pacific index (NP) also show strong interdecadal scale signatures and figure prominently in the chapters of part III. McHugh and Goodin begin the section by examining the climate record at several North American LTER sites for evidence of interdecadal-scale fluctuation. They note that interdecadal-scale contributions to climate variability can best be described in terms of two types of variation: (1) discontinuities in mean value, and (2) the presence of trends in the data. Evaluation of interdecadal periodicities in LTER data is complicated by the relatively short time series of observations available. McHugh and Goodin approach the problem mainly through the use of power spectrum analysis, a widely used tool for evaluating the periodicity in a time series of data. Principal components analysis is used to decompose the time series of growing-season climate data for each of the LTER sites into their principal modes of variability. These modes are then subjected to power spectrum analysis to evaluate the proportions of the variance in the data occurring at various timescales. McHugh and Goodin’s results suggest that significant effects on precipitation and temperature at interdecadal timescales are uncommon in these data, although significant periodicities at both shorter and longer frequencies do emerge from the data (a finding of relevance to other sections of this volume).

2008 ◽  
Vol 08 (03n04) ◽  
pp. L401-L407
Author(s):  
LUCIANO TELESCA ◽  
ROSA CAGGIANO ◽  
VINCENZO LAPENNA ◽  
MICHELE LOVALLO ◽  
SERENA TRIPPETTA ◽  
...  

The temporal fluctuations of particulate matter time series of three reference European stations have been investigated, by using the power spectrum analysis. Our results point out to the presence in particulate matter of annual periodicities superimposed on a scaling behaviour with exponent ranging between ~1.4 and ~1.6, indicating quite high persistent correlations. Furthermore, a crossover timescale at about 1 month, evidenced in all the signals analysed, could be linked with chemical-physical processes in which aerosol particles are involved during their atmospheric lifetimes.


2011 ◽  
Vol 4 (1) ◽  
pp. 96-100 ◽  
Author(s):  
K. M. Hossain ◽  
◽  
D. N. Ghosh ◽  
K. Ghosh ◽  
A. K. Bhattacharya ◽  
...  

2021 ◽  
Author(s):  
Mark D. Risser ◽  
Michael F. Wehner ◽  
John P. O’Brien ◽  
Christina M. Patricola ◽  
Travis A. O’Brien ◽  
...  

AbstractWhile various studies explore the relationship between individual sources of climate variability and extreme precipitation, there is a need for improved understanding of how these physical phenomena simultaneously influence precipitation in the observational record across the contiguous United States. In this work, we introduce a single framework for characterizing the historical signal (anthropogenic forcing) and noise (natural variability) in seasonal mean and extreme precipitation. An important aspect of our analysis is that we simultaneously isolate the individual effects of seven modes of variability while explicitly controlling for joint inter-mode relationships. Our method utilizes a spatial statistical component that uses in situ measurements to resolve relationships to their native scales; furthermore, we use a data-driven procedure to robustly determine statistical significance. In Part I of this work we focus on natural climate variability: detection is mostly limited to DJF and SON for the modes of variability considered, with the El Niño/Southern Oscillation, the Pacific–North American pattern, and the North Atlantic Oscillation exhibiting the largest influence. Across all climate indices considered, the signals are larger and can be detected more clearly for seasonal total versus extreme precipitation. We are able to detect at least some significant relationships in all seasons in spite of extremely large (> 95%) background variability in both mean and extreme precipitation. Furthermore, we specifically quantify how the spatial aspect of our analysis reduces uncertainty and increases detection of statistical significance while also discovering results that quantify the complex interconnected relationships between climate drivers and seasonal precipitation.


2010 ◽  
Vol 34 (2) ◽  
pp. 121-127 ◽  
Author(s):  
P.A. Sturrock ◽  
J.B. Buncher ◽  
E. Fischbach ◽  
J.T. Gruenwald ◽  
D. Javorsek II ◽  
...  

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