Disentangling the confounding effects of PAR and air temperature on net ecosystem exchange at multiple time scales

2014 ◽  
Vol 19 ◽  
pp. 46-58 ◽  
Author(s):  
Zutao Ouyang ◽  
Jiquan Chen ◽  
Richard Becker ◽  
Housen Chu ◽  
Jing Xie ◽  
...  
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Net Ecosystem Exchange ◽  
Multiple Time Scales ◽  
Multiple Time

scholarly journals Biosphere-atmosphere exchange of CO<sub>2</sub> in relation to climate: a cross-biome analysis across multiple time scales

2009 ◽  
Vol 6 (10) ◽  
pp. 2297-2312 ◽  
Author(s):  
P. C. Stoy ◽  
A. D. Richardson ◽  
D. D. Baldocchi ◽  
G. G. Katul ◽  
J. Stanovick ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Time Scales ◽  
Ecosystem Respiration ◽  
Climatic Variability ◽  
Low Frequency ◽  
Net Ecosystem Exchange ◽  
Ecosystem Model ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Terrestrial Carbon

Abstract. The net ecosystem exchange of CO2 (NEE) varies at time scales from seconds to years and longer via the response of its components, gross ecosystem productivity (GEP) and ecosystem respiration (RE), to physical and biological drivers. Quantifying the relationship between flux and climate at multiple time scales is necessary for a comprehensive understanding of the role of climate in the terrestrial carbon cycle. Orthonormal wavelet transformation (OWT) can quantify the strength of the interactions between gappy eddy covariance flux and micrometeorological measurements at multiple frequencies while expressing time series variance in few energetic wavelet coefficients, offering a low-dimensional view of the response of terrestrial carbon flux to climatic variability. The variability of NEE, GEP and RE, and their co-variability with dominant climatic drivers, are explored with nearly one thousand site-years of data from the FLUXNET global dataset consisting of 253 eddy covariance research sites. The NEE and GEP wavelet spectra were similar among plant functional types (PFT) at weekly and shorter time scales, but significant divergence appeared among PFT at the biweekly and longer time scales, at which NEE and GEP were relatively less variable than climate. The RE spectra rarely differed among PFT across time scales as expected. On average, RE spectra had greater low frequency (monthly to interannual) variability than NEE, GEP and climate. CANOAK ecosystem model simulations demonstrate that "multi-annual" spectral peaks in flux may emerge at low (4+ years) time scales. Biological responses to climate and other internal system dynamics, rather than direct ecosystem response to climate, provide the likely explanation for observed multi-annual variability, but data records must be lengthened and measurements of ecosystem state must be made, and made available, to disentangle the mechanisms responsible for low frequency patterns in ecosystem CO2 exchange.

scholarly journals Biosphere-atmosphere exchange of CO<sub>2</sub> in relation to climate: a cross-biome analysis across multiple time scales

2009 ◽  
Vol 6 (2) ◽  
pp. 4095-4141 ◽  
Author(s):  
P. C. Stoy ◽  
A. D. Richardson ◽  
D. D. Baldocchi ◽  
G. G. Katul ◽  
J. Stanovick ◽  
...  
Keyword(s):  
Time Scales ◽  
Ecosystem Respiration ◽  
Climatic Variability ◽  
Low Frequency ◽  
Net Ecosystem Exchange ◽  
Ecosystem Model ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Multiple Frequencies ◽  
Low Dimensional

Abstract. The biosphere-atmosphere flux of CO2 responds to climatic variability at time scales from seconds to years and longer. Quantifying the strength of the interaction between the flux and climate variables at multiple frequencies is necessary to begin understanding the climatic controls on the dynamics of the terrestrial carbon cycle. Orthonormal wavelet transformation (OWT) can quantify the interaction between flux and microclimate at multiple frequencies while expressing time series variance in few energetic wavelet coefficients, offering a low-dimensional view of the measured climate-flux interaction. The variability of the net ecosystem exchange of CO2 (NEE), gross ecosystem productivity (GEP) and ecosystem respiration (RE), and their co-variability with dominant climatic drivers, are explored with a global dataset consisting of 253 eddy covariance research sites. Results demonstrate that the NEE and GEP wavelet spectra are similar amongst plant functional types (PFT) at weekly and shorter time scales, but significant divergence appeared among PFT at the biweekly and longer time scales, at which NEE and GEP are relatively less variable than climate. The RE spectra rarely differ among PFT across time scales. On average, RE spectra had greater low frequency (monthly to interannual) variability than NEE, GEP and climate. The low frequency Fourier coefficients of eight sites with more than eight years of data were compared against CANOAK ecosystem model simulations. Both measurements and theory demonstrate that "multi-annual" spectral peaks in flux may emerge at low (4+ years) time scales. Biological responses to climate and other internal system dynamics provide the likely explanation for observed multi-annual variability, but data records must be lengthened and measurements of ecosystem state must be made, and made available, to disentangle the mechanisms responsible for these patterns.

SOME REMARKS ON THE MULTIPLE TIME SCALES OF DIFFUSION IN MINERALS AND MELTS

2018 ◽  
Author(s):  
Yan Liang ◽  
◽  
Daniele J. Cherniak ◽  
Chenguang Sun
Keyword(s):  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time

scholarly journals Are We Doing ‘Systems’ Research? An Assessment of Methods for Climate Change Adaptation to Hydrohazards in a Complex World

2019 ◽  
Vol 11 (4) ◽  
pp. 1163 ◽  
Author(s):  
Melissa Bedinger ◽  
Lindsay Beevers ◽  
Lila Collet ◽  
Annie Visser
Keyword(s):  
Climate Change ◽  
Human Nature ◽  
Climate Change Adaptation ◽  
Time Scales ◽  
Spatial Scales ◽  
Local Context ◽  
Multiple Time Scales ◽  
Future Research ◽  
Multiple Time ◽  
Systems Research

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

scholarly journals A multistate model incorporating estimation of excess hazards and multiple time scales

2021 ◽  
Vol 40 (9) ◽  
pp. 2139-2154
Author(s):  
Caroline E. Weibull ◽  
Paul C. Lambert ◽  
Sandra Eloranta ◽  
Therese M. L. Andersson ◽  
Paul W. Dickman ◽  
...  
Keyword(s):  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Multistate Model

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

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