Causality of the Drought in the Southwestern United States Based on Observations

Zhang, Feng; Lei, Yadong; Yu, Qiu-Run; Fraedrich, Klaus; Iwabuchi, Hironobu

doi:10.1175/jcli-d-16-0601.1

Cited by 12 publications

(10 citation statements)

References 30 publications

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“…Slow Feature Analysis (SFA) is a representative algorithm that aims to extract the signal of driving forces from nonstationary time series (Konen and Koch 2011;Wiskott 2003). SFA was first applied in the neurobiology field, and then successfully introduced into climate science (Wang, Yang, and Zhou 2016;Yang et al 2016;Wang, Yang, and Zhou 2017;Zhang et al 2017). SFA-derived signal can be regarded as the combined effect of different driving factors.…”

Section: Introductionmentioning

confidence: 99%

Introducing driving-force information increases the predictability of the North Atlantic Oscillation

Pan

Wang

Yang

2019

Atmospheric and Oceanic Science Letters

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The North Atlantic Oscillation (NAO) is the most prominent mode of atmospheric variability in the Northern Hemisphere. Because of the close relationship between the NAO and regional climate in Eurasia, North Atlantic, and North America, improving the prediction skill for the NAO has attracted much attention. Previous studies that focused on the predictability of the NAO were often based upon simulations by climate models. In this study, the authors took advantage of Slow Feature Analysis to extract information on the driving forces from daily NAO index and introduced it into phase-space reconstruction. By computing the largest Lyapunov exponent, the authors found that the predictability of daily NAO index shows a significant increase when its driving force signal is considered. Furthermore, the authors conducted a short-term prediction for the NAO by using a global prediction model for chaotic time series that incorporated the driving-force information. Results showed that the prediction skill for the NAO can be largely increased. In addition, results from wavelet analysis suggested that the driving-force signal of the NAO is associated with three basic drivers: the annual cycle (1.02 yr), the quasi-biennial oscillation (QBO) (2.44 yr), and the solar cycle (11.6 yr), which indicates the critical roles of the QBO and solar activities in the predictability of the NAO.

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Section: Introductionmentioning

confidence: 99%

Introducing driving-force information increases the predictability of the North Atlantic Oscillation

Pan

Wang

Yang

2019

Atmospheric and Oceanic Science Letters

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“…Slow feature analyses Land 2023, 12, 817 2 of 18 cannot separate forcing into climate-and land cover-induced causes but can identify the processes responsible for the long-term rates of change (or time derivatives) by separating the slowly evolving driving forces from fast varying signals; this is a time series diagnostic of the climate-induced large scale attribution. First, analyses have been applied to single climate-related variables (see [6,7]); only Zhang et al [7] focus on rainfall forcing as the water supply on a regional or watershed scale.…”

Section: Introductionmentioning

confidence: 99%

“…This is in contrast (or in comparison) to a causality analysis based on slow feature analysis, which is employed directly to the time series of surface water and energy fluxes (and not their changes) which represent the sources of energy and the water cycle, that is, net radiation and precipitation. Here, a statistical forecast model needs to be constructed to provide the required time series of state changes, before it can be subjected to the spectral causality analysis (by wavelets, see [6], not unlike the final step following the attribution analysis.…”

Section: Introductionmentioning

confidence: 99%

Attribution and Causality Analyses of Regional Climate Variability

Cai

Fraedrich

Sielmann

et al. 2023

Land

Self Cite

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A two-step attribution and causality diagnostic is designed by employing singular spectrum analysis to unfold the attributed climate time series into a trajectory matrix and then subjected to an empirical orthogonal function analysis to identify the evolving driving forces, which can finally be related to major climate modes through their independent frequencies by wavelet analysis. Application results from the arid and drought-prone southern Intermountain region of North America are compared with the climate or larger scale forcing diagnosed from slow feature analysis using the sources of the water and energy flux balance. The following results are noted: (i) The changes between the subsequent four 20-year periods from 1930 to 2010 suggest predominantly climate-induced forcing by the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. (ii) Land cover influences on the changing land cover are of considerably smaller magnitude (in terms of area percentage cover) whose time evolution is well documented from forestation documents. (iii) The drivers of the climate-induced forcings within the last 20 years are identified as the quasi-biennial oscillation and the El Niño–Southern Oscillation by both the inter-annual two-step attribution and the causality diagnostics with monthly scale-based slow feature analysis.

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“…The influences of large-scale climate modes (e.g., El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO) and the Atlantic Multi-decadal Oscillation (AMO)) on the variations of regional-to-global climate (e.g. temperature, rainfall, and atmospheric circulations) have been extensively examined (Bradley et al, 1987;Wu et al, 2003;McCabe et al, 2004;Kenyon and Hegerl, 2008;Steinman et al, 2015;Wang et al, 2016;2017;Zhang et al, 2017;Xie et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Considering that the driving-force signal of dynamic system often consists of different components with various time scales, Pan et al (2017) detected the independent driving-force factors that contain significant peak-periods from the SFA-extracted signals robustly through combing the SFA with wavelet analysis (Torrence et al, 1998). Recently, this kind of technique that combines the SFA with wavelet analysis also has been successfully applied to detect the external and internal driving-forces signals responsible for the variations of regional climate, such as the drought variability in the southwestern United States (Zhang et al, 2017), the temperature variations in the Central England and the Northern Hemisphere (Yang et al, 2016), and the oscillations of stratospheric ozone concentration (Wang et al, 2016). Thus, it is reasonable to anticipate that this new approach can serve for the study of the interconnections among major climate modes and their primary driving factors.…”

Section: Introductionmentioning

confidence: 99%

On the interconnections among major climate modes and their common driving factors

Pan¹,

Wang²,

Yang³

et al. 2019

Preprint

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Abstract. The variations in oceanic and atmospheric modes on various timescales play important roles in generating regional and global climate variability. Many efforts have been devoted to identify the relationships between the variations in climate modes and regional climate variability, but rarely explored the interconnections among these climate modes. Here we use climate indices to represent the variations in major climate modes and we examine the harmonic relationship among the driving forces of climate modes by the combination of Slow Feature Analysis (SFA) and wavelet analysis. We find that all of the significant peak-periods of driving-force signals in the climate indices can be represented as the harmonics of four base periods: 2.32 yr, 3.90 yr, 6.55 yr and 11.02 yr. We infer that the period of 2.32 yr is associated with the signal of Quasi Biennial Oscillation (QBO). The periods of 3.90 yr and 6.55 yr are connected with the intrinsic variability of El Niño-Southern Oscillation (ENSO), and the period of 11.02 yr arises from the sunspot cycle. Results suggest that the base periods and their harmonic oscillations linked to QBO, ENSO and solar activities act as the key connections among the climatic modes with synchronous behaviors, highlighting the important roles of these three oscillations in the variability of current climate.

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Causality of the Drought in the Southwestern United States Based on Observations

Cited by 12 publications

References 30 publications

Introducing driving-force information increases the predictability of the North Atlantic Oscillation

Introducing driving-force information increases the predictability of the North Atlantic Oscillation

Attribution and Causality Analyses of Regional Climate Variability

On the interconnections among major climate modes and their common driving factors

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