Dataset Information

Factors affecting ENSO predictability in a linear empirical model of tropical air-sea interactions.

ABSTRACT: Understanding and extending the predictability of El Niño?Southern Oscillation (ENSO) has been an important research topic because of ENSO's large influence on global weather and climate. Here, we develop an empirical model of tropical atmosphere-ocean interactions that has high ENSO prediction skill, comparable to the skills of well performing dynamical models. The model is used to investigate the effects of the main atmosphere-ocean interaction processes-thermocline and zonal wind feedbacks and zonal wind forcing-on its ENSO predictability. We find that all these processes significantly affect ENSO predictability and extend the predictability limit by up to four months, with the largest effect coming from the thermocline feedback followed by the total zonal wind forcing. The other processes with progressively smaller effects are the external zonal wind forcing and zonal wind feedback. The two most influential processes, however, affect ENSO predictability in the VAR model differently. The thermocline feedback improves the forecast skill by predominantly maintaining the correct phase, whereas the total zonal wind forcing improves the skill by maintaining the correct amplitude of the forecast ENSO events. This result suggests that the dynamical seasonal prediction models must have good representations of the major ENSO processes to make skilful ENSO predictions.

SUBMITTER: Rashid HA

PROVIDER: S-EPMC7054313 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Factors affecting ENSO predictability in a linear empirical model of tropical air-sea interactions.

Rashid Harun A HA

Scientific reports 20200303 1

Understanding and extending the predictability of El Niño‒Southern Oscillation (ENSO) has been an important research topic because of ENSO's large influence on global weather and climate. Here, we develop an empirical model of tropical atmosphere-ocean interactions that has high ENSO prediction skill, comparable to the skills of well performing dynamical models. The model is used to investigate the effects of the main atmosphere-ocean interaction processes-thermocline and zonal wind feedbacks an ...[more]

PMID: 32127554

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Project description:BackgroundMobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians' participation in mHealth apps.ObjectiveThis study aims to empirically explore factors affecting physicians' usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings.MethodsWe collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians' usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias.ResultsOur results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians' cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention.ConclusionsThis study contributes to the existing literature on UTAUT2 extension of physicians' acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians' usage behaviors on mHealth apps in China and provide such apps' managers with an insight into the promotion of physicians' active acceptance and usage behaviors.

Dataset Information

Factors affecting ENSO predictability in a linear empirical model of tropical air-sea interactions.

Publications

Factors affecting ENSO predictability in a linear empirical model of tropical air-sea interactions.

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