Global Journal of Science Frontier Research, H: Environment & Earth Science, Volume 22 Issue 5

differences rapidly amplify over time) most geophysical time series are more appropriately called “chaotic” than “deterministic” (MCWILLIAMS, 2006). Vallis (2016) also discusses the role of GFD in understanding the natural environment in the search for the phenomenological essence; noting that complex systems of interaction present “fluid dynamics emergent phenomena” (which emerge from the collective behavior of the system's constituents – not being a property of its components). According to the author, “at each new level of complexity, new properties arise that do not depend on the details of the underlying laws and qualitatively different behavior occurs” (VALLIS, 2016). As some of the main goals (and past triumphs) of the GFD are in the explanation of “emergent fluid dynamic phenomena,” Vallis (2016) presents two reasons why such phenomena should be understood: a) the scientific understanding of the natural world is “an end in himself,” which affords admiration and respect, in proportion to his greatness; b) the understanding of phenomena enables better prevention, and finally, practical social benefits (public policies can be implemented with a focus on atmospheric and oceanic dynamics, for example). In many geophysical systems of interest, information about systemic behavior can be obtained by quasi-direct numerical simulation of governing equations, for Vallis (2016). In this sense, GFD describes a method and an object of study (VALLIS, 2016). It turns out that there is little scientific understanding regarding the intricate details of ocean circulation, the limits of its biological productivity, its interaction with the atmosphere, or its tolerance for waste dumped by the growing human population (KANTHA and CLAYSON, 2000a; KANTHA and CLAYSON, 2000b). The task of faithfully modeling the circulation of the oceans (across the spectrum of their temporal and spatial variability), for example, is highly complex, challenging, and arduous – meticulous attention to detail is required, both dynamically and numerically (KANTHA and CLAYSON, 2000a; KANTHA and CLAYSON, 2000b). IV. P lausibility of R are Event by G eophysical P rocess (for S cenario) In a world of great uncertainty, scenarios are tools for achieving a long-term vision, and a method for understanding, articulating and moving between the different possible future paths, from “[...] stories that can help us recognize and adapt to changing aspects of our current environment” (SCHWARTZ, 1996). “The study of scenario-based planning is the study of learning and invention” (VAN DER HEIJDEN, 2005). There is a debate among scenario planners about the preference for plausible or probabilistic methodologies, according to Ramirez And Selin (2014). As the two species have incompatible conceptions (about knowledge and uncertainty), both can be approached by critical and etymological examining (from techniques and tools, schools of thought, criteria of effectiveness, epistemological and ontological differences) (RAMIREZ and SELIN, 2014). Regarding the etymology of the words “plausibility” and “probability,” Ramirez and Selin (2014) address three different historical periods: a) in the first period (until the 16th century), both terms were used confusingly, for its origin (derived from classical Latin) – probability denoted “seeming true” (a perception associated with “likelihood”), and plausibility “seeming reasonable or probable” (a perception about “false appearance”); b) in the second period (17th century), probability started to be seen in a more scientific way, related to observational rigor; c) in the third period (from the 18th century onwards), probability becomes a central aspect of statistics (mathematically defined); and plausibility continued in its original sense of providing "the appearance of credibility and reasonableness" (RAMIREZ and SELIN, 2014). The probabilistic approach for scenarios is deductive, positivist, and reductive, approximating absolute claims by exclusion and simplification (captured in formulas, statistics, and regressions) through a scientific, predictive vision, that providing the measurement of occurrences based on a degree of objective and rational belief. However, “this approach is based on facts, which are all in the past” (RAMIREZ and SELIN, 2014). On the other hand, “plausibility is considered a characteristic of credible cause and effect relationships” which, and not objectively, proposes “new beliefs” (RAMIREZ and SELIN, 2014). Probabilistic scenarios would be epistemologically close to a prediction (RAMIREZ and SELIN, 2014). But prediction only makes sense in a domain where probabilities can be evaluated. Another issue regarding forecasts is the fact that, generally, people assimilate a specific routine and settle down, according to Van der Heijden (2005) (in reality, there is always a point in time when structural changes will occur, and behavior will have to change). “Forecasts can work very well for a period, but forecasters need to be aware of variables that will suddenly break the relationship with the past, creating a trend break” (VAN DER HEIJDEN, 2005). For plausibility, the scenarios would only help, in intrinsically uncertain and unpredictable situations, according to Ramirez and Selin (2014). “The final confrontation concerns the roles of creativity and codified knowledge in alternative futures, making scenario planning something that involves “art” or “science” (RAMIREZ and SELIN, 2014). © 2022 Global Journals 1 Global Journal of Science Frontier Research Volume XXII Issue V Year 2022 22 ( H ) Version I Autonomous Technology in Scenario by Rare Geophysical Processes (Underwater Focus)