Would be the Atmosphere Chaotic?

Abstract

The atmosphere has often been considered "chaotic" when in fact the "chaos" is a manifestation of the models that simulate it, which do not include all the physical mechanisms that exist within it. A weather prediction cannot be perfectly verified after a few days of integration due to the inherent nonlinearity of the equations of the hydrodynamic models. The innovative ideas of Lorenz led to the use of the ensemble forecast, with clear improvements in the quality of the numerical weather prediction. The present study addresses the statement that "even with perfect models and perfect observations, the 'chaotic' nature of the atmosphere would impose a finite limit of about two weeks to the predictability of the weather" as the atmosphere is not necessarily "chaotic", but the models used in the simulation of atmospheric processes are. We conclude, therefore, that potential exists for developments to increase the horizon of numerical weather prediction, starting with better models and observations. Keywords: atmospheric modeling; chaos; numerical weather prediction ResumoA atmosfera tem sido muitas vezes considerada "caótica" quando de fato o "caos" é uma manifestação dos modelos que a simulam, os quais não incluem todos os mecanismos físicos nela existentes. Uma previsão do tempo não se verifica perfeitamente depois de alguns dias de integração devido a não linearidade inerente às equações dos modelos da hidrodinâmica. As ideias inovadoras de Lorenz conduziram ao uso da previsão por conjunto, com melhorias flagrantes na qualidade das previsões. O presente estudo se contrapõe à afirmação de que "mesmo com modelos e observações perfeitas, a natureza 'caótica' da atmosfera imporia um limite finito de cerca de duas semanas para a previsibilidade do tempo", uma vez que a atmosfera não é necessariamente "caótica", mas sim os modelos usados na simulação de seus processos. Conclui-se, portanto, que há espaço para o desenvolvimento no sentido de aumentar os horizontes da previsão numérica do tempo, a partir de melhores modelos e melhores observações. Palavras-chave: modelagem atmosférica; caos; previsão numérica do tempo A n u á r i o d o I n s t i t u t o d e G e o c i ê n c i a s -U F R J

Revisiting the Question of Atmospheric Predictability

“…Dr. Isidoro Orlansky working at NOAA (Personal Communication, 2014) read the article of Santos & Buchmann (2013) and agreed with some conclusions. He agrees that models themselves reproduce a 'chaotic' system, but even in those systems there is a large external force that maintains some periodicity that could be predicted for longer periods.…”

“…Due to this characteristic, very small input changes in initial values lead to great divergence in the outputs some days later, and these changes are associated with the difficulty to know what is the 'real atmospheric state'. Since their prior studies, the authors suggest that possibly 'better models' and 'better observational data' could lead to atmospheric simulations and predictions more accurate, including to extend the limit of useful forecasts to beyond two weeks (Santos & Buchmann, 2013).…”

“…It is 'obvious' that these models are 'chaotic'. Santos & Buchmann (2013) disagree with the term 'atmospheric chaos' which was considered different from 'deterministic chaos'. This in turn is a problem related with the initial value 'inconsistencies', the nonlinear models, and the iterative methods to integrate the equations.…”

“…In this second case, were included stochastic terms and prescribed forcing, and both solutions show no dependence on initial value influences. Kalnay (2003) asserts that "even with perfect models and perfect observations, the 'chaotic' nature of the atmosphere would impose a finite limit of about two weeks to the predictability of the weather", and she attributes this statement to four articles published by Lorenz between 1963and 1968. Santos & Buchmann (2013 associate the "finite limit of about two weeks to the predictability of the weather" with the 'deterministic chaos', which should be considered an attribute of the models, not of the atmosphere being simulated.…”

“…2; 5. Improving the Atmospheric Predictability Santos & Buchmann (2013) suggest that the term of the atmospheric predictability is not necessarily limited to two weeks. It is more reasonable to think that, in view of all the advances in weather and climate numerical prediction obtained in the last decades, 'better models' and 'better observational data' should lead to more accurate predictions, instead of worrying if a 'butterfly' beating its wings in Brazil could or not form a 'tornado' in Texas.…”