Revisiting the Question of Atmospheric Predictability

Abstract

This study seeks to demystify the claim that the 'atmospheric chaos' imposes a two-week limit on reliable weather forecasts. 'Deterministic chaos' indeed occurs due to the use of nonlinear numerical models for these forecasts. This 'deterministic chaos' does impose time limits on valid predictions, but it also facilitates, through the ensemble forecasting technique, the use of interesting statistical indicators that define regions and the duration these predictions are more or less reliable. Recently published articles show that the 'uncertainties' in the initial conditions are an inherent difficulty in meteorological observations and have nothing to do with the atmospheric behavior. These studies demonstrate two important aspects regarding 'uncertainties' in data used to initialize models. First, to achieve improvements in numerical weather forecasts, these 'uncertainties' must be skillfully introduced in the large scale and not in the small scale. Secondly, the numerical models must include equations or parameterizations that reproduce nature's ways that let different scales 'interact', that is, the models should reproduce how the energy of different atmospheric modes 'travels'.In the 1960s and 1970s the academic meteorology community debated if more degrees of freedom could reduce the system instabilities in forecast model equations. With this line of reasoning the articles of Charney (1963) and Lorenz (1963) should be highlighted. Charney thought that with more degrees of freedom, the system of equations could stabilize, and thus extend the effective forecast limits. However, at that time, Lorenz, using a very simple convection model based on an approximate system of nonlinear ordinary differential equations, discovered that two runs of the model starting from slightly different initial conditions gave surprisingly divergent responses after a non-long period of integrations. Lorenz called this unexpected result 'deterministic chaos'. Reinforcing the Lorenz results, Kalnay (2003) stated that nothing could be done to ameliorate the models because, as the atmosphere was 'chaotic', the fourteen-day predictability limit could not be surpassed.Nowadays, nonlinear models are used instead of linear to forecast weather, because nonlinear models produce more 'realistic' or substantiated results. This is probably because nonlinear models take into account the disturbance products in the advection and others nonlinear terms, which are important to simulate the real atmosphere and which have aperiodic solutions, contrary to linear models. This study revisits the question of atmospheric predictability, suggesting the research community should invest its effort in two approaches. The first should endeavor to find modeling strategies that better reproduce the realistic ways large-scale interact with the small-scale in geophysical fluid systems, especially the atmosphere, in what are here named 'better models'. The second approach, more evident to the meteorological community, is to strongly invest to obtain more and 'bette...