Exploring the Origin of the Two-Week Predictability Limit: A Revisit of Lorenz’s Predictability Studies in the 1960s

Author:

Shen Bo-Wen1ORCID,Pielke Roger A.2,Zeng Xubin3ORCID,Zeng Xiping4ORCID

Affiliation:

1. Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA

2. Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80203, USA

3. Department of Hydrology and Atmospheric Science, The University of Arizona, Tucson, AZ 85721, USA

4. DEVCOM Army Research Laboratory, Adelphi, MD 20783, USA

Abstract

The 1960s was an exciting era for atmospheric predictability research: a finite predictability of the atmosphere was uncovered using Lorenz’s models and the well-acknowledged predictability limit of two weeks was estimated using a general circulation model (GCM). Here, we delve into details regarding how a correlation between the two-week predictability limit and a doubling time of five days was established, recognize Lorenz’s pioneering work, and suggest non-impossibility for predictability beyond two weeks. We reevaluate the outcomes of three different approaches—dynamical, empirical, and dynamical-empirical—presented in Lorenz’s and Charney et al.’s papers from the 1960s. Using the intrinsic characteristics of the irregular solutions found in Lorenz’s studies and the dynamical approach, a doubling time of five days was estimated using the Mintz–Arakawa model and extrapolated to propose a predictability limit of approximately two weeks. This limit is now termed “Predictability Limit Hypothesis”, drawing a parallel to Moore’s Law, to recognize the combined direct and indirect influences of Lorenz, Mintz, and Arakawa under Charney’s leadership. The concept serves as a bridge between the hypothetical predictability limit and practical model capabilities, suggesting that long-range simulations are not entirely constrained by the two-week predictability hypothesis. These clarifications provide further support to the exploration of extended-range predictions using both partial differential equation (PDE)-physics-based and Artificial Intelligence (AI)—powered approaches.

Publisher

MDPI AG

Reference109 articles.

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3. National Academies of Sciences, Engineering, and Medicine (2016). Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts, The National Academies Press.

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