Linear scaling of energy across temporal scales from right to left for Sea Surface Temperature (SST). This behavior is indicative of potential predictability at longer timescales and in-between scales. Credit: Lovejoy, 2022, Figure 1
Source: AGU Advances
Editors’ Highlights are summaries of recent papers by AGU’s journal editors.

Interactions among weather and, or climate processes at different scales (e.g., hurricanes and hot ocean eddies in the Gulf of Mexico) are highly nonlinear, and useful predictability is limited to a few days (e.g., 5 days’ lead time for Hurricane Ian in September 2022). In climate models, very small differences due to uncertainty in the representation of environmental conditions and states and how they change are amplified, and evolution patterns diverge with timescale, thus limiting the prediction lead time. In nonlinear geophysics, multifractal stochastic processes can be used to transfer pattern geometry across scales thus extending the prediction lead time. Lovejoy [2022] highlights the complementarity of stochastic and physical modeling approaches in climate prediction. In this commentary, Lovejoy makes the case that for the last 100 years there has been little communication between atmospheric sciences and nonlinear geophysics, and this must change to improve useful predictability (i.e., lead time) of climate models. 

Citation: Lovejoy, S. [2022]. The 2021 “complex systems” Nobel prize: The climate, with and without geocomplexity. AGU Advances, 3, e2021AV000640.

—Ana Barros, Editor, AGU Advances

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