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Abstract This Letter reports the first observation of the onset of fully developed turbulence in the solar corona. Long time series of white-light coronal images, acquired by Metis aboard Solar Orbiter at 2 minutes cadence and spanning about 10 hr, were studied to gain insight into the statistical properties of fluctuations in the density of the coronal plasma in the time domain. From pixel-by-pixel spectral frequency analysis in the whole Metis field of view, the scaling exponents of plasma fluctuations were derived. The results show that, over timescales ranging from 1 to 10 hr and corresponding to the photospheric mesogranulation-driven dynamics, the density spectra become shallower moving away from the Sun, resembling a Kolmogorov-like spectrum at 3R_⊙. According to the latest observation and interpretive work, the observed 5/3 scaling law for density fluctuations is indicative of the onset of fully developed turbulence in the corona. Metis observation-based evidence for a Kolmogorov turbulent form of the fluctuating density spectrum casts light on the evolution of 2D turbulence in the early stages of its upward transport from the low corona. 

Aiming to assess the progress and current challenges on the formidable problem of the prediction of solar energetic events since the COSPAR/ International Living With a Star (ILWS) Roadmap paper of Schrijver et al. (2015) , we attempt an overview of the current status of global research efforts. By solar energetic events we refer to flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. The emphasis, therefore, is on the prediction methods of solar flares and eruptions, as well as their associated SEP manifestations. This work complements the COSPAR International Space Weather Action Teams (ISWAT) review paper on the understanding of solar eruptions by Linton et al. (2023) (hereafter, ISWAT review papers are conventionally referred to as ’Cluster’ papers, given the ISWAT structure). Understanding solar flares and eruptions as instabilities occurring above the nominal background of solar activity is a core solar physics problem. We show that effectively predicting them stands on two pillars: physics and statistics. With statistical methods appearing at an increasing pace over the last 40 years, the last two decades have brought the critical realization that data science needs to be involved, as well, as volumes of diverse ground- and space-based data give rise to a Big Data landscape that cannot be handled, let alone processed, with conventional statistics. Dimensionality reduction in immense parameter spaces with the dual aim of both interpreting and forecasting solar energetic events has brought artificial intelligence (AI) methodologies, in variants of machine and deep learning, developed particularly for tackling Big Data problems. With interdisciplinarity firmly present, we outline an envisioned framework on which statistical and AI methodologies should be verified in terms of performance and validated against each other. We emphasize that a homogenized and streamlined method validation is another open challenge. The performance of the plethora of methods is typically far from perfect, with physical reasons to blame, besides practical shortcomings: imperfect data, data gaps and a lack of multiple, and meaningful, vantage points of solar observations. We briefly discuss these issues, too, that shape our desired short- and long-term objectives for an efficient future predictive capability. A central aim of this article is to trigger meaningful, targeted discussions that will compel the community to adopt standards for performance verification and validation, which could be maintained and enriched by institutions such as NASA’s Community Coordinated Modeling Center (CCMC) and the community-driven COSPAR/ISWAT initiative.