Abstract The Lower Thermosphere–Ionosphere (LTI) is the interface region between the Earth’s atmosphere and space. It is modulated by the energy and momentum deposition from the magnetosphere above and by the impacting waves from the lower atmosphere. The LTI region is defined by the co-existence and interaction of neutral and ionized species within the region’s electric and magnetic fields. This interplay results in unique and complex interactions between neutrals and plasmas, that are not fully understood and quantified to this date. In this paper we present an overview of some of the most important open questions related to ion/neutral coupling and the resulting collisional electrodynamics, collisional energetics and collisional dynamics processes. We outline the key reasons for addressing these questions, and highlight methodologies that can lead to their closure in the upcoming years.

Abstract The subauroral region, located equatorward of the auroral oval, is a highly dynamic and complex interface between the magnetosphere, ionosphere, and thermosphere. While traditionally associated with stable optical structures such as stable auroral red arcs, recent observations have revealed a wide range of transient and extreme phenomena—such as subauroral ion drifts and strong thermal emission velocity enhancement—which highlight the region’s variability and intense coupling. The dynamics of the subauroral ionosphere are not only influenced by processes occurring at higher latitudes within the auroral oval but are also shaped by interactions across multiple regions of geospace, including the inner magnetosphere, ring current, inner plasma sheet, and the lower-altitude thermosphere. This growing body of research has underscored both the scientific richness of the subauroral region and the many outstanding questions regarding its drivers and chemical processes. In this paper, we present a in-depth review of observed subauroral structures, available ground-based and satellite datasets, and current modeling efforts aimed at understanding the region’s dynamics. We also examine the state of knowledge surrounding the subauroral ionospheric/thermospheric chemistry and outline critical gaps that require further investigation. Finally, we discuss the pressing need for targeted experiments and new space missions to advance our understanding of this key geospace region.

In satellite remote sensing of land surface Essential Climate Variables (ECVs) using optical sensors, an atmospheric correction step is typically required to convert top-of-atmosphere (TOA) bi-directional reflectances into top-of-canopy (TOC) bi-directional reflectances. We analyse the error covariance structure of TOC reflectances that arises specifically from uncertainties in atmospheric correction. Using SMAC as the atmospheric correction model and Automatic Differentiation (AD) for efficient Jacobian computation, we quantify these error covariances across different scenarios. Our results show that uncertainties in the atmospheric state introduce non-negligible error correlations of both signs between different satellite bands. Since these error correlations are often overlooked, explicitly accounting for them could improve the accuracy of land surface ECV retrievals.

Physics-based numerical models are essential for understanding atmosphere–space interactions. These include mechanisms that perturb the upper-atmosphere dynamics and energetics associated with terrestrial weather (impact from below) and solar-geomagnetic input (impact from above). The relevant physical phenomena range from global and synoptic scales down to the kilometer-scale of mesoscale dynamics. This manuscript discusses the impact of fine-scale processes in the ionosphere–thermosphere–mesosphere (ITM) region initiated from above and below, emphasizing the current status and perspectives of their realistic representation in whole-atmosphere and thermosphere–ionosphere models. The possible pathways to address challenges in realistic predictions of the ITM and its fast variability on global and regional scales are examined. They include three powerful modeling and data analysis techniques, namely nesting, nudging, and assimilation, that need to be carefully implemented for the highly dynamical ITM domain. Reasons for potential problems to constrain the high-resolution ITM predictions by data can be attributed to sparse and diverse observations performed in different layers and the increased complexity of the multi-scale ITM processes that need to be taken into account and properly coupled (e.g., dynamics, radiation, chemistry, and electrodynamics). The multi-scale ITM dynamics are dominated by tides, gravity waves, and small-scale eddies. Constraining these with assimilation and/or nudging algorithms requires rapid data analysis to properly adjust the global and regional diurnal and sub-diurnal oscillations to match space-borne and ground-based observations.

Abstract Climate services often require observational climate data to inform decision-making on mitigation and adaptation activities. Understanding the uncertainties in the climate datasets that are used for this purpose, and how these uncertainties relate to the context of the climate service is critical to making well-informed decisions. Recent developments in the production of climate-relevant satellite datasets has focused on characterising uncertainties from a bottom-up perspective with a high degree of mathematical rigour. Using the example of three essential climate variables: sea surface temperature, soil moisture and carbon dioxide we discuss how to translate the highly-detailed uncertainty information provided with high-resolution datasets into something appropriate to the scale of a climate service, where the decision-making context might be local, regional or global. Close engagement between climate data producers and climate service providers is essential to ensure we have the best possible platform to make decisions as we adapt to climate change.

Abstract An estimate of uncertainty is essential to understanding what information is conveyed by data and how it relates to the wider context of what one intended to measure. It can be difficult to know how to use uncertainty during the analysis of environmental data and the best way to present that information within a dataset. In many common uses, such as calculating statistical significance, it is easy to make mistakes due to incomplete or inappropriate use of the available uncertainty information. Uncertainty is itself uncertain, such that many practical or empirical solutions are available when a comprehensive uncertainty budget is impractical to produce. This manuscript collects actionable guidance on how uncertainty can be used, presented, and calculated when working with essential climate variables (ECVs). This includes qualitative discussions of the utility of uncertainties, explanations of common misconceptions, advice on presentation style, and plain descriptions of the essential equations. Selected worked examples are included on the propagation of uncertainties, particularly for data aggregation and merging. Uncertainty need not be off-putting as even incomplete uncertainty budgets add value to any observation. This paper aims to provide a starting point, or refresher, for researchers in the environmental sciences to make more complete use of uncertainty in their work.