Increasing carbon dioxide concentrations in the mesosphere and lower thermosphere are increasing radiative cooling in the upper atmosphere, leading to thermospheric contraction and decreased neutral mass densities at fixed altitudes. Previous studies of the historic neutral density trend have shown a dependence upon solar activity, with larger F10.7 values resulting in lower neutral density reductions. To investigate the impact on the future thermosphere, the Whole Atmosphere Community Climate Model with ionosphere and thermosphere extension (WACCM-X) has been used to simulate the thermosphere under increasing carbon dioxide concentrations and varying solar activity conditions. These neutral density reductions have then been mapped onto the Shared Socioeconomic Pathways (SSPs) published by the Intergovernmental Panel on Climate Change (IPCC). The neutral density reductions can also be used as a scaling factor, allowing commonly used empirical models to account for CO$_2$ trends. Under the“best case’ SSP1-2.6 scenario, neutral densities reductions at 400 km altitude peak (when CO$_2$ = 474 ppm) at a reduction of 13 to 30\% (under high and low solar activity respectively) compared to the year 2000. Higher CO$_2$ concentrations lead to greater density reductions, with the largest modelled concentration of 890 ppm resulting in a 50 to 77 \% reduction at 400 km, under high and low solar activity respectively.

A new one-dimensional variational (1D-Var) retrieval method for ionospheric GNSS ra- dio occultation (GNSS-RO) measurements is described. The forward model implicit in the retrieval calculates the bending angles produced by a one-dimensional ionospheric electron density profile, modeled with multiple “Vary-Chap” layers. It is demonstrated that gradient based minimisation techniques can be applied to this retrieval problem. The use of ionospheric bending angles is discussed. This approach circumvents the need for Differential Code Bias (DCB) estimates when using the measurements. This new, general retrieval method is applicable to both standard GNSS-RO retrieval problems, and the truncated geometry of EUMETSAT’s Metop Second Generation (Metop-SG), which will provide GNSS-RO measurements up to about 600 km above the surface. The climatological a priori information used in the 1D-Var is effectively a starting point for the 1D-Var minimisation, rather than a strong constraint on the final solution. In this paper the approach has been tested with 143 COSMIC-1 measurements. We find that the method converges in 135 of the cases, but around 25 of those have high “cost at convergence” values. In the companion paper (Elvidge et al., 2023), a full statistical analysis of the method, using over 10,000 COSMIC-2 measurements, has been made.

Multi-model ensembles (MMEs) are used to improve the forecasts of thermospheric neutral densities. A variety of algorithms for constructing the model weights for the MMEs are described and have been implemented including: performance weighting, independence weighting and non-negative least squares. Using both empirical and physics-based models, compared against in-situ CHAMP observations, the skill of each MME weighting approach has been tested in both solar minimum and maximum conditions. In both cases the MME performs better than any individual model. A non-negative least squares weighting for the MME on a set of bias corrected models provides a 68% and 50% reduction in the mean square error compared to the best model (Jacchia-Bowman 2008) in the solar minimum and maximum cases respectively.

Culverwell et al. (2023) described a new one-dimensional variational (1D-Var) retrieval approach for ionospheric GNSS radio occultation (GNSS-RO) measurements. The approach maps a one-dimensional ionospheric electron density profile, modeled with multiple ”Vary-Chap’ layers, to bending angle space. This paper improves the computational performance of the the 1D-Var retrieval using an improved background model and validates the approach by comparing with the COSMIC-2 profile retrievals, based on an Abel Transform inversion, and co-located (within 200 km) ionosonde observations using all suitable data from 2020. A three or four layer Vary-Chap in the 1D-Var retrieval shows improved performance compared to COSMIC-2 retrievals in terms of percentage error for the F2 peak parameters (NmF2 and hmF2). Furthermore, skill in retrieval (compared to COSMIC-2 profiles) throughout the bottomside (~90 km to 300 km) has been demonstrated. With a single Vary-Chap layer the performance is similar, but this improves by approximately 40% when using four-layers.

Prior to use in operational systems, it is essential to validate ionospheric models in a manner relevant to their intended application to ensure satisfactory performance. For Over-the-Horizon radars (OTHR) operating in the high-frequency (HF) band (3-30 MHz), the problem of model validation is severe when used in Coordinate Registration (CR) and Frequency Management Systems (FMS). It is imperative that the full error characteristics of models is well understood in these applications due to the critical relationship they impose on system performance. To better understand model performance in the context of OTHR, we introduce an ionospheric model validation technique using the oblique ground backscatter measurements in soundings from the Super Dual Auroral Radar Network (SuperDARN). Analysis is performed in terms of the F-region leading edge (LE) errors and assessment of range-elevation distributions using calibrated interferometer data. This technique is demonstrated by validating the International Reference Ionosphere (IRI) 2016 for January and June in both 2014 and 2018. LE RMS errors of 100-400 km and 400-800 km are observed for winter and summer months, respectively. Evening errors regularly exceeding 1,000 km across all months are identified. Ionosonde driven corrections to the IRI-2016 peak parameters provide improvements of 200-800 km to the LE, with the greatest improvements observed during the nighttime. Diagnostics of echo distributions indicate consistent underestimates in model NmF2 during the daytime hours of June 2014 due to offsets of -8° being observed in modelled elevation angles at 18:00 and 21:00 UT.

The solar radio flux at 10.7 cm, known as F10.7, is a critical operational space weather index. However, without a clear backup, any interruption to the service can result in substantial errors in model outputs. In this paper we show the impact of one such outage in March 2022 and present a number of alternative solutions for any future outages. The approach resulting in the smallest reconstruction error of F10.7 uses the solar radio flux observations at alternative wavelengths (the best giving a percentage error of 3.1%). Alternatively, use of Sunspot Number, a regular, robust alternative observation, results in a mean percentage error of 8.2% and is also a reliable fallback solution. Additionally, analysis of the error on the use of the conversion between the 12-month rolling sunspot number (R12) and its conversion to F10.7 as used by the IRI is included.

In this paper extreme value theory (EVT) has been used to estimate the return levels for geomagnetic activity based on the aa index. The aa index is the longest, continuously recorded, geomagnetic dataset (from 1868 - Present). This long, 150 year, dataset is an ideal candidate for extreme value analysis. However the data are not independent and identically distributed as required for EVT since they are impacted by the approximately 11 year solar cycle. The Hilbert-Huang Transform has been used to identify the solar cycle component in the data and the data has been split into solar maximum and minimum times. In these two regimes the generalised extreme value distribution has been fit to the datasets. These have also been combined for an estimate of the overall return times. The results suggest that the largest event in the database (March 1989) is a one in 25 year event. However, considering separate solar maximum and minimum times has a large impact on the return times. During solar minimum conditions the return time of the March 1989 event is 130 years. This suggests that the occurrence of extreme space weather events is conditionally dependent on where in the Solar Cycle we are.

In this study, we use measurements from over 4,735 globally distributed Global Navigation Satellite System (GNSS) receivers to track the progression of travelling ionospheric disturbances (TIDs) associated with the 15 January 2022 Hunga Tonga-Hunga Ha’apai submarine volcanic eruption. We identify two distinct Large Scale TIDs (LSTIDs) and several subsequent Medium Scale TIDs (MSTIDs) that propagate radially outward from the eruption site. Within 3000 km of epicenter, LSTIDs of >1600 km and ~1350 km wavelengths are initially observed propagating at speeds of ~950 ms-1 and ~555 ms-1, before substantial slowing to ~600 ms-1 and ~390 ms-1, respectively. MSTIDs with speeds of 200-400 ms-1 are observed for six hours following eruption, the first of which comprises the dominant global ionospheric response and coincides with the atmospheric surface pressure disturbance associated with the eruption. These are the first results demonstrating the global impact of the Tonga eruption on the ionospheric state.

Here we assess to what extent the Empirical Canadian High Arctic Ionospheric Model (E-CHAIM) can reproduce the climatological variations of vertical Total Electron Content (vTEC) in the Canadian sector. Within the auroral oval and polar cap, E-CHAIM is found to exhibit Root Mean Square (RMS) errors in vTEC as low 0.4 TECU during solar minimum summer but as high as 5.0 TECU during solar maximum equinox conditions. These errors represent an improvement of up to 8.5 TECU over the errors of the International Reference Ionosphere (IRI) in the same region. At sub-auroral latitudes, E-CHAIM RMS errors range between 1.0 TECU and 7.4 TECU, with greatest errors during the equinoxes at high solar activity. This represents an up to 0.5 TECU improvement over the IRI during summer but worse performance by up to 2.4 TECU during the winter. Comparisons of E-CHAIM performance against in situ measurements from the European Space Agency’s Swarm mission are also conducted, ultimately finding behaviour consistent with that of vTEC. In contrast to the vTEC results, however, E-CHAIM and the IRI exhibit comparable performance at Swarm altitudes, except within the polar cap, where the IRI exhibits systematic underestimation of electron density by up to 1.0e11 e/m^3. Conjunctions with mid-latitude ionosondes demonstrate that E-CHAIM’s errors appear to result from compounding same-signed errors in its NmF2, hmF2, and topside thickness at these latitudes. Overall, E-CHAIM exhibits strong performance within the polar cap and auroral oval but performs comparably to the IRI at sub-auroral latitudes.