Atmospheric inversions have been used for the past two decades to derive large-scale constraints on the sources and sinks of CO₂ into the atmosphere. The development of dense in situ surface observation networks, such as ICOS in Europe, enables in theory inversions at a resolution close to the country scale in Europe. This has led to the development of many regional inversion systems capable of assimilating these high-resolution data, in Europe and elsewhere. The EUROCOM (European atmospheric transport inversion comparison) project is a collaboration between seven European research institutes, which aims at producing a collective assessment of the net carbon flux between the terrestrial ecosystems and the atmosphere in Europe for the period 2006–2015. It aims in particular at investigating the capacity of the inversions to deliver consistent flux estimates from the country scale up to the continental scale. The project participants were provided with a common database of in situ-observed CO₂ concentrations (including the observation sites that are now part of the ICOS network) and were tasked with providing their best estimate of the net terrestrial carbon flux for that period, and for a large domain covering the entire European Union. The inversion systems differ by the transport model, the inversion approach, and the choice of observation and prior constraints, enabling us to widely explore the space of uncertainties. This paper describes the intercomparison protocol and the participating systems, and it presents the first results from a reference set of inversions, at the continental scale and in four large regions. At the continental scale, the regional inversions support the assumption that European ecosystems are a relatively small sink ($-\mathrm{0.21}\pm \mathrm{0.2}$ Pg C yr⁻¹). We find that the convergence of the regional inversions at this scale is not better than that obtained in state-of-the-art global inversions. However, more robust results are obtained for sub-regions within Europe, and in these areas with dense observational coverage, the objective of delivering robust country-scale flux estimates appears achievable in the near future.

Near-Earth solar-wind conditions, including disturbances generated by coronal mass ejections (CMEs), are routinely forecast using three-dimensional, numerical magnetohydrodynamic (MHD) models of the heliosphere. The resulting forecast errors are largely the result of uncertainty in the near-Sun boundary conditions, rather than heliospheric model physics or numerics. Thus ensembles of heliospheric model runs with perturbed initial conditions are used to estimate forecast uncertainty. MHD heliospheric models are relatively cheap in computational terms, requiring tens of minutes to an hour to simulate CME propagation from the Sun to Earth. Thus such ensembles can be run operationally. However, ensemble size is typically limited to $10^{1}$101 to $10^{2}$102 members, which may be inadequate to sample the relevant high-dimensional parameter space. Here, we describe a simplified solar-wind model that can estimate CME arrival time in approximately 0.01 seconds on a modest desktop computer and thus enables significantly larger ensembles. It is a one-dimensional, incompressible, hydrodynamic model, which has previously been used for the steady-state solar wind, but it is here used in time-dependent form. This approach is shown to adequately emulate the MHD solutions to the same boundary conditions for both steady-state solar wind and CME-like disturbances. We suggest it could serve as a “surrogate” model for the full three-dimensional MHD models. For example, ensembles of $10^{5}$105 to $10^{6}$106 members can be used to identify regions of parameter space for more detailed investigation by the MHD models. Similarly, the simplicity of the model means it can be rewritten as an adjoint model, enabling variational data assimilation with MHD models without the need to alter their code. The model code is available as an Open Source download in the Python language.

Variational data assimilation (DA) has enabled huge improvements in the skill of operational weather forecasting. In this study, we use a simple solar‐wind propagation model to develop the first solar‐wind variational DA scheme. This scheme enables solar‐wind observations far from the Sun, such as at 1 AU, to update and improve the inner‐boundary conditions of the solar wind model (at 30 solar radii). In this way, observational information can be used to improve estimates of the near‐Earth solar wind, even when the observations are not directly downstream of the Earth. Using controlled experiments with synthetic observations, we demonstrate this method's potential to improve solar wind forecasts, though the best results are achieved in conjunction with accurate initial estimates of the solar wind. The variational DA scheme is also applied to Solar‐Terrestrial Relations Observatory (STEREO) in situ observations using initial solar wind conditions supplied by a coronal model of the observed photospheric magnetic field. We consider the period October 2010 to October 2011, when the STEREO spacecraft were approximately 80^∘ ahead/behind Earth in its orbit. For 12 of 13 Carrington Rotations, assimilation of STEREO data improves the near‐Earth solar wind estimate over the nonassimilated state, with a 18.4% reduction in the root mean square error. The largest gains are made by the DA during times when the steady‐state assumption of the coronal models breaks down. While applying this pure variational approach to complex solar‐wind models is technically challenging, we discuss hybrid DA approaches which are simpler to implement and may retain many of the advantages demonstrated here.

Data assimilation (DA) is used extensively in numerical weather prediction (NWP) to improve forecast skill. Indeed, improvements in forecast skill in NWP models over the past 30 years have directly coincided with improvements in DA schemes. At present, due to data availability and technical challenges, DA is underused in space weather applications, particularly for solar wind prediction. This paper investigates the potential of advanced DA methods currently used in operational NWP centers to improve solar wind prediction. To develop the technical capability, as well as quantify the potential benefit, twin experiments are conducted to assess the performance of the Local Ensemble Transform Kalman Filter (LETKF) in the solar wind model ENLIL. Boundary conditions are provided by the Wang‐Sheeley‐Arge coronal model and synthetic observations of density, temperature, and momentum generated every 4.5 h at 0.6 AU. While in situ spacecraft observations are unlikely to be routinely available at 0.6 AU, these techniques can be applied to remote sensing of the solar wind, such as with Heliospheric Imagers or interplanetary scintillation. The LETKF can be seen to improve the state at the observation location and advect that improvement toward the Earth, leading to an improvement in forecast skill in near‐Earth space for both the observed and unobserved variables. However, sharp gradients caused by the analysis of a single observation in space resulted in artificial wavelike structures being advected toward Earth. This paper is the first attempt to apply DA to solar wind prediction and provides the first in‐depth analysis of the challenges and potential solutions.

Local inversions are often observed in the heliospheric magnetic field (HMF), but their origins and evolution are not yet fully understood. Parker Solar Probe has recently observed rapid, Alfvénic, HMF inversions in the inner heliosphere, known as ‘switchbacks’, which have been interpreted as the possible remnants of coronal jets. It has also been suggested that inverted HMF may be produced by near-Sun interchange reconnection; a key process in mechanisms proposed for slow solar wind release. These cases suggest that the source of inverted HMF is near the Sun, and it follows that these inversions would gradually decay and straighten as they propagate out through the heliosphere. Alternatively, HMF inversions could form during solar wind transit, through phenomena such velocity shears, draping over ejecta, or waves and turbulence. Such processes are expected to lead to a qualitatively radial evolution of inverted HMF structures. Using Helios measurements spanning 0.3–1 au, we examine the occurrence rate of inverted HMF, as well as other magnetic field morphologies, as a function of radial distance r, and find that it continually increases. This trend may be explained by inverted HMF observed between 0.3 and 1 au being primarily driven by one or more of the above in-transit processes, rather than created at the Sun. We make suggestions as to the relative importance of these different processes based on the evolution of the magnetic field properties associated with inverted HMF. We also explore alternative explanations outside of our suggested driving processes which may lead to the observed trend.

Data Assimilation (DA) has enabled huge improvements in the skill of terrestrial operational weather forecasting. In this study, we use a variational DA scheme with a computationally efficient solar wind model and in situ observations from STEREO‐A, STEREO‐B and ACE. This scheme enables solar‐wind observations far from the Sun, such as at 1 AU, to update and improve the inner boundary conditions of the solar wind model (at 30 solar radii). In this way, observational information can be used to improve estimates of the near‐Earth solar wind, even when the observations are not directly downstream of the Earth. This allows improved initial conditions of the solar wind to be passed into forecasting models. To this effect, we employ the HUXt solar wind model to produce 27‐day forecasts of the solar wind during the operational lifetime of STEREO‐B (November 01, 2007–September 30, 2014). In near‐Earth space, we compare the accuracy of these DA forecasts with both non‐DA forecasts and simple corotation of STEREO‐B observations. We find that 27‐day root mean square error (RMSE) for STEREO‐B corotation and DA forecasts are comparable and both are significantly lower than non‐DA forecasts. However, the DA forecast is shown to improve solar wind forecasts when STEREO‐B's latitude is offset from the Earth, which is an issue for corotation forecasts. And the DA scheme enables the representation of the solar wind in the whole model domain between the Sun and the Earth to be improved, which will enable improved forecasting of CME arrival time and speed.