Integration of RAM-SCB into the Space Weather Modeling Framework

Accepted Manuscript Integration of RAM-SCB into the Space Weather Modeling Framework Daniel T. Welling, Gabor Toth, Vania K. Jordanova, Yiqun Yu PII:

S1364-6826(17)30278-X

DOI:

10.1016/j.jastp.2018.01.007

Reference:

ATP 4764

To appear in:

Journal of Atmospheric and Solar-Terrestrial Physics

Received Date: 30 April 2017 Revised Date:

2 January 2018

Accepted Date: 3 January 2018

Please cite this article as: Welling, D.T., Toth, G., Jordanova, V.K., Yu, Y., Integration of RAM-SCB into the Space Weather Modeling Framework, Journal of Atmospheric and Solar-Terrestrial Physics (2018), doi: 10.1016/j.jastp.2018.01.007. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Integration of RAM-SCB into the Space Weather Modeling Framework Daniel T. Wellinga,∗, Gabor Totha , Vania K. Jordanovab , Yiqun Yub,c,1 a University

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of Michigan Department of Climate and Space, 2455 Hayward St., Ann Arbor, MI 48109-2143 b Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545 c New Mexico Consortium, Los Alamos, NM 87545

Abstract

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Numerical simulations of the ring current are a challenging endeavor. They require a large set of inputs, including electric and magnetic fields and plasma sheet fluxes. Because the ring current broadly affects the magnetosphere-ionosphere system, the input set is dependent on the ring current region itself. This makes obtaining a set of inputs that are self-consistent with the ring current difficult. To overcome this challenge, researchers have begun coupling ring current models to global models of the magnetosphere-ionosphere system. This paper describes the coupling between the Ring current Atmosphere interaction Model with Self-Consistent Magnetic field (RAM-SCB) to the models within the Space Weather Modeling Framework. Full details on both previously introduced and new coupling mechanisms are defined. The impact of self-consistently including the ring current on the magnetosphere-ionosphere system is illustrated via a set of example simulations.

1. Introduction

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The ring current, or the inner magnetospheric region consisting of ions and electrons in the 100s eV to 100s keV energy range, plays a critical role in magnetospheric and ionospheric dynamics [6]. As the main pressure carrying population, it controls the magnetic geometry of the inner magnetosphere via pressure balance [3, 65, 83]. The pressure gradients drive region 2 field-aligned currents [76, 37, 1, 86, 2]. The ring current also deposits energy into the ionosphere via direct particle precipitation [19, 11, 12, 13] and precipitation caused by ring current-driven waves [18, 51, 26, 54] With such far reaching implications, the ring current is a critical region to understand.

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∗ Corresponding

author Email addresses: [email protected] (Daniel T. Welling ), [email protected] (Gabor Toth), [email protected] (Vania K. Jordanova), [email protected] (Yiqun Yu) 1 Now at Beihang University School of Space and Environment, Beijing, China 100191

Preprint submitted to Journal of Atmospheric and Solar-Terrestrial PhysicsFebruary 2, 2018

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Conversely, the ring current is a product of the greater space environment. Ring current development is dependent on electric and magnetic fields, which determine the strength and direction of particle drifts. Plasma fluxes from the tail dictate the rate of new particles into the ring current. These regions are directly affected by the ring current, creating a tightly coupled, non-linear global system. These two-way interactions makes the ring current challenging to model numerically. Early efforts to model the system in a stand-alone fashion required a broad set of inputs, typically obtained via a combination of observations and empirical models [e.g., 30, 17, 36]. Such combinations are rarely consistent with each other. Further, they produce model results that become inconsistent with the inputs as the ring current cannot change the conditions affecting the surrounding fields and fluxes. Recognizing this, modelers have changed their models to be consistent. This led to models with self-consistent electric and magnetic field calculations [58, 9, 35, 31]. They also began using inputs from global magnetosphere models to ensure the inputs were consistent with each other [57, 15, 16]. To achieve full self-consistency between the ring current and the full magnetosphereionosphere system, inter-region coupling is required. On a global scale, this was first achieved by De Zeeuw et al. [8], which coupled the Rice Convection Model (RCM) to the Block Adaptive Tree Solar wind Roe-type Upwind Scheme (BATS-R-US) global magnetohydrodynamic (MHD) model. This effort has been repeated with other combinations of models [43, 20, 5]. In each case, the result is a ring current-ionosphere-magnetosphere system that is fully self consistent. Over the past decade, the Ring current Atmosphere interaction Model with Self Consistent Magnetic field (RAM-SCB) has undergone an evolution from a purely stand-alone model to a flexible tool that is fully coupled to the Space Weather Modeling Framework (SWMF). This began as an effort to use inputs from global MHD models [84], but grew into a run-time coupling capability [74] that now includes two-way coupling [78, 73, 79, 80]. To date, these capabilities have only been partially and incrementally described. This paper completely describes the coupling capabilities between RAM-SCB and the SWMF. Full details on both previously introduced and new coupling mechanisms are defined. The impact of self-consistently including the ring current on the magnetosphereionosphere system is illustrated via a set of example simulations. 2. Description of Models 2.1. RAM-SCB

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RAM-SCB is a combination of two models: the Ring current Atmosphere interaction Model (RAM) [28, 27] and a 3D force-equilibrium model of the magnetic field (herein referred to as SCB) [4, 85, 82]. RAM solves the bounceaveraged kinetic equation to obtain the warm plasma distribution function, Q (r, φ, E, µ), where r is radial distance from the center of the Earth, φ is

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magnetic local time, E is guiding-center kinetic energy, and µ is the cosine of the equatorial pitch angle. A full description of the kinetic equation and its derivation may be found in Khazanov [33]. Advection is calculated through a prescribed electric and magnetic field. In order to determine the evolution of Q (r, φ, E, µ), field geometry integral quantities, h(µ) [50], must be calculated, requiring full knowledge of the magnetic field in the inner magnetosphere. Four species are solved for: electrons, protons, helium and oxygen ions. RAM’s polar grid spans from 1.75 to 6.75 RE in the Solar-Magnetic (SM) equatorial plane, including boundary cells. In this coordinate system, the magnetic dipole axis is aligned with the SM-Z axis. It has an energy range of approximately 100 eV to 600 KeV . The original version of the RAM code [27] includes charge exchange losses, Coulomb collision losses, and atmospheric loss at low altitudes. It has been updated to work with non-dipole field geometries [29, 32]. The SCB model calculates the magnetic field required to reach force balance with a prescribed anisotropic pressure distribution,

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To do this, it uses an Euler potential representation of the field [55], B = ∇ψ × ∇α

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. . . where ψ is the magnetic flux function, α is a azimuthal angle-like coordinate, and magnetic field lines are defined as the intersections of surfaces of constant ψ and α. An iterative method is used to solve Equation 1 in terms of ψ and α, bounded by the fields at the inner and outer boundary. At a set frequency, RAM and SCB are coupled together to achieve magnetic self-consistency with the ring current. The RAM pressure distribution is handed to SCB, which solves for the self-consistent field. The resulting field geometry is used to calculate the field-aligned integral quantities [10] used in the RAM solver. The coupling frequency defaults to five minutes, which is appropriate for studying dynamics on the scale of geomagnetic storms. The frequency can be increased for periods where the field is more quickly evolving, such as strong field dipolarizations associated with substorm expansions. Like most ring current models, RAM-SCB requires a set of three critical inputs: 1. Magnetic field is required as an initial condition to the self-consistent solver and as a radial inner and outer boundary condition. In the past, RAM-SCB has used a simple dipole field, fields from the empirical models of Tsyganenko [65, 63, 64, 66, 62, 67], and output from an MHD model running independently [84]. 2. Electric field must be prescribed across the entire domain. Previously, empirical models of ionospheric convection potential have been leveraged heavily, including the Volland-Stern model [68, 56] and the Weimer model [70, 71]. Ionospheric potentials from MHD simulations have been used as well [84]. These values must be mapped from the ionosphere to the equatorial plane along magnetic field lines. Recently, the RAM-SCB Model has been extended to include a self-consistent electric field [81].

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Comparisons of how different combinations of inputs affect the development of the RAM-SCB ring current have been performed in a variety of studies [29, 32, 45]. A persistent theme of such work is concern over self-consistency between the ring current and the complex input set.

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3. The geosynchronous plasma environment must also be specified to account for the fresh supply of plasma from the tail plasma sheet. The distribution function, Q, is required at all local times, all pitch angles, and for energies 100 eV to 600 keV . Nominally, the full particle distribution for all species would be provided at all local times, but this is rarely available. Previous studies have leveraged observations and interpolation to fill this critical need [29].

2.2. SWMF & Other Models

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The Space Weather Modeling Framework (SWMF [60, 61]) handles the execution, synchronization, and coupling of RAM-SCB to other models. The SWMF achieves this by first dividing up the space environment into different physics components, each representing a different sub-domain of the heliosphere. Example components include the Global Magnetosphere component (abbreviated GM), which handles the interaction between the solar wind and the Earth’s magnetic field; the Ionospheric Electrodynamics component (IE), which simulates the electric fields and currents in the ionosphere; and the Inner Magnetosphere (IM) component, which is responsible for the pressure-carrying, keV -energy protons and electrons. The SWMF is a true framework in that it allows an arbitrary code to be integrated as one of the physics models so long as software interface wrappers have been developed for that code. For example, RAM-SCB represents the IM component, but two other models are also available to be used in IM: the Rice Convection Model [8] and the Comprehensive Ring Current Model [20]. Models coupled to the SWMF are expected to provide and receive a set of coupling values on demand. At compile time, the user selects which components to activate and which numerical models will be used for each component. At present, there are 14 different physics components and 18 different models available in the SWMF. The IM component is currently coupled to two other components: GM and IE. GM is represented by the Block Adaptive Tree Solar wind Roe-type Upwind Scheme (BATS-R-US) global MHD model [44, 7]. BATS-R-US is a welldeveloped, broadly-used code that is uniquely configurable [48]. Its signature feature is its flexible, adaptive grid that reserves the highest resolution to regions of interest, ensuring the best combination of performance and accuracy. It can solve the ideal, multi-species [39, 75], multi-fluid [22, 38], anisotropic [42, 41], Hall resistive [59], and other forms of the MHD equations. As is common in the field, the Boris speed of light correction factor [24] is used to limit wave speeds in the code, thereby increasing the allowable time step, reducing numerical diffusion, and increasing robustness. The domain spans from 32 Earth Radii (RE ) upstream to 224 RE downstream and 128 RE in each orthogonal direction. The inputs for BATS-R-US are solar wind and interplanetary magnetic field

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(IMF) values, allowing for global experiments as a function of upstream conditions. The IE physics component is handled by the Ridley Ionosphere Model (RIM [46, 47]). RIM is a height integrated electrodynamics model that, given field-aligned currents (FACs) and a prescribed ionospheric conductance pattern, solves for the ionospheric electric potential. For results shown in this study, the configuration of the SWMF, BATS-R-US, RIM, and RAM-SCB follows that of [73].

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3. Coupling Overview

Figure 1: Coupling schematic for the SWMF when three components are used, including RAM-SCB.

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Generically, inter-model coupling is a simple process. During simulation initialization, some basic information about each code configuration is shared with the SWMF, such as the grid setup for RAM. Which couplings are used, the order of couplings, and coupling frequency are selected by the user in the configuration input file. The SWMF is fully parallelized such that each component can (and typically does) run concurrently with the others. The user selects how to divide the available CPUs amongst the components at run time. This means that the overall execution speed is limited by the slowest model being used; RAM-SCB runs faster-than-real-time on a single processor (i.e., one second of simulation takes less than one second to calculate) and does not contribute significantly to the overall execution time. The SWMF maintains synchronization

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between each model, preventing any one from passing a coupling epoch. For each coupling, the SWMF gathers the requisite values from one component then passes it to the other. All passed values are expected in SI units. Figure 1 shows a schematic of the coupling between models. The existing coupling between the GM and IE physics components (BATS-R-US and RIM, respectively) are shown in the upper-left portion of the diagram. RIM receives FACs from BATS-R-US, solves for the ionospheric electric potential. The electric potential is used to calculated the E × B drift speed, which is returned to BATS-R-US to set the tangential velocity about the inner boundary of the MHD model. [23, 46]. The two codes that compose RAM-SCB (RAM and the 3D magnetic equilibrium model) are shown in lower-right portion of the diagram, including the internal couplings described above. The GM-IE combination provide the necessary inputs to drive RAM-SCB: magnetic field (GM), plasma conditions about geosynchronous distance (GM), and the convection electric potential (IE). In return, RAM-SCB passes the total plasma pressure within its domain to the GM component to correct the MHD solution and electron precipitation to the IE component to drive conductance associated with the diffuse aurora. Performing these couplings requires the careful collection of 3D data from the ionosphere out to the equatorial plane about the inner magnetosphere. BATSR-US interpolates its solution to every RAM grid cell center. Care is taken to properly change coordinates between the SM system used by RAM and the system used by BATS-R-US (typically the Geocentric Solar Magnetospheric, or GSM system). This ensures that the MHD values are extracted from the proper location for an aribtrary dipole tilt angle. At each of these points, the magnetic field lines are traced to both the northern and southern hemisphere. Fluid information and field line geometry are passed from BATS-R-US to RAMSCB. In addition to this information, names of all shared variables are passed to help RAM-SCB differentiate between different MHD equation set solutions. Meanwhile, RIM provides the entire 2D northern hemisphere electric potential to RAM-SCB. In return, the species-specific and total thermal pressure is collected by RAM and passed to BATS-R-US. A typical SWMF GM-IM-IE simulation (using BATS-R-US, RAM-SCB, and RIM in these components) employs the following procedure. First, a pseudo steady-state magnetosphere-ionosphere is generated in BATS-R-US and RIM by holding the solar wind input values at the start time of the event constant and running the code for many (typically thousands) iterations. This result is used as the initial state of the magnetosphere. RAM-SCB is initialized with a quiet-time solution based on observations [34, 14, 30]. Before the first timeaccurate iteration, the couplings shown in Figure 1 are performed for the first time. For each coupling, two routines are called: a get routine that collects all data from one code, and a put routine that passes the data to the receiving code, which organizes and utilizes the information. For example, to couple the IM component (RAM-SCB) to the GM component (BATS-R-US), the SWMF will call the get routine to collect information from IM, then call the put routine to hand the information to GM. The precise order of the coupling is set by the user

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at run time. The default order is IE to GM, IE to IM, GM to IE, GM to IM, and IM to GM. After the initial coupling, the SWMF allows all models to step forward in time until the highest coupling frequency between models is reached. Coupling routines are called, and the models are allowed to step forward in time until the next coupling time is reached. This process is repeated until the simulation is complete.

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4. Plasma Coupling

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Upon receiving the coupling data from the GM component, RAM-SCB converts the MHD values into particle fluxes about its outer boundary. The first step is to use the variable list to determine what MHD solution has been provided: single-fluid, single-fluid multi-species, multi-fluid, or anisotropic MHD. Based on the available information, number densities and temperatures are calculated for each of RAM’s three ion species (H+ , He+ , and O+ ). Using these fluid moments and an assumed distribution (either Maxwellian or Kappa as set by the user), fluxes are calculated for each of RAM’s energy bins. If anisotropic MHD is used, a bi-maxwellian or bi-kappa distribution is used; if not, isotropy is assumed for the pitch angle distribution. A complication in this process is that of composition. For single-species, single fluid MHD (the most common way to run BATS-R-US), there is neither composition nor partial pressure information. In this case, the fraction of the total number density dedicated to each species is obtained via the Young et al. empirical formula [77]. Dividing up the MHD solution into separate species is problematic because MHD does not provide number density, it provides mass density. Ignoring helium for the time being, the following relationships connect mass density and number density:

These can be rearranged to yield, ρtotal − 15nO+ mp

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Combined with the composition information from the empirical model, solving for the total and species number densities is now trivial. This equation reveals a deeper problem, however: as the assumed O+ density increases for a constant mass density, the total number density drops precipitously. Further, the temperature is obtained via the ideal gas law. This means that as O+ increases, ntotal decreases, and the temperature increases. These changes can have drastic effects on ring current development and are inconsistent with the single-fluid, single-species MHD solution. Adding information removes the uncertainty in this calculation. Multispecies, single-fluid MHD can keep track of composition, yielding a number 7

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density consistent with the total mass density. However, because there is only a single pressure, there is still ambiguity in the species temperature. When coupling with multi-species, single-fluid MHD, RAM-SCB assumes all species have the same temperature. Coupling with true multi-fluid MHD removes all uncertainties, as each fluid has its own associated pressure. When coupling with BATS-R-US through the SWMF, RAM-SCB automatically detects which MHD equation set is being used and applies the appropriate calculation.

Figure 2: MHD-obtained plasma drivers about geosynchronous orbit for the August 31, 2001 weak storm. The top three frames show the MHD inputs as a function of time (x-axis) and MLT about geosynchronous orbit (y-axis). Total number density (top frame), composition (second frame), and temperature (third frame) are shown. The bottom three frames show IMF BZ (black line) and BY gray line; solar wind number density (green line) and earthward velocity (blue line). The observed (dashed line) and RAM-SCB modeled (blue line) DST are shown in the bottom frame.

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Figure 2 shows plasma coupling values from single-fluid, single-species MHD for a moderate geomagnetic storm that occurred on August 31, 2001. This

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storm was one of four events examined by the National Science Foundation Geospace Environment Modeling (GEM) program’s Disturbance Storm Time (DST ) index validation challenge [45]. The top three frames of Figure 2 illustrate the MHD-derived plasma values that are passed to RAM-SCB and used as inputs at RAM-SCB’s outer boundary. The values shown are the total density (top frame), percent oxygen by number (2nd frame from top), and total plasma temperature (3rd frame). Because these results are from single-fluid MHD, the percent oxygen is obtained via an empirical relationship [77]. The values are shown as a function of time (x-axis, shared by the line plots below) and magnetic local time (MLT) about the outer boundary of RAM (y-axis) such that values in the middle of a frame represent conditions at local midnight. These plots are arranged such that a single vertical slice represents the conditions about geosynchronous distance at a single epoch. The Y and Z components (in Geocentric Solar Magnetosphere, GSM, coordinates) of the IMF are shown in fourth frame from the top, followed by the solar wind Earthward velocity (blue line) and number density (green line). In the bottom frame, the observed DST (dashed line) is shown along with the RAM-SCB modeled DST (blue line) when driven by the values in the top three frames. Figure 2 illustrates how dynamics in the BATS-R-US plasma sheet are transferred to RAM-SCB. The MLT extent of the hot central plasma sheet can be seen in the temperature plot. In the density frame, a substorm injection is observed as a sudden, midnight-centered increase in density at 11:00 UT. At 17:00 UT, two strong but cold plasma injections pinch in from the dawn and dusk flanks. These are the result of the solar wind dynamic pressure pulse (seen in the solar wind density values) which compress the magnetosphere and push boundary layer plasma into geosynchronous locations [75, 38]. These interesting features directly feed into RAM-SCB. In contrast to these structures in temperature and density, the composition slowly varies in time and is constant in MLT. This is because this simulation relies on the empirical composition model rather than dynamic MHD results. Overall, this simulation produced a DST response in RAM-SCB that was far weaker than the observed model. Clearly, with oneway coupling, some features are missed that would drive a more realistic DST response.

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Like similar MHD-ring current model coupling schemes [8, 20], RAM-SCB uses the ionospheric electric potential to calculate particle drifts. However, unlike previous efforts, the RAM-SCB grid lies in the SM equatorial plane, not the ionosphere. With an ionospheric grid, transferring the electric potential was a simple matter of interpolating the potential from the IE grid to the IM grid. With RAM-SCB, the potential must be mapped along magnetic field lines to the equatorial plane. Fortunately, this mapping exists within the SCB solution, so the methodology does not deviate strongly from past efforts. A new coupling developed by Yu et al. [80] provides feed back to the IE electric field calculation. Precipitating electrons from RAM are converted into a 9

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Figure 3: The RIM electric potential in the northern ionosphere (left frame) during an SWMF simulation of the August 31, 2001 event. The same potential pattern has been mapped along SCB field lines onto the RAM-SCB equatorial plane grid (right frame). Both frames use the same scale.

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Figure 3 shows an example of the results of the potential mapping. The left frame shows the RIM electric potential over the northern hemisphere at 7:00 UT during a two-way coupled simulation of the August 31, 2001 event. For this simulation, RAM-SCB received all inputs from the SWMF while providing plasma pressure to the GM component. During this time, the negative potential cell stretches deep into the post-midnight region, suggesting a strong substorm-related electric field (e.g., Weimer [69]). The right frame shows this same potential pattern mapped along SCB field lines and into the equatorial plane. The strong, substorm-related electric field is prominent on the night side of the RAM-SCB domain (lower portion of the circle plot). Future work will involve coupling between the full u×B field as calculated in the MHD equatorial plane. This approach will have several advantages over the current implementation. Most importantly, by leveraging the full electric field, the inductive portion due to the changing magnetic field is included. Additionally, it will remove any ambiguity that arises from using the northern hemisphere electrostatic potential over the southern. While the two hemispheres are very similar overall, the MHD field lines are not perfect conductors so small differences can appear. Switching to the u × B field avoids any issues associated with this.

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6. Pressure Coupling Pressure coupling from RAM-SCB to BATS-R-US follows published examples [8, 20, 40] with few deviations. Pressure moments across the RAM domain 10

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Figure 4: Equatorial total fluid pressure maps from BATS-R-US without RAM-SCB coupling (left column), with RAM-SCB pressure coupling (center column), and the corresponding RAM-SCB solution (right column). Rows correspond to different epochs during the August 31, 2001 storm: early storm onset (top row), storm main phase (middle row), and recovery phase (bottom row). Each frame extends out to 6.5 RE in each direction.

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Figure 4 demonstrates the effect of coupling pressure between RAM-SCB and single-fluid, single-species BATS-R-US during an SWMF simulation of the August 31, 2001 storm event. The observed DST curve is shown by the dashedblack curve in the bottom frame of Figure 2. Each row shows a different epoch during the storm: storm onset (6:00 UT), storm main phase (10:00 UT), and storm recovery phase (16:00 UT). Without this coupling (left column), very little 11

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Passing magnetic field information from BATS-R-US to the SCB solver is particularly challenging because of the fundamentally different ways in which the models represent the field. The BATS-R-US field line traces must be converted into SCB ψ magnetic flux surfaces. This involves sorting and grouping the MHD magnetic field lines by radial distance, extending the traces through the MHD inner boundary (typically at a geocentric radius of 2.5 RE ) down to ionosphere altitudes along assumed dipole field lines, and interpolating the field into smooth flux surfaces. Figure 5 shows an example shell (blue surface) created from MHD field traces (red lines) at SCB’s outer boundary. This process has previously been performed using empirical Tsyganenko fields [83] and using independentlyrun SWMF results [84], but as a pre-processing step that produced input for a stand-alone RAM-SCB simulation. These routines have been converted into a set of internalized subroutines that are called on demand during the coupling process. Once the surfaces are successfully built, SCB uses them as initial and boundary conditions for its force-balance calculation. When full two-way coupling is included, however, the MHD field is already force-balanced with the total pressure provided by RAM-SCB. Early results show that, compared to simulations where initial conditions are provided by empirical models, fewer SCB iterations are required to converge to a force-balanced solution when initial conditions are provided by the SWMF with two-way coupling. However, the because the spatial distribution of the plasma pressure still differs between the two (e.g., Figure 4), the final force-balanced field of SCB will differ slightly from the initial condition supplied by BATS-R-US. The typical difference is 10% or less from the initial condition; the difference peaks during early storm periods when the ring current is changing most rapidly. This capability is a new feature that will be used extensively in the future.

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plasma pressure builds in the inner magnetosphere of BATS-R-US. Conversely, there is a clear build up of pressure in RAM-SCB (right column), driving an asymmetric ring current during the early and main phase of the storm. During the recovery phase, the ring current pressure symmetrizes and decays (right column, bottom frame). When pressure coupling is activated, these signatures manifest within the BATS-R-US solution (center column). The pressure features are slightly weaker and more diffuse in the MHD model for several reasons. Foremost is the higher spatial resolution, polar grid, and less diffuse flux limiter employed by the RAM model (superbee versus the monotized-central limiter with βM C = 1.2 [25]), all acting to allow RAM to maintain sharp radial pressure gradients. Additionally, because the pressure solution in MHD is not overwritten, but only nudged towards the RAM solution, the two pressures will never be exactly the same. Despite the discrepancies, the improvement in the coupled versus uncoupled MHD solutions is significant.

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Figure 5: Illustration of the SCB outer boundary magnetic surface (blue transparent surfaces) as created from the MHD magnetic field traces (red lines). Top, side, and perspective views (top left, bottom left, and right hand frames, respectively) are shown.

8. Coupling Consequences

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Coupling the different SWMF components together yields far reaching effects on all models involved. These effects extend outside the spatial regions of the explicit couplings shown in Figure 1. In some cases, the effects are strong enough to be considered implicit couplings: clear effects on the models via a chain of processes rather than a direct connection defined in the software. These extended effects and implicit couplings allow for the development of feedback loops and exciting dynamics within the self-consistent system. Figure 6 shows the most well known consequence of IM-GM coupling: stretching of the MHD magnetotail. Without this coupling, the MHD tail is very short and dipolar (Figure 6, upper left frame.) The plasma sheet is also relatively cold, reaching only a few keV (bottom left frame). Conversely, the additional pressure from the direct coupling between RAM-SCB and BATS-R-US causes the magnetosphere to inflate, pushing the neutral line far down tail and yielding a long, thin current sheet (upper right panel). The plasma in the near-Earth sheet is now reaching higher temperatures (bottom right panel). Even though these regions are outside of the direct coupling domain, they are strongly influenced by it. As coupling from RAM-SCB drastically changes the geometry of the MHD plasma sheet, so changes the characteristics of the fluxes at geosynchronous distance. These changes, and their effect on the resulting RAM-SCB DST , are shown in Figure 7. This figure is the same as Figure 2, but now the top three frames show the MHD plasma conditions when two-way coupling with RAMSCB is used. Again, the composition is obtained via an empirical relationship. Unlike the previous, one-way coupled case (Figure 2), the plasma sheet material

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Figure 6: Noon-midnight meridian cuts of the MHD magnetosphere during the August 31, 2001 magnetic storm. The left column shows results when the simulation does not include coupling from RAM-SCB; the right column shows the results when RAM-SCB pressure is coupled to the MHD model. The top row shows closed and open magnetic field lines (white and black lines, respectively) and contours of total plasma pressure. The bottom frames shows contours of total fluid temperature.

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undergoes more heating and arrives at the inner magnetosphere far warmer (Figure 7, third frame from the top). The additional energy flux delivered to RAM-SCB helps drive the DST towards more realistic values (bottom frame, blue line) compared to the one-way coupled simulation. The DST also shows a two-stage recovery. The brief fast recovery is associated to the temperaturedependent charge exchange rate [53, 52]: hot oxygen decays very quickly, driving a brief period of fast recovery. The improved tail dynamics in the MHD model (Figure 6) result in plamsa inputs into RAM-SCB that drive a more realistic DST dip and recovery. This shows how these models resonate in unexpected ways via two-way coupling. Careful examination of the differences between the geosynchronous plasma conditions in the one-way coupled MHD simulation (top three frames of Figure 2) and two-way coupled MHD simulation (top three frames of Figure 7) reveals another way in which two-way coupling changes the global configuration of the magnetosphere. Shortly before 17:00UT, a sharp density pulse (Figure 2, second frame from the bottom, green line) arrives, compressing the magnetosphere. During such times, the low latitude boundary layer (LLBL) plasma impinges

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into the plasma sheet and into geosynchronous locations [75, 38]. This dense plasma becomes suddenly apparent as dawn- and dusk-centered injections in the top frame of Figure 2. In the two-way coupled simulation, this injection does not appear (top frame, Figure 7). With RAM-SCB providing thermal pressure to BATS-R-US, the magnetosphere becomes inflated and more resilient to the solar wind sudden impulse. Again, two-way coupling has unexpected consequences. Figure 8 shows further feedback in the form of FACs. Each frame shows the FACs being fed into the ionosphere model from BATS-R-US, similar to the the left frame of Figure 3. The top row shows FACs at 6:00 UT of the August 31, 2001 event, which is at the start of the storm. As seen in the top row of Figure 4, a weak partial ring current has formed in RAM-SCB. Without pressure coupling between BATS-R-US and RAM-SCB, very weak region 2 FACs form (Figure 8, upper left frame). When pressure coupling is turned on, the partial ring current is passed to BATS-R-US (e.g., Figure 4, top-center frame). This leads to a slight

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Figure 7: Similar to Figure 2, but for a two-way coupled IM-GM simulation.

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intensification of the region 2 currents and a strong shift of the position of their peaks. As the storm intensifies, (bottom row), the uncoupled simulation fails to form considerable region 2 FACs. In contrast, the two-way coupled run develops strong region 2 FACs. It is important to note that in this simulatin, there is no electron precipitation coupling used. Therefore, there is no direct coupling from RAM-SCB to the ionosphere. Rather, there is an implicit coupling: RAM-SCB drives strong pressure gradients in the MHD model which leads to strong FACs. Previous studies during stronger events have shown that this effect improves comparisons to observed values [73].

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Figure 8: A comparison of FACs passed to the IE component (following the format of Figure 3, left frame) when the MHD model is used without (left column) and with (right column) coupling to RAM-SCB. The top row shows conditions during the early storm period of the August 31, 2001 event; the bottom row shows conditions during the storm main phase.

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As more SWMF physics components are added, more opportunities for implicit couplings arise. For example, when first-principles-based outflow is included via the Polar Wind Outflow Model [21], the increased region 2 FACs from the ring current coupling have been found to drive additional O+ outflow. This additional mass then circulates through the plasma sheet and into the ring current [73]. This creates a two-way feedback loop between the outflow dynamics and the ring current energy content [72]. Many more opportunities exist

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to explore how the ring current interacts with the magnetosphere-ionospherethermosphere system.

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RAM-SCB now has robust and extensive coupling capabilities within the Space Weather Modeling Framework. Full two-way coupling of plasma fluxes from MHD and ring current pressure to MHD exist for single fluid, multi-species, and full multi-fluid approaches as well as anisotropic MHD. Electric potential values are mapped from the RIM ionosphere to be used by RAM. Magnetic field coupling from BATS-R-US to the SCB solve now happens in during the simulation and not as a post-processing step. These capabilities make RAMSCB one of the most fully-featured ring current models. More importantly, this work enables rich investigations into the ring current and its relationship to the global magnetosphere-ionosphere system. The explicit couplings outlined here allow for researchers to test for implicit couplings, or ways in which the ring current affects magnetosphere dynamics in unexpected ways. The flexibility of the codes allows scientists to turn on and off different features, isolating relationships they discover. RAM-SCB within the SWMF will be a rich source of knowledge for years to come. Acknowledgments This research was conducted as part of the Space Hazards Induced near Earth by Large, Dynamic Storms (SHIELDS) project, funded by the U.S. Department of Energy through the LANL/LDRD-DR Program under contract DE-AC52-06NA25396.

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● RAM-SCB now has robust and extensive coupling capabilities within the Space Weather Modeling Framework. ● Two-way coupling has far reaching effects on simulation results beyond explicit couplings. ● This work enables rich investigations into the ring current and its relationship to the global magnetosphere-ionosphere system