2020 Workshop:Poynting Flux

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Understanding the Electromagnetic Energy Input to Earth’s Atmosphere

Location, Date/Time and Duration

4 hours(GC only)


Alex Chartier
Tomoko Matsuo
Gareth Perry
Bill Bristow
Wenbin Wang
Seebany Datta-Barua

Workshop Categories

Altitudes: IT - Latitudes: polar - Other:

Format of the Workshop

Short Presentations

Estimated attendance


Requested Specific Days


Special technology requests

Grand Challenge Workshop

Request/Justification for Grand Challenge Workshop

This is a major, ongoing area of research that requires a coordinated response from the observational and modeling communities. We plan to organize a measurement campaign, and to combine our data analysis with first-principles models (either as specified forcings or through data assimilation where appropriate). This effort cannot be completed in a single year, so we have requested a Grand Challenge Workshop.

Approximate Timeline and Duration for Grand Challenge Workshop

Three years total

Objectives (specific targets in brackets)

Year 1:

   - Highlight the major data sources relevant to Poynting flux in the ITM and identify their strengths/weaknesses 
     (Identify specific observational gaps)
   - Determine the important spatio-temporal scales to characterize based on current models 
     (validate conclusions using available data, e.g. LEO satellite, radar estimates)
   - Address the relation between different Poynting flux modes (e.g. DC vs AC) and between EM and kinetic energy
     (Quantify the relevant importance of the different modes)

Year 2:

   - Develop an observational campaign to measure the Poynting flux over a specific region 
     (execute the campaign during this year)
   - Produce a data-driven characterization of the high-latitude Poynting flux and related parameters 
     (make available to the community, compare against MHD models)

Year 3:

   - Drive whole-atmosphere models with this data-driven characterization 
     (validate the output against other parameters, e.g. TEC, ion drifts, thermospheric winds/temperatures/densities)
   - Assess relevance to space weather impacts on technology 
     (feed back to user community)

Suggested Tutorial Speakers for your Grand Challenge Workshop

Jeffrey Thayer: Sources, types and magnitudes of electromagnetic energy in the upper atmosphere, and the relation between electromagnetic and kinetic energy fluxes

Brian Anderson: Observing high-latitude electrodynamics using spacecraft constellations

Delores Knipp: What was learnt from the GEM-CEDAR Challenge addressing Poynting flux using DMSP

Dan Weimer: Empirical models of the Poynting flux input: status and future directions


Understanding the Electromagnetic Energy Input to Earth’s Atmosphere

At high latitudes, electromagnetic energy from the Solar wind and magnetosphere flows into the upper atmosphere through the ionosphere (e.g. Gary et al., 1994). This is quantified in terms of Poynting flux. During active magnetic periods, this energy source can be larger than Solar radiation (Luhr and Liu, 2006) and is certainly harder to characterize at all times. The Poynting flux is transformed into thermal or kinetic energy through particle acceleration in the magnetosphere and Joule/frictional heating in the upper atmosphere (e.g. Thayer and Semeter, 2004). Despite the obvious importance of this energy source to the coupled Ionosphere-Thermosphere-Mesosphere (ITM) system, an accurate global picture of the Poynting flux at relevant spatio-temporal scales remains elusive. Current models of the ITM typically still rely on empirical models based on historic datasets (e.g. Heelis et al., 1982; Weimer, 2005) to estimate this forcing. Recent major advances in modeling from the ground up to 600-km (e.g. WACCM-X, WAM, GAIA) and, separately, from the Solar wind down to the ionosphere (e.g. GAMERA, BATS-R-US) essentially meet here.

Science Question: What is the electromagnetic energy input into the Earth’s atmosphere? This topic is aligned with the strategic thrusts outlined in the CEDAR strategic vision. It explores an exchange of energy between all of the space-atmosphere-interaction region (SAIR), it merges many geoscience datasets and models, and it synthesizes knowledge from several disciplines of the solar-terrestrial sciences. Therefore, this Grand Challenge is expected to have a major impact on a large segment of the CEDAR research community.

Alignment with Strategic Plan: This Grand Challenge directly addresses the Space-Atmosphere Interaction Region and an energy transfer process that defines its global behavior. The goal is to understand how, and how much, electromagnetic energy from the Solar wind and magnetosphere is transferred into the upper atmosphere and how that affects the dynamics of the region. This addresses GS Plan Goal #3: "[to understand] How mass, energy, and momentum are transported through the heliosphere, magnetosphere, ionosphere, and atmosphere"

Societal Relevance: Geospace system dynamics driven by electromagnetic energy input, including the high-latitude convective and kinetic processes, produce ionospheric and atmospheric phenomena that have major impacts on technology. For example, at high latitudes, the dominant pattern of GPS signal loss observed by Xiong et al. (2018) appears to be directly associated with the convection of plasma into the polar caps from the sub-auroral dayside ionosphere (Chartier et al., 2019). Both the high-latitude plasma convection cycle and particle precipitation are byproducts of the divergence of the Poynting flux in ITM system.



Chartier, A. T., Huba, J. D., & Mitchell, C. N. (2019). On the Annual Asymmetry of High‐Latitude Sporadic F. Space Weather, 17(11), 1618-1626.

Cosgrove, R. B., Bahcivan, H., Chen, S., Strangeway, R. J., Ortega, J., Alhassan, M., … Cahill, N. (2014). Empirical model of Poynting flux derived from FAST data and a cusp signature. Journal of Geophysical Research: Space Physics, 119(1), 411–430. https://doi.org/10.1002/2013JA019105.

Gary, J. B., Heelis, R. A., Hanson, W. B., & Slavin, J. A. (1994). Field‐aligned Poynting flux observations in the high‐latitude ionosphere. Journal of Geophysical Research: Space Physics, 99(A6), 11417-11427.

Thayer, J. P., & Semeter, J. (2004). The convergence of magnetospheric energy flux in the polar atmosphere. Journal of atmospheric and solar-terrestrial physics, 66(10), 807-824.

Luhr H. and Liu H. (2006): The thermopheric response to geomagnetic storms. ILWS Workshop. GOA Xiong, C., Stolle, C., & Park, J. (2018, April). Climatology of GPS signal loss observed by Swarm satellites. In Annales Geophysicae (Vol. 36, No. 2, pp. 679-693). Copernicus GmbH.


We plan to bring together observationalists, modelers and data assimilation experts to address a major challenge of the CEDAR community: understanding the electromagnetic energy input to Earth's atmosphere. The idea is to bring these experts together to exchange information, specifically to identify, highlight and ultimately address the current challenges faced by the modeling community in coupling whole-atmosphere models with magnetospheric models. A strong presence from the observational community is required to address the challenges faced by modelers, and to highlight the major deficiencies (in terms of spatio-temporal resolution and overall accuracy). Following the first-year meeting we will mount a coordinated observational campaign focused on determining the Poynting flux as accurately as possible over a specific region (to be chosen in the first-year meeting). In parallel to this, we will work on developing existing approaches to the problem (e.g. AmGEO, MIX) and coupling those specification efforts to models that include the ITM. By the third year, we expect to have validated these specifications and quantified their effects on other model parameters, as well as quantifying the uncertainties and the important covariances between errors at different points and in different variables. This information will be critical in feeding the development of whole-atmosphere-magnetosphere modeling efforts, both through validation of those models and possibly through direct data assimilation.

Workshop Summary


The first public session of the CEDAR Grand Challenge: “Understanding the Electromagnetic Energy Input to Earth’s Atmosphere” was held virtually on 25 June 2020. Over 100 people attended the live zoom call, and the audience is still growing through the recording posted on YouTube: https://www.youtube.com/watch?v=FZIKuhEYGBs

Motivation for Poynting flux investigations

Poynting flux is important in redistributing thermospheric mass through heating and mechanical action, and also in driving the polar wind outflow. Poynting flux is also a major energy input to Ionosphere-Thermosphere models that needs to be specified correctly if we are to understand the subsequent propagation of energy throughout the system. In particular, it is important to understand the partitioning of electromagnetic energy between frictional heating and ion drag. Because of its effects on the neutral atmosphere, Poynting flux is relevant to satellite drag estimation.

Theoretical points

It is critical to understand the rest frame of the neutral gas (the 3D neutral wind distribution) to understand the partitioning of electromagnetic energy. This unknown neutral wind variability presents a major challenge.

There are both curl-free and divergence-free terms present in the currents (curl-free due to conductivity variations). In general the conductivity is a major unknown for understanding the transitions between EM energy and thermal/mechanical energy.

There is a perhaps ubiquitous assumption that Poynting flux does not exit through the “sides” of the high-latitude ionosphere. No evidence was presented to challenge that assumption here.

Relevant datasets

There were important contributions from electric field instruments, ground and space magnetometers, SuperDARN ExB velocity data, Incoherent Scatter Radar, neutral wind data, in situ thermospheric mass density and temperature, particle precipitation data, and auroral imaging.

Observational challenges

Data consistency is important – what happens if one dataset (e.g. E) has convection reversal somewhere, other one (e.g. dB) has it somewhere else? There can be a lot of uncertainty in different estimates – a factor of two or more. Subgrid-scale (unresolved) can be another factor of two. Neutral winds are also a huge source of uncertainty, and could be considered the biggest observational “gap” at the moment. Due to transport, the problem has to be addressed globally, across both hemispheres. Energy partitioning from EM to thermal and kinetic has been addressed locally, highlighting the importance of the problem, but the coverage needs to be extended in altitude and across the entire polar caps.

Current observational status

Despite the many challenges and limitations identified, in some cases thermospheric density observations have been reproduced accurately by large-scale specifications and empirical models. However, existing models/specifications are often not close to matching the observed heating rates, or to each other – often not within a factor of two (e.g. RISR observations compared to IPWM driven by different specifications, different Halloween 03 estimates).

DMSP provides a statistical characterization of Poynting flux. A hotspot found on the dayside, correlated with By. There are possible interhemispheric asymmetries in the data.

The new AmGeo data assimilation and data services are in development. This provides an open-source toolkit for estimating high-latitude ionospheric electrodynamics parameters. The project is new software, based on heritage of the KRM/AMIE development line. Currently it has SuperDARN/SuperMAG, next to be added is AMPERE, then other datasets.

New SuperDARN local divergence-free fitting technique indicates mesoscale flow patterns consistent with expectations of small-scale flows based on single-point/single-satellite data. Expansion of this technique will benefit from other SuperDARN developments, e.g. move to imaging radar capability at an increasing number of radars.


The community should aim to constrain models from a range of different perspectives to reduce dependence on internal parameterizations that can be hidden from view. To that end, we encourage the publication of negative results – it is important to find out where our current model-based understanding breaks down. Likewise, the community should develop statistical characterizations of model uncertainties, based on systematic tests of the outputs across the full range of internal parameterization values.

Observationally, a major challenge is aligning physical boundaries across diverse datasets. For example, small systematic errors in the location of the convection reversal boundary could cause large errors in estimated electrodynamics output parameters.

Another challenge is validation across diverse datasets. As a starting point, we propose the identification of periods of both good optical (allsky camera) and SuperDARN data coverage. Despite the obvious systematic biases introduced (no sunlit data for the cameras, no extreme low density/high absorption for SuperDARN) this would be valuable in performing multi-instrument investigations.

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