HAO 2012 Profiles In Science: Dr. Stanley C. Solomon

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Dr. Stan Solomon is a Senior Scientist at the NCAR High Altitude Observatory, specializing in the physics and chemistry of the upper atmosphere and ionosphere. He received the A.B. from Harvard College, and the M.S. and Ph.D. from the University of Michigan. He studies the impacts of solar radiation and geomagnetic disturbances on the thermosphere and ionosphere using theoretical modeling and data analysis, and investigates changes in the climate of the upper atmosphere. He is a Co-Director of the Center for Integrated Space Weather Modeling, and an Interdisciplinary Scientist on the NASA Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics mission. He has also served as a lecturer at the University of Colorado, teaching topics ranging from solar-terrestrial physics to satellite system design.

Selected Recent Publications

Figure 1: High resolution.

(1) Solomon, S. C., A. G. Burns, B. A. Emery, M. G. Mlynczak, L. Qian, W. Wang, D. R. Weimer, and M. Wiltberger. 2012: Modeling studies of the impact of high-speed streams and co-rotating interaction regions on the thermosphere-ionosphere.
J. Geophys. Res., 117, A00L11, doi:10.1029/2011JA017417.

Abstract: Changes in the thermosphere-ionosphere system caused by high-speed streams in the solar wind, and the co-rotating interaction regions they engender, are studied using a combination of model simulations and data analysis. The magnetospheric responses to these structures and consequent ionospheric drivers are simulated using the numerical Coupled Magnetosphere-Ionosphere-Thermosphere model and the empirical Weimer 2005 model, finding that the interplanetary magnetic field (IMF) is more important than solar wind speed and density per se in controlling magnetosphere-ionosphere coupling. The NCAR Thermosphere-Ionosphere-Electrodynamics General Circulation Model is then employed to calculate neutral density, nitric oxide cooling, and electron density, for comparison to space-based measurements from the STAR instrument on the CHAMP satellite, the SABER instrument on the TIMED satellite, and GPS occultations from the COSMIC mission, respectively. The recurrent, periodic changes observed under solar minimum conditions during 2008, and particularly during the Whole Heliospheric Interval (March-April of 2008), are simulated by the model and compared to these measurements. Numerical experiments were conducted to elucidate the mechanisms of solar wind and IMF forcing, setting the solar wind speed and density to nominal values, smoothing the IMF, and also setting it to zero. The results confirm the importance of IMF variations, particularly its north-south component (Bz), but also show that when the average Bz values are negative (southward), the interaction with increased solar wind speed amplifies the magnetosphere-ionosphere-thermosphere response. Conversely, during events when Bz is on average positive (northward), even large increases in solar wind speed have small effects on the system.

Figure 1 caption: Comparison of thermospheric neutral density at 400 km measured by the STAR accelerometer on the CHAMP satellite with TIE-GCM simulations for the WHI during March–April 2008. (a) CHAMP observations from the ascending node, near 21–18 local solar time as indicated by the white dashed lines. (b) Simulations at the local time and latitude of the satellite using the TIE-GCM, with high-latitude forcing derived from the Weimer 2005 model using all measured solar wind and IMF parameters. (c) Simulations with the solar wind speed set to 400 km/s and density set to 4 per cc. (d) Simulations with the IMF y and z components smoothed using a running 72-hour centered mean. (e) Simulations with the IMF y and z components set to zero.

Figure 2: High resolution.

(2) Qian, L., A. B. Burns, S. C. Solomon, and P. C. Chamberlin. 2012: Solar flare impacts on ionospheric electrodynamics. Geophys. Res. Lett., 39, L06101, doi:1019/2012GL051102.

Abstract: The sudden increase of X-ray and extreme ultra-violet irradiance during flares increases the density of the ionosphere through enhanced photoionization. In this paper, we use model simulations to investigate possible additional contributions from electrodynamics, finding that the vertical E×B drift in the magnetic equatorial region plays a significant role in the ionosphere response to solar flares. During the initial stage of flares, upward E×B drifts weaken in the magnetic equatorial region, causing a weakened equatorial fountain effect, which in turn causes lowering of the peak height of the F2 region and depletion of the peak electron density of the F2 region. In this initial stage, total electron content (TEC) enhancement is predominantly determined by solar zenith angle control of photoionization. As flares decay, upward E×B drifts are enhanced in the magnetic equatorial region, causing increases of the peak height and density of the F2 region. This process lasts for several hours, causing a prolonged F2-region disturbance and TEC enhancement in the magnetic equator region in the aftermath of flares. During this stage, the global morphology of the TEC enhancement becomes predominantly determined by these perturbations to the electrodynamics of the ionosphere.

Figure 2 caption: Horizontal maps at of the ionosphere responses to the X17 flare that occurred on October 28, 2003 at 11:10 UT, about 20 minutes after the peak of the flare. (a): change of TEC; (b) change of NmF2; (c) change of hmF2; and (d) rate of change of electron density due to vertical E×B drifts at pressure level +2 (approximately 250 km altitude). Also plotted in the figure are the terminator and magnetic equator.

Figure 3: High resolution.

(3) Lastovicka, J., S. C. Solomon, and L. Qian. 2012: Trends in the neutral and ionized upper atmosphere. Space Sci. Rev., 168, 113, doi:10.1007/s11214-011-9799-3.

Abstract: This article reviews our knowledge of long-term changes and trends in the upper atmosphere and ionosphere. These changes are part of complex and comprehensive pattern of long-term trends in the Earth's atmosphere. They also have practical impact. For example, decreasing thermospheric density causes the lifetime of orbiting space debris to increase, which is becoming a significant threat to important satellite technologies. Since the first paper on upper atmosphere trends was published in 1989, our knowledge has progressed considerably. Anthropogenic emissions of greenhouse gases affect the whole atmosphere, not only the troposphere. They cause warming in the troposphere but cooling in the upper atmosphere. Greenhouse gases such as carbon dioxide are not the only driver of long-term changes and trends in the upper atmosphere and ionosphere. Anthropogenic changes of stratospheric ozone, long-term changes of geomagnetic and solar activity, and other drivers play a role as well, although greenhouse gases appear to be the main driver of long-term trends. This makes the pattern of trends more complex and variable. A consistent, although incomplete, scenario of trends in the upper atmosphere and ionosphere is presented. Trends in F2-region ionosphere parameters, in mesosphere-lower thermosphere dynamics, and in noctilucent or polar mesospheric clouds, are discussed in more detail. Advances in observational and theoretical analysis have explained some previous discrepancies in this global trend scenario. An important role in trend investigations is played by model simulations, which facilitate understanding of the mechanisms behind the observed trends.

Figure 3 caption: Model simulation of trends in ƒ0F2 and hmF2 at noon, longitude 0°, as a difference between the basic state and the state with doubled CO2 concentration. Dashed curve: hmF2 for basic state. Solid curve: hmF2 for doubled CO2.

Figure 4: High resolution.

(4) Qian, L., and S. C. Solomon. 2012: Thermospheric mass density: An overview of temporal and spatial variations. Space Sci. Rev., 168, 147, doi:10.1007/s11214-011-9810-z.

Abstract: Thermosphere neutral density shows complicated temporal and spatial variations driven by external forcing of the thermosphere/ionosphere system, internal dynamics, and thermosphere and ionosphere coupling. Temporal variations include abrupt changes with a time scale of minutes to hours, diurnal variation, multi-day variation, solar-rotational variation, annual/semiannual variation, solar-cycle variation, and long-term trends with a time scale of decades. Spatial variations include latitudinal and longitudinal variations, as well as variation with altitude. Atmospheric drag on satellites varies strongly as a function of thermospheric mass density. Errors in estimating density cause orbit prediction error, and impact satellite operations including accurate catalog maintenance, collision avoidance for manned and unmanned space flight, and re-entry prediction. In this paper, we summarize and discuss these density variations, their magnitudes, and their forcing mechanisms, using neutral density data sets and modeling results. The neutral density data sets include neutral density observed by the accelerometers onboard the Challenging Mini-satellite Payload (CHAMP), neutral density at satellite perigees, and global-mean neutral density derived from thousands of orbiting objects. Modeling results are from the National Center for Atmospheric Research (NCAR) thermosphere-ionosphere-electrodynamics general circulation model (TIE-GCM), and from the NRLMSISE-00 empirical model.

Figure 4 caption: Solar-rotational and annual/semiannual variations of neutral density. (a) Global-mean neutral density at 400 km for 2003. Blue: daily density data derived from satellite drag; black: 81-day running mean of the daily density data; red: daily density simulated by NCAR TIE-GCM. (b) Global mean neutral density at 400 km for 2008. Blue: daily density data derived from satellite drag; black: 81-day running mean of the daily density data; red: daily density simulated by the TIE-GCM. (c) F10.7 and Ap indices for 2003. (d) F10.7 and Ap indices for 2008.

Figure 5: High resolution.

(5) Wiltberger, M., L. Qian, C.-L. Huang, W. Wang, R. E. Lopez, A. G. Burns, S. C. Solomon, Y. Deng, and Y. Huang. 2012:CMIT study of CR2060 and 2068 comparing L1 and MAS solar wind drivers. J. Atmos. Solar -Terr. Phys., 83, 39, doi:10.1016/j.jastp.2012.01.005.

Abstract: While it is widely know that Coronal Mass Ejections and their related solar wind features are significant drivers of activity with geospace it is less known that Corotating Interaction Regions (CIR) and the high speed stream (HSS) periods that precede them are also drivers of activity within geospace. The most recent extended and weak solar minimum interval has brought renewed attention to the space weather impacts of CIR+HSS periods since the highly structured and relatively stable coronal hole features on the Sun resulted in numerous CIR+HSS periods. In this paper we examine two Carrington Rotations (CR) using the Coupled Magnetosphere Ionosphere Thermosphere (CMIT) model. CR2060 lasted from August 4, 2007 to September 11, 2007 and contained three CIR+HSS periods. CR2068, also known as the Whole Heliosphere Interval (WHI), began on March 20, 2008 and lasted until April 16, 2008 and contained two CIR+HSS periods. For each CR simulations driven by both L1 solar wind observations from the OMNI dataset and L1 conditions extracted from CORHEL heliospheric simulations were conducted. The heliospheric simulation results capture the velocity and density structures seen in the solar wind well for CR2060 and only get one of the CIR+HSS periods in CR2068. In each CR the heliospheric simulations produce a much weaker IMF and have less temporal variability in all parameters. We compare the results of the CMIT simulations for each CR to observations of the cross polar cap potential (CPCP), hemispheric power (HP), and SYMH index. We examine the response of the thermospheric density during these intervals by utilizing data from the CHAMP satellite. In the magnetosphere we use magnetic field data from the GOES spacecraft to asses the different simulations ability to describe the distribution and intensity the ULF wave power.

Figure 5 caption: Comparison of ULF wave power in the magnetic field obtained from GOES-12 observations to results from the simulations. In each sub-plot the ULF PSD is plotted as a function of frequency versus MLT in 3 hour bins obtained from the magnetic field data from the entire interval. The first column contains the wave power in the field aligned, Bb direction. The azimuthal direction, B∮, is plotted in the second column, and the radial direction is shown in the third and final column. The first row contains the results obtained from the GOES-12 observations while the second and third rows contain the results from the CMIT-L1 and CMIT-MAS simulations.

Figure 6: High resolution.

(6) Yue, X., W. S. Schreiner, Y.-H. Kuo, D. C. Hunt, W. Wang, S. C. Solomon, A. G. Burns, D. Bilitza, J.-Y. Liu, W. Wan, and J. Wickert. 2012: Global 3-D ionospheric electron density reanalysis based on multi-source data assimilation. J. Geophys. Res., 117, A09325, doi:10.1029/2012JA017968.

Abstract: We report preliminary results of a global 3-D ionospheric electron density reanalysis during 2002–2011 based on multi-source data assimilation. The monthly global ionospheric electron density reanalysis has been done by assimilating the quiet days ionospheric data into a data assimilation model constructed using the international reference ionosphere (IRI) model and a Kalman filter. These data include global navigation satellite system (GNSS) observations of ionospheric total electron content (TEC) from ground based stations, ionospheric radio occultations by CHAMP, GRACE, COSMIC, SAC-C, Metop-A, and TerraSAR-X satellites, and Jason-1/2 altimeter TEC measurements. The output of the reanalysis is 3-D gridded ionospheric electron densities with temporal and spatial resolutions of 1 hour in universal time, 5° in latitude, 10° in longitude, and ~ 30 km in altitude. The climatological features of the reanalysis results, such as solar activity and seasonal variations, and the global morphology of the ionosphere, agree well with those in the empirical models and observations. The global electron content (GEC) derived from the international GNSS service (IGS) global ionospheric maps (GIM), Poker Flat Incoherent Scatter Radar (PFISR) observed electron density profiles during 2007–2010, and global ionosonde network observed ƒ0F2 during 2002–2011 are used to make an independent validation of the reanalysis method. All the comparisons show that the reanalysis results have smaller deviations and bias than the IRI predictions. Especially after April 2006 when the six COSMIC satellites were launched, the reanalysis shows significant improvements over the IRI predictions. The obvious overestimation of the global ionospheric F-region peak electron density by the IRI model during the 23/24 solar minimum is corrected well by the reanalysis. The potential application and improvements of the reanalysis are also discussed.

Figure 6 caption: Example of the reanalyzed global 3-D electron density and the corresponding peak density (NmF2) and vertical TEC map at 1900 UT in September 2006.

(7) Burns, A. G., S. C. Solomon, L. Qian, W. Wang, B. A. Emery, M. Wiltberger, and D. R. Weimer. 2012: The effects of corotating interaction region / high speed stream storms on the thermosphere and ionosphere during the last solar minimum. J. Atmos. Solar -Terr. Phys., 83, 79, doi:10.1016/j.jastp.2012.02.006.

Abstract: Geomagnetic storms at solar minimum are driven by the interaction between high speed streams and low speed streams (Corotating Interactions regions/High Speed Streams—CIR/HSSs), rather than by Coronal Mass Ejections (CMEs). Solar minimum storms are generally of smaller amplitude, but they also have other characteristics that affect the response of the thermosphere/ionosphere (TI) system to them. Burns et al. [2011] explore both the background upper atmosphere and the characteristics of these CIR/HSS events in 2008 using both models and data. Several features are apparent: 1) The nature of the CIR/HSS event was different in the second half of 2008 when the HSS were of short duration compared with the first half of that year when the onset of the next HSS overlapped with the declining phase of the previous one; 2) Individual HSSs had different impacts depending on the average sign of Bz and the duration of the event; 3) Equatorial NmF2 maximized at the equinox in both data and model; 4) No winter anomaly was apparent in the data, but one was present in the model runs; 5) Local time variations dominated the mean neutral densities; 6) NO cooling is maximum in the summer high latitudes; 7) CIR/HSS effects with extended recovery periods were very important if not dominant features of the plots of the first half of 2008; 8) Positive storm effects in NmF2 maximized on the day of the onset of the event at middle latitudes, whereas negative storm effects maximized on the next day at these latitudes; 9) Negative storm effects, neutral density increases and enhanced NO cooling continued for several days after the storm began, indicating the importance of continued forcing associated with Alfven waves and elevated solar wind speeds in the HSS.

(8) Peterson, W. K., T. N. Woods, J. M. Fontenla, P. G. Richards, P. C. Chamberlin, S. C. Solomon, W. K. Tobiska, and H. P. Warren. 2012: Solar EUV and XUV energy input to thermosphere on solar rotation time scales derived from photoelectron observations. J. Geophys. Res., 117, A05320, doi:10.1029/2011JA017382.

Abstract: Solar radiation below ~100 nm produces photoelectrons, a substantial portion of the F region ionization, most of the E region ionization, and drives chemical reactions in the thermosphere. Unquantified uncertainties in thermospheric models exist because of uncertainties in solar irradiance models used to fill spectral and temporal gaps in solar irradiance observations. We investigate uncertainties in solar energy input to the thermosphere on solar rotation time scales using photoelectron observations from the FAST satellite. We compare observed and modeled photoelectron energy spectra using two photoelectron production codes driven by five different solar irradiance models. We observe about 1.7% of the ionizing solar irradiance power in the escaping photoelectron flux. Most of the code/model pairs used reproduce the average escaping photoelectron flux over a 109-day interval in late 2006. The code/model pairs we used do not completely reproduce the observed spectral and solar rotation variations in photoelectron power density. For the interval examined, 30% of the variability in photoelectron power density with equivalent wavelengths between 18 and 45 nm was not captured in the code/model pairs.