RESEARCH ARTICLE
10.1002/2014JA020579 Key Points:• Tropospheric waves produce extreme short-term thermospheric tidal variations • Current satellite-based
observational strategies cannot capture this variability
• Our model reproduces many salient observations but overestimates eastward tides
Correspondence to: K. Häusler, kathrin@ucar.edu
Citation:
Häusler, K., M. E. Hagan, J. M. Forbes, X. Zhang, E. Doornbos, S. Bruinsma, and G. Lu (2015), Intraannual variability of tides in the thermosphere from model simulations and in situ satellite observations, J. Geophys. Res. Space Physics, 120, 751–765, doi:10.1002/2014JA020579.
Received 4 SEP 2014 Accepted 13 DEC 2014
Accepted article online 20 DEC 2014 Published online 24 JAN 2015
Intraannual variability of tides in the thermosphere
from model simulations and in situ
satellite observations
K. Häusler1,2, M. E. Hagan1, J. M. Forbes3, X. Zhang3, E. Doornbos4, S. Bruinsma5, and G. Lu1
1High Altitude Observatory, National Center for Atmospheric Research, Boulder, Colorado, USA,2Advanced Study Program, National Center for Atmospheric Research, Boulder, Colorado, USA,3Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, Colorado, USA,4Aerospace Engineering, Delft University of Technology, Delft, Netherlands,5Department of Terrestrial and Planetary Geodesy, CNES, Toulouse, France
Abstract
In this paper, we provide insights into limitations imposed by current satellite-based strategies to delineate tidal variability in the thermosphere, as well as the ability of a state-of-the-art model to replicate thermospheric tidal determinations. Toward this end, we conducted a year-long thermosphere-ionosphere-mesosphere-electrodynamics general circulation model (TIME-GCM) simulation for 2009, which is characterized by low solar and geomagnetic activity. In order to account for tropospheric waves and tides propagating upward into the ∼30–400 km model domain, we used 3-hourly MERRA (Modern-Era Retrospective Analysis for Research and Application) reanalysis data. We focus on exospheric tidal temperatures, which are also compared with 72 day mean determinations from combined Challenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) satellite observations to assess the model’s capability to capture the observed tidal signatures and to quantify the uncertainties associated with the satellite exospheric temperature determination technique. We found strong day-to-day tidal variability in TIME-GCM that is smoothed out when averaged over as few as ten days. TIME-GCM notably overestimates the 72 day mean eastward propagating tides observed by CHAMP/GRACE, while capturing many of the salient features of other tidal components. However, the CHAMP/GRACE tidal determination technique only provides a gross climatological representation, underestimates the majority of the tidal components in the climatological spectrum, and moreover fails to characterize the extreme variability that drives the dynamics and electrodynamics of the ionosphere-thermosphere system. A multisatellite mission that samples at least six local times simultaneously is needed to provide this quantification.1. Introduction
Over the last decades continuous and globally distributed measurements by low-Earth orbiting satellites led to the discovery that atmospheric tides play an important role in driving the dynamics and electrodynamics of the ionosphere-thermosphere-mesosphere (ITM) system [e.g., Oberheide and Forbes, 2008; Oberheide et al., 2009; Hagan et al., 2007, 2009; Lühr et al., 2008; Immel et al., 2009; He et al., 2011;
Jin et al., 2011; Liu et al., 2010, 2013]. In particular, the especially large ITM impacts of upward propagating
tides that originate in the troposphere were unanticipated. Due to the nature of satellite orbits, measurements are commonly averaged over numerous days and sometimes even months in order to obtain measurements over a full diurnal cycle and thus derive tidal signals. Nguyen and Palo [2013] reported on a new technique and computed the first daily satellite-based estimates of the diurnal migrating tide in the lower and middle atmosphere by combining temperature data from two satellite instruments. Relatedly, Lieberman et al. [2004] invoked a technique developed by Oberheide et al. [2002] to infer daily estimates of nonmigrating diurnal tidal temperatures from the middle atmospheric satellite measurements. However, daily global-scale tidal determinations have yet to be inferred from satellite diagnostics above 80 km. Recently, Forbes et al. [2014] presented exospheric tidal temperature results derived from combining CHAMP (Challenging Minisatellite Payload) and GRACE (Gravity Recovery and Climate Experiment) measurements over 72 days, thus dividing the essential averaging time period in half. Notably, this method strongly depends on the location of the satellites with respect to each other and cannot be applied continuously as detailed in section 4.
A recent study by Lu et al. [2014] showed that even the combination of three low-Earth orbiting (LEO) satellites taking simultaneous in situ measurements of the upper atmosphere is not sufficient to fully capture a global perspective of the dynamical thermosphere and ionosphere. The authors focused on the global ionospheric and thermospheric response to an April 2010 geomagnetic storm using CHAMP, GRACE, and GOCE (Gravity field and steady-state Ocean Circulation Explorer) measurements along with results from the NCAR (National Center for Atmospheric Research) TIME-GCM (thermosphere-ionosphere-mesosphere-electrodynamics general circulation model). Their results demonstrate that reliable general circulation models are necessary to support the interpretation of satellite and ground-based measurements in order to answer some of the open questions concerning the geospace environment. The aforementioned TIME-GCM is a three-dimensional time-dependent global grid-point model, which simulates the circulation, temperature, electrodynamics, and compositional structure of the upper atmosphere and ionosphere from first principles. We refer the reader to Roble and Ridley [1994], Roble [1995, 1996] and references therein for a more complete description of the TIME-GCM. Traditionally, daily-averaged ECMWF (European Centre for Medium-Range Weather Forecasts) or NCEP (National Centers for Environmen-tal Prediction) data and climatological tides from the Global Scale Wave Model (GSWM) were introduced at the TIME-GCM lower boundary (10 hPa or ≈ 30 km). There exist two versions of the GSWM, GSWM-02 and GSWM-09. The latter includes updated tropospheric tidal heating rates using International Satellite Cloud Climatology Project (ISCCP) radiative fluxes and Tropical Rainfall Measuring Mission (TRMM) latent heating profiles and TRMM rainfall rates [Zhang et al., 2010a, 2010b]. An initial comparison between TIME-GCM/GSWM-02 simulated tides and CHAMP observations revealed some similarities but significant discrepancies for particular components [Häusler et al., 2010], which motivated the desire to pursue alternative TIME-GCM lower boundary conditions.
Recently, Häusler et al. [2014] reported on the development of a new lower boundary condition (LBC) for TIME-GCM based on 3-hourly MERRA (Modern-Era Retrospective Analysis for Research and Application) temperatures, horizontal winds, and geopotential heights, which self-consistently accounts for tropospheric day-to-day tidal and planetary wave variability. Because the new MERRA LBC introduces daily tidal variability in addition to planetary waves into TIME-GCM, it renders the use of two different inputs at the lower boundary unnecessary. The MERRA LBC produced measurable variability in upper thermospheric density and captured the salient features observed by GOCE [Häusler et al., 2014].
In this paper we present the intraannual variations of representative TIME-GCM atmospheric tides (section 3) during 2009 using the new MERRA LBC and compare them to the combined CHAMP and GRACE tidal temperature determinations (section 4) during the second half of the year when the observational tides are available. We also quantify the uncertainties associated with the tidal determination technique from the combined CHAMP and GRACE measurements by comparing tides determined from the full model results with those that correspond to the tides determined by analyzing the TIME-GCM results along the satellite orbits (section 5). Hereafter, we refer to the latter as the sampled model results. Discussions and conclusions follow in section 6. The paper finishes with a summary and a description of future work in section 7.
2. The 2009 TIME-GCM/MERRA Simulations
For the year-long 2009 simulations reported herein, we utilized the new TIME-GCM lower boundary condition based on 3-hourly MERRA temperatures, horizontal winds, and geopotential heights [Häusler
et al., 2014]. We used the 10.7 cm solar radio flux (F10.7) values along with hemispheric power and cross-cap
potential values based on the prevailing Kp indices during 2009 to represent solar radiative and auroral forcings. These are shown in Figure 1. The year 2009 is characterized by low solar and geomagnetic activity. The annual averages amount to 70.5 sfu and 3.9 nT for F10.7 and Ap, respectively, as indicated with the red dashed line in Figure 1. Solar activity increased toward the end of the year, but the maximum value reached on 17 December was still only 84.2 sfu. Further, only five geomagnetic storms, each lasting for 3 h, were measured during 2009; three were categorized as minor storms (G1) and two were categorized as moderate storms (G2) (see http://www.swpc.noaa.gov/NOAAscales/NOAAscales.pdf for more details on storm definitions). The minor storms occurred on 13 March at 00–03 UT (Kp = 4.7), on 24 June at 18–21 UT (Kp = 5.0), and on 22 July at 06–09 UT (Kp = 5.0). Both moderate storms had a Kp value of 5.7 and occurred on 22 July at 03–06 UT and 30 August at 15–18 UT.
a)
b)
Figure 1. Yearly course of the (a) 10.7 cm solar radio flux (F10.7,
sfu =10−22W∕m2Hz) and (b) the dailyApindex (nT) for the year
2009. The red dashed line in each plot represents the yearly mean; F10.7year= 70.5sfu,Apyear= 3.9nT.
In order to properly resolve upward propagating tides, it is necessary to run TIME-GCM with a resolution of 2.5◦ by 2.5◦in latitude and longitude and four grid points per scale height in the vertical direction. With the aim to compare the model output with satel-lite measurements, we converted the model output from the pressure grid to an altitude-based grid with a vertical resolution of 5 km. For each latitude, altitude, and day, we then calculated the tides using a function of the form:
An,scos(nΩEt + s𝜆 − 𝜙n,s) where An,s
represents the amplitude of the n, s component, n denotes the subhar-monic of a solar day, ΩEis the rotation rate of Earth (2𝜋∕24 h), t is universal time, s is zonal wave number,𝜆 is
a)
b)
c)
Figure 2. Contours of TIME-GCM/MERRA DE3 temperature amplitudes
(K) versus latitude and days in July 2009 at 340 km altitude. Shown are (a) the daily values, (b) a 10 day running mean moved forward in time by 1 day, and (c) a 72 day running mean moved forward in time by 1 day. See text for details.
longitude, and𝜙n,sis the phase (i.e., the time of maximum amplitude) of the n, s component [Forbes et al., 2006]. We calculated diurnal tides (n = 1) for zonal wave numbers s = −4 to 6 and semidiurnal tides (n = 2) for zonal wave numbers s = −3 to 7. For the terdiurnal (i.e., n = 3) harmonic, we solved only for the migrating component (i.e.,
s = 3). Thereby, positive (negative)
wave numbers s represent westward (eastward) propagating tides.
In this paper we use the nomenclature DWs and DEs to refer to a diurnal tide with zonal wave number s that is either propagating westward (W) or eastward (E). When considering a semidiurnal or terdiurnal tide, we replace D with S or T, respectively.
Figure 2a shows an example of the daily variability for a nonmigrating tide: DE3 temperature amplitudes for the month of July 2009 as a function of latitude at 340 km altitude. The DE3 tide is primarily excited by latent heat release in the tropical troposphere [Hagan and Forbes, 2002]. The day-to-day variations are big, ranging from 4 K to 18 K. Liu [2014] also reported on significant DE3 day-to-day variability seen in the NCAR Whole Atmosphere Community Climate Model with thermosphere extension (WACCAM-X) results. Figure 2 also shows the same DE3 amplitudes but as
the respective 10 day (Figure 2b) and 72 day (Figure 2c) running means, moved forward in time by 1 day. The averaging over several days significantly reduces the DE3 amplitudes by up to 50% for the 10 day means and even more for the 72 day means. Also, the strong day-to-day variation observed in the daily values is smoothed out. This result suggests that the inherent averaging that characterizes observational tidal climatologies leads to significant mischaracterizations of these important dynamical drivers of the atmosphere-ionosphere system, both in terms of inherent variability and amplitude maxima and minima. In the subsequent sections 3–5, we will report on the migrating tides DW1, SW2, and TW3, and the nonmigrating tides DE3, DW2, and SW1. These components represent a cross section of the results that also dominate the modeled and measured thermospheric variations. In section 6, we will also discuss in more detail SE1 and briefly the other calculated tides.
3. Intraannual Variability of TIME-GCM Tides
Figure 3 shows the intraannual variation of the DE3 temperature amplitude for the year 2009. In order to enhance the legibility of these results, we present here and in Figure 4 the 10 day running mean moved forward in time by 1 day. The intraannual variation resembles the one shown by Häusler and Lühr [2009] for the zonal wind based on CHAMP measurements with a maximum in July/August and smaller secondary peak in March/April. However, the TIME-GCM/MERRA simulations also depict a maximum in January, which is almost comparable in size to the July/August peak and is not present in the CHAMP observations. Note that the CHAMP results discussed by Häusler and Lühr [2009] are climatological means based on 4 years of data (2002–2005) and thus can mask the year-to-year variation. In addition, in January 2009 a record strong and prolonged major stratospheric sudden warming (SSW) occurred, which altered the stratosphere distinctly [Harada et al., 2009; Labitzke and Kunze, 2009; Manney et al., 2009]. Several studies [e.g.,
Goncharenko et al., 2010; Fang et al., 2012; Liu et al., 2014, and references therein] showed that a SSW event
influences ionospheric and thermospheric variability. Thus, we presume that the strong DE3 TIME-GCM temperature amplitudes shown in Figure 3 may be related to the unusual 2009 SSW event. This assumption warrants further investigation, which is beyond the scope of our report.
Figure 4a displays the intraannual variation of the migrating diurnal tide (DW1) in temperature. This tidal component is primarily excited in situ by the absorption of extreme ultraviolet radiation (EUV). The strongest amplitudes are found in the northern midlatitudes around both equinoxes and in December 2009. In the polar regions, we find the lowest amplitudes and note that the minimum amplitudes extend toward middle latitudes during times of maximum sunlight, i.e., in December/January in the Southern Hemisphere and in July/August in the Northern Hemisphere. There is an interesting quasi-periodicity in the DW1 amplitude during the last 100 days of the year which mimics the F10.7 variability illustrated in Figure 1. Figure 4b shows the annual variation of the migrating semidiurnal tide (SW2) in temperature. There is more temporal and latitudinal variability in SW2 than in DW1, but the magnitude is only half of DW1. Peak amplitudes exceeding 32 K are found between ±30◦latitude with the maximum slightly shifted to the north of the equator during December/January and south from the equator from June to October. The upward propagating component of SW2 is primarily attributable to the absorption of UV radiation by ozone in the stratosphere-mesosphere along with secondary contributions by forcing in the troposphere [Hagan, 1996;
Zhang et al., 2010a, 2010b].
The TW3 component shown in Figure 4c is of comparable magnitude as SW2 in the beginning of the year as well as toward the end of the year. Peak amplitudes are mostly located in the Southern Hemisphere. Yet in June/July, the maximum of ≈20 K is found around 15◦N.
Strong latitudinal and temporal variation is seen in the DW2 component (Figure 4d), but the magnitude is quite small with peak amplitudes of only 4 K. The maxima in midlatitudes display an annual variation. They are usually located in the hemisphere with the most sunlight. This is also true for the SW1 component shown in Figure 4e. Peak amplitudes of up to 21 K are centered around 60◦latitude of each summer hemisphere. Further, the southern winter hemisphere is about 3 K colder than the northern winter hemisphere at 60◦ latitude. The SW1 component is likely generated in situ and due to wave-wave interaction between SW2 and the stationary planetary wave with wave number 1 (SPW1) at high latitudes [e.g., Forbes and Wu, 2006;
Figure 3. Contours of TIME-GCM/MERRA DE3 temperature amplitudes (K) versus latitude and days of year 2009 at 340 km altitude split up in 12 monthly panels
starting with January in the top left corner to December in the bottom right corner. Shown is the 10 day running mean moved forward in time by 1 day.
4. Comparison With CHAMP and GRACE
Realistic comparisons between tides determined from satellite observations and model simulations require comparable analysis methodologies. Therefore, in this section we show the comparison between tides derived from combined CHAMP and GRACE measurements with tides calculated from TIME-GCM results that were determined from the temperatures along the CHAMP and GRACE orbits and calculated with the identical determination technique. The time period is 1 June 2009 to 31 December 2009.
a)
b)
c)
e)
d)
Figure 4. Contours of TIME-GCM/MERRA (a) DW1, (b) SW2, (c) TW3, (d) DW2, and (e) SW1 temperature amplitudes (K) versus latitude and days in 2009 at 340 km
altitude. Shown is the 10 day running mean moved forward in time by 1 day.
CHAMP was a near-polar orbiting satellite with an inclination of 87.3◦orbiting Earth from 15 July 2000 to 19 September 2010. During its 10 years in space, it provided the first long-time measurements of neutral density and cross-track wind at approximately 400 km altitude. Due to atmospheric drag on the satellite, the orbit altitude actually changed throughout the mission from 450 km in the beginning to below 300 km in the end. If one combines the ascending and descending portions of the orbit one gets 24 h solar local time (SLT) coverage after approximately 130 days.
The GRACE satellites were launched on 17 March 2002 into a near-polar orbit (89◦inclination) but higher in altitude (500 km) than CHAMP. Because of the higher initial altitude, GRACE is still in orbit and providing measurements about 10 out of 12 months per year. The precession rate of GRACE is such that 24 h LST coverage is achieved after approximately 160 days.
Figure 5. CHAMP and GRACE solar local time (h) evolution for the year 2009 around the equator. Upward pointing
triangles (CHAMP=blue, GRACE=pink) depict the ascending part of the orbit, while downward pointing triangles (CHAMP=orange, GRACE=green) depict the descending part of the orbit.
If one combines CHAMP and GRACE measurements together, it is possible to get 24 h local time coverage after 72 days, thereby dividing the 24 h LST period time coverage nearly in half. This is desirable because it reduces the smoothing of the results (viz. Figure 2). It is noteworthy that CHAMP and GRACE actually measure total mass density at different altitudes [see Bruinsma et al., 2004, 2006, and references therein] which necessitates a conversion from total mass density to exosphere temperature so that the data can be combined together in a least squares tidal fit. The conversion procedure involves an intercalibration between the two satellites to remove possible biases due to differences in drag coefficients and other effects and subsequently applies the barometric law to derive exosphere temperatures. For a more detailed description of the tidal determination technique, the reader is referred to Forbes et al. [2009, 2011].
Following the Forbes et al. [2011] approach, we interpolate the TIME-GCM temperatures in space and time to find the corresponding modeled temperatures along the CHAMP and GRACE orbits. Hereby, it is noteworthy that the CHAMP and GRACE measurements are averaged into 3◦latitude bins ranging from 84◦S to 84◦N (i.e., 56 latitude bins in total for each satellite). In retrieving the model output along the satellite orbits, we interpolate the TIME-GCM data to the midpoint of each latitude bin, e.g., to 4.5◦N for the latitude bin 3◦N–6◦N. Interpolating to the midpoint of each latitude bin is justified given the 2.5◦latitude resolution of TIME-GCM. Due to the polar orbits of CHAMP and GRACE, we interpolate TIME-GCM to the actual satellite longitudes. Figure 5 shows the CHAMP and GRACE local time evolution during the year 2009 near the equator. At the beginning of the year, the two satellites are in the same local time plane. Over the course of the year, they slowly drift apart in local time due to the higher inclination of GRACE. At the end of 2009, the difference between CHAMP and GRACE is 7 h. A greater separation than 4 h in local time between the satellites is more favorable for the tidal determination method [Forbes et al., 2011], and thus, we only show results starting with 1 June 2009.
Figure 6 shows model-measurement comparisons and diagnostics for, from left to right, the migrating tides DW1, SW2, and TW3, while Figure 7 depicts the nonmigrating tides DE3, DW2, and SW1, respectively. In this section, we concentrate on Figures 6a–6f, 7a–7f, and 6j–6l, 7j–7l with the first row (Figures 6a–6c and 7a–7c) illustrating the observational results from the combined CHAMP/GRACE observations, second row (Figures 6d–6f and 7d–7f ) displaying the sampled TIME-GCM results, and the fourth row (Figures 6j-6l and 7j-7l) which depicts the difference between observational and sampled TIME-GCM results for the various tides. Positive differences (yellow-to-reddish colors) mean that the tides based on observations are bigger than the modeled tides and vice versa for negative differences. TIME-GCM tides are shown at 340 km alti-tude. The actual upper boundary height of TIME-GCM depends on solar activity because TIME-GCM is a hydrostatic model in which constant pressure levels express the vertical axis. Due to the very low solar activ-ity, the chosen 340 km altitude is close to the upper boundary and represents the altitude where we have coverage at all latitudes and days in 2009. However, at this altitude the tides are already height independent so that a comparison with CHAMP and GRACE at higher altitudes is still valid.
As expected based on in situ forcing, the DW1 (Figure 6a) is by far the biggest tide measured by CHAMP-GRACE reaching a peak amplitude of 115 K near the equator at the end of the year. It also exhibits comparatively little temporal variability and hardly any latitudinal structure. Further, the highest DW1 amplitudes were observed between ±30◦with the maximum (> 100 K) moving from the Northern Hemisphere in June to the Southern Hemisphere in September and back toward the equator. The sampled
g)
Figure 6. Contours of (left) DW1, (middle) SW2, and (right) TW3 temperature amplitudes (K) versus latitude and day at 340 km altitude for (a–c) combined
CHAMP/GRACE observations, (d–f ) sampled TIME-GCM/MERRA simulations, (g–i) full TIME-GCM/MERRA 72 day averaged tides, (j–l) the amplitude difference between observation and sampled model results, and (m–o) the amplitude difference between sampled model and full model results. The time period shown is 1 June to 31 December 2009.
i)
l)
TIME-GCM DW1 also shows hardly any spacial structure, but the amplitudes are much smaller than the observations with differences as high as 40 K (Figure 6j). The discrepancy between the observation and the model is the greatest near the equator. Also TIME-GCM exhibits a latitudinal double peak structure with values exceeding 70 K around days 240–280 which is not present in the observations. The TIME-GCM systematic underestimate of DW1 CHAMP-GRACE results suggests that our EUV forcing warrants further examination, since it is the primary driver of the DW1 thermospheric tide.
TIME-GCM SW2 agrees best with observations around the autumnal equinox (Figure 6k) where the difference between the two data sets is less than 4 K. The biggest differences of more than 24 K can be found north of the equator in June/July when TIME-GCM gives amplitudes exceeding 30 K (Figure 6e), while CHAMP and GRACE only observe single digit amplitudes (Figure 6b). Also at the end of the year, TIME-GCM SW2 amplitudes exceed 39 K slightly south of the equator while CHAMP and GRACE only give amplitudes around 21 K.
We find the best agreement between the migrating tidal observations and the sampled TIME-GCM in the TW3 component. The model accurately captures the overall latitudinal structure as well as the temporal TW3 variability observed by CHAMP and GRACE (Figures 6c and 6f ). However, TIME-GCM amplitudes are generally 5–25 K smaller than the observed TW3.
The overall agreement between observations and TIME-GCM for DE3 is satisfying. Specifically, starting in November the two data sets agree within 1 K with each other (Figure 7j). Also the small increase in CHAMP-GRACE DE3 amplitude in late December between the equator and 30◦N is captured by the model. However, peak DE3 amplitudes are observed by CHAMP and GRACE at the end of August/beginning of September (Figure 7a) centered around the equator, while sampled TIME-GCM peak amplitudes are found a month earlier at ≈20◦S at the end of July/beginning of August (Figure 7d).
Like SW2 and TW3, the CHAMP-GRACE DW2 also exhibits strong latitudinal and temporal variability but the amplitudes are much smaller with a maximum value of only 5 K in the southern winter hemisphere around 60◦S (Figure 7b). TIME-GCM does not replicate this maximum but rather shows it later in the year in the month of November with an amplitude close to 4 K (Figure 7e). In general, the agreement between model and observations is more favorable in the Northern Hemisphere compared to the Southern Hemisphere as shown in Figure 7k.
With the exception of the midlatitude winter Southern Hemisphere, the agreement between CHAMP-GRACE and TIME-GCM SW1 is altogether decent with both amplitudes matching each other to within 2 K most of the time (Figure 7l). Both SW1 tides show increased amplitudes in summer midlatitudes with TIME-GCM showing slightly higher amplitudes in the Southern Hemisphere summer, while CHAMP/GRACE show slightly higher amplitudes in the Northern Hemisphere summer (Figures 7c and 7f ). However, the two SW1 results diverge by 20 K in June at southern midlatitudes where CHAMP/GRACE display a maximum of ≈22 K that is not captured by TIME-GCM.
5. Full Model Versus Sampled Model Results
In order to asses the uncertainties associated with the incomplete atmospheric tidal perspective that the CHAMP and GRACE satellite orbits provide, we compare the sampled TIME-GCM tides derived following the method described in Forbes et al. [2011] (cf. section 4) with the tides calculated from the full model results. These uncertainties are attributable to the aggregate evolution of the diurnal mean temperature as well as the tidal temperature amplitudes over the 72 day period needed for CHAMP-GRACE to provide measurements over the full 24 h LST coverage. To account for the temporal averaging inherent in the CHAMP-GRACE tidal determination technique, we calculated 72 day running mean tides, moved forward 1 day at a time in our analysis of the full model output; compare Figure 2 (bottom). The results depicted in Figures 6g–6i are for the migrating tides and in Figures 7g–7i for the selected nonmigrating tides discussed in section 4. The differences between sampled model minus full model results are shown in the bottom row of Figures 6m–6o and 7m–7o. Positive differences (yellow-to-reddish colors) mean that the tides from the sampled TIME-GCM results (Figures 6d–6f and 7d–7f ) are bigger than the 72 day running mean tides from the full model results and vice versa for negative differences.
Comparing Figure 6d with Figure 6g, we find that the sampled and full model DW1 display essentially similar latitudinal and temporal variations with the same magnitude. However, the full model results do not display
the double peak structure seen in the sampled DW1 on days 240–280. This is also where the biggest DW1 amplitude differences of up to 14 K are found (Figure 6m).
The difference between sampled and full model TIME-GCM SW2 can be as high as 12 K (Figure 6n), but the overall latitudinal and temporal behavior is preserved when this tide is determined from results along the orbit (Figures 6e and 6h). This is not the case for TW3, particularly prior to October (Figure 6o). Both TW3 peaks around day 240 illustrated in Figures 6c and 6f are absent from the full model results, and thus, we find the biggest differences there (≈14 K).
Looking at the nonmigrating tidal results, we find that the full model amplitudes are generally bigger than the tides calculated along the combined CHAMP-GRACE orbits. In Figure 7g, we also find evidence of the impacts of averaging discussed in section 3. Specifically, while the sampled model results show a drop in DE3 amplitudes after day 240 to less than 1 K (Figure 7d), the full model results remain in excess of 4 K in the tropics and increase again as the year progresses.
The full model DW2 (Figure 7h) does not resemble the sampled model DW2 (Figure 7e). While the latter varies rapidly in time and space, there are large patches of the same magnitude for the full model DW2. However the differences in magnitude are small. The SW1 agreement is somewhat better, but the differences can be as high as 7 K (Figure 7o) between sampled and full model results.
6. Discussion
In section 3, we present TIME-GCM/MERRA tidal components for the year 2009. They all display unique annual and latitudinal patterns. Tides like DE3 and DW2 exhibit strong day-to-day variability, while DW1 varies slowly over the course of the year. We also presented the effects that averaging has on the magnitude of the simulated tidal amplitude. Taking DE3 as an example, we showed that a 10 day average not only smoothes out the variability but also reduces the peak amplitude by a factor of 2. However, as the averaging window increases, there is additional ambiguity in the amplitudes, which can even result in an increase of amplitude at certain points in time. Thus, model simulations give us valuable insight into the daily variability of thermospheric tides that cannot be observed with the satellites currently in orbit and can only be determined from a few dispersed ground-based observatories.
In order to make reasonable comparisons between model simulations and in situ observations, it is necessary to apply similar analysis methods. For that reason, we extracted TIME-GCM temperatures along the orbits of CHAMP and GRACE and applied the same tidal determination procedure that was employed for the CHAMP/GRACE analysis. The results of those comparisons were disclosed in section 4 for six selected tides. Only the CHAMP/GRACE DW1 and TW3 migrating components feature stronger amplitudes than the sampled TIME-GCM DW1 and TW3. The difference between observed and sampled DW1 can be as high as 50%, suggesting that we should undertake a future reexamination of TIME-GCM EUV forcing and response. Otherwise, the agreement between CHAMP/GRACE and TIME-GCM thermospheric tides highly depends on the latitude and time of the year with no systematic pattern perceptible.
Figures 8a and 8b exemplify a systematic issue with the TIME-GCM eastward propagating tides and depicts the comparison between CHAMP/GRACE and the sampled TIME-GCM for the SE1 component. For the first 90 days, they agree to within 2 K, but after day 240, TIME-GCM starts to notably deviate from the CHAMP/GRACE SE1. The difference amounts to 10 K in mid-December at 50◦N (Figure 8d) when the CHAMP/GRACE SE1 is negligible. The TIME-GCM SE2 and SE3 tides are also systematically stronger than the CHAMP/GRACE components. The same is true for SW5, SW6, and SW7, but those components are not commonly observed because amplitudes are within the noise level. The systematically high TIME-GCM semidiurnal eastward tides are of concern and the SE2 component in particular, since the TIME-GCM amplitudes can be as much as five times stronger than observations at certain times and locations (not shown). We suspect that the gravity-wave parameterization, which is based on a modified Lindzen [1981] type scheme and was previously tuned to exclusively account for migrating tides, is the underlying shortcoming. However, we need to confirm this with a detailed follow-on study which will include comparisons between TIME-GCM tides and tidal temperature determinations from TIMED/SABER (Thermo-sphere Iono(Thermo-sphere Meso(Thermo-sphere Energetics and Dynamics/Sounding of the Atmo(Thermo-sphere using Broadband Emission Radiometry) results in the middle atmosphere and lower thermosphere, as well as a
reexamina-Figure 8. Contours of SE1 temperature amplitudes (K) versus latitude and day at 340 km altitude for (a) combined
CHAMP/GRACE observations, (b) sampled TIME-GCM/MERRA simulations, (c) full TIME-GCM/MERRA 72 day averaged tides, (d) the amplitude difference between observation and sampled model results, and (e) the amplitude difference between sampled model and full model results. The time period shown is 1 June to 31 December 2009.
tion of the TIME-GCM gravity wave parameterization scheme and its related lower thermospheric molecular damping extension. The results of these investigations will be the subject of a future report.
In section 5, we compared the sampled TIME-GCM tidal components with 72 day averages of the full model results to assess the uncertainties that are inherent in the CHAMP/GRACE tidal determination technique [Forbes et al., 2011]. The SE1 component (Figures 8b and 8c) is an example of how the sampled tides underestimate the full model results, as is true for most of the nonmigrating components. The
Table 1. Forbes et al. [2011] Tidal Determination Assessment Summary: The First Row Shows the Tidal Components That Are Underestimated by the Method,
While the Second Row Lists Tidal Components Which Are Overestimateda
Underestimated DW3, DW2b, D0, DE1, DE2, DE3b, DE4, SW4, SW1, S0, SE1, SE2, SE3
Overestimated DW6, DW5, SW7, SW6, SW3
Bothc DW1, SW2, TW3, DW4, SW5
aThe third row depicts the tidal components that are either underestimated or overestimated depending on time of year and location. bMostly underestimated.
differences between the sampled and full model SE1 amplitudes (Figure 8e) vary with time of year and location between 1 and 8 K. Table 1 summarizes our findings for the remaining components. The majority of tidal components is underestimated by the tidal determination method. The underestimates can be as high as 90% for DE2 around the equator at day 190 (not shown). Therefore, we conclude that the tidal determination technique itself leads to an amplitude reduction over and above the reduction associated with the inherent 72 day averaging. Yet, to resolve how much is attributable to either factor is beyond the scope of this paper and could be a subject of another follow-up study. However, there are also examples of sampled model tides that actually display larger amplitudes than the full model results. They are shown in the second row of Table 1, and apart from SW3, they are usually not significant in the upper atmosphere. However, Forbes et al. [2014] report on a weak SW6 component in the CHAMP/GRACE observations which they could not explain. Considering our findings with respect to the overestimation of SW6 in the sampled model results, it may be possible that the tidal determination technique itself produced the SW6 component and that the signal in the CHAMP/GRACE observations is not real. Interestingly, we cannot make any general claims about the impact of the determination technique on the migrating components. All the migrating tides are both overestimated or underestimated by the determination technique depending on time of year and location as indicated in the third row of Table 1.
7. Summary and Conclusions
We presented results of the first year-long TIME-GCM simulation forced with 3-hourly MERRA reanalysis data at the lower boundary of the model. We subsequently compared the simulated tidal results to combined CHAMP/GRACE tidal determinations and assessed the CHAMP/GRACE tidal determination technique. Our major findings can be summarized as follows:
1. Our model results confirm that realistic forcing from below exerts extreme short-term variability on the atmosphere-ionosphere system, which current satellite-based observational strategies, with their inherent averaging, are not capable of capturing (cf. Figure 2).
2. There is mixed agreement between TIME-GCM/MERRA tidal components and CHAMP/GRACE tidal determinations. However, TIME-GCM/MERRA overestimates most of the observed eastward components and the semidiurnal tides in particular.
3. The tidal determination technique developed by Forbes et al. [2011] for combined satellite observations usually underestimates nonmigrating tidal amplitude, but there is also evidence that it can generate components that are in reality insignificant. We cannot draw a single definitive conclusion about the reliability of the migrating tidal determinations. They are both overestimated and underestimated by the tidal determination method depending on time of year and location.
Our results quantify the shortcomings of current strategies to infer tides from slowly precessing LEO satellite measurements and demonstrate the inherent limitations of this approach. Specifically, while they produce invaluable perspectives on middle and upper atmospheric tidal climatologies, current strategies are incapable of providing insight into the day-to-day, weekly, and even monthly variability that these tides and planetary waves impose on the near space environment. Thus, we are still unable to fully observe and anticipate the prevailing ITM space weather conditions that underlie any response to extreme solar geomagnetic events. Three polar-orbiting satellites providing observations at six local times is the minimum configuration needed to quantify and understand the impacts of vertically propagating diurnal and semidiurnal tides on the atmosphere-ionosphere system (e.g., reference missions in the 2013 National Research Council Solar and Space Physics Decadal Survey [National Research Council, 2013]) and to properly resolve planetary wave modulation of the tides and other sources of day-to-day variability, whatever they may be.
Finally, we need to further assess TIME-GCM thermospheric day-to-day variability via comparisons with ground-based incoherent scatter radar neutral temperature and wind determinations and Fabry-Pérot interferometer measurements. In order to address the systematic overestimation of the semidiurnal eastward components in TIME-GCM we also need to conduct a follow-on study to verify the accuracy of model predictions in the mesosphere and lower thermosphere, evoking comparisons with TIMED satellite observations. In addition, we need to investigate whether the current TIME-GCM gravity wave parameterization and dissipation scheme appropriately accounts for effects on eastward propagating tides.
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Acknowledgments
TIME-GCM results were archived on the National Center for Atmospheric Research High Performance Storage System and are available on request. MERRA data used in the present study were available through the Goddard Earth Sciences Data and Information Services Center (http://disc.sci.gsfc. nasa.gov/mdisc/data-holdings/merra/ merra_products_nonjs.shtml). The CHAMP and GRACE observations were available through http://thermosphere. tudelft.nl/acceldrag/data.php. F10.7 and Ap data were downloaded from http://spidr.ngdc.noaa.gov. This work was supported by the U.S. Partici-pating Investigator (USPI) Program under NASA Grant NNX12AD26G. K.H. was also supported by the Advanced Study Program Postdoctoral Fellowship of the National Center for Atmospheric Research. The National Center for Atmospheric Research is sponsored by the National Science Foundation. We would like to acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR’s Computational and Informa-tion Systems Laboratory, sponsored by the National Science Foundation. Visualizations and part of the data analysis were done with the NCAR Command Language (NCL). Alan Rodger thanks the reviewers for their assistance in evaluating the paper.
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