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Keywords:

  • infrasound;
  • microbaroms;
  • propagation;
  • atmosphere;
  • global change;
  • IMS

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information

[1] Microbaroms are permanent infrasonic waves produced by interacting open ocean swells near low-pressure systems. Continuous infrasound monitoring over 5 years show that microbaroms are globally observed at several middle- and high-latitude infrasound stations that are part of the International Monitoring System (IMS). The arrival azimuths and amplitude of the signals exhibit clear seasonal trends driven primarily by the seasonal reversal of the zonal stratospheric wind. A scaling relation between the signal amplitude and the strength of the upper wind suggests that most of the microbarom energy propagates in the ground to stratosphere waveguide. We show that continuous microbarom measurements can help to evaluate global infrasound detection capabilities, providing new insights on quantitative relationships between infrasonic observables and atmospheric specifications.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information

[2] Marine storms produce open ocean swells which may interact to generate low-frequency acoustic waves over a narrow spectrum of frequencies. Past studies showed that these infrasonic waves, so-called microbaroms, are related to standing ocean waves near low-pressure systems and the resulting high ocean surface waves [Daniels, 1962; Posmentier, 1967]. A precise source mechanism describing the nonlinear interaction of ocean waves with the atmosphere has been proposed by Arendt and Fritts [2000] and Garcés et al. [2002]. The predominant microbarom peak usually has a maximum around 0.2 Hz corresponding to the 10 s periods common to open ocean swells but may have a secondary peak between ∼0.12–0.15 Hz corresponding to large, long-period swells that can be sufficiently energetic to dominate the coherent infrasound field in the microbarom band [Willis, 2004]. The propagation of microbaroms are substantially affected by the thermal and wind structure of the atmosphere [Tabulevich, 1995; Kulichkov et al., 2004]. Depending on the atmospheric wind structure, microbarom signals may propagate in acoustic waveguides between the ground and troposphere, stratosphere and lower thermosphere [Garcés et al., 2004]. In addition to information about the source, microbarom waveform characteristics reveal significant features of the vertical structure of the winds. Therefore microbaroms are then valuable potential source for global atmospheric monitoring since pressure waves can be generated continuously over long duration, allowing investigations in the seasonal and diurnal fluctuations of the atmosphere.

[3] Microbaroms have been proposed to recover the characteristics of high-altitude winds [Rind and Donn, 1975]. Donn and Rind [1971] and Rind [1978] related microbarom amplitude variability to the solar tide fluctuations in the thermosphere during winter and stratospheric wind force during summer. These studies concentrated on the results from an infrasound station in the Eastern US that was primarily exposed to ocean swells arriving from the North Atlantic. The storm sources considered were between hundreds and one thousand kilometers away. Willis et al. [2004] and Garcés et al. [2004] demonstrated that microbarom observations in Hawaii match the seasonal distribution of large swells in the Pacific and the dominant upper wind direction up to the lower mesosphere. More recently, the coherence of microbarom wave trains has been studied in order to help identifying multiple sources or propagation paths [Olson and Szuberla, 2005]. At larger scale, other studies highlighted a clear correlation between the prevailing direction of the stratospheric winds and microbarom arrival azimuths observed by Austral stations [Le Pichon et al., 2005]. Ocean waves that propagate from major storm systems to the coastlines also generate infrasound from surf activity. Arrowsmith and Hedlin [2005] pointed out that continuous monitoring of surf signals at a long-range from the source region provides firm evidence that the amplitude of surf signals depends on the stratospheric winds.

[4] Such studies are now possible with the development of a network of 60 infrasound stations that are part of the International Monitoring System (IMS) for the enforcement of the Comprehensive Nuclear Test Ban Treaty (CTBT). This network already allows a global Earth's coverage for microbarom monitoring [Hedlin et al., 2002]. The main objective of this paper is to demonstrate that for propagation ranges exceeding several thousands of kilometers (telesonic ranges), both number of detections and signal amplitude are strongly affected by the reversibility of the stratospheric wind with season. We focus on middle- and high-latitude IMS stations operating for several years. From the observed cyclical variations of infrasonic observations, we first evaluate the detection capability of microbaroms throughout the year. Then, we use a 3-D propagation modeling associated with a realistic climatological database in order to explain seasonal trends in the observations. Finally, an empirical scaling law relating the signal amplitude to the stratospheric wind speed is derived and discussed.

2. Methodology

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information

[5] The wave parameters of the signals are calculated with the progressive multichannel correlation method (PMCC) [Cansi, 1995]. Used as a real-time detector, this method proved to be very efficient for monitoring routinely low-amplitude coherent waves within noncoherent noise in the 0.05–4 Hz band. In this study, the detection results of the automatic processing are analyzed over several years for a set of IMS infrasound stations mainly distributed in the southern hemisphere (Table 1). All detections lasting several hours with a dominant frequency of 0.1–0.3 Hz and stable azimuths are selected from the detection bulletins.

Table 1. Name and Location of the Studied IMS Infrasound Stations
StationsLatitudeLongitudeAltitude, mBulletins Available Since
I08BO, Bolivia16.21°S68.45°W41002000
I22FR, New Caledonia22.18°S166.85°E2702003
I24FR, Tahiti17.75°S149.29°W1202000
I26DE, Germany, Freyung48.85°N13.71°E11002000
I27DE, Germany, Antartica70.66°S8.32°W46502003
I33MG, Madagascar19.01°S47.30°E13802001

[6] For the propagation modeling, we use the Mass Spectrometer and Incoherent Radar Model (MSIS-90, NRL/MSISE-00) and the Horizontal Wind Model (HWM-93) [Hedin et al., 1996]. These empirical reference models provide time-dependent estimates of temperatures, pressures, winds, and major species concentrations. They account for the major seasonal variations, daily solar tidal variability, geomagnetic and solar forcing effects in the mesosphere and lower thermosphere (55–150 km). Accurate atmospheric specifications are also used to quantify more precisely the relationship between infrasonic observables and the dynamics of the upper wind. The Naval Research Laboratory Ground to Space (NRL-G2S) model [Drob et al., 2003] was run to provide a self-consistent climatological database covering the globe, with a spatial resolution of ∼2°.

[7] The long range propagation is simulated using the Windy Atmospheric Sonic Propagation ray theory-based method (WASP-3D) which account for the spatiotemporal variations of the horizontal wind terms along the raypaths in spherical coordinates [Dessa et al., 2005]. Assuming limited pressure perturbations, the acoustic propagation is governed by the linearized equations for a compressible fluid. This implies that the signal wavelengths are smaller than those of atmospheric property variations. Considering the wavelength of microbaroms, the high-frequency asymptotic approximation is fulfilled for the purpose of seasonal studies. A paraxial approach for the amplitude computation is used where small perturbations of the slowness vector and position around a central ray of reference are considered. This provides a three-dimensional description of the evolution of the cross section of a ray tube, hence giving the local amplitude of the signal. The atmospheric absorption is integrated using range-dependent attenuation coefficients varying with altitude, frequency of the propagating wave and atmospheric parameters (gas composition, density, pressure, temperature and humidity) [Bass and Sutherland, 2004]. Even the ray theory derived amplitude fails to deal with wave front folding phenomena in an inhomogeneous medium and the formation of caustics along the raypaths, one may expect the paraxial approach relevant enough to provide rough estimates of the seasonal trend of the amplitude variations. Finally, by applying a shooting procedure, the ray trajectories and amplitudes are then computed for values of ray parameters derived from the measured horizontal trace velocities.

3. Spatial and Seasonal Distribution of Microbarom Detections

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information

[8] Figure 1 presents the results of 5 years of continuous processing of microbarom signals at stations I08BO (Bolivia) and I26DE (Germany). The German station is situated inside a tall forest and is sheltered from the ambient wind noise fluctuations, whereas the Bolivian station is located on a high exposed plain. Both stations are equipped with efficient noise reducing systems which significantly improve the detection capability of coherent infrasonic energy [Alcoverro and Le Pichon, 2005; Bowman et al., 2005]. Microbaroms in the 0.1–0.3 Hz band are consistently detected throughout the year. Clear seasonal trend in the arrival azimuths are observed. At I08BO, azimuths range between 200° and 225° from May to November, and less prominently between 130° and 155° from December to April. At I26DE, detections around 270°–320° originate from ocean swells in the North Atlantic. They are mainly observed from October to June, while southwest microbarom signals are poorly detected in Bolivia. For both stations, the arrivals have an horizontal trace velocity close to the sound speed at the ground (0.34–0.36 km/s), suggesting that pressure waves propagate near the horizontal at low incidence angle. As for the arrival azimuths, there is also an obvious seasonal trend in the amplitude variation (Figure 2). The temporal amplitude distributions in the southern and northern hemispheres are anticorrelated. In Bolivia, the amplitude increases during the Austral winter, while in Germany it decreases during the same period.

image

Figure 1. Results of automatic PMCC processing at I08BO and I26DE in the 0.05–0.5 Hz band showing the azimuthal variation of microbaroms from June 2000 to June 2005. Arrival azimuths are clockwise from north. Detections at I08BO are contained within the white rectangle. Color refers to the number of detections per week.

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image

Figure 2. Results of automatic PMCC processing at I08BO and I26DE in the 0.05–0.5 Hz band showing the variation of microbarom amplitude from June 2000 to June 2005. Color refers to the number of detections per week.

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[9] The Antarctic Circumpolar Current (ACC) links the major southern oceans in the 50°–60°S range. As large swells systems driven by strong continuous eastward surface winds move along this peri-Antartic belt, the same seasonal pattern in the microbarom signals is observed for others Austral IMS stations. Figure 3 shows the seasonal transition in the bearings along with the stratospheric general circulation between summer and winter. The main bearings follow the reversal in the prevailing zonal wind direction. In the southern hemisphere, around and above the stratopause (altitude of 40–50 km), zonal winds reverse from east to west during the transition between Austral winter to summer (green and yellow curves, respectively). These results show that the empirical HWM-93 model can provide a good description of the general seasonal changes. During the Austral winter, the number of detections is on average 2–3 times larger than the number of detections observed in the Austral summer. Similarly, around latitude of 50°S, stratospheric wind speeds decrease from ∼80 m/s to ∼40 m/s from winter to summer. In the northern hemisphere, it is the opposite.

image

Figure 3. Seasonal variations in the arrival azimuths of microbaroms for several middle- and high-latitude IMS stations in 2003. The azimuthal distributions are plotted for each station in Austral winter (green bars) and Austral summer (yellow bars). The strength of the zonal wind (HWM-93) is averaged in longitude (180°W–180°E) and in altitude (35–40 km) for the winter and summer seasons (green and yellow curves, respectively, according to the scale on the top). For all stations the dominant wind directions match the seasonal variability of microbarom detections.

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4. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information

[10] Continuous monitoring of microbaroms generated by storm systems distributed all around the peri-Antarctic belt reveals similar trends in the measurements. Stratospheric winds in the southern hemisphere blow eastward from June to November and westward the rest of the year, which is consistent with the observed bearings of microbaroms for Austral stations. For these stations, the weak number of detections originating from the northern hemisphere oceans could be explained by the stratospheric wind inversion in the equator region. In the northern hemisphere, the same azimuthal variations are observed showing an approximately 6 month delay. Globally and on large temporal scales of weeks to months, microbarom azimuths are rather constant whereas wider ranges are expected if we consider the extent of the ocean swells distribution. Such wider ranges can be identified in the life span of a typical swell with timescales of days [Garcés et al., 2004]. This observation confirms a strong directionality induced by the prevailing zonal wind. For these measurements, the arrival azimuths roughly coincide with the stratospheric wind direction at the latitude of the ocean swells. Rays launched eastward from the ACC during the Austral wintertime and westward in summer, reach all IMS stations with predicted arrival azimuths close to the observed ones.

[11] The seasonal variation in the number of detections follow the variation of the stratospheric wind strength along the source-receiver path, while some daily variability can either be related to short timescale variability of the atmosphere along the raypaths, or explained by changes in the amount of ocean swell energy. At ∼10 km altitude, the wind speed is mainly governed by large storm systems. Because of its variability, tropospheric waveguides generally do not persist for over long propagation ranges. Considering the strong temperature gradient above 90 km, thermospheric paths are always predicted. However, due to the low particle density and dissipation in the upper atmosphere, thermospheric returns are strongly attenuated. For propagation range of 1000 km and for a frequency of 0.25 Hz, rays refracting at 100–120 km are attenuated by ∼50–100 dB [Bass and Sutherland, 2004]. At shorter distances (less than ∼2000 km), previous studies demonstrated that microbaroms from energetic swells refracted back to the ground at the thermosphere may be observed [Rind, 1978; Garcés et al., 2004]. At larger distances, thermospheric returns are unlikely. Thus we assume that the observed signals propagate efficiently for thousands of kilometers in the stratospheric duct, which is consistent with the low trace velocity values generally observed.

[12] Figure 4a presents the seasonal variations of the maximum amplitude of signals from the South Pacific detected in Bolivia, along with fluctuations in the NRL-G2S wind-corrected sound speed in the 30 to 49 km range. These arrivals are prominent from May to November. Microbarom detections start in April with positive increasing values (and disappear in November with negative values) of the effective sound speed above ∼35 km. The maximum amplitude coincides with the highest wind speed which is greater than 50 m/s between 47 and 49 km in July. Downward refraction occurs when the effective sound speed averaged along the raypaths exceeds the sound speed at the ground level. As a result, downwind propagation with increasing wind speed in the stratosphere decreases the refracting height. Figures 4b and 4c compare the annual variations of the observed signal amplitude at I08BO and I26DE to the results of ray tracing simulations. To interpret the observations, we consider the dissipation of acoustic energy for a frequency of 0.25 Hz. The upper boundaries of the stratospheric ducts reach as high as 45 km in April and November during the seasonal reversal in the stratospheric general circulation, and as low as 30 km during the downwind season. The simulation results show that the acoustic signal suffers a negligible amplitude decrease below 45 km, while rays refracting at higher altitudes are subject to increased attenuation. These simulations are in agreement with the observed seasonal trend in the amplitude for both stations. From winter to summer, the decrease in amplitude can partly be explained by acoustic attenuation in the stratosphere, where the additional absorption due to the difference in travel paths between 30 and 45 km is slightly lower than 10−3 dB/km. Earlier researches dealing with atmospheric tidal circulation in the thermosphere pointed out semidiurnal fluctuations of the amplitude of infrasound signals [Rind and Donn, 1975; Le Pichon et al., 2005]. Such fluctuations, typical of long-duration infrasound propagating through the lower thermosphere, were not clearly observed in these measurements. Figure 4d presents, on a semilogarithmic scale, the measured amplitude versus the stratospheric wind speed. Using a standard least squares procedure, the following linear amplitude-scale relationship is derived:

  • equation image

where P is the zero-to-peak amplitude (in Pa), R is an approximate distance between the source and the receiver (in km), n = 0.3 ± 0.1 is the distance-scaling component, k = 0.0096 s/m is the wind effect normalization parameter estimated with a unit standard deviation of ∼10%, and Vs is the component of the stratospheric wind velocity (in m/s) in the direction of propagation. The scattering in our scaling relation may either be related to stochastic variations in the atmosphere not predicted by the atmospheric models used, or changes in the amount of ocean swell energy. Blanc et al. [1997] proposed for k a value of 0.0116 s/m derived from a combination of atmospheric nuclear and chemical explosion data covering ranges of 400–7000 km. Mutschlecner et al. [1999] adopted a value of 0.016 s/m from observations of nuclear tests carried out at the Nevada test site for ranges of 100–300 km. Stevens et al. [2002] provided an evaluation of various scaling laws derived from nuclear tests with k = 0.019 s/m. These discrepancies may be explained by differences in the frequency content of the signals and the dependence of amplitude upon yield and propagation ranges. In order to investigate small-scale variations of the amplitude, (1) an improved knowledge of the swell distribution over the oceans, (2) a more precise radiation modeling of microbarom signals, and (3) a more realistic long-range propagation modeling are needed. Willis [2004] used global ocean wave spectra provided by the National Oceanic and Atmospheric Administration's (NOAA's) Wavewatch3 (WW3) model [Toldman, 2002], and developed a detailed source pressure formulation base upon interactions of surface wind and ocean waves. Such works provide a basis for a better quantification of the relationship between infrasonic observables and atmospheric specification problems.

image

Figure 4. Correlation between the seasonal variations of microbarom amplitude measured at I08BO in 2003 and the NRL-G2S effective sound speed relative to the ground. Stratospheric waveguides appear with positive values of the relative effective sound speed. (a) Variation of the effective sound speed (in m/s) averaged in various ranges of altitude (colored curves) along raypaths launched eastward from the ACC in the direction of I08BO. The amplitude distribution (in Pa) is presented by a 2-D histogram (color refers to the number of detections per week). (b) Seasonal variations of 5 year cumulative distribution of microbarom amplitude (left; color refers to the number of detections per week), upper boundaries of stratospheric paths (middle), and predicted attenuation (right) for signals detected at I08BO from the South Pacific. Time is in Julian day. (c) Same as Figure 4b for signals detected at I26DE. (d) Relation between amplitude and stratospheric wind speed at 35–40 km in the direction of propagation.

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5. Concluding Remarks

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information

[13] In this study, ocean swells are used as a natural source of infrasonic waves for continuous measurements of high-altitude winds over propagation ranges that exceed several thousands of kilometers. For the studied Boreal and Austral stations, the main bearings of microbaroms reverse from summer to winter, and are anticorrelated from the northern to southern hemispheres. A 3-D paraxial ray-tracing model associated with the NRL-G2S climatological database is used to simulate the propagation and explain seasonal trends in the observations. Simulation results show that time- and range-dependent propagation modeling provide a good description of the general changes. We suggest that the cyclical variations of microbarom azimuths essentially result from seasonal zonal wind reversals in the 35–50 km range, since for large ranges (1) thermospheric returns are strongly attenuated and (2) tropospheric ducts are unstable due to the high variability of the wind in that region. A clear correlation between the observed signal amplitude and the stratospheric wind speed is noted. To interpret these results, we consider the additional absorption resulting from differences in travel paths between 30 and 45 km. These results confirm the strong influence of the prevailing zonal wind on infrasonic detection capabilities, as microbarom signals almost disappear during the counterwind season. Further work is needed in order to evaluate the effects of the source (frequency, size, and extent) on the amplitude-scale relations for long propagation ranges. Other stations should also be considered. We demonstrate that in conjunction with other technologies, microbaroms may yield further information on the seasonal and short timescale variability of the atmosphere below ∼50 km. By taking advantage of new signal processing methods and recent advances in modeling techniques, continuing investigations into global monitoring of natural infrasound may allow continuous, passive acoustic tomography of the stratosphere and lower mesosphere. Furthermore, it is expected stratospheric warming a global monitoring of microbaroms could detect temporary reversals of the temperature gradient at middle and high latitudes significantly affects the structure of the stratospheric waveguide.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information

[14] The authors are grateful to E. Blanc and N. Brachet for their interests in this study and for the helpful discussions we had during the completion of this work. We would like also to thank the NASA Goddard Space Flight Center, Global Modeling and Assimilation Office (GSFC-GMAO), and the NOAA National Centers for Environmental Prediction (NCEP) for providing the NWP data that went into the NRL-G2S atmospheric specifications.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methodology
  5. 3. Spatial and Seasonal Distribution of Microbarom Detections
  6. 4. Discussion
  7. 5. Concluding Remarks
  8. Acknowledgments
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
jgrd12656-sup-0001-t01.txtplain text document0KTab-delimited Table 1.

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