Temporal and spatial distributions of atmospheric wave energy in the equatorial stratosphere revealed by GPS radio occultation temperature data obtained with the CHAMP satellite during 2001–2006

Abstract

Using stratospheric temperature profiles derived from GPS radio occultation (RO) measurements made by the German CHAMP satellite from June 2001 to May 2006, we studied the climatological behavior of atmospheric wave activity in the tropics. The wave potential energy, E p, is calculated from temperature fluctuations with vertical scales shorter than 7 km in a longitude and latitude cell of 20° × 10° at 19–26 km. E p is then averaged every 3 months (June–July–August (JJA), September–October–November (SON), December–January–February (DJF), March–April–May (MAM)), and the averages are compared with the cloud top temperature from outgoing long-wave radiation (OLR) and the convective rain rate from the TRMM precipitation radar (PR). E p at 19–26 km in the western Pacific to Indian Ocean is found to show a clear seasonal variation, with a large E p during DJF and MAM and a considerably enhanced E p in SON; it becomes minimum during JJA near the equator, when the center of the enhanced E p region appears over north India and the Indochina peninsula. Localized enhancement of E p seems to be mainly due to atmospheric gravity waves. In addition, the longitudinally elongated portion of E p is partially affected by Kelvin wave-like disturbances with short horizontal scales. In DJF and MAM, the convective clouds are located over the western Pacific and around Indonesia, at which time the Kelvin wavelike disturbances are effectively generated. The spatial and seasonal variations of E p are closely related to the distribution of clouds, implying that convective wave generation is very important in the tropics. However, wave-mean flow interactions due to the wind shear of the QBO become important in the lower stratosphere, which considerably modifies our analysis of the E p distribution at 19–26 km. Therefore, both wave generation and propagation characteristics must be taken into account in describing the climatological behavior of atmospheric wave activity in the equatorial stratosphere.

The Roles of Equatorial Trapped Waves and Internal Inertia–Gravity Waves in Driving the Quasi-Biennial Oscillation. Part II: Three-Dimensional Distribution of Wave Forcing

“…The difference in the vertical shear of the Walker circulation between the Eastern and Western Hemispheres plays an important role in wave filtering, which results in zonally different wave potential energy distributions in the upper troposphere and lower stratosphere (UTLS). The model results were essentially consistent with recent results obtained from GPS radio occultation (RO) data (Alexander et al 2008b;Tsuda et al 2009). However, Kawatani et al (2009) did not study wave momentum flux and wave forcing.…”

“…Intensive or periodic observations with fine vertical resolution have revealed characteristics of gravity waves in the tropical region (e.g., Tsuda et al 1994;Maruyama 1994;Sato et al 1994;Sato and Dunkerton 1997;Wada et al 1999). Radiosonde observations have revealed that momentum transported by short-period (#;3 days) gravity waves is comparable to that transported by long-period Kelvin waves (Maruyama 1994;Sato and Dunkerton 1997).…”

“…Because mean wind distributions and tropospheric cumulus convection activities are not zonally uniform in equatorial regions, gravity waves propagating upward into the stratosphere are not zonally uniform (Bergman and Salby 1994). Satellite-based instruments have the advantage of observing gravity waves globally (e.g., Tsuda et al 2000Tsuda et al , 2009Ratnam et al 2004;Randel and Wu 2005;Ern et al 2008;Alexander et al 2008a,b). However, satellite data allow calculation of temperature but not of wind components in the equatorial lower stratosphere, and the analyzable spatial and temporal spectrum is limited.…”

Diagnosis of Annual Synchronization of the Quasi-Biennial Oscillation: Results from JRA-25/JCDAS Reanalysis and MRI Chemistry-Climate Model Data

“…For the simulation results, the GWF parameterization assumes a seasonally constant GW source, releasing the simulated QBO from annual synchronization. Observations show strong seasonal changes in convection and GWs (e.g., Tsuda et al 2009). …”

“…That is, if G in the CCM had as clear seasonality as X in the JRA-25 data, the CCM would exhibit the annual synchronization tendency of the QBO. Observations have shown that GW activity, in fact, differs among seasons reflecting convective activity (e.g., Tsuda et al 2009). …”