Long‐Term Variability and Tendencies in Middle Atmosphere Temperature and Zonal Wind From WACCM6 Simulations During 1850–2014

Abstract

Long-term variability of middle atmosphere temperature (T) and zonal wind (U) is investigated using a three-member ensemble of historical simulations of NCAR's Whole Atmospheric Community Climate Model latest version 6 (WACCM6) for 1850-2014 (165 years). The model reproduces the climatological features of T and U. The contributions of Quasi Biennial Oscillation (QBO) at 10 and 30 hPa, solar cycle (SC), El Niño-Southern Oscillation (ENSO), ozone depleting substances (ODS), carbon dioxide (CO 2), and stratospheric sulfate aerosol (volcanic eruptions) to change in monthly zonal mean T and U are analyzed using multiple linear regression. The signal due to CO 2 increase dominates as a predictor of the net multidecadal global annual mean temperature change at all levels in the middle atmosphere. Contributions from ODS also affect the net multidecadal global mean temperature trend in the stratosphere. Because of similarities in the time evolution of the emissions of CO 2 and ODS, the analysis of existing model output cannot accurately separate the attributions of cooling to these two dominant forcing processes. On shorter time scales, solar flux variations are the largest source of variability in the mesosphere while volcanic eruptions are the largest in the stratosphere. In the stratosphere and mesosphere, both QBO and ENSO can significantly impact zonal mean temperature and zonal-mean zonal wind depending on latitudes, but their impact on the multidecadal global mean temperature trend is very small.

Unusual Quasi 10‐Day Planetary Wave Activity and the Ionospheric Response During the 2019 Southern Hemisphere Sudden Stratospheric Warming

“…Given that background, it is unlikely the symmetric Q10DW‐W1 observed in 2019 Southern SSW is a NM since the equivalent depth for the symmetric Q10DW‐W1 is too short to be amplified as a resonant oscillation mode in the middle atmosphere. The mean temperature required to result in such a NM translates to an equivalent depth of 2.5 km for the symmetric Q10DW‐W1 is 61 K, which is ∼100 K lower than the expected mean temperature in the middle atmosphere during the austral summer (Ramesh et al., 2020). This result suggests that there is more likely a forcing mechanism to excite the upward propagating symmetric Q10DW‐W1 observed during the 2019 SSW.…”

Long‐Term Variability and Tendencies in Migrating Diurnal Tide From WACCM6 Simulations During 1850–2014

“…There is a trend in the NINO3.4 index in the most recent decades that is consistent with an overall warming in the ocean temperature. More details on the predictors can be obtained from Ramesh et al (2020).…”

“…The regression analysis attributes the long-term increase in DW1 amplitude mainly to increasing trend in CO 2 . However, as shown in Ramesh et al (2020), a complete separation of trends due to CO 2 and EESC is not possible because of similarities in the time series of forcing. Although the stratospheric aerosols (AOD) have negligible impact on global mean tidal amplitudes, they show negative contribution in all the three components for E23 with slightly larger effect on U 24 than V 24 .…”

“…This suggests that T 24 at the equator is not dominated by the trapped mode, which leads in turn to the conclusion that the propagating tide has larger amplitude than the trapped tide there. As shown in Ramesh et al (2020), the MLR analysis cannot cleanly separate the responses to forcing from CO 2 and from ODS. The low-latitude responses of T 24 , U 24 , and V 24 to ODS appear to be contradictory, suggesting that a decrease in ozone concentration leads to a larger tide.…”

Interannual Variability of Winds in the Antarctic Mesosphere and Lower Thermosphere Over Rothera (67°S, 68°W) During 2005–2021 in Meteor Radar Observations and WACCM‐X

“…This scaling choice allows the regression results interpreted as the change in the winds given a change in the index from its minimum to maximum value (whilst allowing for outliers). Ramesh et al (2020). Finally, Ford et al (2009) and Ramesh et al (2020) found links between the MLT and QBO.…”