Interannual variability of winds in the Antarctic mesosphere and lower thermosphere over Rothera (67° S, 68° W) in radar observations and WACCM-X

Abstract

Abstract. The mesosphere and lower thermosphere (MLT), at heights of 80–100 km, is critical in the coupling of the middle and upper atmosphere and controls the momentum and energy transfer between these two regions. However, despite its importance, many General Circulation Models (GCMs) do not extend upwards into the MLT and those that do remain poorly constrained. In this study, we use a long-term meteor radar wind dataset from Rothera (67° S, 68° W) on the Antarctic Peninsula to test the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (WACCM-X). This radar has an interferometer to determine meteor heights and has been running since 2005. This unique combination yields a dataset ideally suited to investigate interannual variability. We find that although some characteristic features in monthly median winds are represented well in WACCM-X, the model exhibits significant biases. In particular, the observations reveal a ∼10 ms-1 eastward wind at heights of 85–100 km in Antarctic winter, whereas the model predicts winds of the same magnitude but of opposite direction. We propose that this bias exists because WACCM-X is missing eastward momentum forcing in the MLT from the breaking of secondary gravity waves. Both the model and observations reveal significant interannual variability in monthly median winds. We investigate the role of particular key external phenomena in driving the winds in this region. These phenomena are; i) variations in Solar activity, ii) the El Nino Southern Oscillation (ENSO), iii) the Quasi-Biennial Oscillation (QBO) and iv) the Southern Annular Mode (SAM). We use a linear regression method to investigate how the observed and modelled winds, and modelled gravity wave tendencies in the Antarctic MLT vary in relation to the indices that quantify these phenomena. We find that there are some times of year and some height ranges at which there are significant correlations between the indices and the observed/modelled winds. In particular, in summer, there is a strong positive correlation in the modelled and observed zonal winds with the 11-year Solar cycle of magnitude up to 9 ms-1 per 70 Solar flux units. However, there appears to be little significant influence of the ENSO on the winds observed by the radar although WACCM-X zonal winds display a negative correlation throughout January–February and a positive correlation during March–May. Results from the QBO indices are varied and we find differing correlations in the model and observations. Finally, we find a positive correlation between observed summertime zonal winds and the SAM which has a magnitude of 9 ms-1 per 2.5 hPa change in the SAM index. However, in WACCM-X zonal winds the summertime response is negative and around 10 ms-1 per 2.5 hPa. The significance of this work lies in our quantifying the biases in a leading GCM and demonstrating there is significant interannual variability in both modelled and observed winds, some of which are consistent with the proposal of external forcing.

Interannual Variability of the 12‐hr Tide in the Mesosphere and Lower Thermosphere in 15 Years of Meteor‐Radar Observations Over Rothera (68°S, 68°W)

“…In contrast, Noble et al. (2022) found a correlation between the SAM and MLT winds measured by meteor radar at Rothera in the same data set as considered in our study. It is possible that the SAM modulation of MLT winds could impact MLT tidal amplitudes.…”

Evaluation of Polar Winter Mesopause Wind in WACCMX+DART

“…One of the most widely used models is the Whole Atmosphere Community Climate Model (WACCM) (Marsh et al., 2013). Unfortunately, there is a limitation to using WACCM as a tool to study these processes in the polar winter mesopause region because it has a well‐known easterly (westward) wind bias in the polar winter upper mesosphere (e.g., Eswaraiah et al., 2016; Harvey et al., 2019; Hindley et al., 2022; Lieberman et al., 2015; Liu, 2016; Marsh et al., 2013; Noble et al., 2022; Rüfenacht et al., 2018; Smith, 2012; Yuan et al., 2008; Zhang et al., 2021). This bias is not unique to WACCM, and other comprehensive high‐top general circulation models with parameterized GWs show the same deficiencies in simulating the observed zonal wind structure (e.g., Wilhelm et al., 2019) in the winter upper mesosphere (e.g., Griffith et al., 2021; McCormack et al., 2017, 2021; McLandress et al., 2006; Pedatella, Fuller‐Rowell, et al., 2014; Schmidt et al., 2006).…”

Simulation of the Seasonal Variation of Mesospheric Zonal Wind Reversal with Anisotropic Gravity Waves

“…The reversal strength also varies with seasons: weaker in the winter and stronger in the summer. This seasonal variation is also reported in ground-based wind manuscript submitted to JGR: Atmospheres observations (Stober et al, 2021;Hindley et al, 2022;Noble et al, 2022). While such seasonal variation is not properly captured in some of the GCMs, as indicated in the comparative analysis by Stober et al (2021), Hindley et al (2022), andNoble et al (2022).…”

“…This seasonal variation is also reported in ground-based wind manuscript submitted to JGR: Atmospheres observations (Stober et al, 2021;Hindley et al, 2022;Noble et al, 2022). While such seasonal variation is not properly captured in some of the GCMs, as indicated in the comparative analysis by Stober et al (2021), Hindley et al (2022), andNoble et al (2022). For example, in WACCM-X as well as WACCM, the reversal levels are about the same in the winter and summer.…”