On the intermittency of orographic gravity wave hotspots and its importance for middle atmosphere dynamics

Abstract

Abstract. When orographic gravity waves (OGWs) break, they dissipate their momentum and energy and thereby influence the thermal and dynamical structure of the atmosphere. This OGW forcing mainly takes place in the middle atmosphere. It is zonally asymmetric and strongly intermittent. So-called “OGW hotspot regions” have been shown to exert a large impact on the total wave forcing, in particular in the lower stratosphere (LS). Motivated by this we investigate the asymmetrical distribution of the three-dimensional OGW drag (OGWD) for selected hotspot regions in the specified dynamics simulation of the chemistry-climate model CMAM (Canadian Middle Atmosphere Model) for the period 1979–2010. As an evaluation, we first compare zonal mean OGW fluxes and GW drag (GWD) of the model simulation with observations and reanalyses in the Northern Hemisphere. We find an overestimation of GW momentum fluxes and GWD in the model's LS, presumably attributable to the GW parameterizations which are tuned to correctly represent the dynamics of the Southern Hemisphere. In the following, we define three hotspot regions which are of particular interest for OGW studies, namely the Himalayas, the Rocky Mountains and East Asia. The GW drags in these hotspot regions emerge as strongly intermittent, a result that can also quantitatively be corroborated with observational studies. Moreover, a peak-detection algorithm is applied to capture the intermittent and zonally asymmetric character of OGWs breaking in the LS and to assess composites for the three hotspot regions. This shows that LS peak OGW events can have opposing effects on the upper stratosphere and mesosphere depending on the hotspot region. Our analysis constitutes a new method for studying the intermittency of OGWs, thereby facilitating a new possibility to assess the effect of particular OGW hotspot regions on middle atmospheric dynamics.

Multidecadal Measurements of UTLS Gravity Waves Derived From Commercial Flight Data

“…Due to the inhomogeneous data distribution, while these spatial clusters represent both large and small scales well, they contain highly uneven numbers of measurements, with a spread of 3 orders of magnitude in population (compared to 5 orders of magnitude using a regular 3° grid). While suitable for mapping, this may introduce spurious intercluster variations in the statistics we compute, namely, the median and Gini's coefficient of concentration G (used previously for GW studies in, e.g., Hindley et al, 2019; Kuchar et al, 2020; Plougonven et al, 2012; Wright et al, 2013). Means were also calculated for all clusters and showed spatially consistent results but with spike values in some clusters due to outliers.…”

Diverse Dynamical Response to Orographic Gravity Wave Drag Hotspots—A Zonal Mean Perspective

“…As shown in Figure 8 of Kuchar et al. (2020), the peak is already preceeded by a few days of significant OGWD anomalies in the region of the hotspot. Therefore, at lag = 0 we can see a response that has already been evolving over a few days prior to lag = 0 under an increasing OGWD forcing overlapping with an imprint of the “hotspot preconditioning.” To assess if nudging has a dynamical impact on the composites, we derived the nudging strength as a residual term from the TEM momentum budget (see Equation ).…”

“…The identification of strong OGWD events allows to calculate composite anomalies of different variables by subtracting daily values from the monthly climatology of SSW‐free winters. Composites of variables that are analyzed in this study and all processed data are available via the Mendeley Data repository (Kuchar, 2020).…”

“…All processed data files for this study are provided via Mendeley Data Kuchar (2020), and all codes to reproduce our figures are provided via GitHub (Kuchar, 2021).…”

“…To account for the intermittency of OGWD and its distribution into hotspots, we build on the methodology of strong OGWD composites presented by Kuchar et al. (2020). The underlying method and model data used are briefly described in Section 2.…”