Abstract

Abstract. Due to their increasing spatial resolution, numerical weather prediction (NWP) models and the associated analyses resolve a growing fraction of the gravity wave (GW) spectrum. However, it is unclear how well this “resolved” part of the spectrum truly compares to the actual atmospheric variability. In particular, the Lagrangian variability, relevant, for example, to atmospheric dispersion and to microphysical modeling in the upper troposphere–lower stratosphere (UTLS), has not yet been documented in recent products. To address this shortcoming, this paper presents an assessment of the GW spectrum as a function of the intrinsic (air parcel following) frequency in recent (re)analyses (ERA-Interim, ERA5, the ECMWF operational analysis and MERRA-2). Long-duration, quasi-Lagrangian balloon observations in the equatorial and Antarctic lower stratosphere are used as a reference for the atmospheric spectrum and are compared to synthetic balloon observations along trajectories calculated using the wind and temperature fields of the reanalyses. Overall, the reanalyses represent realistic features of the spectrum, notably the spectral gap between planetary and gravity waves and a peak in horizontal kinetic energy associated with inertial waves near the Coriolis frequency f in the polar region. In the tropics, they represent the slope of the spectrum at low frequency. However, the variability is generally underestimated even in the low-frequency portion of the spectrum. In particular, the near-inertial peak, although present in the reanalyses, has a reduced magnitude compared to balloon observations. We compare the observed and modeled variabilities of temperature, zonal momentum flux and vertical wind speed, which are related to low-, mid- and high-frequency waves, respectively. The probability density function (PDF) distributions have similar shapes but show increasing disagreement with increasing intrinsic frequency. Since at those altitudes they are mainly caused by gravity waves, we also compare the geographic distribution of vertical wind fluctuations in the different products, which emphasizes the increase of both GW variance and intermittency with horizontal resolution. Finally, we quantify the fraction of resolved variability and its dependency on model resolution for the different variables. In all (re)analysis products, a significant part of the variability is still missing, especially at high frequencies, and should hence be parameterized. Among the two polar balloon datasets used, one was broadcast on the Global Telecommunication System for assimilation in NWP models, while the other consists of independent observations (unassimilated in the reanalyses). Comparing the Lagrangian spectra between the two campaigns shows that the (re)analyses are largely influenced by balloon data assimilation, which especially enhances the variance at low GW frequency.

Highlights

  • Atmospheric gravity waves (GWs) are mesoscale motions with large-scale impacts notably through three mechanisms

  • We initially considered four recent reanalysis systems (ERA-Interim, ERA5, MERRA-2 and JRA-55) and the ECMWF operational analysis

  • This paper examined the representation of gravity-waveinduced fluctuations in the lower stratosphere of modern reanalyses through the computation of Lagrangian trajectories and their comparison with quasi-Lagrangian balloon observations

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Summary

Introduction

Atmospheric gravity waves (GWs) are mesoscale motions with large-scale impacts notably through three mechanisms. They transport momentum from lower levels and deposit it higher up in the atmosphere, which forces large-scale circulations (Andrews et al, 1987), such as the quasi-biennial oscillation (QBO; Baldwin et al, 2001). They generate small-scale turbulence (e.g., when breaking), which contributes to mixing atmospheric trace constituents A. Podglajen et al.: Lagrangian gravity wave spectra in the lower stratosphere et al, 2017) and diabatic heating. GWs induce temperature and wind fluctuations which impact the formation and microphysical properties of clouds (e.g., cirrus clouds; Potter and Holton, 1995) and aerosols

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