The Response of the Polar Vortex to Tropospheric Temperature Eddies in an Idealized General Circulation Model

A dry-core idealized general circulation model with a stratospheric polar vortex in the northern hemisphere is run with a combination of simplified topography and imposed tropospheric temperature perturbations, each located in the northern hemisphere with a zonal wave number of one. The phase difference between the imposed temperature wave and the topography is varied to understand what effect this has on the occurrence of polar vortex displacements. Geometric moments are used to identify the centroid of the polar vortex for the purposes of classifying whether or not the polar vortex is displaced. Displacements of the polar vortex are a response to increased tropospheric wave activity. Compared to a model run with only topography, the likelihood of the polar vortex being displaced increases when the warm region is located west of the topography peak, and decreases when the cold region is west of the topography peak. This response from the polar vortex is due to the modulation of vertically propogating wave activity by the temperature forcing. When the southerly winds on the western side of the topographically forced anticyclone are collocated with warm or cold temperature forcing, the vertical wave activity flux in the troposphere becomes more positive or negative, respectively. This is in line with recent reanalysis studies which showed that anomalous warming west of the surface pressure high, in the climatological standing wave, precedes polar vortex disturbances.

Tibetan anti cyclone as simulated in a general circulation model

The a im of this study is \0 investiga te the sensitivity of the mean posit ion and intensity ofthe T ibetan anticyclon e simula ted in a Gener al Circ ula tion Model (GeM) to presc ri bed sur face bou nda ry conditions. Results of some numerica l experiments show tha t the intensity and position of the anticyclo ne a rc influencedby the global sea surface temperat ures. The simulated Ti betan High is close 10 its climatologicalla titudi na l positiondur ing monsoon mon ths when the seasonal var iations in the surfac e bo un dary co ndi tions a re introdu cedand the model i.;, integrated from the initia l atmospheric conditions co rrespo ndi ng 10 winte r.

Turbulent parameters in the middle atmosphere: Theoretical estimates deduced from a gravity-wave resolving general circulation model

We present a scaling analysis for the stratified turbulent and small-scale turbulent regimes of atmospheric flow with emphasis on the mesosphere. We distinguish rotating-stratified macroturbulence turbulence (SMT), stratified turbulence (ST), and small-scale isotropic Kolmogorov turbulence (KT), and we specify the length and time scales and the characteristic velocities for these regimes. It is shown that the buoyancy scale (Lb) and the Ozmidov scale (Lo) are the main parameters that describe the transition from SMT to KT. We employ the buoyancy Reynolds number and horizontal Froude number to characterize ST and KT in the mesosphere. This theory is applied to simulation results from a high-resolution general circulation model with a Smagorinsky-type turbulent diffusion scheme for the sub-grid scale parameterization. The model allows us to derive the turbulent root-mean-square (RMS) velocity in the KT regime. It is found that the turbulent RMS velocity has a single maximum in summer and a double maximum in winter months. The secondary maximum in the winter MLT we associate with a secondary gravity wave breaking phenomenon. The turbulent RMS velocity results from the model agree well with Full Correlation Analyses based on MF-radar measurements. A new scaling for the mesoscale horizontal velocity based on the idea of direct energy cascade in masoscales is proposed. The latter findings for mesoscale and small-scale characteristic velocities supports the idea proposed in this research that mesoscale and small-scale dynamics in the mesosphere are governed by SMT, ST, and KT in the statistical average.

Simulation of winter and summer climates with PRL Atmospheric General Circulation Model

A global, spectral Atmospheric General Circulation Model (AGCM) has been developed indigenously at Physical Research Laboratory (PRL) for climate studies. The model has six a levels in the vertical and has horizontal resolution of 21 waves with rhomboidal truncation. The model includes smooth topography, planetary boundary layer, deep convection, large scale condensation, interactive hydrology, radiation with interactive clouds and diurnal cycle. Sea surface temperature and sea ice values were fixed based on climatological data for different calender months. The model was integrated for six years starting with an isothermal atmosphere (2400K), zero winds initial conditions and forcing from incoming solar radiation. After one year the model stabilizes. The seasonal averages of various fields of the last five years are discussed in this paper. It is found that the model reproduces reasonably well the seasonal features of atmospheric circulation, seasonal variability and hemispheric differences.

Robustness of neural network emulations of radiative transfer parameterizations in a state-of-the-art general circulation model

The ability of machine-learning-based (ML-based) model components to generalize to the previously unseen inputs and its impact on the stability of the models that use these components have been receiving a lot of recent attention, especially in the context of ML-based parameterizations. At the same time, ML-based emulators of existing physically based parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural-network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art general circulation model (GCM) are robust with respect to the substantial structural and parametric change in the host model: when used in two 7-month-long experiments with a new GCM, they remain stable and generate realistic output. We concentrate on the stability aspect of the emulators' performance and discuss features of neural network architecture and training set design potentially contributing to the robustness of ML-based model components.