The quasi-biennial oscillation in a warmer climate: sensitivity to different gravity wave parameterizations

In order to simulate the quasi-biennial oscillation (QBO) with a realistic period and amplitude, general circulation models commonly include parameterizations of small scale gravity waves (GW). In this work, we explore how different GW parameterization setups determine the response of QBO properties to a warmer climate. Atmosphere-only experiments in both present day and warmer climate serve as testbed to analyze the effect of four different GW parameterization setups, active in the tropics. Having tuned the GW parameterizations to produce a realistic QBO in present day climate, we analyze changes of QBO properties in the warmer climate. The QBO period decreases in two parameterization setups by similar to 30 %, while the QBO period remains unchanged in the remaining two parameterization setups. In all parameterization setups, the QBO amplitude in the warmer climate weakens below 10 hPa but shows different behaviour above 10 hPa. We show that changes in QBO amplitude and changes in QBO period are inconsistent among experiments. In the chosen experimental design, the inconsistent future change in QBO properties among the suite of experiments depends solely on the choice of the GW parameterization setup

Deep Learning Based Cloud Cover Parameterization for ICON

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub-grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. The NNs accurately estimate sub-grid scale cloud cover from coarse-grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub-grid scale cloud cover of the regional SRM simulation. Using the game-theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column-based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood-based models may be a good compromise between accuracy and generalizability

Large eddy simulation using the general circulation model ICON

ICON (ICOsahedral Nonhydrostatic) is a unified modeling system for global numerical weather prediction (NWP) and climate studies. Validation of its dynamical core against a test suite for numerical weather forecasting has been recently published by Zängl et al. (2014). In the present work, an extension of ICON is presented that enables it to perform as a large eddy simulation (LES) model. The details of the implementation of the LES turbulence scheme in ICON are explained and test cases are performed to validate it against two standard LES models. Despite the limitations that ICON inherits from being a unified modeling system, it performs well in capturing the mean flow characteristics and the turbulent statistics of two simulated flow configurations - one being a dry convective boundary layer and the other a cumulus-topped planetary boundary layer.BMBF/01LK1202

Nonlinearity of the combined warm ENSO and QBO effects on the Northern Hemisphere polar vortex in MAECHAM5 simulations

The influence of the quasi-biennial oscillation (QBO) on the Northern Hemisphere (NH) polar vortex response to warm El Nino-Southern Oscillation (ENSO) events and the impact of the warm ENSO events on the QBO signal in the NH polar stratosphere have been analyzed using the Middle Atmosphere ECHAM5 model. The experiment setup was designed to include simulations of extended NH winter seasons for either strong easterly or strong westerly phases of the tropical QBO, forced with either sea surface temperatures (SSTs) from the strong ENSO event that occurred in 1997/1998 or with climatological SSTs. It has been found that the weakening and warming of the polar vortex associated with a warm ENSO are intensified at the end of the winter during both QBO phases. In addition, the westerly QBO phase delays the onset of the warm ENSO signal, while the easterly QBO phase advances it. Warm ENSO events also impact the extratropical signal of the QBO by intensifying ( weakening) the QBO effects in early ( late) winter. Therefore, it appears that during warm ENSO events the duration of QBO signal in the northern extratropics is shortened while its downward propagation accelerated. Our dynamical analysis has revealed that these results are due to changes in the background flow caused by the QBO combined with changes in the anomalous propagation and dissipation of extratropical waves generated by warm ENSO. In both cases, a nonlinear behavior in the response of the polar vortex is observed when both warm ENSO and the easterly phase of the QBO operate together. These results suggest that the Arctic polar vortex response to combined forcing factors, in our case warm ENSO and the QBO phenomena, is expected to be nonlinear also for other coexistent forcing factors able to affect the variability of the vortex in the stratosphere

Influences of the Indian Summer Monsoon on water vapour and ozone concentrations in the UTLS as simulated by Chemistry-Climate Models

The representation of the Indian summer monsoon (ISM) circulation in some current chemistry–climate models (CCMs) is assessed. The main assessment focuses on the anticyclone that forms in the upper troposphere and lower stratosphere and the related changes in water vapor and ozone during July and August for the recent past. The synoptic structures are described and CCMs and reanalysis models are compared. Multiannual means and weak versus strong monsoon cases as classified by the Monsoon–Hadley index (MHI) are discussed. The authors find that current CCMs capture the average synoptic structure of the ISM anticyclone well as compared to the 40-yr ECMWF Re-Analysis (ERA-40) and NCEP–NCAR reanalyses. The associated impact on water vapor and ozone in the upper troposphere and lower stratosphere as observed with the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat is captured by most models to some degree. The similarities for the strong versus weak monsoon cases are limited, and even for present-day conditions the models do not agree well for extreme events. Nevertheless, some features are present in the reanalyses and more than one CCM, for example, ozone increases at 380 K eastward of the ISM. With the database available for this study, future changes of the ISM are hard to assess. The modeled monsoon activity index used here shows slight weakening of the ISM circulation in a future climate, and some of the modeled water vapor increase seems to be contained in the anticyclone at 360 K and sometimes above. The authors conclude that current CCMs capture the average large-scale synoptic structure of the ISM well during July and August, but large differences for the interannual variability make assessments of likely future changes of the ISM highly uncertain