The connection between the dominant mode of interannual variability in the tropical troposphere, the El Niño–Southern Oscillation (ENSO), and the entry of stratospheric water vapor is analyzed in a set of model simulations archived for the Chemistry-Climate Model Initiative (CCMI) project and for Phase 6 of the Coupled Model Intercomparison Project. While the models agree on the temperature response to ENSO in the tropical troposphere and lower stratosphere, and all models and observations also agree on the zonal structure of the temperature response in the tropical tropopause layer, the only aspect of the entry water vapor response with consensus in both models and observations is that La Niña leads to moistening in winter relative to neutral ENSO. For El Niño and for other seasons, there are significant differences among the models. For example, some models find that the enhanced water vapor for La Niña in the winter of the event reverses in spring and summer, some models find that this moistening persists, and some show a nonlinear response, with both El Niño and La Niña leading to enhanced water vapor in both winter, spring, and summer. A moistening in the spring following El Niño events, the signal focused on in much previous work, is simulated by only half of the models. Focusing on Central Pacific ENSO vs. East Pacific ENSO, or temperatures in the mid-troposphere compared with temperatures near the surface, does not narrow the inter-model discrepancies. Despite this diversity in response, the temperature response near the cold point can explain the response of water vapor when each model is considered separately. While the observational record is too short to fully constrain the response to ENSO, it is clear that most models suffer from biases in the magnitude of the interannual variability of entry water vapor. This bias could be due to biased cold-point temperatures in some models, but others appear to be missing forcing processes that contribute to observed variability near the cold point.

A flash flood is a rapid and intense response of a drainage area to heavy rainfall events. In the arid and semiarid parts of the Eastern Mediterranean (EM) region, the spatiotemporal distribution of rainfall is the most important factor for flash flood generation. A possible precursor to heavy rainfall events is the rise in tropospheric water vapor amount, which can be remotely sensed using ground-based global navigation satellite system (GNSS) stations. Here, we use the precipitable water vapor (PWV) derived from nine GNSS ground-based stations in the arid part of the EM region in order to predict flash floods. Our approach includes using three types of machine learning (ML) models in a binary classification task, which predicts whether a flash flood will occur given 24 h of PWV data. We train our models with 107 unique flash flood events and vigorously test them using a nested cross-validation technique. The results indicate a good agreement between all three types of models and across various score metrics. In addition, the models are further improved by adding more features such as surface pressure measurements. Finally, a feature importance analysis shows that the most important features are the PWV values from 2 to 6 h prior to a flash flood. These promising results indicate that it is possible to augment the current flash flood warning systems with a near real-time GNSS ground-based data-driven approach as demonstrated in this work.

The relative importance of two processes that help control the concentrations of stratospheric water vapor, the Quasi-Biennial Oscillation (QBO) and El Niño–Southern Oscillation (ENSO), are evaluated in observations and in comprehensive coupled ocean–atmosphere-chemistry models. The possibility of nonlinear interactions between these two is evaluated both using multiple linear regression (MLR) and three additional advanced machine learning techniques. The QBO is found to be more important than ENSO; however nonlinear interactions are nonnegligible, and even when ENSO, the QBO, and potential nonlinearities are included, the fraction of entry water vapor variability explained is still substantially less than what is accounted for by cold-point temperatures. While the advanced machine learning techniques perform better than an MLR in which nonlinearities are suppressed, adding nonlinear predictors to the MLR mostly closes the gap in performance with the advanced machine learning techniques. Comprehensive models suffer from too weak a connection between entry water and the QBO; however a notable improvement is found relative to previous generations of comprehensive models. Models with a stronger QBO in the lower stratosphere systematically simulate a more realistic connection with entry water.

The Northern Hemisphere and tropical circulation response to interannual variability in Arctic stratospheric ozone is analyzed in a set of the latest model simulations archived for the Chemistry-Climate Model Initiative (CCMI) project. All models simulate a connection between ozone variability and temperature/geopotential height in the lower stratosphere similar to that observed. A connection between Arctic ozone variability and polar cap surface air pressure is also found, but additional statistical analysis suggests that it is mediated by the dynamical variability that typically drives the anomalous ozone concentrations. While the CCMI models also show a connection between Arctic stratospheric ozone and the El Niño–Southern Oscillation (ENSO), with Arctic stratospheric ozone variability leading to ENSO variability 1 to 2 years later, this relationship in the models is much weaker than observed and is likely related to ENSO autocorrelation rather than any forced response to ozone. Overall, Arctic stratospheric ozone is related to lower stratospheric variability. Arctic stratospheric ozone may also influence the surface in both polar and tropical latitudes, though ozone is likely not the proximate cause of these impacts and these impacts can be masked by internal variability if data are only available for ∼40 years.

The analytic wave solutions obtained by Matsuno (1966) in his seminal work on equatorial waves provide a simple and informative way of assessing the performance of atmospheric models by measuring the accuracy with which they simulate these waves. These solutions approximate the solutions of the shallow-water equations on the sphere for low gravity-wave speeds such as those of the baroclinic modes in the atmosphere. This is in contrast to the solutions of the non-divergent barotropic vorticity equation, used in the Rossby–Haurwitz test case, which are only accurate for high gravity-wave speeds such as those of the barotropic mode. The proposed test case assigns specific values to the wave parameters (gravity-wave speed, zonal wave number, meridional wave mode and wave amplitude) for both planetary and inertia-gravity waves, and suggests simple assessment criteria suitable for zonally propagating wave solutions. The test is successfully applied to a spherical shallow-water model in an equatorial channel and to a global-scale model. By adding a small perturbation to the initial fields it is demonstrated that the chosen initial waves remain stable for at least 100 wave periods. The proposed test case can also be used as a resolution convergence test.

The precipitable water vapour (PWV) of 21 Global Navigation Satellite System (GNSS) ground receivers from the Survey Of Israel Active Permanent Network (SOI‐APN) in the Eastern Mediterranean area are processed and analysed. We focus on the annual cycles, inter‐annual variations and long‐term trends. The PWV monthly mean values peak during September–October where October has the highest variability for all stations. PWV values reach minimum level during February–March where Apr is the least variable month for most stations. Generally, the PWV variability decreases with station height. Additionally, harmonic analysis indicates that the annual mode's amplitude decreases with station height. In contrast, the semi‐annual mode's accounting for the sub‐annual variance increases with station height. The PWV monthly mean anomalies are consistent among all the stations suggesting a common regional driver. Furthermore, a comparison between the PWV station average anomalies and the regional mean anomalies from the ERA5 reanalysis shows a strong correlation with r = 0.95. In addition, a correlation coefficient of r = 0.72 was found with the regional mean moisture flux anomalies at 750 hPa taken from ERA5 and the station average PWV anomalies, thus implying that horizontal moisture flow accounts for most of the inter‐annual variability. However, the significance of the 750 hPa pressure level requires future investigation. For the long‐term analysis, we find an increasing regional mean trend of 0.5 mm ⋅ decade⁻¹ for the entire data period (1997–2019) as compared to 1 mm ⋅ decade⁻¹ for the last decade (2010–2019) suggesting a recent accelerated moistening of the Eastern Mediterranean region.