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Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime stratosphere. Such extreme events are characterized by substantial disruption to the stratospheric polar vortex, which can be categorized into displacement and splitting types depending on the morphology of the disrupted vortex. Moreover, SSWs are usually followed by anomalous tropospheric circulation regimes that are important for subseasonal-to-seasonal prediction. Thus, monitoring the genesis and evolution of SSWs is crucial and deserves further advancement. Despite several analysis methods that have been used to study the evolution of SSWs, the ability of deep learning methods has not yet been explored, mainly due to the relative scarcity of observed events. To overcome the limited observational sample size, we use data from historical simulations of the Whole Atmosphere Community Climate Model version 6 to identify thousands of simulated SSWs, and use their spatial patterns to train the deep learning model. We utilize a convolutional neural network combined with a variational auto-encoder (VAE)—a generative deep learning model—to construct a phase diagram that characterizes the SSW evolution. This approach not only allows us to create a latent space that encapsulates the essential features of the vortex structure during SSWs, but also offers new insights into its spatiotemporal evolution mapping onto the phase diagram. The constructed phase diagram depicts a continuous transition of the vortex pattern during SSWs. Notably, it provides a new perspective for discussing the evolutionary paths of SSWs: the VAE gives a better-reconstructed vortex morphology and more clearly organized vortex regimes for both displacement-type and split-type events than those obtained from principal component analysis. Our results provide an innovative phase diagram to portray the evolution of SSWs, in which particularly the splitting SSWs are better characterized. Our findings support the future use of deep learning techniques to study the underlying dynamics of extreme stratospheric vortex phenomena, and to establish a benchmark to evaluate model performance in simulating SSWs.

 

Weather regimes have been defined over multiple regions and used in a range of practical applications, including subseasonal‐to‐seasonal forecasting and climate model evaluation. Despite their widespread use, the extent to which regimes reflect physical modes of the atmosphere is seldom investigated. Here, we adopt a year‐round classification of four North American weather regimes, with a fifth “no regime” class, and leverage dynamical systems theory to investigate their dynamical properties. We find that when the atmospheric flow is assigned to a regime, it displays persistent characteristics and a lifecycle‐like temporal evolution. We further find that, regardless of season, these characteristics are enhanced when the atmospheric flow displays a comparatively strong projection onto the cluster‐mean of the regime to which it is assigned (while the reverse is true for a weaker projection). We interpret these results as evidence that the four North American weather regimes are physically‐meaningful, with a clear dynamical footprint.

 

Misalignments between planetary orbits and the equatorial planes of their host stars are clues about the formation and evolution of planetary systems. Earlier work found evidence for a peak near 90° in the distribution of stellar obliquities, based on frequentist tests. We performed hierarchical Bayesian inference on a sample of 174 planets for which either the full three-dimensional stellar obliquity has been measured (72 planets) or for which only the sky-projected stellar obliquity has been measured (102 planets). We investigated whether the obliquities are best described by a Rayleigh distribution or by a mixture of a Rayleigh distribution representing well-aligned systems and a different distribution representing misaligned systems. The mixture models are strongly favored over the single-component distribution. For the misaligned component, we tried an isotropic distribution and a distribution peaked at 90° and found the evidence to be essentially the same for both models. Thus, our Bayesian inference engine did not find strong evidence favoring a “perpendicular peak,” unlike the frequentist tests. We also investigated selection biases that affect the inferred obliquity distribution, such as the bias of the gravity-darkening method against obliquities near 0° or 180°. Further progress in characterizing the obliquity distribution will probably require the construction of a more homogeneous and complete sample of measurements.

 

Apicomplexa are ancient and diverse organisms which have been poorly characterized by modern genomics. To better understand the evolution and diversity of these single-celled eukaryotes, we sequenced the genome ofOphryocystis elektroscirrha, a parasite of monarch butterflies,Danaus plexippus. We contextualize our newly generated resources within apicomplexan genomics before answering longstanding questions specific to this host-parasite system. To start, the genome is miniscule, totaling only 9 million bases and containing fewer than 3,000 genes, half the gene content of two other sequenced invertebrate-infecting apicomplexans,Porospora giganteaandGregarina niphandrodes. We found thatO. elektroscirrhashares different orthologs with each sequenced relative, suggesting the true set of universally conserved apicomplexan genes is very small indeed. Next, we show that sequencing data from other potential host butterflies can be used to diagnose infection status as well as to study diversity of parasite sequences. We recovered a similarly sized parasite genome from another butterfly,Danaus chrysippus, that was highly diverged from theO. elektroscirrhareference, possibly representing a distinct species. Using these two new genomes, we investigated potential evolutionary response by parasites to toxic phytochemicals their hosts ingest and sequester. Monarch butterflies are well-known to tolerate toxic cardenolides thanks to changes in the sequence of their Type II ATPase sodium pumps. We show thatOphryocystiscompletely lacks Type II or Type 4 sodium pumps, and related proteins PMCA calcium pumps show extreme sequence divergence compared to other Apicomplexa, demonstrating new avenues of research opened by genome sequencing of non-model Apicomplexa.

 

Weather regimes defined through cluster analysis concisely categorize the anomalous regional circulation pattern on any given day. Owing to their persistence and low dimensionality, regimes are increasingly used in subseasonal-to-seasonal prediction and in analysis of climate variability and change. However, a limitation of existing regime classifications for North America is their seasonal dependence, with most existing studies defining regimes for winter only. Here, we normalize the seasonal cycle in daily geopotential height variance and use empirical orthogonal function analysis combined withk-means clustering to define a new set of year-round North American weather regimes: the Pacific Trough, Pacific Ridge, Alaskan Ridge, and Greenland High regimes. We additionally define a “No Regime” state to represent conditions close to climatology. To demonstrate the robustness of the classification, a thorough assessment of the sensitivity of the clustering solution to various methodological choices is provided. The median persistence of all four regimes, obtained without imposing a persistence criterion, is found to be one week, approximately 3 times longer than the median persistence of the No Regime state. Regime-associated temperature and precipitation anomalies are reported, together with the relationship between the regimes and modes of climate variability. We also quantify historical trends in the frequency of the regimes since 1979, finding a decrease in the annual frequency of the Pacific Trough regime and an increase in the summertime frequency of the Greenland High regime. This study serves as a foundation for the future use of these regimes in a variety of weather and climate applications.

Weather regimes provide a simple way of classifying daily large-scale regional weather patterns into a few predefined types. Existing methods usually define regimes for a specific season (typically winter), which limits their use, or provides only a minimal assessment of their robustness. In this study, we objectively quantify four weather regimes for use year-round over North America, while we classify near-normal conditions as No Regime. The four regimes represent persistent large-scale weather types that last for about a week and occasionally much longer. Our new classification can be applied to subseasonal-to-seasonal forecasts and climate model output to diagnose recurrent weather types across the North American continent.

 

Abstract Observational evidence shows changes to North American weather regime occurrence depending on the strength of the lower-stratospheric polar vortex. However, it is not yet clear how this occurs or to what extent an improved stratospheric forecast would change regime predictions. Here we analyze four North American regimes at 500 hPa, constructed in principal component (PC) space. We consider both the location of the regimes in PC space and the linear regression between each PC and the lower-stratospheric zonal-mean winds, yielding a theory of which regime transitions are likely to occur due to changes in the lower stratosphere. Using a set of OpenIFS simulations, we then test the effect of relaxing the polar stratosphere to ERA-Interim on subseasonal regime predictions. The model start dates are selected based on particularly poor subseasonal regime predictions in the European Centre for Medium-Range Weather Forecasts CY43R3 hindcasts. While the results show only a modest improvement to the number of accurate regime predictions, there is a substantial reduction in Euclidean distance error in PC space. The average movement of the forecasts within PC space is found to be consistent with expectation for moderate-to-large lower-stratospheric zonal wind perturbations. Overall, our results provide a framework for interpreting the stratospheric influence on North American regime behavior. The results can be applied to subseasonal forecasts to understand how stratospheric uncertainty may affect regime predictions, and to diagnose which regime forecast errors are likely to be related to stratospheric errors. Significance Statement Predicting the weather several weeks ahead is a major challenge with large potential benefits to society. The strength of the circulation more than 10 km above the Arctic during winter (i.e., the polar vortex) is one source of predictability. This study investigates how forecast error and uncertainty in the polar vortex can impact predictions of large-scale weather patterns called “regimes” over North America. Through statistical analysis of observations and experiments with a weather forecast model, we develop an understanding of which regime changes are more likely to be due to changes in the polar vortex. The results will help forecasters and researchers understand the contribution of the stratosphere to changes in weather patterns, and in assessing and improving weather forecast models. 

In Europe, the increase in temperatures caused by climate change has been particularly fast in the cold season. Although the magnitude of this change is relatively well known, less research has been done on how the increase of temperatures is manifested in different large‐scale weather types, called weather regimes. For example, one could expect that the weather patterns in which air is flowing from the rapidly‐warming Arctic would have warmed faster than other weather patterns in recent decades. Here we show that such an asymmetric warming actually occurs in the four Euro‐Atlantic weather regimes. In northern Europe, the weather regime which is typically associated with cold airmasses from the Arctic (NAO–) has warmed about 25% faster than the cold‐season days on average, and about 60% faster than the regime where the air flows from the North Atlantic (NAO+). Consequently, the weather regime that on average brings the coldest weather is warming the fastest in a large part of northern Europe. In contrast, the weather regime that typically brings the warmest weather has warmed the slowest, especially in the continental Europe. Our results provide a new perspective on the reported decrease of sub‐seasonal temperature variability.

 

The separation of xylene isomers still remains an industrially challenging task. Here, porous purine-based metal–organic frameworks (MOFs) have been synthesized and studied for their potential in xylene separations. In particular, Zn(purine)I showed excellent para -xylene/ ortho -xylene separation capability with a diffusion selectivity of 6 and high equilibrium adsorption selectivity as indicated by coadsorption experiments. This high selectivity is attributed to the shape and size of the channel aperture within the rigid framework of Zn(purine)I. 

We report the confirmation and characterisation of TOI-1820 b, TOI-2025 b, and TOI-2158 b, three Jupiter-sized planets on short-period orbits around G-type stars detected by TESS. Through our ground-based efforts using the FIES and Tull spectrographs, we have confirmed these planets and characterised their orbits, and find periods of around 4.9 d, 8.9 d, and 8.6 d for TOI-1820 b, TOI-2025 b, and TOI-2158 b, respectively. The sizes of the planets range from 0.96 to 1.14 Jupiter radii, and their masses are in the range from 0.8 to 4.4 Jupiter masses. For two of the systems, namely TOI-2025 and TOI-2158, we see a long-term trend in the radial velocities, indicating the presence of an outer companion in each of the two systems. For TOI-2025 we furthermore find the star to be well aligned with the orbit, with a projected obliquity of 9 −31 +33 °. As these planets are all found in relatively bright systems ( V ~ 10.9–11.6 mag), they are well suited for further studies, which could help shed light on the formation and migration of hot and warm Jupiters. 

Variability in atmospheric river (AR) frequency can drive hydrometeorological extremes with broad societal impacts. Mitigating the impacts of increased or decreased AR frequency requires forewarning weeks to months ahead. A key driver of Northern Hemisphere wintertime mid‐latitude subseasonal‐to‐seasonal climate variability is the stratospheric polar vortex. Here, we quantify AR frequency, landfall, genesis, and termination depending on the strength of the lower stratospheric polar vortex. We find large differences between weak and strong vortex states consistent with a latitudinal shift of the eddy‐driven jet, with the greatest differences over the British Isles, Scandinavia, and Iberia. Significant differences are also found for the Pacific Northwest of North America. Most of the seasonal‐scale stratospheric modulation of precipitation over Europe is explained by modulation of ARs. Our results provide potentially useful statistics for extended‐range prediction, and highlight the importance of ARs in bringing about the precipitation response to anomalous vortex states.