The study of the stratosphere-troposphere interaction is important as it can contribute to boosting predictability in the subseasonal-to-seasonal timescale, particularly regarding extremes. This manuscript investigates the relationship between the stratospheric polar vortex and its sudden stratospheric warming and the troposphere in regard to the reflective and absorptive states of the vortex. We explore the eddy heat flux in relation to vertical wave propagation and sudden stratospheric warming, in addition to using the reflective index for comparison and checking. To find reflective and absorptive vortex regime and associated tropospheric flow, the analysis is complemented by clustering analysis. Using northern winter stratospheric and mid-tropospheric Reanalysis heights as well as sea level pressure and 2 m-temperature, absorptive and reflective states are identified and their coherent structures investigated in relation to the vortex state and surface climate. While the reflective index is not consistent with the eddy heat flux on the classification, the absorptive type for both methods consistently trigger a response in the annular mode with a negative Arctic Oscillation imprint. It also exhibits longer lasting wave propagation, compared to reflective types, suggesting sustained disruption of the circulation and occurrence of blocking. The clustering analysis reveals specific characteristics within vortex states affecting wave propagation. Precisely, weak and displaced or split vortex over the eastern hemisphere is associated with absorptive type, and yields more persistence, compared to the reflective type, associated with strong or quite weak vortex and also displaced vortex over North America. Effect on the surface climate are also discussed.

A severe cold air outbreak hit the US and parts of Canada in January 2019, leaving behind many casualties where at least 21 people died as a consequence. According to Insurance Business America, the event cost the US about 1 billion dollars. In the Midwest, surface temperatures dipped to the lowest on record in decades, reaching −32 °C in Chicago, Illinois, and down to −48 °C wind chill temperature in Cotton and Dakota, Minnesota, giving rise to broad media attention. A zonal wavenumber 1–3 planetary wave forcing caused a sudden stratospheric warming, with a displacement followed by a split of the polar vortex at the beginning of 2019. The common downward progression of the stratospheric anomalies stalled at the tropopause and, thus, they did not reach tropospheric levels. Instead, the stratospheric trough, developing in a barotropic fashion around 70° W, turned the usually baroclinic structure of the Aleutian high quasi-barotropic. In response, upward propagating waves over the North Pacific were reflected at its lower stratospheric, eastward tilting edge toward North America. Channeled by a dipole structure of positive and negative eddy geopotential height anomalies, the waves converged at the center of the latter and thereby strengthened the circulation anomalies responsible for the severely cold surface temperatures in most of the Midwest and Northeast US.

Spatiotemporal variations in temperature (T) and equivalent temperature (Te) significantly impact agricultural production across Pakistan, highlighting the need for enhanced weather and climate modeling. This study utilized four reanalysis datasets spanning a 38-year period (1981–2018): the fifth-generation European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5), Interim ECMWF reanalysis (ERA-Interim), Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), and the Japanese 55-year reanalysis (JRA55). We employed National Oceanic and Atmospheric Administration/Advanced Very High-Resolution Radiometer (NOAA/AVHRR) Normalized Difference Vegetation Index (NDVI) data, a proxy for crop health, to assess the relationship between T, Te, and NDVI. This relationship is examined via regression and correlation analyses, and significance is assessed using the Mann–Kendall test and t-test. Our results show that near-surface T significantly contributes to the magnitude of Te (> 90%), whereas specific humidity (SH) has a smaller impact (< 10%). Both T and Te increase significantly across the entire tropospheric column, at 0.15 – 0.31 and 0.38 – 0.77 °C/decade, respectively. Notably, the mid-tropospheric level exhibits less warming than the upper and lower tropospheric levels. Correlation analyses of T and Te with NDVI reveal that Te exhibits a significantly stronger relationship with NDVI compared to T on both seasonal and annual timescales. The highest correlation occurs in the warm and humid summer monsoon (June – August), with Te showing a correlation of 0.50 and T correlating at 0.22 with NDVI. This study suggests that Te can serve as an additional metric for analysing near-surface heating trends in relation to crop health.

The interannual rainfall variability over the southeast Australian state of Victoria is known to be influenced by a number of large scale and regional phenomena, including the Indian Ocean Dipole (IOD), Southern Oscillation Index (SOI), and Southern Annular Mode (SAM). However, the role of ‘upstream’ regional circulation or pressure anomalies has received only modest attention. The amount of winter (May-August) rainfall over the state has declined over the past few decades, especially from 1960 to 2017. Using the Center of Action (COA) technique this study examines the relationship between winter precipitation over Victoria and the characteristics of the Indian Ocean High (IOH) over the period 1951–2021. We show that variations of the IOH are strongly linked with those of precipitation over Victoria. The strongest link is with the Indian Ocean High pressure (IOH_P) and its longitudinal position (IOH_LN), whereas the Indian Ocean High latitude (IOH_LT) has little impact. Less precipitation is observed across the state when IOH_P anomalies are positive, whereas the eastward shift of the IOH_LN is a major factor in the reduction of precipitation. Using correlation and multiple regression analyses, we find that the IOH indices explain 54 % of the winter precipitation variation. The strength of this relationship is somewhat weaker in the northern part of the state, partly because of the additional influence of ‘north-west cloud bands’ north of the Great Diving Range. Finally, we perform composite analyses of anomalous high (low) years of IOH to establish evidence of IOH influencing Victorian rainfall. This allows us to reveal the dynamical mechanisms behind the revealed associations.

With the increase in the volume of climate model simulations for past, present and future climate, from various institutions across the globe, there is a need for efficient and robust methods for model comparison and/or evaluation. This manuscript discusses common empirical orthogonal function analysis with a step-wise algorithm, which can be used for the above objective. The method looks for simultaneous diagonalisation of several covariance matrices in a step-wise fashion ensuring thus simultaneous monotonic decrease of the eigenvalues in all groups, and allowing therefore for dimension reduction. The method is applied to a number of tropospheric and stratospheric fields from the main four reanalysis products, and also to several historical climate model simulations from CMIP6, the Coupled Model Intercomparison Project (Phase 6). Monthly means as well as winter daily gridded data are considered over the Northern Hemisphere. The method shows consistency between mass fields as well as mid-tropospheric and stratospheric fields of the reanalyses, but also reveals significant differences in the 2 m surface-air temperature in terms of explained variance. CMIP6 models, on the other hand, show differences reflected in the percentage of explained variance of the leading common EOFs with inter-group variation ranging from 5–10% in the troposphere to about 25% in the stratosphere. Higher order statistics within the leading common modes of variability, in addition to further merits of the method are also discussed. 

Persistent boreal winter cold spells (PCEs) can heavily strain the economy and significantly impact everyday life. While sudden stratospheric warmings are considered a precursor for Eurasian (EUR) cold events, these temperature extremes may occur during the full range of stratospheric variability. We investigate PCEs relative to the prevailing stratospheric polar vortex regime before their onset, with a particular focus on extremely weak (SSW) and strong (SPV) stratospheric winds by performing (lagged) composite analysis based on ERA5 reanalysis. On average, SPV PCEs that are concentrated over central EUR, are colder, shorter and set in more abruptly compared to SSW PCEs. A quasi-stationary, mid-tropospheric anticyclone over the Arctic Ocean that blocks warm air advection toward EUR is connected to the canonical downward progression of the negative North Atlantic Oscillation for SSW PCEs. In contrast, during SPV PCEs, the anticyclone is part of a Rossby wave having an origin co-located with negative wave activity flux anomalies over and being influenced by stratospheric wave reflection toward the North Atlantic. Its slow east-ward propagation is likely related to Arctic surface warming and unusually weak zonal winds over EUR. 

Study region: The study is carried out for northern Tunisia.

Study focus: Precipitations are often analysed via intensity or accumulation for a specified timescale (e.g., annual, seasonal, etc). We propose in this study to analyse regional rainfall variability by adopting a variable time step through the rain event concept. This event-based approach, ensures the integration of information related to rain intermittency, which is one of the fundamental properties of precipitations. This study focuses essentially on wet spells characteristics derived from the aggregation of daily winter dataset over a 50 years period (1960–2009). The multivariate analysis, based on the combination of two clustering approaches, i.e., self-organizing map and hierarchical clustering, allows the identification of different rainfall regimes. This study helps to understand rainfall variability patterns and to address rainfall regionalization and water use management issues.

New hydrological insights for the region: The winter precipitations of northern Tunisia are classified into 4 typical situations: Extremely dry seasons with a few short and weak rainfall events, dry seasons, with high frequency of weak events, intermediate seasons with medium amount of rain and intermittent events and rainiest seasons with long and intense events. The regionalization yields two geographical regions: northern sector characterized by rainy seasons, whereas the stations of the southern sector are mostly dry. The temporal variability analysis shows that the dry season classes dominate extending over three consecutive decades from 1970 to 2000.

Monthly evaporation data for 1958–2015 from the Woods Hole Oceanographic Institution dataset are used to investigate inter-annual variability of Mediterranean evaporation and its links to regional climate during the extended (June–September) summer season. An EOF (Empirical Orthogonal Functions) analysis performed on the monthly means (i.e., separately for June, July, August and September time series) revealed two leading modes of evaporation variability, characterized by the monopole (EOF-1) and zonal dipole (EOF-2) patterns. These modes explain together more than 50% of the total variability of Mediterranean evaporation. It is shown that the EOF-1 reflects interdecadal changes characterized by below normal evaporation during the period 1970–2000, and above normal evaporation before and after that period. The EOF-2 reflects inter-annual and decadal scale variations of Mediterranean evaporation. Analysis of correlations between the leading PCs (principal components) of evaporation and indexes of large-scale teleconnections suggests moderate, but statistically significant links between Mediterranean evaporation and the North Atlantic Oscillation, Scandinavian teleconnection, East Atlantic teleconnection, Atlantic Multi-decadal Oscillation and the Asian monsoon. It is revealed that the dynamic impact on the North Atlantic Oscillation on evaporation weakens toward the end of the summer season, whereas thermodynamic impact from the Asian monsoon increases.

The low-frequency variability of the extratropical atmosphere involves hemispheric-scale recurring, often persistent, states known as teleconnection patterns or regimes, which can have a profound impact on predictability on intra-seasonal and longer timescales. However, reliable data-driven identification and dynamical representation of such states are still challenging problems in modeling the dynamics of the atmosphere. We present a new method, which allows us both to detect recurring regimes of atmospheric variability and to obtain dynamical variables serving as an embedding for these regimes. The method combines two approaches from nonlinear data analysis: partitioning a network of recurrent states with studying its properties by the recurrence quantification analysis and the kernel principal component analysis. We apply the method to study teleconnection patterns in a quasi-geostrophical model of atmospheric circulation over the extratropical hemisphere as well as to reanalysis data of geopotential height anomalies in the mid-latitudes of the Northern Hemisphere atmosphere in the winter seasons from 1981 to the present. It is shown that the detected regimes as well as the obtained set of dynamical variables explain large-scale weather patterns, which are associated, in particular, with severe winters over Eurasia and North America. The method presented opens prospects for improving empirical modeling and long-term forecasting of large-scale atmospheric circulation regimes.

This research focuses on extracting the statistical features, in space and time, of the monthly rainfall in Saudi Arabia (SA) and the relation to the large-scale atmospheric variability through teleconnection for strategic water resources planning. These features are useful for future predictions. 28 stations distributed over SA for a period between 1970 and 2012 are utilized. According to the Kolmogorov–Smirnov (K-S) test, the Log-normal and Gamma distributions are dominant, while for the Chi-squared (Chi2) test, the Beta distribution is dominant. The K-S is preferable since it works with the original data rather than the Chi² that uses binning, and therefore, some information is lost. The L-moment analysis showed that Person type III is dominant for the wet season while there is no obvious distribution for the dry season. Empirical Orthogonal Function (EOF) analysis is applied to seasonal rainfall data for studying the dominant modes of climate variability and associated large-scale circulation patterns. Our results demonstrate a robust relationship between the wet season (November – April) with El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO), whereas the dry season (June – September) is associated with the Indian Ocean Dipole (IOD). Moreover, the warm (cold) phase of PDO is associated with excess (deficit) rainfall, indicating some predictability of the seasonal rainfall over SA.