Study region: The study focuses on northern Tunisia, where rainfed cereal crops are predominantly cultivated. This area is particularly vulnerable to droughts, which significantly impact agricultural productivity. Drought data on cereal crop damage were obtained from the National Reports (JORT) over 21 years (2000/01–2020/21).

Study focus: This study proposes to identify droughts using the productivity index (KV), a satellite-derived ratio of actual to potential evapotranspiration based on MODIS data, which reflects climate, soil, and vegetation conditions. Three drought identification methods were evaluated: (1) order statistics (M1a: 25th percentile of the minimum; M1b: minimum of the median; M1c: 25th percentile of the median); (2) a four-class classification based on percentiles (M2: severe, moderate, mild humid, and humid); and (3) archetype analysis (M3), which identifies extreme states on the convex hull of the data.

New hydrological insights for the region: The results demonstrate the effectiveness of KV in drought detection. Method M1a and M1b missed one drought year (2019–20), while M1c produced a false detection. Method M2 correctly identified the four most severe droughts and classified four additional years as moderate droughts, aligning with JORT reports. Archetype analysis (M3) revealed that the three archetypes best distinguished drought conditions, with declared drought years showing the smallest weights (<0.045) relative to the favorable crop archetype (A1). A four-archetype model introduced minor errors (one false alarm and one undetected drought). Notably, the weights associated with favorable (A1) and unfavorable (A2, A3) archetypes correlated strongly with reported crop damage percentages. These findings highlight the robustness of satellite-derived productivity indices and archetype analysis for large-scale drought monitoring in semi-arid regions like northern Tunisia.

A novel method is developed and applied to identify high-dimensional weather and climate extremes located on the envelope of the data set within its state space. The method is based on formulating and integrating dynamical systems whose attractive set, that is, stable fixed points, is constituted of extreme states residing on the convex hull, namely archetypes. It is shown that these states tend to organize into clusters leading to the concept of ’hull clustering’. The method is applied to tropical monthly sea surface temperature (SST) anomalies over the period 1950–2014 and to the 850-K northern hemispheric winter potential vorticity over the period 1979–2021. The SST archetype clusters yield broadly five clusters reflecting El-Nino Southern Oscillation (ENSO) flavors, representing conventional and Modoki El-Niño and La-Niña phases. The application to the stratospheric polar vortex yields broadly three to five clusters representing different states of the winter polar vortex including splitting and displacement over parts of the northern hemisphere. Implication and usefulness of the developed method in long-range forecasting are also discussed.

Ongoing deforestation and global warming are driving critical changes in the Amazon rainforest, a region essential to the global climate system. Model and observational studies have found drier conditions and changes in rainfall patterns in the Amazon; however, fewer studies have examined the role of clouds. Using the state-of-the-art CLARA-A3 satellite climate data record and ERA5 reanalysis data between 1982 and 2020, this study analyzes spatiotemporal trends in cloud cover and surface properties. Our results show a slight but statistically insignificant increase in total cloud cover across the Amazon, but regional and seasonal variations are pronounced. The northern Amazon exhibits a significant increase in high-level cloud fractional cover during the dry–wet transition, while the southern Amazon shows significant declining trends during the dry season across all cloud types. A significant correlation is observed between El Niño–Southern Oscillation and high-level cloud cover variability, especially in the northeastern Amazon, while mid- and low-level clouds show a weak relationship with sea surface temperature variability. Consistent trends in surface heat fluxes and humidity, likely tied to land use and surface changes, may influence these lower cloud layers. However, the application of multiple linear regression reveals the analyzed variables poorly predict medium- and low-level cloud cover, leaving the exact drivers of the observed cloud cover trends uncertain. These findings are consistent with prior research on hydroclimatic trends, implying a drier dry season and a wetter wet season. Further research is recommended to clarify causality and implications for the Amazon climate system.

The method of archetypal analysis is used to generate a set of monthly timescale rainfall archetypes for the Australian region. The patterns associated with the archetypes reflect continental and regional-scale wet and dry. The dominant pattern in terms of occurrence and persistence is one in which most of the continent is dry. This pattern is typically expressed over winter and spring. The next most frequent pattern is one where most of the continent is wet, mostly expressed during summer. It is rare to find periods where the whole continent is wet outside summer, though this does occur and is associated with very wet years for the continent. The archetype patterns have preferred seasonal expressions, and preferred transitions from one pattern to another. The continent-wide dry pattern is mostly followed by patterns in which both south-west and south-east Australia are wet during the autumn and winter. However, if the dry continental archetype persists through to spring, then it is usually followed by a pattern that is wet in the south-east but not the south-west. The analysis reveals pivotal months, such as April and November. These months mark the end of periods when only a few archetypes are expressed, allow expression of almost all the archetypes, and are then succeeded by periods when a smaller number of archetypes are expressed again. The archetype patterns successfully capture the large-scale spatial patterns of monthly rainfall in Australia, and provide a diagnostic tool to evaluate the onset, duration and transitions between wet and dry periods.

The low-frequency variability of the mid-latitude atmosphere involves complex nonlinear and chaotic dynamical processes posing predictability challenges. It is characterized by sporadically recurring, often long-lived patterns of atmospheric circulation of hemispheric scale known as weather regimes. The evolution of these circulation regimes in addition to their link to large-scale teleconnections can help to extend the limits of atmospheric predictability. They also play a key role in sub- and inter-seasonal weather forecasting. Their identification and modeling remains an issue, however, due to their intricacy, including a clear conceptual picture. In recent years, the concept of metastability has been developed to explain regimes formation. This suggests an interpretation of circulation regimes as communities of states in the neighborhood of which the atmospheric system remains abnormally longer than typical baroclinic timescales. Here we develop a new and effective method to identify such communities by constructing and analyzing an operator of the system’s evolution via hidden Markov model (HMM). The method makes use of graph theory and is based on probabilistic approach to partition the HMM transition matrix into weakly interacting blocks – communities of hidden states – associated with regimes. The approach involves nonlinear kernel principal component mapping to consistently embed the system state space for HMM building. Application to northern winter hemisphere using geopotential heights from reanalysis yields four persistent and recurrent circulation regimes. Statistical and dynamical characteristics of these circulation regimes and surface impacts are discussed. In particular, unexpected high correlations are obtained with EL-Niño Southern Oscillation and Pacific decadal oscillation with lead times of up to one year.

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.

We study the hemispheric- to continental-scale regimes that lead to summertime heatwaves in the Northern Hemisphere. By using a powerful data mining methodology—archetype analysis—we identify the characteristic spatial patterns consisting of blocking high pressure systems embedded within a meandering upper atmosphere circulation that is longitudinally modulated by coherent Rossby wave packets. Periods when these atmospheric regimes are strongly expressed correspond to large increases in the likelihood of extreme surface temperature. Most strikingly, these regimes are shown to be typical of surface extremes and frequently reoccur. Three well-publicized heatwaves are studied in detail—the June–July 2003 western European heatwave, the August 2010 “Russian” heatwave, and the June 2021 “heatdome” event across western North America, and are shown to be driven by blocking high pressure systems linked to stalled Rossby wave packets. We discuss the implications of our work for long-range prediction or early warning, climate model assessment, and postevent diagnosis.

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.