By bringing together experts from diverse disciplines, this session aims to provide a comprehensive platform for discussing the latest advances in AR science. We invite all contributions that aim at a better understanding of AR uncertainties, processes, and impacts across past, present, and future climates at regional to global scales. Relevant topics of the session include, but are not limited to:
• Observation, identification, and monitoring of ARs
• Physical, dynamical, & microphysical aspects of ARs
• Aerosol & biochemical aspects of ARs
• ARs and the surface energy budget
• Environmental and socioeconomic impacts of AR-induced weather extremes
• ARs as a component of compound events
• AR dynamics and impacts in understudied regions
• Role of ARs in the changing Cryosphere
• Forecasting of ARs
• ARs in past, present, and future climates
Atmospheric rivers are transient filaments of high integrated water vapour transport (IVT), spanning across oceanic basins, that can be associated with heavy precipitation. Possible feedbacks between convection at the mesoscale and moisture transport could modulate impacts at the leading end of atmospheric rivers, but while the link with synoptic-scale dynamics and more specifically with extratropical cyclones has been the object of numerous studies, finer scale phenomena remain less investigated, apart from case-studies in specific regions such as the Californian coast.
This work aims at characterizing convection within atmospheric rivers and its interactions with moisture fluxes. We investigate the extent to which the conceptual scheme for the structure of atmospheric rivers is valid, and whether it should be refined.
A Lagrangian perspective on atmospheric rivers is key in order to study their internal structure from tail to head, from genesis to termination. We therefore use the tARget database of ERA5 atmospheric rivers (Guan and Waliser, 2024), detected globally on the basis of a relative regional threshold for high-IVT structures. We point out that it catches high-IVT objects that differ from the classical picture of atmospheric rivers and that could be separately classified through the description of their structure. We then develop an algorithm that detects the internal features of atmospheric rivers. We show that there can be multiple moisture transport axes, with varying connections to a cold front. Moreover, atmospheric rivers associated with extreme precipitation or IVT exhibit specific internal structures in terms of overturning circulations and tilted updrafts.
This work underlines the need to consider the diversity of atmospheric rivers to better understand their impacts.
How to cite: Le Gall, E., Fildier, B., and Bony, S.: Anatomy of atmospheric rivers: the internal signature of extreme events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11549, https://doi.org/10.5194/egusphere-egu26-11549, 2026.
A large amount of work has been devoted to identifying and characterizing the main drivers associated with Extreme Precipitation Events (EPEs). Among these drivers the main ones are Extratropical Cyclones (ETCs) and in particular their Warm Conveyor Belts (WCBs) and Atmospheric Rivers (ARs). These features can be intrinsically linked to the other through powerful feedbacks involving moisture, latent heat, and potential vorticity.
This study aims to increase the understanding of the intricate association between ARs, WCBs, and ETCs in driving EPEs on the North Atlantic basin through a comprehensive composite analysis. Using ERA-5 data from 1979 to 2023, we investigate first the characteristics of ARs based on their interaction with the ascent phase of WCBs, a key mechanism for moisture uplift and precipitation generation within ETCs. Results show that the influence of the ascent phase intensifies the precipitation values within the AR, and that those values extend northwestward towards the cyclone location. This clearly shows the influence of the ascent phase on the precipitation generation within both ARs and ETCs.
We then develop a composite analysis of AR cases, examining the evolution of meteorological fields at 12-hour intervals from 24 hours prior to the maximum deepening point (MDP) of the associated ETC, until 24 hours after this point. This detailed temporal analysis provides insights into how the structure and intensity of ARs and WCBs evolve in relation to the dynamic development of ETCs, which is critical for understanding extreme weather phenomena (e.g., EPEs). Results show that the moisture content within the AR is at its peak on the MDP timestep, and that the precipitation values start within the AR but as the ETC develops, the pattern extends northwestward (coinciding with the ascent phase occurrence composite), with the highest values occurring also at the MDP timestep.
These results suggest that WCB-influenced ARs are characterized by a more intense and focused precipitation core, well aligned with the cyclone’s warm sector, and exhibiting a stronger coupling with cyclone deepening. This research will contribute to a more comprehensive understanding of the link between the three systems, potentially improving their predictability and supporting more effective flood and landslide mitigation strategies. Such insights are vital given the increasing frequency and intensity of extreme weather events in a changing climate.
This work was supported by the Portuguese Fundação para a Ciência e Tecnologia, FCT, I.P./MCTES through the project AMOTHEC (DRI/India/0098/2020) with DOI 10.54499/DRI/India/0098/2020 and also through national funds (PIDDAC): LA/P/0068/2020 - https://doi.org/10.54499/LA/P/0068/2020, UID/50019/2025, https://doi.org/10.54499/UID/PRR/50019/2025, UID/PRR2/50019/2025. Tiago M. Ferreira was supported by FCT through PhD grant UI/BD/154496/2022.
How to cite: Ferreira, T. M., Trigo, R. M., Christ, S., Quinting, J., Pinto, J. G., and Ramos, A. M.: The role of Warm Conveyor Belts Ascent in Modulating Atmospheric River Characteristics and Cyclone Interaction: a composite analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7669, https://doi.org/10.5194/egusphere-egu26-7669, 2026.
Atmospheric rivers (ARs) play a major role in both global moisture and energy transport. There has been substantial research exploring the sources and pathways of moisture in these features, which often cooccur with extratropical cyclone systems and spatially overlap with the cyclone’s warm conveyor belt. However, how these features contribute to the convergence and transport of energy at a local and global scale is less well understood. Our new work uses Isca, an idealized modeling framework, to construct a hierarchy of models with varying complexity. By varying the radiation scheme from a simple, gray radiation scheme, to a scheme including water vapor feedbacks, to a full radiative transfer scheme, this model hierarchy allows us to use mechanism denial to better understand the physical processes that govern the AR size, frequency, and their role in energy convergence and transport. We examine how moisture and energy transport change throughout the AR lifecycle, and with varying levels of CO2 forcing. We also present a new, threshold-free AR identification method that performs equally well across a variety of warming and cooling experiments, without arbitrary adjustments of thresholds, allowing us to accurately assess changes to AR frequency, size, and energy and moisture transport in a variety of climate states. This work provides new insight into the nature of ARs, their internal structure and lifecycles, and their role in the global energy budget.
How to cite: Scholz, S. and Lora, J.: Atmospheric rivers and energy transport in a hierarchy of idealized models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11748, https://doi.org/10.5194/egusphere-egu26-11748, 2026.
We investigates the remote influence of diabatic heating over the Tibetan Plateau (TP) on atmospheric river (AR) activity in the North Pacific. We first identify a heating-sensitive region over the southern TP, where enhanced diabatic heating is significantly and positively correlated with AR frequency. This relationship is primarily associated with latent heat release sustained by abundant moisture supply. Further analyses indicate that the dynamical effects of eastward-propagating Rossby waves, originating over the Atlantic and modulated by the TP, promote upward moisture transport.Using the Water Accounting Model–2Layers, we show that the anomalous heating over the southern TP is mainly driven by increased moisture transport from the Indian Ocean, Arabian Sea, and Bay of Bengal, which is further intensified by the westward extension of the western North Pacific subtropical high (WNPSH). Additional moisture contributions are also detected from Eurasia. Moreover, Rossby wave activity emanating from the TP propagates eastward toward Japan, strengthening the westerlies and generating upper-level divergence that induces a coupled cyclonic–anticyclonic circulation over the North Pacific. This circulation enhances moisture convergence, thereby increasing AR activity in the region.In addition, a positive feedback is identified between southern TP heating and an eastward-propagating upper-level anticyclone, which further reinforces the westward extension of the WNPSH. These results highlight the TP’s far-reaching climatic influence and underscore its critical role in regulating atmospheric river activity over the North Pacific.
How to cite: Zhao, Y.: Impacts of Thermal Heating over the Tibetan Plateau on Atmospheric River Activity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9462, https://doi.org/10.5194/egusphere-egu26-9462, 2026.
Atmospheric rivers (ARs) are key agents regulating global hydroclimate and extreme precipitation. Climate models project the increase and intensification of ARs in a warming climate, but their responses to CO2 mitigation remain unclear. Based on large-ensemble climate model experiments in which CO2 concentrations are systematically increased and then decreased to the present-day levels, we show that AR frequency and intensity do not fully return to their present-day states when CO2 concentrations are reduced. Instead ARs are projected to become more frequent and intense after CO2 removal, particularly along the western coasts of North America, Europe and South America, in East Asia, and along the Antarctic coast, leading to increased extreme precipitation in the midlatitudes and potential threat to Antarctic ice shelf stability. These hysteretic responses of ARs are attributed to both thermodynamic and dynamic changes that manifest differently by region but are closely related to the delayed recovery of the Atlantic meridional overturning circulation and the Southern Ocean temperature.
How to cite: Son, S.-W., Chung, S., Park, C., Kwon, Y., Winters, A., and Dong, W.: Hysteresis of global atmospheric rivers to carbon dioxide removal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2671, https://doi.org/10.5194/egusphere-egu26-2671, 2026.
Atmospheric rivers (ARs) are the conduit for the majority of atmospheric moisture transport into the polar regions and influence the evolution of the polar ice sheets and sea ice. Their impacts on the polar cryosphere are expected to intensify as atmospheric moisture content and temperatures increase in a warming climate. In order to assess these polar AR impacts, some method for identifying ARs in reanalysis and/or model datasets must be chosen. Prior studies facilitated by the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) show that quantitative and qualitative conclusions about the global climatology, impacts, and future changes of ARs depend on the choice of AR detection tool (ARDT) used in the analysis. There is a community need for a similar comparison of ARDTs in the polar regions (both the Arctic and Antarctica), where the unique atmospheric conditions require different parameters for AR detection relative to global ARDTs.
In this presentation, we report initial findings from the ARTMIP polar synthesis project, a collaborative effort to provide community guidance on best practices for ARDT selection in polar studies. This project builds upon the existing ARTMIP framework to curate and compare AR catalogues from a number of ARDTs that have been developed for polar AR identification. We first analyze polar AR climatology across ARDTs in the historical record using MERRA-2 atmospheric reanalysis, focusing on similarities and differences among ARDTs linked to their algorithm design. These historical catalogues are also used to analyze how differences in AR detection across ARDTs affect interpretation of ice sheet and sea ice impacts. We then extend our analysis to future AR projections by applying each ARDT to three members of the CESM2 large ensemble under the SSP3-7.0 emissions scenario. We focus on cross-algorithm differences in AR detection and associated cryosphere impacts that may be accentuated by future atmospheric warming and moistening. Finally, we use a subset of four ARDTs in a case study to assess how existing ARDTs may be tuned to more accurately identify polar ARs.
How to cite: Mattingly, K., Maclennan, M., Schnaubelt, J., and Shields, C. and the ARTMIP Polar Synthesis Team: The ARTMIP Polar Synthesis: A comparative analysis of polar atmospheric river tracking methods in historical and future climate states, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14978, https://doi.org/10.5194/egusphere-egu26-14978, 2026.
Atmospheric rivers (ARs) are increasingly recognized as key contributors to moisture and heat transport into Antarctica, yet their dominant time scales of variability and links to large-scale climate modes remain insufficiently quantified. We analyze sector-resolved AR frequency and integrated vapor transport around the Antarctic margin using band-pass filtering and canonical correlation analysis applied to reanalysis-based circulation and thermodynamic fields. The results show a pronounced scale dependence of AR variability, with weak and spatially incoherent signals at interannual (6–18-month) time scales, but robust and hemispherically organized patterns at multiyear (36–72-month) periods.
At these longer time scales, AR activity is strongly coupled to tropical–extratropical modes, in particular ENSO, the Indian Ocean Dipole, and the Southern Annular Mode, through their modulation of storm-track intensity, subtropical jet position, and meridional moisture transport. The strongest canonical responses occur in the Weddell Sea and Atlantic–Indian sectors, characterized by negative sea-level pressure anomalies, enhanced westerlies, and intensified poleward integrated vapor transport. In contrast, the East Antarctic and Ross–Bellingshausen sectors exhibit weaker and more localized circulation anomalies, indicating a strong modulation by regional geometry and background flow.
The associated wind and pressure patterns reveal preferred pathways for AR intrusions, involving strengthened midlatitude westerlies, anticyclonic anomalies over the Amundsen–Bellingshausen Seas, and shifts in the subtropical jet that facilitate tropical–polar moisture exchange. These results demonstrate that Antarctic ARs are organized by large-scale tropical–extratropical coupling acting predominantly at multiyear time scales, with pronounced sectoral contrasts. Such scale-dependent behavior has important implications for understanding and predicting variability in Antarctic precipitation, surface temperature, and surface mass balance.
How to cite: Justino, F., Bromwich, D., and Gurjao, C.: Dominant time scales of tropical–extratropical coupling in atmospheric rivers over the Southern Ocean and coastal Antarctica, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13680, https://doi.org/10.5194/egusphere-egu26-13680, 2026.
Heavy precipitation in Colorado (CO) is key to water resources, and the presence or absence of a few strong storms can make or break the yearly snowpack that delivers water to four major river basins. However, predicting precipitation in CO is challenging because it has high spatial and temporal variability. Atmospheric rivers (ARs) are one type of storm that results in a large fraction of extreme precipitation in the western U.S. and lends itself to improved forecasts over the region. Extensive knowledge of AR frequency, intensity, impacts, and key meteorological processes has been developed for U.S. West Coast landfalling ARs; however, relatively limited research has examined AR characteristics further inland, particularly for Colorado (CO), where high and complex topography, as well as the distance from the coast, complicate attempts to track ARs, AR-derived moisture, and AR-related impacts. Previous research efforts attributing precipitation to ARs based on their spatial footprint have yielded less than 30% of cool-season precipitation in CO as related to ARs. However, a large volume of anecdotal evidence suggests that ARs play a larger role in CO precipitation. To quantify this, we used trajectory-based methods to quantify the contribution of landfalling ARs to top-decile precipitation in subbasins throughout CO. Moisture sourced from landfalling ARs penetrates inland along relatively low-elevation corridors through the Interior West, and exhibits substantial geographic and interannual variability. Using the backward trajectory approach, we found that landfalling ARs contribute 21–78% of western CO’s top-decile cool season precipitation. Most of the AR-related precipitation across western CO during the cool-season is sourced from landfalling ARs near Southern California, the Baja Peninsula, and the Pacific Northwest. These results indicate a larger role for ARs in CO weather and hydroclimate than previous research suggests and highlight the importance of AR representation in forecast models to improve predictability of precipitation in CO.
How to cite: Nash, D., Rutz, J., Cordeira, J., Zhang, Z., Ralph, F. M., Sanders, K., and Walter, E.: Using Backwards Trajectories to Estimate Atmospheric Rivers’ Contributions to Colorado’s Wettest Days, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-260, https://doi.org/10.5194/egusphere-egu26-260, 2026.
Atmospheric Rivers (ARs) play a fundamental role in global and regional hydroclimate, accounting for up to 35% of annual mean precipitation, approximately 50% of extreme precipitation, and around 85% of flood events in the midlatitudes. Using multiple observational and reanalysis datasets spanning 1979-2025 and applying several AR detection techniques, including the SCAlable Feature Extracting and Tracking (SCAFET) method, we systematically examine recent changes in AR characteristics and their associated hydroclimate extremes. Our analysis shows no significant trend in the total annual frequency of all AR events, despite a pronounced long-term increase in integrated vapor transport over the last several decades. In contrast, the frequency and maximum intensity of moderate-to-extreme ARs have increased significantly, accompanied by a robust intensification of AR-related extreme precipitation. We further find pronounced seasonal dependence in these changes, characterized by a robust poleward shift of ARs, associated with storm-track displacement and moisture-transport pathway migration during boreal winter, and by a notable increase in AR activity over East Asia and the western North Pacific during boreal summer. These findings are consistent across different reanalysis products and detection algorithms, underscoring the robustness of the detected signals. The recent increase in moderate-to-extreme AR events highlights an emerging amplification of hydroclimate extremes, with important implications for water resources management, flood risk assessment, and climate adaptation strategies in midlatitude regions.
How to cite: Lee, J.-Y., Konda, G. N., Nelllikkattil, A. B., and Guan, B.: Recent Intensification of Moderate-to-Extreme Atmospheric Rivers and Associated Hydroclimate Extremes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17184, https://doi.org/10.5194/egusphere-egu26-17184, 2026.
In the province of Québec in eastern Canada, ice-related floods (IRF) have affected several municipalities and caused over 50 million dollars in personal damage over the past 35 years. Dynamic river ice breakup occurs when river discharge increases due to snowmelt runoff and rainfall, before any significant thermal deterioration of the ice cover. Fragmented ice blocks then jam at river constrictions, triggering the formation of ice jams and consequently flooding adjacent urban areas. Atmospheric rivers (AR) are long and narrow corridors of high-water vapor transport that travel poleward and often result in large amounts of rainfall. Although the frequency of mid-winter ice breakup and AR have increased in recent years in eastern Canada, the effect of AR on IRF has never been investigated systematically at the regional scale. This study assesses the impact of AR on IRF in Québec from 1990 to 2022. To investigate the influence of streamflow, surface, and atmospheric conditions on IRF, we leveraged a provincial flood-related insurance claim database along with the publicly available repository of historical ice jams (IJ) in Québec, the Québec hydroclimatic Atlas dataset of simulated river discharge, the version 3.1 of the Canadian Surface Reanalysis and the EDARA atmospheric river database. Our results show that more than 81% of the 732 analyzed IJ were related to AR conditions, defined as an integrated water transport (IVT) greater than 250 kg m–1 s–1. The IJ-related IVT and rainfall intensity were significantly higher in mid-winter (n = 325) than spring (n = 407). In contrast, greater snowmelt contribution during spring IJ resulted in larger streamflow when compared to mid-winter events. Among the mid-winter IJ, those associated to a flood (n = 26) happened under significantly more intense AR conditions. This research demonstrates the significant role of AR on mid-winter IRF and provides new insights for improving winter flood awareness and early warning systems. Next analyses will focus on the characteristics of AR during IJ and IRF.
How to cite: Anctil, F., Bouchard, B., F. Nadeau, D., Bourgault, M.-A., Hamon, R., Brault, B., Roy, N., Gagnon, C., Bédard-Therrien, A., and Ghobrial, T.: The contribution of atmospheric rivers to ice-related flooding in Québec, Canada, from 1990 to 2022, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14258, https://doi.org/10.5194/egusphere-egu26-14258, 2026.
Extreme precipitation and flooding during the monsoon season in India are closely linked to anomalies in atmospheric moisture transport. Two of the primary mechanisms that govern moisture flux into the Indian region are Monsoon Low-Level Jets (LLJ) and Atmospheric Rivers (ARs), the latter of which are under-recognized over the tropics, especially in the monsoon dominated regions. The low level jets are characterized by nocturnal intensification and boundary-layer thermal forcing and ARs are synoptic scale, transient corridors of intense horizontal water vapor transport. While these two phenomena have been extensively characterized individually, their degree of structural correspondence, coexistence, and synergistic impact on monsoon rainfall and extremes over India remains poorly quantified. This study presents a comparative structural analysis of LLJ and ARs that landfall over the Indian subcontinent during the Indian summer monsoon season (June to September). We apply standard detection methodologies on the horizontal winds and Integrated Water Vapor Transport (IVT) fields, derived from high resolution reanalysis data for AR and LLJ detection. We then stratify events into combinations of LLJ and AR occurrences, and construct composites to characterize the horizontal structural properties and vertical structures quantitatively. The results reveal quantitative distinctions in the wind and moisture characteristics associated with the LLJs and ARs that traverse. This study addresses a critical gap in distinguishing the ARs from the large-scale monsoon moisture transported by LLJs, by quantifying the structural distinctness between these transport mechanisms. Providing process level insights on these mechanisms may have implications in improving their representation in weather and climate models and potential predictability.
Keywords: Atmospheric Rivers, Low Level Jets, Indian Summer Monsoon, Monsoon Moisture Transport
How to cite: Pandidurai, D. and Agarwal, A.: Structural Characteristics of Moisture Transport Systems over the Indian Subcontinent during the Summer Monsoon, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1034, https://doi.org/10.5194/egusphere-egu26-1034, 2026.
Atmospheric rivers (ARs), narrow and intense moisture corridors typically extending poleward, significantly shape the hydrometeorological patterns across mid-latitudes. In this study, summer days with AR-related precipitation in eastern China (EC) during 1979−2022 were identified and categorized into six distinct levels based on precipitation intensity percentiles, derived from both the ERA5 and CN05.1 datasets. Results reveal a significant positive correlation between the maximum AR precipitation and maximum integrated water vapor transport (IVT) within each category, while no such correlation exists between mean AR precipitation and mean IVT. As precipitation strengthens, the proportion of areas experiencing precipitation on AR days progressively expands, approaching 100% in the strongest cases. The Sichuan Basin, Northeast China, and coastal South and East China exhibit relatively higher precipitation intensity and efficiency under weak–moderate categories. For moderate−heavy categories, the middle and lower Yangtze River and North China emerge as additional key AR precipitation-affected areas, while the influence on coastal regions significantly decreases. The frequency of AR precipitation days shows a distinct north–south gradient, with hotspots shifting systematically from Northeast to South China as intensity rises. Moisture budget analysis shows that the primary factor controlling AR precipitation intensity is vertical moisture convection, particularly its dynamic component, and zonal advection ranks second. Vertical motion, which governs these processes, is mainly driven by anomalous convergence and divergence linked to the subtropical westerly jet, with topography and atmospheric instabilities further enhancing its impact. These findings may offer valuable insights for future research on AR precipitation and related disasters in China.
How to cite: Yang, Y., Peng, D., Hua, L., Zhong, L., Gong, Z., Huang, W., and Li, H.: Dynamic Factors Dominate the Summer Precipitation Intensity of Atmospheric Rivers Landfalling Eastern China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4381, https://doi.org/10.5194/egusphere-egu26-4381, 2026.
Atmospheric Rivers (ARs) are long (>2000 km) and narrow (<1000 km) corridors of enhanced moisture transport, with typical water vapour fluxes of 400–500 kg m⁻¹ s⁻¹. When these moisture-laden systems encounter the steep Himalayan terrain, strong orographic uplift produces intense precipitation, supplying much of the seasonal snowpack that sustains regional rivers and water resources. Approximately 56–73% of extreme precipitation events and floods in the Himalayas occur during the presence of ARs, underscoring their critical role in hydrological extremes and downstream water availability for millions of people.
Numerical Weather Prediction (NWP) models play a crucial role in forecasting extreme weather systems, and evaluating their performance over the complex terrain of the Himalayas is a vital first step toward improving regional predictability. In this study, we assessed the capability of multiple NWP models, including ECMWF, IMD, NCEP, and NCMRWF, to detect and forecast ARs at various lead times. ARs were identified using the tARget algorithm based on Integrated Vapour Transport (IVT) thresholds. Our analysis shows that the Hit Rate varies between 0.3 and 0.6 across models and lead times, while the False Alarm Rate ranges from 0.03 to 0.09, indicating considerable uncertainty in AR prediction. The ECMWF generally performs better at short lead times, capturing a larger fraction of observed AR events, whereas the NCEP model exhibits comparatively better skill at longer lead times, extending beyond 10 days. For all models, forecast skill consistently decreases with increasing lead time, reflecting the growing uncertainty associated with longer-range predictions. The relatively low hit rate of the IMD model can be largely attributed to its tendency to overestimate IVT over the Indian subcontinent. This positive bias leads to an exaggerated frequency of AR detections, thereby inflating false alarms and reducing the overall reliability of the forecasts.
Beyond event detection, substantial discrepancies are also found in AR characteristics, including their intensity, spatial extent, geographical position, and orientation. These differences highlight limitations in how current NWP models represent moisture transport and orographic interactions over the Himalayas. Consequently, further improvements in physical processes, parameterizations, and model resolution are required to achieve more accurate and reliable AR forecasts for this highly complex and hydrologically sensitive region.
How to cite: Khan, A. K., Nayak, M. A., and Fayaz, S. I.: Multi-Model Evaluation of Atmospheric River Forecast Skill and Uncertainty over the Himalayas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9930, https://doi.org/10.5194/egusphere-egu26-9930, 2026.
Long, narrow zones of the Integrated Vapor Transport (IVT) in the lower troposphere are known as Atmospheric Rivers (ARs). ARs are major causes of heavy rain, and they are often associated with serious cases of flooding. For instance, the 2014 Kashmir flood and the 2013 Uttarakhand flood are linked to Himalayan ARs. Therefore, ARs are important in causing extreme weather and risk of floods in the Himalayan region. Thus, skillful prediction of ARs can be helpful in better severe weather risk management. The most widely accepted metric for identifying ARs is IVT as it integrates moisture content and its transport. Although the Global Forecast System (GFS) forecasts IVT globally, it is shown to suffer from systematic error over the West Coast of USA, especially for high magnitude IVT, and also fails in the accurate spatial organization of AR events. Recently, Chapman et al. (2019) proposed a Convolutional Neural Network (CNN) to the enhance the skill of GFS IVT forecasts in mid-latitude areas on the West Coast. However, the model lacks correction of IVT direction, which is critical in defining the precipitation produced from an AR upon impacting a mountain barrier. In addition, there is no machine learning model that is specifically designed for the Himalayan region. This work modifies the Chapman CNN architecture, in the South Asian region, incorporating the Himalayan region for correcting both the magnitude and direction of GFS IVT. In our work we take Modern Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) as a proxy to ground truth. The model significantly reduced various metrics, such as the Root Mean Square Error (RMSE) and Mean Angular Error (MAE), in comparison to GFS IVT, in the Himalayan region and in the entire study domain. When the model was tested for AR events, its performance significantly improved the AR forecast. These advances show that the model offers a powerful deep learning framework for AR prediction as compared to the raw GFS baseline.
How to cite: Fayaz, S. I., Nayak, M. A., and Khan, A. K.: Improving Atmospheric River Forecast Over Himalayas using Convolutional Neural Network , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9523, https://doi.org/10.5194/egusphere-egu26-9523, 2026.
Atmospheric Rivers (ARs) are lengthy, narrow atmospheric corridors that transport substantial moisture over great distances, often resulting in heavy precipitation upon landfall. Characterization of the connectivity and spatial organization of AR events across regions is highly challenging due to their dynamic nature and the complex nonlinear interactions governing moisture transport pathways. This study applies complex network theory to analyse AR moisture transport patterns and connectivity over the West Coast of North America. The ERA5 reanalysis data and 7 CMIP6 climate model outputs over the period 1970–2014 are studied. We first employ the Mann-Kendall trend analysis to examine long-term changes in integrated water vapor transport intensity, thereby establishing the temporal evolution of AR characteristics that the network analysis will contextualize. We next evaluate model performance through Spearman correlation analysis and develop a hybrid network construction methodology that integrates six different threshold selection techniques to determine the optimal correlation threshold for network construction. We then apply several network measures, including degree centrality, clustering coefficient, and closeness centrality, to characterize the organization of an AR system. The Mann-Kendall analysis reveals significant intensification on the West Coast (+0.5-1.0 kg/m/s per year), strengthening in the Gulf of Alaska (p < 0.05). Climatological composites reveal the primary AR corridor at 40–50°N, with peak intensities of 450–500 kg/m/s in the central Pacific and making landfall along the Northern California/Oregon coast at intensities exceeding 400 kg/m/s. Model evaluation identifies EC-Earth3 and EC-Earth3-CC as the best-performing (Spearman r > 0.40), substantially outperforming other CMIP6 models (r = 0.02–0.24). Network validation establishes optimal parameters at r = 0.35 correlation threshold and 5% edge density, with network stability exceeding 0.95 and >90% inter-model agreement on top 100 nodes. Network centrality analysis reveals a hierarchical organization with uniform clustering coefficients (0.6–0.8) across the North Pacific, a north-south gradient in degree centrality (0.08–0.11 in the northern, 0.02–0.04 in the subtropical region), and identifies a critical moisture transport hub at 30–40°N, 120–140°W. The northern Pacific storm corridor (40–55°N) dominates all network measures, confirming primary AR pathways, with the EC-Earth models reliably reproducing the observed patterns. These findings demonstrate that network theory offers a quantitative framework for understanding AR connectivity and organization, with applications for climate change assessment and water resource management.
How to cite: Sharma, J. and Sivakumar, B.: Network Analysis of Atmospheric River Moisture Transport: Connectivity, Trend, and Climatology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18470, https://doi.org/10.5194/egusphere-egu26-18470, 2026.
Aerosol Atmospheric Rivers (AARs) are long, narrow regions in the atmosphere transporting large concentrations of aerosols. Previous literature focused on AARs detected only based on MERRA-2 reanalysis data. In this study, global AAR catalogues of different species (organic carbon, black carbon, and dust) are constructed for the 2003 – 2023 period by applying an AAR detection algorithm to two reanalysis products: MERRA-2 and CAMS. These catalogues provide information regarding the location and extension of the AARs found at 6-hourly timesteps, as well as their geometric characteristics (length, width) and their tracking over time. Results show large discrepancies between the catalogues based on the two reanalyses. Specifically, substantial differences are found between the spatial and temporal frequencies of occurrence of AARs in for each aerosol species considered. These findings underscore the need for caution when using AAR catalogues obtained from only one reanalysis product.
How to cite: Gaetani, M., Sturlese, G., and Pohl, B.: Major differences between two atmospheric reanalyses seen by an aerosol atmospheric river detection algorithm, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18467, https://doi.org/10.5194/egusphere-egu26-18467, 2026.
The increasing frequency of Arctic atmospheric rivers has significantly slowed the recovery of Arctic sea ice in recent decades. However, existing studies primarily focused on the local impacts of Arctic-internal atmospheric rivers, while how polar-external atmospheric rivers influence Arctic sea ice remains largely unexplored. This study reveals a significant decline in the genesis and poleward tracks of southeastern North Atlantic atmospheric rivers (NAARs) during the sea ice recovery season (October to March) since the mid-2000s. Both reanalysis and simulations suggest that large-scale atmospheric teleconnection wave trains associated with southeastern NAARs play a critical role in Barents Sea ice recovery by enhancing local Arctic cooling. However, the decline in southeastern NAARs activity after the mid-2000s has weakened this restorative effect, leading to a 31% slowdown in Barents Sea ice growth. These findings highlight the important influence of mid- to low-latitude climate changes on Arctic sea ice decline.
How to cite: Zhang, S., Zeng, G., Chen, H. W., and Chen, D.: Impeded Arctic sea ice recovery: The role of declining southeastern North Atlantic atmospheric rivers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4283, https://doi.org/10.5194/egusphere-egu26-4283, 2026.
The global mean surface temperature is the primary metric used to track how the climate is changing. While variability and change in global mean temperatures on interannual scales has been studied extensively, there has been limited analysis of daily global temperature variability. This is despite record-setting daily global average temperature events, such as in July 2024, generating widespread media interest.
Here, we explore the characteristics of spikes and dips in daily global average temperatures using the ERA5 reanalysis. We find that daily global temperature spikes are typically associated with Antarctic heatwaves while dips are related to Antarctic cold spells. For other parts of the world, the relationship between local and global average temperatures is much weaker. As Antarctic heatwaves are often preceded by atmospheric rivers, we examine poleward integrated water vapour transport and atmospheric river coverage in the days prior to daily global temperature spikes and dips. We find a strong signal of heightened poleward moisture transport 3-6 days prior to spikes in daily global mean temperature and the opposite pattern ahead of global temperature dips. We then examine to see if atmospheric river activity around Antarctica can help explain annual global mean temperature anomalies and find some effect, albeit weaker relative to daily scales.
This work highlights the importance of Southern Ocean atmospheric rivers in explaining variability in global mean temperatures across scales. Further study of modelling and prediction of global average temperatures based on atmospheric river activity is envisaged.
How to cite: King, A., Reid, K., Wille, J., and Alastrué de Asenjo, E.: Atmospheric rivers around Antarctica are behind daily global temperature spikes and dips, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10210, https://doi.org/10.5194/egusphere-egu26-10210, 2026.
The U.S. National Centers for Environmental Information (NCEI) is sponsoring the development of a reanalysis-based global atmospheric river (AR) historical data record (HDR) to serve as a valuable resource for the scientific, operations-based, and decision-making communities. The AR HDR uses a novel combination of two techniques: (1) the AR scale, which broadly characterizes AR strength from 1-5 based on the peak integrated water vapor transport (IVT) and duration of AR conditions (i.e., IVT ≥ 250 kg m-1 s-1) at a given location, and (2) the tARget AR detection algorithm–a tool that uses climatological, geometric, and directional thresholds to identify ARs. Since the AR scale has no geometric criteria (and thus identifies/ranks non-AR events such as tropical cyclones, cutoff lows, and monsoons) and tARget does not provide characterization of AR strength, these two methods complement each other: tARget differentiates between ARs and other storm types, while the AR scale provides rankings. The resulting AR database is used to examine select global cases, interannual variability, long-term climatologies of global AR characteristics categorized by rank, and reanalysis-based precipitation. The results demonstrate the combined capability of tARget to identify ARs and the AR scale to subsequently characterize their intensity, such that AR-related impacts globally can be better understood. The AR HDR will be hosted by NCEI. Future work includes implementation of the HDR method in forecasts, comparison to observed hydrometeorological datasets where available such as precipitation, streamflow, and drought, and an examination of AR scale modifications in polar and mountainous regions.
How to cite: Slinskey, E., Rutz, J., Guan, B., and Ralph, F. M.: Global Atmospheric River Historical Data Record: Combining AR Detection and Intensity Categorization , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3564, https://doi.org/10.5194/egusphere-egu26-3564, 2026.
Atmospheric rivers (ARs) are narrow corridors of concentrated moisture that play a key role in global precipitation and extreme hydroclimatic events. Despite their importance, their contribution to precipitation variability, flood risk, and long-term climate change remains poorly quantified. In this study, we combine global hydrological observations, high-resolution precipitation datasets, and multi-model climate simulations to assess the impact of ARs on interannual variability, extreme precipitation, and rare flooding. Our results indicate that ARs account for 70–90% of year-to-year precipitation variability across mid-latitude regions and are linked to more than 70% of the largest precipitation and streamflow events globally. Their presence can increase the likelihood of rare flood events by up to an order of magnitude in parts of North America, Europe, and Australia. Additionally, there have been notable increases in the frequency of ARs and the associated precipitation totals over the past decades. Climate model projections further suggest that AR-induced precipitation is likely to become more frequent and intense in the future, even in areas where mean precipitation may decline, potentially amplifying their role in hydroclimatic extremes. Together, these findings highlight that ARs are not only key drivers of present-day precipitation and flood events but will also increasingly shape future global hydroclimatic conditions. Understanding AR processes is therefore essential for anticipating changes in regional water availability, managing flood hazards, and adapting to a changing climate.
How to cite: Pradhan, S., Wasko, C., and Peel, M.: Atmospheric Rivers as Drivers of Precipitation Variability and Flood Extremes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3710, https://doi.org/10.5194/egusphere-egu26-3710, 2026.
Atmospheric rivers (ARs) transport high concentrations of water vapor in narrow bands from the tropical and subtropical Atlantic to western European coasts. Ireland frequently falls in their paths and receives ~50 ARs annually. To better understand AR-specific synoptic states and behaviour in Ireland, we examine how statistical and machine learning analysis fares in identifying and characterising ARs at both 6-hourly and daily resolutions. We use a dataset based on 1949 landfalling Irish ARs detected using the “tARget” AR detection tool for a 42-year period of 1980-2021, and which are linked to ~80% of Ireland’s daily extreme precipitation events.
Notably, traditional statistical analyses (e.g., correlations, PCA) of daily weather parameters (e.g., 10-m wind, 10-m highest wind gust, air temperature) loosely identify AR days for different landfall regions, but 6-hourly reanalysis variables such as Integrated Vapor Transport (IVT), 850 hpa vertical velocity (ω), and 500 hpa geopotential height strongly distinguish ARs. K-means clustering shows that persistent ARs with high IVT and long overland durations are most common with southern and western Ireland landfalls, whereas northern and eastern landfall sites receive weaker ARs. When trained with daily observational data, machine learning models (Random Forest, XGBoost, and LSTM) identify AR vs. non-AR days with 75-85% F1 scores (precision/recall efficiency). With reanalysis data, the models score ~75% at multi-class classification for AR ranks detection but are less successful for high-intensity ARs (ranked 4 and 5). The Random Forest model performs the best at predicting daily maximum precipitation (R2: 0.63), with key predictors being the 850 hpa upward motion of air (-ω, in %) and maximum IVT. The important reanalysis and observation variables identified above can be selected to reduce model complexity and to train specialized hybrid models for future AR studies.
How to cite: Sharif, S. and Akers, P. D.: Landfalling Atmospheric Rivers in Ireland: Statistical and Machine Learning Insights, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22609, https://doi.org/10.5194/egusphere-egu26-22609, 2026.
