the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Mar 2026
Atmospheric river trajectories organise along a global transport network
Abstract. Atmospheric rivers (ARs) transport vast amounts of water vapor and cause weather extremes. However, they have typically been studied as isolated events rather than as components of a global transport system. By mapping ARs worldwide, we reveal that their transport is organized along a sparse set of preferred pathways forming a global network. Recognizing ARs as a globally interconnected system is highly relevant, not only for advancing atmospheric science but also for improving forecasts of extreme precipitation, droughts, and polar ice melt under climate change. Beyond the familiar storm tracks, we identify hubs of pronounced vapor transport changes and demonstrate that polar regions act as structural accumulation regions for persistent ARs. ARs preferentially travel along circumglobal atmospheric highways shaped by teleconnection patterns and circulation regimes, providing new opportunities for AR prediction. While previous research recognized only five AR basins, we uncover a larger, hierarchically organized set of interconnected basins that provides a more comprehensive understanding of how regional AR hotspots are embedded within large-scale flow. The global AR transport network links synoptic storms to planetary circulation, illuminating hidden pathways in the global water cycle.
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-332', Anonymous Referee #1, 01 Apr 2026
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RC2: 'Comment on egusphere-2026-332', Anonymous Referee #2, 13 Apr 2026
The authors put forward a framework that identifies the major Atmospheric River (AR) basins on a global scale, speculate on the respective AR drivers, and explore how they change in response to seasonality and the El Nino Southern Oscillation (ENSO) climate mode. The paper is well written and insightful, with the figures conveying the results concisely. However, a justification of some of the choices made is required and a stronger link to meteorology is also needed. I believe that after a major revision the manuscript will be in a suitable form to be published in this journal.
Major Comments:
1. The ARs used to construct the global atmospheric river transport network (ARTN) are extracted from the Potsdam Institute for Climate Impact Research (PIK) Atmospheric River Trajectories version 1 (PIKART-1) and the Tracking Rivers Globally as Elongated Targets Version 4 (tARget-4) catalogues. Why are these catalogues selected? How sensitive are the results to the choice of the AR catalogues and hence to the way ARs are diagnosed? The authors should also provide in the text further information on how the ARs are identified in these two products, this is not clear from lines 76-79.
2. The AR basins displayed in Fig. 5a may not be always active during the full study period. For example, ARs in the northern Arabian Peninsula are more prominent in El Nino winters and following spring season (Dasari et al., 2017; Esfandiari and Rezaei, 2022) when the mid-latitude storm track is also shifted equatorwards (as seen Fig. 4h). In summer following El Nino winters, ARs are less frequent in the subtropics over southeast Asia (Liang and Yong, 2021). Can the authors identify the meteorological conditions under which each AR basin is more prominent? This would allow for predictability: if a similar environmental set up is forecasted, we have an idea of the regions more likely to be impacted by ARs.
3. Have the authors explored trends in the AR features and basins? For example, are some AR basins becoming less relevant in detriment of others? What about the AR IVT changes? Can these trends be linked to changes in the background state as given by ERA-5 that is used to extract the ARs? The long study period allows for a statistically robust trend analysis to be performed.
4. From a meteorological perspective, are all the AR communities in Fig. 5a independent from each other? For example, take the one over western Greenland. The ARs that occur here develop locally or originate from those that occur in the communities around it? In lines 465-467 it is stated "Physically, AR communities can be understood as enclosed geographical regions whose boundaries are determined by persistent steering flows, topography, coastal moisture gradients and thermodynamic limits on AR life cycles (depending on the rates of evaporation and precipitation over an AR’s life cycle)." Have the authors considered adding a table with the major features of each AR basin? Can the authors quantify the relative contribution of local vs. remote moisture sources for the ARs in each basin? This may help justify some of the considered AR communities.
5. For the ENSO results displayed in Fig. 4, and based on what is shown in Fig. S18, I believe the authors took all El Nino and La Nina months in the period 1950-2023 irrespective of the season. Given the seasonal contrast in AR highways that is more prominent in the Northern Hemisphere (Figs. 4e-f), it would be better to generate seasonal maps for El Nino and La Nina as well. There are also different flavours of ENSO (Newman et al., 2011), which can also be considered.
Minor Comments:
1. Line 17: "narrow and long channels of anomalously high water vapor transport"
2. Lines 341-343: This is in line with the findings of Ramos et al. (2019), which also stresses the role of moisture from South America in driving cold-season ARs over the western CAPE.
3. Lines 438-440: This should go into the Methods section
4. Line 445: "Washington state" or "northwestern North America"
5. Lines 571-573: What is meant by "dangerous impacts" here? Aren't ARs also impactful outside of the polar regions?
6. Lines 604-606: The methodology used here can also be applied to aerosol atmospheric rivers (AARs; Lapere et al., 2024), in which aerosols such as dust, black carbon, organic carbon, and sea salt and transported polewards within a dry or moist air mass.
References:
Dasari, H. P., Langodan, S., Viswanadhapalli, Y., Vadlamudi, B. R., Papadopoulos, V. P. and Hoteit, I. (2018) ENSO influence on the interannual variability of the Red Sea convergence zone and associated rainfall. International Journal of Climatology, 38, 761-775. https://doi.org/10.1002/joc.5208
Esfandiari, N., Rezaei, M. (2022) Automatic detection, classification, and long-term investigation of temporal–spatial changes of atmospheric rivers in the Middle East. International Journal of Climatology, 42, 7730–7750. https://doi.org/10.1002/joc.7674
Lapere, R., Thomas, J. L., Favier, V., Angot, H., Asplund, J., Ekman, A. M. L., Marelle, L., Raut, J.-C., Da Silva, A., Wille, J. D., Zieger, P. (2024) Polar aerosol atmospheric rivers: Detection, characteristics, and potential applications. Journal of Geophysical Research: Atmospheres, 129, e2023JD039606. https://doi.org/10.1029/2023JD039606
Liang, J., and Yong, Y. (2021) Climatology of atmospheric rivers in the Asian monsoon region. International Journal of Climatology, 41, E801–E818. https://doi.org/10.1002/joc.6729
Newman, M., Shin, S.-I., Alexander, M. A. (2011) Natural variation in ENSO flavors. Geophysical Research Letters, 38, L14705. https://doi.org/10.1029/2011GL047658
Ramos, A. M., Blamey, R. C., Algarra, I., Nieto, R., Gimeno, L., Tomé, R., Reason, C. J. C.,Trigo, R.M. (2019) From Amazonia to southern Africa: atmospheric moisture transport through low-level jets and atmospheric rivers. Annals of the New York Academy of Sciences, 1436, 217-230. https://doi.org/10.1111/nyas.13960
Citation: https://doi.org/10.5194/egusphere-2026-332-RC2
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Referee Report for "Atmospheric river trajectories organise along a global transport network" by Tobias Braun et al.
The manuscript presents a network-based representation of atmospheric river (AR) trajectories to identify large-scale transport pathways, hubs, and basins. The idea of organizing AR dynamics within a global transport network is clear and well-motivated, and provides a coherent link between regional behavior and planetary-scale structure. In particular, extending the analysis to a global perspective is important, as AR transport is inherently non-local: moisture pathways span ocean basins and connect distant regions, such that regional impacts depend on large-scale circulation patterns and upstream conditions. The proposed framework captures this connectivity in a unified way.
The work is structured around three relevant questions on transport changes, pathways, and basin organization. The contribution is primarily conceptual and methodological: several identified structures align with known circulation features, while the novelty lies in their systematic extraction within a unified network framework, particularly in the identification of hierarchical basin organization.
From a methodological standpoint, the framework is well formulated and constitutes a major strength. The use of multiple AR catalogs, Lagrangian trajectory-based construction, and null models provides a solid foundation. At the same time, the approach relies on several modeling choices—such as centroid-based representation, edge definition, thresholding, and consensus construction—that may influence the resulting topology.
The results are clearly presented and internally consistent. The framework successfully recovers known large-scale transport structures and provides a useful representation of AR dynamics. The extraction of a global “highway” structure using edge betweenness centrality is a particularly strong result, supported by statistical validation. The identification of basins and their hierarchical organization is also convincing and represents a meaningful extension beyond previous classifications.
At the same time, several interpretations would benefit from more cautious framing. In particular, some findings (e.g., hubs, IVT changes) largely reflect known physical processes expressed in the network representation. Interpretations based on PageRank or shortest-path structures appear to reflect topological properties of the constructed network rather than independently demonstrated dynamical mechanisms. Similarly, claims regarding predictability are based on moderate correlations and structural arguments, and could be stated more conservatively.
I have a few points that would help strengthen the manuscript:
1) The results depend on the underlying AR catalogs and trajectory definitions. It would be useful to more explicitly quantify how robust the main structures are across detection methods, and how differences between catalogs and the chosen consensus construction propagate into the network.
2) The connection between network structures and atmospheric dynamics could be clarified. Some interpretations (e.g., “highways” or accumulation regions) appear to reflect known circulation features or topological properties of the network, rather than independent mechanisms.
3) The analysis depends on several methodological choices (e.g., AR representation, thresholding, spatial resolution, and consensus construction). A brief discussion of the sensitivity of the results to these choices would strengthen confidence in the conclusions.
4) The predictability perspective is interesting but currently limited. A more cautious framing or clearer statement of limitations would improve the discussion.
Overall, the manuscript presents a clear and useful framework with relevant insights for the community. The study is well executed and suitable for publication after minor clarifications and a more careful framing of interpretation and predictability claims.