Articles | Volume 27, issue 14
https://doi.org/10.5194/hess-27-2645-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-2645-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2023
Research article |
| 19 Jul 2023
| 19 Jul 2023
The role of atmospheric rivers in the distribution of heavy precipitation events over North America
Sara M. Vallejo-Bernal
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association,
P.O. Box 60 12 03,
14412 Potsdam, Germany
Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany
Frederik Wolf
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association,
P.O. Box 60 12 03,
14412 Potsdam, Germany
Niklas Boers
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association,
P.O. Box 60 12 03,
14412 Potsdam, Germany
Earth System Modelling, School of Engineering and Design, Technical University of Munich, 85521 Ottobrunn, Germany
Global Systems Institute and Department of Mathematics, University of Exeter, Exeter, EX4 4QF, UK
Dominik Traxl
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association,
P.O. Box 60 12 03,
14412 Potsdam, Germany
Norbert Marwan
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association,
P.O. Box 60 12 03,
14412 Potsdam, Germany
Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany
Jürgen Kurths
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association,
P.O. Box 60 12 03,
14412 Potsdam, Germany
Institute of Geosciences, University of Potsdam, 14476 Potsdam, Germany
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Paleoclimate proxy records show that the North Atlantic climate repeatedly transitioned between two regimes during the last glacial interval. This study investigates a bivariate proxy record from a Greenland ice core which reflects past Greenland temperatures and large-scale atmospheric conditions. We reconstruct the underlying deterministic drift by estimating first-order Kramers–Moyal coefficients and identify two separate stable states in agreement with the aforementioned climatic regimes.
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Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Renee van Dongen, Dirk Scherler, Dadiyorto Wendi, Eric Deal, Luca Mao, Norbert Marwan, and Claudio I. Meier
EGUsphere, https://doi.org/10.5194/egusphere-2022-1234, https://doi.org/10.5194/egusphere-2022-1234, 2022
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El Niño Southern Oscillation (ENSO) is a climatic phenomenon that causes abnormal climatic conditions in Chile. We investigated how ENSO affects catchment hydrology and found strong seasonal and spatial differences in the hydrological response to ENSO which was caused by different hydrological processes in catchments that are dominated by snowmelt-generated runoff or rainfall-generated runoff. These results are relevant for water resources management and ENSO mitigation in Chile.
Eirik Myrvoll-Nilsen, Keno Riechers, Martin Wibe Rypdal, and Niklas Boers
Clim. Past, 18, 1275–1294, https://doi.org/10.5194/cp-18-1275-2022, https://doi.org/10.5194/cp-18-1275-2022, 2022
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In layer counted proxy records each measurement is accompanied by a timestamp typically measured by counting periodic layers. Knowledge of the uncertainty of this timestamp is important for a rigorous propagation to further analyses. By assuming a Bayesian regression model to the layer increments we express the dating uncertainty by the posterior distribution, from which chronologies can be sampled efficiently. We apply our framework to dating abrupt warming transitions during the last glacial.
Keno Riechers, Takahito Mitsui, Niklas Boers, and Michael Ghil
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Building upon Milancovic's theory of orbital forcing, this paper reviews the interplay between intrinsic variability and external forcing in the emergence of glacial interglacial cycles. It provides the reader with historical background information and with basic theoretical concepts used in recent paleoclimate research. Moreover, it presents new results which confirm the reduced stability of glacial-cycle dynamics after the mid-Pleistocene transition.
Cinthya Esther Nava Fernandez, Tobias Braun, Bethany Fox, Adam Hartland, Ola Kwiecien, Chelsea Pederson, Sebastian Hoepker, Stefano Bernasconi, Madalina Jaggi, John Hellstrom, Fernando Gázquez, Amanda French, Norbert Marwan, Adrian Immenhauser, and Sebastian Franz Martin Breitenbach
Clim. Past Discuss., https://doi.org/10.5194/cp-2021-172, https://doi.org/10.5194/cp-2021-172, 2022
Manuscript not accepted for further review
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We provide a ca. 1000 year long (6.4–5.4 ka BP) stalagmite-based reconstruction of mid-Holocene rainfall variability in the tropical western Pacific. The annually laminated multi-proxy (δ13C, δ18O, X/Ca, gray values) record comes from Niue island and informs on El Nino-Southern Oscillation and South Pacific Convergence Zone dynamics. Our data suggest that ENSO was active and influenced rainfall seasonality over the covered time interval. Rainfall seasonality was subdued during active ENSO phases
Cited articles
Adhikari, A. and Behrangi, A.: Assessment of satellite precipitation products
in relation with orographic enhancement over the western United States, Earth
Space Sci., 9, e2021EA001906, https://doi.org/10.1029/2021EA001906, 2022. a
Agarwal, A., Marwan, N., Ozturk, U., and Maheswaran, R.: Unfolding community structure in rainfall network of Germany using complex network-based approach, Springer, Singapore, https://doi.org/10.1007/978-981-13-2038-5, 2019. a, b
Baggett, C. F., Lee, S., and Feldstein, S.: An investigation of the presence
of atmospheric rivers over the North Pacific during planetary-scale wave life
cycles and their role in Arctic warming, J. Atmos. Sci., 73, 4329–4347,
https://doi.org/10.1175/JAS-D-16-0033.1, 2016. a
Baggett, C. F., Barnes, E. A., Maloney, E. D., and Mundhenk, B. D.: Advancing
atmospheric river forecasts into subseasonal-to-seasonal time scales,
Geophys. Res. Lett., 44, 7528–7536, https://doi.org/10.1002/2017GL074434, 2017. a, b
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., and Hwang, D. U.: Complex
networks: Structure and dynamics, Phys. Rep., 424, 175–308, 2006. a
Boers, N., Bookhagen, B., Marwan, N., Kurths, J., and Marengo, J. A.: Complex
networks identify spatial patterns of extreme rainfall events of the South
American Monsoon System, Geophys. Res. Lett., 40, 4386–4392,
https://doi.org/10.1002/grl.50681, 2013. a, b, c, d
Boers, N., Bookhagen, B., Barbosa, H. M. J., Marwan, N., Kurths, J., and
Marengo, J. A.: Prediction of extreme floods in the eastern Central Andes
based on a complex networks approach, Nat. Commun., 5, 5199, https://doi.org/10.1038/ncomms6199,
2014a. a, b, c
Boers, N., Donner, R. V., and Bookhagen, B.: Complex network analysis helps to
identify impacts of the El Niño Southern Oscillation on moisture
divergence in South America, Clim. Dynam., 45, 619–632,
https://doi.org/10.1007/s00382-014-2265-7, 2014b. a, b
Boers, N., Rheinwalt, A., Bookhagen, B., Barbosa, H. M. J., Marwan, N.,
Marengo, J. A., and Kurths, J.: The South American rainfall dipole: A
complex network analysis of extreme events, Geophys. Res. Lett., 41,
7397–7405, 2014c. a
Boers, N., Bookhagen, B., Marwan, N., and Kurths, J.: Spatiotemporal
characteristics and synchronization of extreme rainfall in South America with
focus on the Andes Mountain range, Clim. Dynam., 46, 601–617,
https://doi.org/10.1007/s00382-015-2601-6, 2015. a, b
Corringham, T. W., Martin Ralph, F., Gershunov, A., Cayan, D. R., and Talbot,
C. A.: Atmospheric rivers drive flood damages in the western United States,
Sci. Adv., 5, eaax4631, https://doi.org/10.1126/sciadv.aax4631, 2019. a
Dettinger, M. D., Ralph, F. M., Das, T., Neiman, P. J., and Cayan, D. R.:
Atmospheric rivers, floods and the water resources of California, Water, 3,
445–478, https://doi.org/10.3390/w3020445, 2011. a
Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.: The backbone of the
climate network, Europhys. Lett., 87, 48007, https://doi.org/10.1209/0295-5075/87/48007, 2009a. a
Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.: Complex networks in
climate dynamics, Eur. Phys.-J. Spec. Top., 174, 157–179,
https://doi.org/10.1140/epjst/e2009-01098-2, 2009b. a
Donges, J. F., Schultz, H. C. H., Marwan, N., Zou, Y., and Kurths, J.:
Investigating the topology of interacting networks: Theory and application
to coupled climate subnetworks, Eur. Phys. J. B, 84, 635–651,
https://doi.org/10.1140/epjb/e2011-10795-8, 2011. a
Eiras-Barca, J., Ramos, A. M., Pinto, J. G., Trigo, R. M., Liberato, M. L. R., and Miguez-Macho, G.: The concurrence of atmospheric rivers and explosive cyclogenesis in the North Atlantic and North Pacific basins, Earth Syst. Dynam., 9, 91–102, https://doi.org/10.5194/esd-9-91-2018, 2018. a, b
Eiras-Barca, J., Ramos, A. M., Algarra, I., Vázquez, M., Dominguez, F.,
Miguez-Macho, G., Nieto, R., Gimeno, L., Taboada, J., and Ralph, F. M.:
European West Coast atmospheric rivers: A scale to characterize strength and
impacts, Weather and Climate Extremes, 31, 100305, https://doi.org/10.1016/j.wace.2021.100305, 2021. a, b, c
Gao, J., Barzel, B., and Barabási, A.-l.: Universal resilience patterns
in complex networks, Nature, 530, 307–312, https://doi.org/10.1038/nature16948, 2016. a
Gao, Y., Lu, J., Leung, L. R., Yang, Q., Hagos, S., and Qian, Y.: Dynamical
and thermodynamical modulations on future changes of landfalling atmospheric
rivers over western North America, Geophys. Res. Lett., 42, 7179–7186,
https://doi.org/10.1002/2015GL065435, 2015. a
Gershunov, A., Shulgina, T., Ralph, F. M., Lavers, D. A., and Rutz, J. J.:
Assessing the climate-scale variability of atmospheric rivers affecting
western North America, Geophys. Res. Lett., 44, 7900–7908,
https://doi.org/10.1002/2017GL074175, 2017 (data available at: https://weclima.ucsd.edu/data-products/, last access: 5 April 2022). a, b, c, d, e, f
Guan, B. and Waliser, D. E.: Detection of atmospheric rivers: Evaluation and
application of an algorithm for global studies, J. Geophys.
Res.-Atmos., 120, 12514–12535, 2015. a
Guirguis, K., Gershunov, A., DeFlorio, M. J., Shulgina, T., Delle Monache, L.,
Subramanian, A. C., Corringham, T. W., and Ralph, F. M.: Four atmospheric
circulation regimes over the North Pacific and their relationship to
California precipitation on daily to seasonal timescales, Geophys.
Res. Lett., 47, e2020GL087609, https://doi.org/10.1029/2020GL087609, 2020. a
Hagos, S. M., Leung, L. R., Yoon, J.-H., Lu, J., and Gao, Y.: A projection of changes in landfalling atmospheric river frequency and extreme precipitation over western North America from the Large Ensemble CESM simulations, Geophys. Res. Lett., 43, 1357–1363,
https://doi.org/10.1002/2015GL067392, 2016. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023a. a, b
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023b. a, b
Huang, H., Patricola, C. M., Bercos-Hickey, E., Zhou, Y., Rhoades, A., Risser,
M. D., and Collins, W. D.: Sources of subseasonal-to-seasonal predictability of atmospheric rivers and precipitation in the western United States, J. Geophys. Res.-Atmos., 126, e2020JD034053, https://doi.org/10.1029/2020JD034053, 2021. a
Huning, L. S., Margulis, S. A., Guan, B., Waliser, D. E., and Neiman, P. J.:
Implications of detection methods on characterizing atmospheric river
contribution to seasonal snowfall across Sierra Nevada, USA, Geophys.
Res. Lett., 44, 10–445, 2017. a
Krichak, S. O., Barkan, J., Breitgand, J. S., Gualdi, S., and Feldstein, S. B.:
The role of the export of tropical moisture into midlatitudes for extreme
precipitation events in the Mediterranean region, Theor. Appl. Climatol.,
121, 499–515, https://doi.org/10.1007/s00704-014-1244-6, 2015. a, b
Lavers, D. A. and Villarini, G.: The nexus between atmospheric rivers and
extreme precipitation across Europe, Geophys. Res. Lett., 40, 3259–3264,
https://doi.org/10.1002/grl.50636, 2013. a
Lora, J. M., Shields, C., and Rutz, J.: Consensus and disagreement in
atmospheric river detection: ARTMIP global catalogues, Geophys. Res.
Lett., 47, e2020GL089302, https://doi.org/10.1029/2020GL089302, 2020. a, b
Mahoney, K., Swales, D., Mueller, M. J., Alexander, M., Hughes, M., and Malloy,
K.: An examination of an inland-penetrating atmospheric river flood event
under potential future thermodynamic conditions, J. Climate, 31,
6281–6297, 2018. a
Malik, N., Bookhagen, B., Marwan, N., and Kurths, J.: Analysis of spatial and
temporal extreme monsoonal rainfall over South Asia using complex networks,
Clim. Dynam., 39, 971–987, https://doi.org/10.1007/s00382-011-1156-4, 2012. a, b, c
Messier, C., Bauhus, J., Doyon, F., Maure, F., Sousa-Silva, R., Nolet, P.,
Mina, M., Aquilué, N., Fortin, M.-J., and Puettmann, K.: The functional
complex network approach to foster forest resilience to global changes,
Forest Ecosystems, 6, 1–16, 2019. a
Mo, R. and Lin, H.: Tropical–mid-latitude interactions: Case study of an
inland-penetrating atmospheric river during a major winter storm over North
America, Atmos. Ocean, 57, 208–232, 2019. a
Mundhenk, B. D., Barnes, E. A., Maloney, E. D., and Baggett, C. F.: Skillful
empirical subseasonal prediction of landfalling atmospheric river activity
using the Madden–Julian oscillation and quasi-biennial oscillation, npj
Clim. Atmos. Sci., 1, 19–21, https://doi.org/10.1038/s41612-017-0008-2, 2018. a, b
Neiman, P. J., Ralph, F. M., Wick, G. A., Lundquist, J. D., and Dettinger,
M. D.: Meteorological characteristics and overland precipitation impacts of
atmospheric rivers affecting the West coast of North America based on eight
years of SSM/I satellite observations, J. Hydrometeorol., 9, 22–47,
https://doi.org/10.1175/2007JHM855.1, 2008. a, b, c
Newell, R. E., Newell, N. E., Zhu, Y., and Scott, C.: Tropospheric rivers? – A
pilot study, Geophys. Res. Lett., 19, 2401–2404, 1992. a
Newman, M., Kiladis, G. N., Weickmann, K. M., Ralph, F. M., and Sardeshmukh,
P. D.: Relative contributions of synoptic and low-frequency eddies to
time-mean atmospheric moisture transport, including the role of atmospheric
rivers, J. Climate, 25, 7341–7361, https://doi.org/10.1175/JCLI-D-11-00665.1, 2012. a
Ozturk, U., Malik, N., Cheung, K., Marwan, N., and Kurths, J.: A network-based comparative study of extreme tropical and frontal storm rainfall over
Japan, Clim. Dynam., 53, 521–532, https://doi.org/10.1007/s00382-018-4597-1, 2019. a
O’Brien, T. A., Wehner, M. F., Payne, A. E., Shields, C. A., Rutz, J. J., Leung, L.-R., Ralph, F. M., Collow, A., Gorodetskaya, I., Guan, B., Lora, J. M., McClenny, E., Nardi, K. M., Ramos, A. M., Tomé, R., Sarangi, C., Shearer, E. J., Ullrich, P. A., Zarzycki, C., Loring, B., Huang, H., Inda-Díaz, H. A., Rhoades, A. M., and Zhou, Y.:
Increases in future AR count and size: Overview of the ARTMIP Tier 2 CMIP5/6
experiment, J. Geophys. Res.-Atmos., 127,
e2021JD036013, https://doi.org/10.1029/2021JD036013, 2022. a, b, c, d
Pan, M. and Lu, M.: A novel atmospheric river identification algorithm, Water
Resour. Res., 55, 6069–6087, 2019. a
Payne, A. E., Demory, M.-E., Leung, L. R., Ramos, A. M., Shields, C. A., Rutz,
J. J., Siler, N., Villarini, G., Hall, A., and Ralph, F. M.: Responses and
impacts of atmospheric rivers to climate change, Nat. Rev. Earth Environ.,
1, 143–157, https://doi.org/10.1038/s43017-020-0030-5, 2020. a, b, c
Prabhat, Kashinath, K., Mudigonda, M., Kim, S., Kapp-Schwoerer, L., Graubner, A., Karaismailoglu, E., von Kleist, L., Kurth, T., Greiner, A., Mahesh, A., Yang, K., Lewis, C., Chen, J., Lou, A., Chandran, S., Toms, B., Chapman, W., Dagon, K., Shields, C. A., O'Brien, T., Wehner, M., and Collins, W.: ClimateNet: An expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather, Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, 2021. a
Quiroga, R. Q., Kreuz, T., and Grassberger, P.: Event synchronization: A
simple and fast method to measure synchronicity and time delay patterns,
Phys. Rev. E, 66, 041904, https://doi.org/10.1103/PhysRevE.66.041904, 2002. a
Ralph, F. M. and Dettinger, M. D.: Storms, floods, and the science of
atmospheric rivers, Eos, T. Am. Geophys. Un., 92, 265–266,
https://doi.org/10.1029/2011EO320001, 2011. a
Ramos, A. M., Nieto, R., Tomé, R., Gimeno, L., Trigo, R. M., Liberato, M. L. R., and Lavers, D. A.: Atmospheric rivers moisture sources from a Lagrangian perspective, Earth Syst. Dynam., 7, 371–384, https://doi.org/10.5194/esd-7-371-2016, 2016. a
Ramseyer, C. A. and Teale, N.: On the emerging global relevance of atmospheric
rivers and impacts on landscapes and water resources, Prog. Phys.
Geog.-Earth and Environment, 45, 965–978, 2021. a
Rivoire, P., Martius, O., and Naveau, P.: A comparison of moderate and extreme
ERA-5 daily precipitation with two observational data sets, Earth Space
Sci., 8, e2020EA001633, https://doi.org/10.1029/2020EA001633, 2021. a
Rutz, J. J., Shields, C. A., Lora, J. M., Payne, A. E., Guan, B., Ullrich, P., O’Brien, T., Leung, L. R., Ralph, F. M., Wehner, M., Brands, S., Collow, A., Goldenson, N., Gorodetskaya, I., Griffith, H., Kashinath, K., Kawzenuk, B., Krishnan, H., Kurlin, V., Lavers, D., Magnusdottir, G., Mahoney, K., McClenny, E., Muszynski, G., Nguyen, P. D., Prabhat, M., Qian, Y., Ramos, A. M., Sarangi, C., Sellars, S., Shulgina, T., Tome, R., Waliser, D., Walton, D., Wick, G., Wilson, A. M., and Viale, M.: The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying uncertainties in atmospheric river climatology, J.
Geophys. Res.-Atmos., 124, 13777–13802, 2019. a, b
Sharma, A. R. and Déry, S. J.: Contribution of atmospheric rivers to annual, seasonal, and extreme precipitation across British Columbia and southeastern Alaska, J. Geophys. Res.-Atmos., 125, 1–21,
https://doi.org/10.1029/2019JD031823, 2020. a
Shields, C. A. and Kiehl, J. T.: Atmospheric river landfall-latitude changes in
future climate simulations, Geophys. Res. Lett., 43, 8775–8782,
2016. a
Shields, C. A., Rutz, J. J., Leung, L.-Y., Ralph, F. M., Wehner, M., Kawzenuk, B., Lora, J. M., McClenny, E., Osborne, T., Payne, A. E., Ullrich, P., Gershunov, A., Goldenson, N., Guan, B., Qian, Y., Ramos, A. M., Sarangi, C., Sellars, S., Gorodetskaya, I., Kashinath, K., Kurlin, V., Mahoney, K., Muszynski, G., Pierce, R., Subramanian, A. C., Tome, R., Waliser, D., Walton, D., Wick, G., Wilson, A., Lavers, D., Prabhat, Collow, A., Krishnan, H., Magnusdottir, G., and Nguyen, P.: Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project goals and experimental design, Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018, 2018. a, b, c, d, e
Smith, B. L., Yuter, S. E., Neiman, P. J., and Kingsmill, D. E.: Water vapor
fluxes and orographic precipitation over northern California associated with
a landfalling atmospheric river, Mon. Weather Rev., 138, 74–100,
https://doi.org/10.1175/2009MWR2939.1, 2010. a
Steinhaeuser, K., Ganguly, A. R., and Chawla, N. V.: Multivariate and
multiscale dependence in the global climate system revealed through complex
networks, Clim. Dynam., 39, 889–895, 2012. a
Stolbova, V., Martin, P., Bookhagen, B., Marwan, N., and Kurths, J.: Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka, Nonlin. Processes Geophys., 21, 901–917, https://doi.org/10.5194/npg-21-901-2014, 2014. a, b
Tan, X., Gan, T. Y., and Chen, Y. D.: Synoptic moisture pathways associated
with mean and extreme precipitation over Canada for summer and fall, Clim.
Dynam., 52, 2959–2979, 2019. a
Traxl, D.: dominiktraxl/artracks: Release v1.0.0, Zenodo [data set], https://doi.org/10.5281/zenodo.7130642,
2022. a, b, c
Tsonis, A. A. and Roebber, P. J.: The architecture of the climate network,
Phys. A, 333, 497–504, 2004. a
Tsonis, A. A. and Swanson, K. L.: Topology and predictability of El Niño
and la Niña networks, Phys. Rev. Lett., 100, 1–4,
https://doi.org/10.1103/PhysRevLett.100.228502, 2008. a
Tsonis, A. A., Swanson, K. L., and Roebber, P. J.: What do networks have to do
with climate?, B. Am. Meteorol. Soc., 87, 585–595, 2006. a
Waliser, D. and Guan, B.: Extreme winds and precipitation during landfall of
atmospheric rivers, Nat. Geosci., 10, 179–183, https://doi.org/10.1038/ngeo2894,
2017. a, b, c, d
Warner, M. D., Mass, C. F., and Salathe Jr, E. P.: Changes in winter
atmospheric rivers along the North American west coast in CMIP5 climate
models, J. Hydrometeorol., 16, 118–128, 2015. a
Wolf, F. and Donner, R. V.: Spatial organization of connectivity in functional
climate networks describing event synchrony of heavy precipitation,
Eur. Phys. J.-Spec. Top., 230, 3045–3063,
https://doi.org/10.1140/epjs/s11734-021-00166-1, 2021. a
Wolf, F., Bauer, J., Boers, N., and Donner, R. V.: Event synchrony measures
for functional climate network analysis: A case study on South American
rainfall dynamics, Chaos, 30, 033102, https://doi.org/10.1063/1.5134012,
2020. a, b
Wolf, F., Ozturk, U., Cheung, K., and Donner, R. V.: Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season, Earth Syst. Dynam., 12, 295–312, https://doi.org/10.5194/esd-12-295-2021, 2021.
a, b
Xu, G., Ma, X., and Chang, P.: IPART: A Python Package for Image-Processing
based Atmospheric River Tracking, Journal of Open Source Software, 5, 2407,
https://doi.org/10.21105/joss.02407, 2020. a, b
Yamasaki, K., Gozolchiani, A., and Havlin, S.: Climate networks around the
globe are significantly affected by El Niño, Phys. Rev. Lett., 100,
1–4, https://doi.org/10.1103/PhysRevLett.100.228501, 2008. a
Zhang, Z., Ralph, F. M., and Zheng, M.: The relationship between extratropical
cyclone strength and atmospheric river intensity and position, Geophys.
Res. Lett., 46, 1814–1823, 2019. a
Zhu, Y. and Newell, R. E.: A proposed algorithm for moisture fluxes from
atmospheric rivers, Mon. Weather Rev., 126, 725–735,
https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2, 1998. a
Short summary
Employing event synchronization and complex networks analysis, we reveal a cascade of heavy rainfall events, related to intense atmospheric rivers (ARs): heavy precipitation events (HPEs) in western North America (NA) that occur in the aftermath of land-falling ARs are synchronized with HPEs in central and eastern Canada with a delay of up to 12 d. Understanding the effects of ARs in the rainfall over NA will lead to better anticipating the evolution of the climate dynamics in the region.
Employing event synchronization and complex networks analysis, we reveal a cascade of heavy...
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