Articles | Volume 12, issue 1
https://doi.org/10.5194/esd-12-295-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-12-295-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season
Frederik Wolf
CORRESPONDING AUTHOR
Research Domain IV – Complexity Science, Potsdam Institute for Climate Impact Research (PIK) – Member of the Leibniz Association, Telegrafenberg, 14473 Potsdam, Germany
Department of Physics, Humboldt University, Newtonstraße 15, 12489 Berlin, Germany
Ugur Ozturk
Research Domain IV – Complexity Science, Potsdam Institute for Climate Impact Research (PIK) – Member of the Leibniz Association, Telegrafenberg, 14473 Potsdam, Germany
Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Kevin Cheung
Climate Research, Climate and Atmospheric Science, NSW Department of Planning, Industry and Environment, 4 Parramatta Square, Parramatta NSW 2150, Australia
Reik V. Donner
Research Domain IV – Complexity Science, Potsdam Institute for Climate Impact Research (PIK) – Member of the Leibniz Association, Telegrafenberg, 14473 Potsdam, Germany
Department of Water, Environment, Construction, and Safety, Magdeburg-Stendal University of Applied Sciences, Breitscheidstraße 2, 39114 Magdeburg, Germany
Related authors
Frederik Wolf, Aiko Voigt, and Reik V. Donner
Earth Syst. Dynam., 12, 353–366, https://doi.org/10.5194/esd-12-353-2021, https://doi.org/10.5194/esd-12-353-2021, 2021
Short summary
Short summary
In our work, we employ complex networks to study the relation between the time mean position of the intertropical convergence zone (ITCZ) and sea surface temperature (SST) variability. We show that the information hidden in different spatial SST correlation patterns, which we access utilizing complex networks, is strongly correlated with the time mean position of the ITCZ. This research contributes to the ongoing discussion on drivers of the annual migration of the ITCZ.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Noemie Ehstand, Reik V. Donner, Cristobal Lopez, Marcelo Barreiro, and Emilio Hernandez-Garcia
EGUsphere, https://doi.org/10.5194/egusphere-2025-343, https://doi.org/10.5194/egusphere-2025-343, 2025
Short summary
Short summary
The Madden-Julian Oscillation (MJO) is a large-scale tropical wave of enhanced and suppressed rainfalls, slowly moving eastward at the equator, influencing the weather and climate globally. We study the MJO using a simplified model designed to capture its large-scale features. We introduce new, more realistic, inputs into the model, show that this enhanced model successfully replicates key characteristics of the MJO, and identify some of its limitations.
Julianna Carvalho-Oliveira, Giorgia Di Capua, Leonard F. Borchert, Reik V. Donner, and Johanna Baehr
Weather Clim. Dynam., 5, 1561–1578, https://doi.org/10.5194/wcd-5-1561-2024, https://doi.org/10.5194/wcd-5-1561-2024, 2024
Short summary
Short summary
We demonstrate with a causal analysis that an important recurrent summer atmospheric pattern, the so-called East Atlantic teleconnection, was influenced by the extratropical North Atlantic in spring during the second half of the 20th century. This causal link is, however, not well represented by our evaluated seasonal climate prediction system. We show that simulations able to reproduce this link show improved surface climate prediction credibility over those that do not.
Wolfgang Schwanghart, Ankit Agarwal, Kristen Cook, Ugur Ozturk, Roopam Shukla, and Sven Fuchs
Nat. Hazards Earth Syst. Sci., 24, 3291–3297, https://doi.org/10.5194/nhess-24-3291-2024, https://doi.org/10.5194/nhess-24-3291-2024, 2024
Short summary
Short summary
The Himalayan landscape is particularly susceptible to extreme events, which interfere with increasing populations and the expansion of settlements and infrastructure. This preface introduces and summarizes the nine papers that are part of the special issue,
Estimating and predicting natural hazards and vulnerabilities in the Himalayan region.
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024, https://doi.org/10.5194/npg-31-115-2024, 2024
Short summary
Short summary
Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.
Giorgia Di Capua, Dim Coumou, Bart van den Hurk, Antje Weisheimer, Andrew G. Turner, and Reik V. Donner
Weather Clim. Dynam., 4, 701–723, https://doi.org/10.5194/wcd-4-701-2023, https://doi.org/10.5194/wcd-4-701-2023, 2023
Short summary
Short summary
Heavy rainfall in tropical regions interacts with mid-latitude circulation patterns, and this interaction can explain weather patterns in the Northern Hemisphere during summer. In this analysis we detect these tropical–extratropical interaction pattern both in observational datasets and data obtained by atmospheric models and assess how well atmospheric models can reproduce the observed patterns. We find a good agreement although these relationships are weaker in model data.
Kamal Rana, Nishant Malik, and Ugur Ozturk
Nat. Hazards Earth Syst. Sci., 22, 3751–3764, https://doi.org/10.5194/nhess-22-3751-2022, https://doi.org/10.5194/nhess-22-3751-2022, 2022
Short summary
Short summary
The landslide hazard models assist in mitigating losses due to landslides. However, these models depend on landslide databases, which often have missing triggering information, rendering these databases unusable for landslide hazard models. In this work, we developed a Python library, Landsifier, consisting of three different methods to identify the triggers of landslides. These methods can classify landslide triggers with high accuracy using only a landslide polygon shapefile as an input.
Ivana Čavlina Tomašević, Kevin K. W. Cheung, Višnjica Vučetić, Paul Fox-Hughes, Kristian Horvath, Maja Telišman Prtenjak, Paul J. Beggs, Barbara Malečić, and Velimir Milić
Nat. Hazards Earth Syst. Sci., 22, 3143–3165, https://doi.org/10.5194/nhess-22-3143-2022, https://doi.org/10.5194/nhess-22-3143-2022, 2022
Short summary
Short summary
One of the most severe and impactful urban wildfire events in Croatian history has been reconstructed and analyzed. The study identified some important meteorological influences related to the event: the synoptic conditions of the Azores anticyclone, cold front, and upper-level shortwave trough all led to the highest fire weather index in 2017. A low-level jet, locally known as bura wind that can be explained by hydraulic jump theory, was the dynamic trigger of the event.
Michael Dietze, Rainer Bell, Ugur Ozturk, Kristen L. Cook, Christoff Andermann, Alexander R. Beer, Bodo Damm, Ana Lucia, Felix S. Fauer, Katrin M. Nissen, Tobias Sieg, and Annegret H. Thieken
Nat. Hazards Earth Syst. Sci., 22, 1845–1856, https://doi.org/10.5194/nhess-22-1845-2022, https://doi.org/10.5194/nhess-22-1845-2022, 2022
Short summary
Short summary
The flood that hit Europe in July 2021, specifically the Eifel, Germany, was more than a lot of fast-flowing water. The heavy rain that fell during the 3 d before also caused the slope to fail, recruited tree trunks that clogged bridges, and routed debris across the landscape. Especially in the upper parts of the catchments the flood was able to gain momentum. Here, we discuss how different landscape elements interacted and highlight the challenges of holistic future flood anticipation.
Tommaso Alberti, Reik V. Donner, and Stéphane Vannitsem
Earth Syst. Dynam., 12, 837–855, https://doi.org/10.5194/esd-12-837-2021, https://doi.org/10.5194/esd-12-837-2021, 2021
Short summary
Short summary
We provide a novel approach to diagnose the strength of the ocean–atmosphere coupling by using both a reduced order model and reanalysis data. Our findings suggest the ocean–atmosphere dynamics presents a rich variety of features, moving from a chaotic to a coherent coupled dynamics, mainly attributed to the atmosphere and only marginally to the ocean. Our observations suggest further investigations in characterizing the occurrence and spatial dependency of the ocean–atmosphere coupling.
Frederik Wolf, Aiko Voigt, and Reik V. Donner
Earth Syst. Dynam., 12, 353–366, https://doi.org/10.5194/esd-12-353-2021, https://doi.org/10.5194/esd-12-353-2021, 2021
Short summary
Short summary
In our work, we employ complex networks to study the relation between the time mean position of the intertropical convergence zone (ITCZ) and sea surface temperature (SST) variability. We show that the information hidden in different spatial SST correlation patterns, which we access utilizing complex networks, is strongly correlated with the time mean position of the ITCZ. This research contributes to the ongoing discussion on drivers of the annual migration of the ITCZ.
Giorgia Di Capua, Jakob Runge, Reik V. Donner, Bart van den Hurk, Andrew G. Turner, Ramesh Vellore, Raghavan Krishnan, and Dim Coumou
Weather Clim. Dynam., 1, 519–539, https://doi.org/10.5194/wcd-1-519-2020, https://doi.org/10.5194/wcd-1-519-2020, 2020
Short summary
Short summary
We study the interactions between the tropical convective activity and the mid-latitude circulation in the Northern Hemisphere during boreal summer. We identify two circumglobal wave patterns with phase shifts corresponding to the South Asian and the western North Pacific monsoon systems at an intra-seasonal timescale. These patterns show two-way interactions in a causal framework at a weekly timescale and assess how El Niño affects these interactions.
Cited articles
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
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
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, Geophys. Res. Lett., 41, 7397–7405,
https://doi.org/10.1002/2014GL061829, 2014b. a
Chen, G. T.-J.: Large-Scale Circulations Associated with the East Summer
Monsoon and the Mei-Yu over South China and Taiwan, J. Meteorol. Soc.
Jpn., 72, 959–983, 1994. a
Cheung, K. K. W. and Ozturk, U.: Synchronization of extreme rainfall during the
Australian summer monsoon: Complex network perspectives, Chaos, 30, 063117,
https://doi.org/10.1063/1.5144150, 2020. a
Choi, K.-S., Wang, B., and Kim, D.-W.: Changma onset definition in Korea using
the available water resources index and its relation to the Antarctic
oscillation, Clim. Dynam., 38, 547–562, https://doi.org/10.1007/s00382-010-0957-1,
2012. a
Ciemer, C., Boers, N., Barbosa, H. M. J., Kurths, J., and Rammig, A.: Temporal
evolution of the spatial covariability of rainfall in South America, Clim.
Dynam., 51, 371–382, https://doi.org/10.1007/s00382-017-3929-x, 2018. a
Di Capua, G., Kretschmer, M., Donner, R. V., van den Hurk, B., Vellore, R., Krishnan, R., and Coumou, D.: Tropical and mid-latitude teleconnections interacting with the Indian summer monsoon rainfall: a theory-guided causal effect network approach, Earth Syst. Dynam., 11, 17–34, https://doi.org/10.5194/esd-11-17-2020, 2020. a
Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.: The backbone of the
climate network, EPL, 87, 48007, https://doi.org/10.1209/0295-5075/87/48007, 2009. a
Donges, J. F., Schultz, H. C., 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
Donges, J. F., Heitzig, J., Beronov, B., Wiedermann, M., Runge, J., Feng,
Q. Y., Stolbova, V., Donner, R. V., Marwan, N., Dijkstra, H. A., and Kurths,
J.: Unified functional network and nonlinear time series analysis for
complex systems science: The pyunicorn package, Chaos, 25, 113101, https://doi.org/10.1063/1.4934554, 2015. a
Donges, J. F., Schleussner, C. F., Siegmund, J. F., and Donner, R. V.: Event
coincidence analysis for quantifying statistical interrelationships between
event time series, Eur. Phys. J. Spec. Top., 225, 471–487,
https://doi.org/10.1140/epjst/e2015-50233-y, 2016. a, b
Fortunato, S.: Community detection in graphs, Phys. Rep., 486, 75–174,
https://doi.org/10.1016/j.physrep.2009.11.002, 2010. a
Fortunato, S. and Hric, D.: Community detection in networks: A user guide,
Phys. Rep., 659, 1–44, https://doi.org/10.1016/j.physrep.2016.09.002, 2016. a
Fukui, E.: Distribution of extraordinarily heavy rainfalls in Japan, Geogr.
Rev. Jpn., 43, 581–593, 1970. a
Gelbrecht, M., Boers, N., and Kurths, J.: Phase coherence between precipitation in South America and Rossby waves, Sci. Adv., 4, eaau3191, https://doi.org/10.1126/sciadv.aau3191, 2018. a
Guan, P., Chen, G., Zeng, W., and Liu, Q.: Corridors of Mei-Yu-Season Rainfall
over Eastern China, J. Climate, 23, 2603–2626,
https://doi.org/10.1175/JCLI-D-19-0649.1, 2020. a, b, c
Hassanibesheli, F. and Donner, R. V.: Network inference from the timing of
events in coupled dynamical systems, Chaos, 29, 083125, https://doi.org/10.1063/1.5110881, 2019. a
He, S.-H., Feng, T.-C., Gong, Y.-C., Huang, Y.-H., Wu, C.-G., and Gong, Z.-Q.:
Predicting extreme rainfall over eastern Asia by using complex networks,
Chinese Phys. B, 23, 059202, https://doi.org/10.1088/1674-1056/23/5/059202, 2014. a
Heitzig, J., Donges, J. F., Zou, Y., Marwan, N., and Kurths, J.: Node-weighted
measures for complex networks with spatially embedded, sampled, or
differently sized nodes, Eur. Phys. J. B, 85, https://doi.org/10.1140/epjb/e2011-20678-7, 2012. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woolen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteorol. Soc., 77, 437–472, 1996. a
Kanamitsu, B. Y. M., Ebisuzaki, W., Jack, W., Yang, S.-K., Hnilo, J. J.,
Fiorino, M., and Potter, G. L.: NCEP-DOE AMIP-II Reanalysis (R-2), B.
Am. Meteorol. Soc., 83, 1631–1644, 2002. a
Kawale, J., Liess, S., Kumar, A., Steinbach, M., Ganguly, A., Samatova, N.,
Semazzi, F., Snyder, P., and Kumar, V.: Data guided discovery of dynamic
climate dipoles, in: Proceedings of the nASA Conference on Intelligent Data
Understanding, 30–44, Computer History Museum, Mountain View, CA, USA, 19–21 October 2011. a
Kretschmer, M., Coumou, D., Donges, J. F., and Runge, J.: Using Causal Effect
Networks to Analyze Different Arctic Drivers of Midlatitude Winter
Circulation, J. Climate, 29, 4069–4081,
https://doi.org/10.1175/JCLI-D-15-0654.1, 2016. a
Liu, Y. and Ding, Y.: Teleconnection between the Indian summer monsoon onset
and the Meiyu over the Yangtze River Valley, Sci. China Ser. D Earth Sci.,
51, 1021–1035, https://doi.org/10.1007/s11430-008-0073-9, 2008. a, b, c, d
Malik, N., Marwan, N., and Kurths, J.: Spatial structures and directionalities in Monsoonal precipitation over South Asia, Nonlin. Processes Geophys., 17, 371–381, https://doi.org/10.5194/npg-17-371-2010, 2010. 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
NASA: Global Precipitation measurement, available at: https://pmm.nasa.gov/data-access/downloads/trmm, last access: 24 February 2021. a
Newman, M. E. J.: Modularity and community structure in networks, Proc. Natl.
Acad. Sci., 103, 8577–8582, https://doi.org/10.1073/pnas.0601602103, 2006. a
Okada, Y. and Yamazaki, K.: Climatological Evolution of the Okinawa Baiu and
Differences in Large-Scale Features during May and June, J. Climate, 25,
6287–6303, https://doi.org/10.1175/JCLI-D-11-00631.1, 2012. a, b, c
Preethi, B., Mujumdar, M., Kripalani, R. H., Prabhu, A., and Krishnan, R.:
Recent trends and tele-connections among South and East Asian summer
monsoons in a warming environment, Clim. Dynam., 48, 2489–2505,
https://doi.org/10.1007/s00382-016-3218-0, 2017. 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
Rheinwalt, A., Marwan, N., Kurths, J., Werner, P., and Gerstengabe, F.-W.:
Boundary effects in network measures of spatially embedded networks, EPL,
100, 28002, https://doi.org/10.1209/0295-5075/100/28002, 2012. a
Rosvall, M. and Bergstrom, C. T.: Maps of random walks on complex networks
reveal community structure, PNAS, 105, 1118–1123, 2008. a
Runge, J., Petoukhov, V., Donges, J. F., Hlinka, J., Jajcay, N., Vejmelka, M.,
Hartman, D., Marwan, N., Palus, M., and Kurths, J.: Identifying causal
gateways and mediators in complex spatio-temporal systems, Nat.
Commun., 6, 8502, https://doi.org/10.1038/ncomms9502, 2015. a
Sampe, T. and Xie, S.-P.: Large-Scale Dynamics of the Meiyu-Baiu Rainband:
Environmental Forcing by the Westerly Jet, J. Climate, 23, 113–134,
https://doi.org/10.1175/2009JCLI3128.1, 2010. 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
Suda, K. and Asakura, T.: A study on the Unusual Baiu Season in 1954 by Means
of Northern Atmosphere Upper Air Mean Charts, J. Meteorol. Soc. Jpn.,
33, 233–244, 1955. a
Tan, J., Jakob, C., Rossow, W. B., and Tselioudis, G.: Increases in tropical
rainfall driven by changes in frequency of organized deep convection,
Nature, 519, 451–454, https://doi.org/10.1038/nature14339, 2015. a
Tomita, T., Yamaura, T., and Hashimoto, T.: Interannual Variability of the
Baiu Season near Japan Evaluated from the Equivalent Potential Temperature,
J. Meteorol. Soc. Jpn., 89, 517–537, https://doi.org/10.2151/jmsj.2011-507, 2011. a
Tropical Rainfall Measuring Mission (TRMM): TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25 degree × 0.25 degree V7, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/TRMM/TMPA/3H/7, 2011. a
Tsonis, A. A. and Roebber, P. J.: The architecture of the climate network,
Phys. A, 333, 497–504, 2004. a
Ueda, H. and Yasunari, T.: Abrupt Seasonal Change of Large-Scale Convective
Activity, J. Meteorol. Soc. Jpn., 73, 795–809, 1995. a
US National Center for Environmental Prediction: NCEP/NCAR Reanalysis 1, available at: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html, last access: 3 June 2020.
a
US National Center for Environmental Prediction and Department of Energy: NCEP-DOE Reanalysis 2, available at: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html, last access: 20 August 2020. a
US National Oceanic and Atmospheric Administration: NOAA Interpolated Outgoing Longwave Radiation (OLR), available at: https://psl.noaa.gov/data/gridded/data.interp_OLR.html, last access: 20 August 2020. a
Wang, S. S.-Y., Kim, H., Coumou, D., Yoon, J.-H., Zhao, L., and Gillies, R. R.:
Consecutive extreme flooding and heat wave in Japan: Are they becoming a
norm?, Atmos. Sci. Lett., 20, e933, https://doi.org/10.1002/asl.933, 2019. a
Wiedermann, M., Donges, J. F., Kurths, J., and Donner, R. V.: Mapping and
discrimination of networks in the complexity-entropy plane, Phys. Rev. E,
96, 042304, https://doi.org/10.1103/PhysRevE.96.042304, 2017. a
Yihui, D. and Chan, J. C. L.: The East Asian summer monsoon: an overview,
Meteorol. Atmos. Phys., 89, 117–142, https://doi.org/10.1007/s00703-005-0125-z, 2005. a
Zhu, J., Huang, D.-Q., Zhang, Y.-C., Huang, A.-N., Kuang, X.-Y., and Huang, Y.:
Decadal changes of Meiyu rainfall around 1991 and its relationship with two
types of ENSO, J. Geophys. Res.-Atmos., 118, 9766–9777,
https://doi.org/10.1002/jgrd.50779, 2013. a
Zhu, X., Wu, Z., and He, J.: Anomalous Meiyu onset averaged over the Yangtze
River valley, Theor. Appl. Climatol., 94, 81–95,
https://doi.org/10.1007/s00704-007-0347-8, 2008. a
Short summary
Motivated by a lacking onset prediction scheme, we examine the temporal evolution of synchronous heavy rainfall associated with the East Asian Monsoon System employing a network approach. We find, that the evolution of the Baiu front is associated with the formation of a spatially separated double band of synchronous rainfall. Furthermore, we identify the South Asian Anticyclone and the North Pacific Subtropical High as the main drivers, which have been assumed to be independent previously.
Motivated by a lacking onset prediction scheme, we examine the temporal evolution of synchronous...
Altmetrics
Final-revised paper
Preprint