[1]
|
Ångström, A. (1935) Teleconnections of Climatic Changes in Present Time. Geografiska Annaler, 17, 242-258.
https://doi.org/10.1080/20014422.1935.11880600
|
[2]
|
Schwing, F.B., Mendelssohn, R., Bograd, S.J., et al. (2010) Climate Change, Teleconnection Patterns, and Regional Processes Forcing Marine Populations in the Pacific. Journal of Marine Systems, 79, 245-257.
https://doi.org/10.1016/j.jmarsys.2008.11.027
|
[3]
|
Bridgman, H.A. and Oliver, J.E. (2014) The Global Climate System: Patterns, Processes, and Teleconnections. Cambridge University Press, Cambridge.
|
[4]
|
Wang, J., Wang, X., et al. (2020) Teleconnection Analysis of Monthly Streamflow Using Ensemble Empirical Mode Decomposition. Journal of Hydrology, 582, Article ID: 124411. https://doi.org/10.1016/j.jhydrol.2019.124411
|
[5]
|
曹若兰, 莫宏伟. 韶山市土地利用变化对周围土地生态服务价值的影响[J]. 水土保持通报, 2022, 42(2): 307-314+388.
|
[6]
|
Liebhold, A., Koenig, W.D. and Bjørnstad, O.N. (2004) Spatial Synchrony in Population Dynamics. Annual Review of Ecology, Evolution, and Systematics, 35, 467-490. https://doi.org/10.1146/annurev.ecolsys.34.011802.132516
|
[7]
|
Shestakova, T.A., Gutiérrez, E. and Voltas, J. (2018) A Roadmap to Disentangling Ecogeographical Patterns of Spatial Synchrony in Dendrosciences. Trees, 32, 359-370. https://doi.org/10.1007/s00468-017-1653-0
|
[8]
|
Deza, J.I., Masoller, C. and Barreiro, M. (2014) Distinguishing the Effects of Internal and Forced Atmospheric Variability in Climate Networks. Nonlinear Processes in Geophysics, 21, 617-631. https://doi.org/10.5194/npg-21-617-2014
|
[9]
|
Routson, C.C., Woodhouse, C.A., Overpeck, J.T., et al. (2016) Teleconnected Ocean Forcing of Western North American Droughts and Pluvials during the Last Millennium. Quaternary Science Reviews, 146, 238-250.
https://doi.org/10.1016/j.quascirev.2016.06.017
|
[10]
|
Chang, N.B., Imen, S., Bai, K., et al. (2017) The Impact of Global Unknown Teleconnection Patterns on Terrestrial Precipitation across North and Central America. Atmospheric Research, 193, 107-124.
https://doi.org/10.1016/j.atmosres.2017.04.018
|
[11]
|
Yang, R. and Xing, B. (2022) Teleconnections of Large-Scale Climate Patterns to Regional Drought in Mid-Latitudes: A Case Study in Xinjiang, China. Atmosphere, 13, Article No. 230. https://doi.org/10.3390/atmos13020230
|
[12]
|
Harwood, N., Hall, R., Di Capua, G., et al. (2021) Using Bayesian Networks to Investigate the Influence of Subseasonal Arctic Variability on Midlatitude North Atlantic Circulation. Journal of Climate, 34, 2319-2335.
https://doi.org/10.1175/JCLI-D-20-0369.1
|
[13]
|
Silva, F.N., Vega-Oliveros, D.A., Yan, X., et al. (2021) Detecting Climate Teleconnections with Granger Causality. Geophysical Research Letters, 48, e2021GL094707. https://doi.org/10.1029/2021GL094707
|
[14]
|
Gao, M., Zhang, H., Zhang, A., et al. (2022) Nonhomogeneous Poisson Process Model of Summer High Temperature Extremes over China. Stochastic Environmental Research and Risk Assessment.
https://doi.org/10.21203/rs.3.rs-919065/v1
|
[15]
|
Kim, H., Kang, S.M., Kay, J.E., et al. (2022) Subtropical Clouds Key to Southern Ocean Teleconnections to the Tropical Pacific. Proceedings of the National Academy of Sciences, 119, e2200514119.
https://doi.org/10.1073/pnas.2200514119
|
[16]
|
Graafland, C.E., Gutierrez, J.M., Lopez, J.M., et al. (2020) The Probabilistic Backbone of Data-Driven Complex Networks: An Example in Climate. Scientific Reports, 10, Article No. 11484. https://doi.org/10.1038/s41598-020-67970-y
|
[17]
|
Agarwal, A., Caesar, L., Marwan, N., et al. (2019) Network-Based Identification and Characterization of Teleconnections on Different Scales. Scientific Reports, 9, Article No. 8808. https://doi.org/10.1038/s41598-019-45423-5
|
[18]
|
Ciemer, C., Boers, N., Barbosa, H.M.J., et al. (2018) Temporal Evolution of the Spatial Covariability of Rainfall in South America. Climate Dynamics, 51, 371-382. https://doi.org/10.1007/s00382-017-3929-x
|
[19]
|
Donges, J.F., Zou, Y., Marwan, N., et al. (2009) Complex Networks in Climate Dynamics: Comparing Linear and Nonlinear Network Construction Methods. The European Physical Journal Special Topics, 174, 157-179.
https://doi.org/10.1140/epjst/e2009-01098-2
|
[20]
|
Su, Z., Meyerhenke, H. and Kurths, J. (2022) The Climatic Interdependence of Extreme-Rainfall Events around the Globe. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32, Article ID: 043126. https://doi.org/10.1063/5.0077106
|
[21]
|
Boers, N., Goswami, B., Rheinwalt, A., et al. (2019) Complex Networks Reveal Global Pattern of Extreme-Rainfall Teleconnections. Nature, 566, 373-377. https://doi.org/10.1038/s41586-018-0872-x
|
[22]
|
Wang, X., Xie, F., Zhang, Z., et al. (2021) Complex Network of Synchronous Climate Events in East Asian Tree-Ring Data. Climatic Change, 165, Article No. 54. https://doi.org/10.1007/s10584-021-03008-0
|
[23]
|
Gregory, W., Tsamados, M., Stroeve, J., et al. (2020) Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes. Weather and Forecasting, 35, 793-806. https://doi.org/10.1175/WAF-D-19-0107.1
|
[24]
|
Gong, Z.-Q., Wang, X.-J., Zhi, R. and Feng, A.-X. (2011) Circulation System Complex Networks and Teleconnections. Chinese Physics B, 20, 495-503.
|
[25]
|
Ekhtiari, N., Agarwal, A., Marwan, N., et al. (2019) Disentangling the Multi-Scale Effects of Sea-Surface Temperatures on Global Precipitation: A Coupled Networks Approach. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29, Article ID: 063116. https://doi.org/10.1063/1.5095565
|
[26]
|
Ying, N., Zhou, D., Chen, Q., et al. (2019) Long-Term Link Detection in the CO2 Concentration Climate Network. Journal of Cleaner Production, 208, 1403-1408. https://doi.org/10.1016/j.jclepro.2018.10.093
|
[27]
|
Yang, X., Wang, Z.H. and Wang, C. (2022) Critical Transitions in the Hydrological System: Early-Warning Signals and Network Analysis. Hydrology and Earth System Sciences, 26, 1845-1856. https://doi.org/10.5194/hess-26-1845-2022
|
[28]
|
Ekhtiari, N., Ciemer, C., Kirsch, C., et al. (2021) Coupled Network Analysis Revealing Global Monthly Scale Co-Variability Patterns between Sea-Surface Temperatures and Precipitation in Dependence on the ENSO State. The European Physical Journal Special Topics, 230, 3019-3032. https://doi.org/10.1140/epjs/s11734-021-00168-z
|
[29]
|
Quiroga, R.Q., Kreuz, T. and Grassberger, P. (2002) Event Synchronization: A Simple and Fast Method to Measure Synchronicity and Time Delay Patterns. Physical Review E, 66, Article ID: 041904.
https://doi.org/10.1103/PhysRevE.66.041904
|
[30]
|
Boers, N., Bookhagen, B., Barbosa, H.M.J., et al. (2014) Prediction of Extreme Floods in the Eastern Central Andes Based on a Complex Networks Approach. Nature Communications, 5, Article No. 5199.
https://doi.org/10.1038/ncomms6199
|
[31]
|
Perry, S.J., McGregor, S., Gupta, A.S., et al. (2017) Future Changes to El Niño-Southern Oscillation Temperature and Precipitation Teleconnections. Geophysical Research Letters, 44, 10608-10616.
https://doi.org/10.1002/2017GL074509
|
[32]
|
Yeh, S.W., Cai, W., Min, S.K., et al. (2018) ENSO Atmospheric Teleconnections and Their Response to Greenhouse Gas Forcing. Reviews of Geophysics, 56, 185-206. https://doi.org/10.1002/2017RG000568
|
[33]
|
Shraddha, G., Zhen, S., Niklas, B., et al. (2022) Interconnection between the Indian and the East Asian Summer Monsoon: Spatial Synchronization Patterns of Extreme Rainfall Events. International Journal of Climatology, 43, 1034-1049.
https://doi.org/10.1002/joc.7861
|
[34]
|
Qiao, P., Gong, Z., Liu, W., et al. (2021) Extreme Rainfall Synchronization Network between Southwest China and Asia-Pacific Region. Climate Dynamics, 57, 3207-3221. https://doi.org/10.1007/s00382-021-05865-y
|
[35]
|
Qiao, P., Gong, Z., Liu, W., et al. (2022) Asymmetrical Synchronization of Extreme Rainfall Events in Southwest China. International Journal of Climatology, 42, 5935-5948. https://doi.org/10.1002/joc.7569
|
[36]
|
Mao, Y., Zou, Y., Alves, L.M., et al. (2022) Phase Coherence between Surrounding Oceans Enhances Precipitation Shortages in Northeast Brazil. Geophysical Research Letters, 49, e2021GL097647. https://doi.org/10.1029/2021GL097647
|
[37]
|
Qiao, P., Liu, W., Zhang, Y., et al. (2021) Complex Networks Reveal Teleconnections between the Global SST and Rainfall in Southwest China. Atmosphere, 12, Article No. 101. https://doi.org/10.3390/atmos12010101
|
[38]
|
Li, K., Wang, M. and Liu, K. (2021) The Study on Compound Drought and Heatwave Events in China Using Complex Networks. Sustainability, 13, Article No. 12774. https://doi.org/10.3390/su132212774
|
[39]
|
Li, K., Wang, M. and Liu, K. (2022) The Study of Temperature Regionalization in China Using Complex Networks. International Journal of Climatology, 42, 4445-4459. https://doi.org/10.1002/joc.7478
|
[40]
|
Kurths, J., Agarwal, A., Shukla, R., et al. (2019) Unravelling the Spatial Diversity of Indian Precipitation Teleconnections via a Non-Linear Multi-Scale Approach. Nonlinear Processes in Geophysics, 26, 251-266.
https://doi.org/10.5194/npg-26-251-2019
|
[41]
|
营娜, 叶谦, 韩战钢, 等. 全球地表温度大气遥相关路径研究[J]. 北京师范大学学报(自然科学版), 2021, 57(3): 314-319.
|
[42]
|
营娜, 陈建华, 李冬, 等. 基于复杂网络的中国臭氧拓扑特征[J]. 环境科学, 2022, 43(5): 2395-2402.
|
[43]
|
Zhao, Z.D., Zhao, N. and Ying, N. (2021) Association, Correlation, and Causation among Transport Variables of PM2.5. Frontiers in Physics, 9, Article ID: 684104. https://doi.org/10.3389/fphy.2021.684104
|
[44]
|
Ying, N., Zhou, D., Han, Z., et al. (2020) Climate Networks Suggest Rossby-Waves-Related CO2 Concentrations to Surface Air Temperature. Europhysics Letters, 132, Article No. 19001. https://doi.org/10.1209/0295-5075/132/19001
|
[45]
|
Zhou, D., et al. (2015) Teleconnection Paths via Climate Network Direct Link Detection. Physical Review Letters, 115, Article ID: 268501. https://doi.org/10.1103/PhysRevLett.115.268501
|
[46]
|
Runge, J., Petoukhov, V., Donges, J.F., et al. (2015) Identifying Causal Gateways and Mediators in Complex Spatio-Temporal Systems. Nature Communications, 6, Article No. 8502. https://doi.org/10.1038/ncomms9502
|
[47]
|
Boers, N., Bookhagen, B., Marwan, N., et al. (2016) Spatiotemporal Characteristics and Synchronization of Extreme Rainfall in South America with Focus on the Andes Mountain Range. Climate Dynamics, 46, 601-617.
https://doi.org/10.1007/s00382-015-2601-6
|
[48]
|
Liu, T., Chen, D., Yang, L., et al. (2023) Teleconnections among Tipping Elements in the Earth System. Nature Climate Change, 13, 67-74. https://doi.org/10.1038/s41558-022-01558-4
|
[49]
|
Ahmadi, M., Kamangar, M., Salimi, S., et al. (2022) A New Approach in Evaluation Impacts of Teleconnection Indices on Temperature and Precipitation in Iran. Theoretical and Applied Climatology, 150, 15-33.
https://doi.org/10.1007/s00704-022-04138-w
|
[50]
|
Kalu, I., Ndehedehe, C.E., Okwuashi, O., et al. (2022) An Assimilated Deep Learning Approach to Identify the Influence of Global Climate on Hydrological Fluxes. Journal of Hydrology, 614, Article ID: 128498.
https://doi.org/10.1016/j.jhydrol.2022.128498
|
[51]
|
Gao, L., Yang, Y.M., Li, Q., et al. (2022) Deep Learning for Predicting Winter Temperature in North China. Atmosphere, 13, Article No. 702. https://doi.org/10.3390/atmos13050702
|
[52]
|
Builes-Jaramillo, A., et al. (2018) Nonlinear Interactions between the Amazon River Basin and the Tropical North Atlantic at Interannual Timescales. Climate Dynamics: Observational, Theoretical and Computational Research on the Climate System, 50, 2951-2969. https://doi.org/10.1007/s00382-017-3785-8
|
[53]
|
Xu, F., Shi, Y., Deng, M., et al. (2017) Multi-Scale Regionalization Based Mining of Spatio-Temporal Teleconnection Patterns between Anomalous Sea and Land Climate Events. Journal of Central South University, 24, 2438-2448.
https://doi.org/10.1007/s11771-017-3655-x
|