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关于遥相关问题的研究现状综述
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Abstract:
地球科学中的气候系统是一个复杂的物理系统。人类在远古时期就注意到了一些独特的自然现象，通过生活实践想要理解地球系统各个要素之间的联系，也总结了一些规律，比如瑞雪兆丰年等。但是这些基于人类经验得到的规律不能准确地描述地球系统的变化，复杂网络是探究地球系统变化的一个新角度。识别遥相关现象最主要可以为遥远地区的气候变化提供一定程度的预测。本文分类整理了近十年对遥相关问题的最新研究，重点对利用复杂网络方法和事件同步方法研究遥相关问题的文献进行了概述，以及对遥相关路径和预测的研究。我们旨在想要为研究遥相关问题的人们提供一些研究思路和方法总结。
The climate system in earth science is a complex physical system. Since ancient times, human beings have noticed some unique natural phenomena. Through life practice, they want to understand the relationship between various elements of the earth system, and they have also summarized some rules, such as good snow and good years. But these rules based on human experience do not accurately describe changes in the Earth system. A complex network is a new angle to explore the change in the earth’s system. The identification of teleconnection phenomena can, above all, provide a degree of prediction of climate change in distant regions. We categorize the latest research on teleconnection problems in the last ten years, focusing on the literature on teleconnection problems using complex network methods and event synchronization methods, as well as the research on teleconnection paths and predictions. We aim to provide some research ideas and methods summary for those who study teleconnection problems.
| [1] | ？ngstr？m, 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 Bj？rnstad, 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 Ni？o-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 |
