{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:19:25Z","timestamp":1775132365999,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19070404"],"award-info":[{"award-number":["XDA19070404"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["42030602"],"award-info":[{"award-number":["42030602"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA19070404"],"award-info":[{"award-number":["XDA19070404"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42030602"],"award-info":[{"award-number":["42030602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The thermal condition over the Tibetan Plateau (TP) plays a vital role in the South Asian high (SAH) and the Asian summer monsoon (ASM); however, its prediction skill is still low. Here, two machine learning models are employed to address this problem. Expert knowledge and distance correlation are used to select the predictors from observational datasets. Both linear and nonlinear relationships are considered between the predictors and predictands. The predictors are utilized for training the machine learning models. The prediction skills of the machine learning models are higher than those of two state-of-the-art dynamic operational models and can explain 67% of the variance in the observations. Moreover, the SHapley Additive exPlanation method results indicate that the important predictors are mainly from the Southern Hemisphere, Eurasia, and western Pacific, and most show nonlinear relationships with the predictands. Our results can be applied to find potential climate teleconnections and improve the prediction of other climate signals.<\/jats:p>","DOI":"10.3390\/rs14174169","type":"journal-article","created":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T02:04:32Z","timestamp":1661479472000},"page":"4169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["The Prediction of the Tibetan Plateau Thermal Condition with Machine Learning and Shapley Additive Explanation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9639-3958","authenticated-orcid":false,"given":"Yuheng","family":"Tang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"College of Earth Science, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Anmin","family":"Duan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"College of Earth Science, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Chunyan","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"College of Earth Science, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yue","family":"Xin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"},{"name":"College of Earth Science, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"ref_1","unstructured":"Ye, D., and Gao, Y. (1979). Meteorology of the Qinghai-Xizang (Tibet) Plateau, Science Press. (In Chinese)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1093\/nsr\/nwu045","article-title":"Tibetan Plateau climate dynamics: Recent research progress and outlook","volume":"2","author":"Wu","year":"2014","journal-title":"Natl. Sci. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/srep00404","article-title":"Thermal Controls on the Asian Summer Monsoon","volume":"2","author":"Wu","year":"2012","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1093\/nsr\/nwaa011","article-title":"Land\u2013atmosphere\u2013ocean coupling associated with the Tibetan Plateau and its climate impacts","volume":"7","author":"Liu","year":"2020","journal-title":"Natl. Sci. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s13351-017-6045-2","article-title":"Relationship between atmospheric heat source over the Tibetan Plateau and precipitation in the Sichuan\u2013Chongqing region during summer","volume":"31","author":"Lai","year":"2017","journal-title":"J. Meteorol. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1007\/s00376-001-0007-3","article-title":"Interannual variability of atmospheric heat source\/sink over the Qinghai\u2014Xizang (Tibetan) Plateau and its relation to circulation","volume":"18","author":"Zhao","year":"2001","journal-title":"Adv. Atmos. Sci."},{"key":"ref_7","first-page":"108","article-title":"The wind structure and heat balance in the lower troposphere over Tibetan Plateau and its surrounding","volume":"28","author":"Ye","year":"1957","journal-title":"Acta Meteorol. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1175\/1520-0493(1998)126<0913:TPFATT>2.0.CO;2","article-title":"Tibetan Plateau Forcing and the Timing of the Monsoon Onset over South Asia and the South China Sea","volume":"126","author":"Wu","year":"1998","journal-title":"Mon. Weather Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1007\/s00382-015-2506-4","article-title":"Location and variation of the summertime upper-troposphere temperature maximum over South Asia","volume":"45","author":"Wu","year":"2015","journal-title":"Clim. Dyn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1007\/s00382-018-4246-8","article-title":"Variability of summertime Tibetan tropospheric temperature and associated precipitation anomalies over the central-eastern Sahel","volume":"52","author":"Nan","year":"2019","journal-title":"Clim. Dyn."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105212","DOI":"10.1016\/j.atmosres.2020.105212","article-title":"Links between the thermal condition of the Tibetan Plateau in summer and atmospheric circulation and climate anomalies over the Eurasian continent","volume":"247","author":"Nan","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.accre.2020.08.001","article-title":"Evaluation of CMIP6 for historical temperature and precipitation over the Tibetan Plateau and its comparison with CMIP5","volume":"11","author":"Zhu","year":"2020","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1007\/s00376-018-7217-4","article-title":"Interannual Variability of Late-spring Circulation and Diabatic Heating over the Tibetan Plateau Associated with Indian Ocean Forcing","volume":"35","author":"Zhao","year":"2018","journal-title":"Adv. Atmos. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s00382-017-3906-4","article-title":"Tibetan Plateau capacitor effect during the summer preceding ENSO: From the Yellow River climate perspective","volume":"51","author":"Jin","year":"2018","journal-title":"Clim. Dyn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1007\/s00382-014-2417-9","article-title":"Interannual variability of the spring atmospheric heat source over the Tibetan Plateau forced by the North Atlantic SSTA","volume":"45","author":"Cui","year":"2015","journal-title":"Clim. Dyn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1002\/joc.6676","article-title":"Connection between winter Arctic sea ice and west Tibetan Plateau snow depth through the NAO","volume":"41","author":"Chen","year":"2021","journal-title":"Int. J. Climatol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1038\/s41558-020-0881-2","article-title":"Arctic sea-ice loss intensifies aerosol transport to the Tibetan Plateau","volume":"10","author":"Li","year":"2020","journal-title":"Nat. Clim. Chang."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1175\/JCLI3391.1","article-title":"The Effect of ENSO on Tibetan Plateau Snow Depth: A Stationary Wave Teleconnection Mechanism and Implications for the South Asian Monsoons","volume":"18","author":"Shaman","year":"2005","journal-title":"J. Clim."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1175\/JCLI-D-19-0111.1","article-title":"The Impact of Preceding Spring Antarctic Oscillation on the Variations of Lake Ice Phenology over the Tibetan Plateau","volume":"33","author":"Liu","year":"2020","journal-title":"J. Clim."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"7701","DOI":"10.1175\/JCLI-D-17-0327.1","article-title":"Southern Hemisphere Origins for Interannual Variations of Snow Cover over the Western Tibetan Plateau in Boreal Summer","volume":"31","author":"Dou","year":"2018","journal-title":"J. Clim."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1038\/s41586-019-1559-7","article-title":"Deep learning for multi-year ENSO forecasts","volume":"573","author":"Ham","year":"2019","journal-title":"Nature"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e2021GL093787","DOI":"10.1029\/2021GL093787","article-title":"Using machine learning to analyze physical causes of climate change: A case study of US Midwest extreme precipitation","volume":"48","author":"Davenport","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1038\/548379a","article-title":"How machine learning could help to improve climate forecasts","volume":"548","author":"Jones","year":"2017","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9684","DOI":"10.1073\/pnas.1810286115","article-title":"Deep learning to represent subgrid processes in climate models","volume":"115","author":"Rasp","year":"2018","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_25","first-page":"7113","article-title":"Seasonal Forecast of Non-monsoonal Winter Precipitation over the Eurasian Continent using Machine Learning Models","volume":"34","author":"Qian","year":"2021","journal-title":"J. Clim."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"124006","DOI":"10.1088\/1748-9326\/ac34bc","article-title":"Using deep learning to predict the East Asian summer monsoon","volume":"16","author":"Tang","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105337","DOI":"10.1016\/j.atmosres.2020.105337","article-title":"CNN-based near-real-time precipitation estimation from Fengyun-2 satellite over Xinjiang, China","volume":"250","author":"Xue","year":"2020","journal-title":"Atmos. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1007\/s00376-019-9023-z","article-title":"A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area","volume":"36","author":"Li","year":"2019","journal-title":"Adv. Atmos. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15481603.2019.1650447","article-title":"Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data","volume":"57","author":"Abdi","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wagle, N., Acharya, T.D., Kolluru, V., Huang, H., and Lee, D.H. (2020). Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods. Appl. Sci., 10.","DOI":"10.3390\/app10228083"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"064005","DOI":"10.1088\/1748-9326\/ab7df9","article-title":"Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest","volume":"15","author":"Kang","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"nwaa307","DOI":"10.1093\/nsr\/nwaa307","article-title":"Robust prediction of hourly PM2.5 from meteorological data using LightGBM","volume":"8","author":"Zhong","year":"2021","journal-title":"Natl. Sci. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lee, Y., Han, D., Ahn, M.-H., Im, J., and Lee, S.J. (2019). Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11151741"},{"key":"ref_35","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume":"Volume 30","author":"Lundberg","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e12984","DOI":"10.1111\/ina.12984","article-title":"Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach","volume":"32","author":"Yang","year":"2022","journal-title":"Indoor Air"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3979","DOI":"10.1175\/JCLI-D-21-0665.1","article-title":"Response of the South Asian High in May to the early spring North Pacific Victoria Mode","volume":"35","author":"Yang","year":"2022","journal-title":"J. Clim."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1175\/1520-0442(1995)008<0248:TEOESC>2.0.CO;2","article-title":"The Effect of Eurasian Snow Cover on the Indian Monsoon","volume":"8","author":"Vernekar","year":"1995","journal-title":"J. Clim."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1002\/2013JD020316","article-title":"The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2: 1. Sea ice concentrations","volume":"119","author":"Titchner","year":"2014","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_41","unstructured":"Robinson, D.A., Estilow, T.W., and Program, N.C. (2021, December 30). NOAA Climate Data Record (CDR) of Northern Hemisphere (NH) Snow Cover Extent (SCE) Version 1, Available online: https:\/\/www.ncei.noaa.gov\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.5194\/essd-12-3469-2020","article-title":"The Berkeley Earth land\/ocean temperature record","volume":"12","author":"Rohde","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_43","unstructured":"Chen, T., and Guestrin, C. (2022, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA."},{"key":"ref_44","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). Lightgbm: A highly efficient gradient boosting decision tree. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_45","first-page":"202","article-title":"Solar radiation prediction using different machine learning algorithms and implications for extreme climate events","volume":"9","author":"Huang","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1038\/s41598-020-80820-1","article-title":"Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt","volume":"11","author":"Shahhosseini","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.tra.2018.02.009","article-title":"Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo","volume":"110","author":"Ding","year":"2018","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106545","DOI":"10.1016\/j.aap.2021.106545","article-title":"Predicting effects of built environment on fatal pedestrian accidents at location-specific level: Application of XGBoost and SHAP","volume":"166","author":"Chang","year":"2022","journal-title":"Accid. Anal. Prev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4439","DOI":"10.1038\/s41467-020-18297-9","article-title":"Developing a COVID-19 mortality risk prediction model when individual-level data are not available","volume":"11","author":"Barda","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_51","first-page":"2769","article-title":"Measuring and testing dependence by correlation of distances","volume":"35","author":"Rizzo","year":"2007","journal-title":"Ann. Stat."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1080\/01621459.2012.695654","article-title":"Feature Screening via Distance Correlation Learning","volume":"107","author":"Li","year":"2012","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1175\/1520-0442(1996)009<0358:TOAIVO>2.0.CO;2","article-title":"The Onset and Interannual Variability of the Asian Summer Monsoon in Relation to Land\u2013Sea Thermal Contrast","volume":"9","author":"Li","year":"1996","journal-title":"J. Clim."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"103625","DOI":"10.1016\/j.earscirev.2021.103625","article-title":"Warming amplification over the Arctic Pole and Third Pole: Trends, mechanisms and consequences","volume":"217","author":"You","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.5194\/gmd-12-1087-2019","article-title":"SEAS5: The new ECMWF seasonal forecast system","volume":"12","author":"Johnson","year":"2019","journal-title":"Geosci. Model Dev."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e2020MS002101","DOI":"10.1029\/2020MS002101","article-title":"The German climate forecast system: GCFS","volume":"13","author":"Dobrynin","year":"2021","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1007\/s00382-014-2241-2","article-title":"Potential influence of the November\u2013December Southern Hemisphere annular mode on the East Asian winter precipitation: A new mechanism","volume":"44","author":"Wu","year":"2015","journal-title":"Clim. Dyn."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1007\/s00382-014-2303-5","article-title":"The impact of South Pacific extratropical forcing on ENSO and comparisons with the North Pacific","volume":"44","author":"Ding","year":"2015","journal-title":"Clim. Dyn."},{"key":"ref_59","first-page":"231","article-title":"The Relationship between Circulation and SST Anomaly East of Australia and the Summer Rainfall in the Middle and Lower Reaches of the Yangtze River","volume":"32","author":"Liu","year":"2008","journal-title":"Chin. J. Atmos. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"L03703","DOI":"10.1029\/2010GL046278","article-title":"Decadal to bi-decadal rainfall variation in the western Pacific: A footprint of South Pacific decadal variability?","volume":"38","author":"Hsu","year":"2011","journal-title":"Geophys. Res. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1175\/1520-0442(2000)013<1517:PEATHD>2.0.CO;2","article-title":"Pacific-East Asian Teleconnection: How Does ENSO Affect East Asian Climate?","volume":"13","author":"Wang","year":"2000","journal-title":"J. Clim."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1007\/s00382-017-3692-z","article-title":"PDO modulation of the ENSO impact on the summer South Asian high","volume":"50","author":"Xue","year":"2018","journal-title":"Clim. Dyn."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.rse.2005.01.006","article-title":"Evaluation of spring snow covered area depletion in the Canadian Arctic from NOAA snow charts","volume":"95","author":"Wang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.rse.2006.09.035","article-title":"Assessment of spring snow cover duration variability over northern Canada from satellite datasets","volume":"111","author":"Brown","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_65","first-page":"22","article-title":"Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback","volume":"34","author":"Brown","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"D16","DOI":"10.1029\/2010JD013975","article-title":"A multi-data set analysis of variability and change in Arctic spring snow cover extent, 1967\u20132008","volume":"115","author":"Brown","year":"2010","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1038\/s41586-021-03854-z","article-title":"Skilful precipitation nowcasting using deep generative models of radar","volume":"597","author":"Ravuri","year":"2021","journal-title":"Nature"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4501","DOI":"10.1175\/2009JCLI2524.1","article-title":"Statistical Prediction of ENSO from Subsurface Sea Temperature Using a Nonlinear Dimensionality Reduction","volume":"22","author":"Lima","year":"2009","journal-title":"J. Clim."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1175\/JCLI-D-21-0110.1","article-title":"A Statistical Intraseasonal Prediction Model of Extended Boreal Summer Western North Pacific Tropical Cyclone Genesis","volume":"35","author":"Zhao","year":"2022","journal-title":"J. Clim."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"e2019JD030923","DOI":"10.1029\/2019JD030923","article-title":"The Role of the Stratosphere in Subseasonal to Seasonal Prediction: 2. Predictability Arising From Stratosphere-Troposphere Coupling","volume":"125","author":"Domeisen","year":"2020","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1002\/joc.5411","article-title":"Climatic and associated cryospheric, biospheric, and hydrological changes on the Tibetan Plateau: A review","volume":"38","author":"Bibi","year":"2018","journal-title":"Int. J. Climatol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1175\/2008JCLI2544.1","article-title":"Indian Ocean Capacitor Effect on Indo\u2013Western Pacific Climate during the Summer following El Ni\u00f1o","volume":"22","author":"Xie","year":"2009","journal-title":"J. Clim."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"4937","DOI":"10.1007\/s00382-019-04837-7","article-title":"Distinguishing interannual variations and possible impacted factors for the northern and southern mode of South Asia High","volume":"53","author":"Xue","year":"2019","journal-title":"Clim. Dyn."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.1007\/s00382-016-3490-z","article-title":"Two interannual dominant modes of the South Asian High in May and their linkage to the tropical SST anomalies","volume":"49","author":"Liu","year":"2017","journal-title":"Clim. Dyn."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s00376-010-9224-y","article-title":"The impact of the tropical Indian Ocean on South Asian High in boreal summer","volume":"28","author":"Huang","year":"2011","journal-title":"Adv. Atmos. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1175\/1520-0477(1999)080<0629:COSASM>2.0.CO;2","article-title":"Choice of South Asian Summer Monsoon Indices. Bull","volume":"80","author":"Wang","year":"1999","journal-title":"Amer. Meteorol. Soc."},{"key":"ref_77","unstructured":"Gang, H., and Guijie, Z. (2019). The East Asian Summer Monsoon Index (1851\u20132021), National Tibetan Plateau Data Center."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"9977","DOI":"10.1175\/JCLI-D-15-0272.1","article-title":"A New Upper-Level Circulation Index for the East Asian Summer Monsoon Variability","volume":"28","author":"Zhao","year":"2015","journal-title":"J. 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