Abstract
The occurrence of summer extreme rainfall over southern China (SCER) is closely related to the boreal summer intraseasonal oscillation (BSISO), and whether operational models can reasonably predict the BSISO evolution and its modulation on SCER probability is crucial for disaster prevention and mitigation. Here, we find that the skill of subseasonal-to-seasonal (S2S) operational models in predicting the first component of BSISO (BSISO1) might determine the forecast skill of SCER. A systematic assessment is conducted on the reforecast data from two operational models that participated in the S2S project, i.e., the model of European Centre for Medium-Range Weather Forecasts (ECMWF) and the model of China Meteorological Administration (CMA). The results show that the ECMWF model can yield skillful prediction of the BSISO1 index up to 24 days in advance, while the skill of the CMA model is about 10 days. Accordingly, the SCER occurrence is correctly predicted by ECMWF (CMA) model at a forecast lead time of ~ 14 (7) days. The diagnostic results of modeled moisture processes further suggest that the anomalous moisture convergence (advection) induced by the BSISO1 activity serves as the primary (secondary) source of subseasonal predictability of SCER. With better prediction of the moisture convergence anomaly in the specific phases of BSISO1, higher skills can be obtained in the probability prediction of SCER. The present study implies that a further improvement in predicting the BSISO and its related moisture processes is crucial to promoting the subseasonal prediction skill of SCER probability.
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References
Ajayamohan RS, Goswami BN (2007) Dependence of simulation of boreal summer tropical intraseasonal oscillations on the simulation of seasonal mean. J Atmos Sci 64(2):460–478. https://doi.org/10.1175/JAS3844.1
Bo Z, Liu X, Gu W, Huang A, Fang Y, Wu T, Jie W, Li Q (2020) Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model. Theor Appl Climatol 142(1):393–406. https://doi.org/10.1007/s00704-020-03312-2
Brunet G, Shapiro M, Hoskins B, Moncrieff M, Dole R, Kiladis GN, Kirtman B, Lorenc A, Mills B, Morss R et al (2010) Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction. Bull Am Meteorol 91(10):1397–1406. https://doi.org/10.1175/2010bams3013.1
de Andrade FM, Coelho CAS, Cavalcanti IFA (2019) Global precipitation hindcast quality assessment of the subseasonal to seasonal (S2S) prediction project models. Clim Dyn 52(9):5451–5475. https://doi.org/10.1007/s00382-018-4457-z
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553. https://doi.org/10.1002/qj.828
Ding R, Li J, Seo KH (2011) Estimate of the predictability of boreal summer and winter intraseasonal oscillations from observations. Mon Weather Rev 139:2421–2438
Drbohlav H-KL, Wang B (2005) Mechanism of the northward-propagating intraseasonal oscillation: insights from a zonally symmetric model. J Clim 18(7):952–972. https://doi.org/10.1175/JCLI3306.1
Fang Y, Li B, Liu X (2019) Predictability and prediction skill of the boreal summer intra-seasonal oscillation in BCC_CSM model. J Meteorol Soc Jpn. https://doi.org/10.2151/jmsj.2019-019
Ford TW, Dirmeyer PA, Benson DO (2018) Evaluation of heat wave forecasts seamlessly across subseasonal timescales. Clim Atmos Sci 1(1):20. https://doi.org/10.1038/s41612-018-0027-7
Fu X, Wang B (2004) The boreal summer intraseasonal oscillations simulated in a hybrid coupled atmosphere–ocean model. Mon Weather Rev 132:2628–2649
Fu X, Wang B, Waliser DE, Li T (2007) Impact of atmosphere-ocean coupling on the predictability of monsoon intraseasonal oscillations. J Atmos Sci 64(1):157–174. https://doi.org/10.1175/JAS3830.1
Fu X, Lee JY, Wang B, Wang W, Vitart F (2013) Intraseasonal forecasting of the Asian summer monsoon in four operational and research models. J Clim 26:4186. https://doi.org/10.1175/JCLI-D-12-00252.1
Goswami BN, Shukla J (1984) Quasi-periodic oscillations in a symmetric general circulation model. J Atmos Sci 41:20–37
Guo Q, Liu X, Wu T, Cheng B, Li R, Wei L (2017) Verification and correction of East China summer rainfall prediction based on BCC_CSM model. Chin J Atmos Sci 41(1):71–90. https://doi.org/10.3878/j.issn.1006-9895.1602.15280
He Z, Hsu PC, Liu X, Wu T, Gao Y (2019) Factors limiting the forecast skill of the boreal summer intraseasonal oscillation in a subseasonal-to-seasonal model. Adv Atmos Sci 36(1):104–118. https://doi.org/10.1007/s00376-018-7242-3
Heidke P (1926) Berechnung des Erfolges und der Güte der Windstärkevorhersagen im Sturmwarnungsdienst. Geogr Ann 8:301–349. https://doi.org/10.2307/519729
Hsu PC, Lee JY, Ha KJ (2016) Influence of boreal summer intraseasonal oscillation on extreme rainfall in southern China. Int J Climatol 36:1403. https://doi.org/10.1002/joc.4433
Hsu PC, Lee JY, Ha KJ, Tsou CH (2017) Influences of boreal summer intraseasonal oscillation on heat waves in monsoon Asia. J Clim 30(18):7191–7211. https://doi.org/10.1175/JCLI-D-16-0505.1
Inness PM, Slingo JM, Guilyardi E, Cole J (2003) Simulation of the Madden–Julian oscillation in a coupled general circulation model. Part II: the role of the basic state. J Clim 16(3):365–382. https://doi.org/10.1175/1520-0442(2003)016%3c0365:SOTMJO%3e2.0.CO;2
Jeong J-H, Linderholm HW, Woo S-H, Folland C, Kim B-M, Kim S-J, Chen D (2013) Impacts of snow initialization on subseasonal forecasts of surface air temperature for the cold season. J Clim 26(6):1956–1972. https://doi.org/10.1175/JCLI-D-12-00159.1
Jiang X, Li T, Wang B (2004) Structures and mechanisms of the northward propagating boreal summer intraseasonal oscillation. J Clim 17(5):1022–1039. https://doi.org/10.1175/1520-0442(2004)017%3c1022:SAMOTN%3e2.0.CO;2
Jiang X, Waliser DE, Li J-L, Woods C (2011) Vertical cloud structures of the boreal summer intraseasonal variability based on CloudSat observations and ERA-Interim reanalysis. Clim Dyn 36:2219–2232. https://doi.org/10.1007/s00382-010-0853-8
Jiang X, Waliser DE, Xavier PK, Petch J, Klingaman NP, Woolnough SJ, Guan B, Bellon G, Crueger T, DeMott C et al (2015) Vertical structure and physical processes of the Madden–Julian oscillation: exploring key model physics in climate simulations. J Geophys Res Atmos 120(10):4718–4748. https://doi.org/10.1002/2014JD022375
Wu J, Vitart F, Wu T, Liu X (2017) Simulations of Asian summer monsoon in the sub-seasonal to seasonal prediction project (S2S) database. Q J R Meteorol Soc 143(706):2282–2295
Jones PD, Horton EB, Folland CK, Hulme M, Parker DE, Basnett TA (1999) The use of indices to identify changes in climatic extremes. Clim Change 42(1):131–149. https://doi.org/10.1023/A:1005468316392
Kim HM, Webster PJ, Toma VE, Kim D (2014) Predictability and prediction skill of the MJO in two operational forecasting systems. J Clim 27(14):5364–5378. https://doi.org/10.1175/JCLI-D-13-00480.1
Kumar S, Dirmeyer PA, Lawrence DM, DelSole T, Altshuler EL, Cash BA, Fennessy MJ, Guo Z, Kinter JL, Straus DM (2014) Effects of realistic land surface initializations on subseasonal to seasonal soil moisture and temperature predictability in North America and in changing climate simulated by CCSM4. J Geophys Res Atmos 119(23):13250–13270. https://doi.org/10.1002/2014JD022110
Lee JY, Wang B (2014) Future change of global monsoon in the CMIP5. Clim Dyn 42:101. https://doi.org/10.1007/s00382-012-1564-0
Lee SS, Wang B (2016) Regional boreal summer intraseasonal oscillation over Indian Ocean and Western Pacific: comparison and predictability study. Clim Dyn 46(7):2213–2229. https://doi.org/10.1007/s00382-015-2698-7
Lee JY, Wang B, Wheeler MC, Fu X, Waliser DE, Kang IS (2013) Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Clim Dyn 40(1):493–509. https://doi.org/10.1007/s00382-012-1544-4
Lee SS, Wang B, Waliser DE, Neena JM, Lee JY (2015) Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment. Clim Dyn 45(7):2123–2135. https://doi.org/10.1007/s00382-014-2461-5
Lee SS, Moon JY, Wang B, Kim HJ (2017) Subseasonal prediction of extreme precipitation over Asia: boreal summer intraseasonal oscillation perspective. J Clim 30(8):2849–2865. https://doi.org/10.1175/JCLI-D-16-0206.1
Li J, Wang B (2018) Predictability of summer extreme precipitation days over eastern China. Clim Dyn 51(11):4543–4554. https://doi.org/10.1007/s00382-017-3848-x
Li J, Dong W, Yan Z (2012) Changes of climate extremes of temperature and precipitation in summer in eastern China associated with changes in atmospheric circulation in East Asia during 1960–2008. Chin Sci Bull 57:1856–1861
Li J, Zhu Z, Dong W (2017) Assessing the uncertainty of CESM-LE in simulating the trends of mean and extreme temperature and precipitation over China. Int J Climatol 37(4):2101–2110. https://doi.org/10.1002/joc.4837
Li W, Chen J, Li L, Chen H, Liu BY, Xu CY, Li XQ (2019) Evaluation and bias correction of S2S precipitation for hydrological extremes. J Hydrometeorol 20(9):1887–1906. https://doi.org/10.1175/JHM-D-19-0042.1
Li J, Wang B, Yang YM (2020a) Diagnostic metrics for evaluating model simulations of the east Asian monsoon. J Clim 33(5):1777–1801. https://doi.org/10.1175/JCLI-D-18-0808.1
Li J, Yan H, Zhu Z (2020b) Quantitative analysis of changes of summer extremes temperature and precipitation days over China with respect to the mean temperature increase. Chin Plateau Meteorol 39(3):532–542. https://doi.org/10.7522/j.issn.1000-0534.2019.00042
Li W, Hu S, Hsu PC, Guo W, Wei J (2020c) Systematic bias of Tibetan Plateau snow cover in subseasonal-to-seasonal models. Cryosphere 14(10):3565–3579. https://doi.org/10.5194/tc-14-3565-2020
Liang P, Lin H (2018) Sub-seasonal prediction over East Asia during boreal summer using the ECCC monthly forecasting system. Clim Dyn 50:1007
Lin H, Brunet G, Derome J (2008) Forecast skill of the Madden–Julian oscillation in two Canadian atmospheric models. Mon Weather Rev 136:4130. https://doi.org/10.1175/2008MWR2459.1
Liu RF, Wang W (2015) Multi-week prediction of South-East Asia rainfall variability during boreal summer in CFSv2. Clim Dyn 45(1):493–509. https://doi.org/10.1007/s00382-014-2401-4
Liu B, Yan Y, Zhu C, Ma S, Li J (2020) Record-breaking Meiyu rainfall around the Yangtze River in 2020 regulated by the subseasonal phase transition of the North Atlantic Oscillation. Geophys Res Lett 47(22):e2020GL090342. https://doi.org/10.1029/2020GL090342
Liu X, Yao J, Wu T, Zhang S, Xu F, Zhang L, Jie W, Zhou W, Li Q, Liang X et al (2021) Development of coupled data assimilation with the BCC climate system model: highlighting the role of sea-ice assimilation for global analysis. J Adv Model Earth Syst 13(4):e2020MS002368. https://doi.org/10.1029/2020MS002368
Loriaux JM, Lenderink G, Siebesma AP (2016) Peak precipitation intensity in relation to atmospheric conditions and large-scale forcing at midlatitudes. J Geophys Res Atmos 121(10):5471–5487. https://doi.org/10.1002/2015JD024274
Lu R, Dong H, Su Q, Ding H (2014) The 30–60-day intraseasonal oscillations over the subtropical western North Pacific during the summer of 1998. Adv Atmos Sci 31:1–7
O’Gorman PA, Schneider T (2009) The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc Natl Acad Sci USA 106(35):14773–14777. https://doi.org/10.1073/pnas.0907610106
Orsolini YJ, Senan R, Balsamo G, Doblas-Reyes FJ, Vitart F, Weisheimer A, Carrasco A, Benestad RE (2013) Impact of snow initialization on sub-seasonal forecasts. Clim Dyn 41(7):1969–1982. https://doi.org/10.1007/s00382-013-1782-0
Pegion K, Kirtman BP, Becker E, Collins DC, LaJoie E, Burgman R, Bell R, DelSole T, Min D, Zhu Y et al (2019) The subseasonal experiment (SubX): a multimodel subseasonal prediction experiment. Bull Am Meteorol Soc 100(10):2043–2060. https://doi.org/10.1175/BAMS-D-18-0270.1
Ren HL, Liu Y, Jin FF, Yan YP, Liu XW (2014) Application of the analogue-based correction of errors method in ENSO prediction. Atmos Ocean Sci Lett 7(2):157–161. https://doi.org/10.3878/j.issn.1674-2834.13.0080
Ren P, Ren HL, Fu JX, Wu J, Du L (2018) Impact of boreal summer intraseasonal oscillation on rainfall extremes in southeastern China and its predictability in CFSv2. J Geophys Res Atmos 123(9):4423–4442. https://doi.org/10.1029/2017JD028043
Sabeerali CT, Ramu Dandi A, Dhakate A, Salunke K, Mahapatra S, Rao SA (2013) Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs. J Geophys Res Atmos 118(10):4401–4420. https://doi.org/10.1002/jgrd.50403
Sperber KR, Annamalai H (2008) Coupled model simulations of boreal summer intraseasonal (30–50 day) variability, part 1: systematic errors and caution on use of metrics. Clim Dyn 31(2):345–372. https://doi.org/10.1007/s00382-008-0367-9
Vitart F (2017) Madden–Julian oscillation prediction and teleconnections in the S2S database. Q J R Meteorol Soc 143:2210–2220
Vitart F, Woolnough S, Balmaseda MA, Tompkins AM (2007) Monthly forecast of the Madden–Julian Oscillation using a coupled GCM. Mon Weather Rev 135(7):2700–2715. https://doi.org/10.1175/MWR3415.1
Vitart F, Ardilouze C, Bonet A, Brookshaw A, Chen M, Codorean C, Déqué M, Ferranti L, Fucile E, Fuentes M et al (2017) The subseasonal to seasonal (S2S) prediction project database. Bull Am Meteorol Soc 98(1):163–176. https://doi.org/10.2307/26243670
Wang B, Xie X (1997) A model for the boreal summer intraseasonal oscillation. J Atmos Sci 54:72–86
Wang S, Sobel AH, Tippett MK, Vitart F (2019) Prediction and predictability of tropical intraseasonal convection: seasonal dependence and the Maritime Continent prediction barrier. Clim Dyn 52(9):6015–6031. https://doi.org/10.1007/s00382-018-4492-9
Wang T, Chu C, Sun X, Li T (2020) Improving real-time forecast of intraseasonal variabilities of Indian summer monsoon precipitation in an empirical scheme. Front Earth Sci. https://doi.org/10.3389/feart.2020.577311
Wang J, Yang J, Ren HL, Li J, Bao Q, Gao M (2021) Dynamical and machine learning hybrid seasonal prediction of summer rainfall in China. J Meteorol 35(4):583–593. https://doi.org/10.1007/s13351-021-0185-0
Wheeler MC, Hendon HH (2004) An all-season real-time multivariate MJO index: development of an index for monitoring and prediction. Mon Weather Rev 132(8):1917–1932. https://doi.org/10.1175/1520-0493(2004)132%3c1917:AARMMI%3e2.0.CO;2
Woolnough SJ, Vitart F, Balmaseda MA (2007) The role of the ocean in the Madden–Julian Oscillation: Implications for MJO prediction. Q J R Meteorol Soc 133(622):117–128. https://doi.org/10.1002/qj.4
Wu J, Gao XJ (2013) A gridded daily observation dataset over China region and comparison with the other datasets. Chin J Geophys 56(4):1102–1111. https://doi.org/10.6038/cjg20130406
Xavier P, Rahmat R, Cheong WK, Wallace E (2014) Influence of Madden-Julian Oscillation on Southeast Asia extreme rainfall: observations and predictability. Geophys Res Lett 41(12):4406–4412. https://doi.org/10.1002/2014GL060241
Yan Z, Jones PD, Davies TD, Moberg A, Bergström H, Camuffo D, Cocheo C, Maugeri M, Demarée GR, Verhoeve T et al (2002) Trends of extreme temperatures in Europe and China based on daily observations. Clim Change 53(1):355–392. https://doi.org/10.1023/A:1014939413284
Yang J, Zhu T, Gao M, Lin H, Wang B, Bao Q (2018) Late-July barrier for subseasonal forecast of summer daily maximum temperature over Yangtze River basin. Geophys Res Lett 45(22):12610–12615. https://doi.org/10.1029/2018GL080963
Yang J, He S, Bao Q (2021a) Convective/large-scale rainfall partitions of tropical heavy precipitation in CMIP6 atmospheric models. Adv Atmos Sci 38(6):1020–1027. https://doi.org/10.1007/s00376-021-0238-4
Yang YM, Cho JA, Moon JY, Kim KY, Wang B (2021b) Improved boreal summer intraseasonal oscillation simulations over the Indian Ocean by modifying moist parameterizations in climate models. Clim Dyn. https://doi.org/10.1007/s00382-021-05822-9
Yao J, Sun X, Tang J, Ji Y, Xu Y, Yang XQ (2020) Summer regional pentad heat wave in eastern China and their possible causes. Front Earth Sci. https://doi.org/10.3389/feart.2020.598027
Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A (2012) APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorol Soc 93:1401–1415. https://doi.org/10.1175/BAMS-D-11-00122.1
Zhang X, Alexander L, Hegerl GC, Jones P, Tank AK, Peterson TC, Trewin B, Zwiers FW (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wires Clim Change 2(6):851–870. https://doi.org/10.1002/wcc.147
Zhang T, Yang S, Jiang X, Dong S (2016) Sub-seasonal prediction of the maritime continent rainfall of wet-dry transitional seasons in the NCEP climate forecast version 2. Atmosphere 7(2):28. https://doi.org/10.3390/atmos7020028
Zhang KY, Li J, Zhu ZW, Li T (2021) Subseasonal prediction skill of the persistent snowstorm event over southern China during early 2008 in ECMWF and CMA S2S prediction models. Adv Atmos Sci. https://doi.org/10.1007/s00376-021-0402-x
Zhu C, Nakazawa T, Li J, Chen L (2003) The 30–60 day intraseasonal oscillation over the western North Pacific Ocean and its impacts on summer flooding in China during 1998. Geophys Res Lett. https://doi.org/10.1029/2003GL017817
Zhu X, Liu X, Huang A, Zhou Y, Wu Y, Fu Z (2021) Impact of the observed SST frequency in the model initialization on the BSISO prediction. Clim Dyn 57(3):1097–1117. https://doi.org/10.1007/s00382-021-05761-5
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This work was supported by the National Natural Science Foundation of China (Grant no. 42088101), and the National Key R&D Program of China (Grant no. 2018YFC1505905).
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Wu, J., Li, J., Zhu, Z. et al. Factors determining the subseasonal prediction skill of summer extreme rainfall over southern China. Clim Dyn 60, 443–460 (2023). https://doi.org/10.1007/s00382-022-06326-w
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DOI: https://doi.org/10.1007/s00382-022-06326-w