Abstract
The seasonal prediction skills in the CAMS-CSM (the acronym stands for the Chinese Academy of Meteorological Sciences Climate System Model) climate forecast system is evaluated with a set of retrospective forecast experiments during the period of 1981–2019. The CAMS-CSM, which has been registered for the sixth phase of the coupled model intercomparison project (CMIP6), is an atmosphere–ocean–land–sea ice fully coupled general circulation model. The assimilation scheme used in the forecast system is the 3-dimentional nudging, including both the atmospheric and oceanic components. The analyses mainly focus on the seasonal predictable skill of sea surface temperature, 2-m air temperature, and precipitation anomalies. The analyses revealed that the model shows a good prediction skill for the SST anomalies, especially in the tropical Pacific, in association with El Niño-Southern Oscillation (ENSO) events. The anomaly correlation coefficient (ACC) score for ENSO can reach 0.75 at 6-month lead time. Furthermore, the extreme warm/cold Indian Ocean dipole (IOD) events are successfully predicted at 3- and even 6-month lead times. The whole ACC of IOD events between the observation and the prediction can reach 0.51 at 2-month lead time. There are reliable seasonal prediction skills for 2-m air temperature anomalies over most of the Northern Hemisphere, where the correlation is mainly above 0.4 at 2-month lead time, especially over the East Asia, North America and South America. However, the seasonal prediction for precipitation still faces a big challenge. The source of precipitation predictability over the East Asia can be partly related to strong ENSO events. Additionally, the anomalous anticyclone over the western North Pacific (WPAC) which connects the ENSO events and the East Asian summer monsoon (EASM) can be well predicted at 6-month lead time.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adler RF et al (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167. https://doi.org/10.1175/1525-7541(2003)004%3c1147:Tvgpcp%3e2.0.Co;2
Alexander MA, Bladé I, Newman M, Lanzante JR, Lau N-C, Scott JD (2002) The atmospheric bridge: the influence of ENSO teleconnections on air–sea interaction over the global oceans. J Clim 15:2205–2231. https://doi.org/10.1175/1520-0442(2002)015%3c2205:Tabtio%3e2.0.Co;2
Anderson J, Hoar T, Raeder K, Liu H, Collins N, Torn R, Avellano A (2009) The data assimilation research testbed: a community facility. Bull Am Meteor Soc 90:1283–1296. https://doi.org/10.1175/2009bams2618.1
Ashok K, Guan ZY, Saji NH, Yamagata T (2004) Individual and combined influences of ENSO and the Indian Ocean Dipole on the Indian summer monsoon. J Clim 17:3141–3155. https://doi.org/10.1175/1520-0442(2004)017%3c3141:Iacioe%3e2.0.Co;2
Baehr J et al (2014) The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model. Clim Dyn 44:2723–2735. https://doi.org/10.1007/s00382-014-2399-7
Balmaseda M, Mogensen K, Weaver A (2013) Evaluation of the ECMWF ocean reanalysis ORAS4. Quart J R Meteorol Soc 139:1132–1161
Barker DM, Huang W, Guo YR, Bourgeois AJ, **ao QN (2004) A three-dimensional variational data assimilation system for MM5: implementation and initial results. Mon Weath Rev 132:897–914. https://doi.org/10.1175/1520-0493(2004)132%3c0897:Atvdas%3e2.0.Co;2
Becker E et al (2014) The NCEP climate forecast system version 2. J Clim 27:2185–2208. https://doi.org/10.1175/jcli-d-12-00823.1
Behringer DW, Xue Y (2004) Eighth symposium on integrated observing and assimilation systems for atmosphere, oceans, and land surface, AMS 84th annual meeting, Washington State Convention and Trade Center, Seattle. Wash Am Meteor Soc 23:11–15
Behringer DW, Ji M, Leetmaa A (1998) An improved coupled model for ENSO prediction and implications for ocean initialization Part i: the ocean data assimilation system. Mon Weath Rev 126:1013–1021. https://doi.org/10.1175/1520-0493(1998)126%3c1013:Aicmfe%3e2.0.Co;2
Cai W et al (2019) Pantropical climate interactions. Science. https://doi.org/10.1126/science.aav4236
Cazes Boezio G, Ortelli S (2019) Use of the WRF-DA 3D-var data assimilation system to obtain wind speed estimates in regular grids from measurements at wind farms in Uruguay. Data. https://doi.org/10.3390/data4040142
Chen L, Dong M, Shao Y (1992) The characteristics of interannual variations on the East Asian monsoon. J Meteorol Soc Jpn Ser II 70:397–421. https://doi.org/10.2151/jmsj1965.70.1B_397
Chen DK, Zebiak SE, Cane MA, Busalacchi AJ (1997) Initialization and predictability of a coupled ENSO forecast model. Mon Weath Rev 125:773–788
Chen DK, Cane M, Kaplan A, Zebiak S, Huang D (2004) Predictability of El Niño over the past 148 years. Nature 428:733–736
Dai YJ et al (2003) The common land model. Bull Am Meteor Soc 84:1013–1023. https://doi.org/10.1175/Bams-84-8-1013
Du Y, **e S-P, Huang G, Hu K (2009) Role of air–sea interaction in the long persistence of El Niño–induced north Indian ocean warming*. J Clim 22:2023–2038. https://doi.org/10.1175/2008jcli2590.1
Dunstone N et al (2016) Skilful predictions of the winter North Atlantic Oscillation one year ahead. Nat Geosci 9:809–814. https://doi.org/10.1038/ngeo2824
Eliashiv J, Subramanian AC, Miller AJ (2019) Tropical climate variability in the community earth system model: data assimilation research testbed. Clim Dyn 54:793–806. https://doi.org/10.1007/s00382-019-05030-6
Evensen G (1994) Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res 99:10143–10162
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Fan Y, Allen MR, Anderson DLT, Balmaseda MA (2000) How predictability depends on the nature of uncertainty in initial conditions in a coupled model of ENSO. J Clim 13:3298–3313. https://doi.org/10.1175/1520-0442(2000)013%3c3298:Hpdotn%3e2.0.Co;2
Gao C, Wu X, Zhang R-H (2016) Testing a four-dimensional variational data assimilation method using an improved intermediate coupled model for ENSO. Anal Predict Adv Atmos Sci 33:875–888. https://doi.org/10.1007/s00376-016-5249-1
Gao C, Zhang R-H, Wu X, Sun J (2018) Idealized experiments for optimizing model parameters using a 4D-variational method in an intermediate coupled model of ENSO. Adv Atmos Sci 35:410–422. https://doi.org/10.1007/s00376-017-7109-z
Graham NE, Barnett JM, Barnett TP (1987) An investigation of the El Nin˜o-Southern Oscillation cycle with statistical models: II. Model results. J Geophys Res 92:14271–14289
Griffies SM, Harrison MJ, Pacanowski PC (2004) A technical guide to MOM4 GFDL ocean group technical report No. 5. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, p 339
Guan Y, Zhu J, Huang B, Hu Z-Z, Kinter Iii JL (2014) South pacific ocean dipole: a predictable mode on multiseasonal time scales. J Clim 27:1648–1658. https://doi.org/10.1175/jcli-d-13-00293.1
Guilyardi E (2006) El Niño–mean state–seasonal cycle interactions in a multi-model ensemble. Clim Dyn 26:329–348. https://doi.org/10.1007/s00382-005-0084-6
He S, Yu J-Y, Yang S, Fang S-W (2020) ENSO’s impacts on the tropical Indian and Atlantic Oceans via tropical atmospheric processes: observations versus CMIP5 simulations. Clim Dyn 54:4627–4640. https://doi.org/10.1007/s00382-020-05247-w
Hu Z-Z, Kumar A (2014) How Variable Is the uncertainty in ENSO sea surface temperature prediction? J Clim 27:2779–2788. https://doi.org/10.1175/jcli-d-13-00576.1
Hua LJ, Chen L, Rong XY, Li J, Zhang G, Wang L (2019) An assessment of ENSO stability in CAMS climate system model simulations. J Meteorol Res 33:80–88. https://doi.org/10.1007/s13351-018-8092-8
Huang BY et al (2017) Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J Clim 30:8179–8205. https://doi.org/10.1175/Jcli-D-16-0836.1
Ji M, Behringer DW, Leetmaa A (1998) An improved coupled model for ENSO prediction and implications for ocean initialization. Part II: the coupled model. Mon Weath Rev 126:1022–1034. https://doi.org/10.1175/1520-0493(1998)126%3c1022:Aicmfe%3e2.0.Co;2
Jiang W, Huang G, Hu K, Wu R, Gong H, Chen X, Tao W (2017) Diverse relationship between ENSO and the Northwest Pacific summer climate among CMIP5 models: dependence on the ENSO decay pace. J Clim 30:109–127. https://doi.org/10.1175/jcli-d-16-0365.1
Jiang X, Zhang T, Tam CY, Chen J, Lau NC, Yang S, Wang Z (2019) Impacts of ENSO and IOD on snow depth over the Tibetan Plateau: roles of convections over the western North Pacific and Indian Ocean. J Geophys Res Atmos 124:11961–11975. https://doi.org/10.1029/2019jd031384
** EK et al (2008) Current status of ENSO prediction skill in coupled ocean–atmosphere models. Clim Dyn 31:647–664. https://doi.org/10.1007/s00382-008-0397-3
Johnson C, Bowler N (2009) On the reliability and calibration of ensemble forecasts. Mon Weather Rev 137:1717–1720. https://doi.org/10.1175/2009mwr2715.1
Johnson SJ et al (2019) SEAS5: the new ECMWF seasonal forecast system. Geosci Model Dev 12:1087–1117. https://doi.org/10.5194/gmd-12-1087-2019
Keenlyside N, Latif M, Botzet M, Jungclaus J, Schulzweida UWE (2005) A coupled method for initializing El Nino southern oscillation forecasts using sea surface temperature. Tellus A 57:340–356. https://doi.org/10.1111/j.1600-0870.2005.00107.x
Keenlyside N, Latif M, Botzet M, Jungclaus J, Schulzweida U (2016) A coupled method for initializing el niño southern oscillation forecasts using sea surface temperature. Tellus A 57:340–356. https://doi.org/10.3402/tellusa.v57i3.14661
Kirtman BP (2003) The COLA anomaly coupled model: ensemble ENSO prediction. Mon Weather Rev. https://doi.org/10.1175/1520-0493(2003)131%3c2324:TCACME%3e2.0.CO;2
Kobayashi S et al (2015) The JRA-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Jpn Ser II 93:5–48. https://doi.org/10.2151/jmsj.2015-001
Kumar A, Zhu J (2018) Spatial variability in seasonal prediction Skill of SSTs: inherent predictability or forecast errors? J Clim 31:613–621. https://doi.org/10.1175/jcli-d-17-0279.1
Latif M et al (1994) A review of ENSO prediction studies. Clim Dyn 9:167–179. https://doi.org/10.1007/bf00208250
Latif M et al (2001) ENSIP: the El Niño simulation intercomparison project. Clim Dyn 18:255–276. https://doi.org/10.1007/s003820100174
Lau N-C, Nath MJ (2003) Atmosphere-Ocean variations in the indo-pacific sector during ENSO episodes. J Clim 16:3–20. https://doi.org/10.1175/1520-0442(2003)016%3c0003:Aoviti%3e2.0.Co;2
Liu Y, Liu Z, Zhang S, Rong X, Jacob R, Wu S, Lu F (2004) Ensemble-based parameter estimation in a coupled GCM using the adaptive spatial average method. J Clim 27:4002–4014
Liu XW et al (2015) Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m). Adv Atmos Sci 32:1156–1172. https://doi.org/10.1007/s00376-015-4194-8
Liu H, Tang Y, Chen D, Lian T (2016) Predictability of the Indian ocean dipole in the coupled models. Clim Dyn 48:2005–2024. https://doi.org/10.1007/s00382-016-3187-3
Liu B, Huang G, Hu K, Wu R, Gong H, Wang P, Zhao G (2017) The multidecadal variations of the interannual relationship between the East Asian summer monsoon and ENSO in a coupled model. Clim Dyn 51:1671–1686. https://doi.org/10.1007/s00382-017-3976-3
Lorenc A (1986) Analysis methods for numerical weather prediction. Q J R Meteorol Soc 112:1177–1194
Lu B, Ren HL (2019) ENSO features dynamics, and teleconnections to east Asian climate as simulated in CAMS-CSM. J Meteorol Res 33:46–65. https://doi.org/10.1007/s13351-019-8101-6
Luo JJ, Masson S, Behera S, Shingu S, Yamagata T (2005) Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J Clim 18:4474–4497. https://doi.org/10.1175/Jcli3526.1
Luo JJ, Masson S, Behera S, Yamagata T (2007) Experimental forecasts of the Indian ocean dipole using a coupled OAGCM. J Clim 20:2178–2190. https://doi.org/10.1175/Jcli4132.1
Luo JJ, Masson S, Behera SK, Yamagata T (2008) Extended ENSO predictions using a fully coupled ocean-atmosphere model. J Clim 21:84–93. https://doi.org/10.1175/2007jcli1412.1
McPhaden MJ (1995) The tropical atmosphere ocean (TAO) array is completed. Bull Am Meteorol Soc 76:739–741. https://doi.org/10.1029/97JC02906
McPhaden MJ et al (1998) The tropical ocean-global atmosphere observing system: a decade of progress. J Geophys Res Oceans 103:14169–14240. https://doi.org/10.1029/97jc02906
Molteni F et al (2011) The new ECMWF seasonal forecast system (system 4) ECMWF. Tech Memorand 656:49
Moore AM, Kleeman R (1996) The dynamics of error growth and predictability in a coupled model of ENSO. Q J R Meteorol Soc 122:1405–1446
Moore AM, Zavala-Garay J, Tang YM et al (2006) Optimal forcing patterns for coupled models of ENSO. J Clim 19:4683–4699
Nur’utami MN, Hidayat R (2016) Influences of IOD and ENSO to indonesian rainfall variability: role of atmosphere-ocean interaction in the indo-pacific sector. Proc Environ Sci 33:196–203. https://doi.org/10.1016/j.proenv.2016.03.070
Ren H-L, Nie Y (2020) Skillful prediction of winter Arctic Oscillation from previous summer in a linear empirical model. Sci China Earth Sci. https://doi.org/10.1007/s11430-020-9665-3
Ren H-L, Zuo J, Deng Y (2018) Statistical predictability of Niño indices for two types of ENSO. Clim Dyn 52:5361–5382. https://doi.org/10.1007/s00382-018-4453-3
Roeckner E, Bäuml G, Bonaventura L, Brokopf R (2003) The atmospheric general circulation model ECHAM5. Part I: model description report 349, Max Planck Institute for Meteorology, pp 140
Rong XY et al (2018) The CAMS climate system model and a basic evaluation of its climatology and climate variability simulation. J Meteorol Res 32:839–861. https://doi.org/10.1007/s13351-018-8058-x
Rong XY, Li J, Chen HM, Su JZ, Hua LJ, Zhang ZQ, **n YF (2020) The CMIP6 historical simulation datasets produced by the climate system model CAMS-CSM. Adv Atmos Sci. https://doi.org/10.1007/s00376-020-0171-y
Saha S et al (2006) The NCEP climate forecast system. J Clim 19:3483–3517. https://doi.org/10.1175/Jcli3812.1
Saji NH, Goswami BN, Vinayachandran PN, Yamagata T (1999) A dipole mode in the tropical Indian Ocean. Nature 401:360–363. https://doi.org/10.1038/43854
Shi H, Wang B (2018) How does the Asian summer precipitation-ENSO relationship change over the past 544 years? Clim Dyn 52:4583–4598. https://doi.org/10.1007/s00382-018-4392-z
Shi L, Hendon HH, Alves O, Luo J-J, Balmaseda M, Anderson D (2012) How predictable is the Indian ocean dipole? Mon Weather Rev 140:3867–3884. https://doi.org/10.1175/mwr-d-12-00001.1
Small RJ et al (2014) A new synoptic scale resolving global climate simulation using the community earth system model. J Adv Model Earth Syst 6:1065–1094. https://doi.org/10.1002/2014ms000363
Su J, Zhang R, Rong X, Min Q, Zhu C (2018) Sea surface temperature in the subtropical pacific boosted the 2015 El Niño and hindered the 2016 La Niña. J Clim 31:877–893. https://doi.org/10.1175/jcli-d-17-0379.1
Sugiura N et al (2008) Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations. J Geophys Res. https://doi.org/10.1029/2008jc004741
Tang Y, Kleeman R, Moore AM, Weaver A, Vialard J (2003) The use of ocean reanalysis products to initialize ENSO predictions. Geophys Res Lett. https://doi.org/10.1029/2003gl017664
Tang Y, Lin H, Moore AM (2008) Measuring the potential predictability of ensemble climate predictions. J Geophys Res. https://doi.org/10.1029/2007jd008804
Tang Y et al (2018) Progress in ENSO prediction and predictability study. Natl Sci Rev 5:826–839. https://doi.org/10.1093/nsr/nwy105
Tao W, Huang G, Hu K, Gong H, Wen G, Liu L (2015) A study of biases in simulation of the Indian Ocean basin mode and its capacitor effect in CMIP3/CMIP5 models. Clim Dyn 46:205–226. https://doi.org/10.1007/s00382-015-2579-0
Wajsowicz RC (2004) Climate variability over the tropical indian ocean sector in the NSIPP seasonal forecast system. J Clim 17:4783–4804. https://doi.org/10.1175/jcli-3239.1
Wang B, Wu R, Fu X (2000) Pacific-east Asian teleconnection: how does enso affect east Asian climate? J Clim 13:1517–1536. https://doi.org/10.1175/1520-0442(2000)013%3c1517:Peathd%3e2.0.Co;2
Wang Y et al (2019) Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF. Clim Dyn 53:5777–5797. https://doi.org/10.1007/s00382-019-04897-9
Winton M (2000) A reformulated three-layer sea ice model. J Atmos Oceanic Tech 17:525–531. https://doi.org/10.1175/1520-0426(2000)017%3c0525:Artlsi%3e2.0.Co;2
Wu R, Kirtman BP (2003) On the impacts of the Indian summer monsoon on ENSO in a coupled GCM. Q J R Meteorol Soc 129:3439–3468. https://doi.org/10.1256/qj.02.214
Wu B, Zhou T, Li T (2009) Seasonally evolving dominant interannual variability modes of east Asian climate. J Clim 22:2992–3005. https://doi.org/10.1175/2008jcli2710.1
**e S-P, Hu K, Hafner J, Tokinaga H, Du Y, Huang G, Sampe T (2009) Indian ocean capacitor effect on indo-western pacific climate during the summer following El Niño. J Clim 22:730–747. https://doi.org/10.1175/2008jcli2544.1
Xu Q, Guan Z (2017) Interannual variability of summertime outgoing longwave radiation over the maritime continent in relation to east Asian summer monsoon anomalies. J Meteorol Res 31:665–677. https://doi.org/10.1007/s13351-017-6178-3
Xue Y, Cane MA, Zebiak SE, Blumenthal MB (1994) On the prediction of ENSO: a study with a low-order markov model. Tellus A 46:512–528. https://doi.org/10.3402/tellusa.v46i4.15641
Yang J, Liu Q, **e S-P, Liu Z, Wu L (2007) Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys Res Lett. https://doi.org/10.1029/2006gl028571
Yu RC (1994) A two-step shape-preserving advection scheme. Adv Atmos Sci 11:479–490
Zhang R-H, Gao C (2016) The IOCAS intermediate coupled model (IOCAS ICM) and its real-time predictions of the 2015–2016 El Niño event. Sci Bull 61:1061–1070. https://doi.org/10.1007/s11434-016-1064-4
Zhang R, Sumi A, Kimoto M (1999) A diagnostic study of the impact of el niño on the precipitation in China. Adv Atmos Sci 16:229–241. https://doi.org/10.1007/bf02973084
Zhang RH, Zebiak SE, Kleeman R, Keenlyside N (2003) A new intermediate coupled model for El Nino simulation and prediction. Geophys Res Lett. https://doi.org/10.1029/2003GL018010
Zhang H, Shi GY, Nakajima T, Suzuki T (2006a) The effects of the choice of the k-interval number on radiative calculations. J Quant Spectrosc Radiat Transfer 98:31–43. https://doi.org/10.1016/j.jqsrt.2005.05.090
Zhang H, Suzuki T, Nakajima T, Shi GY, Zhang XY, Liu Y (2006b) Effects of band division on radiative calculations. Opt Eng. https://doi.org/10.1117/12160521
Zhang S, Harrison M, Rosati A, Wittenberg A (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev 133:3176–3201
Zhang R-H et al (2020) A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China. J Oceanol Limnol 38:930–961. https://doi.org/10.1007/s00343-020-0157-8
Zheng F, Zhu J, Zhang R-H, Zhou G (2006) Improved ENSO forecasts by assimilating sea surface temperature observations into an intermediate coupled model. Adv Atmos Sci 23:615–624. https://doi.org/10.1007/s00376-006-0615-z
Zhong W, Cai W, Zheng XT, Yang S (2019) Unusual anomaly pattern of the 2015/2016 extreme El Niño Induced by the 2014 warm condition. Geophys Res Lett 46:14772–14781. https://doi.org/10.1029/2019gl085681
Zhu J, Huang B, Marx L, Kinter JL, Balmaseda MA, Zhang R-H, Hu Z-Z (2012) Ensemble ENSO hindcasts initialized from multiple ocean analyses. Geophys Res Lett. https://doi.org/10.1029/2012gl051503
Zhu J, Huang B, Balmaseda MA, Kinter JL, Peng P, Hu Z-Z, Marx L (2013) Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization. Clim Dyn 41:2785–2795. https://doi.org/10.1007/s00382-013-1965-8
Zhu J, Kumar A, Lee H-C, Wang H (2017) Seasonal predictions using a simple ocean initialization scheme. Clim Dyn 49:3989–4007. https://doi.org/10.1007/s00382-017-3556-6
Acknowledgements
This work was supported by the National Key Research and Development Program of China (2019YFC1510001), the Basic Research Fund of the Chinese Academy of Meteorological Sciences (2020Y005, 2020Y006 and 2021Z005).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, B., Su, J., Ma, L. et al. Seasonal prediction skills in the CAMS-CSM climate forecast system. Clim Dyn 57, 2953–2970 (2021). https://doi.org/10.1007/s00382-021-05848-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-021-05848-z