Progress in ENSO prediction and predictability study

National Science Review - Tập 5 Số 6 - Trang 826-839 - 2018
Youmin Tang1,2, Rong‐Hua Zhang3,4, Ting Liu1,2, Wansuo Duan5, Dejian Yang6, Fei Zheng7, Hong‐Li Ren8, Tao Lian2, Chuan Gao3,4, Dake Chen2, Mu Mu9
1Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia V2N 4Z9, Canada
2State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou 310012, China
3Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China
4Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China
5State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
6College of Oceanography, Hohai University, Nanjing, 210098, China
7International Center for Climate and Environment Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
8Laboratory for Climate Studies & CMA-NJU Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China
9College of Atmospheric and Oceanic Science, Fudan University, Shanghai 200438, China

Tóm tắt

Abstract

ENSO is the strongest interannual signal in the global climate system with worldwide climatic, ecological and societal impacts. Over the past decades, the research about ENSO prediction and predictability has attracted broad attention. With the development of coupled models, the improvement in initialization schemes and the progress in theoretical studies, ENSO has become the most predictable climate mode at the time scales from months to seasons. This paper reviews in detail the progress in ENSO predictions and predictability studies achieved in recent years. An emphasis is placed on two fundamental issues: the improvement in practical prediction skills and progress in the theoretical study of the intrinsic predictability limit. The former includes progress in the couple models, data assimilations, ensemble predictions and so on, and the latter focuses on efforts in the study of the optimal error growth and in the estimate of the intrinsic predictability limit.

Từ khóa


Tài liệu tham khảo

Philander, 1990, 538

Luo, 2016, Current status of intraseasonal–seasonal-to-interannual prediction of the Indo-Pacific climate, Indo-Pacific Climate Variability and Predictability, 63, 10.1142/9789814696623_0003

Latif, 1998, A review of the predictability and prediction of ENSO, J Geophys Res, 103, 14375, 10.1029/97JC03413

Jin, 2008, Current status of ENSO prediction skill in coupled ocean–atmosphere models, Clim Dyn, 31, 647, 10.1007/s00382-008-0397-3

Barnston, 2012, Skill of real-time seasonal ENSO model predictions during 2002–11: is our capability increasing?, Bull Amer Meteor Soc, 93, 631, 10.1175/BAMS-D-11-00111.1

Chen, 2008, El Niño prediction and predictability, J Comput Phys, 227, 3625, 10.1016/j.jcp.2007.05.014

Lian, 2017, Genesis of the 2014–2016 El Niño events, Sci China Earth Sci, 60, 1589, 10.1007/s11430-016-8315-5

Mu, 2017, The predictability of atmospheric and oceanic motions: retrospect and prospects, Sci China Earth Sci, 60, 2001, 10.1007/s11430-016-9101-x

Kumar, 2018, Spatial variability in seasonal prediction skill of SSTs: inherent predictability or forecast errors, J Climate, 31, 613, 10.1175/JCLI-D-17-0279.1

Bjerknes, 1969, Atmospheric teleconnections from the equatorial Pacific, Mon Wea Rev, 97, 163, 10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2

Yang, 2000, Correction of systematic errors in coupled GCM forecasts, J Climate, 13, 2072, 10.1175/1520-0442(2000)013<2072:COSEIC>2.0.CO;2

Liang, 2012, The effect of ENSO events on the tropical Pacific mean climate: insights from an analytical model, J Climate, 25, 7590, 10.1175/JCLI-D-11-00490.1

Liang, 2017, Factors determining the asymmetry of ENSO, J Climate, 30, 6097, 10.1175/JCLI-D-16-0923.1

Penland, 1996, A stochastic model of IndoPacific sea surface temperature anomalies, Physica D, 98, 534, 10.1016/0167-2789(96)00124-8

Tseng, 2017, An ENSO prediction approach based on ocean conditions and ocean–atmosphere coupling, Clim Dyn, 48, 2025, 10.1007/s00382-016-3188-2

Hsieh, 2009, Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels, 10.1017/CBO9780511627217

Cane, 1986, Experimental forecasts of El Niño, Nature, 321, 827, 10.1038/321827a0

Zebiak, 1987, A model El Niño–Southern Oscillation, Mon Wea Rev, 115, 2262, 10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2

Hirst, 1986, Unstable and damped equatorial modes in simple coupled ocean-atmosphere models, J Atmos Sci, 43, 606, 10.1175/1520-0469(1986)043<0606:UADEMI>2.0.CO;2

Barnett, 1993, ENSO and ENSO-related predictability. Part I: Prediction of equatorial Pacific sea surface temperature with a hybrid coupled ocean–atmosphere model, J Climate, 6, 1545, 10.1175/1520-0442(1993)006<1545:EAERPP>2.0.CO;2

Tang, 2002, Hybrid coupled models of the tropical Pacific—II ENSO prediction, Clim Dyn, 19, 343, 10.1007/s00382-002-0231-2

Ren, 2016, The new generation of ENSO prediction system in Beijing Climate Centre and its predictions for the 2014/2016 super El Niño event, Meteor Mon, 42, 521

Ren, 2013, Recharge oscillator mechanisms in two types of ENSO, J Climate, 26, 6506, 10.1175/JCLI-D-12-00601.1

Liu, 2017, Improving ENSO prediction in CFSv2 with an analogue-based correction method, Int J Climatol, 37, 5035, 10.1002/joc.5142

Ren, 2017, Prediction of primary climate variability modes at the Beijing Climate Center, J Meteorol Res, 31, 204, 10.1007/s13351-017-6097-3

Ren, 2017, Upper-ocean dynamical features and prediction of the super El Niño in 2015/16: a comparison with the cases in 1982/83 and 1997/98, J Meteorol Res, 31, 278, 10.1007/s13351-017-6194-3

Li, 2015, An ENSO hindcast experiment using CESM, Acta Oceanol Sin, 37, 39

Zhang, 2017, Processes involved in the second-year warming of the 2015 El Niño event as derived from an intermediate ocean model, Sci China Earth Sci, 9, 1

Zhang, 2016, The IOCAS intermediate coupled model (IOCAS ICM) and its real-time predictions of the 2015–2016 El Niño event, Science Bulletin, 61, 1061, 10.1007/s11434-016-1064-4

Zhang, 2013, A successful real-time forecast of the 2010–11 La Niña event, Sci Rep, 3, 1108, 10.1038/srep01108

Zheng, 2010, Coupled assimilation for an intermediated coupled ENSO prediction model, Ocean Dyn, 60, 1061, 10.1007/s10236-010-0307-1

Zheng, 2016, Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model, Clim Dyn, 47, 3901, 10.1007/s00382-016-3048-0

Cheng, 2010, Ensemble construction and verification of the probabilistic ENSO prediction in the LDEO5 model, J Climate, 23, 5476, 10.1175/2010JCLI3453.1

Chen, 2004, Predictability of El Niño over the past 148 years, Nature, 428, 733, 10.1038/nature02439

Deber, 1989, A global oceanic data assimilation system, J Phys Oceanogr, 19, 1333, 10.1175/1520-0485(1989)019<1333:AGODAS>2.0.CO;2

Oberhuber, 1998, Predicting the ’97 El Niño event with a global climate model, Geophys Res Lett, 25, 2273, 10.1029/98GL51782

Kirtman, 1997, Multiseasonal predictions with a coupled tropical ocean–global atmosphere system, Mon Wea Rev, 125, 789, 10.1175/1520-0493(1997)125<0789:MPWACT>2.0.CO;2

Tang, 2004, SST assimilation experiments in a tropical Pacific Ocean model, J Phys Oceanogr, 34, 623, 10.1175/3518.1

Keenlyside, 2005, A coupled method for initializing El Nino Southern Oscillation forecasts using sea surface temperature, Tellus A, 57, 340

Merryfield, 2013, The Canadian seasonal to interannual prediction system. Part I: Models and initialization, Mon Wea Rev, 141, 2910, 10.1175/MWR-D-12-00216.1

Gill, 1982, Atmosphere-Ocean Dynamics

Zheng, 2007, Impact of altimetry data on ENSO ensemble initializations and predictions, Geophys Res Lett, 34, 256, 10.1029/2007GL030451

Deng, 2010, Assimilation of Argo temperature and salinity profiles using a bias-aware localized EnKF system for the Pacific Ocean, Ocean Modell, 35, 187, 10.1016/j.ocemod.2010.07.007

Zhu, 2015, Salinity anomaly as a trigger for ENSO events, Sci Rep, 4, 6821, 10.1038/srep06821

Lu, 2015, Strongly coupled data assimilation using leading averaged coupled covariance (LACC). Part II: CGCM experiments, Mon Wea Rev, 143, 4645, 10.1175/MWR-D-15-0088.1

Tang, 2016, An introduction to ensemble-based data assimilation method in the Earth sciences, Nonlinear Systems-Design, Analysis, Estimation and Control, 10.5772/64718

Cai, 2003, Bred vectors of the Zebiak–Cane model and their potential application to ENSO predictions, J Climate, 16, 40, 10.1175/1520-0442(2003)016<0040:BVOTZC>2.0.CO;2

Tang, 2006, ENSO predictability of a fully coupled GCM model using singular vector analysis, J Climate, 19, 3361, 10.1175/JCLI3771.1

Mu, 2003, A new approach to studying ENSO predictability: conditional nonlinear optimal perturbation, ChinSciBull, 48, 1045

Zheng, 2009, Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles, Adv Atmos Sci, 26, 359, 10.1007/s00376-009-0359-7

Moore, 1998, Skill assessment for ENSO using ensemble prediction, QJ Royal Met Soc, 124, 557, 10.1002/qj.49712454609

Kirtman, 2009, Multimodel ensemble ENSO prediction with CCSM and CFS, Mon Wea Rev, 137, 2908, 10.1175/2009MWR2672.1

Ding, 2007, Nonlinear finite-time Lyapunov exponent and predictability, Phys Lett A, 364, 396, 10.1016/j.physleta.2006.11.094

Moore, 1996, The dynamics of error growth and predictability in a coupled model of ENSO, QJ Royal Met Soc, 122, 1405, 10.1002/qj.49712253409

Mu, 2007, A kind of initial errors related to ‘Spring Predictability Barrier’ for El Niño events in Zebiak-Cane model, Geophys Res Lett, 34, 3709, 10.1029/2006GL027412

Tang, 2011, Bred vector and ENSO predictability in a hybrid coupled model during the period 1881–2000, J Climate, 24, 298, 10.1175/2010JCLI3491.1

Hou, 2018, The application of nonlinear local Lyapunov vectors to the Zebiak–Cane model and their performance in ensemble prediction, Clim Dyn, 51, 283, 10.1007/s00382-017-3920-6

Cheng, 2010, Further analysis of singular vector and ENSO predictability in the Lamont model—Part II: Singular value and predictability, Clim Dyn, 35, 827, 10.1007/s00382-009-0728-z

Kleeman, 2003, The calculation of climatically relevant singular vectors in the presence of weather noise as applied to the ENSO problem, J Atmos Sci, 60, 2856, 10.1175/1520-0469(2003)060<2856:TCOCRS>2.0.CO;2

Palmer, 2013, Singular vectors, predictability and ensemble forecasting for weather and climate, J Phys A: Math Theor, 46, 254018, 10.1088/1751-8113/46/25/254018

Barkmeijer, 2003, Forcing singular vectors and other sensitive model structures, Q J R Meteorol Soc, 129, 2401, 10.1256/qj.02.126

Duan, 2013, Non-linear forcing singular vector of a two-dimensional quasi-geostrophic model, Tellus A: Dynamic Meteorology and Oceanography, 65, 18452, 10.3402/tellusa.v65i0.18452

Duan, 2016, The role of nonlinear forcing singular vector tendency error in causing the ‘spring predictability barrier’ for ENSO, J Meteorol Res, 30, 853, 10.1007/s13351-016-6011-4

Webster, 1992, Monsoon and ENSO: selectively interactive systems, QJ Royal Met Soc, 118, 877, 10.1002/qj.49711850705

Yu, 2009, Dynamics of nonlinear error growth and season-dependent predictability of El Niño events in the Zebiak–Cane model, QJR Meteorol Soc, 135, 2146, 10.1002/qj.526

Ashok, 2007, El Niño Modoki and its possible teleconnection, J Geophys Res, 112, C11007, 10.1029/2006JC003798

Hendon, 2009, Prospects for predicting two flavors of El Niño, Geophys Res Lett, 36, L19713, 10.1029/2009GL040100

Tian, 2016, Comparison of the initial errors most likely to cause a spring predictability barrier for two types of El Niño events, Clim Dyn, 47, 779, 10.1007/s00382-015-2870-0

Duan, 2018, Towards optimal observational array for dealing with challenges of El Niño-Southern Oscillation predictions due to diversities of El Niño, Clim Dyn, 51, 3351, 10.1007/s00382-018-4082-x

Ren, 2016, Distinct persistence barriers in two types of ENSO, Geophys Res Lett, 43, 10973, 10.1002/2016GL071015

Mu, 2014, Similarities between optimal precursors for ENSO events and optimally growing initial errors in El Niño predictions, Theor Appl Climatol, 115, 461, 10.1007/s00704-013-0909-x

DelSole, 2004, Predictability and information theory. Part I: Measures of predictability, J Atmos Sci, 61, 2425, 10.1175/1520-0469(2004)061<2425:PAITPI>2.0.CO;2

Rowell, 1998, Assessing potential seasonal predictability with an ensemble of multidecadal GCM simulations, J Climate, 11, 109, 10.1175/1520-0442(1998)011<0109:APSPWA>2.0.CO;2

Tang, 2008, Comparison of information-based measures of forecast uncertainty in ensemble ENSO prediction, J Climate, 21, 230, 10.1175/2007JCLI1719.1

Kumar, 2000, Analysis of a conceptual model of seasonal climate variability and implications for seasonal prediction, Bull Amer Meteor Soc, 81, 255, 10.1175/1520-0477(2000)081<0255:AOACMO>2.3.CO;2

Kumar, 2001, Seasonal predictions, probabilistic verifications, and ensemble size, J Climate, 14, 1671, 10.1175/1520-0442(2001)014<1671:SPPVAE>2.0.CO;2

Kumar, 2015, Inherent predictability, requirements on the ensemble size, and complementarity, Mon Wea Rev, 143, 3192, 10.1175/MWR-D-15-0022.1

Kumar, 2014, How variable is the uncertainty in ENSO sea surface temperature prediction?, J Climate, 27, 2779, 10.1175/JCLI-D-13-00576.1

Cheng, 2011, Relationship between predictability and forecast skill of ENSO on various time scales, J Geophys Res, 116, C12006, 10.1029/2011JC007249

Tang, 2013, Methods of estimating uncertainty of climate prediction and climate change projection, Climate Change-Realities, Impacts Over Ice Cap, Sea Level and Risks, 10.5772/54810

Li, 2013, Temporal-spatial distribution of the predictability limit of monthly sea surface temperature in the global oceans, Int J Climatol, 33, 1936, 10.1002/joc.3562

Palmer, 2002, The economic value of ensemble forecasts as a tool for risk assessment: from days to decades, Q J R Meteorol Soc, 128, 747, 10.1256/0035900021643593

Kirtman, 2003, The COLA anomaly coupled model: ensemble ENSO prediction, Mon Wea Rev, 131, 2324, 10.1175/1520-0493(2003)131<2324:TCACME>2.0.CO;2

Yang, 2016, Probabilistic versus deterministic skill in predicting the western North Pacific-East Asian summer monsoon variability with multimodel ensembles, J Geophys Res: Atmos, 121, 1079, 10.1002/2015JD023781

Xue, 2012, A comparative analysis of upper-ocean heat content variability from an ensemble of operational ocean reanalyses, J Climate, 25, 6905, 10.1175/JCLI-D-11-00542.1

Zhu, 2012, An ensemble estimation of the variability of upper-ocean heat content over the tropical atlantic ocean with multi-ocean reanalysis products, Clim Dyn, 39, 1001, 10.1007/s00382-011-1189-8

Zhu, 2012, Ensemble enso hindcasts initialized from multiple ocean analyses, Geophys Res Lett, 39, L09602, 10.1029/2012GL051503

Zhu, 2013, Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization, Clim Dyn, 41, 2785, 10.1007/s00382-013-1965-8

Tippett, 2008, Skill of multimodel ENSO probability forecasts, Mon Wea Rev, 136, 3933, 10.1175/2008MWR2431.1

Tippett, 2017, Assessing probabilistic predictions of ENSO phase and intensity from the North American Multimodel Ensemble, Clim Dyn, 687, 1

Mu, 2017, Enlightenments from researches and predictions of 2014–2016 super El Niño event, Sci China Earth Sci, 60, 1569, 10.1007/s11430-017-9094-5

Gao, 2017, The roles of atmospheric wind and entrained water temperature (Te) in the second-year cooling of the 2010–12 La Niña event, Clim Dyn, 48, 597, 10.1007/s00382-016-3097-4

Zhang, 2001, Effect of penetrating momentum flux over the surface boundary/mixed layer in a z-coordinate OGCM of the tropical Pacific, J Phys Oceanogr, 32, 3616, 10.1175/1520-0485(2002)032<3616:EOPMFO>2.0.CO;2

Large, 1994, Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization, Rev Geophys, 32, 363, 10.1029/94RG01872

Zhu, 2018, An Argo-derived background diffusivity parameterization for improved ocean simulations in the tropical Pacific, Geophys Res Lett, 45, 1509, 10.1002/2017GL076269

Zhang, 2018, Ocean chlorophyll-induced heating feedbacks on ENSO in a coupled ocean physics–biology model forced by prescribed wind anomalies, J Climate, 31, 1811, 10.1175/JCLI-D-17-0505.1

Zhu, 2016, The role of off-equatorial surface temperature anomalies in the 2014 El Niño prediction, Sci Rep, 6

Zhu, 2017, Importance of convective parameterization in ENSO predictions, Geophys Res Lett, 44, 6334, 10.1002/2017GL073669

Chen, 2015, Strong influence of westerly wind bursts on El Niño diversity, Nat Geosci, 8, 339, 10.1038/ngeo2399