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The spring prediction barrier in ENSO hindcast experiments using the FGOALS-g model
- Journal of Oceanology and Limnology Vol. 30, Issue 6, Pages: 1093-1104(2012)
- Affiliations：
1. State Key Laboratory of Tropical Oceanography South China Sea Institute of Oceanology Chinese Academy of Sciences
2. State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences
- Author bio：
- Funds：
DOI：10.1007/s00343-012-1271-z
CLC：-
Online First：17 November 2012，
Published：2012
- Accepted：
Scan QR Code
Yan, L., Yu, Y. The spring prediction barrier in ENSO hindcast experiments using the FGOALS-g model., Chin. J. Ocean. Limnol. 30, 1093–1104 (2012). https://doi.org/10.1007/s00343-012-1271-z
摘要
Abstract
The Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g) was used to study the spring prediction barrier (SPB) in an ensemble system. This coupled model was developed and maintained at the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). There are two steps in our hindcast experiments. The first is to integrate the coupled model continuously with sea surface temperature (SST) nudging
from 1971 to 2006. The second is to carry out a series of one-year hindcasts without SST nudging
by adopting initial values from the first step on January 1 st
April 1 st
July 1 st
and October 1 st
from 1982 to 2005. We generate 10 ensemble members for a particular start date (1 st ) by choosing different atmospheric and land conditions around the hindcast start date (1 st through 10 th ). To estimate the predicted SST
two methods are used: (1) Anomaly Correlation Coefficient and its rate of decrease; and (2) Talagrand distribution and its standard deviation. Results show that FGOALS-g offers a reliable ensemble system with realistic initial atmospheric and oceanic conditions
and high anomaly correlation (>0.5) within 6 month lead time. Further
the ensemble approach is effective
in that the anomaly correlation of ensemble mean is much higher than that of most individual ensemble members. The SPB exists in the FGOALS-g ensemble system
as shown by anomaly correlation and equal likelihood. Nevertheless
the role of the ensemble mean in reducing the SPB of ENSO prediction is significant. The rate of decrease of the ensemble mean is smaller than the largest deviations by 0.04–0.14. At the same time
the ensemble system “equal likelihood” declines during spring. An ensemble mean helps give a correct prediction direction
departing from largely-deviated ensemble members.
关键词
Keywords
references
Balmaseda M A, Anderson D L T, Davey M K. 1994. ENSO prediction using a dynamical ocean model coupled to statistical atmospheres. Tellus, 46A: 497–511.
Barnston A G, Dool V D, Rodenhuis H M et al. 1994. Longlead seasonal forecasts—where do we stand? Bull. Amer. Meteor. Soc., 75: 2 097–2 114.
Bettge T W, Weatherly J W, Washington W M et al. 1996. “The CSM Sea Ice Model.” NCAR Technical Note NCAR/TN-425+STR. National Center for Atmospheric Research, Boulder, Colorado.
Biz C M, Lipscomb W H. 1999. An energy-conserving thermodynamic model of sea ice. J. Geophy. Res., 104: 15 669–15 677.
Bonan G B, Oleson K W, Vertenstein M et al. 2002. The land surface climatology of the community land model coupled to the NCAR community climate model. J. Climate, 15: 3 123–3 149.
Buizza R, Palmer T N. 1998. Impact of ensemble size on ensemble prediction. Mon. Wea. Rev., 129: 550–560.
Chen D, Cane M A, Kaplan A et al. 2004. Predictability of El Niño over the past 148 years. Nature, 428: 733–736.
Chen D, Zebiak S E, Busalacchi A J et al. 1995. An improved procedure for El Niño forecasting: implications for predictability. Science, 269: 1 699–1 702.
Chen D, Zebiak S E, Cane M A et al. 1997. Initialization and predictability of a coupled ENSO forecast model. Mon. Wea. Rev., 125: 773–788.
Clarke A J, van Gorder S. 1999. The connection between the boreal spring Southern Oscillation persistence barrier and biennial variability. J. Climate, 12: 610–620.
DeWitt D G. 2005. Retrospective Forecasts of Interannual sea surface temperature anomalies from 1982 to present using a directly coupled atmospheric-ocean general circulation model. Mon. Wea. Rev., 133: 2 972–2 995.
Duan W, Liu X, Zhu K et al. 2009. Exploring the initial errors that cause a significant “spring predictability barrier” for El Niño events, J. Geophys. Res., 114: C04022, http://dx.doi.org/10.1029/2008JC004925.
Hamill T M. 2001. Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129: 550–560.
Hunke E C, Dukowicz J K. 1997. An elastic-viscous-plastic model for sea ice dynamics. J. Phys. Oceanogr., 27: 1 849–1 867.
Jin E K, Kinter J L, Wang B et al. 2008. Current status of ENSO prediction skill in coupled ocean-atmosphere models. Clim. Dyn., 31: 647–664.
Kalnay E. 2003. Atmospheric modeling, data assimilation and predictability. Cambridge University Press. 341p.
Kiehl J T, Hack J, Bonan G et al. 1996. Description of the NCAR Community Climate Model (CCM3), Technical Report NCAR/TN-420+STR. National Center for Atmospheric Research, Boulder, Colorado, USA. 152p.
Li L, Wang B, Wang Y et al. 2007. Improvements in climate simulation with modifications to the Tiedtke convective parameterization in the grid-point atmospheric model of IAP LASG (GAMIL). Adv. Atmos. Sci., 24: 323–335.
Li H M, Feng L, Zhou T J. 2010. Multi-model projection of July-August climate extreme changes over China under CO2 doubling. Part II: temperature. Adv. Atmos. Sci., 28: 448–463.
Li H M, Feng L, Zhou T J. 2011. Multi-model projection of July-August climate extreme changes over China under CO2 doubling. Part I: precipitation. Adv. Atmos. Sci., 28: 433–447.
Liu H L, Zhang X H, Li W et al. 2004. An eddy-permitting oceanic general circulation model and its preliminary evaluation. Adv. Atmos. Sci., 21: 675–690.
Luo J J, Masson S, Behera S K et al. 2005. Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J. Climate, 18: 4 474–4 497.
Mu M, Duan W S, Wang B. 2007. Season-dependent dynamics of nonlinear optimal error growth and El Niño-Southern Oscillation predictability in a theoretical model. J. Geophys. Res., 112: D10113, http://dx.doi.org/10.1029/2005JD006981.
Oberhuber J M, Christoph E R, Esch M et al. 1998. Predicting the’ 97 El Niño event with a global climate model. Geophys. Res. Lett., 25: 2 273–2 276.
Palmer T N, Alessandri A, Andersen U et al. 2004. Development of a European multimodel ensemble system for seasonalto-interannual prediction (DEMETER). Bull. Amer. Meteor. Soc., 85: 853–872.
Rayner N A, Parker D E, Horton E B et al. 2006. UKMO — GISST/MOHMATN4/MOHSST6 — Global Ice coverage and SST (1856–2006), [Internet]. UK Meteorological Office, available from http://badc.nerc.ac.uk/data/gisst/.
Reynolds R W, Rayner N A, Smith T M et al. 2002. An improved in situ and satellite SST analysis for climate. J. Climate, 15: 1 609–1 625.
Schneider E K, DeWitt D G, Rosati A et al. 2003. Retrospective ENSO forecasts: sensitivity to atmospheric model and ocean resolution. Mon. Wea. Rev., 131: 3 038–3 060.
Talagrand O, Vautard R, Strauss B. 1997. Evaluation of probabilistic prediction systems. ECMWF Workshop on Predictability.
Torrence C, Webster P J. 1998. The annual cycle of persistence in the El Niño/Southern Oscillation. Q. J. R. Meteorol. Soc., 124: 1 985–2 004.
Wang B, Wan H, Ji Z Z et al. 2004. Design of a new dynamical core for global atmospheric models based on some efficient numerical methods. Science in China, 47: 4–21.
Webster P J. 1995. The annual cycle and the predictability of the tropical coupled ocean-atmosphere system. Meteor. Atmos. Phys., 56: 33–55.
Webster P J, Yang S. 1992. Monsoon and ENSO: selectively interactive systems. Q. J. R. Meteor. Soc., 118: 825–877.
Wei C, Duan W S. 2010. The “Spring Predictability Barrier” phenomenon of ENSO predictions generated with the FGOALS-g Model. Atmos. Oceanic Sci. Lett., 3: 87–92.
Xue Y, Cane M A, Zebiak S E et al. 1994. On the prediction of ENSO: a study with a low order Markov model. Tellus, 46A: 512–528.
Xue Y, Behringer D W, Huang B. 2008. introduction_godas_web.pdf, [Internet]. NOAA Climate Prediction Center, available from http://www.cpc.ncep.noaa.gov/products/GODAS/pl/.
Yan L, Yu Y Q, Wang B et al. 2009. ENSO hindcast experiments using a coupled GCM. Atmos. Oceanic Sci. Lett., 2: 7–13.
Yu Y Q, Yu R C, Zhang X H et al. 2002. A flexible coupled ocean-atmosphere general circulation model. Adv. Atmos. Sci., 19: 169–190.
Yu Y Q, Zhou Z Y, Zhang X H. 2003. Impact of the closure of Indonesian seaway on climate: a numerical modeling study. Chinese Science Bulletin, 48 Supp.II: 88–93.
Yu Y Q, Zhang X H, Guo Y F. 2004. Global coupled oceanatmosphere general circulation models in LASG/IAP. Adv. Atmos. Sci., 21: 444–455.
Yu Y Q, Liu X Y. 2004. ENSO and Indian Ocean Dipole mode in three coupled GCMs. Acta Oceanologica Sinica, 23: 581–595.
Yu Y Q, Zheng W P, Liu H L et al. 2007. The LASG Coupled Climate System Model FGCM-1.0. Chinese Journal of Geophysics, 50: 1 454–1 465.
Yu Y Q, Zhi H, Wang B et al. 2008. Coupled model simulations of climate changes in the 20 th century and beyond. Adv. Atmos. Sci., 25(4): 641–654.
Yu Y S, Duan W S, Xu H et al. 2009. Dynamics of nonlinear error growth and season-dependent predictability of El Niño events in the Zebiak-Cane model. Quart. J. Roy. Meteor. Soc. 135(645): 2 146–2 160.
Zhang X H, Yu Y Q, Liu H L. 2003. The development and application of the oceanic general circulation models. Part I. The global oceanic general circulation models. Chinese J. of Atmos. Sci., 27: 607–617. (in Chinese with English abstract)
Zhang Y L, Yu Y Q, Duan W S. 2012. The spring prediction barrier of ENSO in retrospective prediction experiments from coupled ocean-atmosphere models. Acta Meteorologica Sinica, in press. (in Chinese with English abstract)
Zheng F, Zhu J. 2010. Spring predictability barrier of ENSO events from the perspective of an ensemble prediction system. Global and Planetary Change, 72: 108–117.
Zheng F, Zhu J, Wang H et al. 2009. Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles. Adv. Atmos. Sci., 26: 359–372.
Zheng W P, Braconnot P, Guilyardi E et al. 2008. ENSO at 6ka and 21ka from ocean-atmosphere coupled model simulations. Clim. Dyn., 30: 745–762.
Zheng W P, Yu Y Q. 2007. ENSO phase-locking in an oceanatmosphere coupled model FGCM-1.0. Adv. Atmos. Sci., 24: 833–844.
Zhou G Q, Li X, Zeng Q C. 1998. A coupled oceanatmosphere general circulation model for ENSO prediction and 1997/1998 ENSO forecast. Climatic and Environmental Research, 3: 349–357. (in Chinese with English abstract)
Zhou T J, Yu R C. 2006. Twentieth century surface air temperature over China and the globe simulated by Coupled Climate Models. J. Climate, 19(22): 5 843–5 858.
Zhou T J, Yu Y Q, Liu H L et al. 2007. Progress in the development and application of Climate Ocean Models and Ocean-atmosphere Coupled Models in China. Adv. Atmos. Sci., 24(6): 729–738.
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