doi:10.1007/s00376-015-5174-8

An S. I., F. F. Jin, 2001: Collective role of thermocline and zonal advective feedbacks in the ENSO mode. J.Climate, 14( 16), 3421- 3432.7142b668-36b0-40b9-a855-1be5c86e3c2f

7cced414a9afbb584b1be474e9a03f7b

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2001JCli...14.3421A

refpaperuri:(2987402d9d46b314f43cf81ab7d0c8e4)

/s?wd=paperuri%3A%282987402d9d46b314f43cf81ab7d0c8e4%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2001JCli...14.3421A&ie=utf-8

Ashok K., S. K. Behera, S. A. Rao, H. Y. Weng, and T. Yamagata, 2007: El Niño Modoki and its possible teleconnection. J. Geophys. Res., 112,C11007, doi: 10.1029/2006JC003798.10.1029/2006JC003798

4c9ce4f6-43f6-4657-a703-db6ce800c67d

9ed3e543c8e9bfe16c01f5a1395c1bab

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2006JC003798%2Fabstract%3Bjsessionid%3DF2C5797F7539FC367A07E919851C7401.f03t02

refpaperuri:(42a43d3a8b5a3852d00d87760b9d78f8)

http://onlinelibrary.wiley.com/doi/10.1029/2006JC003798/abstract;jsessionid=F2C5797F7539FC367A07E919851C7401.f03t02[1] Using observed data sets mainly for the period 1979–2005, we find that anomalous warming events different from conventional El Ni09o events occur in the central equatorial Pacific. This unique warming in the central equatorial Pacific associated with a horseshoe pattern is flanked by a colder sea surface temperature anomaly (SSTA) on both sides along the equator. empirical orthogonal function (EOF) analysis of monthly tropical Pacific SSTA shows that these events are represented by the second mode that explains 12% of the variance. Since a majority of such events are not part of El Ni09o evolution, the phenomenon is named as El Ni09o Modoki (pseudo-El Ni09o) (“Modoki” is a classical Japanese word, which means “a similar but different thing”). The El Ni09o Modoki involves ocean-atmosphere coupled processes which include a unique tripolar sea level pressure pattern during the evolution, analogous to the Southern Oscillation in the case of El Ni09o. Hence the total entity is named as El Ni09o–Southern Oscillation (ENSO) Modoki. The ENSO Modoki events significantly influence the temperature and precipitation over many parts of the globe. Depending on the season, the impacts over regions such as the Far East including Japan, New Zealand, western coast of United States, etc., are opposite to those of the conventional ENSO. The difference maps between the two periods of 1979–2004 and 1958–1978 for various oceanic/atmospheric variables suggest that the recen

Barkmeijer J., T. Iversen, and T. N. Palmer, 2003: Forcing singular vectors and other sensitive model structures. Quart. J. Roy. Meteor. Soc., 129( 592), 2401- 2423.10.1256/qj.02.126

da4922be9ac454e4abf8c93b1c648205

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1256%2Fqj.02.126%2Fcitedby

http://onlinelibrary.wiley.com/doi/10.1256/qj.02.126/citedbyABSTRACT Model tendency perturbations can, like analysis perturbations, be an effective way to influence forecasts. In this paper, optimal model tendency perturbations, or forcing singular vectors, are computed with diabatic linear and adjoint T42L40 versions of the European Centre for Medium-Range Weather Forecasts' forecast model. During the forecast time, the spatial pattern of the tendency perturbation does not vary and the response at optimization time (48 hours) is measured in terms of total energy. Their properties are compared with those of initial singular vectors, and differences, such as larger horizontal scale and location, are discussed. Sensitivity calculations are also performed, whereby a cost function measuring the 2-day forecast error is minimized by only allowing tendency perturbations. For a given number of minimization steps, this approach yields larger cost-function reductions than the sensitivity calculation using only analysis perturbations. Nonlinear forecasts using only one type of perturbation confirm an improved performance in the case of tendency perturbations. For a summer experiment a substantial reduction of the systematic error is shown in the case of forcing sensitivity. Copyright 漏 2003 Royal Meteorological Society.

Barreiro M., P. Chang, 2004: A linear tendency correction technique for improving seasonal prediction of SST. Geophys. Res. Lett., 31(23),L23209, doi: 10.1029/2004gl021148.10.1029/2004GL021148

06ab0084843ca7b090edb009f9254252

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2004GL021148%2Ffull

http://onlinelibrary.wiley.com/doi/10.1029/2004GL021148/fullAbstract Top of page Abstract 1.Introduction 2.Linear Tendency Correction Technique 3.Model and Present Application 4.Results 5.Summary Acknowledgments References [1] A methodology is presented to linearly correct the tendency of sea surface temperature (SST) anomalies in a coupled model. Using an atmospheric general circulation model (AGCM) coupled to a slab ocean as an example, we demonstrate the effectiveness of the linear correction methodology in improving the model's skill predicting SST in the tropical Atlantic Ocean during boreal spring. For this particular coupled model, the correction mainly takes into consideration the linear ocean dynamics absent in the slab ocean, thereby improving the skill in the tropical south and equatorial Atlantic. The corrected coupled model is further shown to produce a skillful rainfall forecast in the intertropical convergence zone (ITCZ) region during the boreal spring.

Behringer D. W., M. Ji, and A. Leetmaa, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon. Wea. Rev., 126( 5), 1013- 1021.

Blumenthal M. B., 1991: Predictability of a coupled ocean-atmosphere model. J.Climate, 4( 8), 766- 784.10.1175/1520-0442(1991)0042.0.CO;2

3f9d5714-9e26-47df-8e78-ec2b82eee9e4

cc87c95915f17771852a1c52568374ea

http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F092479639190028S

refpaperuri:(70af9186f72d36879e1ffc80ec54a654)

http://www.sciencedirect.com/science/article/pii/092479639190028SAbstract The predictability and variability of a coupled ocean-atmosphere model has been investigated by examining the growth of small initial perturbations during the evolution of the coupled system. The ocean model is first integrated in a forced mode for a duration of over 24 years beginning with January 1964 in which wind stress forcing for each month is prescribed from the observations. This provides surrogate analysis or control run with which predictions from the coupled model can be initiated and compared. Starting from January 1970 with each of the next 181 initial states from the control run, a prediction experiment was carried out for a duration of 36 months each using the fully coupled model. With this large ensemble of prediction experiments, a detailed analysis of growth of initial error and forecast errors was carried out. The SST forecasts are compared with observations as well as the control run. The root-mean-square difference between control and forecasts becomes larger than the standard deviation of the control as well as persistence error in about three months. As a result of differences between the simulated SST in the control run and observations, the forecasts are forced to have initial errors that are comparable to the standard deviation of the observations. Some significant systematic errors in the model are also noted. There is an indication that the forecasts may be improved to some extent by averaging a few of the most recent available forecasts and removing the known systematic error. Also carried out is a large ensemble of identical twin experiments, each for a duration of 15 years. In one of each pair of experiments a small random perturbation is introduced at the initial time in the surface winds. These experiments have shown that the growth of small initial errors in the coupled model is governed by processes with two well-separated time scales. The fast time scale process introduces errors that have a doubling time of about 5 months, while the slow time scale process introduces errors that have a typical doubling time of about 15 months. The existence of a slow time scale gives us optimism about long-range forecasts of ENSO-type events. However, the fast growth rate tends to saturate at a level that is comparable to the climatological standard deviation. Thus, a key to long-range forecasting of ENSO-type events may lie in the ability to identify those initial states that are not too sensitive to the processes associated with fast growth rate. The diagnostic analysis shows that the first three empirical orthogonal functions (EOF) of the observed wind stress together explain only about 36% of the total variance. Although the observed wind stress has considerable amplitude in the higher EOFs, it is shown that only the first three components are important for forcing the observed interannual variations using this model. The atmospheric component of the coupled model is not able to simulate these large-scale components of the observed wind stress accurately. This is partly because the atmospheric model is mainly driven by the underlying sea surface temperature anomalies (SSTA) and partly due to the structural differences between the SSTA simulated by the model and the observed SSTA. Thus, a combination of the atmospheric component's tight coupling to the ocean and the ocean model's inability to simulate the SST anomalies correctly seems to be responsible for the rather rapid growth of prediction errors.

Bourassa, M. A., Rosario Romero, Shawn R. Smith, James J. O'Brien, 2005: A new FSU winds climatology. J. Climate,18, 3686-3698, doi: http://dx.doi.org/10.1175/JCLI3487.1.10.1175/JCLI3487.1

096be2241d0bd4e7a17b8654bb5e2ee7

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2005JCli...18.3686B

http://adsabs.harvard.edu/abs/2005JCli...18.3686BA new objective time series of in situased monthly surface winds has been developed as a replacement for the subjective tropical Pacific Florida State University (FSU) winds. The new time series begins in January 1978, and it is ongoing. The objective method distinguishes between observations from volunteer observing ships (VOSs) and buoys, allowing different weights for these different types of observations. An objective method is used to determine these weights and accounts for the differences in error characteristics and in spatial/temporal sampling. A comparison is made between the objective and subjective products, as well as scatterometer winds averaged monthly on the same grid. The scatterometer fields are a good proxy for truth. These three sets of fields have similar magnitudes, directions, and derivative fields. Both in situ wind products underestimate convergence about the intertropical convergence zone; however, the objective FSU product is a much better match to the scatterometer observations. Furthermore, the objective winds have smaller month-to-month variation than the subjective winds. Composites of ENSO phases are also examined and show minor differences between the subjective and objective wind products. The strengths and weaknesses of the objective and subjective winds are discussed.

Chen D. K., S. E. Zebiak, A. J. Busalacchi, and M. A. Cane, 1995: An improved procedure for El Niño forecasting: Implications for predictability. Science, 269, 1699- 1702.f0c8e6c1-a7a5-42ca-a30d-b9e4b80bf729

f10992904d7c885966104328d9c51383

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F245343887_An_improved_procedure_for_El_Nino_forecasting

refpaperuri:(20ab5bab11cc11dc54936082b810ee79)

http://www.researchgate.net/publication/245343887_An_improved_procedure_for_El_Nino_forecasting

Chen D. K., C. M. Cane, S. E. Zebiak, R. Ca\nizares, and A. Kaplan, 2000: Bias correction of an ocean-atmosphere coupled model. Geophys. Res. Lett.,27(16), 2585-2588, doi: 10.1029/ 1999gl011078.10.1029/1999GL011078

4dcd5975-3950-4ba2-a85c-064ac6223a1f

0e32511d08e58f56ce53c88292dcee18

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F1999GL011078%2Ffull

refpaperuri:(ced32067b187c6f7134ae41af4e87caa)

http://onlinelibrary.wiley.com/doi/10.1029/1999GL011078/fullAbstract Top of page Abstract References A serious problem in the initialization of a climate forecast model is the model-data incompatibility caused by systematic model biases. Here we use the Lamont model to demonstrate that these biases can be effectively reduced with a simple statistical correction, and the bias-corrected model can have a more realistic internal variability as well as an improved forecast performance. The results reported here should be of practical use to other ocean-atmosphere coupled models for climate prediction.

Chen D. K., M. A. Cane, A. Kaplan, S. E. Zebiak, and D. J. Huang, 2004: Predictability of El Niño over the past 148 years. Nature, 428( 6984), 733- 736.10.1038/nature02439

15085127

0e566377-8b8f-4424-8c42-09e1127596eb

8b9772bafd37304a03809c39c21a610b

http%3A%2F%2Fmed.wanfangdata.com.cn%2FPaper%2FDetail%2FPeriodicalPaper_PM15085127

refpaperuri:(996444e2daa89c031d9c25496a0d31f9)

http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM15085127Forecasts of El Ni09o climate events are routinely provided and distributed, but the limits of El Ni09o predictability are still the subject of debate. Some recent studies suggest that the predictability is largely limited by the effects of high-frequency atmospheric `noise', whereas others emphasize limitations arising from the growth of initial errors in model simulations. Here we present retrospective forecasts of the interannual climate fluctuations in the tropical Pacific Ocean for the period 1857 to 2003, using a coupled ocean-atmosphere model. The model successfully predicts all prominent El Ni09o events within this period at lead times of up to two years. Our analysis suggests that the evolution of El Ni09o is controlled to a larger degree by self-sustaining internal dynamics than by stochastic forcing. Model-based prediction of El Ni09o therefore depends more on the initial conditions than on unpredictable atmospheric noise. We conclude that throughout the past century, El Ni09o has been more predictable than previously envisaged.

D'andrea F., R. Vautard, 2000: Reducing systematic errors by empirically correcting model errors. Tellus A, 52( 2), 21- 41.10.1034/j.1600-0870.2000.520103.x

37abd90094c0916ae5df873f9b1776b1

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1034%2Fj.1600-0870.2000.520103.x%2Fcitedby

http://onlinelibrary.wiley.com/doi/10.1034/j.1600-0870.2000.520103.x/citedbyA methodology for the correction of systematic errors in a simplified atmospheric general‐circulation model is proposed. First, a method for estimating initial tendency model errors is developed, based on a 4‐dimensional variational assimilation of a long‐analysed dataset of observations in a simple quasi‐geostrophic baroclinic model. Then, a time variable potential vorticity source term is added as a forcing to the same model, in order to parameterize subgrid‐scale processes and unrepresented physical phenomena. This forcing term consists in a (large‐scale) flow dependent parametrization of the initial tendency model error computed by the variational assimilation. The flow dependency is given by an analogues technique which relies on the analysis dataset. Such empirical driving causes a substantial improvement of the model climatology, reducing its systematic error and improving its high frequency variability. Low‐frequency variability is also more realistic and the model shows a better reproduction of Euro‐Atlantic weather regimes. A link between the large‐scale flow and the model error is found only in the Euro‐Atlantic sector, other mechanisms being probably the origin of model error in other areas of the globe.

Duan W. S., B. Tian, and H. Xu, 2014: Simulations of two types of El Niño events by an optimal forcing vector approach. Climate Dyn.,43, 1677-1692, doi: 10.1007/s00382-013-1993-4.10.1007/s00382-013-1993-4

4b647156-3ec6-45dd-8bf0-f833b4c619d8

4ebc6b100c586b439019d3b0b03ea509

http%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs00382-013-1993-4

refpaperuri:(796236032515a4c4ebd5299e25d13233)

http://onlinelibrary.wiley.com/resolve/reference/XREF?id=10.1007/s00382-013-1993-4In this paper, an optimal forcing vector (OFV) approach is proposed. The OFV offsets tendency errors and optimizes the agreement of the model simulation with observation. We apply the OFV approach to the well-known Zebiak–Cane model and simulate several observed eastern Pacific (EP) El Ni09o and central Pacific (CP) El Ni09o events during 1980–2004. It is found that the Zebiak–Cane model with a proper initial condition often reproduces the EP-El Ni09o events; however, the Zebiak–Cane model fails to reproduce the CP-El Ni09o events. The model may be much more influenced by model errors when simulating the CP-El Nino events. As expected, when we use the OFV to correct the Zebiak–Cane model, the model reproduces the three CP-El Ni09o events well. Furthermore, the simulations of the corresponding winds and thermocline depths are also acceptable. In particular, the thermocline depth simulations for the three CP-El Ni09o events lead us to believe that the discharge process of the equatorial heat content associated with the CP-El Ni09o is not efficient and emphasizes the role of the zonal advection in the development of the CP-El Nino events. The OFVs associated with the three CP-El Ni09o events often exhibit a sea surface temperature anomaly (SSTA) tendency with positive anomalies in the equatorial eastern Pacific; therefore, the SST tendency errors occurring in the equatorial eastern Pacific may dominate the uncertainties of the Zebiak–Cane model while simulating CP-El Nino events. A further investigation demonstrates that one of the model errors offset by the OFVs is of a pattern similar to the SST cold-tongue cooling mode, which may then provide one of the climatological conditions for the frequent occurrence of CP-El Nino events. The OFV may therefore be a useful tool for correcting forecast models and then for helping improve the forecast skill of the models.

Evensen G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99( C5), 10 143- 10 162.10.1029/94JC00572

ef8053eecb8d37c88057c7928546f3a5

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F94JC00572%2Fcitedby

http://onlinelibrary.wiley.com/doi/10.1029/94JC00572/citedbySequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics EVENSEN G. Journal of Geophysical Research 99(5), 10143-10162, 1994

Feng F., W. S. Duan, 2013: The role of constant optimal forcing in correcting forecast models. Science China Earth Sciences, 56( 4), 434- 443.10.1007/s11430-012-4568-z

a30b82ade78da91c735a01d50cf294f5

http%3A%2F%2Fwww.cnki.com.cn%2FArticle%2FCJFDTotal-JDXG201303010.htm

http://www.cnki.com.cn/Article/CJFDTotal-JDXG201303010.htmIn this paper,the role of constant optimal forcing(COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model.The results show that when we only consider model error caused by parameter error,which also changes with the development of state variables in a numerical model,the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model.The COF can also offset the impact of model error caused by stochastic processes.In reality,the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions.Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results.In summary,although the variation in the model error due to physical process is time-dependent,the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results.Therefore,the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.

Guilyardi E., H. Bellenger, M. Collins, S. Ferrett, W. J. Cai, and A. T. Wittenberg, 2012: A first look at ENSO in CMIP5. CLIVAR Exchange, 17, 29- 32.c88676fa67cf88170ce9b2ce482b172f

http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F257979825_A_first_look_at_ENSO_in_CMIP5

http://www.researchgate.net/publication/257979825_A_first_look_at_ENSO_in_CMIP5The El Ni09o–Southern Oscillation (ENSO) is a naturally occurring fluctuation that originates in the tropical Pacific region with severe weather and societal impacts worldwide (McPhaden et al. 2006). Despite considerable progress in our understanding of the impact

Ham Y. G., J. S. Kug, 2012: How well do current climate models simulate two types of El Niño? Climate Dyn.,39(1-2), 383-398, doi: 10.1007/s00382-011-1157-3.

Kalnay E., Coruthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77( 4), 437- 471.f539a4fb-a013-4942-ac7e-7f15017eedac

23d674534321ec5c56bf181fd85f5561

http%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Di1536-1098-69-2-93-Kalnay1%26dbid%3D16%26doi%3D10.3959%252F1536-1098-69.2.93%26key%3D10.1175%252F1520-0477%281996%29077%3C0437%253ATNYRP%3E2.0.CO%253B2

refpaperuri:(fe1c070047a030c900beb40441caee5a)

/s?wd=paperuri%3A%28fe1c070047a030c900beb40441caee5a%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Di1536-1098-69-2-93-Kalnay1%26dbid%3D16%26doi%3D10.3959%252F1536-1098-69.2.93%26key%3D10.1175%252F1520-0477%281996%29077%253C0437%253ATNYRP%253E2.0.CO%253B2&ie=utf-8

Kao H. Y., J. Y. Yu, 2009: Contrasting eastern-Pacific and central-Pacific types of ENSO. J.Climate, 22( 4), 615- 632.10.1175/2008JCLI2309.1

45c44667cdecca214fcb46319cfa9a89

http%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20093117308.html

http://www.cabdirect.org/abstracts/20093117308.htmlAbstract Surface observations and subsurface ocean assimilation datasets are examined to contrast two distinct types of El Ni帽o鈥揝outhern Oscillation (ENSO) in the tropical Pacific: an eastern-Pacific (EP) type and a central-Pacific (CP) type. An analysis method combining empirical orthogonal function (EOF) analysis and linear regression is used to separate these two types. Correlation and composite analyses based on the principal components of the EOF were performed to examine the structure, evolution, and teleconnection of these two ENSO types. The EP type of ENSO is found to have its SST anomaly center located in the eastern equatorial Pacific attached to the coast of South America. This type of ENSO is associated with basinwide thermocline and surface wind variations and shows a strong teleconnection with the tropical Indian Ocean. In contrast, the CP type of ENSO has most of its surface wind, SST, and subsurface anomalies confined in the central Pacific and tends to onset, develop, and decay in situ. This type of ENSO appears less related to the thermocline variations and may be influenced more by atmospheric forcing. It has a stronger teleconnection with the southern Indian Ocean. Phase-reversal signatures can be identified in the anomaly evolutions of the EP-ENSO but not for the CP-ENSO. This implies that the CP-ENSO may occur more as events or epochs than as a cycle. The EP-ENSO has experienced a stronger interdecadal change with the dominant period of its SST anomalies shifted from 2 to 4 yr near 1976/77, while the dominant period for the CP-ENSO stayed near the 2-yr band. The different onset times of these two types of ENSO imply that the difference between the EP and CP types of ENSO could be caused by the timing of the mechanisms that trigger the ENSO events.

Kim S. T., J. Y. Yu, A. Kumar, and H. Wang, 2012: Examination of the two types of ENSO in the NCEP CFS model and its extratropical associations. Mon. Wea. Rev., 140( 6), 1908- 1923.10.1175/MWR-D-11-00300.1

bd0de78bd16348431738d80e7470980a

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012MWRv..140.1908K

http://adsabs.harvard.edu/abs/2012MWRv..140.1908KAbstract Two types of El Nioouthern Oscillation (ENSO) simulated by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) model are examined. The model is found to produce both the eastern Pacific (EP) and central Pacific (CP) types of ENSO with spatial patterns and temporal evolutions similar to the observed. The simulated ENSO intensity is comparable to the observed for the EP type, but weaker than the observed for the CP type. Further analyses reveal that the generation of the simulated CP ENSO is linked to extratropical forcing associated with the North Pacific Oscillation (NPO) and that the model is capable of simulating the coupled airea processes in the subtropical Pacific that slowly spreads the NPO-induced SST variability into the tropics, as shown in the observations. The simulated NPO, however, does not extend as far into the deep tropics as it does in the observations and the coupling in the model is not sustained as long as it is in the observations. As a result, the extratropical forcing of tropical central Pacific SST variability in the CFS model is weaker than in the observations. An additional analysis with the Bjerknes stability index indicates that the weaker CP ENSO in the CFS model is also partially due to unrealistically weak zonal advective feedback in the equatorial Pacific. These model deficiencies appear to be related to an underestimation in the amount of the marine stratus clouds off the North American coasts inducing an ocean surface warm bias in the eastern Pacific. This study suggests that a realistic simulation of these marine stratus clouds can be important for the CP ENSO simulation.

Kug J. S., F. F. Jin, and S. I. An, 2009: Two types of El Niño events: Cold tongue El Niño and warm pool El Niño. J.Climate, 22( 6), 1499- 1515.

Kug J. S., J. Choi, S. I. An, F. F. Jin, and A. T. Wittenberg, 2010: Warm pool and cold tongue El Niño events as simulated by the GFDL 2.1 coupled GCM. J.Climate, 23( 6), 1226- 1239.10.1175/2009JCLI3293.1

56c55c26-c682-46bb-a106-284f1d8732b9

1a0bad1ebddf618704afd9c391af3091

http%3A%2F%2Fwww.cabdirect.org%2Fabstracts%2F20103118385.html

refpaperuri:(205b7ff4e18f55f36d3fd66f1c2cc0ae)

http://www.cabdirect.org/abstracts/20103118385.htmlAbstract Recent studies report that two types of El Ni09o events have been observed. One is the cold tongue (CT) El Ni09o, which is characterized by relatively large sea surface temperature (SST) anomalies in the eastern Pacific, and the other is the warm pool (WP) El Ni09o, in which SST anomalies are confined to the central Pacific. Here, both types of El Ni09o events are analyzed in a long-term coupled GCM simulation. The present model simulates the major observed features of both types of El Ni09o, incorporating the distinctive patterns of each oceanic and atmospheric variable. It is also demonstrated that each type of El Ni09o has quite distinct dynamic processes, which control their evolutions. The CT El Ni09o exhibits strong equatorial heat discharge poleward and thus the dynamical feedbacks control the phase transition from a warm event to a cold event. On the other hand, the discharge process in the WP El Ni09o is weak because of its spatial distribution of ocean dynamic field. The positive SST anomaly of WP El Ni09o is thermally damped through the intensified evaporative cooling.

Leith C. E., 1978: Objective methods for weather prediction. Annual Review of Fluid Mechanics,10(2), 107-128, doi: 10.1146/annurev.fl.10.010178.000543.10.1146/annurev.fl.10.010178.000543

58123377f897797fea8533cceaee06f6

http%3A%2F%2Fwww.annualreviews.org%2Fdoi%2Fabs%2F10.1146%2Fannurev.fl.10.010178.000543

http://www.annualreviews.org/doi/abs/10.1146/annurev.fl.10.010178.000543ABSTRACT This review deals with the objective methods for weather prediction that have been developed during this century and now supplement the subjective methods that have been used for millenia. From a technical point of view, the problem of weather prediction is especially intriguing in that the atmospheric system is essentially nonlinear and cannot be decomposed into independently acting modes. The inevitable errors in observing the smallest scales of motion must then contaminate larger scales and, in fact, they eventually destroy the accuracy of any prediction. Thus, weather prediction is seen as an unstable problem in the sense that small initial differences have large final consequences. Furthermore, uncertainties both at the beginning and later on in the prediction process require that prediction be treated as a statisti

Lorenz E. N., 1965: A study of the predictability of a 28-variable atmospheric model. Tellus, 17( 4), 321- 333.9327e0fe-8035-465a-a20c-2187e271a72a

5290b716f057ec79a3c6ace9c6fd2bd7

http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.2153-3490.1965.tb01424.x%2Fpdf

refpaperuri:(3c5589799a6ba39ebdc5944cf81c3457)

/s?wd=paperuri%3A%283c5589799a6ba39ebdc5944cf81c3457%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.2153-3490.1965.tb01424.x%2Fpdf&ie=utf-8

Morss R. E., K. A. Emanuel, and C. Snyder, 2001: Idealized adaptive observation strategies for improving numerical weather prediction. J. Atmos. Sci., 58( 3), 210- 232.10.1175/1520-0469(2001)058<0210:IAOSFI>2.0.CO;2

8c38f303527a1ca601a3a4511a2456c7

http%3A%2F%2Fwww.ams.org%2Fmathscinet-getitem%3Fmr%3D1812005

http://www.ams.org/mathscinet-getitem?mr=1812005Presents a study which examined two simplified adaptive strategies in a simulated idealized system used for improving numerical weather prediction. Description of the three-dimensional quasigeostrophic model and the data assimilation system; Comparison between global adaptive observations and global nonadaptive observations; Capability of the simplified adaptive observations to reduce analysis and weather forecast errors.

Mu M., W. S. Duan, and B. Wang, 2003: Conditional nonlinear optimal perturbation and its applications. Nonlinear Processes in Geophysics, 10( 6), 493- 501.e25c177dc6379606b920ad8427a3dded

http%3A%2F%2Fnsr.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.5194%2Fnpg-10-493-2003%26link_type%3DDOI

http://nsr.oxfordjournals.org/external-ref?access_num=10.5194/npg-10-493-2003&link_type=DOI

Rayner N. A., D. E. Parker, E. B. Horton, C. K. Folland , L. V. Alexand er, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108(D14),4407, doi: 10.1029/2002JD 002670.10.1029/2002JD002670

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refpaperuri:(bb68c298dcb75e337b3531e27068a3b0)

http://onlinelibrary.wiley.com/doi/10.1029/2002JD002670/full[1] We present the Met Office Hadley Centre's sea ice and sea surface temperature (SST) data set, HadISST1, and the nighttime marine air temperature (NMAT) data set, HadMAT1. HadISST1 replaces the global sea ice and sea surface temperature (GISST) data sets and is a unique combination of monthly globally complete fields of SST and sea ice concentration on a 1 latitude-longitude grid from 1871. The companion HadMAT1 runs monthly from 1856 on a 5掳 latitude-longitude grid and incorporates new corrections for the effect on NMAT of increasing deck (and hence measurement) heights. HadISST1 and HadMAT1 temperatures are reconstructed using a two-stage reduced-space optimal interpolation procedure, followed by superposition of quality-improved gridded observations onto the reconstructions to restore local detail. The

Roads J. O., 1987: Predictability in the extended range. J. Atmos. Sci., 44( 23), 3495- 3527.10.1175/1520-0469(1987)044<3495:PITER>2.0.CO;2

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1987JAtS...44.3495R

http://adsabs.harvard.edu/abs/1987JAtS...44.3495RAbstract A two-level spherical quasi-geostrophic model is formulated for predictability experiments. The stationary external forcing for this model is calculated from observations. Both barotropic and baroclinic forcings are required in order to achieve a realistic model climatology. Realistic transient behavior is also present in the model. The most notable difference is that the observed transient kinetic energy has more energy in the smallestscales. Predictability experiments have an initial rms doubling time of approximately two days. This growth rate along with an initial error of about l/2 the initial error of present operational models produces an rms error equal to the climatological rms error and a correlation of 0.5 on about day 12 of the forecast. At the largest scales, this limiting point is reached shortly thereafter. The error continues to grow at a decreasing rate until at about 30 days the forecast skill is extremely small and comparable to the skill of a persistence forecast. Various time averages at various lags were examined for skill in the extended range. Filters that weighted most strongly the initial forecast days.were shown to provide increased skill. At the furthest limits (60-day time averages), filters improve the skill of prediction by an amount comparable to that which a numerical forecast is an improvement over a persistence forecast. A window filter improves forecasts of time averages by simply eliminating forecast days beyond about day 15. Besides the overall limit, no stable geographical or spectralvariations in the cutoff time could be determined from the limited sample of forecasts described in this paper.

Talagrand O., 1997: Assimilation of observations, an introduction. J. Meteor. Soc.Japan, 75, 191- 209.10.1175/1520-0469(1997)054<0679:OTRBTS>2.0.CO;2

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F229086740_Assimilation_of_observations_an_introduction

http://www.researchgate.net/publication/229086740_Assimilation_of_observations_an_introductionABSTRACT Assimilation of meteorological or oceanographical observations can be described as the process through which all the available information is used in order to estimate as accurately as possible the state of the atmospheric or oceanic flow. The available information essentially consists of the observations proper, and of the physical laws which govern the evolution of the flow. The latter are available in practice under the form of a numerical model. The existing assimilation algorithms can be described as either sequential or variational. The links between these algorithms and the theory of statistical estimation are discussed. The performances of present algorithms, and the perspectives for future development, are also briefly discussed.

Tang Y. M., Z. W. Deng, X. B. Zhou, Y. J. Cheng, and D. K. Chen, 2008: Interdecadal variation of ENSO predictability in multiple models. J.Climate, 21( 18), 4811- 4833.10.1175/2008JCLI2193.1

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http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008JCli...21.4811T

http://adsabs.harvard.edu/abs/2008JCli...21.4811TIn this study, El Ni01±o09outhern Oscillation (ENSO) retrospective forecasts were performed for the 120 yr from 1881 to 2000 using three realistic models that assimilate the historic dataset of sea surface temperature (SST). By examining these retrospective forecasts and corresponding observations, as well as the oceanic analyses from which forecasts were initialized, several important issues related to ENSO predictability have been explored, including its interdecadal variability and the dominant factors that control the interdecadal variability. The prediction skill of the three models showed a very consistent interdecadal variation, with high skill in the late nineteenth century and in the middle09恪發ate twentieth century, and low skill during the period from 1900 to 1960. The interdecadal variation in ENSO predictability is in good agreement with that in the signal of interannual variability and in the degree of asymmetry of ENSO system. A good relationship was also identified between the degree of asymmetry and the signal of interannual variability, and the former is highly related to the latter. Generally, the high predictability is attained when ENSO signal strength and the degree of asymmetry are enhanced, and vice versa. The atmospheric noise generally degrades overall prediction skill, especially for the skill of mean square error, but is able to favor some individual prediction cases. The possible reasons why these factors control ENSO predictability were also discussed.

Toth Z., E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125( 12), 3297- 3319.

Vannitsem S., Z. Toth, 2002: Short-term dynamics of model errors. J. Atmos. Sci., 59( 17), 2594- 2604.9df6e05668950f71c684e94ed3ac3696

http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2002JAtS...59.2594V

/s?wd=paperuri%3A%28fed28bd18ff3c07d1a5f95ebeadb593f%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2002JAtS...59.2594V&ie=utf-8

Xue Y., M. A. Cane, S. E. Zebiak, and M. B. Blumenthal, 1994: On the prediction of ENSO: A study with a low-order Markov model. Tellus A, 46( 5), 512- 528.10.1034/j.1600-0870.1994.00013.x

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http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1034%2Fj.1600-0870.1994.00013.x%2Fcitedby

refpaperuri:(cd7a6e8694ac35228fcc3b5a74c4b6a2)

http://onlinelibrary.wiley.com/doi/10.1034/j.1600-0870.1994.00013.x/citedbyA linear model best fit to the Zebiak and Cane (1987) ENSO forecast model (ZC) is used to study the model's prediction skill. Multivariate empirical orthogonal functions (MEOFs) obtained from the sea surface temperature anomaly, sea level and wind stress anomaly fields in a suite of 3-year forecast runs of ZC starting from the monthly initial conditions in the period January 1970 to December 1991, are used to construct a series of seasonally varying linear Markov models. It is found that the model with 18 MEOFs fits the original nonlinear model reasonably well and has comparable or better forecast skill. Assimilating the observed SST into the initial conditions further improves forecast skill at short lead times (< 9 months). The transient initial error growth in the model's prediction is attributed to the non-self-adjoint property as in Farrell and Blumenthal. Initial error grows fastest starting from spring and slowest starting from late summer and is sensitive to the initial error structures. Two singular vectors (SVs) of the linear evolution operator have significant transient growth dominating the total error growth. Since the optimal perturbation (fastest SV) has mostly high MEOF components, the error growth tends to be larger when there are more high mode components in the initial error fields. This result suggests a way to filter the initial condition fields: the MEOFs higher than the 18th in the initial fields are mostly noise and removing them improves prediction skill. The forecasts starting from late summer have the best predictability because the fastest growth season (summer) is just avoided. The well known, very rapid decline in forecast skill in the boreal spring (the “spring barrier”) is here attributed to the smallness of the signal to be forecast: the standard deviation of the NIN03 SST anomaly is smallest in spring.

Yu J. Y., S. T. Kim, 2010: Three evolution patterns of central-Pacific El Niño. Geophys. Res. Lett., 37,L08706, doi: 10.1029/2010GL042810.

Zebiak S. E., M. A. Cane, 1987: A model El Niño-southern oscillation. Mon. Wea. Rev., 115( 10), 2262- 2278.10.1038/302295a0

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http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F244948692_1987_A_model_El-Nino-Southern_Oscillation

refpaperuri:(02aa79a86a12d930aec389c613c4f943)

http://www.researchgate.net/publication/244948692_1987_A_model_El-Nino-Southern_OscillationAt intervals that vary from 2 to 10 yr sea-surface temperatures and rainfall are unusually high and the tradewinds are unusually weak over the tropical Pacific Ocean. These Southern Oscillation El Ni帽o events which devastate the ecology of the coastal zones of Ecuador and Peru, which affect the global atmospheric circulation and which can contribute to severe winters over northern America, often develop in a remarkably predictable manner. But the event which began in 1982 has not followed this pattern.