Investigating ENSO sensitivity to mean climate in an intermediate model using a novel statistical technique
|
|
- Emmeline Webster
- 5 years ago
- Views:
Transcription
1 GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L07705, doi: /2007gl029348, 2007 Investigating ENSO sensitivity to mean climate in an intermediate model using a novel statistical technique Faming Wang 1 Received 14 January 2007; revised 28 February 2007; accepted 8 March 2007; published 6 April [1] Recent studies using general circulation models to project the response of ENSO to greenhouse warming find, despite a shift in the mean state toward a warmer climate, no significant change in the amplitude of ENSO. Here, an intermediate coupled model of tropical Pacific is shown to capture such insensitivity of ENSO to a change in mean climate. This is accomplished by systematically searching in the model parameter space for the configurations that enable the model to reproduce the observed variance, skewness, kurtosis, and autocorrelation of the Niño 3 index of the past 150 years. Three model configurations are identified to give similar approximation of Niño 3 index, which represent the realization of ENSO in different climate regimes. Citation: Wang, F. (2007), Investigating ENSO sensitivity to mean climate in an intermediate model using a novel statistical technique, Geophys. Res. Lett., 34, L07705, doi: / 2007GL Introduction [2] The El Niño Southern Oscillation (ENSO) is the strongest mode of interannual variability of the coupled atmosphere-ocean system in the tropical Pacific with global impacts because of its ability to force changes in the global planetary wave structure and precipitation patterns. There is much interest in understanding the evolution of ENSO behavior upon a background of changing climate. [3] Despite the recent improvements in the ENSO simulations in coupled ocean-atmosphere models [AchutaRao and Sperber, 2006], the projections of ENSO s response to global warming are generally diverse and highly uncertain. Zelle et al. [2005], using the Community Climate System Model (CCSM, version 1.4), found no significant change in the period, amplitude and spatial pattern of ENSO as greenhouse gas concentration increases. Toniazzo [2006a], using the Third Hadley Centre Coupled Model (HadCM3), found that ENSO in the 4 CO 2 integration exhibits a westward shift in the sea surface temperature (SST) pattern and an increase in the frequency. Meehl et al. [2006] analyzed the stabilized greenhouse gases integrations of the Parallel Climate Model (PCM) and the CCSM version 3, found reduction in the amplitude of ENSO, but no appreciable change in the frequency. Survey of the multiple General Circulation Models (GCMs) experiments for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change shows that the projections of future ENSO are largely model-dependent. Under doubling CO 2 scenarios, models dominated by local SST-wind feedback show decrease in the amplitude of ENSO, whereas models dominated by remote thermocline feedback show an amplitude increase [Merryfield, 2006]. The largest ENSO amplitude change occurs in those models that exhibit an increase in the strength of thermocline feedback as CO 2 increases [Guilyardi, 2006]. For the models with the most realistic simulations of present day ENSO, there is a reasonable balance of feedback mechanisms, and the feedbacks achieve a new equilibrium as climate moves toward a warming state [Philip and van Oldenborgh, 2006], where no statistically significant amplitude changes are observed in the projections of future ENSO [van Oldenborgh et al., 2005]. [4] The dependence of ENSO properties on the local SST feedback and the remote thermocline feedback has been proposed by Fedorov and Philander [2001]. In their linear stability analysis of the tropical ocean-atmosphere system, the ENSO mode is uniquely controlled by mean background climate parameters that include the intensity of the trade winds, the mean depth of the thermocline, the temperature difference across the thermocline. The mean climate change affects the strength of the ocean-atmosphere feedbacks, hence the period and growth rate of the ENSO mode. However, there is an obvious gap in trying to relate the easily observed ENSO statistics (variance, skewness, persistence) from observation and GCM simulations to the more understandable ENSO stability from linear theory [Toniazzo, 2006b]. For example, the GCMs (at least some) exhibits an insensitivity of ENSO to mean climate change, i.e., different equilibrium climates corresponding to similar ENSO variability [van Oldenborgh et al., 2005; Philip and van Oldenborgh, 2006], while Fedorov and Philander [2001] suggest a high sensitivity of ENSO to changes in the background climate state. [5] In an attempt to bridge the gap, here we apply the Bayesian stochastic inversion to an intermediate coupled model (ICM) under the constraint of 150-year observation of present day ENSO. First, six parameters of the ICM are selected to describe the mean climate states, then the ICM simulation under a mean climate state is compared to the observed ENSO in terms of Niño 3 index. By exploring the parameter space, we try to determine the climate regimes that produce interannual variability similar to ENSO observation. Furthermore, we discuss the implications of these climate regimes for interpreting the GCM projections of the response of ENSO to greenhouse warming. 1 Institute of Oceanology, Chinese Academy of Sciences, Qingdao, Shandong, China. Copyright 2007 by the American Geophysical Union /07/2007GL Model and Parameters [6] The model used in this study is an intermediate coupled model. It consists of an empirical atmosphere and L of5
2 a 1.5 layer reduced gravity ocean embedded with a constantdepth mixed surface layer. The model domain covers the tropical Pacific Ocean (30 S 30 N, 120 E 80 W) with a resolution of 1 in latitude and 2 in longitude. It was developed to study the physical processes controlling the seasonal cycle and the interannual variability in the tropical Pacific [Chang, 1994] Model Parameters to Perturb [7] In the ICM, the wind stress is represented by a linear stochastic model in vector form t ¼ a^t þ AT 0 þ bh where ^t is the mean wind stress, T 0 is the SST anomaly, h is the atmospheric white noise forcing. The linear feedback AT 0 describes the dynamic response of the atmosphere to the SST anomalies. a and b are two nondimensional parameters, controlling the strength of the mean winds and the noise forcing. [8] The oceanic component is a derivate of the Lamont model [Zebiak and Cane, 1987] with a parameterization of the temperature of the entrained water T e ¼ ð1 gþt þ gt Hmix þ z T Hmix h 0 þ ah 02 þ bh 03 ð2þ where T Hmix z T Hmix are the observed mean temperature and its vertical derivative at the base of mixed layer, g and d are two adjustable parameters controlling the dependence of T e on the sea surface temperature T and the thermocline perturbations h 0, a and b are predetermined coefficients. In the SST equation, the effectiveness of the vertical upwelling term W(T T e )/H mix is determined by the contributions of the local SST-wind interaction g and the remote SST-thermocline feedback d. [9] Within the ICM, six parameters were identified as controlling key characteristics of the model simulation: the coefficient of Rayleigh friction in the Ekman layer r s,the sensitivity of entrained temperature to SST g, the sensitivity of entrained temperature to thermocline anomaly d, the mean thermocline depth H, the strength of mean wind stress a, and the strength of noise forcing b. To explore the full range of model possibilities, we allow these parameters to vary freely within subjectively chosen uncertainty ranges Optimal Parameter Estimation [10] For each combination of parameter values r s, g, d, H, a, b (hereafter referred to as a model configuration), we run the model forward for 300 years and calculate the Niño 3 index as the SST anomalies averaged over the eastern equatorial Pacific (5 S 5 N, 150 W 90 W). Then, we evaluate the model performance by comparing the 300-year Niño 3 simulation with the 150-year Niño 3 observation of Kaplan Extended SST [Kaplan et al., 1998]. The model-data comparison is quantitatively measured by a cost function, which is defined as a weighted average of the mean square error of multiple characteristics of ENSO statistics, cost ¼ 1 n ½ varmod var obs Š 2 ½ þ skw mod skw obs Š 2 5 s var s skw ½ þ kur mod kur obs Š 2 þ 1 X ½cor mod ðtþ cor obs ðtþš 2 o s kur 6 s cor ðtþ t ð1þ ð3þ [11] Here var, skw, kur, and cor are variance, skewness, kurtosis, and autocorrelation (persistence) of Niño 3 time series; ss are the data uncertainties; and t = 3,6,...,36 months is the time lag. We choose these statistics because they are easy to calculate and they are directly related to properties of the system s dynamics. For example, variance depends on stability [An et al., 2004]; skewness and kurtosis measure nonlinearity [Burgers and Stephenson, 1999; Hannachi et al., 2003]; autocorrelation is connected to power spectrum (via Fourier transform). As a sensitivity test, we also try different cost functions by adding more (less) weight to the individual terms of equation (3). As long as the general statistics of Niño 3 index is concerned, our results are insensitive to the changing cost. [12] The data uncertainty s consists of measurement error (which we ignore) as well as the natural fluctuations. Here, we first divide the 150-year observation of Niño 3 index into 15 ten-year segments, then calculate variance, skewness, kurtosis, and persistence for these ten-year time series and get 15 (independent) estimates. ss are the variances of these statistical quantities. It can be viewed as a measure of the decadal changes in ENSO statistics. Large s (mainly the skewness and autocorrelation at long lag time) indicates large data uncertainty, thus a great tolerance in the model-data mismatch and a small contribution to the total cost. Since data uncertainty does not vary with model parameters, we can compare the model results for different parameter values by using the cost function (equation (3)). Smaller costs mean smaller errors, hence better ENSO simulations. [13] In order to find the optimal parameters, i.e., the parameter values corresponding to the minimum cost, one needs to search through the six-dimensional parameter space spanned by (r s, g, d, H, a, b). In this study we apply an effective and efficient technique to sample the parameter space Bayesian stochastic inversion with fast annealing [see Jackson et al., 2004; Sen and Stoffa, 1996]. The solution is cast in terms of Bayesian statistics and the result is an estimate of the joint probability density function (pdf) for the model parameters under the constraint of Niño 3 observation. From this joint pdf, we can determine the likelihood of parameter settings in reproducing the properties of observed ENSO. The optimal parameter setting is then inferred through the maximum likelihood estimation. 3. Parameter Regimes Consistent With Present ENSO 3.1. Probability Distribution in Parameter Space [14] In our experiment, totally 5000 model configurations were explored, which represents 5000 (nonuniform) sampling points in the parameter space. The likelihood of a model configuration is the probability of the ICM s simulation close to the observation at that particular parameter value. By integrating the six-dimensional pdf over four parameters, we obtain the marginal probability of two parameters as well as the correlation between them. Figure 1 shows the marginal probability as a function of the mean thermocline depth H and the mean wind strength a. Obviously, probability tends to distribute within three distinct regions of parameter space. Taking H = 120 m, a = 1 as threshold for mean thermocline and wind, 2of5
3 we shall refer to these high-probability regions (shaded areas) as weak wind-shallow thermocline (WWSH), weak wind-deep thermocline (WWDH), and strong wind-deep thermocline (SWDH). Given the intermediate model and the observational data, these are the parameter regimes where present-day ENSO is most likely to reside, with 56% chance in WWSH, 15% in WWDH, and 5% in SWDH. [15] To test whether these high probability features truly reflect the six-dimensional distribution, we select the 100 best simulations (smallest cost) and mark them on various two-dimensional parameter maps of marginal probability. If the identified regime is really a simply connected domain in six-dimensional parameter space, then these best solutions should fall into the high-probability areas of twodimensional maps. Our results show that most of the best solutions indeed concentrate in the three identified regimes, with some scattering between WWDH and SWDH. [16] The congregation of best solutions in parameter space also reflects the inter-dependence between model parameters, which is well illustrated by the correlation among parameters (Table 1). As we can see, subsurface sensitivity (g and d), mean thermocline depth (H), and strength of noise forcing (b) are all significantly correlated. Here, high correlation means that in the model the effect of change in one parameter could be counteracted by change in the other parameter. For example, an increase in H is stabilizing while an increase in d is destabilizing [Fedorov and Philander 2001]. In order for the ICM to reproduce observation, the effect of deepening H should be balanced by the effect of strengthening d, which implies a positive correlation between H and d. Such parameter correlation poses a challenge for parameter tuning in the development of climate models. As model parameters are not independent, simply tuning them one by one will not achieve the best performance [Stainforth et al., 2005]. Figure 1. 2-D marginal probability as function of mean thermocline depth and mean wind strength. The shading indicates the high probability density areas, i.e., the parameter regimes where ENSO likely resides (see text for more details). The locations of optimal solutions are also marked (plus, SWDH; circle, WWDH; square, WWSH). The contour interval is Optimal Solutions [17] The three parameter regimes are represented by their optimal solutions, whose parameter values are listed in Table 2. It is interesting to notice that these three optimals differ not only in mean thermocline depth and mean wind strength, but also in other parameters. The only exception is the Rayleigh friction coefficient, which is characterized by a damping timescale of 3 4 days, indicating little parameter uncertainty and thus being ignored from the rest of discussion. Another observation is the coherent behavior of H and d, confirming their high correlation shown in Table 1. [18] Figure 2 shows the simulated Niño 3 indices from optimal solutions along with the 150-year observation. Observation indicates that ENSO is an interannual variability with variance of 0.65 K 2, skewness of 0.72, and kurtosis of 4.0 [Burgers and Stephenson, 1999]. The three model simulations all produce some sort of interannual variations, resemble the observation in terms of variance, skewness, kurtosis, and persistence. Considering the intermediate complexity of the model, we might conclude that the characteristics of observed ENSO are well reproduced within these three parameter regimes. [19] Nevertheless, there are also some differences among the optimal solutions. For example, WWSH and SWDH produce more skewness (skw = 0.92) while WWDH produces less (skw = 0.2). An intuitive explanation is that saturation of SST is easier to occur in the cases of WWSH and SWDH than WWDH, which limits the full development of cold events. Spectral analysis of the simulated Niño 3 time series reveals a 2 3 year power-band for SWDH, 3 4 year band for WWDH, and dual peaks (3 year and 5 year) for WWSH. By switching on/off the noise forcing (set b =0), we further investigate the free oscillations in these three regimes. Our study show that the ENSO-like variability in the WWSH regime is a self-sustaining oscillation disturbed by stochastic forcing (b = 1.3); WWDH is a highly damped oscillation forced by strong stochastic noise (b = 3.9); SWDH is also a damped oscillation but with moderate damping and modest stochastic forcing (b = 1.9). An in-depth explanation of these differences requires a dynamic understanding of the coupled tropical ocean-atmosphere system, which will be reported elsewhere. 4. Conclusion and Discussion [20] With a novel inversion technique, we thoroughly sample the parameter space of an intermediate coupled Table 1. Correlation Between Model Parameters a Rayleigh Friction T e to T T e to h 0 Mean Thermocline Noise Wind r s g d H b a a It is calculated by integrating the probability density functions over parameters. Bold numbers indicate high correlations which are further discussed in the text. 3of5
4 Table 2. Optimal Parameter Values for the Three Climate Regimes a Climate Regimes, b d Rayleigh Friction T e to T T e to h 0 Mean Thermocline, m Noise Wind WWSH WWDH SWDH a These values are obtained by fitting the intermediate model to the Niño 3 observation via the Bayesian stochastic inversion (see the text for details). b WWSH: weak wind shallow thermocline; WWDH: weak wind deep thermocline; SWDH: strong wind deep thermocline. model of the tropical Pacific, find three climate regimes, weak wind-shallow thermocline, weak wind-deep thermocline, strong wind-deep thermocline, that are consistent with the variance, skewness, kurtosis and autocorrelation of the Niño 3 index in the present day ENSO observation. This result has important implications for the interpretation of GMC results concerning the future state of ENSO under greenhouse warming. In those GCM experiments, the models first are validated by comparing with present day ENSO observation, then are used to make projection about ENSO under CO 2 doubling or quadrupling. Only results from models with realistic ENSO simulations are considered reliable [Collins et al., 2005; van Oldenborgh et al., 2005; Merryfield, 2006]. Those models, however, differ in their configuration and parameterization. The simulated ENSOs then may represent a realization in Figure 2. Time series of Niño 3 anomalies of (a) Kaplan data set of , (b) 150-year simulation of WWSH, (c) 150-year simulation of WWDH, and (d) 150-year simulation of SWDH. The variance, skewness, and kurtosis of Niño 3 index are also listed for comparison (see text for the detail). The unit of Niño 3 anomaly is degree. different dynamic regimes, just like the ICM simulations in section 3. The diversity in model dynamics is definitely going to affect the projection of ENSO s sensitivity to mean climate change caused by greenhouse warming [Zelle et al., 2005; Merryfield, 2006; Toniazzo, 2006a; Yeh and Kirtman, 2007]. [21] We further note that the deep thermocline regimes are dominated by the remote SST-thermocline feedback, while the shallow thermocline regime is dominated by the local SST-wind feedback, implying a shift in the coupling loop during regime transition. Such shift has been demonstrated in several GCM experiments concerning the impact of global warming on ENSO. Namely, as the mean climate of equatorial Pacific warms, the trade winds becomes weaker, and the thermocline becomes shallower [Philip and van Oldenborgh, 2006], the coupling processes shift accordingly between the local wind feedback and the remote thermocline feedback [Guilyardi, 2006; Toniazzo, 2006a], however no statistical significant changes in the amplitude and period of ENSO variability are detected [van Oldenborgh et al., 2005]. [22] This paper primarily focuses on changes in the statistics of Niño 3 index in response to changes in the mean climate. Other aspects of ENSO, including pattern, phase, and propagation of the oscillation are not explored in our analysis. Therefore, the similarity of Niño 3 indices in three climate regimes does not necessarily suggest that ENSO also behaves similarly in all the other aspects. In addition, we assume that the combination of parameter values is self-consistent. In other words, every model configuration identified is physical realizable by assumption. In reality, wind, current, thermocline, and SST form a coupled system of mean climate states which involve feedbacks just like ENSO [Latif et al., 2001]. In addition, many important feedbacks associated with increasing greenhouse gases (e.g., the cloud forcing) are not contained in our intermediate model, which make it difficult to draw a firm conclusion on the projection of ENSO. It would require a full GCM to investigate all these feedbacks and determine the future state of the mean climate as well as ENSO. [23] Acknowledgments. We thank Charles Jackson for initiating this research and providing his BSI code for the calculation. He is also greatly thanked for the stimulating discussions and constructive suggestions during the course of the work. Ping Chang, Matthew Collins, and an anonymous reviewer are acknowledged for valuable comments. This work has been supported by the G. Unger Vetlesen Foundation and the Chinese NSF grant F. Wang is supported by the Hundred Talents Program, Chinese Academy of Sciences. References AchutaRao, K., and K. R. Sperber (2006), ENSO simulation in coupled ocean-atmosphere models: Are the current models better?, Clim. Dyn., 27, 1 15, doi: /s of5
5 An, S.-I., A. Timmermann, L. Bejarano, F.-F. Jin, F. Justino, Z. Liu, and A. W. Tudhope (2004), Modeling evidence for enhanced El Niño Southern Oscillation amplitude during the Last Glacial Maximum, Paleoceanography, 19, PA4009, doi: /2004pa Burgers, G., and D. B. Stephenson (1999), The normality of El Niño, Geophys. Res. Lett., 26(8), Chang, P. (1994), A study of the seasonal cycle of sea surface temperature in the tropical Pacific Ocean using reduced gravity models, J. Geophys. Res., 99, Collins, M., and the CMIP Modelling Groups (2005), El Niño or La Niña like climate change?, Clim. Dyn., 24, Fedorov, A. V., and S. G. Philander (2001), A stability analysis of tropical ocean-atmosphere interactions: Bridging measurements and theory for El Niño, J. Clim., 14, Guilyardi, E. (2006), El Niño-mean state-seasonal cycle interactions in a multi-model ensemble, Clim. Dyn., 26, Hannachi, A., D. B. Stephenson, and K. R. Sperber (2003), Probabilitybased methods for quantifying nonlinearity in the ENSO, Clim. Dyn., 20, Jackson, C., M. K. Sen, and P. L. Stoffa (2004), An efficient stochastic Bayesian approach to optimal parameter and uncertainty estimation for climate model predictions, J. Clim., 17, Kaplan, A., M. Cane, Y. Kushnir, A. Clement, M. Blumenthal, and B. Rajagopalan (1998), Analyses of global sea surface temperature , J. Geophys. Res., 103, 18,567 18,589. Latif, M., et al. (2001), ENSIP: The El Niño simulation intercomparison project, Clim. Dyn., 18, Meehl, G. A., H. Teng, and G. Branstator (2006), Future changes of El Niño in two global coupled climate models, Clim. Dyn., 26, Merryfield, W. J. (2006), Changes to ENSO under CO 2 doubling in a multimodel ensemble, J. Clim., 19, Philip, S., and G. J. van Oldenborgh (2006), Shifts in ENSO coupling processes under global warming, Geophys. Res. Lett., 33, L11704, doi: /2006gl Sen, M. K., and P. L. Stoffa (1996), Bayesian inference, Gibbs sampler and uncertainty estimation in geophysical inversion, Geophys. Prospect., 44, Stainforth, D. A., et al. (2005), Uncertainty in predictions of the climate response to rising levels of greenhouse gases, Nature, 433, Toniazzo, T. (2006a), Properties of El Niño Southern Oscillation in different equilibrium climates with HadCM3, J. Clim., 19, Toniazzo, T. (2006b), A study of the sensitivity of ENSO to the mean climate, Adv. Geosci., 6, van Oldenborgh, G. J., S. Y. Philip, and M. Collins (2005), El Niño in a changing climate: A multi-model study, Ocean Sci., 1, Yeh, S.-W., and B. P. Kirtman (2007), ENSO amplitude changes due to climate change projections in different coupled models, J. Clim., 20, Zebiak,S.E.,andM.A.Cane(1987),AmodelElNiño Southern Oscillation, Mon. Weather Rev., 115, Zelle, H., G. J. van Oldenborgh, G. Burgers, and H. Dijkstra (2005), El Niño and greenhouse warming: Results from ensemble simulations with the NCAR CCSM, J. Clim., 18, F. Wang, Institute of Oceanology, Chinese Academy of Sciences, 7 Nanhai Road, Qingdao, Shandong, , China. (faming_wang@ms. qdio.ac.cn) 5of5
An Introduction to Coupled Models of the Atmosphere Ocean System
An Introduction to Coupled Models of the Atmosphere Ocean System Jonathon S. Wright jswright@tsinghua.edu.cn Atmosphere Ocean Coupling 1. Important to climate on a wide range of time scales Diurnal to
More informationENSO Amplitude Change in Observation and Coupled Models
ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 25, NO. 3, 28, 361 366 ENSO Amplitude Change in Observation and Coupled Models ZHANG Qiong 1 (Ü ), GUAN Yue 1,3 (' ff), and YANG Haijun 2 (fl ) 1 State Key Laboratory
More informationENSO amplitude changes in climate change commitment to atmospheric CO 2 doubling
GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L13711, doi:10.1029/2005gl025653, 2006 ENSO amplitude changes in climate change commitment to atmospheric CO 2 doubling Sang-Wook Yeh, 1 Young-Gyu Park, 1 and Ben
More informationThe Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 1, 25 30 The Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO HU Kai-Ming and HUANG Gang State Key
More informationRole of the upper ocean structure in the response of ENSO-like SST variability to global warming
Clim Dyn DOI 10.1007/s00382-010-0849-4 Role of the upper ocean structure in the response of ENSO-like SST variability to global warming Sang-Wook Yeh Boris Dewitte Bo Young Yim Yign Noh Received: 4 February
More informationSignificant changes to ENSO strength and impacts in the twenty-first century: Results from CMIP5
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl052759, 2012 Significant changes to ENSO strength and impacts in the twenty-first century: Results from CMIP5 S. L. Stevenson 1 Received 14 June
More informationAtmospheric properties and the ENSO cycle: models versus observations
Noname manuscript No. (will be inserted by the editor) Atmospheric properties and the ENSO cycle: models versus observations Sjoukje Y. Philip Geert Jan van Oldenborgh Received: date / Accepted: date Abstract
More informationUC Irvine Faculty Publications
UC Irvine Faculty Publications Title A linear relationship between ENSO intensity and tropical instability wave activity in the eastern Pacific Ocean Permalink https://escholarship.org/uc/item/5w9602dn
More informationJP1.7 A NEAR-ANNUAL COUPLED OCEAN-ATMOSPHERE MODE IN THE EQUATORIAL PACIFIC OCEAN
JP1.7 A NEAR-ANNUAL COUPLED OCEAN-ATMOSPHERE MODE IN THE EQUATORIAL PACIFIC OCEAN Soon-Il An 1, Fei-Fei Jin 1, Jong-Seong Kug 2, In-Sik Kang 2 1 School of Ocean and Earth Science and Technology, University
More informationSensitivity of Nonlinearity on the ENSO Cycle in a Simple Air-Sea Coupled Model
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2009, VOL. 2, NO. 1, 1 6 Sensitivity of Nonlinearity on the ENSO Cycle in a Simple Air-Sea Coupled Model LIN Wan-Tao LASG, Institute of Atmospheric Physics, Chinese
More informationAnatomizing the Ocean s Role in ENSO Changes under Global Warming*
15 DECEMBER 2008 Y A N G A N D Z H A N G 6539 Anatomizing the Ocean s Role in ENSO Changes under Global Warming* HAIJUN YANG Department of Atmospheric Science, School of Physics, Peking University, Beijing,
More informationThe 1970 s shift in ENSO dynamics: A linear inverse model perspective
GEOPHYSICAL RESEARCH LETTERS, VOL. 4, 1612 1617, doi:1.12/grl.5264, 213 The 197 s shift in ENSO dynamics: A linear inverse model perspective Christopher M. Aiken, 1 Agus Santoso, 1 Shayne McGregor, 1 and
More informationThe Spring Predictability Barrier Phenomenon of ENSO Predictions Generated with the FGOALS-g Model
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2010, VOL. 3, NO. 2, 87 92 The Spring Predictability Barrier Phenomenon of ENSO Predictions Generated with the FGOALS-g Model WEI Chao 1,2 and DUAN Wan-Suo 1 1
More informationInfluence of reducing weather noise on ENSO prediction
PICES 2009 annual meeting W8 POC workshop Influence of reducing weather noise on ENSO prediction Xiaohui Tang 1, Ping Chang 2, Fan Wang 1 1. Key Laboratory of Ocean Circulation and Waves, Institute of
More informationlecture 11 El Niño/Southern Oscillation (ENSO) Part II
lecture 11 El Niño/Southern Oscillation (ENSO) Part II SYSTEM MEMORY: OCEANIC WAVE PROPAGATION ASYMMETRY BETWEEN THE ATMOSPHERE AND OCEAN The atmosphere and ocean are not symmetrical in their responses
More informationEl Niño Southern Oscillation: Magnitudes and asymmetry
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi:10.1029/2009jd013508, 2010 El Niño Southern Oscillation: Magnitudes and asymmetry David H. Douglass 1 Received 5 November 2009; revised 10 February 2010;
More informationLocal versus non-local atmospheric weather noise and the North Pacific SST variability
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L14706, doi:10.1029/2007gl030206, 2007 Local versus non-local atmospheric weather noise and the North Pacific SST variability Sang-Wook
More informationLecture 2 ENSO toy models
Lecture 2 ENSO toy models Eli Tziperman 2.3 A heuristic derivation of a delayed oscillator equation Let us consider first a heuristic derivation of an equation for the sea surface temperature in the East
More informationThe Impact of increasing greenhouse gases on El Nino/Southern Oscillation (ENSO)
The Impact of increasing greenhouse gases on El Nino/Southern Oscillation (ENSO) David S. Battisti 1, Daniel J. Vimont 2, Julie Leloup 2 and William G.H. Roberts 3 1 Univ. of Washington, 2 Univ. of Wisconsin,
More informationImproving ENSO in a Climate Model Tuning vs. Flux correction
Improving ENSO in a Climate Model Tuning vs. Flux correction Tobias Bayr, Mojib Latif, Joke Lübbecke, Dietmar Dommenget and Wonsun Park GEOMAR Kiel, Germany Improving ENSO in a Climate Model Tuning vs.
More informationPotential impact of initialization on decadal predictions as assessed for CMIP5 models
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl051974, 2012 Potential impact of initialization on decadal predictions as assessed for CMIP5 models Grant Branstator 1 and Haiyan Teng 1 Received
More informationEl Niño Southern Oscillation: Magnitudes and Asymmetry
El Niño Southern Oscillation: Magnitudes and Asymmetry David H. Douglass Department of Physics and Astronomy University of Rochester, Rochester, NY 14627-0171 Abstract The alternating warm/cold phenomena
More informationLinear solutions for the frequency and amplitude modulation of ENSO by the annual cycle
SERIES A DYNAMIC METEOROLOGY AND OCEANOGRAPHY PUBLISHED BY THE INTERNATIONAL METEOROLOGICAL INSTITUTE IN STOCKHOLM Printed in Singapore. All rights reserved C 2010 The Authors Tellus A C 2010 International
More informationOceanic origin of the interannual and interdecadal variability of the summertime western Pacific subtropical high
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L13701, doi:10.1029/2008gl034584, 2008 Oceanic origin of the interannual and interdecadal variability of the summertime western Pacific
More informationPredictability of the duration of La Niña
Predictability of the duration of La Niña Pedro DiNezio University of Texas Institute for Geophysics (UTIG) CESM Winter Meeting February 9, 2016 Collaborators: C. Deser 1 and Y. Okumura 2 1 NCAR, 2 UTIG
More informationSimple Mathematical, Dynamical Stochastic Models Capturing the Observed Diversity of the El Niño Southern Oscillation (ENSO)
Simple Mathematical, Dynamical Stochastic Models Capturing the Observed Diversity of the El Niño Southern Oscillation (ENSO) Lecture 5: A Simple Stochastic Model for El Niño with Westerly Wind Bursts Andrew
More informationDepartment of Earth System Science University of California Irvine, California, USA. Revised, April 2011 Accepted by Journal of Climate
Reversed Spatial Asymmetries between El Niño and La Niña and their Linkage to Decadal ENSO Modulation in CMIP Models 1 1 1 1 1 1 0 1 0 1 Jin-Yi Yu * and Seon Tae Kim Department of Earth System Science
More informationThe Two Types of ENSO in CMIP5 Models
1 2 3 The Two Types of ENSO in CMIP5 Models 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Seon Tae Kim and Jin-Yi Yu * Department of Earth System
More informationThe modeling of the ENSO events with the help of a simple model. Abstract
The modeling of the ENSO events with the help of a simple model Vladimir N. Stepanov Proudman Oceanographic Laboratory, Merseyside, England February 20, 2007 Abstract The El Niño Southern Oscillation (ENSO)
More informationthe 2 past three decades
SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE2840 Atlantic-induced 1 pan-tropical climate change over the 2 past three decades 3 4 5 6 7 8 9 10 POP simulation forced by the Atlantic-induced atmospheric
More informationToward understanding the double Intertropical Convergence Zone pathology in coupled ocean-atmosphere general circulation models
Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi:10.1029/2006jd007878, 2007 Toward understanding the double Intertropical Convergence Zone pathology in coupled ocean-atmosphere
More informationTwentieth-Century Sea Surface Temperature Trends M.A. Cane, et al., Science 275, pp (1997) Jason P. Criscio GEOS Apr 2006
Twentieth-Century Sea Surface Temperature Trends M.A. Cane, et al., Science 275, pp. 957-960 (1997) Jason P. Criscio GEOS 513 12 Apr 2006 Questions 1. What is the proposed mechanism by which a uniform
More informationMozambique. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1
UNDP Climate Change Country Profiles Mozambique C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2.Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk
More informationENSO Irregularity. The detailed character of this can be seen in a Hovmoller diagram of SST and zonal windstress anomalies as seen in Figure 1.
ENSO Irregularity The detailed character of this can be seen in a Hovmoller diagram of SST and zonal windstress anomalies as seen in Figure 1. Gross large scale indices of ENSO exist back to the second
More informationAn observational study of the impact of the North Pacific SST on the atmosphere
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L18611, doi:10.1029/2006gl026082, 2006 An observational study of the impact of the North Pacific SST on the atmosphere Qinyu Liu, 1 Na
More informationHigh initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming
GEOPHYSICAL RESEARCH LETTERS, VOL. 37,, doi:10.1029/2010gl044119, 2010 High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming Yuhji Kuroda 1 Received 27 May
More informationPredictability of Tropical Pacific Decadal Variability in an Intermediate Model*
2842 JOURNAL OF CLIMATE Predictability of Tropical Pacific Decadal Variability in an Intermediate Model* ALICIA R. KARSPECK, RICHARD SEAGER, AND MARK A. CANE Lamont-Doherty Earth Observatory, Columbia
More informationHow Will Low Clouds Respond to Global Warming?
How Will Low Clouds Respond to Global Warming? By Axel Lauer & Kevin Hamilton CCSM3 UKMO HadCM3 UKMO HadGEM1 iram 2 ECHAM5/MPI OM 3 MIROC3.2(hires) 25 IPSL CM4 5 INM CM3. 4 FGOALS g1. 7 GISS ER 6 GISS
More informationThe Success of Weather Prediction required A. MEASUREMENTS that describe the phenomena to be predicted
The character Henchard in Thomas Hardyʼs novel, The Mayor of Casterbridge, has the following exchange with a man of curious repute as a forecaster or weather-prophet. Now, for instance, can ye charm away
More informationThe two types of ENSO in CMIP5 models
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2012gl052006, 2012 The two types of ENSO in CMIP5 models Seon Tae Kim 1 and Jin-Yi Yu 1 Received 12 April 2012; revised 14 May 2012; accepted 15 May
More informationLecture 28. El Nino Southern Oscillation (ENSO) part 5
Lecture 28 El Nino Southern Oscillation (ENSO) part 5 Understanding the phenomenon Until the 60s the data was so scant that it seemed reasonable to consider El Nino as an occasional departure from normal
More informationChanges to ENSO under CO 2 Doubling in a Multimodel Ensemble
15 AUGUST 2006 M E R R Y F I E L D 4009 Changes to ENSO under CO 2 Doubling in a Multimodel Ensemble WILLIAM J. MERRYFIELD Canadian Centre for Climate Modelling and Analysis, Meteorological Service of
More informationClimate Risk Profile for Samoa
Climate Risk Profile for Samoa Report Prepared by Wairarapa J. Young Samoa Meteorology Division March, 27 Summary The likelihood (i.e. probability) components of climate-related risks in Samoa are evaluated
More informationLETTERS. The cause of the fragile relationship between the Pacific El Niño and the Atlantic Niño
Vol 443 21 September 2006 doi:10.1038/nature05053 The cause of the fragile relationship between the Pacific El Niño and the Atlantic Niño Ping Chang 1, Yue Fang 1, R. Saravanan 2, Link Ji 1 & Howard Seidel
More informationModelling ENSO in GCMs: overview, progress and challenges
Modelling ENSO in GCMs: overview, progress and challenges Eric Guilyardi IPSL/LOCEAN, Paris, France & NCAS-Climate, Univ. Reading, UK With contributions from: Andrew Wittenberg, Alexey Fedorov, Mat Collins,
More informationSimple Mathematical, Dynamical Stochastic Models Capturing the Observed Diversity of the El Niño-Southern Oscillation
Simple Mathematical, Dynamical Stochastic Models Capturing the Observed Diversity of the El Niño-Southern Oscillation Lectures 2 and 3: Background and simple ENSO models 14 september 2014, Courant Institute
More informationCHINESE JOURNAL OF GEOPHYSICS. Analysis of the characteristic time scale during ENSO. LIU Lin 1,2, YU Wei2Dong 2
49 1 2006 1 CHINESE JOURNAL OF GEOPHYSICS Vol. 49, No. 1 Jan., 2006,. ENSO., 2006, 49 (1) : 45 51 Liu L, Yu W D. Analysis of the characteristic time scale during ENSO. Chinese J. Geophys. (in Chinese),
More informationContents of this file
Geophysical Research Letters Supporting Information for Future changes in tropical cyclone activity in high-resolution large-ensemble simulations Kohei Yoshida 1, Masato Sugi 1, Ryo Mizuta 1, Hiroyuki
More informationRole of atmospheric adjustments in the tropical Indian Ocean warming during the 20th century in climate models
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L08712, doi:10.1029/2008gl033631, 2008 Role of atmospheric adjustments in the tropical Indian Ocean warming during the 20th century in
More informationSPECIAL PROJECT PROGRESS REPORT
SPECIAL PROJECT PROGRESS REPORT Progress Reports should be 2 to 10 pages in length, depending on importance of the project. All the following mandatory information needs to be provided. Reporting year
More informationSeparation of a Signal of Interest from a Seasonal Effect in Geophysical Data: I. El Niño/La Niña Phenomenon
International Journal of Geosciences, 2011, 2, **-** Published Online November 2011 (http://www.scirp.org/journal/ijg) Separation of a Signal of Interest from a Seasonal Effect in Geophysical Data: I.
More informationForced and internal variability of tropical cyclone track density in the western North Pacific
Forced and internal variability of tropical cyclone track density in the western North Pacific Wei Mei 1 Shang-Ping Xie 1, Ming Zhao 2 & Yuqing Wang 3 Climate Variability and Change and Paleoclimate Working
More informationRetrospective El Niño Forecasts Using an Improved Intermediate Coupled Model
SEPTEMBER 2005 Z H A N G E T A L. 2777 Retrospective El Niño Forecasts Using an Improved Intermediate Coupled Model RONG-HUA ZHANG* AND STEPHEN E. ZEBIAK International Research Institute for Climate Prediction,
More informationIrregularity and Predictability of ENSO
Irregularity and Predictability of ENSO Richard Kleeman Courant Institute of Mathematical Sciences New York Main Reference R. Kleeman. Stochastic theories for the irregularity of ENSO. Phil. Trans. Roy.
More informationDecadal changes of ENSO persistence barrier in SST and ocean heat content indices:
Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi:10.1029/2006jd007654, 2007 Decadal changes of ENSO persistence barrier in SST and ocean heat content indices: 1958 2001 Jin-Yi
More informationImpact of atmospheric CO 2 doubling on the North Pacific Subtropical Mode Water
GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L06602, doi:10.1029/2008gl037075, 2009 Impact of atmospheric CO 2 doubling on the North Pacific Subtropical Mode Water Hyun-Chul Lee 1,2 Received 19 December 2008;
More informationDoing science with multi-model ensembles
Doing science with multi-model ensembles Gerald A. Meehl National Center for Atmospheric Research Biological and Energy Research Regional and Global Climate Modeling Program Why use a multi-model ensemble
More informationWill a warmer world change Queensland s rainfall?
Will a warmer world change Queensland s rainfall? Nicholas P. Klingaman National Centre for Atmospheric Science-Climate Walker Institute for Climate System Research University of Reading The Walker-QCCCE
More informationSUPPLEMENTARY INFORMATION
Effect of remote sea surface temperature change on tropical cyclone potential intensity Gabriel A. Vecchi Geophysical Fluid Dynamics Laboratory NOAA Brian J. Soden Rosenstiel School for Marine and Atmospheric
More informationCoupled ocean-atmosphere ENSO bred vector
Coupled ocean-atmosphere ENSO bred vector Shu-Chih Yang 1,2, Eugenia Kalnay 1, Michele Rienecker 2 and Ming Cai 3 1 ESSIC/AOSC, University of Maryland 2 GMAO, NASA/ Goddard Space Flight Center 3 Dept.
More informationThe Climate System and Climate Models. Gerald A. Meehl National Center for Atmospheric Research Boulder, Colorado
The Climate System and Climate Models Gerald A. Meehl National Center for Atmospheric Research Boulder, Colorado The climate system includes all components of the physical earth system that affect weather
More informationReversed Spatial Asymmetries between El Niño and La Niña and Their Linkage to Decadal ENSO Modulation in CMIP3 Models
15 OCTOBER 2011 Y U A N D K I M 5423 Reversed Spatial Asymmetries between El Niño and La Niña and Their Linkage to Decadal ENSO Modulation in CMIP3 Models JIN-YI YU AND SEON TAE KIM Department of Earth
More informationCharacterizing unforced multi-decadal variability of ENSO: a case study with the GFDL CM2.1 coupled GCM
Clim Dyn DOI 1.17/s382-16-3477-9 Characterizing unforced multi-decadal variability of ENSO: a case study with the GFDL coupled GCM A. R. Atwood 1,2 D. S. Battisti 3 A. T. Wittenberg 4 W. H. G. Roberts
More informationTropical Pacific response to 20th century Atlantic warming
GEOPHYSICAL RESEARCH LETTERS, VOL. 38,, doi:10.1029/2010gl046248, 2011 Tropical Pacific response to 20th century Atlantic warming F. Kucharski, 1 I. S. Kang, 2 R. Farneti, 1 and L. Feudale 1 Received 16
More informationDecadal variability of the IOD-ENSO relationship
Chinese Science Bulletin 2008 SCIENCE IN CHINA PRESS ARTICLES Springer Decadal variability of the IOD-ENSO relationship YUAN Yuan 1,2 & LI ChongYin 1 1 State Key Laboratory of Numerical Modeling for Atmospheric
More informationTropical Pacific decadal variability and ENSO amplitude modulation in a CGCM
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi:10.1029/2004jc002442, 2004 Tropical Pacific decadal variability and ENSO amplitude modulation in a CGCM Sang-Wook Yeh and Ben P. Kirtman 1 Center for Ocean-Land-Atmosphere
More informationOne of the main characteristics of the earth s climate is its strong
El Niño/Southern Oscillation response to global warming M. Latif 1 and N. S. Keenlyside Ocean Circulation and Climate Dynamics Division, Leibniz Institut für Meereswissenschaften an der Universität Kiel,
More informationForecasting. Theory Types Examples
Forecasting Theory Types Examples How Good Are Week Out Weather Forecasts? For forecasts greater than nine days out, weather forecasters do WORSE than the climate average forecast. Why is there predictability
More informationCauses of the El Niño and La Niña Amplitude Asymmetry in the Equatorial Eastern Pacific
1FEBRUARY 2010 S U E T A L. 605 Causes of the El Niño and La Niña Amplitude Asymmetry in the Equatorial Eastern Pacific JINGZHI SU,* RENHE ZHANG, 1 TIM LI, # XINYAO RONG, 1 J.-S. KUG, # AND CHI-CHERNG
More informationInitialized decadal climate predictions focusing on the Pacific Gerald Meehl
Initialized decadal climate predictions focusing on the Pacific Gerald Meehl National Center for Atmospheric Research Boulder Colorado Following Zhang, Wallace and Battisti (1997) the Interdecadal Pacific
More informationRelationships between Extratropical Sea Level Pressure Variations and the Central- Pacific and Eastern-Pacific Types of ENSO
1 2 3 4 Relationships between Extratropical Sea Level Pressure Variations and the Central- Pacific and Eastern-Pacific Types of ENSO 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
More informationSensitivity of Tropical Tropospheric Temperature to Sea Surface Temperature Forcing
Sensitivity of Tropical Tropospheric Temperature to Sea Surface Temperature Forcing Hui Su, J. David Neelin and Joyce E. Meyerson Introduction During El Niño, there are substantial tropospheric temperature
More information2013 ATLANTIC HURRICANE SEASON OUTLOOK. June RMS Cat Response
2013 ATLANTIC HURRICANE SEASON OUTLOOK June 2013 - RMS Cat Response Season Outlook At the start of the 2013 Atlantic hurricane season, which officially runs from June 1 to November 30, seasonal forecasts
More informationImpact of the Atlantic Multidecadal Oscillation on the Asian summer monsoon
GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L24701, doi:10.1029/2006gl027655, 2006 Impact of the Atlantic Multidecadal Oscillation on the Asian summer monsoon Riyu Lu, 1,2 Buwen Dong, 3 and Hui Ding 2,4 Received
More informationNatural Centennial Tropical Pacific Variability in Coupled GCMs
Natural Centennial Tropical Pacific Variability in Coupled GCMs Jason E. Smerdon 1 Kris Karnauskas 2 Richard Seager 1 J. Fidel Gonzalez-Rouco 3 1 LDEO, 2 WHOI and 3 UCM Millennial Climate Model Simulations
More informationEnsemble Hindcasts of ENSO Events over the Past 120 Years Using a Large Number of Ensembles
ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 26, NO. 2, 2009, 359 372 Ensemble Hindcasts of ENSO Events over the Past 120 Years Using a Large Number of Ensembles ZHENG Fei 1 (x ), ZHU Jiang 2 (Á ô), WANG Hui
More informationTROPICAL METEOROLOGY Ocean-Atmosphere Interaction and Tropical Climate Shang-Ping Xie OCEAN-ATMOSPHERE INTERACTION AND TROPICAL CLIMATE
OCEAN-ATMOSPHERE INTERACTION AND TROPICAL CLIMATE Shang-Ping Xie International Pacific Research Center and Department of Meteorology University of Hawaii, Honolulu, HI 96822, USA Keywords: ocean-atmospheric
More informationAnthropogenic climate change is now well established as a
Published online: 23 MaY 21 doi: 1.138/ngeo868 the impact of global warming on the tropical Pacific ocean and el niño Mat collins 1 *, soon-il an 2, Wenju cai 3, alexandre ganachaud 4, eric guilyardi 5,
More informationENSO Cycle: Recent Evolution, Current Status and Predictions. Update prepared by Climate Prediction Center / NCEP 23 April 2012
ENSO Cycle: Recent Evolution, Current Status and Predictions Update prepared by Climate Prediction Center / NCEP 23 April 2012 Outline Overview Recent Evolution and Current Conditions Oceanic Niño Index
More informationMechanisms of Seasonal ENSO Interaction
61 Mechanisms of Seasonal ENSO Interaction ELI TZIPERMAN Department of Environmental Sciences, The Weizmann Institute of Science, Rehovot, Israel STEPHEN E. ZEBIAK AND MARK A. CANE Lamont-Doherty Earth
More informationarxiv: v1 [physics.ao-ph] 18 Mar 2015
A Climate Network Based Stability Index for El Niño Variability Qing Yi Feng and Henk A. Dijkstra arxiv:13.449v1 [physics.ao-ph] 18 Mar 21 Institute for Marine and Atmospheric research Utrecht (IMAU),
More informationInterdecadal and Interannnual Variabilities of the Antarctic Oscillation Simulated by CAM3
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2014, VOL. 7, NO. 6, 515 520 Interdecadal and Interannnual Variabilities of the Antarctic Oscillation Simulated by CAM3 XUE Feng 1, SUN Dan 2,3, and ZHOU Tian-Jun
More informationAssessing the Quality of Regional Ocean Reanalysis Data from ENSO Signals
ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2012, VOL. 5, NO. 1, 55 61 Assessing the Quality of Regional Ocean Reanalysis Data from ENSO Signals WANG Lu 1, 2 and ZHOU Tian-Jun 1 1 State Key Laboratory of
More informationAn Equatorial Ocean Recharge Paradigm for ENSO. Part I: Conceptual Model
811 JOURNAL OF THE ATMOSPHERIC SCIENCES An Equatorial Ocean Recharge Paradigm for ENSO. Part I: Conceptual Model FEI-FEI JIN Department of Meteorology, School of Ocean and Earth Science and Technology,
More informationPotential of Equatorial Atlantic Variability to Enhance El Niño Prediction
1 Supplementary Material Potential of Equatorial Atlantic Variability to Enhance El Niño Prediction N. S. Keenlyside 1, Hui Ding 2, and M. Latif 2,3 1 Geophysical Institute and Bjerknes Centre, University
More informationEvaluating a Genesis Potential Index with Community Climate System Model Version 3 (CCSM3) By: Kieran Bhatia
Evaluating a Genesis Potential Index with Community Climate System Model Version 3 (CCSM3) By: Kieran Bhatia I. Introduction To assess the impact of large-scale environmental conditions on tropical cyclone
More informationENSO-Like and ENSO-Induced Tropical Pacific Decadal Variability in CGCMs
1MARCH 2013 C H O I E T A L. 1485 ENSO-Like and ENSO-Induced Tropical Pacific Decadal Variability in CGCMs JUNG CHOI AND SOON-IL AN Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
More informationOcean-Atmosphere Interactions and El Niño Lisa Goddard
Ocean-Atmosphere Interactions and El Niño Lisa Goddard Advanced Training Institute on Climatic Variability and Food Security 2002 July 9, 2002 Coupled Behavior in tropical Pacific SST Winds Upper Ocean
More informationCuba. General Climate. Recent Climate Trends. UNDP Climate Change Country Profiles. Temperature. C. McSweeney 1, M. New 1,2 and G.
UNDP Climate Change Country Profiles Cuba C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk
More informationMalawi. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1
UNDP Climate Change Country Profiles Malawi C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk
More informationHybrid coupled modeling of the tropical Pacific using neural networks
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110,, doi:10.1029/2004jc002595, 2005 Hybrid coupled modeling of the tropical Pacific using neural networks Shuyong Li, William W. Hsieh, and Aiming Wu Department of
More informationMid-Holocene ENSO: Issues in quantitative model-proxy data comparisons
Click Here for Full Article PALEOCEANOGRAPHY, VOL. 23,, doi:10.1029/2007pa001512, 2008 Mid-Holocene ENSO: Issues in quantitative model-proxy data comparisons J. Brown, 1,2 A. W. Tudhope, 3 M. Collins,
More informationWCRP/CLIVAR efforts to understand El Niño in a changing climate
WCRP/CLIVAR efforts to understand El Niño in a changing climate Eric Guilyardi IPSL/LOCEAN, Paris & NCAS-Climate, U. Reading Thanks to Andrew Wittenberg, Mike McPhaden, Matthieu Lengaigne 2015 El Niño
More informationIMPLICATIONS OF THE VAST PLIOCENE WARMPOOL. Chris Brierley and Alexey Fedorov
IMPLICATIONS OF THE VAST PLIOCENE WARMPOOL Chris Brierley and Alexey Fedorov Outline Introduction to the Early Pliocene When & why should we care? A vast warmpool in the Pacific Paleo-observations & comparison
More informationWhich Climate Model is Best?
Which Climate Model is Best? Ben Santer Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory, Livermore, CA 94550 Adapting for an Uncertain Climate: Preparing
More informationNOTES AND CORRESPONDENCE. El Niño Southern Oscillation and North Atlantic Oscillation Control of Climate in Puerto Rico
2713 NOTES AND CORRESPONDENCE El Niño Southern Oscillation and North Atlantic Oscillation Control of Climate in Puerto Rico BJÖRN A. MALMGREN Department of Earth Sciences, University of Göteborg, Goteborg,
More informationDynamics of annual cycle/enso interactions
Dynamics of annual cycle/enso interactions A. Timmermann (IPRC, UH), S. McGregor (CCRC), M. Stuecker (Met Dep., UH) F.-F. Jin (Met Dep., UH), K. Stein (Oce Dep., UH), N. Schneider (IPRC, UH), ENSO and
More informationMultidecadal variability in the transmission of ENSO signals to the Indian Ocean
Click Here for Full Article GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L09706, doi:10.1029/2007gl029528, 2007 Multidecadal variability in the transmission of ENSO signals to the Indian Ocean G. Shi, 1 J. Ribbe,
More informationImpacts of Long-term Climate Cycles on Alberta. A Summary. by Suzan Lapp and Stefan Kienzle
Impacts of Long-term Climate Cycles on Alberta A Summary by Suzan Lapp and Stefan Kienzle Large Scale Climate Drivers The Pacific Decadal Oscillation (PDO) [Mantua et al., 1997] is the dominant mode of
More informationRobust GEFA Assessment of Climate Feedback to SST EOF Modes
ADVANCES IN ATMOSPHERIC SCIENCES, VOL. 28, NO. 4, 2011, 907 912 Robust GEFA Assessment of Climate Feedback to SST EOF Modes FAN Lei 1 ( [), Zhengyu LIU 2,3 (4ffi ), and LIU Qinyu 1 (4 ) 1 Physical Oceanography
More information