Abstract

Tropical ocean wave dynamics associated with the El Niño–Southern Oscillation cycle in a coupled model are examined. The ocean–atmosphere model consists of statistical atmosphere coupled to a simple reduced gravity model of the tropical Pacific Ocean. The statistical atmosphere is simple enough to allow for the structure and position of the wind stress anomalies to be externally specified. In a control simulation, where the structure of the wind stress anomaly is determined from observations, the model produces a regular 5-yr oscillation. This simulation is consistent with the so-called delayed oscillator theory in that subsurface wave dynamics determine the slow timescale of the oscillation and surface-layer processes are found to be of secondary importance. Kelvin and Rossby wave propagation is detected along the equator, with periods considerably shorter than the simulated oscillation period. The way in which these relatively fast waves are related to the simulated 5-yr oscillation is discussed.

In order to understand the mechanism responsible for the 5-yr period in the control simulation, two sets of sensitivity experiments were conducted. The first set of experiments focused on how the meridional structure of the wind stress anomaly influences the model ENSO period. Relatively broad (narrow) meridional structures lead to relatively long (short) periods. While the gravest Rossby wave appears to be important in these simulations, it is found that the maximum variability in the thermocline is associated with off-equatorial Rossby waves (i.e., Rossby waves that have a maximum amplitude beyond ±7° of the equator). The second set of sensitivity experiments was designed to examine how these off-equatorial Rossby waves influence the ENSO cycle. Without the effects of the off-equatorial Rossby waves at the western boundary, the model produces a 2-yr oscillation regardless of the meridional structure of the wind stress anomaly. The mechanism by which these off-equatorial Rossby waves influence the ENSO period is described. Based on these experiments, it is shown that the reflection of the gravest Rossby wave off the western boundary is required to produce oscillatory behavior in the model, but the period of the oscillation is determined by the off-equatorial Rossby waves and the latitude at which they are forced.

Abstract

Results are described from a large sample of coupled ocean–atmosphere retrospective forecasts during 1980–99. The prediction system includes a global anomaly coupled general circulation model and a state-of-the-art ocean data assimilation system. The retrospective forecasts are initialized each January, April, July, and October of each year, and ensembles of six forecasts are run for each initial month, yielding a total of 480 1-yr predictions.

In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective. The probabilistic approach is used to quantify the uncertainty in any given forecast. The deterministic measures of skill for eastern tropical Pacific SST anomalies (SSTAs) suggest that the ensemble mean forecasts are useful up to lead times of 7–9 months. At somewhat shorter leads, the forecasts capture some aspects of the variability in the tropical Indian and Atlantic Oceans. The ensemble mean precipitation anomaly has disappointingly low correlation with observed rainfall. The probabilistic measures of skill (relative operating characteristics) indicate that the distribution of the ensemble provides useful forecast information that could not easily be gleaned from the ensemble mean. In particular, the prediction system has more skill at forecasting cold ENSO events compared to warm events. Despite the fact that the ensemble mean rainfall is not well correlated with the observed, the ensemble distribution does indicate significant regions where there is useful information in the forecast ensemble. In fact, it is possible to detect that droughts over land are more predictable than floods. It is argued that probabilistic verification is an important complement to any deterministic verification, and provides a useful and quantitative way to measure uncertainty.

Abstract

This study investigates the impacts of the Indian Ocean on the relationship between the Indian summer monsoon and the El Niño–Southern Oscillation (ENSO) through numerical simulations with a coupled atmosphere–ocean general circulation model and atmospheric general circulation model (AGCM) experiments with specified sea surface temperature (SST) and surface heat flux (SHF) forcing. Previous studies have shown that this particular coupled model captures many aspects of the observed Indian summer monsoon–ENSO relationship. However, it is found that, when the Indian Ocean is decoupled from the atmosphere, the Indian monsoon–ENSO relationship reverses. This change is linked to the relationships between surface evaporation, surface wind, and SST in the north Indian Ocean. In the coupled case, surface evaporation anomalies are positively correlated with surface wind anomalies during April–June and are of the same sign as SST anomalies during July–September. In the decoupled Indian Ocean case, surface evaporation anomalies are of the same sign as surface wind anomalies during the entire April–September period.

Numerical experiments with an AGCM were performed with SST or SHF anomalies specified in the tropical Indian–Pacific Ocean, tropical Pacific Ocean only, and tropical Indian Ocean only. These experiments confirm the importance of local coupled air–sea feedback in the Indian Ocean for a proper simulation of the Indian monsoon–ENSO relationship.

Abstract

This study investigates the relationship between spring and summer rainfall in East Asia and the preceding winter and spring snow cover/depth over Eurasia, using station rainfall observations, satellite-observed snow cover, satellite-derived snow water equivalent, and station observations of the number of days of snow cover and snow depth. Correlation analysis shows that snow-depth anomalies can persist from winter to spring whereas snow cover anomalies cannot in most regions of Eurasia. Locally, snow cover and snow-depth anomalies in February are not related in most regions to the north of 50°N, but those anomalies in April display consistent year-to-year variations. The results suggest that the winter snow cover cannot properly represent all the effects of snow and it is necessary to separate the winter and spring snow cover in addressing the snow–monsoon relationship.

Spring snow cover in western Siberia is positively correlated with spring rainfall in southern China. The circulation anomalies associated with the western Siberian spring snow cover variations show an apparent wave pattern over the eastern Atlantic through Europe and midlatitude Asia. Spring snow cover over the Tibetan Plateau shows a moderate positive correlation with spring rainfall in southern China. Analysis shows that this correlation includes El Niño–Southern Oscillation (ENSO) effects. In contrast to the Indian summer monsoon rainfall for which the ENSO interferes with the snow effects, the Tibetan Plateau snow cover and ENSO work cooperatively to enhance spring rainfall anomalies in southern China. In comparison, ENSO has larger impacts than the snow on spring rainfall in southern China.

Abstract

This study investigates whether air–sea interactions contribute to differences in the predictability of the boreal winter tropical intraseasonal oscillation (TISO) using the NCEP operational climate model. A series of coupled and uncoupled, “perfect” model predictability experiments are performed for 10 strong model intraseasonal events. The uncoupled experiments are forced by prescribed SST containing different types of variability. These experiments are specifically designed to be directly comparable to actual forecasts. Predictability estimates are calculated using three metrics, including one that does not require the use of time filtering. The estimates are compared between these experiments to determine the impact of coupled air–sea interactions on the predictability of the tropical intraseasonal oscillation and the sensitivity of the potential predictability estimates to the different SST forcings.

Results from all three metrics are surprisingly similar. They indicate that predictability estimates are longest for precipitation and outgoing longwave radiation (OLR) when the ensemble mean from the coupled model is used. Most importantly, the experiments that contain intraseasonally varying SST consistently predict the control events better than those that do not for precipitation, OLR, 200-hPa zonal wind, and 850-hPa zonal wind after the first 10 days. The uncoupled model is able to predict the TISO with similar skill to that of the coupled model, provided that an SST forecast that includes these intraseasonal variations is used to force the model. This indicates that the intraseasonally varying SSTs are a key factor for increased predictability and presumably better prediction of the TISO.

Abstract

The processes for the coupled tropospheric biennial oscillation involving the Indian monsoon and El Niño–Southern Oscillation (ENSO) are studied through composites of sequential wet monsoon/La Niña year followed by dry monsoon/El Niño year using observations and outputs from the Center for Ocean–Land–Atmosphere Studies (COLA) interactive ensemble coupled general circulation model. The previous composites emphasize the role of ENSO in the monsoon transition but do not exclude the possible role of factors other than ENSO.

It is found that the central eastern equatorial Pacific sea surface temperature (SST) anomalies can affect the Indian monsoon transition by two processes: (i) a shift of large-scale east–west circulation across the equatorial Indian–Pacific Oceans; and (ii) a Rossby wave–type response over the eastern north Indian Ocean–western North Pacific. The former provides the low-level anomalous moisture convergence (divergence) that preconditions the atmosphere for a wet (dry) Indian monsoon. The latter induces anomalous downward (upward) motion and low-level anomalous anticyclonic (cyclonic) circulation over the eastern north Indian Ocean–South China Sea. Associated shortwave radiation and surface evaporation changes favor the eastward shift of cold (warm) SST anomalies and in turn low-level easterly (westerly) anomalies through SST gradient changes.

The moistening (drying) of the atmosphere before a wet (dry) Indian monsoon is due to the low-level anomalous moisture convergence (divergence) that is dominated by anomalous wind convergence of the mean humidity. A large increase (decrease) in surface evaporation is observed in conjunction with the transition to a wet (dry) monsoon. The Indian Ocean SST change is mainly a response to the monsoon and ENSO-induced surface heat flux changes. The Asian land surface condition anomalies are not necessary in this coupled biennial variability of the monsoon–ENSO system in the model.

Abstract

A negative correlation is observed between interannual variations of the Australian summer monsoon (ASM) and El Niño–Southern Oscillation (ENSO). This negative relationship is well simulated in the Center for Ocean–Land–Atmosphere (COLA) interactive ensemble coupled general circulation model (CGCM). The present study investigates roles of the Indian Ocean in the ASM–ENSO relationship through controlled numerical experiments with the COLA CGCM. It is found that air–sea coupling in the Indian Ocean plays an important role in maintaining the negative ASM–ENSO relationship. When the Indian Ocean is decoupled from the atmosphere, the ASM–ENSO relationship is significantly weakened or even masked by the internal atmospheric variability. This change in the ASM–ENSO relationship is related to complementary roles of Indian Ocean sea surface temperature (SST) anomalies in the ASM variability and feedbacks from the Indian Ocean on ENSO. Without a coupled Indian Ocean, the ENSO amplitude is reduced, leading to a decrease in the ENSO-induced ASM variability, and the constructive impacts of the Indian Ocean SST anomalies on the ASM variability are substantially reduced. This reduces the ASM variability related to ENSO. Consistent with the change in the ASM–ENSO relationship, the local air–sea relationship in the ASM region displays important differences with and without a coupled Indian Ocean.

The long-term change in the ASM–ENSO relationship is related to that in ENSO amplitude in the interactive ensemble coupled model. A relatively higher (lower) negative correlation occurs in periods of larger (smaller) ENSO amplitude. This relationship, however, is not clear in the anomaly coupled model with only one atmospheric realization. This difference is attributed to changes in the signal-to-noise ratio in the ASM variability. A comparison is made with observations and the long-term change in the Indian summer monsoon (ISM)–ENSO relationship in the model.

Abstract

Calculations of the optimal perturbations of an anomaly coupled ocean–atmosphere general circulation model (ACGCM) have been performed using a set of linear (Markov) models best fit to 300 years of continuous simulations. This study aims (i) to verify some of the findings regarding optimals from earlier studies based on relatively less complex coupled models and (ii) to assess how well a linear stochastic model reproduces the interannual variability of the tropical Pacific in the ACGCM.

The Markov models are built in a multivariate (i.e., sea surface temperature, heat content, zonal and meridional components of wind stress, and total heat flux) empirical orthogonal function (MEOF) space with reduced dimension while retaining the important covariability. These empirical models are trained in the first 300 yr and verified in the last 50 yr of the data. The Markov model with eight retained MEOFs (MK8) shows, in terms of the anomaly correlations, the best predictive skill of interannual variability in the ACGCM for up to about a year.

The main conclusions of this study are as follows:

(i) The singular values of MK8 show a strong seasonal dependence, indicating seasonal variation in the Markov models, which in turn implies the importance of seasonality in the internal dynamics. From the perspective of a stochastic model, this may also indicate that neither the nonlinearity in the system nor the seasonality in the stochastic forcing is necessary for the phase locking of the model ENSO to the annual cycle.

(ii) In the tropical Pacific, a narrow region straddling the equator is conducive for the transient growth of perturbations, independent of the season; however, a migration of the maximum growth region from the central to the eastern Pacific from spring to summer to winter is also observed. The western warm pool region is capable of generating transient growth throughout the year. However, the amplitude of the optimal shows some modulation with the annual cycle. Another region where considerable transient growth can occur is the northern subtropics. Therefore, the optimals in this study encompass the features of a coupled model with a statistical atmosphere as well as the features of the coupled model with a dynamical atmosphere in which deep convective parameterization is included.

(iii) In general, the geographical locations of the optimals do not depend on whether EOF analysis is performed on a covariance matrix or on a correlation matrix. Nevertheless, their spatial extent is larger when the correlation matrix is used.

(iv) The spatial distribution of the ratio of local amplitudes of the residual (misfit of the linear models) to the respective local anomaly points to the fact that, in general, a narrow region between 5°S and 5°N in the Pacific Ocean is in a linear regime. However, in the off-equatorial region this linear approximation generally fails. In the equatorial western Pacific west of 160°E, a region where abundant convective activity occurs both in the model and in nature, nonlinear dynamics is important. Therefore, this analysis hints at the possibility of accommodating the two competing points of views of ENSO into a single framework, by virtue of the fact that both linear and nonlinear dynamics seems to operate in a nonoverlapping manner in both space and time.

Abstract

The present study documents the influence of El Niño and La Niña events on the spread and predictability of rainfall, surface pressure, and 500-hPa geopotential height, and contrasts the relative contribution of signal and noise changes to the predictability change based on a long-term integration of an interactive ensemble coupled general circulation model. It is found that the pattern of the El Niño–Southern Oscillation (ENSO)-induced noise change for rainfall follows closely that of the corresponding signal change in most of the tropical regions. The noise for tropical Pacific surface pressure is larger (smaller) in regions of lower (higher) mean pressure. The ENSO-induced noise change for 500-hPa height displays smaller spatial scales compared to and has no systematic relationship with the signal change.

The predictability for tropical rainfall and surface pressure displays obvious contrasts between the summer and winter over the Bay of Bengal, the western North Pacific, and the tropical southwestern Indian Ocean. The predictability for tropical 500-hPa height is higher in boreal summer than in boreal winter. In the equatorial central Pacific, the predictability for rainfall is much higher in La Niña years than in El Niño years. This occurs because of a larger percent reduction in the amplitude of noise compared to the percent decrease in the magnitude of signal from El Niño to La Niña years. A consistent change is seen in the predictability for surface pressure near the date line. In the western North and South Pacific, the predictability for boreal winter rainfall is higher in El Niño years than in La Niña years. This is mainly due to a stronger signal in El Niño years compared to La Niña years. The predictability for 500-hPa height increases over most of the Tropics in El Niño years. Over western tropical Pacific–Australia and East Asia, the predictability for boreal winter surface pressure and 500-hPa height is higher in El Niño years than in La Niña years. The predictability change for 500-hPa height is primarily due to the signal change.

Abstract

This study investigates the impacts of the Indian Ocean on El Niño–Southern Oscillation (ENSO) variability through numerical simulations with a coupled atmosphere–ocean general circulation model, composite analyses with the coupled model output, and simple atmosphere model experiments with specified sea surface temperature (SST) forcing. It is found that, when the Indian Ocean is decoupled from the atmosphere, the ENSO variability in the coupled model is significantly reduced. Conditional SST distributions indicate that the warm (cold) ENSO state is stronger and occurs more frequently when the Indian Ocean SST in summer is relatively cold (warm), whereas it is weaker and occurs less frequently when the Indian Ocean is relatively warm (cold). The impacts of the Indian Ocean are suggested by a comparison of SST composites under warm, normal, and cold Indian Ocean SST conditions in the developing stage of ENSO.

It is demonstrated that the Indian Ocean affects the ENSO variability through modulating convective heating over the Indian Ocean and the Walker circulation over the tropical Indian and Pacific Oceans. Warmer (colder) Indian Ocean SST induces easterly (westerly) surface wind anomalies over the eastern Indian Ocean and western-central equatorial Pacific. Numerical experiments of a simple atmosphere model with specified SST forcing support the roles of imposed Indian Ocean SST anomalies.

The applicability of the model results to nature is discussed. It is shown that the observed SST anomalies in the Indian Ocean were out of phase with those in the Pacific Ocean in some ENSO developing years. As such, the Indian Ocean SST anomalies could contribute to the intensity of ENSO. This impact is significant for the cold ENSO events, and there is some evidence for this impact during some warm ENSO events.