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Ben P. Kirtman

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.

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Ben P. Kirtman

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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.

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Renguang Wu and Ben P. Kirtman

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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.

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Renguang Wu and Ben P. Kirtman

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.

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Renguang Wu and Ben P. Kirtman

Abstract

Equatorial Pacific sea surface temperature (SST) anomalies in the Center for Ocean–Land–Atmosphere Studies (COLA) interactive ensemble coupled general circulation model show near-annual variability as well as biennial El Niño–Southern Oscillation (ENSO) variability. There are two types of near-annual modes: a westward propagating mode and a stationary mode. For the westward propagating near-annual mode, warm SST anomalies are generated in the eastern equatorial Pacific in boreal spring and propagate westward in boreal summer. Consistent westward propagation is seen in precipitation, surface wind, and ocean current. For the stationary near-annual mode, warm SST anomalies develop near the date line in boreal winter and decay locally in boreal spring. Westward propagation of warm SST anomalies also appears in the developing year of the biennial ENSO mode. However, warm SST anomalies for the westward propagating near-annual mode occur about two months earlier than those for the biennial ENSO mode and are quickly replaced by cold SST anomalies, whereas warm SST anomalies for the biennial ENSO mode only experience moderate weakening.

Anomalous zonal advection contributes to the generation and westward propagation of warm SST anomalies for both the westward propagating near-annual mode and the biennial ENSO mode. However, the role of mean upwelling is markedly different. The mean upwelling term contributes to the generation of warm SST anomalies for the biennial ENSO mode, but is mainly a damping term for the westward propagating near-annual mode. The development of warm SST anomalies for the stationary near-annual mode is partially due to anomalous zonal advection and upwelling, similar to the amplification of warm SST anomalies in the equatorial central Pacific for the biennial ENSO mode. The mean upwelling term is negative in the eastern equatorial Pacific for the stationary near-annual mode, which is opposite to the ENSO mode.

The development of cold SST anomalies in the aftermath of warm SST anomalies for the westward propagating near-annual mode is coupled to large easterly wind anomalies, which occur between the warm and cold SST anomalies. The easterly anomalies contribute to the cold SST anomalies through anomalous zonal, meridional, and vertical advection and surface evaporation. The cold SST anomalies, in turn, enhance the easterly anomalies through a Rossby-wave-type response. The above processes are most effective during boreal spring when the mean near-surface-layer ocean temperature gradient is the largest. It is suggested that the westward propagating near-annual mode is related to air–sea interaction processes that are limited to the near-surface layers.

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Renguang Wu and Ben P. Kirtman

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.

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Kathy Pegion and Ben P. Kirtman

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The impact of coupled air–sea feedbacks on the simulation of tropical intraseasonal variability is investigated in this study using the National Centers for Environmental Prediction Climate Forecast System. The simulation of tropical intraseasonal variability in a freely coupled simulation is compared with two simulations of the atmospheric component of the model. In one experiment, the uncoupled model is forced with the daily sea surface temperature (SST) from the coupled run. In the other, the uncoupled model is forced with climatological SST from the coupled run. Results indicate that the overall intraseasonal variability of precipitation is reduced in the coupled simulation compared to the uncoupled simulation forced by daily SST. Additionally, air–sea coupling is responsible for differences in the simulation of the tropical intraseasonal oscillation between the coupled and uncoupled models, specifically in terms of organization and propagation in the western Pacific. The differences between the coupled and uncoupled simulations are due to the fact that the relationships between precipitation and SST and latent heat flux and SST are much stronger in the coupled model than in the uncoupled model. Additionally, these relationships are delayed by about 5 days in the uncoupled model compared to the coupled model. As demonstrated by the uncoupled simulation forced with climatological SST, some of the intraseasonal oscillation can be simulated by internal atmospheric dynamics. However, the intraseasonally varying SST appears to be important to the amplitude and propagation of the oscillation beyond the Maritime Continent.

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Ray Bell and Ben Kirtman

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This study assesses the skill of multimodel forecasts of 10-m wind speed, significant wave height, and mean wave period in the North Atlantic for the winter months. The 10-m winds from four North American multimodel ensemble models and three European Multimodel Seasonal-to-Interannual Prediction project (EUROSIP) models are used to force WAVEWATCH III experiments. Ten ensembles are used for each model. All three variables can be predicted using December initial conditions. The spatial maps of rank probability skill score are explained by the impact of the North Atlantic Oscillation (NAO) on the large-scale wind–wave relationship. Two winter case studies are investigated to understand the relationship between large-scale environmental conditions such as sea surface temperature, geopotential height at 500 hPa, and zonal wind at 200 hPa to the NAO and the wind–wave climate. The very strong negative NAO in 2008/09 was not well forecast by any of the ensembles while most models correctly predicted the sign of the event. This led to a poor forecast of the surface wind and waves. A Monte Carlo model combination analysis is applied to understand how many models are needed for a skillful multimodel forecast. While the grand multimodel ensemble provides robust skill, in some cases skill improves once some models are not included.

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Cristiana Stan and Ben P. Kirtman

Abstract

The influence of atmospheric stochastic forcing and uncertainty in initial conditions on the limit of predictability of the NCEP Climate Forecast System (CFS) is quantified based on comparisons of idealized identical twin prediction experiments using two different coupling strategies. In the first method, called the interactive ensemble, a single oceanic general circulation model (GCM; the Modular Ocean Model version 3 of GFDL) is coupled to the ensemble average of multiple realizations (in this case six ensemble members) of an atmospheric GCM (NCEP Global Forecast System). In the second method the standard CFS is used. The interactive ensemble is specifically designed to reduce the internal atmospheric dynamic fluctuations that are unrelated to the sea surface temperature anomalies via ensemble averaging at the air–sea interface, whereas in the standard CFS, the atmospheric noise (i.e., stochastic forcing) plays an active role in the evolution of the coupled system. In the identical twin experiments presented here, the perfect model approach is taken, thereby explicitly excluding the impact of model error on the estimate of the limit of predictability. The experimental design and the analysis separately consider how uncertainty in the ocean initial conditions (i.e., initial condition noise) versus uncertainty as the forecast evolves (i.e., noise due to internal dynamics of the atmosphere) impact estimates of the limit of predictability. Estimates of the limit of predictability are based on both deterministic measures (ensemble spread and root-mean-square error) and probabilistic measures (relative operating characteristics). The analysis examines both oceanic and atmospheric variables in the tropical Pacific. The overarching result is that noise in the initial condition is the primary factor limiting predictability, whereas noise as the forecast evolves is of secondary importance.

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