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Heat is one of the most serious hazards in the world as it affects human health and is extremely dangerous to vulnerable populations such as the elderly, people with preexisting cardiovascular or respiratory conditions, and even healthy people with prolonged sunlight exposure during heat waves. As the globe has warmed over the past several decades, extreme heat has become more frequent and intense than ever before, and Africa, especially the Sahel in West Africa, is one of the regions of the world where heat is a major public health concern exacerbated by livelihood activities during the heat season. Yet, there is a major gap in monitoring and forecasting heat waves in Africa. This paper describes NOAA's role in enabling heat–health early warning in Africa, working with meteorological services and health professionals. Emphasis is on real-time heat wave forecasting at week 2, including the postprocessing of the NCEP model outputs, and providing the information to the meteorological services in Africa to serve as guidance in national heat wave forecasts. In addition, the paper describes the end-to-end process of heat hazard outlooks and translating the forecasts into early action and early planning to reduce heat risk to human health. Furthermore, the paper addresses the very important aspect of capacity development tailored at enhancing forecasters' skills to prepare and issue heat wave forecasts and training of a cadre of health professionals to work with meteorologists to coproduce heat–health bulletins and to issue heat–health early warnings. [ABSTRACT FROM AUTHOR]

ABSTRACT Seasonal climate forecasts are operationally produced at various climate prediction centres around the world. However, these forecasts may not necessarily be appropriately integrated into application models in order to help with decision-making processes. This study investigates the use of a combination of physical and empirical models to predict seasonal inflows into Lake Kariba in southern Africa. Two predictions systems are considered. The first uses antecedent seasonal rainfall totals over the upper Zambezi catchment as predictor in a statistical model for estimating seasonal inflows into Lake Kariba. The second and more sophisticated method uses predicted low-level atmospheric circulation of a coupled ocean-atmosphere general circulation model ( CGCM) downscaled to the inflows. Forecast verification results are presented for five run-on 3-month seasons; from September to June over an independent hindcast period of 14 years (1995/1996 to 2008/2009). Verification is conducted using the relative operating characteristic ( ROC) and the reliability diagram. In addition to the presented verification statistics, the hindcasts are also evaluated in terms of their economic value as a usefulness indicator of forecast quality for bureaucrats and to the general public. The models in general perform best during the austral mid-summer season of DJF (seasonal onset of inflows) and the autumn season of MAM (main inflow season). Moreover, the prediction system that uses the output of the CGCM is superior to the simple statistical approach. An additional forecast of a recent flooding event (2010/2011), which lies outside of the 14-year verification window, is presented to demonstrate the forecast system's operational capability further during a season of high inflows that caused societal and infrastructure problems over the region. [ABSTRACT FROM AUTHOR]

ABSTRACT An assessment of probabilistic prediction skill of seasonal temperature extremes over Southern Africa is presented. Verification results are presented for six run-on seasons; September to November, October to December, November to January, December to February, January to March, and February to April over a 15-year retroactive period. Comparisons are drawn between downscaled seasonal 850 hPa geopotential height field forecasts of a two-tiered system versus downscaled height forecasts from a coupled ocean-atmosphere system. The ECHAM4.5 atmospheric general circulation model (GCM) is used for both systems; in the one-tiered system the ECHAM4.5 is directly coupled to the ocean model Modular Ocean Model version three (MOM3), and in the two-tiered system the ECHAM4.5 is coupled with Van den Dool sea surface temperature (SST) hindcasts. Model output statistical equations are developed using canonical correlation analysis (CCA) to reduce system deficiencies. Probabilistic verification is conducted using the relative operating characteristic (ROC) and reliability diagram. The coupled model performs best in capturing seasonal maximum temperature extremes. Seasons demonstrating the highest ROC scores coincide with the period of highest seasonal temperatures found over Southern Africa. The above-normal category of the one-tiered system indicates the highest skill in predicting maximum temperature extremes, implying the coupled model predicts skilfully when there is a high likelihood of experiencing extremely high seasonal maximum temperatures during mid to late summer. The downscaled coupled maximum temperature hindcasts are evaluated additionally in terms of their monetary value and quality to the general public. The seasonal forecast system presented in this study should be able to reduce risks in decision making by the health industry in Southern Africa. [ABSTRACT FROM AUTHOR]

The recent U.S. National Academies report, Assessment of Intraseasonal to Interannual Climate Prediction and Predictability, was unequivocal in recommending the need for the development of a North American Multimodel Ensemble (NMME) operational predictive capability. Indeed, this effort is required to meet the specific tailored regional prediction and decision support needs of a large community of climate information users. The multimodel ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation and has proven to produce better prediction quality (on average) than any single model ensemble. This multimodel approach is the basis for several international collaborative prediction research efforts and an operational European system, and there are numerous examples of how this multimodel ensemble approach yields superior forecasts compared to any single model. Based on two NOAA Climate Test bed (CTB) NMME workshops (18 February and 8 April 2011), a collaborative and coordinated implementation strategy for a NMME prediction system has been developed and is currently delivering real-time seasonal-to-interannual predictions on the NOAA Climate Prediction Center (CPC) operational schedule. The hindcast and real-time prediction data are readily available (e.g., ) and in graphical format from CPC (). Moreover, the NMME forecast is already currently being used as guidance for operational forecasters. This paper describes the new NMME effort, and presents an overview of the multimodel forecast quality and the complementary skill associated with individual models. [ABSTRACT FROM AUTHOR]

The recent increase in availability of high-performance computing (HPC) resources in South Africa allowed the development of an ocean-atmosphere coupled general circulation model (OAGCM). The ECHAM4.5-South African Weather Service (SAWS) Modular Oceanic Model version 3 (MOM3-SA) is the first OAGCM to be developed in Africa for seasonal climate prediction. This model employs an initialization strategy that is different from previous versions of the model that coupled the same atmosphere and ocean models. Evaluation of hindcasts performed with the model revealed that the OAGCM is successful in capturing the development and maturity of El Niño and La Niña episodes up to 8 months ahead. A model intercomparison also indicated that the ECHAM4.5-MOM3-SA has skill levels for the Niño-3.4 region SST comparable with other coupled models administered by international centers. Further analysis of the coupled model revealed that La Niña events are more skillfully discriminated than El Niño events. However, as is typical for OAGCM, the model skill was generally found to decay faster during the spring barrier. The analysis also showed that the coupled model has useful skill up to several-months lead time when predicting the equatorial Indian Ocean dipole (IOD) during the period spanning between the middle of austral spring and the start of the summer seasons, which reaches its peak in November. The weakness of the model in other seasons was mainly caused by the western segment of the dipole, which eventually contaminates the dipole mode index (DMI). The model is also able to forecast the anomalous upper air circulations, particularly in the equatorial belt, and surface air temperature in the Southern African region as opposed to precipitation. [ABSTRACT FROM AUTHOR]

Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacific sea surface temperatures, warm water volumes (WWV), and zonal winds (ZW). Contrasting cross-validated predictive skills are found between the 'dry' January-June and 'rainy' July-December crop-production seasons. For the dry season, both irrigated and rain-fed rice production are shown to depend strongly on rainfall in the previous October-December. Furthermore, rice-crop hindcasts based on the two coupled GCMs, or on the observed WWV and ZW, are each able to account for more than half of the total variance of the dry-season national detrended rice production with about a 6-month lead time prior to the beginning of the harvest season. At regional and provincial levels, predictive skills are generally low. The relationships are found to be more complex for rainy-season rice production. Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June-September and in October-December of the harvested year, respectively. Tropical cyclone activity is also shown to be a contributing factor in the latter 3-month season. Hindcasts based on the WWV and ZW are able to account for almost half of the variance of the detrended rice production data in Luzon with a few months' lead time prior to the beginning of the rainy season. [ABSTRACT FROM AUTHOR]

Real-time model predictions of ENSO conditions during the 2002-11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Niño- 3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981-2011, this finding is explained by the relatively greater predictive challenge posed by the 2002-11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002-11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of statistical models for start times just before the middle of the year when prediction has proven most difficult. The greater skill of dynamical models is largely attributable to the subset of dynamical models with the most advanced, highresolution, fully coupled ocean-atmosphere prediction systems using sophisticated data assimilation systems and large ensembles. This finding suggests that additional advances in skill remain likely, with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes. [ABSTRACT FROM AUTHOR]

This paper examines the quality of seasonal probabilistic forecasts of near-global temperature and precipitation issued by the International Research Institute for Climate and Society (IRI) from late 1997 through 2008, using mainly a two-tiered multimodel dynamical prediction system. Skill levels, while modest when globally averaged, depend markedly on season and location and average higher in the tropics than extratropics. To first order, seasons and regions of useful skill correspond to known direct effects as well as remote teleconnections from anomalies of tropical sea surface temperature in the Pacific Ocean (e.g., ENSO related) and in other tropical basins. This result is consistent with previous skill assessments by IRI and others and suggests skill levels beneficial to informed clients making climate risk management decisions for specific applications. Skill levels for temperature are generally higher, and less seasonally and regionally dependent, than those for precipitation, partly because of correct forecasts of enhanced probabilities for above-normal temperatures associated with warming trends. However, underforecasting of above-normal temperatures suggests that the dynamical forecast system could be improved through inclusion of time-varying greenhouse gas concentrations. Skills of the objective multimodel probability forecasts, used as the primary basis for the final forecaster-modified issued forecasts, are comparable to those of the final forecasts, but their probabilistic reliability is somewhat weaker. Automated recalibration of the multimodel output should permit improvements to their reliability, allowing them to be issued as is. IRI is currently developing single-tier prediction components. [ABSTRACT FROM AUTHOR]

This study examines skill of retrospective forecasts using the ECHAM4.5 atmospheric general circulation model (AGCM) forced with predicted sea surface temperatures (SSTs) from methods of varying complexity. The SST fields are predicted in three ways: persisted observed SST anomalies, empirically predicted SSTs, and predicted SSTs from a dynamically coupled ocean–atmosphere model. Investigation of relative skill of the three sets of retrospective forecasts focuses on the ensemble mean, which constitutes the portion of the model response attributable to the prescribed boundary conditions. The anomaly correlation skill analyses for precipitation and 2-m air temperature indicate that dynamically predicted SSTs generally improve upon persisted and empirically predicted SSTs when they are used as boundary forcing in the AGCM predictions. This is particularly the case for precipitation forecasts. The skill differences in these experiments are ascribed to the skill of SST predictions in the tropical ocean basins. The multiscenario forecast by averaging the three retrospective experiments performs, overall, as well as or better than the best of the three individual experiments in specific seasons and regions. The advantage of multiscenario forecast manifests both in the deterministic and probabilistic skill. In particular, the multiscenario precipitation forecast for the December–February season demonstrates better skill than the best of the three scenarios over several regions, such as the western United States and southeastern South America. These results suggest the potential value in producing superensembles spanning different SST prediction scenarios. [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) has been observed to exhibit decadal changes in its properties; the cause and implication of such changes are strongly debated. Here the authors examine the influences of two particular attributors of the ocean–atmospheric system. The roles of stochastic forcing (SF) in the atmosphere and decadal changes in the temperature of subsurface water entrained into the mixed layer (Te) in modulating ENSO are compared to one another using coupled ocean–atmosphere models of the tropical Pacific climate system. Two types of coupled models are used. One is an intermediate coupled model (ICM) and another is a hybrid coupled model (HCM), both of which consist of the same intermediate ocean model (IOM) with an empirical parameterization for Te, constructed via singular value decomposition (SVD) analysis of the IOM simulated historical data. The differences in the ICM and HCM are in the atmospheric component: the one in the ICM is an empirical feedback model for wind stress (τ), and that in the HCM is an atmospheric general circulation model (AGCM; ECHAM4.5). The deterministic component of atmospheric τ variability, representing its signal response (τSig) to an external SST forcing, is constructed statistically by an SVD analysis from a 24-member ensemble mean of the ECHAM4.5 AGCM simulations forced by observed SST; the SF component (τSF) is explicitly estimated from the ECHAM4.5 AGCM ensemble and HCM simulations. Different SF representations are specified in the atmosphere: the SF effect can be either absent or present explicitly in the ICM, or implicitly in the HCM where the ECHAM4.5 AGCM is used as a source for SF. Decadal changes in the ocean thermal structure observed in the late 1970s are incorporated into the coupled systems through the Te parameterizations for the two subperiods before (1963–79) and after (1980–96) the climate shift (T63–79e and T80–96e), respectively. The ICM and HCM simulations well reproduce interannual variability associated with El Niño in the tropical Pacific. Model sensitivity experiments are performed using these two types of coupled models with different realizations of SF in the atmosphere and specifications of decadal Te changes in the ocean. It is demonstrated that the properties of ENSO are modulated differently by these two factors. The decadal Te changes in the ocean can be responsible for a systematic shift in the phase propagation of ENSO, while the SF in the atmosphere can contribute to the amplitude and period modulation in a random way. The relevance to the observed decadal ENSO variability in the late 1970s is discussed. [ABSTRACT FROM AUTHOR]

The annual cycle of sea surface temperature (SST) in the tropical Atlantic of a directly coupled atmosphere–ocean general circulation model (CGCM) is decomposed into the parts forced by different surface fluxes (denoted as modes) for the two extreme months of March and August using forced ocean experiments. Almost all previous diagnostic work of the forcing of the SST annual cycle in the Atlantic has concentrated on the near-equatorial region. Here, the annual cycle is examined within the latitude range of 25°S–25°N to facilitate comparison with the interannual variability. The structure of the response to the different surface flux forcings bears some resemblance to the interannual SST modes in the tropical Atlantic, which are diagnosed using rotated empirical orthogonal function (REOF) analysis. Diagnosis of the forcing of the annual cycle modes and the interannual modes shows that they do not always have a common cause. Hence, the simple interpretation that the leading interannual modes are perturbations to the annual cycle is not always valid. In particular, the equatorial SST annual cycle mode is primarily driven by variations in vertical velocity while the equatorial interannual mode is associated with eastward-propagating thermocline anomalies and is forced by both thermocline anomalies and vertical velocity anomalies. As for the interannual modes, there exist off-equatorial annual cycle modes in both the Northern and Southern Hemispheres. The annual cycle off-equatorial mode in both hemispheres is shown to be primarily driven by heat flux variations. The Southern Hemisphere interannual mode is primarily driven by heat flux variations while the Northern Hemisphere interannual mode shows a strong influence of thermocline depth anomalies. In addition, the Southern Hemisphere interannual mode is centered about 10° south of the annual cycle mode. An interannual mode that has maximum variability along the South American coast south of the equator is shown to be associated with thermocline depth anomalies. This interannual mode has no analog in the annual cycle modes. The coupled model simulation of the annual cycle is found to be fairly realistic so that the results presented here could have applicability to the observed Atlantic. [ABSTRACT FROM AUTHOR]

This paper is about the statistical correction of systematic errors in dynamical sea surface temperature (SST) prediction systems using linear regression approaches. The typically short histories of model forecasts create difficulties in developing regression-based corrections. The roles of sample size, predictive skill, and systematic error are examined in evaluating the benefit of a linear correction. It is found that with the typical 20 yr of available model SST forecast data, corrections are worth performing when there are substantial deviations in forecast amplitude from that determined by correlation with observations. The closer the amplitude of the uncorrected forecasts is to the optimum squared error-minimizing amplitude, the less likely is a correction to improve skill. In addition to there being less “room for improvement,” this rule is related to the expected degradation in out-of-sample skill caused by sampling error in the estimate of the regression coefficient underlying the correction. Application of multivariate [canonical correlation analysis (CCA)] correction to three dynamical SST prediction models having 20 yr of data demonstrates improvement in the cross-validated skills of tropical Pacific SST forecasts through reduction of systematic errors in pattern structure. Additional beneficial correction of errors orthogonal to the CCA modes is achieved on a per-gridpoint basis for features having smaller spatial scale. Until such time that dynamical models become freer of systematic errors, statistical corrections such as those shown here can make dynamical SST predictions more skillful, retaining their nonlinear physics while also calibrating their outputs to more closely match observations. [ABSTRACT FROM AUTHOR]

A large number of ensemble hindcasts (or retrospective forecasts) of tropical Pacific sea surface temperature (SST) have been made with a coupled atmosphere–ocean general circulation model (CGCM) that does not employ flux correction in order to evaluate the potential skill of the model as a seasonal forecasting tool. Oceanic initial conditions are provided by an ocean data assimilation system. Ensembles of seven forecasts of 6-month length are made starting each month in the 1982 to 2002 period. Skill of the coupled model is evaluated from both a deterministic and a probabilistic perspective. The skill metrics are calculated using both the bulk method, which includes all initial condition months together, and as a function of initial condition month. The latter method allows a more objective evaluation of how the model has performed in the context in which forecasts are actually made and applied. The deterministic metrics used are the anomaly correlation and the root-mean-square error. The coupled model deterministic skill metrics are compared with those from persistence and damped persistence reference forecasts. Despite the fact that the coupled model has a large cold bias in the central and eastern equatorial Pacific this coupled model is shown to have forecast skill that is competitive with other state-of-the-art forecasting techniques. Potential skill from probabilistic forecasts made using the coupled model ensemble members are evaluated using the relative operating characteristics method. This analysis indicates that for most initial condition months this coupled model has more skill at forecasting cold events than warm or neutral events in the central Pacific. In common with other forecasting systems, the coupled model forecast skill is found to be lowest for forecasts passing through the Northern Hemisphere (NH) spring. Diagnostics of this so-called spring predictability barrier in the context of this coupled model indicate that two factors likely contribute to this predictability barrier. First, the coupled model shows a too-weak coupling of the surface and subsurface temperature anomalies during NH spring. Second, the coupled-model-simulated signal-to-noise ratio for SST anomalies is much lower during NH spring than at other times of the year, indicating that the model’s potential predictability is low at this time. [ABSTRACT FROM AUTHOR]

The International Research Institute (IRI) for Climate Prediction seasonal forecast system is based largely on the predictions of ensembles of several atmospheric general circulation models (AGCMs) forced by two versions of an SST prediction—one consisting of persisted SST anomalies from the current observations and one of evolving SST anomalies as predicted by a set of dynamical and statistical SST prediction models. Recently, an objective multimodel ensembling procedure has replaced a more laborious and subjective weighting of the predictions of the several AGCMs. Here the skills of the multimodel predictions produced retrospectively over the first 4 years of IRI forecasts are examined and compared with the skills of the more subjectively derived forecasts actually issued. The multimodel ensemble predictions are generally found to be an acceptable replacement, although the precipitation forecasts do benefit from inclusion of empirical forecast tools. Planned pattern-level model output statistics (MOS) corrections for systematic biases in the AGCM forecasts may render them more sufficient in their own right. [ABSTRACT FROM AUTHOR]

Describes results from a series of 40 retrospective forecasts of tropical Pacific SST, starting January 1 and July 1 1980-1999, performed with several coupled ocean-atmosphere general circulation models sharing the same ocean model and the same initial conditions. Indication that the systematic errors of the mean evolution in the coupled models depend strongly on the atmospheric model; Suggestion of a spring prediction barrier.

Changes in the tropical oceans caused by a shift of 6 months in the date of perihelion are examined using a coupled atmosphere-ocean general circulation model (GCM). The changes in the annual cycle of sea surface temperature (SST) near the equator are described, and the mechanism for the changes is diagnosed. The GCM results are diagnosed using the ocean component model forced by the time mean fluxes from the coupled integration. This diagnosis shows that the changes in the annual cycle of near-equatorial SST are caused by different mechanisms in different regions. An extensive analysis of the changes in the eastern Pacific is given because of the importance of this region in modulating the global climate through teleconnections associated with ENSO. In the eastern Pacific, the change in the annual cycle of SST is found to be primarily due to zonal wind stress differences. The zonal wind stress differences are caused for the most part by changes in the precipitation distribution. The changes in the precipitation distribution are consistent with being caused by changes in both the low-level convergence forced by the surface temperature gradients and changes in the local evaporation. The physical process responsible for the change in low-level zonal wind and convergence are diagnosed using a steady-state linearized version of the atmospheric model that is forced by time mean fields from the coupled model. Interannual variability of eastern Pacific (Niño-3) SST under the modified solar forcing is found to have an amplitude and period similar to those observed in modern times. The only major difference in the interannual variability of tropical Pacific SST is found to be the timing of SST anomalies. This occurs because the interannual SST variability in the tropical eastern Pacific is phase locked to the annual cycle of SST, whose phase is itself dependent on the solar forcing. [ABSTRACT FROM AUTHOR]

A scheme for making seasonal to interannual predictions of El Nino-Southern Oscillation with a coupled atmosphere-ocean general circulation model that incorporates subsurface ocean measurements in the initial conditions is described. Anomaly initial conditions are used in order to reduce initial shock and climate drift. The ocean component of the prediction model has a nearly global domain, and the coupled model does not employ anomaly coupling or empirical statistical corrections. Initial conditions for the ocean were obtained from a near-global ocean analysis produced by an ocean data assimilation system. The assimilation system uses a variationally formulated optimal interpolation method to analyze oceanic fields from temperature observations and a first-guess field provided by integrating a global ocean general circulation model. The period of the analysis was 1986 through 1992. The anomaly initial conditions for the ocean were generated by adding the anomalies of the assimilated fields from the assimilation climatology to the coupled model climatology. A series of 28 1-yr hindcast experiments, four each year for the years 1986-1992, was carried out to test the scheme. The hindcasts show considerable skill in the equatorial Pacific. [ABSTRACT FROM AUTHOR]

The origin of the annual cycle of equatorial sea surface temperature (SST) is diagnosed using a global coupled atmosphere-ocean general circulation model (CGCM) that realistically simulates this annual cycle. The simulated heat flux, wind stress, upper ocean thermal structure, and mixed-layer depth, which are critical to the correct simulation of the near-equatorial SST, are compared with observations for realism. Based on this analysis, it is concluded that the model results should be applicable to the actual coupled system in the Indian and Atlantic Oceans. In the Pacific, errors in the simulated zonal wind stress and heat flux imply that, even though the SST annual cycle is realistic, the processes that govern its evolution may be different than those found in nature. The processes controlling the annual cycle of SST in the CGCM are diagnosed with experiments in which the ocean component model is forced with the CGCM surface fluxes of heat, momentum, and freshwater. In the eastern Pacific, the annual cycle of SST is due in large part to upwelling variations. Near the coast of South America, the upwelling is caused in nearly equal parts by the zonal and meridional wind stress, whereas farther to the west the upwelling induced by the zonal wind stress is dominant. The surface heat flux forces a significant portion of the annual cycle in the eastern Atlantic. However, as was found for the eastern Pacific, upwelling is the dominant process determining the annual cycle of SST. The annual cycle of SST in the Indian Ocean is dominated by the response to the varying heat flux. The annual cycle of surface heat flux in the Atlantic and Indian Oceans is due about equally to the latent and shortwave fluxes. In both the Atlantic and Indian Oceans, variations in the magnitude of the surface heat flux are more important than mixed-layer depth variations in determining the SST response to the flux of heat across the ocean surface. [ABSTRACT FROM AUTHOR]

The Geophysical Fluid Dynamics Laboratory ocean model has been used to diagnose the sensitivity of the Center for Ocean--Land--Atmosphere Studies atmospheric general circulation model wind stress to convective parameterization. In a previous study this atmospheric model was integrated for seven years with observed sea surface temperatures to test three different convective parameterizations: Kuo, Betts--Miller, and relaxed Arakawa-- Schubert. In this study, the three wind stress fields are then used to force the ocean model. For comparison, an ocean model simulation with the subjectively analyzed Florida State University wind stress product is also made. The resulting ocean temperature field is compared with the National Centers for Environmental Prediction ocean analyses in terms of the annual cycle and interannual variability in order to identify errors in the ocean model and errors that are unique to the particular wind stress field. Overall, the ocean simulation with the wind stress resulting from the relaxed Arakawa--Schubert parameterization gives a thermal structure that is in best agreement with the analyzed wind stress simulation and the ocean analyses. Based on the simulation of the annual cycle of sea surface temperature and heat content in the deep Tropics, it is concluded that the wind stress is too strong with the Betts--Miller parameterization and too weak with the Kuo parameterization, particularly in the boreal spring. The simulation with the wind stress from the relaxed Arakawa--Schubert parameterization minimizes the errors in the heat content between 108S and 108N and has smaller errors than the analyzed wind stress simulation in some regions. In terms of interannual variability, the anomalies from all three wind stress products produce sea surface temperature anomalies that are concentrated in the western Pacific with little or no sea surface temperature anomaly in the east indicating that the ocean model has some... [ABSTRACT FROM AUTHOR]

Explores the roles played by the large-scale motion induced by vertical diffusion of heat from the lower boundary and condensational heating due to deep convection in maintaining the precipitation zones in the Tropics of an atmospheric general circulation model. Moisture flux convergence; Sigma-level wind field over the tropical Pacific Ocean.

A table is presented which lists several El Niño-Southern Oscillation prediction models for use in analyzing weather forecasts and statistics.

A correction to the second sentence in the abstract in the article is needed to be clarified to avoid ambiguity regarding the discussed model that was published in the previous issue is presented.