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J. Shukla

Abstract

The global general circulation model of the Geophysical Fluid Dynamics Laboratory has been integrated with and without a cold sea surface temperature (SST) anomaly over the Somali coast and the western Arabian Sea. The temperature anomaly is −3°C near the Somali coast and linearly decreases eastward having zero anomaly at about 1500 km east of the coast. Comparison of the mean of the two model states indicates that the rainfall over India and the adjoining region is drastically reduced due to the colder SST anomaly over the western Arabian Sea. The other associated features due to the cold anomaly are an increase in sea surface pressure over the Arabian Sea, a decrease in local evaporation, and a reduction in the cross equatorial component of the wind at the surface and hence a reduction in the cross equatorial moisture flux. Statistical analysis of the results has been done by comparing the difference between the two mean states (“signal”) and the standard deviation of the errors (“noise”) in estimating the mean due to the finiteness of the averaging period. It is found that the results of the present numerical experiment are statistically significant.

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J. Shukla

Abstract

We have attempted to determine the theoretical upper limit of dynamical predictability of monthly means for prescribed nonfluctuating external forcings. We have extended the concept of “classical” predictability, which primarily refers to the lack of predictability due mainly to the instabilities of synoptic-scale disturbances, to the predictability of time averages, which are determined by the predictability of low-frequency planetary waves. We have carded out 60-day integrations of a global general circulation model with nine different initial conditions but identical boundary conditions of sea surface temperature, snow, sea ice and soil moisture. Three of these initial conditions are the observed atmospheric conditions on 1 January of 1975, 1976 and 1977. The other six initial conditions are obtained by superimposing over the observed initial conditions a random perturbation comparable to the errors of observation. The root-mean-square (rms) error of random perturbations at all the grid points and all the model levels is 3 m s−1 in u and v components of wind. The rms vector wind error between the observed initial conditions is >15 m s−1.

It is hypothesized that for a given averaging period, if the rms error among the time averages predicted from largely different initial conditions becomes comparable to the rms error among the time averages predicted from randomly perturbed initial conditions, the time averages are dynamically unpredictable. We have carried out the analysis of variance to compare the variability, among the three groups, due to largely different initial conditions, and within each group due to random perturbations.

It is found that the variances among the first 30-day means, predicted from largely different initial conditions, are significantly different from the variances due to random perturbations in the initial conditions, whereas the variances among 30-day means for days 31–60 are not distinguishable from the variances due to random initial perturbations. The 30-day means for days 16–46 over certain areas are also significantly different from the valances due to random perturbations.

These results suggest that the evolution of long waves remains sufficiently predictable at least up to one month, and possibly up to 45 days, so that the combined effects of their own nonpredictability and their depredictabilization by synoptic-scale instabilities is not large enough to degrade the dynamical prediction of monthly means. The Northern Hemisphere appears to be more predictable than the Southern Hemisphere.

It is noteworthy that the lack of predictability for the second month is not because the model simulations relax to the same model state but because of very large departures in the simulated model states. This suggests that, with improvements in model resolution and physical parameterizations, there is potential for extending the predictability of time averages even beyond one month.

Here, we have examined only the dynamical predictability, because the boundary conditions are identical in all the integrations. Based on these results, and the possibility of additional predictability due to the influence of persistent anomalies of sea surface temperature, sea ice, snow and soil moisture, it is suggested that there is sufficient physical basis to undertake a systematic program to establish the feasibility of predicting monthly means by numerical integrations of realistic dynamical models.

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J. Shukla

Abstract

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J. Shukla

Abstract

A combined CISK-barotropic-baroclinic instability analysis of the observed monsoon flow has been performed using the quasi-equilibrium assumption for the parameterization of moist convection. Linear perturbation equations for a three-layer quasi-geostrophic model are numerically integrated to get the most unstable mode. A deep cloud model, in which the height of the base of the cloud does not change with time and entrainment occurs for the whole depth of the cloud but detrainment occurs only at the top, is used to parameterize the effects of moist convection.

It is found that the maximum growth rate occurs for the smallest scales. The mechanism for scale selection is therefore not clear. The structure and energetics of the computed linear perturbations for a wavelength corresponding to that of the observed monsoon depressions is compared with the observations. The dominant energy transformation for the computed and the observed perturbations is found to be from eddy available potential energy to eddy kinetic energy. The primary source of heating is condensational heating. Reasonable agreements between the structure and the energetics of the computed perturbations and the observed monsoon depressions suggest that CISK may provide the primary driving mechanism for the growth of monsoon depressions.

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J. Shukla

Abstract

No abstract available.

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Bohua Huang
and
J. Shukla

Abstract

The monthly mean surface wind stress and winds in the lower troposphere for 1986–92 simulated by the Center for Ocean–Land–Atmosphere Studies atmospheric general circulation model (AGCM) forced with observed sea surface temperature (SST) is compared with observations. It is found that the AGCM surface stress has weak equatorial easterlies during boreal spring and weak El Niño–Southern Oscillation (ENSO) signals over the central and eastern Pacific Ocean. On the other hand, the AGCM winds at 850 mb are found to be in much better agreement with the observations.

An empirical scheme is developed to reconstruct the AGCM surface wind stress, based on the AGCM winds from 850 mb. The reconstructed wind stress is more consistent with observations for both annual and interannual variability. A series of numerical experiments are conducted using the observed, AGCM, and reconstructed surface stress to force an ocean general circulation model. The results demonstrate that the low-frequency ENSO signals are significantly improved in the OGCM when the reconstructed dataset replaces the original AGCM stress. Improvements are evident in more realistic SST anomalies and variability of the thermocline depth.

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Bohua Huang
and
J. Shukla

Abstract

A numerical simulation has been conducted using a general circulation model of the tropical Atlantic Ocean forced with observed monthly surface wind stress for 1964–87 and parameterized surface heat flux. The simulated sea surface temperature (SST) and upper-ocean heat content (HC) are used to examine the low-frequency variability in the ocean. A comparison with the SST observations shows that the model realistically simulates the major features of the decadal variability at the sea surface, such as the fluctuation of the SST dipole pattern (or the meridional gradient). It also produces interannual variations with timescales of two to three years.

The simulated HC anomalies are used to examine the variations of the thermocline depth and the effects of ocean dynamics. A principal oscillation pattern (POP) analysis is performed to distinguish the spatial structures of decadal and interannual variations. It is found that the interannual variations are associated with tropical oceanic waves, stimulated by the fluctuations of the equatorial easterlies, which propagate eastward along the equator and westward to the north and south, resulting in an essentially symmetric structure about the equator at these scales. The periods of these modes are determined by the meridional width of the equatorial wind anomaly. The decadal mode, however, is associated with the ocean’s adjustment in response to a basinwide out-of-phase fluctuation between the northeast and southeast trade winds. For instance, forced by a weakening of the northeast winds and a simultaneous strengthening of the southeast winds, the thermocline deepens in a belt extending from 5°N in the west to the North African coast. At the same time, the thermocline shoals from the southeast coast to the equatorial ocean. The associated SST pattern exhibits a strong dipole structure with positive anomalies in the north and negative anomalies in the south. When the wind anomalies weaken, the warm water accumulated in the northern tropical ocean is released and redistributed within the basin. At this stage, the SST dipole disappears. In the framework of this separation of the variability into two dominant timescales, the extraordinarily large warm SST anomalies in the southeast ocean in the boreal summer of 1984 are a result of in-phase interference of the decadal and interannual modes.

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Timothy DelSole
and
J. Shukla

Abstract

This paper proposes a strategy for selecting the best linear prediction model for Indian monsoon rainfall. In this strategy, a cross-validation procedure first screens out all models that perform poorly on independent data, then the error variance of every remaining model is compared to that of every other model to test whether the difference in error variances is statistically significant. This strategy is shown to produce better forecasts on average than selecting either the model that utilizes all predictors, the model that explains the most variance in the independent data, or the model with the most favorable statistic suggested by Mallow. All of the model selection criteria suggest that regression models based on two to three predictors produce better forecasts on average than regression models using a larger number of predictors. For the period up to 1967, the forecast strategy selected the prior climatology as the best predictor. For the period 1967 to the present, the strategy performed better than forecasts based on the prior climatology and all other methodologies investigated. Indexes of Pacific Ocean temperature in the Tropics (called Niño-3) and indexes of pressure fluctuations in the Northern Atlantic (called NAO), at 1–6 lead months, failed to provide prediction models that performed better on average than a prediction based on the antecedent climatology. Forecasts based on the prior 25-yr climatology had especially high skill during the recent period 1989–2000, a fact that appears to be a mere coincidence, but which may be relevant to interpreting the skill of the power regression model currently used by the India Meteorological Department.

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V. Krishnamurthy
and
J. Shukla

Abstract

A gridded daily rainfall dataset prepared from observations at 3700 stations is used to analyze the intraseasonal and interannual variability of the summer monsoon rainfall over India. It is found that the major drought years are characterized by large-scale negative rainfall anomalies covering nearly all of India and persisting for the entire monsoon season. The intraseasonal variability of rainfall during a monsoon season is characterized by the occurrence of active and break phases. During the active phase, the rainfall is above normal over central India and below normal over northern India (foothills of the Himalaya) and southern India. This pattern is reversed during the break phase.

It is found that the nature of the intraseasonal variability is not different during the years of major droughts or major floods. This suggests that a simple conceptual model to explain the interannual variability of the Indian monsoon rainfall should consist of a linear combination of a large-scale persistent seasonal mean component and a statistical average of intraseasonal variations. The large-scale persistent component can be part of low-frequency components of the coupled ocean–land–atmosphere system including influences of sea surface temperature, snow, etc. The mechanisms responsible for the intraseasonal variations are not well understood. This simple conceptual framework suggests that the ability to predict the seasonal mean rainfall over India will depend on the relative contributions of the externally forced component and the intraseasonal component. To the extent that the intraseasonal component is intrinsically unpredictable, success in long-range forecasting will largely depend on accurate quantitative estimates of the externally forced component.

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Bohua Huang
and
J. Shukla

Abstract

A series of experiments are conducted using a coupled ocean–atmosphere general circulation model in regional coupled mode, which permits active air–sea interaction only within the Indian Ocean to the north of 30°S, with sea surface temperatures (SSTs) prescribed over the rest of the world oceans. In this paper, an ensemble of nine simulations has been analyzed with the observed SST anomalies for 1950–98 prescribed over the uncoupled region. The purpose of this study is to determine the major patterns of interannual variability in the tropical Indian Ocean that could be related to the global low-frequency fluctuations and to understand the physical links between the remote forcing and the regional coupled variations.

The ensemble coupled simulations with prescribed SST outside the Indian Ocean are able to reproduce a considerable amount of observed variability in the tropical Indian Ocean during 1950–98. The first EOF modes of the simulated upper-ocean heat content and SST anomalies show structures that are quite consistent with those from the historical upper oceanic temperature and SST analyses. The dominant pattern of response is associated with an oceanic dynamical adjustment of the thermocline depth in the southwestern Indian Ocean. In general, a deepening of the thermocline in the southwest is usually accompanied by the enhanced upwelling and thermocline shoaling centered near the Sumatra coast. Further analysis shows that the leading external forcing is from the El Niño–Southern Oscillation (ENSO), which induces an anomalous fluctuation of the atmospheric anticyclones on both sides of the equator over the Indian Ocean, starting from the evolving stage of an El Niño event in boreal summer. Apart from weakening the Indian monsoon, the surface equatorial easterly anomalies associated with this circulation pattern first induce equatorial and coastal upwelling anomalies near the Sumatra coast from summer to fall, which enhance the equatorial zonal SST gradient and stimulate intense air–sea feedback in the equatorial ocean. Moreover, the persistent anticyclonic wind curl over the southern tropical Indian Ocean, reinforced by the equatorial air–sea coupling, forces substantial thermocline change centered at the thermocline ridge in the southwestern Indian Ocean for seasons. The significant thermocline change has profound and long-lasting influences on the SST fluctuations in the Indian Ocean.

It should be noted that the ENSO forcing is not the only way that this kind of basinwide Indian Ocean fluctuations can be generated. As will be shown in the second part of this study, similar low-frequency fluctuations can also be generated by processes within the Indian and western Pacific region without ENSO influence. The unique feature of the ENSO influence is that, because of the high persistence of the atmospheric remote forcing from boreal summer to winter, the life span of the thermocline anomalies in the southwestern Indian Ocean is generally longer than that generated by regional coupled processes.

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