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  • 1
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 21 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, . pp.
    Publication Date: 2020-03-25
    Type: Book , NonPeerReviewed
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  • 2
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    Max-Planck-Institut für Meteorologie
    In:  MPG-Spiegel : aktuelle Informationen für Mitarbeiter und Freunde der Max-Planck-Gesellschaft, 3/88 . pp. 20-23.
    Publication Date: 2017-02-27
    Type: Article , NonPeerReviewed
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  • 3
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 251 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, . pp.
    Publication Date: 2019-08-07
    Type: Report , NonPeerReviewed
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  • 4
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 268 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, . pp.
    Publication Date: 2019-08-07
    Type: Report , NonPeerReviewed
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  • 5
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 223 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, . pp.
    Publication Date: 2019-08-07
    Type: Report , NonPeerReviewed
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  • 6
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 184 . Max-Planck-Institut für Meteorologie, Bremerhaven, Germany, pp. 703-720, . pp.
    Publication Date: 2019-08-07
    Type: Report , NonPeerReviewed
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  • 7
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 170 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, 30 pp.
    Publication Date: 2019-08-07
    Description: The EL Nino-Southern Oscillation phenomenon (ENSO) is an interannual perturbation of the climate system. It is characterized by a weakening of the trade winds and a warming of the sea surface temperature in the equatorial Pacific. ENSO occurs every 4-7 years and its impacts are felt worldwide. Recently, coupled ocean-atmosphere models have been used to describe ENSO and to predict it at lead times up to one to two years. The predictability of Enso is determined by the oceanic part of the coupled system. For forecasts, the accuracy of the initial state from which the prediction is started is crucial. Hence, data assimilation into ocean models should be a powerful tool to improve ENSO forecasts. The two different types of observational data, most relevant for ENSO predictions are temperature measurements down to a depth of a few hundred meters and sea level observations. In general the accuracy of temperature measurements is quite good and they contain the required information to initialize a coupled ocean atmosphere forecast system. However, these data are mainly taken from ships or buoys which may lead to poor spatial and temporal data coverage in certain areas. In contrast, sea level data are available almost continuously in space and time since they can be measured with good accuracy by satellites. In principle they both contain the ENSO signal, and it is an interesting question, whether the impact on ENSO forecasts is comparable for both types of data. (orig.)
    Type: Report , NonPeerReviewed
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  • 8
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 196 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, 22 pp.
    Publication Date: 2019-08-07
    Description: An oscillation with a period of about 30 months has been identified in the equatorial Atlantic by analyzing sea surface temperature (SST) observations for the period 1949-1991. The 30-month time scale was also found in numerical simulations with an atmospheric general circulation model (AGCM) that was forced by these SSTs and a coupled ocean atmosphere general circulation model (CGCM). Consistent with the theory of tropical air-sea interactions, the Atlantic oscillation (El Hermanito) is an inherently coupled air-sea mode and can be viewed as the Atlantic analogon of the El Nino/Southern Oscillation (ENSO) phenomenon in the equatorial Pacific. El Hermanito is an internal Atlantic mode and appears to be independent of the quasi-biennial (QB) variability observed in the tropical Indian and Pacific Oceans. The discovery of El Hermanito is important to the prediction of Atlantic climate anomalies. (orig.)
    Type: Report , NonPeerReviewed
    Format: text
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  • 9
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    Max-Planck-Institut für Meteorologie
    In:  MPI-Report, 306 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, 7 pp.
    Publication Date: 2019-08-07
    Description: Analysis of sea surface temperature (SST) observations of the last 150 years reveals strong changes in the tropical Pacific climate system during the most recent decades. These changes can be best described as a slow variation of the mean state of the tropical Pacific. The superimposed interannual variability associated with the El Niño/Southern Oscillation (ENSO) phenomenon does not exhibit any significant changes. However, the change in the mean state is “El Niño-like”, with many aspects observed during present-day El Niño events. Thus, the change in the mean state biasses the SSTs in the tropical Pacific towards the warm side, which explains the stronger and more frequent El Niños observed during the recent decades.
    Type: Report , NonPeerReviewed
    Format: text
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  • 10
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    Max-Planck-Institut für Meteorologie
    In:  Max-Planck-Institut für Meteorologie: Report, 326 . Max-Planck-Institut für Meteorologie, Hamburg, Germany, pp. 1358-1368, 21 pp.
    Publication Date: 2019-08-08
    Description: The interannual variability of the tropical Indian Ocean sea surface temperature (SST) is studied with observational data and a hierarchy of coupled general circulation models (CGCMs). Special attention is given to the question whether an oscillatory dipole mode exists in the tropical Indian Ocean region with centers east and west of 80°E. Our observational analyses indicate that dipole-like variability can be explained as an oscillatory mode only in the context of ENSO (El Nino/Southern Oscillation). A dipole-like structure in the SST anomalies independent of ENSO was found also. Our series of coupled model experiments shows that ocean dynamics is not important to this type of dipole-like SST variability. It is forced by surface heat flux anomalies that are integrated by the thermal inertia ofthe oceanic mixed layer, which reddens the SST spectrum.
    Type: Report , PeerReviewed
    Format: text
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