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The Indian Ocean: The Region of Highest Skill Worldwide in Decadal Climate Prediction

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  • 1 Institut Català de Ciències del Clima, Barcelona, Spain
  • 2 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom, and Istituto di Scienze dell’Atmosfera e del Clima, Consiglio Nazionale delle Riceche, Bologna, Italy
  • 3 Institut Català de Ciències del Clima, Barcelona, Spain
  • 4 Institut Català de Ciències del Clima and Instituciò Catalana de Recerca i Estudis Avancats, Barcelona, Spain
  • 5 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
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Abstract

The Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts taking into account or not the effect of volcanic stratospheric aerosols and by the drop in skill when filtering out their effect in hindcasts that take them into account. Indeed, the Indian Ocean is shown to be the region where the ratio of the internally generated over the externally forced variability is the lowest, where the amplitude of the internal variability has been estimated by removing the effect of long-term warming trend and volcanic aerosols by a multiple least squares linear regression on observed SSTs.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-00049.s1.

Corresponding author address: Virginie Guemas, Institut Català de Ciències del Clima, Carrer Trueta, 203, 08005 Barcelona, Spain. E-mail: vguemas@ic3.cat

Abstract

The Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts taking into account or not the effect of volcanic stratospheric aerosols and by the drop in skill when filtering out their effect in hindcasts that take them into account. Indeed, the Indian Ocean is shown to be the region where the ratio of the internally generated over the externally forced variability is the lowest, where the amplitude of the internal variability has been estimated by removing the effect of long-term warming trend and volcanic aerosols by a multiple least squares linear regression on observed SSTs.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-12-00049.s1.

Corresponding author address: Virginie Guemas, Institut Català de Ciències del Clima, Carrer Trueta, 203, 08005 Barcelona, Spain. E-mail: vguemas@ic3.cat

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