The Indian Ocean stands out as the region where the state-of-the-art decadal climate pre10 dictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and RMSE (Root Mean Square Error) 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 simu15 lations 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 find17 ing 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 which 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-square linear regression on observed SSTs.
The Indian Ocean: the region of highest skill worldwide in decadal climate prediction
Corti S;
2013
Abstract
The Indian Ocean stands out as the region where the state-of-the-art decadal climate pre10 dictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and RMSE (Root Mean Square Error) 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 simu15 lations 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 find17 ing 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 which 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-square linear regression on observed SSTs.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
