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Wayman E. Baker
,
Stephen C. Bloom
,
John S. Woollen
,
Mark S. Nestler
,
Eugenia Brin
,
Thomas W. Schlatter
, and
Grant W. Branstator

Abstract

A three-dimensional (3D), multivariate, statistical objective analysis scheme (referred to as optimum interpolation or OI) has been developed for use in numerical weather prediction studies with the FGGE data. Some novel aspects of the present scheme include 1) a multivariate surface analysis over the oceans, which employs an Ekman balance instead of the usual geostrophic relationship, to model the pressure-wind error cross correlations, and 2) the capability to use an error correlation function which is geographically dependent.

A series of 4-day data assimilation experiments are conducted to examine the importance of some of the key features of the OI in terms of their effects on forecast skill, as well as to compare the forecast skill using the OI with that utilizing a successive correction method (SCM) of analysis developed earlier. For the three cases examined, the forecast skill is found to be rather insensitive to varying the error correlation function geographically. However, significant differences are noted between forecasts from a two-dimensional (2D) version of the OI and those from the 3D OI, with the 3D OI forecasts exhibiting better forecast skill. The 3D OI forecasts are also more accurate than those from the SCM initial conditions.

The 3D OI with the multivariate oceanic surface analysis was found to produce forecasts which were slightly more accurate, on the average, than a univariate version.

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Gerald A. Meehl
,
Lisa Goddard
,
George Boer
,
Robert Burgman
,
Grant Branstator
,
Christophe Cassou
,
Susanna Corti
,
Gokhan Danabasoglu
,
Francisco Doblas-Reyes
,
Ed Hawkins
,
Alicia Karspeck
,
Masahide Kimoto
,
Arun Kumar
,
Daniela Matei
,
Juliette Mignot
,
Rym Msadek
,
Antonio Navarra
,
Holger Pohlmann
,
Michele Rienecker
,
Tony Rosati
,
Edwin Schneider
,
Doug Smith
,
Rowan Sutton
,
Haiyan Teng
,
Geert Jan van Oldenborgh
,
Gabriel Vecchi
, and
Stephen Yeager

This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.

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