SPECIFICATION AND PREDICTION OF GLOBAL SURFACE-TEMPERATURE AND PRECIPITATION FROM GLOBAL SST USING CCA

A reconstructed sea surface temperature (SST) dataset is used to exami ne relationships between SST and seasonal mean surface temperature (T) and total precipitation (P) over most of the global continents for th e 1950-92 period. Both specification (i.e., simultaneous) and predicti ve relations are studied. Canonical correlation analysis (CCA) is used to describe the relationships and to provide information aiding in ph ysical interpretation. A sequence of four consecutive 3-month periods of global SST anomalies is related to T and P anomalies during the fou rth period for the specification analyses, and to 3-month periods rang ing from one to four seasons later for the predictive analyses. Dynami cal specifications of the National Centers for Environmental Predictio n (NCEP) atmospheric model, using observed SST anomalies as boundary c onditions, are also examined for confirmation of and comparison with t he statistical specification relationships suggested by the CCA. Speci fication and predictive cross-validated skill is modest except for cer tain regions and/or times of the year having correlations of 0.5 and g reater. Seasonal T is generally specified/predicted with greater skill than P. Some regions have seasonality in their specificability/predic tability, where skill varies more strongly as a function of the target season than lead time for T, P, or both. In these cases, such as Sahe l African rainfall in northern summer or northeastern Australian rainf all in May through July, the skill of specification is not substantial ly higher than the skill of shea or even moderately long lead predicti on. Specifications and predictions are skillful in areas affected by t he ENSO, including the tropical Pacific islands for all seasons, and d uring specific seasons in northern and eastern Australia, and parts of Africa and North and South America. Skill is lowest in Europe and mid latitude Asia where ENSO's direct influence is lacking. However, non-E NSO predictive skill sources also contribute substantially to final sk ill; these exist both in regions strongly and minimally influenced by ENSO. The most important of these is an interdecadal trend from the 19 50s to the 1980s-90s defined by a warming in the Indian and South Atla ntic Oceans paralleling a cooling in the North Pacific and Atlantic ba sins. Another controlling SST dipole with a less obvious trend include s mainly the tropical SST of all three ocean basins versus the extratr opical (especially Northern Hemisphere) SST. Still other, more localiz ed, SST patterns are suggested as critical. Some of the regions that s how modest but usable seasonal predictive potential have no prior spec ificative or predictive history because they are not directly influenc ed by ENSO and/or have marginal data quality or density. This is encou raging, since the statistical skill realized here should be reproducib le, and hopefully surpassable, using dynamical models.