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Growinginterestintraditionalknowledge(TK),particularlyinrelationtothepredictionofweatherorclimate extremes, raises issues concerning the appropriate storage and management of the information collected. The Traditional Knowledge Database (TK Database) for the storage and use of TK associated with weather and climate prediction in the Paci c was designed with the following principles in mind: (1) preservation of the knowledge, maintaining cultural context wherever possible; (2) respect for intellectual property and cultural sensitivities around data sharing and use; (3) appropriate system design, accounting for ongoing costs of system maintenance and often intermittent Internet access; and (4) moving beyond data preservation to ensure continued use and growth of the TK. The TK Database was successfully deployed to four countries in the south Paci c and is regularly used by their national meteorological services, and partner organizations, both to preserve TK related to weather and climate and as a tool to assist in monitoring the TK indicators. As the rst database of its kind, the TK Database lls a critical gap in the appropriate storage and application of TK and provides an important foundation for future developments.

In most countries, national meteorological services either generate or have access to seasonal climate forecasts.However,inanumberofregions,theuptakeoftheseforecastsbylocalcommunitiescanbelimited, with the locals instead relying on traditional knowledgeto make their climate forecasts.Both approachesto seasonal climate forecasting have bene ts, and the incorporation of traditional forecast methods into contemporaryforecastsystemscanleadtoforecaststhatarelocallyrelevantandbettertrustedbytheusers.This in turn could signi cantly improve the communication and application of climate information, especially to remote communities. A number of different methodologies have been proposed for combining these forecasts. Through considering the bene ts and limitations of each approach, practical recommendations are providedonselectingamethod,intheformofadecisionframework,thattakesintoconsiderationbothuser and provider needs. The framework comprises four main decision points: 1) consideration of the level of involvement of traditional-knowledge experts or the community that is required, 2) existing levels of traditional knowledge of climate forecasting and its level of cultural sensitivity, 3) the availability of long-term data—both traditional-knowledge and contemporary-forecast components, and 4) the level of resourcing available.Noonemethodissuitableforeveryoneandeverysituation;however,thedecisionframeworkhelps to select the most appropriate method for a given situation.