The Republic of Korea-Pacific Islands Climate Prediction Services Project

Abstract

Seasonal prediction provides critical information for the tropical Pacific region, where the economy and livelihood is highly dependent on climate variability. While the highest skills of dynamical prediction systems are usually found in the tropical Pacific, National Hydrological and Meteorological Services (NHMS) in the Pacific Islands Countries (PICs) do not take full advantage of such scientific achievements. The Republic of Korea-Pacific Islands Climate Prediction Services (ROK-PI CliPS) project aims to help PICs produce regionally tailored climate prediction information using a dynamical seasonal prediction system. The project is being jointly implemented by the APEC Climate Center (APCC) and the Secretariat of the Pacific Regional Environment Programme (SPREP), in close collaboration with NHMSs in PICs. The regionally tailored, dynamical-statistical hybrid climate prediction system uses predictors that were identified through communications with NHMSs. The predictors were selected based on the empirical physical relationship of the local climate fluctuations, indicated by multi-institutional and multimodel ensembles. This hybrid system makes full use of dynamical seasonal predictions, which have not been commonly utilized in current operation in PICs. In accordance with system development, additional efforts have been made for PIC NHMSs to build capacity by increasing their knowledge and skill needed to develop such methodologies and systems. Nonetheless, the successive and strategic efforts to sustain and further improve climate predictions in the Pacific Islands region are required.

Advances and challenges of operational seasonal prediction in Pacific Island Countries

“…Around 50% of the selected optimal predictors are not directly related to ENSO (referred to as ‘OTHER’ category). This implies the usefulness of PICASO and the further benefits of dynamical seasonal prediction when the empirical association between the local rainfall and ENSO is neither clear nor robust (see Figure 2 of Sohn et al 29 for more details).…”

“…Climate experts from both APCC (expertise in APCC MME products) and the PICs NMHSs (expertise in local climate system) extracted large-scale patterns of climate variables that are associated with observed rainfall. The optimal predictors are then identified based on the discussions among APCC and PICs NMHSs experts (see also Sohn et al (2018)). We then performed probabilistic Bayesian regressions onto the historical rainfall observation for each station to generate station-level prediction information.…”

“…1 ). It was developed through the Republic of Korea-Pacific Islands Climate Prediction Services project (ROK-PI CliPS; 2014–2017) funded by the Republic of Korea-Pacific Islands Forum Cooperation Fund (RPCF) to assist the Pacific Island NMHSs in generating high quality climate forecasts 29 . Many PICs have islands smaller than the MME grid representation or small-scale topographic features that affect local climate states, making global- or regional-scale prediction information potentially unsuitable for their operational domestic climate outlooks.…”