CR4D: Regional Workshop on Seamless Climate Forecasts to Improve Decision-Making at the Sub-Seasonal to Seasonal Scale (S2S)
Wednesday, February 8, 2017 to Thursday, February 9, 2017
UNCC, Addis Ababa, Ethiopia
The Climate Research for Development (CR4D) in Africa is an African-led initiative supported by partnership between African Climate Policy Centre (ACPC) of the United Nations Economic Commission for Africa (UNECA), African Ministerial Conference on Meteorology (AMCOMET), World Meteorological Organization (WMO), and Global Framework for Climate Services (GFCS) to promote and nurture collaborative, user-driven, climate research activities to improve climate information needed for decision making and development planning in various climate sensitive socio-economic sectors. To achieve this key objective, different pilot research projects are being undertaken by CR4D secretariat including the sub-seasonal to seasonal (S2S) forecasting.
During the African Climate Conference in 2013 (ACC2013), participants drawn from different expertise and disciplines initiated the process of developing four regional proposals that would address the continental priority research needs. One of these proposals was focused on improving S2S forecasting including the use of user-based metrics to verify the skill of S2S predictions or prototype predictions. This research frontier has been recognized by WWRP/WCRP as a grand challenge in their Sub-Seasonal to Seasonal (S2S) Prediction Project. As part of the S2S Project, WCRP/WWRP are fostering the creation of multi-model research archives of sub-seasonal forecasts (15–90 days ahead) from global producing centers (GPCs). Archives of retrospective seasonal forecasts (3-9 months ahead), as well as real-time forecasts, are available from the WMO Lead Centre for Long-Range Forecast Multi-Model Ensembles (LC-LRFMME). Retrospective forecasts from additional modelling centres are also available from the WCRP Climate System Historical Forecast Project (CHFP) database, and from the North American Multi-Model Ensemble (NMME) database that includes both hind casts and forecasts from the same set of models in real time.