RESUMO
The limited skill of seasonal climate predictions in some regions of the Southern African Development Community (SADC) restricts their potential application to the development of climate services. This study explores the feasibility of improving the quality of these predictions by using the observed relationship between Essential Climate Variables (ECVs) and large-scale teleconnection indices, namely Niño3.4, the Atlantic Niño and the Indian Ocean Dipole. The underlying hypothesis is that, for certain areas, the empirical observed teleconnections could improve the predictions offered by the seasonal forecasting systems. This is achieved by implementing linear regression models between each index and ECV, for up to 12 months into the past, and for each season and grid point. After obtaining the index-derived estimates of the variables, the correlation coefficients and fair Ranked Probability Skill Scores (fRPSS) are computed and compared to those of the ECMWF SEAS5 (SEAS5) predictions for different lead times. The results show that 10-25 % of the entire domain exhibits improved correlations for the index-derived precipitation in all seasons. In the case of temperature, though, higher correlations could be observed only in six seasons (and solely for Niño3.4). Regarding fRPSS, up to 7 % of the entire area shows an improvement when using Niño3.4 to estimate temperature (in four seasons). Conversely, for precipitation there is no detected enhancement. In future work, it would be worth investigating whether a combined multi-index regression can further raise the observed increase in performance.
RESUMO
Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health-Climate Risk framework.
RESUMO
Predicting the variations in climate for the coming 1-10 years is of great interest for decision makers, as this time horizon coincides with the strategic planning of stakeholders from climate-vulnerable sectors such as agriculture. This study attempts to illustrate the potential value of decadal predictions in the development of climate services by establishing interactions and collaboration with stakeholders concerned with food production and security. Building on our experience from interacting with users and the increased understanding of their needs gathered over the years through our participation in various European activities and initiatives, we developed a decadal forecast product that provides tailored and user-friendly information about multi-year dry conditions for the coming five years over global wheat harvesting regions. This study revealed that the coproduction approach, where the interaction between the user and climate service provider is established at an early stage of forecast product development, is a fundamental step to successfully provide useful and ultimately actionable information to the interested stakeholders. The study also provides insights that shed light on the reasons for the delayed entry of decadal predictions in the climate services discourse and practice, obtained from surveying climate scientists and discussing with decadal prediction experts. Finally, it shows the key challenges that this new source of climate information still faces.