| ABSTRACT | In view of the observed and yet imminent climate change, climate data are exploited due to the impact they exert: on the environment, on human health, on agriculture and on economy. The emerging question is how raw scientific data can be transformed to actionable information, ready for use by stakeholders, the ones who are responsible to make the climate resilient decisions. This task requires that the raw climatic information is efficiently contextualized and made relevant to specific applications. In this work we attempt to map the path from raw climate data to tangible climate impacts. Emphasis is given on the production and collection of raw climatic information and its downscaling to an appropriate geographical scale that is defined by the application in concern, the quantification of its uncertainty, its post-processing to climate relevant indices and its integration to more complex statistical and dynamical algorithms. The data value chain we present in this work focuses on the climate sensitive vector-borne diseases and namely how we can estimate health risks in view of climatic changes that are in progress and are expected to intensify, using state of the art climate data and multidisciplinary analysis techniques. Malaria is used as a test case over Greece and the impact of climate on its distribution is assessed mainly by the impact of climate on its primary vector, over the study region. An observation that arises from such attempts of assessing climate impacts, is that the production of climate data at the very beginning of the data value chain should be designed in such a way, so as to be suitable for a specific application at the end of the chain. Thus, there will be a consistency between the data production process and their proper exploitation. |