Improved Arctic data to predict climate change Scientists are enhancing the quality of available climate change data covering the previous 30 to 50 years in high latitude regions and the Arctic. Improved predictive models will be critical to policy development and implementation. Climate Change and Environment © Thinkstock High latitude regions (those close to the poles), including the Arctic polar region, are frigid zones historically characterised by ice and snow all year long. They are integral parts of the so-called cryosphere where the Earth is perennially frozen. Fluctuations in parameters associated with these frozen zones affect ocean sea level and the climate of mid-latitude nations and, conversely, global climate change affects these parameters. The Arctic environment is particularly sensitive to global change and global warming (forcing mechanisms) and more closely reflects such changes (feedback) compared to any other region of the cryosphere. European scientists initiated the EU-funded 'Monitoring and assessing regional climate change in high latitudes and the Arctic' (Monarch-A) project to develop an information package devoted to Essential Climate Variables (ECVs) in these areas during the last 30 to 50 years. Eleven multidisciplinary ECVs covering terrestrial, oceanic and atmospheric components were selected for generation of time-series evolutions. They include land cover, snow cover, sea level, sea ice volume and ocean heat transport. The scientists are developing a description of the state and evolution of high latitude and Arctic ECVs in the context of terrestrial carbon and water fluxes, sea level, ocean circulation and the marine carbon cycle. Monarch-A researchers have harmonised different sources of the same variables, reanalysed parameters using improved models and algorithms, and assimilated existing data into new comprehensive databases. Numerous improvements in data accuracy and coverage have already been achieved. Enhancing the time span of existing ECV data records along with their accuracy improves the predictive capability of models through better starting points (initial conditions). Monarch-A outcomes are expected to provide important scientific foundations for the development and implementation of European and international environmental policies including climate adaptation strategies.