Worldwide survey of awareness and needs concerning reanalyses, and respondents views on climate services

A worldwide online survey about user awareness of reanalyses and climate services was conducted in the period from November 2013 to February 2014 by the Coordinating Earth Observation Data Validation for Re-Analysis for Climate Services (CORE-CLIMAX) project. The 2,578 respondents were mostly users of global reanalyses [particularly the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), National Aeronautics and Space Administration (NASA), and Japan Meteorological Agency (JMA) reanalyses]. They answered queries arranged in 11 sections by choosing from preprepared check box responses and left several hundred free comments. Here, we analyze responses related to characteristics of reanalysis data and the perceived obstacles for using reanalysis in climate services. After examining responses from all survey participants, we focus on the answers from subgroups working in specific disciplines related to natural resource management: freshwater, agriculture and food production, forestry, and energy. Although the survey attracted mostly self-selected respondents from the education and public research and development (R&D) sectors, one-third of the energy-related subgroup were from the private sector. A large majority (91%) of the respondents use ECMWF reanalyses, but other reanalysis products are also widely used by them. Respondents expressed desire for reanalysis development in the areas of 1) training and online plotting tools, 2) more frequent updates, 3) explanations about uncertainties (the energy subgroup emphasizes this), 4) smaller biases, 5) less restrictive data policy, and 6) higher temporal and spatial resolution (the energy and water subgroups highlight this). Additionally, the subgroups (excluding energy) expressed interest in including in future climate services activities for applied weather and climate research for impact assessment and/or statistical impact analyses for improving weather warnings and their criteria.Peer reviewe