Speaker
Description
There is a growing need for stakeholders to access relevant and user-friendly climate information to develop action plans addressing climate change challenges across various sectors. The occurrence of future climate events is often presented in a probabilistic context, where multiple projections are produced to represent the uncertainties inherent in climate modeling. However, the practical use of these projections is limited by (1) systematic biases in climate models—despite continuous improvements in their skill—and (2) the large number of available simulations, which complicates effective decision-making.
Indicators based on absolute thresholds are particularly sensitive to such biases, which can strongly distort analyses. To address this, we present a bias-corrected ensemble of simulations generated using statistical correction methods and the CLIMATE GRID, an observational dataset developed by the Royal Meteorological Institute of Belgium (RMI) to accurately represent conditions across Belgium.
Even with a bias-corrected ensemble, potential users often struggle to identify which scenarios and models to prioritize. To tackle this issue, we are developing an interactive, collaborative method that helps users select a representative subset of climate simulations from large ensembles. Inspired by a service developed by MeteoSwiss, this approach identifies specific low-, medium-, and high-impact scenarios, providing plausible statistical outcomes for selected climate indices, emission scenarios, and regions. An example focusing on temperature-related indices will be presented to illustrate how users can efficiently handle large ensembles by selecting a manageable, representative subset of climate projections for further analysis.
| If your abstract is not accepted for an oral presentation, would you be interested in presenting it as a poster instead? | Yes |
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| If accepted by the Scientific Board, I agree to have my presentation/poster and abstract published on the Belgian Climate Centre websites and social media. | Yes |
