3 research outputs found
Integrating Data Science and Earth Science
This open access book presents the results of three years collaboration between earth scientists and data scientists, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows
The regional MiKlip decadal prediction system for Europe: Hindcast skill for extremes and userâoriented variables
Regional climate predictions for the next decade are gaining importance, as this period falls within the planning horizon of politics, economy, and society. The potential predictability of climate indices or extremes at the regional scale is of particular interest. The German MiKlip project (âmidâterm climate forecastâ) developed the first regional decadal prediction system for Europe at 0.44° resolution, based on the regional model COSMOâCLM using global MPIâESM simulations as boundary conditions. We analyse the skill of this regional system focussing on extremes and userâoriented variables. The considered quantities are related to temperature extremes, heavy precipitation, wind impacts, and the agronomy sector. Variables related to temperature (e.g., frost days, heat wave days) show high predictive skill (anomaly correlation up to 0.9) with very little dependence on leadâtime, and the skill patterns are spatially robust. The skill patterns for precipitationârelated variables (e.g., heavy precipitation days) and windâbased indices (like storm days) are less skilful and more heterogeneous, particularly for the latter. Quantities related to the agronomy sector (e.g., growing degree days) show high predictive skill, comparable to temperature. Overall, we provide evidence that decadal predictive skill can be generally found at the regional scale also for extremes and userâoriented variables, demonstrating how the utility of decadal predictions can be substantially enhanced. This is a very promising first step towards impactârelated modelling at the regional scale and the development of individual userâoriented products for stakeholders.The skill of the regional MiKlip decadal prediction system is analysed focussing on extremes and userâoriented variables. Variables related to temperature extremes and the agronomy sector show high predictive skill with very little dependence on leadâtime. Skill patterns for precipitationârelated variables and windâbased indices are less skilful and more heterogeneous, especially for the latter.The study was mainly funded by the Bundesministerium fĂŒr Bildung und Forschung (BMBF) under project FONA MiKlipâII
http://dx.doi.org/10.13039/501100002347AXA Research Fund
http://dx.doi.org/10.13039/50110000196