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- ItemLocal difference measures between complex networks for dynamical system model evaluation(San Francisco, CA : Public Library of Science (PLoS), 2015) Lange, S.; Donges, J.F.; Volkholz, J.; Kurths, J.
- ItemAdjusting climate model bias for agricultural impact assessment: How to cut the mustard(Amsterdam [u.a.] : Elsevier, 2019) Galmarini, S.; Cannon, A.J.; Ceglar, A.; Christensen, O.B.; de Noblet-Ducoudré, N.; Dentener, F.; Doblas-Reyes, F.J.; Dosio, A.; Gutierrez, J.M.; Iturbide, M.; Jury, M.; Lange, S.; Loukos, H.; Maiorano, A.; Maraun, D.; McGinnis, S.; Nikulin, G.; Riccio, A.; Sanchez, E.; Solazzo, E.; Toreti, A.; Vrac, M.; Zampieri, M.[No abstract available]
- ItemTrend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0)(Göttingen : Copernicus GmbH, 2019) Lange, S.In this paper I present new methods for bias adjustment and statistical downscaling that are tailored to the requirements of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In comparison to their predecessors, the new methods allow for a more robust bias adjustment of extreme values, preserve trends more accurately across quantiles, and facilitate a clearer separation of bias adjustment and statistical downscaling. The new statistical downscaling method is stochastic and better at adjusting spatial variability than the old interpolation method. Improvements in bias adjustment and trend preservation are demonstrated in a cross-validation framework.