Revisiting the Potential to Narrow Model Uncertainty in the Projections of Arctic Runoff

• Published in: Geophysical Research Letters
• Main Authors: Dutot, Emma, Douville, Hervé
• Other Authors: Météo-France, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology; [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology; Climate change
• Online Access: https://meteofrance.hal.science/meteo-04439017; https://meteofrance.hal.science/meteo-04439017/document; https://meteofrance.hal.science/meteo-04439017/file/Geophysical%20Research%20Letters%20-%202023%20-%20Dutot%20-%20Revisiting%20the%20Potential%20to%20Narrow%20Model%20Uncertainty%20in%20the%20Projections%20of.pdf; https://doi.org/10.1029/2023GL104039

International audience Abstract Despite multiple advances in the understanding of the water cycle intensification in a warmer climate, climate models still diverge in their hydrological projections. Here we constrain annual runoff projections over individual and aggregated Arctic river basins. For this purpose, we use two ensembles of global climate models and two statistical methods: a regression scheme assuming similar runoff sensitivities at interannual versus climate change timescales, and a Bayesian method where models are used to derive a posterior runoff response conditioned on historical observations. While both techniques are shown to narrow model uncertainties, more or less substantially depending on rivers, the Bayesian method is less sensitive to the choice of the model ensemble and is more skillful when tested with synthetic observations. It has also been applied over the whole Arctic watershed, showing so far a limited narrowing of the inter‐model spread, but its skill will further improve with increasing climate change.