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

• Published in: Geophysical Research Letters
• Main Authors: Emma Dutot, Hervé Douville
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2023
• Subjects: climate change; Arctic; runoff; projections; constraints; uncertainties; Geophysics. Cosmic physics; QC801-809; Climate change
• Online Access: https://doi.org/10.1029/2023GL104039; https://doaj.org/article/0357ab4f1b2a47079aa338c5d3a30960

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.