Abstract
Abstract. Climate change impact assessment related to floods, infrastructure networks, and water resource management applications requires realistic simulations of high-resolution gridded precipitation series under a future climate. This paper proposes to produce such simulations by combining a weather generator for high-resolution gridded daily precipitation, trained on a historical observation-based gridded data product, with coarser-scale climate change information obtained using a regional climate model. The climate change information can be added to various components of the weather generator, related to both the probability of precipitation as well as the amount of precipitation on wet days. The information is added in a transparent manner, allowing for an assessment of the plausibility of the added information. In a case study of nine hydrological catchments in central Norway with the study areas covering 1000–5500 km2, daily simulations are obtained on a 1 km grid for a period of 19 years. The method yields simulations with realistic temporal and spatial structures and outperforms empirical quantile delta mapping in terms of marginal performance.
Highlights
The rate of projected future warming in northern Europe is amongst the highest in the world, driven to a large extent by the strong feedback involving snow and ice as the climate warms (Collins et al, 2013)
A version denoted by WG1.2 and WG2.2 for Regional climate models (RCMs) information derived from RCM1 and RCM2, respectively, includes climate change information from the RCM in the seasonality and trend components of both the gamma model and the probit model for precipitation occurrence
The various weather generator (WG) methods are compared against the reference method in Sect. 3.5 denoted EQM1 and EQM2 derived from RCM1 and RCM2, respectively, as well as a simple method that uses the empirical distributions of the fine-scale seNorge data in the training period directly as predictions for the corresponding empirical distributions of the fine-scale seNorge data in the test period
Summary
The rate of projected future warming in northern Europe is amongst the highest in the world, driven to a large extent by the strong feedback involving snow and ice as the climate warms (Collins et al, 2013). The projected changes in precipitation amounts, snowpack, and snow cover will considerably impact surface hydrology through, for example, changed runoff magnitude as well as timing and amplitude of the spring flood (e.g. Von Storch et al, 2015). In order to study these effects, impact models optimally require inputs that reliably represent precipitation occurrence and intensity at a high spatial resolution, spatial and temporal variability, as well as physical consistency for different regions and seasons (Maraun et al, 2010). Coupled atmosphere–ocean general circulation models (GCMs) remain our main source of information for projections of future climate. These have spatial resolutions that are too coarse for assessing the often localized impacts of changing precipitation patterns. Regional climate models (RCMs) at a spatial resolution of 10–15 km (e.g. Jacob et al, 2014) are able to explicitly resolve mesoscale atmospheric processes and add valuable information for precipitation modelling over a region, with the newest model generations at an even higher resolution and able to include explicit deep convection (Lind et al, 2020; Prein et al, 2020)
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