Pattern scaling the parameters of a Markov‐chain gamma‐distribution daily precipitation generator

Abstract

AbstractGeneral circulation models (GCMs) are the most sophisticated tools at our disposal for studying future climates, but there are limitations to overcome. These include resolutions that may be too coarse for impact assessments, limited or zero availability of some policy‐relevant scenarios, and limited time‐series length for assessing the risk of extreme events. We illustrate how these limitations can be addressed by combining a stochastic precipitation generator (SPG) with pattern scaling (PS) of its key parameters. Computationally inexpensive, SPG parameters can be perturbed to generate time‐series representative of weather under a future climate with high spatial and temporal resolution. If the SPG parameter perturbations are derived directly from GCM simulations projections can only be made for scenarios already simulated by the GCM. Instead, we obtain the parameter perturbations using PS, facilitating emulation of scenarios not necessarily explicitly simulated by the GCM, and where we scale perturbations approximately linearly with global temperature change. PS is commonly applied to estimate perturbations in the mean of climate variables, but rarely to higher‐order parameters as we demonstrate here. We apply PS for the first time, globally, to the parameters of a daily, first‐order Markov‐chain gamma‐distribution SPG using output from the IPSL‐CM6A‐LR GCM to perturb an SPG fitted to observed data from two stations in diverse climates (Santarém, Brazil and Reykjavik, Iceland) to illustrate this novel approach. We produce time series corresponding to a range of GWLs and demonstrate the capability of the combined SPG‐PS approach to study local‐scale, future daily precipitation characteristics, climate and subsequent risk of extreme weather events.