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Petter Lind, David Lindstedt, Erik Kjellström, and Colin Jones

Abstract

High-impact, locally intense rainfall episodes represent a major socioeconomic problem for societies worldwide, and at the same time these events are notoriously difficult to simulate properly in climate models. Here, the authors investigate how horizontal resolution and model formulation influence this issue by applying the HIRLAM–ALADIN Regional Mesoscale Operational NWP in Europe (HARMONIE) Climate (HCLIM) regional model with three different setups: two using convection parameterization at 15- and 6.25-km horizontal resolution (the latter within the “gray zone” scale), with lateral boundary conditions provided by ERA-Interim and integrated over a pan-European domain, and one with explicit convection at 2-km resolution (HCLIM2) over the Alpine region driven by the 15-km model. Seven summer seasons were sampled and validated against two high-resolution observational datasets. All HCLIM versions underestimate the number of dry days and hours by 20%–40% and overestimate precipitation over the Alpine ridge. Also, only modest added value was found for gray-zone resolution. However, the single most important outcome is the substantial added value in HCLIM2 compared to the coarser model versions at subdaily time scales. It better captures the local-to-regional spatial patterns of precipitation reflecting a more realistic representation of the local and mesoscale dynamics. Further, the duration and spatial frequency of precipitation events, as well as extremes, are closer to observations. These characteristics are key ingredients in heavy rainfall events and associated flash floods, and the outstanding results using HCLIM in a convection-permitting setting are convincing and encourage further use of the model to study changes in such events in changing climates.

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Elizabeth J. Kendon, Richard G. Jones, Erik Kjellström, and James M. Murphy

Abstract

Multimodel ensembles, whereby different global climate models (GCMs) and regional climate models (RCMs) are combined, have been widely used to explore uncertainties in regional climate projections. In this study, the extent to which information can be enhanced from sparsely filled GCM–RCM ensemble matrices and the way in which simulations should be prioritized to sample uncertainties most effectively are examined.

A simple scaling technique, whereby the local climate response in an RCM is predicted from the large-scale change in the GCM, is found to often show skill in estimating local changes for missing GCM–RCM combinations. In particular, scaling shows skill for precipitation indices (including mean, variance, and extremes) across Europe in winter and mean and extreme temperature in summer and winter, except for hot extremes over central/northern Europe in summer. However, internal variability significantly impacts the ability to determine scaling skill for precipitation indices, with a three-member ensemble found to be insufficient for identifying robust local scaling relationships in many cases.

This study suggests that, given limited computer resources, ensembles should be designed to prioritize the sampling of GCM uncertainty, using a reduced set of RCMs. Exceptions are found over the Alps and northeastern Europe in winter and central Europe in summer, where sampling multiple RCMs may be equally or more important for capturing uncertainty in local temperature or precipitation change. This reflects the significant role of local processes in these regions. Also, to determine the ensemble strategy in some cases, notably precipitation extremes in summer, better sampling of internal variability is needed.

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Bart van den Hurk, Martin Hirschi, Christoph Schär, Geert Lenderink, Erik van Meijgaard, Aad van Ulden, Burkhardt Rockel, Stefan Hagemann, Phil Graham, Erik Kjellström, and Richard Jones

Abstract

Simulations with seven regional climate models driven by a common control climate simulation of a GCM carried out for Europe in the context of the (European Union) EU-funded Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) project were analyzed with respect to land surface hydrology in the Rhine basin. In particular, the annual cycle of the terrestrial water storage was compared to analyses based on the 40-yr ECMWF Re-Analysis (ERA-40) atmospheric convergence and observed Rhine discharge data. In addition, an analysis was made of the partitioning of convergence anomalies over anomalies in runoff and storage. This analysis revealed that most models underestimate the size of the water storage and consequently overestimated the response of runoff to anomalies in net convergence. The partitioning of these anomalies over runoff and storage was indicative for the response of the simulated runoff to a projected climate change consistent with the greenhouse gas A2 Synthesis Report on Emission Scenarios (SRES). In particular, the annual cycle of runoff is affected largely by the terrestrial storage reservoir. Larger storage capacity leads to smaller changes in both wintertime and summertime monthly mean runoff. The sustained summertime evaporation resulting from larger storage reservoirs may have a noticeable impact on the summertime surface temperature projections.

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