Dynamical Downscaling with Reinitializations: A Method to Generate Finescale Climate Datasets Suitable for Impact Studies - Lucas-Picher, Philippe; Boberg, Fredrik; Christensen, Jens H.; Berg, Peter (Journal of Hydrometeorology, 2013)
Abstract To retain the sequence of events of a regional climate model (RCM) simulation driven by a reanalysis, a method that has not been widely adopted uses an RCM with frequent reinitializations toward its driving field. In this regard, this study highlights the benefits of an RCM simulation with frequent (daily) reinitializations compared to a standard continuous RCM simulation. Both simulations are carried out with the RCM HIRHAM5, driven with the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) data, over the 12-km-resolution European Coordinated Regional Climate Downscaling Experiment (CORDEX) domain covering the period 1989–2009. The analysis of daily precipitation shows improvements in the sequence of events and the maintenance of the added value from the standard continuous RCM simulation. The validation of the two RCM simulations with observations reveals that the simulation with reinitializations indeed improves the temporal correlation. Furthermore, the RCM simulation with reinitializations has lower systematic errors compared to the continuous simulation, which has a tendency to be too wet. A comparison of the distribution of wet day precipitation intensities shows similar added value in the continuous and reinitialized simulations with higher variability and extremes compared to the driving field ERA-Interim. Overall, the results suggest that the finescale climate dataset of the RCM simulation with reinitializations better suits the needs of impact studies by providing a sequence of events matching closely the observations, while limiting systematic errors and generating reliable added value. Downsides of the method with reinitializations are increased computational costs and the introduction of temporal discontinuities that are similar to those of a reanalysis.

On the need for bias correction of regional climate change projections of temperature and precipitation - Christensen, Jens H.; Boberg, Fredrik; Christensen, Ole B.; Lucas‐Picher, Philippe (Geophysical Research Letters, 2008)
Within the framework of the European project ENSEMBLES (ensembles‐based predictions of climate changes and their impacts) we explore the systematic bias in simulated monthly mean temperature and precipitation for an ensemble of thirteen regional climate models (RCMs). The models have been forced with the European Centre for Medium Range Weather Forecasting Reanalysis (ERA40) and are compared to a new high resolution gridded observational data set. We find that each model has a distinct systematic bias relating both temperature and precipitation bias to the observed mean. By excluding the twenty‐five percent warmest and wettest months, respectively, we find that a derived second‐order fit from the remaining months can be used to estimate the values of the excluded months. We demonstrate that the common assumption of bias cancellation (invariance) in climate change projections can have significant limitations when temperatures in the warmest months exceed 4–6 °C above present day conditions.