Received: 30 Jun 2016 – Discussion started: 29 Sep 2016
Abstract. We introduce a method – called ADAMONT (ADAptation of RCM outputs to MOuNTain regions) v1.0 – to downscale and adjust daily climate projections from a regional climate model against a regional reanalysis of hourly meteorological conditions using quantile mapping. The method produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. The ADAMONT method is evaluated through its application to the ALADIN-Climate v5 RCM forced by the ERA-Interim reanalysis, compared to the SAFRAN reanalysis, used as the pseudo-observation database covering the entire French Alps split into 23 massifs within which meteorological conditions are provided for several elevation bands separated by 300 m altitude. Different evaluation criteria are analysed for temperature, precipitation, but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data generated using this method. The impact of the learning period and of the method used to select neighbouring RCM grid points for each SAFRAN massif/altitude configuration is tested. The performance of the method is satisfying, with similar or even better evaluation metrics than previous literature findings. Results for temperature are generally better than for precipitation. Snow depth yields good results, which can be viewed as indicating a reasonably good inter-variable consistency of the meteorological data produced by the method. The temporal transferability of the method is assessed through the comparison of results obtained using different learning periods, and shows that the method is sensitive to the period considered due to the empirical treatment of values beyond the 99.5 th quantile. The use of a complex RCM grid points selection technique taking into account horizontal but also altitudinal proximity to SAFRAN massif centroids/altitude couples generally degrades evaluation metrics for high altitudes, compared to a simpler 2-dimensional proximity selection technique.
How to cite. Verfaillie, D., Déqué, M., Morin, S., and Lafaysse, M.: The downscaling and adjustment method ADAMONT
v1.0 for climate projections in mountainous regions
applicable to energy balance land surface models, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2016-168, 2016.