The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models

Abstract

Abstract. We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM) using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately 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. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally better than for precipitation. Results in terms of snow depth are satisfactory, which can be viewed as indicating a reasonably good intervariable consistency of the meteorological data produced by the method. In terms of temporal transferability (evaluated over time periods of 15 years only), results depend on the learning period. In terms of RCM grid point selection technique, the use of a complex RCM grid points selection technique, taking into account horizontal but also altitudinal proximity to SAFRAN massif centre points/altitude couples, generally degrades evaluation metrics for high altitudes compared to a simpler grid point selection method based on horizontal distance.

Winter tourism under climate change in the Pyrenees and the French Alps: relevance of snowmaking as a technical adaptation

“…This study uses the EURO-CORDEX dataset Kotlarski et al, 2014) for climate projections consisting of six regional climate models (RCMs) forced by five different global climate models (GCMs) from the CMIP5 ensemble (Taylor et al, 2012) over Europe, for the historical, RCP2.6, RCP4.5 and RCP8.5 scenarios (Moss et al, 2010). All EURO-CORDEX data were adjusted using the ADA-MONT method (Verfaillie et al, 2017) using the SAFRAN data as the reference observation dataset . Historical runs generally cover the period 1950-2005 and climate projections (RCPs) cover the period 2006-2100 (Table 1).…”

“…The present work aims at producing snow reliability investigations of a wide range of ski resorts in France (Alps and Pyrenees), Spain and Andorra under past and future conditions using state-of-the-art snowpack modelling. We accounted for snow grooming and snowmaking using a detailed snowpack model (Spandre et al, 2016b) and used adjusted and downscaled climate projections from the EURO-CORDEX dataset (Verfaillie et al, 2017 to compute snow reliability elevations with distinct levels of snow reliability requirements. The mean elevation of residential population in a ski resort (Breiling and Charamza, 1999) and the mean elevation of ski lifts (Falk and Vanat, 2016) were compared to the snow reliability line.…”

Multi-components ensembles of future meteorological and natural snow conditions in the Northern French Alps

“…This could also be due to the fact that Crocus model outputs result 5 from the interaction between various meteorological variables, both in terms of mean values but also their day to day variations, especially precipitation and temperature conditions which together yield either to rain or snow precipitation. By design, the ADAMONT method adjusts the variables independently from each other (Verfaillie et al, 2017). Even if special care is taken to minimize the disadvantages of this approach, such as the use of weather regimes for the quantile mapping statistical adjustment method, or the final quantile mapping applied to rain and snow precipitation separately to mitigate temperature/precipitation 10 detrimental interactions (Verfaillie et al, 2017), some interaction terms probably remain uncorrected.…”

“…By design, the ADAMONT method adjusts the variables independently from each other (Verfaillie et al, 2017). Even if special care is taken to minimize the disadvantages of this approach, such as the use of weather regimes for the quantile mapping statistical adjustment method, or the final quantile mapping applied to rain and snow precipitation separately to mitigate temperature/precipitation 10 detrimental interactions (Verfaillie et al, 2017), some interaction terms probably remain uncorrected. The adjustment method also probably exerts an influence on the variability during the historical period, which may be responsible for the overall lower spread (either expressed in terms of quantile-based coefficient of variation of annual values or the coefficient of variation of the interannual means) compared to future projections.…”

“…Based on the methodological framework introduced here, this will be tackled in subsequent exhaustive investigations, based on the entire wealth of data available in the SAFRAN reanalysis for the French Alps and Pyrenees (Durand et al, 2009b, a;Maris et al, 2009). The method can obviously be applied beyond French borders, provided 10 that an adequate long-term observational dataset can be used as a basis for RCM output adjustment using the ADAMONT method (Verfaillie et al, 2017).…”