A Statistical Adjustment of Regional Climate Model Outputs to Local Scales: Application to Platja de Palma, Spain

Abstract

Projections of climate change effects for the System of Platja de Palma (SPdP) are derived using a novel statistical technique. Socioeconomic activities developed in this settlement are very closely linked to its climate. Any planning for socioeconomic opportunities in the mid-and long term must take into account the possible effects of climate change. To this aim, daily observed series of minimum and maximum temperatures, precipitation, relative humidity, cloud cover, and wind speed have been analyzed. For the climate projections, daily data generated by an ensemble of regional climate models (RCMs) have been used. To properly use RCM data at local scale, a quantile-quantile adjustment has been applied to the simulated regional projections. The method is based on detecting changes in the cumulative distribution functions between the recent past and successive time slices of the simulated climate and applying these, after calibration, to the recent past (observed) series. Results show an overall improvement in reproducing the present climate baseline when using calibrated series instead of raw RCM outputs, although the correction does not result in such clear improvement when dealing with very extreme rainfalls. Next, the corrected series are analyzed to quantify the climate change signal. An increase of the annual means for temperatures together with a decrease for the remaining variables is projected throughout the twenty-first century. Increases in weak and intense daily rainfalls and in high extremes for daily maximum temperature can also be expected. With this information at hand, the experts planning the future of SPdP can respond more effectively to the problem of local adaptation to climate change.

Decreasing Past and Mid-Century Rainfall Indices over the Ouémé River Basin, Benin (West Africa)

“…Before the future precipitation index was calculated, we corrected the bias of the raw output of the RCM with a new quantile-quantile calibration method based on a nonparametric function that amends mean, variability, and shape errors in the simulated cumulative distribution functions (CDFs) of the climatic variables, developed by [44]. Indeed, two studies devoted to the comparison of daily precipitation bias correction methods were done in Benin namely N'Tcha M'Po et al [39] and Obada et al [45].…”

“…In these studies, six daily precipitation bias correction methods were compared and the new quantile method (AQM: Adjusted Quantile Mapping) is the most adapted method to reduce the bias of the daily precipitation simulated by the RCMs in Benin. The procedure consists of calculating the changes, quantile by quantile, in the CDFs of daily RCM outputs between a x-year control period and successive x-year future time slices [39,44]. These changes are rescaled based on the observed CDF for the same control period, and then added, quantile by quantile, to these observations to obtain new calibrated future CDFs that convey the climate change signal [44].…”

Assessing Climate Impacts on Hydropower Production: The Case of the Toce River Basin

“…We used a quantile-based error-correction approach (quantile mapping) to downscale the RCM simulations to a point scale and to reduce its error characteristics [23][24][25][26][27][28][29][30]. In our study, the quantile mapping applied observational data from the gauging stations to climate data from the regional climate models on a daily basis.…”

Bias correction of dynamically downscaled precipitation to compute soil water deficit for explaining year-to-year variation of tree growth over northeastern France

“…Quantile-mapping (QM) transformation (Empirical transformation of Panofsky and Brier, 1968) can overcome these limitations. The QM method has been widely used for correcting biases in simulated meteorological variables (Déqué, 2007;Boé et al, 2007;Themeßl et al, 2011;Amengual et al, 2012;Themeßl et al, 2012;Maraun, 2013).…”

“…A difficulty arises for precipitation since RCMs tend to overestimate the number of days resulting in trace values, but also to underestimate the number of non-rainy days, thus resulting in an unrealistic probability of precipitation in the simulations (Amengual et al, 2012). To overcome this issue, and following Amengual et al (2012), an additional constraint is imposed: the ratio of non-rainy days between predicted and control simulated raw data is maintained for the calibrated versus observed series. This is done by transposing differences between predicted and control precipitation quantile before its implementation over corrected series.…”