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Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

The present contribution constitutes the first comprehensive attempt to (a) record the spatial characteristics of the beaches of the Aegean Archipelago (Greece), a critical resource for both the local and national economy; and (b) provide a rapid assessment of the impacts of the long- term and episodic sea level rise (SLR), under different scenarios. Spatial information and other attributes (e.g. presence of coastal protection works and backshore development) of the beaches of the 58 largest islands of the Archipelago were obtained on the basis of remote-sensed images available in the web. Ranges of SLR-induced beach retreats under different morphological, sedimentological and hydrodynamic forcing and SLR scenarios were estimated, using suitable ensembles of cross-shore (1-D) morphodynamic models. These ranges, combined with empirically-derived estimations of wave run up-induced flooding, were then compared with the recorded maximum beach widths, to provide ranges of retreat/erosion and flooding at the Archipelago scale. The spatial information shows that the Aegean beaches may be particularly vulnerable to mean (MSLR) and episodic SLRs due to: (i) their narrow widths (about 59 % of the beaches have maximum widths < 20 m); (ii) their limited terrestrial sediment supply; (iii) the substantial coastal development and (iv) limited existing coastal protection. Modeling results indeed project severe impacts under MSLR and storm surges, which by 2100 could be devastating. For example, under MSLR of 0.5 m (RCP4.5), a storm surge of 0.6 m is projected to result in complete erosion of between 31 and 88 % of all beaches (29 - 87 % of beaches currently fronting coastal infrastructure and assets), at least temporarily. It appears that, in addition to the significant effort and financial resources required to protect/maintain the critical economic resource of the Aegean Archipelago, appropriate coastal ‘set-back zone’ policies should also be adopted and implemented. JRC.E.1-Disaster Risk Management