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An Artificial Neural Network (ANN) approach is used to downscale ECHAM5 GCM temperature (T ) and rainfall (R) fields to RegCM3 regional model scale over Europe. The main inputs to the neural network were the ECHAM5 fields and topography, and RegCM3 topography. An ANN trained for the period 1960-1980 was able to recreate the RegCM3 1981-2000 mean T and R fields with reasonable accuracy. The ANN showed an improvement over a simple lapse-rate correction method for T , although the ANN R field did not capture all the fine-scale detail of the RCM field. An ANN trained over a smaller area of Southern Europe was able to capture this detail with more precision. The ANN was unable to accurately recreate the RCM climate change (CC) signal between 1981-2000 and 2081-2100, and it is suggested that this is because the relationship between the GCM fields, RCM fields and topography is not constant with time and changing climate. An ANN trained with three ten-year "time-slices" was able to better reproduce the RCM CC signal, particularly for the full European domain. This approach shows encouraging results but will need further refinement before becoming a viable supplement to dynamical regional climate modelling of temperature and rainfall. [ABSTRACT FROM AUTHOR]

We present a weighting scheme specifically designed for regional climate models (RCMs) in that it is based on the model performance in simulating the sub-global climate model (GCM) mesoscale climate signal. The functional form of the weights is based on multiple variables (temperature and precipitation) and metrics (correlation and root mean square error). The weighting scheme is applied to an ensemble of RCM simulations for the European region recently completed as part of the ENSEMBLES project. As a test of the successful implementation of the scheme, the weighting leads to an overall improvement of the performance of the ensemble when measured with the same metrics used in the weighting. The improvement is particularly pronounced over topographically complex regions (e.g. the Alps) in which a larger inter-model range of performance, and thus a more aggressive weighting, is found. When applied to the generation of probabilistic climate change projections, this scheme is designed to be used in conjunction with other RCM weighting metrics developed in the ENSEMBLES project and corresponding weighting schemes for the GCMs driving the regional models. [ABSTRACT FROM AUTHOR]

We design, apply, and validate a methodology for correcting climate model output to produce internally consistent fields that have the same statistical intensity distribution as the observations. We refer to this as a statistical bias correction. Validation of the methodology is carried out using daily precipitation fields, defined over Europe, from the ENSEMBLES climate model dataset. The bias correction is calculated using data from 1961 to 1970, without distinguishing between seasons, and applied to seasonal data from 1991 to 2000. This choice of time periods is made to maximize the lag between calibration and validation within the ERA40 reanalysis period. Results show that the method performs unexpectedly well. Not only are the mean and other moments of the intensity distribution improved, as expected, but so are a drought and a heavy precipitation index, which depend on the autocorrelation spectra. Given that the corrections were derived without seasonal distinction and are based solely on intensity distributions, a statistical quantity oblivious of temporal correlations, it is encouraging to find that the improvements are present even when seasons and temporal statistics are considered. This encourages the application of this method to multi-decadal climate projections. [ABSTRACT FROM AUTHOR]

Soil quality is fundamental for ecosystem long term functionality, productivity and resilience to current climatic changes. Despite its importance, soil is lost and degraded at dramatic rates worldwide. In Europe, the Mediterranean areas are a hotspot for soil erosion and land degradation due to a combination of climatic conditions, soils, geomorphology and anthropic pressure. Soil organic carbon (SOC) is considered a key indicator of soil quality as it relates to other fundamental soil functions supporting crucial ecosystem services. In the present study, the functional relationships among SOC and other important soil properties were investigated in the topsoil of 38 sites under different land cover and management, distributed over three Mediterranean regions under strong desertification risk, with the final aim to define critical SOC ranges for fast loss of important soil functionalities. The study sites belonged to private and public landowners seeking to adopt sustainable land management practices to support ecosystem sustainability and productivity of their land. Data showed a very clear relationship between SOC concentrations and the other analyzed soil properties: total nitrogen, bulk density, cation exchange capacity, available water capacity, microbial biomass, C fractions associated to particulate organic matter and to the mineral soil component and indirectly with net N mineralization. Below 20 g SOC kg -1 , additional changes of SOC concentrations resulted in a steep variation of all the analyzed soil indicators, an order of magnitude higher than the changes occurring between 50 and 100 g SOC kg -1 and 3-4 times the changes observed at 20-50 g SOC kg -1 . About half of the study sites showed average SOC concentration of the topsoil centimetres <20 g SOC kg -1 . For these areas the level of SOC might hence be considered critical and immediate and effective recovery management plans are needed to avoid complete land degradation in the next future., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)