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In the context of the first phase of the Euro-CORDEX Flagship Plot Study (FPS) Land Use and Climate Across Scales (LUCAS), we investigate the afforestation impact on the seasonal cycle of soil temperature over the European continent with an ensemble of ten regional climate models (RCMs). For this purpose, each ensemble member performed two idealized land cover experiments in which Europe is covered either by forests or grasslands. The multi-model mean exhibits a reduction of the annual amplitude of soil temperature (AAST) over all European regions, although this not a robust feature among the models. In Mediterranean, the simulated AAST response to afforestation is between -4 K and +2 K while in Scandinavia the inter-model spread ranges from -7 K to +1 K. We then examine the role of changes in the annual amplitude of ground heat flux (AAGHF) and summer soil moisture content (SMC) in determining the effect of afforestation on AAST response. In contrast with the diverging results in AAST, all the models consistently indicate a widespread AAGHF decrease and summer SMC decline due to afforestation. The AAGHF changes effectively explain the largest part of the inter-model variance in AAST response in most regions, while the changes in summer SMC determine the sign of AAST response within a group of three simulations sharing the same land surface model. Finally, we pair FLUXNET sites to compare the simulated results with observation-based evidence of the impact of forest on soil temperature. In line with models, observations indicate a summer ground cooling in forested areas compared to open lands. The vast majority of models agree with the sign of the observed reduction in AAST, although with a large variation in the magnitude of changes. Overall, we aspire to emphasize the effects of afforestation on soil temperature profile with this study, given that changes in the seasonal cycle of soil temperature potentially perturb crucial biochemical processes. Such perturbations can be of societal relevance as afforestation is proposed as a climate change mitigation strategy. [ABSTRACT FROM AUTHOR]

The region of southern Africa (SAF) is highly vulnerable to the impacts of climate change and is projected to experience severe precipitation shortages in the coming decades. Ensuring that our modelling tools are fit for the purpose of assessing these changes is critical. In this work we compare a range of satellite products along with gauge-based datasets. Additionally, we investigate the behaviour of regional climate simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Africa domain, along with simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6). We identify considerable variability in the standard deviation of precipitation between satellite products that merge with rain gauges and satellite products that do not, during the rainy season (Oct-Mar), indicating high observational uncertainty for specific regions over SAF. Good agreement both in spatial pattern and the strength of the calculated trends is found between satellite and gauge-based products, however. Both CORDEX-Africa and CMIP5 ensembles underestimate the observed trends during the analysis period. The CMIP6 ensemble displayed persistent drying trends, in direct contrast to the observations. The regional ensemble exhibited improved performance compared to its forcing (CMIP5), when the annual cycle and the extreme precipitation indices were examined, confirming the added value of the higher resolution regional climate simulations. The CMIP6 ensemble displayed a similar behaviour to CMIP5, however reducing slightly the ensemble spread. However, we show that reproduction of some key SAF phenomena, like the Angolan Low (which exerts a strong influence on regional precipitation), still poses a challenge for the global and regional models. This is likely a result of the complex climatic process that take place. Improvements in observational networks (both in-situ and satellite), as well as continued advancements in high-resolution modelling will be critical, in order to develop a robust assessment of climate change for southern Africa. [ABSTRACT FROM AUTHOR]

In this work we present a sensitivity study of eight WRF (Weather Research and Forecasting model) regional climate simulations for the EURO-CORDEX domain regarding aerosol implementation and their impact on European climate. The sensitivities differ in the aerosol properties (optical characteristics) and effects implemented (direct/indirect), as well as in the aerosol input data used (Tegen, MACv1, MACC, GOCART). Simulations have a resolution of 0.44° and are forced by the ERA-Interim reanalysis. A basic evaluation has been performed against ground (E-OBS) and satellite-based observational data (CMSAF Sarah, Clara). Implementation of the direct radiative effect of aerosol reduces the direct component of the incoming surface solar radiation by 20-30 % in all seasons, due to enhanced aerosol scattering. The diffuse shortwave component augments 30-40 % in summer and 5-8 % in winter, while downward shortwave radiation at the surface is attenuated by 3-8 %. The resulting aerosol radiative effect is negative and stronger in summer (-12 W/m²) than inwinter (-2 W/m²) due to a balance between the more negative direct aerosol effect (-17 to -5 W/m²) and positive changes in the cloud forcing (+5 W/m²) representing the semi-direct effect. We also show that modeling direct and indirect effects can lead to small changes in cloudiness, mainly regarding low-level clouds, and circulation anomalies in the lower and mid-troposphere, which in some cases can be statistically significant. Precipitation is not affected in a consistent pattern by the aerosol implementation and changes do not exceed ±10 %. Temperature, on the other hand, systematically decreases by -0.1 to -0.5 °C due to the direct effect with regional changes that can be up to -1.5 °C. [ABSTRACT FROM AUTHOR]

The region of southern Africa (SAF) is among the most exposed climate change hotspots and is projected to experience severe impacts on multiple economical and societal sectors. For this reason, producing reliable projections of the expected impacts of climate change is key for local communities. In this work we use a set of 19 regional climate models (RCMs) performed in the context of the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Africa and a set of 10 global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), that were used as the driving GCMs in the RCM simulations. We are concerned about the degree to which RCM simulations are influenced by their driving GCMs, with regards to monthly precipitation climatologies, precipitation biases and precipitation change signal, according to the Representative Concentration Pathway (RCP) 8.5 for the end of the 21st century. We investigate the degree to which RCMs and GCMs are able to reproduce specific climatic features over SAF and over three sub-regions, namely the greater Angola region, the greater Mozambique region and the greater South Africa region. We identify that during the beginning of the rainy season, when regional processes are largely dependent on the coupling between the surface and the atmosphere, the impact of the driving GCMs on the RCMs is smaller, compared to the core of the rainy season, when precipitation is mainly controlled by the large-scale circulation. In addition, we show that RCMs are able to counteract the bias received by their driving GCMs, hence, we claim that the cascade of uncertainty over SAF is not additive, but indeed the RCMs do provide improved precipitation climatologies. The fact that certain bias patterns over the historical period (1985-2005) identified in GCMs are resolved in RCMs, provides evidence that RCMs are reliable tools for climate change impact studies over SAF. [ABSTRACT FROM AUTHOR]

The "Coordinated Regional Climate Downscaling Experiment" (CORDEX) is a scientific effort of the World Climate Research Program (WRCP) for the coordination of regional climate initiatives. In order to accept an experiment, CORDEX provides experiment guidelines, specifications of regional domains and data access/archiving. CORDEX experiments are important to study climate at the regional scale, and at the same time, they also have a very prominent goal in providing regional climate data of high quality. Data requirements are intended to cover all the possible needs of stake holders, and scientists working on climate change mitigation and adaptation policies in various scientific communities. The required data and diagnostics are grouped into different levels of frequency, priority, and some of them even have to be provided as statistics (minimum, maximum, mean) over different time periods. Most commonly, scientists need to post-process the raw output of regional climate models, since the latter was not originally designed to meet the specific CORDEX data requirements. This post-processing procedure includes the computation of diagnostics, statistics, and final homogenization of the data, which is often computationally costly and time consuming. Therefore, the development of specialized software and/or code is required. The current paper presents the development of a specialized module (version 1.3) for the Weather Research and Forecasting (WRF) model, capable of outputting the required CORDEX variables. Additional diagnostic variables not required by CORDEX, but of potential interest to the regional climate modeling community, are also included in the module. "Generic" definitions of variables are adopted in order to overcome model and/or physics parameterization dependence of certain diagnostics and variables, thus facilitating a robust comparison among simulations. The module is computationally optimized, and the output is divided in different priority levels following CORDEX specifications (Core, Tier1, and additional) by selecting pre-compilation flags. This implementation of the module does not add a significant extra cost when running the model, for example the addition of the Core variables slows the model time-step by less than a 5%. The use of the module reduces the requirements of disk storage by about a 50%. [ABSTRACT FROM AUTHOR]