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In this work we present downscaling experiments with the Weather Research and Forecasting model (WRF) to test the sensitivity to resolving aerosol–radiation and aerosol–cloud interactions on simulated regional climate for the EURO-CORDEX domain. The sensitivities mainly focus on the aerosol–radiation interactions (direct and semi-direct effects) with four different aerosol optical depth datasets (Tegen, MAC-v1, MACC, GOCART) being used and changes to the aerosol absorptivity (single scattering albedo) being examined. Moreover, part of the sensitivities also investigates aerosol–cloud interactions (indirect effect). Simulations have a resolution of 0.44 ∘ and are forced by the ERA-Interim reanalysis. A basic evaluation is performed in the context of seasonal-mean comparisons to ground-based (E-OBS) and satellite-based (CM SAF SARAH, CLARA) benchmark observational datasets. The impact of aerosols is calculated by comparing it against a simulation that has no aerosol effects. The implementation of aerosol–radiation interactions reduces the direct component of the incoming surface solar radiation by 20 %–30 % in all seasons, due to enhanced aerosol scattering and absorption. Moreover the aerosol–radiation interactions increase the diffuse component of surface solar radiation in both summer (30 %–40 %) and winter (5 %–8 %), whereas the overall downward solar radiation at the surface is attenuated by 3 %–8 %. The resulting aerosol radiative effect is negative and is comprised of the net effect from the combination of the highly negative direct aerosol effect (-17 to -5 W m -2) and the small positive changes in the cloud radiative effect (+5 W m -2), attributed to the semi-direct effect. The aerosol radiative effect is also stronger in summer (-12 W m -2) than in winter (-2 W m -2). We also show that modelling aerosol–radiation and aerosol–cloud interactions 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, mainly close to the Black Sea in autumn, can be of statistical significance. Precipitation is not affected in a consistent pattern throughout the year by the aerosol implementation, and changes do not exceed ± 5 % except for the case of unrealistically absorbing aerosol. Temperature, on the other hand, systematically decreases by -0.1 to -0.5 ∘ C due to aerosol–radiation interactions with regional changes that can be up to -1.5 ∘ C. [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]

In the framework of AfriCultuReS project, operational deterministic weather forecasts provide valuable information on the expected weather conditions over the African continent as a part of federation of services within the project. In this study, we investigate the performance of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) over pilot regions in Africa by utilizing available surface observations, satellite and reanalysis data. The verification period covers two consecutive years (June 2018–May 2020). In addition, we assess the ability of the model to provide skillful forecasts through three high-impact precipitation events that occurred during this period. The results show that the model presents both positive and negative biases with respect to its predicted near surface air temperature, underestimates the near surface relative humidity and the mean sea-level pressure, while overestimates the wind speed at 10 m. The neighborhood-based statistical verification of the 24-h accumulated precipitation reveals that the model forecasts the precipitation events more accurately as the verification area is increasing but at higher precipitation thresholds its performance deteriorates. Different variability, errors and correlation between simulated and observed precipitation exist in each forecast lead day and region. A range of model behavior and forecast skill is found with respect to the examined three precipitation events. Skillful forecasts up to four days ahead were provided in the cases of the Tropical Cyclone IDAI and the flash flooding events in northern Tunisia, while the lowest performance was found in the region of the West African Monsoon. [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 and archiving. CORDEX experiments are important to study climate at the regional scale, and at the same time, they also have a very prominent role in providing regional climate data of high quality. Data requirements are intended to cover all the possible needs of stakeholders 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 and 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 the 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 into different priority levels following CORDEX specifications (Core, Tier 1, 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 %. The module performs neither additional statistics over different periods of time nor homogenization of the output data. [ABSTRACT FROM AUTHOR]