Supplementary material to "Gains and losses in surface solar radiation with dynamic aerosols in regional climate simulations for Europe"

Abstract. The solar resource can be highly influenced by clouds and atmospheric aerosol, which has been named by the IPCC as the most uncertainty climate forcing agent. Nonetheless, Regional Climate Models (RCMs) hardly ever model dynamically atmospheric aerosol concentration and their interaction with radiation and clouds, in contrast to Global Circulation Models (GCMs). The objective of this work is to evince the role of the interactively modeling of aerosol concentrations and their interactions with radiation and clouds in Weather Research and Forecast (WRF) model simulations with a focus on summer mean surface downward solar radiation (RSDS) and over Europe. The results show that the response of RSDS is mainly led by the aerosol effects on cloudiness, which explain well the differences between the experiments in which aerosol-radiation and aerosol-radiation-cloud interactions are taken into account or not. Under present climate, a reduction about 5% in RSDS was found when aerosols are dynamically solved by the RCM, which is larger when only aerosol-radiation interactions are considered. However, for future projections, the inclusion of aerosol-radiation-cloud interactions results in the most negative RSDS change pattern (while with slight values), showing noticeable differences with the projections from either the other RCM experiments or from their driving GCM (which do hold some significant positive signals). Differences in RSDS among experiments are much more softer under clear-sky conditions.

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Supplementary material to "Convection-permitting regional climate simulations for representing floods in small and medium sized catchments in the Eastern Alps"

10.5194/nhess-2018-17-supplement ◽

2018 ◽

Author(s):

Christian Reszler ◽

Matthew Blasie Switanek ◽

Heimo Truhetz

Keyword(s):

Regional Climate ◽

Eastern Alps ◽

Climate Simulations ◽

Supplementary Material

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Supplementary material to "COSMO-CLM Regional Climate Simulations in the CORDEX framework: a review"

10.5194/gmd-2020-443-supplement ◽

2021 ◽

Author(s):

Silje Lund Sørland ◽

Roman Brogli ◽

Praveen Kumar Pothapakula ◽

Emmanuele Russo ◽

Jonas Van de Walle ◽

...

Keyword(s):

Regional Climate ◽

Climate Simulations ◽

Supplementary Material

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Changes in extreme surface solar radiation in EURO-CORDEX Regional Climate Models

10.5194/egusphere-egu21-16536 ◽

2021 ◽

Author(s):

Blanka Bartok

Keyword(s):

Climate Change ◽

Power Generation ◽

Solar Radiation ◽

Solar Energy ◽

Electricity Generation ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Surface Solar Radiation ◽

The Impact

As solar energy share is showing a significant growth in the European electricity generation system, assessments regarding long-term variation of this variable related to climate change are becoming more and more relevant for this sector. Several studies analysed the impact of climate change on the solar energy sector in Europe (Jerez et al, 2015) finding light impact (-14%; +2%) in terms of mean surface solar radiation. The present study focuses on extreme values, namely on the distribution of low surface solar radiation (overcast situation) and high surface solar radiation (clear sky situation), since the frequencies of these situations have high impact on electricity generation.The study considers 11 high-resolution (0.11 deg) bias-corrected climate projections from the EURO-CORDEX ensemble with 5 Global Climate Models (GCMs) downscaled by 6 Regional Climate Models (RCMs).Changes in extreme surface solar radiation frequencies show different regional patterns over Europe.The study also includes a case study determining the changes in solar power generation induced by the extreme situations.&#160;&#160;Jerez et al (2015): The impact of climate change on photovoltaic power generation in Europe, Nature Communications 6(1):10014, 10.1038/ncomms10014&#160;

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On the ability of RegCM4 regional climate model to simulate surface solar radiation patterns over Europe: an assessment using satellite-based observations

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-13195-2015 ◽

2015 ◽

Vol 15 (22) ◽

pp. 13195-13216 ◽

Cited By ~ 24

Author(s):

G. Alexandri ◽

A. K. Georgoulias ◽

P. Zanis ◽

E. Katragkou ◽

A. Tsikerdekis ◽

...

Keyword(s):

Solar Radiation ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Radiative Transfer Model ◽

Asymmetry Factor ◽

Transfer Model ◽

Potential Contribution ◽

Absolute Deviation ◽

Surface Solar Radiation

Abstract. In this work, we assess the ability of RegCM4 regional climate model to simulate surface solar radiation (SSR) patterns over Europe. A decadal RegCM4 run (2000–2009) was implemented and evaluated against satellite-based observations from the Satellite Application Facility on Climate Monitoring (CM SAF), showing that the model simulates adequately the SSR patterns over the region. The SSR bias between RegCM4 and CM SAF is +1.5 % for MFG (Meteosat First Generation) and +3.3 % for MSG (Meteosat Second Generation) observations. The relative contribution of parameters that determine the transmission of solar radiation within the atmosphere to the deviation appearing between RegCM4 and CM SAF SSR is also examined. Cloud macrophysical and microphysical properties such as cloud fractional cover (CFC), cloud optical thickness (COT) and cloud effective radius (Re) from RegCM4 are evaluated against data from CM SAF. Generally, RegCM4 underestimates CFC by 24.3 % and Re for liquid/ice clouds by 36.1 %/28.3 % and overestimates COT by 4.3 %. The same procedure is repeated for aerosol optical properties such as aerosol optical depth (AOD), asymmetry factor (ASY) and single-scattering albedo (SSA), as well as other parameters, including surface broadband albedo (ALB) and water vapor amount (WV), using data from MACv1 aerosol climatology, from CERES satellite sensors and from ERA-Interim reanalysis. It is shown here that the good agreement between RegCM4 and satellite-based SSR observations can be partially attributed to counteracting effects among the above mentioned parameters. The potential contribution of each parameter to the RegCM4–CM SAF SSR deviations is estimated with the combined use of the aforementioned data and a radiative transfer model (SBDART). CFC, COT and AOD are the major determinants of these deviations on a monthly basis; however, the other parameters also play an important role for specific regions and seasons. Overall, for the European domain, CFC, COT and AOD are the most important factors, since their underestimations and overestimations by RegCM4 cause an annual RegCM4–CM SAF SSR absolute deviation of 8.4, 3.8 and 4.5 %, respectively.

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Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data

10.5194/essd-2019-209 ◽

2019 ◽

Author(s):

Hou Jiang ◽

Ning Lu ◽

Jun Qin ◽

Ling Yao

Keyword(s):

Solar Radiation ◽

Satellite Data ◽

Power Plants ◽

Numerical Models ◽

Regional Climate ◽

Diffuse Radiation ◽

Terrestrial Ecosystems ◽

Global Solar Radiation ◽

Surface Solar Radiation ◽

Diffuse Solar Radiation

Abstract. Surface solar radiation drives the water cycle and energy exchange on the earth's surface, being an indispensable parameter for many numerical models to estimate soil moisture, evapotranspiration and plant photosynthesis, and its diffuse component can promote carbon uptake in ecosystems as a result of improvements of plant productivity by enhancing canopy light use efficiency. To reproduce the spatial distribution and spatiotemporal variations of solar radiation over China, we generate the high-accuracy radiation datasets, including global solar radiation (GSR) and the diffuse radiation (DIF) with spatial resolution of 1/20 degree, based on the observations from the China Meteorology Administration (CMA) and Multi-functional Transport Satellite (MTSAT) satellite data, after tackling the integration of spatial pattern and the simulation of complex radiation transfer that the existing algorithms puzzle about by means of the combination of convolutional neural network (CNN) and multi-layer perceptron (MLP). All data cover a period from 2007 to 2018 in hourly, daily total and monthly total scales. The validation in 2008 shows that the root mean square error (RMSE) between our datasets and in-situ measurements approximates 73.79 W/m2 (0.27 MJ/m2) and 58.22 W/m2 (0.21 MJ/m2) for GSR and DIF, respectively. Besides, the spatially continuous hourly estimates properly reflect the regional differences and restore the diurnal cycles of solar radiation in fine scales. Such accurate knowledge is useful for the prediction of agricultural yield, carbon dynamics of terrestrial ecosystems, research on regional climate changes, and site selection of solar power plants etc. The datasets are freely available from Pangaea at https://doi.org/10.1594/PANGAEA.904136 (Jiang and Lu, 2019).

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