Near-surface wind variability over the broader Adriatic region: insights from an ensemble of regional climate models

2017 ◽  
Vol 50 (11-12) ◽  
pp. 4455-4480 ◽  
Author(s):  
Andreina Belušić ◽  
Maja Telišman Prtenjak ◽  
Ivan Güttler ◽  
Nikolina Ban ◽  
David Leutwyler ◽  
...  
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Surface Wind ◽  
Regional Climate Models ◽  
Near Surface ◽  
Wind Variability ◽  
Adriatic Region

scholarly journals Human influence on European winter wind storms such as those of January 2018

2019 ◽  
Vol 10 (2) ◽  
pp. 271-286 ◽  
Author(s):  
Robert Vautard ◽  
Geert Jan van Oldenborgh ◽  
Friederike E. L. Otto ◽  
Pascal Yiou ◽  
Hylke de Vries ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Roughness ◽  
Western Europe ◽  
Climate Models ◽  
Decadal Variability ◽  
Regional Climate ◽  
Surface Wind ◽  
Regional Climate Models ◽  
Surface Wind Speed ◽  
Near Surface

Abstract. Several major storms pounded western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analysed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend in the frequency of strong winds associated with such storms. High-resolution regional climate models, on the other hand, show no trend up to now and a small increase in storminess in future due to climate change. This shows that factors other than climate change, which are not in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering the Netherlands and in previous studies. This observed trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two mentioned. However, all simulations indicate that global warming could lead to a marginal increase (0 %–20 %) in the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. This excludes other factors, such as surface roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are correctly simulated by climate models, we cannot give credible projections of future storminess over land in Europe.

scholarly journals SPATIO-TEMPORAL DISTRIBUTION OF WIND SPEED AND DAILY MAXIMUM WIND SPEED IN MOROCCO FOR THE PERIOD 2020-2050

2018 ◽  
pp. 68-71
Author(s):  
V. Khokhlov ◽  
Y. El Hadri
Keyword(s):  
Wind Speed ◽  
Climate Models ◽  
Regional Climate ◽  
Atlantic Coast ◽  
Surface Wind ◽  
Regional Climate Models ◽  
Maximum Wind Speed ◽  
Daily Maximum ◽  
Near Surface ◽  
Maximum Wind

The Moroccan energy system is highly dependent on external energy markets. Therefore, the current renewable energy strategy is focused on deployment of large-scale renewable technologies projects. Morocco has abundant wind resources. Estimations made by development organizations in Morocco quantify that the economic and technical potential of wind energy in Morocco amount to 26 GW. The aim of this study is to determine the possible quantitative indicators of wind speed, the daily maximum wind speed and their space-time distribution in the period 2020-2050 on the territory of Morocco. In study used data from regional climate modelling with a high spatial resolution of the project CORDEX. Simulations of regional climate models provide opportunities for a better understanding of atmospheric processes in the region and their possible future change. In the study use of regional climate models simulations for the RCP 4.5 scenario for the Africa region, presented in a rectangular coordinate system with a spatial resolution of ≈ 44 km. As a result of the regional climate models calculation, the mean monthly Near-Surface Wind Speed, and Daily Maximum Near-Surface Wind Speed values for the period 2020-2050 for the territory of Morocco were obtained. Regional climate models simulations showed that in Morocco will be dominated by gentle and moderate winds. The smallest values of the average wind speed are predicted in Fez − Meknes and Beni-Mellal − Henifra regions and will be about 3 m/s, the highest values can reach 9 m/s on the Atlantic coast to the south of Dakhla village. An analysis showed that in the future a character of annual course, in general, will have two types: in central mountain regions of Atlas, in the northeastern part of country and on the Mediterranean coast maximum wind speed will be register in winter; summer seasonal maximum of wind speed will be typical on the flat areas of the Atlantic coast, in the southern part of the country and on areas located behind the ridges of the Atlas mountains on the border with Algeria. The most favorable for the development of wind energy will be areas located on the shore of the Mediterranean Sea and the Atlantic Ocean and in the southern part of Morocco.

scholarly journals Human influence on European winter wind storms such as those of January 2018

2018 ◽  
Author(s):  
Robert Vautard ◽  
Geert Jan van Oldenborgh ◽  
Friederike E. L. Otto ◽  
Pascal Yiou ◽  
Hylke de Vries ◽  
...  
Keyword(s):  
Climate Change ◽  
Western Europe ◽  
Climate Models ◽  
Decadal Variability ◽  
Regional Climate ◽  
Surface Wind ◽  
Regional Climate Models ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Modelling Studies

Abstract. Several major storms pounded Western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analyzed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend of the frequency of strong winds associated with such storms. High-resolution regional climate models on the other hand show no trend up to now and a small increase in the future due to climate change. This shows that that factors other than climate change, which are not represented (well) in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering The Netherlands and in previous studies. This trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two studied. However, all simulations indicate that global warming could lead to a marginal increase (0–20 %) of the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. However, this excludes other factors, such as roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are simulated well by climate models they cannot give credible projections of future storminess over land in Europe.

scholarly journals An Ensemble Framework to Investigate Wind Energy Sustainability Considering Climate Change Impacts

2020 ◽  
Vol 12 (3) ◽  
pp. 876 ◽  
Author(s):  
Shengjin Wang ◽  
Hongru Yang ◽  
Quoc Bao Pham ◽  
Dao Nguyen Khoi ◽  
Pham Thi Thao Nhi
Keyword(s):  
Climate Change ◽  
Wind Power ◽  
Climate Models ◽  
Regional Climate ◽  
Surface Wind ◽  
Regional Climate Models ◽  
High Energy ◽  
Renewable Energy Resources ◽  
Near Surface ◽  
Impacts Of Climate Change

Wind power is a key element for future renewable energy resources and plays a vital role in sustainable development. Global warming and future climate conditions are going to impact many atmospheric, oceanic, and earth systems. In this study, impacts of climate change on wind power resources under future climatic conditions are evaluated for the Persian Gulf to explore the sustainability of this kind of energy for present and future developments. To that end, three regional climate models obtained from coordinated regional downscaling experiment (CRODEX), including daily simulations of near-surface wind speeds for a 20-year period in the present and future, were considered. Prior to computing the wind power at turbine hub-height, historical simulations of CORDEX were evaluated versus ERA-Interim wind outputs to determine the accuracy of the regional climate models. An attempt was made to build an ensemble model from available models by assigning weights to the models based on their merits. Subsequently, the wind power at the turbine hub-height was computed for historical and future periods to detect the impacts of climate change. Some points with a relatively high energy potential were selected as energy hotspots for further investigations. The results revealed that the mean annual wind power over the study area changed remarkably, which is of great importance for sustainable developments. Moreover, the results of the directional investigations showed roughly the same directional distribution for the future period as the past.

Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

2021 ◽  
Author(s):  
Jeremy Carter ◽  
Amber Leeson ◽  
Andrew Orr ◽  
Christoph Kittel ◽  
Melchior van Wessem
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate Change ◽  
Near Surface ◽  
Virtual Lab ◽  
Model Variability ◽  
The Antarctic

<p>Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.</p><p>Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.</p>

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

scholarly journals Comparison of Linear Predictability of Surface Wind Components from Observations with Simulations from RCMs and Reanalysis

2018 ◽  
Vol 57 (4) ◽  
pp. 889-906
Author(s):  
Yiwen Mao ◽  
Adam Monahan
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Surface Wind ◽  
Signal Strength ◽  
Regional Climate Models ◽  
Small Scale ◽  
Mountainous Terrain ◽  
Physical Processes ◽  
Tropospheric Circulation ◽  
Using Data

AbstractThis study compares the predictability of surface wind components by linear statistical downscaling using data from both observations and comprehensive models [regional climate models (RCM) and NCEP-2 reanalysis] in three domains: North America (NAM), Europe–Mediterranean Basin (EMB), and East Asia (EAS). A particular emphasis is placed on predictive anisotropy, a phenomenon referring to unequal predictability of surface wind components in different directions. Simulated predictability by comprehensive models is generally close to that found in observations in flat regions of NAM and EMB, but it is overestimated relative to observations in mountainous terrain. Simulated predictability in EAS shows different structures. In particular, there are regions in EAS where predictability simulated by RCMs is lower than that in observations. Overestimation of predictability by comprehensive models tends to occur in regions of low predictability in observations and can be attributed to small-scale physical processes not resolved by comprehensive models. An idealized mathematical model is used to characterize the predictability of wind components. It is found that the signal strength along the direction of minimum predictability is the dominant control on the strength of predictive anisotropy. The biases in the model representation of the statistical relationship between free-tropospheric circulation and surface winds are interpreted in terms of inadequate simulation of small-scale processes in regional and global models, and the primary cause of predictive anisotropy is attributed to such small-scale processes.

Evaluation of MM5 and HadGEM3-RA hindcast in the CORDEX East Asia Phase Ⅱ: near-surface air temperature

2020 ◽  
Author(s):  
Seok-Woo Shin ◽  
Dong-Hyun Cha ◽  
Taehyung Kim ◽  
Gayoung Kim ◽  
Changyoung Park ◽  
...  
Keyword(s):  
East Asia ◽  
Air Temperature ◽  
Climate Models ◽  
Extreme Values ◽  
Regional Climate ◽  
Surface Air Temperature ◽  
Regional Climate Models ◽  
Near Surface ◽  
The Mean ◽  
Cordex East Asia

<p>Extreme temperature can have a devastating impact on the ecological environment (i.e., human health and crops) and the socioeconomic system. To adapt to and cope with the rapidly changing climate, it is essential to understand the present climate and to estimate the future change in terms of temperature. In this study, we evaluate the characteristics of near-surface air temperature (SAT) simulated by two regional climate models (i.e., MM5 and HadGEM3-RA) over East Asia, focusing on the mean and extreme values. To analyze extreme climate, we used the indices for daily maximum (Tmax) and minimum (Tmin) temperatures among the developed Expert Team on Climate Change Detection and Indices (ETCCDI) indices. In the results of the CORDEX-East Asia phase Ⅰ, the mean and extreme values of SAT for DJF (JJA) tend to be colder (warmer) than observation data over the East Asian region. In those of CORDEX-East Asia phase Ⅱ, the mean and extreme values of SAT for DJF and JJA have warmer than those of the CORDEX-East Asia phase Ⅰ except for those of HadGEM3-RA for DJF. Furthermore, the Extreme Temperature Range (ETR, maximum value of Tmax - minimum value of Tmin) of CORDEX-East Asia phase Ⅰ data, which are significantly different from those of observation data, are reduced in that of CORDEX-East Asia phase Ⅱ. Consequently, the high-resolution regional climate models play a role in the improvement of the cold bias having the relatively low-resolution ones. To understand the reasons for the improved and weak points of regional climate models, we investigated the atmospheric field (i.e., flow, air mass, precipitation, and radiation) influencing near-surface air temperature. Model performances for SAT over East Asia were influenced by the expansion of the western North Pacific subtropical high and the location of convective precipitation in JJA and by the contraction of the Siberian high, the spatial distribution of snowfall and associated upwelling longwave radiation in DJF.</p>

scholarly journals The regional impact of urban emissions on air quality in Europe: the role of the urban canopy effects

2021 ◽  
Vol 21 (18) ◽  
pp. 14309-14332
Author(s):  
Peter Huszar ◽  
Jan Karlický ◽  
Jana Marková ◽  
Tereza Nováková ◽  
Marina Liaskoni ◽  
...  
Keyword(s):  
Air Quality ◽  
Land Surface ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Scale ◽  
Urban Canopy ◽  
Regional Climate Models ◽  
Near Surface ◽  
Urban Emissions ◽  
The Impact

Abstract. Urban areas are hot spots of intense emissions, and they influence air quality not only locally but on a regional or even global scale. The impact of urban emissions over different scales depends on the dilution and chemical transformation of the urban plumes which are governed by the local- and regional-scale meteorological conditions. These are influenced by the presence of urbanized land surface via the so-called urban canopy meteorological forcing (UCMF). In this study, we investigate for selected central European cities (Berlin, Budapest, Munich, Prague, Vienna and Warsaw) how the urban emission impact (UEI) is modulated by the UCMF for present-day climate conditions (2015–2016) using two regional climate models, the regional climate models RegCM and Weather Research and Forecasting model coupled with Chemistry (WRF-Chem; its meteorological part), and two chemistry transport models, Comprehensive Air Quality Model with Extensions (CAMx) coupled to either RegCM and WRF and the “chemical” component of WRF-Chem. The UCMF was calculated by replacing the urbanized surface by a rural one, while the UEI was estimated by removing all anthropogenic emissions from the selected cities. We analyzed the urban-emission-induced changes in near-surface concentrations of NO2, O3 and PM2.5. We found increases in NO2 and PM2.5 concentrations over cities by 4–6 ppbv and 4–6 µg m−3, respectively, meaning that about 40 %–60 % and 20 %–40 % of urban concentrations of NO2 and PM2.5 are caused by local emissions, and the rest is the result of emissions from the surrounding rural areas. We showed that if UCMF is included, the UEI of these pollutants is about 40 %–60 % smaller, or in other words, the urban emission impact is overestimated if urban canopy effects are not taken into account. In case of ozone, models due to UEI usually predict decreases of around −2 to −4 ppbv (about 10 %–20 %), which is again smaller if UCMF is considered (by about 60 %). We further showed that the impact on extreme (95th percentile) air pollution is much stronger, and the modulation of UEI is also larger for such situations. Finally, we evaluated the contribution of the urbanization-induced modifications of vertical eddy diffusion to the modulation of UEI and found that it alone is able to explain the modeled decrease in the urban emission impact if the effects of UCMF are considered. In summary, our results showed that the meteorological changes resulting from urbanization have to be included in regional model studies if they intend to quantify the regional footprint of urban emissions. Ignoring these meteorological changes can lead to the strong overestimation of UEI.

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