Importance of the SRES in projections of climate change impacts on near-surface wind regimes

2010 ◽  
Vol 19 (3) ◽  
pp. 267-274 ◽  
Author(s):  
Sara C. Pryor ◽  
Justin T. Schoof
Keyword(s):  
Climate Change ◽  
Surface Wind ◽  
Climate Change Impacts ◽  
Near Surface ◽  
Wind Regimes

scholarly journals Application of ERA5 and MENA Simulations to Predict Offshore Wind Energy Potential

2020 ◽  
Author(s):  
Shahab Shamshirband ◽  
Amir Mosavi ◽  
narjes nabipour ◽  
Kwok-wing Chau
Keyword(s):  
Climate Change ◽  
Wind Energy ◽  
Power Distribution ◽  
Climate Model ◽  
Surface Wind ◽  
Climate Change Impacts ◽  
Important Variable ◽  
Offshore Wind ◽  
Energy Potential ◽  
Near Surface

This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near-surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data are employed to assess the suitability of the climate model. Moreover, the ERA5 wave data over the study area are applied to compute sea surface roughness as an important variable for converting near-surface wind speeds to those of wind speed at turbine hub height. Considering the power distribution, bathymetry and distance from the coats, some spots as tentative energy hotspots to provide a detailed assessment of directional and temporal variability and also to investigate climate change impact studies. RCP8.5 is a common climatic scenario is used to project and extract future variation of the energy in the selected sites. The results of this study demonstrate that the selected locations have a suitable potential for wind power turbine plans and constructions.

scholarly journals Modeling Climate Change Impact on Wind Power Resources Using Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Author(s):  
Narjes Nabipour ◽  
Amir Mosavi ◽  
Eva Hajnal ◽  
Laszlo Nadai ◽  
Shahab Shamshirband ◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Wind Power ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Climate Change Impacts ◽  
The Caspian Sea ◽  
Inference System ◽  
Near Surface

Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

Characteristics of near-surface wind regimes in the Taklimakan Desert, China

Geomorphology ◽  
2008 ◽  
Vol 96 (1-2) ◽  
pp. 39-47 ◽  
Author(s):  
Ruiping Zu ◽  
Xian Xue ◽  
Mingrui Qiang ◽  
Bao Yang ◽  
Jianjun Qu ◽  
...  
Keyword(s):  
Surface Wind ◽  
Taklimakan Desert ◽  
Near Surface ◽  
The Taklimakan Desert ◽  
Wind Regimes

scholarly journals Climate Change: Anticipated Effects on High-Energy Laser Weapon Systems in Maritime Environments

2011 ◽  
Vol 50 (1) ◽  
pp. 153-166 ◽  
Author(s):  
Steven T. Fiorino ◽  
Robb M. Randall ◽  
Richard J. Bartell ◽  
Adam D. Downs ◽  
Peter C. Chu ◽  
...  
Keyword(s):  
Climate Change ◽  
Marine Boundary Layer ◽  
Climate Change Impacts ◽  
High Energy ◽  
Sea Surface ◽  
Atmospheric Effects ◽  
Near Surface ◽  
Energy Laser ◽  
High Energy Laser ◽  
Mean Differences

Abstract This study quantifies the potential impacts on ship-defense high-energy-laser (HEL) performance due to atmospheric effects in the marine boundary layer driven by recent observations and analysis of worldwide sea surface temperatures (SSTs). The atmospheric effects are defined using the worldwide probabilistic climatic database available in the High Energy Laser End-to-End Operational Simulation (HELEEOS) model, which includes an SST database for the period 1854–1997. A more recent worldwide sea surface temperature database was provided by the Naval Postgraduate School for the period 1990–2008. Mean differences and trends between the two SST databases are used to deduce possible climate change impacts on simulated maritime HEL engagements. The anticipated effects on HEL propagation performance are assessed at an operating wavelength of 1.0642 μm across the world’s oceans and mapped onto a 1° × 1° grid. The scenario evaluated is near surface and nearly horizontal over a range of 5000 m in which anticipated clear-air maritime aerosols occur. Summer and winter scenarios are considered. In addition to realistic vertical profiles of molecular and aerosol absorption and scattering, correlated optical turbulence profiles in probabilistic (percentile) format are used.

What is the added value of using downscaled CMIP5 data for  the study of climate extremes under  climate change?

2021 ◽  
Author(s):  
Dimitri Defrance ◽  
Thomas Noël ◽  
Harilaos Loukos
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
General Circulation ◽  
Heat Waves ◽  
Surface Wind ◽  
Added Value ◽  
Cumulative Distribution ◽  
Climate Projections ◽  
Geophysical Research ◽  
Near Surface

<p>In the beginning of this century, impacts studies due to climate change were carried out directly with the outputs of the general circulation models of the Atmosphere and the Ocean (AOGCM). However, these models had very low resolutions in the order of several degrees and the climate of some areas, such as monsoon regions, was poorly reproduced. These two disadvantages make it difficult to study the evolution of extremes. Recently, more impact studies are using outputs from multiple AOGCM models that are downscaled and unbiased. The ISIMIP consortium (https://www.isimip.org/) participates in the dissemination of this practice by proposing several AOGCM models with a resolution of 0.5° X 0.5°.</p><p>In our study, a high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed  (Noël et al. accepted). This dataset is obtained by using the quantile – quantile method Cumulative Distribution Function transform (CDFt) (Vrac et al. 2012, 2016,, developed over  10 years to bias correct or downscale climate model output, and ERA5 land data as a reference . T</p><p>We propose in this communication to present the climate variability by the end of the century in terms of extreme climate indicators such as heat waves or heavy rainfall at the local/grid point level (e.g. city level). Particular attention will be paid to the magnitude of the changes as well as the associated uncertainty.</p><p> </p><p>References</p><p>Vrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., & Somot, S. (2012). Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment.Nat. Hazards Earth Syst. Sci., 12, 2769–2784.</p><p>Vrac, M., Noël, T., & Vautard, R. (2016). Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter. Journal of Geophysical Research: Atmospheres, 121(10), 5237-5258.</p><p>Noël, T., Loukos, H., Defrance, D., Vrac, M., & Levavasseur, G. (2020). High-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with ERA5 reanalyses for climate change impact assessments. Data in Brief (accepted, https://doi.org/10.31223/X53W3F)</p>

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.

Increased uncertainty in Land-Use change impacts on temperature due to Global Warming in CMIP6-LUMIP experiments

2021 ◽  
Author(s):  
Arshdeep Singh ◽  
Sanjiv Kumar
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Use Change ◽  
Direct Method ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Shortwave Radiation ◽  
Near Surface ◽  
Historical Climate ◽  
The Future

<p>Land-use change (LU) is a major regional climate forcing that affects carbon-water-energy fluxes and, therefore, near-surface air temperature. Although there are uncertainties in LU impacts in the historical climate, there is a growing consensus towards a cooling influence in the mid-latitudes. However, how a drier and warmer land surface condition in the future climate can change the LU impacts are not investigated well.</p><p>We use a comprehensive set of five coupled climate models from the CMIP6-LUMIP project to assess the changing influence of the LU change. We use two methodologies: (1) direct method – where LU impacts are estimated by subtracting the ‘no-LU’ climate experiment from the control experiment that includes LU, and (2) Kumar et al., 2013 (K13) method where LU impacts are estimated by comparing climate change impacts between LU and no-LU neighboring regions.</p><p>First, we compared the LU impacts in the historical climate and between the direct method and K13 methods using the multi-model analysis. In the North America LU change region, the direct method shows a cooling impact of (-0.14 ± 0.13°C). The K13 methods show a smaller cooling impact (-0.09 ± 0.08°C). In terms of energy balance, the direct method shows a reduction of net shortwave radiation (-0.82 ± 0.91 watts/m<sup>2</sup>) the K13 method shows a cleaner result of (-1.25 ± 0.60 watts/m<sup>2</sup>), as expected. We suspect that a more substantial influence of the LU change in the direct method is due to large-scale circulation driven response or due to the internal variability that has been canceled out in the K13 method.</p><p>Next, we extend the K13 method to assess the LU impacts in the future climate. Direct methods are not available for the future climate experiment in CMIP6-LUMIP datasets. We find that a cooling impact of LU change has become statistically insignificant in the future climate (-0.17 ± 0.19°C). A similar influence is also found in the reduction of the net shortwave radiation (-1.92 ± 3.34 watts/m<sup>2</sup>). We also found that climate change impacts on temperature are an order of magnitude greater than LU impact in the future climate. Hence, we hypothesize that higher warming has contributed to the larger uncertainty in LU impacts. We will also discuss LU impacts in Eurasia and Indian subcontinent.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.967d8b47f50063273001161/sdaolpUECMynit/12UGE&app=m&a=0&c=6fbaa64b9acfb208f665dca0184a6955&ct=x&pn=gnp.elif&d=1" alt=""></p><p> </p><p> </p><p>Reference</p><p>Kumar, S., Dirmeyer, P. A., Merwade, V., DelSole, T., Adams, J. M., & Niyogi, D. (2013). Land use/cover change impacts in CMIP5 climate simulations: A new methodology and 21st century challenges. Journal of Geophysical Research: Atmospheres, 118(12), 6337-6353.</p>

Doppler-Lidar Evaluation of HRRR-Model Skill at Simulating Summertime Wind Regimes in the Columbia River Basin during WFIP2

2021 ◽  
Author(s):  
Robert M. Banta ◽  
Yelena L. Pichugina ◽  
Lisa S. Darby ◽  
W. Alan Brewer ◽  
Joseph B. Olson ◽  
...  
Keyword(s):  
Wind Speed ◽  
Pressure Gradient ◽  
Large Scale ◽  
Surface Wind ◽  
Columbia River ◽  
Surface Heating ◽  
Model Errors ◽  
Improvement Project ◽  
Near Surface ◽  
Wind Regimes

AbstractComplex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s−1 and RMSEs of 2-3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.

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.

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