Quantifying some of the impacts of dust and other aerosol on the Caspian Sea region using a regional climate model

2015 ◽  
Vol 46 (1-2) ◽  
pp. 41-55 ◽  
Author(s):  
N. Elguindi ◽  
F. Solmon ◽  
U. Turuncoglu
Keyword(s):  
Regional Climate Model ◽  
Caspian Sea ◽  
Climate Model ◽  
Regional Climate ◽  
The Caspian Sea

scholarly journals Caspian Sea south coast future climate change estimations through regional climate model

2019 ◽  
Vol 6 (1) ◽  
pp. 111-138
Author(s):  
Fardin Saberi Louyeh ◽  
Bohlol Alijani ◽  
Shahriar Khaledi ◽  
◽  
◽  
...  
Keyword(s):  
Climate Change ◽  
Regional Climate Model ◽  
Caspian Sea ◽  
Climate Model ◽  
Regional Climate ◽  
Future Climate ◽  
South Coast ◽  
Future Climate Change

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.

Qinghai-Xizang (Tibetan) Plateau climate simulation using the regional climate model RegCM3

2013 ◽  
Vol 57 (3) ◽  
pp. 173-186 ◽  
Author(s):  
X Wang ◽  
M Yang ◽  
G Wan ◽  
X Chen ◽  
G Pang
Keyword(s):  
Tibetan Plateau ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Simulation

Evaluation of net shortwave radiation over China with a regional climate model

2020 ◽  
Vol 80 (2) ◽  
pp. 147-163
Author(s):  
X Liu ◽  
Y Kang ◽  
Q Liu ◽  
Z Guo ◽  
Y Chen ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Monsoon Region ◽  
Clear Sky ◽  
Sky Conditions

The regional climate model RegCM version 4.6, developed by the European Centre for Medium-Range Weather Forecasts Reanalysis, was used to simulate the radiation budget over China. Clouds and the Earth’s Radiant Energy System (CERES) satellite data were utilized to evaluate the simulation results based on 4 radiative components: net shortwave (NSW) radiation at the surface of the earth and top of the atmosphere (TOA) under all-sky and clear-sky conditions. The performance of the model for low-value areas of NSW was superior to that for high-value areas. NSW at the surface and TOA under all-sky conditions was significantly underestimated; the spatial distribution of the bias was negative in the north and positive in the south, bounded by 25°N for the annual and seasonal averaged difference maps. Compared with the all-sky condition, the simulation effect under clear-sky conditions was significantly better, which indicates that the cloud fraction is the key factor affecting the accuracy of the simulation. In particular, the bias of the TOA NSW under the clear-sky condition was <±10 W m-2 in the eastern areas. The performance of the model was better over the eastern monsoon region in winter and autumn for surface NSW under clear-sky conditions, which may be related to different levels of air pollution during each season. Among the 3 areas, the regional average biases overall were largest (negative) over the Qinghai-Tibet alpine region and smallest over the eastern monsoon region.

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