Projection of Changes in Future Surface Wind around Japan Using a Non-hydrostatic Regional Climate Model

Abstract Yearly percentiles of geostrophic wind speeds serve as a widely used proxy for assessing past storm activity. Here, daily geostrophic wind speeds are derived from a geographical triangle of surface air pressure measurements and are used to build yearly frequency distributions. It is commonly believed, however unproven, that the variation of the statistics of strong geostrophic wind speeds describes the variation of statistics of ground-level wind speeds. This study evaluates this approach by examining the correlation between specific annual (seasonal) percentiles of geostrophic and of area-maximum surface wind speeds to determine whether the two distributions are linearly linked in general. The analyses rely on bootstrap and binomial hypothesis testing as well as on analysis of variance. Such investigations require long, homogeneous, and physically consistent data. Because such data are barely existent, regional climate model–generated wind and surface air pressure fields in a fine spatial and temporal resolution are used. The chosen regional climate model is the spectrally nudged and NCEP-driven regional model (REMO) that covers Europe and the North Atlantic. Required distributions are determined from diagnostic 10-m and geostrophic wind speed, which is calculated from model air pressure at sea level. Obtained results show that the variation of strong geostrophic wind speed statistics describes the variation of ground-level wind speed statistics. Annual and seasonal quantiles of geostrophic wind speed and ground-level wind speed are positively linearly related. The influence of low-pass filtering is also considered and found to decrease the quality of the linear link. Moreover, several factors are examined that affect the description of storminess through geostrophic wind speed statistics. Geostrophic wind from sea triangles reflects storm activity better than geostrophic wind from land triangles. Smaller triangles lead to a better description of storminess than bigger triangles.

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Filling the matrix: an ANOVA-based method to emulate regional climate model simulations for equally-weighted properties of ensembles of opportunity

Climate Dynamics ◽

10.1007/s00382-021-06010-5 ◽

2021 ◽

Author(s):

Ole B. Christensen ◽

Erik Kjellström

Keyword(s):

Regional Climate Model ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Surface Wind ◽

Climate Data ◽

Large Ensembles ◽

Climate Model Simulations

AbstractCollections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less arbitrary weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs. Also, the method is only useful when inter-simulation variability is limited. This is the case for the average fields that have been studied, but not for the extremes. We have developed analytical expressions for the degree of improvement obtained with the present method, which quantify this conclusion.

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Filling the Matrix: An ANOVA-Based Method to Emulate Regional Climate Model Simulations for Equally-Weighted Properties of Ensembles of Opportunity

10.21203/rs.3.rs-366374/v1 ◽

2021 ◽

Author(s):

Ole Bøssing Christensen ◽

Erik Kjellström

Keyword(s):

Regional Climate Model ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Regional Climate ◽

Surface Wind ◽

Climate Data ◽

Large Ensembles ◽

Climate Model Simulations

Abstract Collections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less random weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs.

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

10.20944/preprints202001.0100.v1 ◽

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.

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Modelling a tropical-like cyclone in the Mediterranean Sea under present and warmer climate

10.5194/nhess-2020-187 ◽

2020 ◽

Author(s):

Shunya Koseki ◽

Priscilla A. Mooney ◽

William Cabos ◽

Miguel Ángel Gaertner ◽

Alba de la Vara ◽

...

Keyword(s):

Global Warming ◽

Regional Climate Model ◽

Water Vapour ◽

Climate Model ◽

Regional Climate ◽

Surface Wind ◽

Sea Surface ◽

Cumulus Convection ◽

Warm Core ◽

Projected Change

Abstract. This study focuses on a single Mediterranean hurricane (hearafter medicane), to investigate the medicane response to global warming during the middle of the 21st century and assess the contradictory effects of a warmer ocean and a warmer atmosphere on its development. Our investigation uses the state-of-the-art regional climate model WRF with the optimum combination of physical parameterizations based on a sensitivity assessment study. Results show that our model setup can reproduce a realistic cyclone track and the transition from initial disturbance to tropical-like cyclone with a deep warm core although the transition is earlier than for the observed medicane. To investigate the response of the medicane to future climate change, a pseudo global warming (PGW) approach has been used. This approach adds the projected change of atmospheric and ocean variables obtained by an ensemble of CMIP5 models to the boundary conditions for the regional climate model. A PGW simulation where all variables (PGWALL) are incremented shows that most of the medicane characteristics moderately intensify, e.g., surface wind speed, uptake of water vapour and precipitation. However the maximum depression of sea level pressure (SLP) is almost identical with that under present climate conditions. Two additional PGW simulations were undertaken; One simulation adds the projected change in sea surface and skin temperature only (PGWSST) while the second simulation adds the PGW changes to only atmospheric variables (PGWATMS) i.e. we use present time sea surface temperatures. These simulations show opposite effects on the medicane. In PGWSST, the medicane is reinforced more vigorously than PGWALL: much deeper SLP depression, stronger surface wind, and more intense evaporation and precipitation. In contrast, the medicane in PGWATMS weakens considerably (SLP, surface wind and rainfall decrease) still converts into a tropical-like cyclone with a deep warm core. This difference can be explained by an increased water vapour driven by the warmer ocean surface (favourable for cumulus convection) and the warmer and drier atmosphere in PGWATMS tends to inhibit condensation (unfavourable for cumulus convection). As a result of these counteracting effects of warmer ocean and atmosphere, the medicane is enhanced only modestly by global warming.

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PERFORMA KONVERGENSI ANGIN PERMUKAAN DIURNAL MODEL REANALISIS ERA5 DI BENUA MARITIM INDONESIA

Jurnal Sains & Teknologi Modifikasi Cuaca ◽

10.29122/jstmc.v20i2.3795 ◽

2020 ◽

Vol 20 (2) ◽

pp. 59-65

Author(s):

Achmad Fahruddin Rais ◽

Soenardi Soenardi ◽

Zubaidi Fanani ◽

Pebri Surgiansyah

Keyword(s):

Finite Difference ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Surface Wind ◽

Limited Area ◽

Surface Winds ◽

Limited Area Model ◽

Area Model ◽

Weather Research

IntisariPada penelitian ini, penulis mengkaji uji performa kualitatif konvergensi angin permukaan model reanalisis ERA5 di BMI yang dibandingkan dengan hasil penelitian menggunakan limited area model (LAM) oleh Qian, Im dan Eltahir serta Alfahmi et al. Konvergensi angin permukaan dan anomali angin permukaan dihitung dengan menggunakan finite difference. Hasil penelitian menunjukkan bahwa model reanalisis ERA5 mampu mensimulasikan konvergensi anomali angin permukaan dengan baik terhadap model regional climate model (RegCM) maupun The MIT regional climate model (MRCM) resolusi 27 km di Pulau Jawa dan sekitarnya serta BMI bagian barat dengan nilai konvergensi yang lebih tinggi. Sedangkan terhadap model weather research forecast (WRF) 9 km di BMI bagian timur, model reanalisis ERA5 juga dapat mensimulasikan konvergensi angin permukaan, tetapi dengan nilai yang lebih rendah. Selain itu, model reanalisis ERA5 mensimulasikan konvergensi angin permukaan lebih cepat 2 jam di BMI bagian barat dan timur dibandingkan MRCM27 dan WRF. AbstractIn this study, we discuss the qualitative performance testing of ERA5 surface wind convergence over the Indonesia maritime continent (BMI) compared with research based on limited area model (LAM) by Qian, Im, and Eltahir and also Alfahmi et al. Wind surface convergence and wind surface anomalies convergence is calculated using finite-difference. The results show that the ERA5 reanalysis model can simulate convergence of surface wind anomalies compared with both regional climate model (RegCM) and 27 km MIT regional climate model (MRCM) over Java and also western BMI with higher convergence values. While ERA5 reanalysis model can also simulate convergence of surface winds, but with lower values compared to 9 km weather research forecast (WRF) model over eastern BMI. Besides, the ERA5 reanalysis model simulates convergence of surface winds, which is 2 hours faster over western and eastern BMI compared to MRCM27 and WRF.

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Effects of coastal topography on climate: high-resolution simulation with a regional climate model

Climate Research ◽

10.3354/cr01077 ◽

2012 ◽

Vol 52 ◽

pp. 159-174 ◽

Cited By ~ 26

Author(s):

B Önol

Keyword(s):

High Resolution ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Resolution Simulation ◽

Coastal Topography

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

Climate Research ◽

10.3354/cr01167 ◽

2013 ◽

Vol 57 (3) ◽

pp. 173-186 ◽

Cited By ~ 18

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

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

Climate Research ◽

10.3354/cr01598 ◽

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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