Skill Assessment of an Atmosphere–Wave Regional Coupled Model over the East China Sea with a Focus on Typhoons

Li, Delei; Staneva, Joanna; Grayek, Sebastian; Behrens, Arno; Feng, Jianlong; Yin, Baoshu

doi:10.3390/atmos11030252

Cited by 9 publications

(6 citation statements)

References 56 publications

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“…Furthermore, Ramon et al (2019) revealed that ERA5 features the best agreement with the observations among five global reanalyses for the mean wind speed and wind variability at turbine hub heights. By comparing against satellite and station observations, Li et al (2020) showed that ERA5 features high quality in capturing surface wind conditions over the East China Sea, with bias less than 0.4 m/s. All of the above support the reasonability of using ERA5 as a reference data set in this study.…”

Section: Methodsmentioning

confidence: 99%

Historical Evaluation and Future Projections of 100‐m Wind Energy Potentials Over CORDEX‐East Asia

Feng

Dosio

et al. 2020

JGR Atmospheres

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Countries in East Asia have set ambitious goals for the development of wind energy to meet the increasing energy demand and to mitigate anthropogenic climate change. However, few studies have investigated changes in wind energy over East Asia under future climatic conditions. In this study, we investigate future changes of 100‐m wind speed and wind energy potential over the CORDEX‐East Asia region under the Representative Concentration Pathway (RCP) 8.5 scenario, by using ensemble simulations from the regional climate model Consortium for small‐scale modeling in CLimate Mode (CCLM). A multivariate bias adjustment method based on the N‐dimensional probability density function transform is used to correct raw simulated horizontal wind components. The comparison between future climate (2021–2050 and 2070–2099) and the present climate (1971–2000) shows decreases in wind speed, wind power density, and wind energy output over most of the CORDEX‐East Asia region, especially in the tropics. Projected increases are pronounced over the Himalayan regions, the Indo‐China Peninsula, the South China Sea, and the western Pacific Ocean in summer and over northeastern China, parts of Western China and the Indo‐China Peninsula in winter. Interannual and intra‐annual variability of wind power density are projected to intensify significantly for most of the CORDEX‐East Asia region. The occurrence of weak wind speeds (<3 m/s) is projected to increase, while strong wind speeds (>11 m/s) are projected to decrease over most of the ocean.

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Section: Methodsmentioning

confidence: 99%

Historical Evaluation and Future Projections of 100‐m Wind Energy Potentials Over CORDEX‐East Asia

Feng

Dosio

et al. 2020

JGR Atmospheres

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“…The atmospheric model used herein is the Consortium for Small-Scale Modeling (COSMO)-Climate Mode (CLM) (CCLM) regional climate model (Rockel et al, 2008;Doms and Baldauf, 2013). CCLM is the community model of the German regional climate research community and has already been utilized in several studies employing coupled systems with waves (e.g., Cavaleri et al, 2012b;Wahle et al, 2017;Li et al, 2020) or oceanic and other earth system components (e.g., Ho-Hagemann et al, 2013, 2015, 2017Van Pham et al, 2014;Will et al, 2017;Kelemen et al, 2019). CCLM is based on the primitive thermo-hydrodynamical equations describing a compressible flow in a moist atmosphere.…”

Section: Numerical Modelsmentioning

confidence: 99%

“…The wave model utilized in this study is the third-generation WAve Model WAM (WAMDI Group, 1988;ECMWF, 2019). WAM has also been used successfully in several studies that assessed the wave-atmosphere coupling together with CCLM (e.g., Cavaleri et al, 2012b;Wahle et al, 2017;Li et al, 2020). However, WAM has also been employed with other atmospheric models, such as the ECMWF model (e.g., Janssen and Viterbo, 1996;Janssen et al, 2002) or the Weather Research and Forecasting (WRF) model (e.g., Wu et al, 2017).…”

Section: Numerical Modelsmentioning

confidence: 99%

Internal Model Variability of Ensemble Simulations With a Regional Coupled Wave-Atmosphere Model GCOAST

Wiese¹,

Staneva²,

Ho-Hagemann³

et al. 2020

Front. Mar. Sci.

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Ensemble simulations are performed to quantify the internal variability of both regional atmospheric models and wave-atmosphere coupled model systems. Studies have shown that the internal variability in atmospheric models (e.g., wind or pressure fields) is increased during extreme events, such as storms. Comparing the magnitude of the internal variability of the atmospheric model with the internal variability of the coupled model system reveals that the internal variability can be reduced by coupling a wave model to the atmospheric model. While this effect is most evident during extreme events, it is still present in a general assessment of the mean internal variability during the whole study period. Furthermore, the role of this wave-atmosphere coupling can be distinguished from that of the internal variability of the atmospheric model since the impact of the wave-atmosphere interaction is larger than the internal variability. This is shown to be robust to different boundary conditions. One method to reduce the internal variability of the atmospheric model is to apply spectral nudging, the role of which in both the stand-alone atmospheric model and the coupled wave-atmosphere system is evaluated. Our analyses show that spectral nudging in both coupled and stand-alone ensemble simulations keeps the internal variability low, while the impact of the wave-atmosphere interaction remains approximately the same as in simulations without spectral nudging, especially for the wind speed and significant wave height. This study shows that in operational and climate research systems, the internal variability of the atmospheric model is reduced when the ocean waves and atmosphere are coupled. Clear influences of the wave-atmosphere interaction on both of these earth system components can be detected and differentiated from the internal model variability. Furthermore, the wave-atmosphere coupling has a positive effect on the agreement of the model results with both satellite and in situ observations.

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“…1 We conducted wave hindcast simulations over the NWP and BYE forced by ERA5 wind speed (0.25 • , Hersbach et al, 2020) during 1979-2019 to validate the model's ability to capture the wave climate features in the BYE (thereafter ERA5 driven wave hindcast). ERA5 winds have proved to be robust in forcing wave conditions in the study domain (Li et al, 2020). To study the impact of climate change on wave climate, nested WAM simulations are forced by 3-hourly wind outputs (0.22 • ) from the CCLM-MPIESM RCM simulations (Kim et al, 2020) for the historical climate period and two future periods (2021-2050 and 2071-2100) under the RCP2.6 and RCP8.5 emission scenarios.…”

Section: Methodology and Datasets Wave Dynamical Downscalingmentioning

confidence: 99%

Dynamical Projections of the Mean and Extreme Wave Climate in the Bohai Sea, Yellow Sea and East China Sea

Feng

Zhu

et al. 2022

Front. Mar. Sci.

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Few studies have focused on the projected future changes in wave climate in the Chinese marginal seas. For the first time, we investigate the projected changes of the mean and extreme wave climate over the Bohai Sea, Yellow Sea, and East China Sea (BYE) during two future periods (2021–2050 and 2071–2100) under the RCP2.6 and RCP8.5 scenarios from the WAM wave model simulations with a resolution of 0.1°. This is currently the highest-resolution wave projection dataset available for the study domain. The wind forcings for WAM are from high-resolution (0.22°) regional climate model (RCM) CCLM-MPIESM simulations. The multivariate bias-adjustment method based on the N-dimensional probability density function transform is used to correct the raw simulated significant wave height (SWH), mean wave period (MWP), and mean wave direction (MWD). The annual and seasonal mean SWH are generally projected to decrease (-0.15 to -0.01 m) for 2021–2050 and 2071–2100 under the RCP2.6 and RCP8.5 scenarios, with statistical significance at a 0.1 level for most BYE in spring and for most of the Bohai Sea and Yellow Sea in annual and winter/autumn mean. There is a significant decrease in the spring MWP for two future periods under both the RCP2.6 and RCP8.5 scenarios. In contrast, the annual and summer/winter 99th percentile SWH are generally projected to increase for large parts of the study domain. Results imply that the projected changes in the mean and 99th percentile extreme waves are very likely related to projected changes in local mean and extreme surface wind speeds, respectively.

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Skill Assessment of an Atmosphere–Wave Regional Coupled Model over the East China Sea with a Focus on Typhoons

Cited by 9 publications

References 56 publications

Historical Evaluation and Future Projections of 100‐m Wind Energy Potentials Over CORDEX‐East Asia

Historical Evaluation and Future Projections of 100‐m Wind Energy Potentials Over CORDEX‐East Asia

Internal Model Variability of Ensemble Simulations With a Regional Coupled Wave-Atmosphere Model GCOAST

Dynamical Projections of the Mean and Extreme Wave Climate in the Bohai Sea, Yellow Sea and East China Sea

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