Improving the WRF model's (version 3.6.1) simulation over sea ice surface  through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Yao, Yao; Huang, Jianbin; Luo, Yong; Zhao, Zongci

doi:10.5194/gmd-9-2239-2016

Cited by 7 publications

(5 citation statements)

References 67 publications

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“…A large internal variability within atmospheric circulation systems over the Arctic also contributes to this relationship between observation and understanding. Accordingly, improvements in Arctic modeling have mainly been carried out by improving the presenting physical processes and reducing the diversity of modeling results (Hines et al, ; Vavrus & Waliser, ; Yao et al, ).…”

Section: Introductionmentioning

confidence: 99%

Dynamical Core in Atmospheric Model Does Matter in the Simulation of Arctic Climate

Jun

Choi

Kim

2018

Geophysical Research Letters

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Climate models using different dynamical cores can simulate significantly different winter Arctic climates even if equipped with virtually the same physics schemes. Current climate simulated by the global climate model using cubed‐sphere grid with spectral element method (SE core) exhibited significantly warmer Arctic surface air temperature compared to that using latitude‐longitude grid with finite volume method core. Compared to the finite volume method core, SE core simulated additional adiabatic warming in the Arctic lower atmosphere, and this was consistent with the eddy‐forced secondary circulation. Downward longwave radiation further enhanced Arctic near‐surface warming with a higher surface air temperature of about 1.9 K. Furthermore, in the atmospheric response to the reduced sea ice conditions with the same physical settings, only the SE core showed a robust cooling response over North America. We emphasize that special attention is needed in selecting the dynamical core of climate models in the simulation of the Arctic climate and associated teleconnection patterns.

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Section: Introductionmentioning

confidence: 99%

Dynamical Core in Atmospheric Model Does Matter in the Simulation of Arctic Climate

Jun

Choi

Kim

2018

Geophysical Research Letters

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“…However, the bias pattern of the WRF simulation in cold seasons is different from that of ERA‐Interim; therefore, such cold biases of WRF could not be related to ERA‐Interim. Previous studies also reported similar cold biases of WRF over some mid‐latitude regions (Liu et al ., 2012; Yao et al ., 2016), and they argued that the cold biases could probably be attributed to the WRF deficiency in reproducing the snow process and feedback. The actual reasons for the cold biases over NA should be investigated in the future.…”

Section: Resultsmentioning

confidence: 99%

“…However, compared with ERA‐Interim, the cold biases of WRF are much stronger and over almost all regions of NA. The large cold biases in these two seasons could also be attributed to the model deficiency in reproducing the snow process and feedback (Yu et al ., 2015; Yao et al ., 2016). The biases of atmospheric circulations could also contribute to precipitation biases over some regions of NA.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, we chose the YSU scheme (Hong et al ., 2006) for the planetary boundary layer scheme, the RRTMG scheme (Iacono et al ., 2008) for the radiative transfer scheme, and the NOAH‐MP scheme (Niu et al ., 2011; Yang et al ., 2011) for the land surface process parameterization scheme. These three schemes have often been used in previous regional simulations over the regions of the mid‐latitude Eurasian continent (Wang and Yang, 2008; Yu, 2019; Zhu et al ., 2020) and some high‐latitude regions (e.g., Norway and Arctic Ocean) (Heikkilä et al ., 2011; Yao et al ., 2016). The performances of the configurations with different microphysical processes and cumulus convection parameterization schemes were evaluated here because they have significant influences on air temperature and precipitation simulations (Thompson et al ., 2004; Brown et al ., 2017; Zhang and Wang, 2018).…”

Section: Data and Experimental Designmentioning

confidence: 99%

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Performance of the Weather Research and Forecasting model in simulating climate over northern Asia

Bai

Sun

et al. 2022

Intl Journal of Climatology

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High-resolution simulation is important for understanding climate change and its impact over regions with complex terrain. Northern Asia (NA) is such a region and sensitive to global warming. However, the performance of the regional model over NA is still unclear. In this study, we conducted a twodecade-long high-resolution (25 km) simulation over NA using the Weather Research and Forecasting (WRF) model forced by the ERA-Interim reanalysis and then evaluated the performance of WRF against the observations. The results show that the WRF simulation, set up with selections of the physical schemes, can reasonably reproduce the spatial and temporal features of surface air temperature and precipitation over NA. In addition, the WRF simulation can add more regional details on the climate features than the ERA-Interim data, especially over the NA regions with complex terrain. The spatial-pattern correlation coefficient of summer precipitation over NA between the WRF simulation and observations is higher than that of the ERA-Interim data. WRF can reasonably reproduce the annual cycles of precipitation. Moreover, the observed year-to-year variations in summer surface air temperature and precipitation over most NA subregions could be generally captured by the WRF. In conclusion, although systemic model bias still exists, the evaluations in this study indicate that WRF shows reasonable performance in a high-resolution simulation of climate over NA, consequently providing a scientific basis for future simulation studies over the region.

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“…The one-dimensional high-resolution thermodynamic snow/ice model HIGHTSI has been used to obtain accurate simulations of the growth and melting of ice cover in the Bohai, Baltic, and Arctic Seas (Cheng et al 2006, Karvonen et al 2017, Merkouriadi et al 2017, Yao et al 2016. With parameter optimization and adjustment to account for the differences between sea and lake conditions (Table 3, SI Fig.…”

Section: One-dimensional High-resolution Thermodynamic Snow/ice Modelmentioning

confidence: 99%

Fluorescein isothiocyanate stability in different solvents

et al. 2021

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Nutrient transport in seasonally ice-covered lakes is an important driver of spring algal blooms in eutrophic waters because phase changes during the ice growth process redistribute the nutrients. In this study, nutrient transport under static conditions was simulated using two ice thickness models in combination with an indoor freezing experiment under different segregation coefficient conditions for nutrients. A real-time prediction model for nutrient and pollutant concentrations in ice-covered lakes was established to explore the impact of the ice-on period in eutrophic shallow lakes. The results showed that the empirical degreeday model and the high-resolution thermodynamic snow and sea-ice model (HIGHTSI) could both be used to simulate lake ice thickness. The empirical degree-day model performed better at predicting the maximum ice thickness (measured thickness 0.22-0.55 m; simulated thickness 0.48 m), while the HIGHTSI model was more accurate when estimating the mean thickness (5-6 % error). When simulating ice growth, the HIGHTSI model considered more meteorological factors impacting ice cover ablation; hence, it performed better during the ablation stage relative to the empirical degree-day model. Two nondynamic nutrient transport models were developed by combining the segregation coefficient model and the ice thickness prediction model. The HIGHTSI nutrient transport model can be used to predict real-time changes in nutrient concentrations under ice cover, and the degree-day model can be used to predict changes in the lake water ecosystem.

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Improving the WRF model's (version 3.6.1) simulation over sea ice surface through coupling with a complex thermodynamic sea ice model (HIGHTSI)

Cited by 7 publications

References 67 publications

Dynamical Core in Atmospheric Model Does Matter in the Simulation of Arctic Climate

Dynamical Core in Atmospheric Model Does Matter in the Simulation of Arctic Climate

Performance of the Weather Research and Forecasting model in simulating climate over northern Asia

Fluorescein isothiocyanate stability in different solvents

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