Simulations of the East Asian Winter Monsoon on Subseasonal to Seasonal Time Scales Using the Model for Prediction Across Scales

Hsu, Li‐Che; Chen, Dan-Rong; Chiang, Chou-Chun; Chu, Jung‐Lien; Yu, Yi-Chiang; Wu, Chia‐Chun

doi:10.3390/atmos12070865

Cited by 5 publications

(3 citation statements)

References 92 publications

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“…However, the specific causes of the low prediction skill with regard to changes in equatorial surface temperature variability or Arctic-related circulation, need to be further investigated using a more comprehensive analysis. Consistently with this study, Hsu et al [39] recently revealed that tropical sea surface temperature biases are related to the EAWM variability in the subseasonal simulation for 2011-2020. In this study, although the hindcast period is limited to 1991-2010 due to data availability, it will be extended to more recent periods (e.g., 2011-2020) after updating the model version.…”

Section: Discussionsupporting

confidence: 90%

Characteristics of Subseasonal Winter Prediction Skill Assessment of GloSea5 for East Asia

Ham

Jeong

2021

Atmosphere

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In this study, the characteristics of systematic errors in subseasonal prediction for East Asia are investigated using an ensemble hindcast (1991–2010) produced by the Global Seasonal Forecasting System version 5 (GloSea5). GloSea5 is a global prediction system for the subseasonal-to-seasonal time scale, based on a fully coupled atmosphere, land, ocean, and sea ice model. To examine the fidelity of the system with respect to reproducing and forecasting phenomena, this study assesses the systematic biases in the global prediction model focusing on the prediction skill for the East Asian winter monsoon (EAWM), which is a major driver of weather and climate variability in East Asia. To investigate the error characteristics of GloSea5, the hindcast period is analyzed by dividing it into two periods: 1991–2000 and 2001–2010. The main results show that the prediction skill for the EAWM with a lead time of 3 weeks is significantly decreased in the 2000s compared to the 1990s. To investigate the reason for the reduced EAWM prediction performance in the 2000s, the characteristics of the teleconnections relating to the polar and equatorial regions are examined. It is found that the simulated excessive weakening of the East Asian jet relating to the tropics and a failure in representing the Siberian high pressure relating to the Arctic are mainly responsible for the decreased EAWM prediction skill.

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Section: Discussionsupporting

confidence: 90%

Characteristics of Subseasonal Winter Prediction Skill Assessment of GloSea5 for East Asia

Ham

Jeong

2021

Atmosphere

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“…The Model for Prediction Across Scales-Atmosphere (MPAS-A) is a global, non-hydrostatic atmospheric model with the unstructured Voronoi meshes and C-grid, which developed with the basis of WRF model (Skamarock et al 2012). MPAS model has been used for seasonal and sub-seasonal forecasts (Weber and Mass 2019;Hsu et al 2021). Chen et al (2023 conducetd two set of climate simulations from April to August over China, and evaluated the surface air temperature and precipitation during the East Asia summer mosoon using CN05 observation.…”

Section: Introductionmentioning

confidence: 99%

Impact of parameterization schemes on temperature simulation over China using the variable-resolution MPAS-A

BAI,

Sun

2024

Preprint

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In this study, we investigate how different physical parameterizations impact the simulation of surface (2 m) air temperature over China using the Model for Prediction Across Scales-Atmosphere (MPAS-A) with a customized 92-25 km variable resolution. All simulations use ERA-Interim reanalysis data as the initial conditions for three selected typical years (1987, 1998 and 2011), and seven sub-regions is divided to assess the performance of different parameterization schemes on varying climate types. We performed 12 simulations using combinations of two shortwave–longwave (SW-LW) radiation, three cumulus convective (CUM) and two microphysics (MP) schemes. Model performance is evaluated using CN05 observation. The simulation shows significant sensitivity to SW-LW schemes, with the large difference of RSME scores. And RRTMG exhibits lower bias over more than half of the sub-regions. The influence of CUM schemes is weaker than that of SW-LW schemes on surface air temperature simulation. GF shows best model BIAS over more sub-regions. Followed is KF. For the MP schemes, the difference between the simulation results of Thompson and WSM6 is not significant for the same time and sub-region. Further we analysis the influence of different parameterization combinations. RRTMG_Thompson_NTDK combination performs best relative to other combinations under all conditions, with higher spatial correlation coefficient, lower root-mean-square error and bias. The annual cycles of surface air temperature could also be reasonably reproduced. Our results indicate a better parametrization scheme configuration, evaluate the simulation performance of the MPAS model for surface air temperature over China, and provide a meaningful reference for future researches.

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“…Spherical icosahedral grid is often used as the basic framework of atmospheric models, such as the ICosahedral Nonhydrostatic model (ICON) [1-3], Models for Predication Across Scales (MPAS) [4], the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) [5], DYNAMICO [6], Ocean-Land-Atmosphere Model (OLAM) [7,8], etc. These models are wieldy used for polar lows simulation [9], monsoon simulation [10], ocean tides dissipation [11], etc. Recently, the Spectral Radiation Transport Model for Aerosol Species coupled with NICAM (NICAM-SPRINTARS) [12] has been developed for studying and simulating atmosphere-aerosols interactions and their effects on atmospheric pollution and climate change.…”

Section: Introductionmentioning

confidence: 99%

An Overall Uniformity Optimization Method of the Spherical Icosahedral Grid Based on the Optimal Transformation Theory

et al. 2021

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The improvement of overall uniformity and smoothness of spherical icosahedral grids, the basic framework of atmospheric models, is a key to reducing simulation errors. However, most of the existing grid optimization methods have optimized grid from different aspects and not improved overall uniformity and smoothness of grid at the same time, directly affecting the accuracy and stability of numerical simulation. Although a well-defined grid with more than 12 points cannot be constructed on a sphere, the area uniformity and the interval uniformity of the spherical grid can be traded off to enhance extremely the overall grid uniformity and smoothness. To solve this problem, an overall uniformity and smoothness optimization method of the spherical icosahedral grid is proposed based on the optimal transformation theory. The spherical cell decomposition method has been introduced to iteratively update the grid to minimize the spherical transportation cost, achieving an overall optimization of the spherical icosahedral grid. Experiments on the four optimized grids (the spring dynamics optimized grid, the Heikes and Randall optimized grid, the spherical centroidal Voronoi tessellations optimized grid and XU optimized grid) demonstrate that the grid area uniformity of our method has been raised by 22.60% of SPRG grid, −1.30% of HR grid, 38.30% of SCVT grid and 38.20% of XU grid, and the grid interval uniformity has been improved by 2.50% of SPRG grid, 2.80% of HR grid, 11.10% of SCVT grid and 11.00% of XU grid. Although the grid uniformity of the proposed method is similar with the HR grid, the smoothness of grid deformation has been enhanced by 79.32% of grid area and 24.07% of grid length. To some extent, the proposed method may be viewed as a novel optimization approach of the spherical icosahedral grid which can improve grid overall uniformity and smoothness of grid deformation.

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Simulations of the East Asian Winter Monsoon on Subseasonal to Seasonal Time Scales Using the Model for Prediction Across Scales

Cited by 5 publications

References 92 publications

Characteristics of Subseasonal Winter Prediction Skill Assessment of GloSea5 for East Asia

Characteristics of Subseasonal Winter Prediction Skill Assessment of GloSea5 for East Asia

Impact of parameterization schemes on temperature simulation over China using the variable-resolution MPAS-A

An Overall Uniformity Optimization Method of the Spherical Icosahedral Grid Based on the Optimal Transformation Theory

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