Cheng et al. (2025) Climatological characteristics of tropical cyclones simulated in the global-regional integrated forecasting system (GRIST) model

Identification

• Journal: Climate Dynamics
• Year: 2025
• Date: 2025-10-29
• Authors: Xi Cheng, Xinyao Rong, Yi Zhang, Yuqing Wang, Jian Li, Jing Xu
• DOI: 10.1007/s00382-025-07916-0

Research Groups

• Chinese Academy of Meteorological Sciences, Beijing, China
• CMA Earth System Modeling and Prediction Center, Beijing, China
• State Key Laboratory of Severe Weather Meteorological Science and Technology, CEMC, Beijing, China
• State Key Laboratory of Climate System Prediction and Risk Management (CPRM), and School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, China
• Department of Atmospheric Sciences, School of Ocean and Earth Science and Technology, International Pacific Research Center, University of Hawai‘I at Manoa, Honolulu, HI, USA
• Qingdao Institute of Marine Meteorology, Chinese Academy of Meteorological Sciences, Qingdao, China

Short Summary

This study evaluates the climatological characteristics of tropical cyclones (TCs) simulated by the Global-Regional Integrated Forecasting System (GRIST) model at 60 km and 15 km resolutions. It finds that while the model generally reproduces TC activity well, especially in the North Pacific, it consistently underestimates TC intensity and activity in the North Atlantic due to large-scale circulation biases and poor representation of African easterly waves.

Objective

• To evaluate the climatological characteristics of tropical cyclones (TCs) simulated by the Global-Regional Integrated Forecasting System (GRIST) model at horizontal resolutions of 60 km and 15 km.
• To assess the model’s ability to reproduce large-scale climatological conditions and TC activity, and to diagnose the sources of systematic biases.

Study Configuration

• Spatial Scale: Global atmospheric model with quasi-uniform grids at 60 km (G7) and 15 km (G9) horizontal resolutions, both with 31 vertical levels. Focus on Western North Pacific (WNP), Eastern North Pacific (ENP), North Atlantic (NA), and Indian Ocean basins.
• Temporal Scale: Eight seasonal simulations (June 1 to December 1) covering the years 2016–2023.

Methodology and Data

• Models used:
  • Global-Regional Integrated Forecasting System (GRIST): Unified weather–climate atmospheric model, hydrostatic version with a dry-mass, layer-averaged, unstructured-mesh dynamical core.
  • Physics suite: PhysW, modified Tiedtke-Bechtold convection scheme, WSM6 microphysics, Xu-Randall cloud fraction, YSU boundary layer, RRTMG radiation, Noah-MP land surface, Kim-Arakawa orographic gravity wave drag.
  • TC tracking: TSTORMS algorithm, identifying vortices based on minimum sea level pressure, 850-hPa relative vorticity, near-surface wind speed, and warm-core structure.
  • Diagnostic: Modified Genesis Potential Index (GPI) of Murakami and Wang (2010), including 500-hPa vertical velocity, and its logarithmic decomposition for bias attribution.
• Data sources:
  • TC tracks and intensity: International Best Track Archive for Climate Stewardship (IBTrACS) version v04r01.
  • Precipitation: Integrated Multi-satellitE Retrievals for GPM (IMERG) Version 07 product (0.1° × 0.1° resolution, half-hourly).
  • Reanalysis and Initialization: ERA5 reanalysis dataset (monthly-mean atmospheric variables at 0.25° × 0.25° resolution for diagnostics; 6-hourly 3D atmospheric fields and land surface conditions for initialization; daily skin temperature and sea-ice concentration prescribed).

Main Results

• GRIST effectively reproduces the large-scale climatological mean state (e.g., JJA global mean precipitation: GPM 3.12 mm day⁻¹, G7 3.16 mm day⁻¹, G9 3.22 mm day⁻¹; JJA precipitation pattern correlation: 0.83 for G7 and G9) and captures the spatial and temporal features of TC activity, especially over the Western North Pacific (WNP) and Eastern North Pacific (ENP).
• The 15 km resolution (G9) significantly improves TC genesis frequency, intensity spectrum (generating storms up to Category 4), spatial distribution of tracks, and TC lifetime compared to the 60 km (G7) configuration.
• The model persistently underestimates TC intensity (G9 biased towards Category 1-2, underrepresenting Category 3-5) and exhibits suppressed activity in the North Atlantic (NA) basin across both resolutions, particularly in the central and eastern sectors.
• Diagnostic analysis using the Genesis Potential Index (GPI) reveals that these biases are primarily linked to systematic errors in the large-scale circulation, including excessive vertical wind shear, insufficient mid-level humidity, and weakened vertical motion.
• Regionally, misplacements of the monsoon trough in the WNP and weakened African easterly waves (AEWs) in the NA further contribute to deficiencies in TC genesis. Simulated AEWs remain confined to land and weaken rapidly offshore, failing to seed TC development in the Atlantic main development region.

Contributions

• This study provides the first comprehensive evaluation of the GRIST model's capability to simulate TC climatology at high resolutions (60 km and 15 km), including near convection-permitting scales.
• It demonstrates the significant added value of 15 km resolution for improving TC genesis, intensity, and track simulation, particularly in the North Pacific basin.
• The research offers detailed diagnostic attribution of systematic biases in TC activity, specifically the persistent underestimation in the North Atlantic, linking it to large-scale circulation errors and the misrepresentation of African easterly waves.
• It underscores that while high resolution is crucial, it is not sufficient to address all deficiencies, highlighting the importance of accurately representing environmental conditions and subgrid physics for TC genesis and evolution.
• The findings provide a clear pathway for future model improvements, including moving towards convection-resolving scales and leveraging GRIST's variable-resolution framework for targeted refinement in dynamically sensitive regions.

Funding

• National Key R&D Program of China (Grant 2022YFC3004203)
• Startup Foundation for Introducing Talent of NUIST

Citation

@article{Cheng2025Climatological,
  author = {Cheng, Xi and Rong, Xinyao and Zhang, Yi and Wang, Yuqing and Li, Jian and Xu, Jing},
  title = {Climatological characteristics of tropical cyclones simulated in the global-regional integrated forecasting system (GRIST) model},
  journal = {Climate Dynamics},
  year = {2025},
  doi = {10.1007/s00382-025-07916-0},
  url = {https://doi.org/10.1007/s00382-025-07916-0}
}