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Development of the global chemistry-climate coupled model BCC-GEOS-Chem v2.0: improved atmospheric chemistry performance and new capability of chemistry-climate interactions

Ruize Sun (Co-first Author), Xiao Lu (Co-first Author), Haipeng Lin, Tongwen Wu, Xingpei Ye, Lu Shen, Xuan Wang, Haolin Wang, Jingyu Li, Ni Lu, Jiayin Su, Jie Zhang, Fang Zhang, Xiaoge Xin, Xiong Liu, Xiao Yang, Lin Zhang*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

Interactions between atmospheric chemical compounds and climate have a great impact on the earth system and atmospheric chemistry. However, the online two-way chemistry-climate coupled model, an indispensable tool for quantifying chemistry-climate interactions and projecting future air quality with climate change, remains sparse due to the considerable challenge in model complexity and computational resources. We present the development and evaluation of BCC-GEOS-Chem v2.0, which couples the GEOS-Chem chemical transport model (v14.0.1) with the Beijing Climate Centre Earth System Model (BCC-ESM). Based on the modular framework of BCC-GEOS-Chem v1.0, BCC-GEOS-Chem v2.0 further couples the Harmonized Emissions Component (HEMCO) to manage anthropogenic emission inventories and natural emissions, updates the chemical mechanism, includes the feedback of aerosols and greenhouse gases, and develops the capability for high-resolution simulation. The standard chemical mechanism in the BCC-GEOS-Chem v2.0 features a comprehensive Ox-NOx-VOC-halogen-aerosol chemical scheme for the troposphere and the stratosphere. We further evaluate the performance of the BCC-GEOS-Chem v2.0 simulation in representing atmospheric chemistry and compare with the model outputs from the BCC-GEOS-Chem v1.0 and BCC-AGCM-Chem over the simulated time period (2012–2014) at a spatial resolution of T42L26 (approximately 2.8° × 2.8° and 26 vertical layers with a top at 2.914 hPa). BCC-GEOS-Chem v2.0 accurately depicts the primary seasonal and spatial distributions of tropospheric ozone observed by multiple instruments, showing small global mean biases of −2.1–1.8 ppbv for mid-tropospheric (700–400 hPa) ozone concentrations relative to satellite observations, along with a high spatial correlation coefficient (r) of 0.77–0.92 for individual seasons. It also demonstrates improved performance in simulating tropospheric carbon monoxide (CO), nitrogen dioxide (NO2), formaldehyde (CH2O) and surface PM2.5 compared to both BCC-GEOS-Chem v1.0 and the BCC-AGCM-Chem. The diagnostics of tropospheric ozone budgets (a global tropospheric ozone burden of 355 Tg) and OH concentrations (0.97 × 106 molec. cm-3) are generally consistent with observation-constrained estimates and multi-model assessment. With the inclusions of aerosol-radiation and aerosol-cloud interactions, BCC-GEOS-Chem v2.0 reproduces the expected impacts of aerosols on radiative and cloud properties, e.g., decreasing shortwave downward solar radiation and outgoing longwave radiation, increasing cloud liquid water, and suppressing precipitation. The high-resolution simulation at T159L72 (approximately 0.75° × 0.75° and 72 vertical layers with a top at 0.01 hPa) further improves the model capability in resolving the fine-scale plume transport dynamics and the pollution hotspot of NO2 and PM2.5, as well as the low ozone concentration in high-NOx environment in wintertime China. The development of the BCC-GEOS-Chem v2.0 model provides a powerful tool to study climate-chemistry interactions and for future projection of global atmospheric chemistry and regional air quality. © Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Original languageEnglish
Pages (from-to)2111-2136
JournalGeoscientific Model Development
Volume19
Issue number5
Online published16 Mar 2026
DOIs
Publication statusPublished - 2026

Funding

This research was supported by the National Key Research and Development Program of China (grant no. 2023YFC3706104) and the National Natural Science Foundation of China (grants nos. 42275106 and 42522505). This research is also supported by the Guangdong Basic and Applied Basic Research Foundation (grant no. 2025B1515020034)

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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