the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Seesaw haze pollution in North China modulated by the sub-seasonal variability of atmospheric circulation
Ge Zhang
Wenju Cai
L. Ruby Leung
Shuxiao Wang
Minghuai Wang
Huayao Shan
Xiaohong Yao
Huiwang Gao
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Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.
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A state-of-the-art thermodynamic model has been coupled with the city-scale chemistry transport model EPISODE–CityChem to investigate the equilibrium between the inorganic gas and aerosol phases over the greater Athens area, Greece. The simulations indicate that the formation of nitrates in an urban environment is significantly affected by local nitrogen oxide emissions, as well as ambient temperature, relative humidity, photochemical activity, and the presence of non-volatile cations.
hiddensource of inter-model variability and may be leading to bias in some climate model results.