the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data
Huimin Li
Jianbing Jin
Hailong Wang
Pinya Wang
Hong Liao
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We develop a novel, dual-CTM bias correction framework to attribute summertime PM2.5 and ozone changes over eastern China during 2015–2024. The framework substantially reduces the CTM biases and reconciles the inter-model discrepancies in the attribution. Emission reductions dominate both the PM2.5 decline and ozone increase, but there is a marked transition of their role after 2019. Persistent unfavorable meteorological conditions contribute to ozone increase especially before 2019.
Marine cloud brightening (MCB) is a proposal to emit sea salt aerosols to make clouds more reflective and cool the climate. Here, we use three climate models to study a hypothetical future where MCB is used to maintain temperatures near 2020–2039 conditions. The simulation results indicate that using MCB in midlatitude ocean regions can keep the climate close to present day conditions. This reduces many of the negative impacts shown in previous studies, informing future modeling efforts.
hiddensource of inter-model variability and may be leading to bias in some climate model results.