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Area of Science:

  • Atmospheric Chemistry and Climate Science
  • Environmental Modeling and Machine Learning

Background:

  • Global chemistry-climate models (CCMs) are vital for predicting future surface ozone, essential for air quality management and health risk assessments.
  • Existing CCMs exhibit biases in spatial distribution, magnitude, and trends, limiting their regional application and reliability.
  • Accurate ozone projections are needed to inform effective environmental protection strategies.

Purpose of the Study:

  • To apply a machine learning (ML) algorithm, LightGBM, to correct biases in ozone simulations from the Community Earth System Model version 2 (CESM2).
  • To calibrate future surface ozone projections under Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) for China, the United States, and Europe from 2020 to 2060.
  • To analyze the drivers of future ozone trends, distinguishing between anthropogenic emissions and climate change impacts.

Main Methods:

  • Utilized the LightGBM ML algorithm to correct biases in CESM2-simulated surface ozone concentrations.
  • Applied the bias-corrected model to project future ozone levels under two contrasting emissions scenarios (SSP1-2.6 and SSP5-8.5).
  • Conducted a decomposition analysis to attribute changes in projected ozone to anthropogenic emissions and climate effects.

Main Results:

  • ML-based bias correction significantly improved spatial distribution and reduced ozone bias by 40-60%, also correcting a potentially erroneous trend in eastern China under SSP1-2.6.
  • Under SSP1-2.6, corrected ozone is projected to decrease significantly across China, the US, and Europe by 2060.
  • Under SSP5-8.5, corrected ozone is projected to increase in China, with smaller increases in the US and Europe by 2060.
  • Anthropogenic emission changes were identified as the dominant driver of future ozone trends.
  • A 'climate penalty' (ozone increase due to climate change) was observed in polluted eastern China, while climate benefits (ozone decrease) occurred in western China, the US, and Europe under SSP5-8.5.

Conclusions:

  • Combining ML with CCMs offers a powerful approach to enhance the accuracy of air quality projections.
  • The ML-corrected projections provide more reliable estimates of future surface ozone, supporting better regional environmental policy.
  • Region-specific environmental protection strategies are necessary, considering the varying influences of emissions and climate change on future ozone levels.