Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments.

Area of Science:

• Climate Science
• Environmental Modeling
• Atmospheric Chemistry

Background:

• Integrated assessment models (IAMs) and climate models (CMs) have different spatial resolutions, necessitating emissions data downscaling.
• Accurate downscaling is vital for simulating climate change impacts driven by human activities.

Purpose of the Study:

• To investigate the impact of different downscaling methods on climate variables.
• To assess whether variations in downscaling sulfur emissions affect temperature and precipitation projections.

Main Methods:

• Compared two downscaling methods (emissions intensity convergence vs. inertia) for sulfur emissions from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model.
• Inputted downscaled emissions into the Model for Interdisciplinary Research on Climate (MIROC) for climate simulations.
• Utilized field significance tests to evaluate differences in climate projections.

Main Results:

• Downscaled emissions showed marked differences in intensity but similar densities across methods.
• Field significance tests revealed little evidence of significant differences in projected temperature and precipitation between the two downscaling methods.
• Climate simulations did not show clear differences based on the tested downscaling approaches.

Conclusions:

• The choice of downscaling method for IAM emissions data has a limited impact on climate projections of temperature and precipitation.
• Current downscaling approaches appear robust enough for climate modeling purposes, despite differences in emissions intensity.
• Further research could explore other emissions types or more complex downscaling techniques.