26 JAN 2026 (MON) 16:05 - 16:35

Refining Regional Carbon Emissions Inversion by Integrating Urban-Climate Interactions 

Miss ZHOU Wan 

( Supervisor: Prof Yuyu Zhou )

Abstract:

High-resolution and timely monitoring of urban greenhouse gas (GHG) emissions is pivotal for effective climate mitigation, as cities constitute the dominant source of global anthropogenic carbon. However, traditional "bottom-up" inventories suffer from intrinsic limitations, including multi-year latency, insufficient spatiotemporal granularity, and inherent uncertainties. Alternatively, "top-down" atmospheric inversions offer independent constrains, but rely heavily on the accuracy of atmospheric transport models (ATMs). Crucially, current ATMs frequently neglect the complex meteorological feedbacks induced by urbanization—specifically the effects of urban land surface changes and anthropogenic heat emissions (AHEs)—which significantly alter the atmospheric transport and dispersion of GHGs, thereby biasing emission estimates.

To bridge these gaps, this study establishes a high-resolution regional carbon assimilation system that integrates urban physics, atmospheric chemistry, and data assimilation. The research is conducted in three coupled stages. First, the Weather Research and Forecasting model coupled with the Urban Canopy Model (WRF-UCM) is employed to explicitly resolve the impacts of urban expansion and AHE on local meteorology, yielding precise physical fields (e.g., planetary boundary layer height and wind fields). Second, these refined meteorological constraints drive the WRF coupled with Chemistry (WRF-Chem) model to accurately simulate GHG transport, thereby minimizing systematic errors rooted in inadequate urban representation. Finally, leveraging these reduced transport uncertainties, a four-dimensional local ensemble transform Kalman filter (4D-LETKF) data assimilation system coupled with WRF-Chem is deployed to optimize carbon fluxes by assimilating multi-source observations, including satellite retrievals and ground-based measurements.

This study aims to disentangle the mechanism of urban-climate interactions on GHG transport and to construct a high-precision posterior emission inventory. By incorporating detailed urban canopy processes directly into the inversion framework, this research expects to significantly reduce meteorological biases inherent in forward modeling, ultimately enhancing the accuracy of urban-scale carbon emission estimates. These findings provide robust scientific support for verifying regional carbon budgets and pinpointing emission hotspots.

Keywords: Urbanization; Anthropogenic Heat; Carbon Emission Inversion; Data Assimilation; WRF-Chem; 4D-LETKF