Seminar - Bridge the Gap between Observation and Modeling: Diagnosing Cloud-related Processes in Earth System Models using Field Observations
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Detailed atmospheric observations from field campaigns differ substantially from Earth system model (ESM) output in spatial and temporal coverage, resolution, and variable representativeness. This presentation describes our efforts to bridge this observation–model gap by developing value-added products from field observations and using them, together with single-column model (SCM) simulations, to diagnose, attribute, and reduce errors in ESM physical parameterizations.
By synthesizing field observations through constrained variational analysis, we derive dynamically and thermodynamically consistent large-scale forcing datasets that provide SCMs with realistic large-scale dynamic conditions. Prescribing these constraints helps isolate errors associated with physics parameterizations for further diagnosis and improvement. As an example, multi-SCM intercomparison study of the diurnal cycle of precipitation (DCP) reveal common deficiencies in deep-convection schemes, including premature afternoon convection, excessive coupling between convection and boundary-layer instability, and failure to reproduce nocturnal precipitation. A revised convective trigger improves DCP simulations by incorporating dynamic convective available potential energy (dCAPE) as an additional triggering criterion and an unrestricted parcel launch level (ULL), which primarily improve afternoon and nocturnal precipitation, respectively.
We also developed the ESM Aerosol–Cloud Diagnostics package (ESMAC Diags), which standardizes in situ measurements from aircraft, ships, and surface sites to facilitate routine evaluation of aerosols, clouds, and their interactions in ESMs. Initial evaluations indicate that simulated cloud-droplet number concentration may be excessively sensitive to aerosol concentration, suggesting deficiencies in the representation of aerosol activation. Together, these studies demonstrate how field observations, value-added products, and a hierarchy of modeling approaches can advance process-level understanding and support the development of ESM parameterizations.
Dr. Shuaiqi Tang is an Associate Professor at the School of Atmospheric Sciences, Nanjing University. He received his B.S. and M.S. degrees from Peking University and his Ph.D. from Stony Brook University in 2015. Before joining Nanjing University in 2024, he worked as a Research Scientist at Lawrence Livermore National Laboratory and Pacific Northwest National Laboratory in the United States.
His research focuses on precipitation, clouds, aerosols, and their interactions, combining field observations and Earth system modeling to understand, evaluate, and improve the representation of atmospheric physical processes. His work includes developing atmospheric observational value-added products and aerosol–cloud diagnostics for Earth system models, as well as leading model intercomparison studies to identify and reduce biases in the simulation of the diurnal cycle of precipitation. His current research interests include cloud and precipitation processes, aerosol–cloud interactions, and the evaluation and development of physical parameterizations within Earth system models.