Hi, I am Zilu Meng, a Ph.D. student at the University of Washington, working under the guidance of Prof. Gregory J. Hakim and Prof. Eric J. Steig. Prior to this, I earned my B.S. with honors in Atmospheric Sciences from Nanjing University of Information Science and Technology (NUIST) (Nanjing Institute of Meteorology), where I conducted research on tropical dynamics with Prof. Tim Li.
My research interests include data assimilation, climate dynamics, paleoclimate, and machine learning. Currently, my work focuses on paleoclimate data assimilation and investigating climate dynamics over the last millennium. I develop statistical approaches and physical models to better understand past climate variability and dynamics, with the ultimate goal of improving the accuracy of weather and climate predictions. In my free time, I enjoy playing video games, reading, and hiking.
My Curriculum Vitae can be found here.
Using Deep Learning based AGCMs (Neural GCM, DLESyM, ACE2), we conducted AMIP-like experiments spanning 1890-2020. We investigate two key questions: (1) Can these models simulate atmospheric conditions outside their training range? (2) How well do these models capture climate sensitivity and physical consistency compared to the real world?
Paleoclimate data assimilation is a powerful technique for reconstructing past climate states before the instrumental era. Instrumental records are relatively short and heavily influenced by anthropogenic forcing, limiting our understanding of natural climate variability. Paleoclimate proxies (e.g., tree rings, ice cores, corals) provide valuable indirect information about past conditions. By integrating these proxies with climate models through data assimilation, we can generate comprehensive reconstructions of past climate fields (temperature, precipitation, etc.) and study phenomena like ENSO, PDO, and AMO over longer timescales.
I am currently developing the first seasonal reanalysis dataset covering the last millennium using an "online" or "cycling" data assimilation approach. This dataset will provide gridded climate fields, enabling detailed studies of past climate variability and change. For instance, the comparison (shown right) of the Nino3.4 Index from our reanalysis with the HadISST dataset highlights the potential to accurately capture ENSO behavior over the millennium. This allows for comparisons between past and present climate dynamics.
A manuscript detailing this work has been accepted to the Journal of Climate (preprint available here).
Dataset: LMR Seasonal Data Portal
"Online" data assimilation (DA) is used to generate a seasonal-resolution reanalysis dataset over the last millennium by combining forecasts from an ocean–atmosphere–sea-ice coupled linear inverse model with climate proxy records. Instrumental verification reveals that this reconstruction achieves the highest correlation skill, while using fewer proxies, in surface temperature reconstructions compared to other paleo-DA products, particularly during boreal winter when proxy data are scarce. Reconstructed ocean and sea-ice variables also have high correlation with instrumental and satellite datasets. Verification against independent proxy records shows that reconstruction skill is robust throughout the last millennium. Analysis of the results reveals that the method effectively captures the seasonal evolution and amplitude of El Niño events, seasonal temperature trends that are consistent with orbital forcing over the last millennium, and polar-amplified cooling in the transition from the Medieval Climate Anomaly to the Little Ice Age.
Key Features:
Related Publication: Meng et al. (2025), Journal of Climate
Summary of completed reviews.