My work combines climate records, physical models, and machine learning to reconstruct past climate and understand El Niño, sea ice, and extreme weather.
Earth's instrumental record covers only a small part of climate history. I combine natural archives, including tree rings, ice cores, corals, and boreholes, with physical models and modern AI to estimate how the atmosphere, ocean, and sea ice changed before widespread measurements.
This longer view helps us understand how climate patterns vary, why events such as heat waves and cold spells occur, and how unusual recent changes are. My goal is to turn scattered evidence into climate information that is physically consistent, useful, and easier to understand.
Use natural records and historical observations to recover climate changes across seasons, centuries, and millennia.
Blend incomplete evidence with climate physics through data assimilation, statistical tools, and machine learning.
Study ENSO, ocean and sea-ice change, atmospheric circulation, and the behavior of extreme heat and cold.
Beyond research: I enjoy building open-source scientific tools, playing video games, reading, and hiking.
Peer-reviewed articles and preprints spanning paleoclimate reconstruction, data assimilation, ENSO, and machine-learning Earth system models.
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
From foundational atmospheric dynamics to data assimilation and paleoclimate reconstruction.
University of Washington
Seattle, WA, USA
Data assimilation, machine learning, climate dynamics, and paleoclimate.
Nanjing University of Information Science and Technology
Nanjing, China
GPA: 95/100 Ranked 1st/50
Atmospheric Fluid Dynamics, Atmospheric Physics, and Synoptic Meteorology.
Why Is the Pacific Meridional Mode Most Pronounced in Boreal Spring? Paper Thesis (Chinese)
Invited seminars, conference talks, and posters on paleoclimate reconstruction, data assimilation, machine learning, and atmospheric dynamics.
Seattle, WA, USA
Beyond ECMWF?! Weak-Constraint 4D-Var over the Past 12,000 Years
View title slide
Nanjing, China · Meteorology Building, Room 813
Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Heat and Cold Wave Frequencies
View event poster
Beijing, China
Data Assimilation and Atmospheric Modelling over the Last Millennium
Beijing, China
Data Assimilation over the Last Millennium
New Orleans, LA, USA
Coupled Seasonal Data Assimilation over the Last Millennium
New Orleans, LA, USA
Deep Learning Atmospheric Model Reliably Simulates Out-of-Sample Extreme Weather Events
Honolulu, HI, USA
Coupled Seasonal Data Assimilation over the Last Millennium
Boston, MA, USA
Sacpy: A Python Module for Statistical Analysis of Climate Data
Columbus, OH, USA
Ocean, Atmosphere, and Sea Ice Data Assimilation for the Last Millennium
Nanjing, China
Uncovering Insights from the Last Millennium Using Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics
Nanjing, China
Ocean, Atmosphere, and Sea Ice Data Assimilation for the Last Millennium
Seattle, WA, USA
Uncovering Insights from the Last Millennium Using Coupled Seasonal Data Assimilation
Washington, D.C., USA
Reconstructing the Tropical Pacific Upper Ocean Using Online Data Assimilation with a Deep Learning Model
Seattle, WA, USA
Last Millennium Seasonal Reanalysis
Seattle, WA, USA
Deep Learning for Data Assimilation
Supporting rigorous, open, and reproducible research across climate, weather, and Earth system science.
3×
Earth System Science Data
2×
JAMES
1×
Journal of Climate
2×
npj Climate and Atmospheric Science
1×
Climate Dynamics
1×
Geoscientific Model Development