Zilu Meng (孟子路)

1. Ning, L., Liu, J., Liu, Z., Xing, F., Wu, F., Yan, M., Meng, Z., Chen, K., Qin, Y., Sun, W., & Wen, Q. (2025). Progresses and Prospects of Paleoclimate Data Assimilation. SCIENCE CHINA: Earth Science, https://doi.org/10.1007/s11430-025-1810-2, [PDF], [PDF in Chinese].
2. Hua, Z., Karamperidou, C., Meng, Z. (2025). Extratropical Atmospheric Circulation Response to ENSO in Deep Learning Pacific Pacemaker Experiments. arXiv:2511.20899
3. Meng, Z., Hakim, G. J., Yang, W., & Vecchi, G. A. (2026). Large-Ensemble Simulations Reveal Links Between Atmospheric Blocking Frequency and Sea Surface Temperature Variability. arXiv:2602.05083.
4. Meng, Z., Hakim, G. J., Yang, W., & Vecchi, G. A. (2025). Deep Learning Atmospheric Models Reliably Simulate Out-of-Sample Land Heat and Cold Wave Frequencies, Geophysical Research Letters, in press. https://doi.org/10.1029/2025GL117990.
5. Meng, Z., & Polvani, L. M. (2026). No evidence for significant warming or cooling in Eurasian winter response to major volcanic eruptions over the last millennium.
6. Meng, Z., Hakim, G. J., & Steig, E. J. (2025). Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics over the Last Millennium. Journal of Climate, 38, 7229–7247. https://doi.org/10.1175/JCLI-D-25-0048.1. [PDF], [Supplementary Material], [Data], [Code], [Bilibili Video (Chinese)].
7. Meng, Z., & Hakim, G. J. (2024). Reconstructing the Tropical Pacific Upper Ocean using Online Data Assimilation with a Deep Learning model. Journal of Advances in Modeling Earth Systems, 16, e2024MS004422. https://doi.org/10.1029/2024MS004422. [Code], [Poster].
8. Meng, Z., & Li, T. (2024). Why is the Pacific meridional mode most pronounced in boreal spring? Climate Dynamics, 62(1), 459–471. https://doi.org/10.1007/s00382-023-06914-4. [Code], [Thesis (Chinese)], [Sacpy Code], [Sacpy Poster].
9. Zhu, F., Emile-Geay, J., Anchukaitis, K. J., McKay, N. P., Stevenson, S., & Meng, Z. (2023). A pseudoproxy emulation of the PAGES 2k database using a hierarchy of proxy system models. Scientific Data, 10, 624. https://doi.org/10.1038/s41597-023-02489-1. [CFR Code].
10. Meng, Z., Hu, Z., Ai, Z., Zhang, Y., & Shan, K. (2021). Research on Planar Double Compound Pendulum Based on RK-8 Algorithm. Journal on Big Data, 3, 11–20. https://doi.org/10.32604/jbd.2021.015208, [Sacpy Code].

Multiscale proxy data assimilation using weak-constraint 4D-Var

My recent work develops LMR4D, a new way to reconstruct past climate by combining many kinds of climate evidence that record time differently. Tree rings may record year-to-year changes, sediments and ice cores may average over decades, and boreholes can remember centuries of surface temperature change. LMR4D brings these records into one physically guided reconstruction instead of treating them as separate problems.

The framework optimizes a continuous climate history while allowing the model to be imperfect. This makes it possible to combine PAGES2k, Temp12k, and borehole records, and to estimate not only past surface temperature but also slower parts of the climate system such as upper-ocean heat content. The results suggest that recent upper-ocean warming is large compared with late Common Era variability, and show how long-memory records like boreholes can add information that annual proxies alone cannot provide.

• Big idea: combine climate clues with different time resolutions in one reconstruction.
• Why it matters: a longer view of climate history helps put modern warming and ocean heat uptake in context.
• Method: weak-constraint 4D-Var with a coupled ocean-atmosphere-sea-ice emulator.

How my reconstruction workflow thinks

AMIP-like experiments on Deep learning based AGCM

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?

LMR Seasonal - Last Millennium Reanalysis with Seasonal Resolution

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:

• Seasonal resolution paleoclimate reanalysis spanning the last millennium (850-2000 CE)
• Coupled ocean-atmosphere-sea ice reconstruction using online data assimilation
• High correlation skill with instrumental data, especially during data-sparse winter months
• Captures seasonal evolution and amplitude of ENSO events
• Consistent with orbital forcing patterns over the millennium
• Documents polar-amplified cooling during Medieval Climate Anomaly to Little Ice Age transition

Related Publication: Meng et al. (2025), Journal of Climate

Ph.D. in Climate and Atmospheric Sciences

University of Washington, Seattle, WA, USA (2023 - Present)

Advisors: Prof. Gregory J. Hakim & Prof. Eric J. Steig

Research Interests: Data assimilation, Machine learning, Climate dynamics, Paleoclimate

B.S. in Atmospheric Science (with Honors)

Nanjing University of Information Science and Technology (NUIST,or Nanjing Institute of Meteorology), Nanjing, China (2019 - 2023)

Advisor: Prof. Tim Li

Core Courses: Atmospheric (Fluid) Dynamics, Atmospheric Physics, Synoptic Meteorology

GPA: 95/100 (Ranked 1st/50 in honors class)

Thesis: Why is the Pacific meridional mode most pronounced in boreal spring? [Paper, Thesis (Chinese)]

1. Last Millennium Seasonal Reanalysis: Developed a seasonal reanalysis dataset for the last millennium using online data assimilation. [Poster, Code]
2. Deep Learning for Tropical Pacific Reconstruction: Developed a deep learning model coupled with online data assimilation to reconstruct tropical climate fields. [Paper, Poster, GitHub]
3. AMIP experiments on Deep Learning GCMs: Conducted AMIP experiments on various deep learning GCMs to study climate sensitivity and long-term simulation capabilities.
4. Sacpy: Built an efficient and user-friendly Python module for Statistical Analysis of Climate data. Widely used in the community. [Docs & GitHub, Poster]
5. CFR Framework: Contributed to developing CFR, a universal framework for climate field reconstruction. [GitHub]
6. Deep Learning for ENSO Prediction: Used deep learning (CNNs) and Grad-CAM to study predictability and precursors of El Niño/La Niña events. [Zhihu Article, GitHub]
7. Prof. John Mike Wallace's Website: Assisted Prof. J. M. Wallace in building his personal academic website. [Website]

• Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, Jan 2026. (Invited Talk)
  Data Assimilation and Atmospheric Modelling over the Last Millennium.
• Peking University Earth and Space Sciences Seminar, Beijing, China, Jan 2025. (Invited Talk)
  Data Assimilation over the Last Millennium.
• AGU Fall Meeting, New Orleans, LA, USA, Dec 2025. (Oral Presentation)
  Coupled Seasonal Data Assimilation over the Last Millennium.
• AGU Fall Meeting, New Orleans, LA, USA, Dec 2025. (Poster)
  Deep Learning Atmospheric Model Reliably Simulates out-of-sample extreme weather events, [Poster].
• University of Hawaii at Manoa ATMO Seminar, Honolulu, HI, USA, Nov 2025. (Invited Talk)
  Coupled Seasonal Data Assimilation over the Last Millennium
• Graduate Climate Conference (GCC), Boston, MA, USA, Oct 2025. (Poster)
  Sacpy: A Python Module for Statistical Analysis of Climate Data
• PCC Summer Institute, Seattle, WA, USA, Sep 2025. (Poster)
  Last Millennium Seasonal Reanalysis. [Poster]
• Paleoclimate Seminar, Ohio State University, Columbus, OH, USA, Apr 2025. (Invited Talk)
  Ocean, Atmosphere, and Sea Ice Data Assimilation for the Last Millennium.
• Climate Dynamics Seminar, Nanjing Normal University, Nanjing, China, Mar 2025. (Invited Talk)
  Uncovering Insights from the Last Millennium Using Coupled Seasonal Data Assimilation of Sea Ice, Ocean, and Atmospheric Dynamics. [Slides]
• Data Assimilation Seminar, Nanjing University, Nanjing, China, Mar 2025. (Invited Talk)
  Ocean, Atmosphere, and Sea Ice Data Assimilation for the Last Millennium.
• Climate Dynamics Seminar, University of Washington, Seattle, WA, USA, Feb 2025.
  Uncovering Insights from the Last Millennium Using Coupled Seasonal Data Assimilation...
• AGU Fall Meeting, Washington, D.C., USA, Dec 2024. (Oral Presentation)
  Reconstructing the Tropical Pacific Upper Ocean using Online Data Assimilation with a Deep Learning model. [Paper]
• Graduate Climate Conference (GCC), Seattle, WA, USA, Oct 2024. (Poster)
  Last Millennium Seasonal Reanalysis. [Poster]
• Nanjing Data Assimilation Workshop, Nanjing, China, June 2024. (Poster)
  Deep Learning for Tropical Pacific Reconstruction. [Poster, GitHub]
• PCC Summer Institute Talk, UW, Seattle, WA, USA, May 2024.
  Deep Learning for Data Assimilation.
• AGU Fall Meeting, San Francisco, CA, USA, Dec 2023. (Poster)
  Sacpy: Python Package for Statistical Analysis of Climate. [Poster, GitHub]

• Earth System Science Data (3)
• PLOS ONE (3)
• Journal of Climate (1)
• Climate Dynamics (1)
• JGR: Machine Learning and Computation (1)
• JGR Atmosphere (1)
• Atmosphere (1)
• Radio Science (1)
• Frontiers in Earth Science (1)
• npj Climate and Atmospheric Science (1)

Summary of completed reviews.