Skip to main content

LMR turbo

Project description

https://zenodo.org/badge/DOI/10.5281/zenodo.2655097.svg https://img.shields.io/github/last-commit/fzhu2e/LMRt/master https://img.shields.io/github/license/fzhu2e/LMRt https://img.shields.io/pypi/pyversions/LMRt https://img.shields.io/pypi/v/LMRt.svg

LMR Turbo (LMRt)

LMR Turbo (LMRt) is a lightweight, packaged version of the Last Millennium Reanalysia (LMR) framework, inspired by LMR_lite.py originated by Professor Hakim. LMRt aims to provide following extra features:

  • a package that is easy to install and import in scripts or Jupyter notebooks

  • modularized workflows at different levels:

    • the low-level workflow focuses on the flexibility and customizability

    • the high-level workflow focuses on the convenience of repeating Monte-Carlo iterations

    • the top-level workflow focuses on the convenience of reproducing an experiment purely based on a given configuration YAML file

  • convenient visualization functionalities for diagnosis and validations (leveraging the Series and EnsembleSeries of the Pyleoclim UI)

A preview of the results

Mean temperature

Mean temperature

Niño 3.4 index

Niño 3.4

Documentation

References of the LMR framework

  • Hakim, G. J., J. Emile‐Geay, E. J. Steig, D. Noone, D. M. Anderson, R. Tardif, N. Steiger, and W. A. Perkins, 2016: The last millennium climate reanalysis project: Framework and first results. Journal of Geophysical Research: Atmospheres, 121, 6745–6764, https://doi.org/10.1002/2016JD024751.

  • Tardif, R., Hakim, G. J., Perkins, W. A., Horlick, K. A., Erb, M. P., Emile-Geay, J., et al. (2019). Last Millennium Reanalysis with an expanded proxy database and seasonal proxy modeling. Climate of the Past, 15(4), 1251–1273. https://doi.org/10.5194/cp-15-1251-2019

Published studies using LMRt

  • Zhu, F., Emile‐Geay, J., Hakim, G. J., King, J., & Anchukaitis, K. J. (2020). Resolving the Differences in the Simulated and Reconstructed Temperature Response to Volcanism. Geophysical Research Letters, 47(8), e2019GL086908. https://doi.org/10.1029/2019GL086908

  • Zhu, F., Emile-Geay, J., Anchukaitis, K. J., Hakim, G. J., Wittenberg, A. T., Morales, M. S., Toohey, M., & King, J. (2022). A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation. Nature Communications, 13(1), 747. https://doi.org/10.1038/s41467-022-28210-1

How to cite

If you find this package useful, please cite it with DOI: 10.5281/zenodo.2655097 along with the below studies:

@article{zhu_re-appraisal_2022,
    title = {A re-appraisal of the {ENSO} response to volcanism with paleoclimate data assimilation},
    volume = {13},
    issn = {2041-1723},
    url = {https://www.nature.com/articles/s41467-022-28210-1},
    doi = {10.1038/s41467-022-28210-1},
    language = {en},
    number = {1},
    journal = {Nature Communications},
    author = {Zhu, Feng and Emile-Geay, Julien and Anchukaitis, Kevin J. and Hakim, Gregory J. and Wittenberg, Andrew T. and Morales, Mariano S. and Toohey, Matthew and King, Jonathan},
    month = feb,
    year = {2022},
    pages = {747},
}

@article{zhu_resolving_2020,
    title = {Resolving the {Differences} in the {Simulated} and {Reconstructed} {Temperature} {Response} to {Volcanism}},
    volume = {47},
    issn = {1944-8007},
    url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL086908},
    doi = {10.1029/2019GL086908},
    language = {en},
    number = {8},
    journal = {Geophysical Research Letters},
    author = {Zhu, Feng and Emile‐Geay, Julien and Hakim, Gregory J. and King, Jonathan and Anchukaitis, Kevin J.},
    year = {2020},
    pages = {e2019GL086908},
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

LMRt-0.8.3.tar.gz (71.2 kB view details)

Uploaded Source

File details

Details for the file LMRt-0.8.3.tar.gz.

File metadata

  • Download URL: LMRt-0.8.3.tar.gz
  • Upload date:
  • Size: 71.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for LMRt-0.8.3.tar.gz
Algorithm Hash digest
SHA256 a89d8e0748c6ffc86cfe7ea2fc39a6660b5d9cc8993071c3b8551a37ddc67c43
MD5 062d205c45efaf66afce4d2874198521
BLAKE2b-256 83e4777d63cb746cff55ee2db5a04b451d43eb8086953fa4f9ab6460679f4bd6

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page