Data & code repository for "A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation"
Bibliographic record
Abstract
This repository includes the data and code that can be used to reproduce the figures for the paper entitled A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation (DOI: 10.1038/s41467-022-28210-1). The code is tested with Python v3.8, and the package LMRt (Zhu et. al., 2021) is required to perform essential analysis (e.g. Superposed Epoch Analysis) and the corresponding visualization. Repository Structure recons: the folder that includes our reconstructions of (i) the NINO3.4 series with 0.05, 0.25, 0.5, 0.75, 0.95 quantiles and (ii) the ensemble median surface temperature field recon_Ocn2kCorals_Li13b6.nc: the reconstruction that assimilates all the available Ocean2k corals (Tierney et al., 2015; PAGES2k Consortium, 2017) and the six best ENSO predictors in Li et al. (2013). recon_Corals_Li13b6.nc: the reconstruction that assimilates the corals reaching back before 1750 CE and the six best ENSO predictors in Li et al. (2013). recon_Corals.nc: the reconstruction that assimilates the corals reaching back before 1750 CE. recon_Li13b6.nc: the reconstruction that assimilates the six best ENSO predictors in Li et al. (2013). notebooks: the folder that includes Jupyter notebooks Fig-1.ipynb: the notebook that performs analysis and generates Fig. 1 in the main text Fig-2.ipynb: the notebook that performs analysis and generates Fig. 2 in the main text Fig-3.ipynb: the notebook that performs analysis and generates Fig. 3 in the main text Fig-4.ipynb: the notebook that performs analysis and generates Fig. 4 in the main text data: the folder that includes auxiliary data for the analysis and visualization proxy_locs.pkl: the pickle file that includes the location information of the assimilated proxies eVolv2k_v3_ds_1.nc: the eVolv2k v3 volcanic forcing data (Toohey & Sigl, 2017), which is publicly available and can be downloaded here palmyra2013.txt: the Palmyra coral d18O data (Emile-Geay et al., 2013), which is publicly available and can be downloaded here Li13b6.txt: the six best ENSO predictors in Li et al. (2013), including the first two principle components (PCs) of the North American Drought Atlas (NADA; Cook et al., 2004) and Monsoon Asia Drought Atlas (MADA; Cook et al., 2010) networks, the Kauri tree-ring composite (Fowler et al., 2012), and the South America Altiplano (SA Altiplano) tree-ring composite (Morales et al., 2012) enso-li2013.txt: the Li et al. (2013) NINO3.4 reconstruction, which is publicly available and can be downloaded with the link: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/enso-li2013.txt ERSSTv5_sst_DJF.nc: the ERSSTv5 boreal winter (December-February) SST field data (Huang et al., 2017) figs: the folder that includes figures Fig-1.pdf: Fig. 1 in the main text Fig-2.pdf: Fig. 2 in the main text Fig-3.pdf: Fig. 3 in the main text Fig-4.pdf: Fig. 4 in the main text References Cook, E. R., Woodhouse, C. A., Eakin, C. M., Meko, D. M., & Stahle, D. W. (2004). Long-Term Aridity Changes in the Western United States. Science, 306(5698), 1015–1018. https://doi.org/10.1126/science.1102586 Cook, E. R., Anchukaitis, K. J., Buckley, B. M., D’Arrigo, R. D., Jacoby, G. C., & Wright, W. E. (2010). Asian Monsoon Failure and Megadrought During the Last Millennium. Science, 328(5977), 486–489. https://doi.org/10.1126/science.1185188 Emile-Geay, J., Cobb, K. M., Mann, M. E., & Wittenberg, A. T. (2013). Estimating Central Equatorial Pacific SST Variability over the Past Millennium. Part II: Reconstructions and Implications. Journal of Climate, 26(7), 2329–2352. https://doi.org/10.1175/JCLI-D-11-00511.1 Fowler, A. M., Boswijk, G., Lorrey, A. M., Gergis, J., Pirie, M., McCloskey, S. P. J., et al. (2012). Multi-centennial tree-ring record of ENSO-related activity in New Zealand. Nature Climate Change, 2(3), 172–176. https://doi.org/10.1038/nclimate1374 Huang, B., Thorne, P. W., Banzon, V. F., Boyer, T., Chepurin, G., Lawrimore, J. H., et al. (2017). Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, 30(20), 8179–8205. https://doi.org/10.1175/JCLI-D-16-0836.1 Li, J., Xie, S.-P., Cook, E. R., Morales, M. S., Christie, D. A., Johnson, N. C., et al. (2013). El Niño modulations over the past seven centuries. Nature Climate Change, 3(9), 822–826. https://doi.org/10.1038/nclimate1936 Morales, M. S., Christie, D. A., Villalba, R., Argollo, J., Pacajes, J., Silva, J. S., et al. (2012). Precipitation changes in the South American Altiplano since 1300 AD reconstructed by tree-rings. Climate of the Past, 8(2), 653–666. https://doi.org/10.5194/cp-8-653-2012 PAGES2k Consortium (2017). A global multiproxy database for temperature reconstructions of the Common Era. Scientific Data, 4, 170088. https://doi.org/10.1038/sdata.2017.88 Tierney, J. E., Abram, N. J., Anchukaitis, K. J., Evans, M. N., Giry, C., Kilbourne, K. H., et al. (2015). Tropical sea surface temperatures for the past four centuries reconstructed from coral archives. Paleoceanography, 30(3), 2014PA002717. https://doi.org/10.1002/2014PA002717 Toohey, M., & Sigl, M. (2017). Volcanic stratospheric sulfur injections and aerosol optical depth from 500 BCE to 1900 CE. Earth System Science Data, 9(2), 809–831. https://doi.org/10.5194/essd-9-809-2017 Zhu, F., Emile-Geay, J. Hakim, G. J., Tardif, R., and Perkins., A., (2021). LMR Turbo (LMRt): a lightweight implementation of the LMR framework (0.8.0). Zenodo. http://doi.org/10.5281/zenodo.5205223 How to cite this repo This repo can be cited with DOI: 10.5281/zenodo.5716165.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.323 | 0.211 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".