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5 results for “paleoclimate data assimilation”
Holocene temperature reconstruction using paleoclimate data assimilation
<p>A reconstruction of Holocene temperature made using paleoclimate data assimilation. Spatial and mean quantities are presented, as well as information about the experimental design and proxies. The code used to make this reconstruction is available at https://github.com/Holocene-Reconstruction/Holocene-code.</p>
Data & code repository for "A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation"
<p>This repository includes the data and code that can be used to reproduce the figures for the paper entitled <em>A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation</em>.</p>
Spatiotemporally-completed reconstruction of precipitation during the Holocene over the Northern Hemisphere using paleoclimate data assimilation
<div>(1) <strong>Data Content</strong>: Spatiotemporally complete reconstruction of annual precipitation during the Holocene (i.e., 12-0 ka BP) over the Northern Hemisphere. Based on the sources of the prior ensembles, the dataset comprises three distinct reconstructions, namely PDA (TraCE), PDA (HadCM) and PDA (Mixed), each of which contains: 1) 200 precipitation reconstructions derived from the Monte Carlo realizations for each experimental group, and 2) the corresponding mean and ±1 standard deviation calculated from each set of 200 reconstructions. <strong>(2) Data Production Method</strong>: We reconstructed annual precipitation fields for the Northern Hemisphere during the Holocene using a paleoclimate data assimilation system. This involved assimilating 2,421 Holocene precipitation records from the LegacyClimate 1.0 dataset. In our experiment, we utilized the time-averaged Ensemble Optimal Interpolation (EnIO) data assimilation algorithm. The static prior ensemble of states was constructed from either the TraCE 21 ka BP or the HadCM 23 ka transient climate simulations, or a combination of both in a mixed approach. The data have a temporal resolution of 100 years and a spatial resolution of 3.75°. </div> <div> </div> <div>All prerequisite materials for conducting the PDA-based reconstruction experiments - including prior model simulations, Holocene precipitation records, and Matlab codes- are publicly available on Zenodo repository (<span lang="EN-US">https://doi.org/10.5281/zenodo.17354887</span>). Please contact the author Miao Fang (E-mail: mfang@lzb.ac.cn) for more information about the details of the reconstructions.</div>
An Online Paleoclimate Data Assimilation with a Deep Learning-based Network
<p>OnlinePDA_zenodo.rar (files compressed with RAR compression) includes:</p> <p>1. Directory 'Code' contains the <em>LIM</em>.<em>m</em> and <em>NET</em>.<em>m</em> for the examination of predictive skills of the surrogate models; the <em>Exp_LIM</em>.<em>m</em> and <em>Exp_NET</em>.<em>m</em> for the reconstruction of SAT during the preindustrial period (851-1850 CE) and the instrumental period(1880-2000CE), for both pseudoproxy experiments and real proxy experiments; </p> <p>2. Directory 'Data' contains the necessary data in the <strong>model</strong>, <strong>prior</strong>, <strong>proxy</strong>, and <strong>obs</strong> directory used to perform the resconstruction. e.g. surrogate models, prior samples, pseudoproxy and real proxy, and instrumental data; <em>Figure</em>*.<em>m and</em> <em>CERMSE</em>*.<em>mat</em> provide the necessary code and data for plotting Figures 3-10</p>
Data & code repository for "A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation"
<p>This repository includes the data and code that can be used to reproduce the figures for the paper entitled <em>A re-appraisal of the ENSO response to volcanism with paleoclimate data assimilation</em> (<a href="https://doi.org/10.1038/s41467-022-28210-1">DOI: 10.1038/s41467-022-28210-1</a>).</p> <p>The code is tested with Python v3.8, and the package <a href="https://github.com/fzhu2e/LMRt">LMRt</a> (Zhu et. al., 2021) is required to perform essential analysis (e.g. Superposed Epoch Analysis) and the corresponding visualization.</p> <p><strong>Repository Structure</strong></p> <ul> <li> <p><code>recons</code>: 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</p> <ul> <li><code>recon_Ocn2kCorals_Li13b6.nc</code>: 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).</li> <li><code>recon_Corals_Li13b6.nc</code>: the reconstruction that assimilates the corals reaching back before 1750 CE and the six best ENSO predictors in Li et al. (2013).</li> <li><code>recon_Corals.nc</code>: the reconstruction that assimilates the corals reaching back before 1750 CE.</li> <li><code>recon_Li13b6.nc</code>: the reconstruction that assimilates the six best ENSO predictors in Li et al. (2013).</li> </ul> </li> <li> <p><code>notebooks</code>: the folder that includes Jupyter notebooks</p> <ul> <li><code>Fig-1.ipynb</code>: the notebook that performs analysis and generates Fig. 1 in the main text</li> <li><code>Fig-2.ipynb</code>: the notebook that performs analysis and generates Fig. 2 in the main text</li> <li><code>Fig-3.ipynb</code>: the notebook that performs analysis and generates Fig. 3 in the main text</li> <li><code>Fig-4.ipynb</code>: the notebook that performs analysis and generates Fig. 4 in the main text</li> </ul> </li> <li> <p><code>data</code>: the folder that includes auxiliary data for the analysis and visualization</p> <ul> <li><code>proxy_locs.pkl</code>: the pickle file that includes the location information of the assimilated proxies</li> <li><code>eVolv2k_v3_ds_1.nc</code>: the eVolv2k v3 volcanic forcing data (Toohey & Sigl, 2017), which is publicly available and can be downloaded <a href="https://doi.org/10.26050/WDCC/eVolv2k_v3">here</a></li> <li><code>palmyra2013.txt</code>: the Palmyra coral d18O data (Emile-Geay et al., 2013), which is publicly available and can be downloaded <a href="https://www.ncdc.noaa.gov/cdo/f?p=519:1:::::P1_STUDY_ID:1875">here</a></li> <li><code>Li13b6.txt</code>: 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)</li> <li><code>enso-li2013.txt</code>: the Li et al. (2013) NINO3.4 reconstruction, which is publicly available and can be downloaded with the link: <code>ftp://ftp.ncdc.noaa.gov/pub/data/paleo/treering/reconstructions/enso-li2013.txt</code></li> <li><code>ERSSTv5_sst_DJF.nc</code>: the ERSSTv5 boreal winter (December-February) SST field data (Huang et al., 2017)</li> </ul> </li> <li> <p><code>figs</code>: the folder that includes figures</p> <ul> <li><code>Fig-1.pdf</code>: Fig. 1 in the main text</li> <li><code>Fig-2.pdf</code>: Fig. 2 in the main text</li> <li><code>Fig-3.pdf</code>: Fig. 3 in the main text</li> <li><code>Fig-4.pdf</code>: Fig. 4 in the main text</li> </ul> </li> </ul> <p><strong>References</strong></p> <ul> <li>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. <a href="https://doi.org/10.1126/science.1102586">https://doi.org/10.1126/science.1102586</a></li> <li>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. <a href="https://doi.org/10.1126/science.1185188">https://doi.org/10.1126/science.1185188</a></li> <li>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. <a href="https://doi.org/10.1175/JCLI-D-11-00511.1">https://doi.org/10.1175/JCLI-D-11-00511.1</a></li> <li>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. <a href="https://doi.org/10.1038/nclimate1374">https://doi.org/10.1038/nclimate1374</a></li> <li>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. <a href="https://doi.org/10.1175/JCLI-D-16-0836.1">https://doi.org/10.1175/JCLI-D-16-0836.1</a></li> <li>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. <a href="https://doi.org/10.1038/nclimate1936">https://doi.org/10.1038/nclimate1936</a></li> <li>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. <a href="https://doi.org/10.5194/cp-8-653-2012">https://doi.org/10.5194/cp-8-653-2012</a></li> <li>PAGES2k Consortium (2017). A global multiproxy database for temperature reconstructions of the Common Era. Scientific Data, 4, 170088. <a href="https://doi.org/10.1038/sdata.2017.88">https://doi.org/10.1038/sdata.2017.88</a></li> <li>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. <a href="https://doi.org/10.1002/2014PA002717">https://doi.org/10.1002/2014PA002717</a></li> <li>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. <a href="https://doi.org/10.5194/essd-9-809-2017">https://doi.org/10.5194/essd-9-809-2017</a></li> <li>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. <a href="http://doi.org/10.5281/zenodo.5205223">http://doi.org/10.5281/zenodo.5205223</a></li> </ul> <p><strong>How to cite this repo</strong></p> <p>This repo can be cited with <a href="https://doi.org/10.5281/zenodo.5716165">DOI: 10.5281/zenodo.5716165</a>.</p>
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