Northeast Pacific deoxygenation and volcanism during the last deglaciation
Bibliographic record
Abstract
File structure: Source data Contains source data to main text and extended data figures. Data sources are identified within and listed below. Computer codes Geochemical inversion – “Data for geochemical inversion.xlsx”: contains Gulf of Alaska sediment and volcanic/terrigenous endmember geochemical data used for data inversion. “geochemical inversion.r”: R script used to perform geochemical inversion. “GOA inversion fraction.csv”: Result of the geochemical inversion, including the volcanic and terrigenous fractions in each Gulf of Alaska sediment sample. “GOA inversion residual.csv”: Result of the geochemical inversion, including the residuals of each element. cluster volcanic geochemical data – “Database of volcanic geochemistry.xlsx”: compiled database of the geochemistry of volcanic endmember samples. “cluster.r”: R script used to perform cluster analysis on the volcanic samples. “Clustered volcanic data.csv”: the results of cluster analysis. “Dendroplot.r”: R script used to plot the dendrogram for the cluster analysis. “dendro 15 complete euclidean.pdf”: The dendrogram. “Volcanic endmembers.csv”: final geochemical volcanic endmembers based on the cluster analysis. Global volcanic eruption compilation – “eruption.database.intcal20.xlsx”: This file includes the eruption database compiled by this study, as well as the previous compilation of Huybers and Langmuir 2009 EPSL (referred to as HL09). “eruption.ratio.R” and “volc.freq.R”: These are R scripts that compute the eruption frequency of glaciated and unglaciated volcanoes using the eruption database. “eruption.ratio.R”calls the function inside “volc.freq.R”. “Volcanic eruption summary.xlsx”: This file contains the outputs of the R scripts. PISM sensitivity experiment – “ciscyc.5km.epica.ts.10a.nc” and other netcdf files: The output of PISM sensitivity experiments, from Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3606536. Click the link to see the documentation of these files. “temperature timeseries.xlsx”: the temperature forcing used in the sensitivity experiments. “PISM sensitivity.r”: R script used to analyse the PISM sensitivity experiments, including data binning, lag correlation and regression between ice sheeting response and temperature forcing. “PISM sensitivity.xlsx”: Output of the R script. “GOA.calibration.csv”: SST record from the Gulf of Alaska site 85JC/U1419, calibrated using bayspline. “GOA.Ensemble.csv”: 1000 ensemble output of the bayspline calibration. “predict ice volume SST.r”: R script used to predict the response of CIS ice volume to GOA SST forcing. The script will call the results of PISM sensitivity experiments in “PISM sensitivity.xlsx” and the GOA SST forcing in “GOA.calibration.csv” and “GOA.calibration.csv”. “PISM ice vol GOA SST.csv”: predicted PISM ice vol based on GOA SST forcing and taking into account all sensitivity experiments and the uncertainty in SST reconstruction. “PISM ice vol GOA SST model.csv”: predicted PISM ice vol based on GOA SST forcing based on each sensitivity experiment and the uncertainty in SST reconstruction. Please cite the following studies when using the data, in addition to citing the present study: GOA age model, IRD and MAR: Walczak, M. H. et al. Phasing of millennial-scale climate variability in the Pacific and Atlantic Oceans. Science 370, 716–720 (2020). Velle, J. H. et al. High resolution inclination records from the Gulf of Alaska, IODP Expedition 341 Sites U1418 and U1419. Geophys. J. Int. 229, 345–358 (2022). Heaton, T. J. et al. Marine20—The Marine Radiocarbon Age Calibration Curve (0–55,000 cal BP). Radiocarbon 62, 779–820 (2020). GOA SST: Praetorius, S. K. et al. North Pacific deglacial hypoxic events linked to abrupt ocean warming. Nature 527, 362–366 (2015). Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022). Tierney, J. E. & Tingley, M. P. BAYSPLINE: A New Calibration for the Alkenone Paleothermometer. Paleoceanogr. Paleoclimatology 33, 281–301 (2018). GOA benthic foraminifera assemblage: Belanger, C. L., Sharon, Du, J., Payne, C. R. & Mix, A. C. North Pacific deep-sea ecosystem responses reflect post-glacial switch to pulsed export productivity, deoxygenation, and destratification. Deep Sea Res. Part Oceanogr. Res. Pap. 164, 103341 (2020). Sharon, Belanger, C., Du, J. & Mix, A. Reconstructing Paleo-oxygenation for the Last 54,000 Years in the Gulf of Alaska Using Cross-validated Benthic Foraminiferal and Geochemical Records. Paleoceanogr. Paleoclimatology 36, e2020PA003986 (2021). GOA productivity: Romero, O. E., LeVay, L. J., McClymont, E. L., Müller, J. & Cowan, E. A. Orbital and Suborbital-Scale Variations of Productivity and Sea Surface Conditions in the Gulf of Alaska During the Past 54,000 Years: Impact of Iron Fertilization by Icebergs and Meltwater. Paleoceanogr. Paleoclimatology 37, e2021PA004385 (2022). Addison, J. A. et al. Productivity and sedimentary δ15N variability for the last 17,000 years along the northern Gulf of Alaska continental slope. Paleoceanography 27, PA1206 (2012). GOA bulk sediment neodymium isotopes: Du, J., Haley, B. A., Mix, A. C., Walczak, M. H. & Praetorius, S. K. Flushing of the deep Pacific Ocean and the deglacial rise of atmospheric CO 2 concentrations. Nat. Geosci. 11, 749–755 (2018). GOA volcanic endmember data compilation: Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120. Sarbas, B., Jochum, K. P., Nohl, U. & Hofmann, A. W. GEOROC, the MPI geochemical rock database: a new tool for geochemists. Eos Trans. AGU 80, F1184 (1999). Global and regional volcanic eruption data compilation: Huybers, P. & Langmuir, C. Feedback between deglaciation, volcanism, and atmospheric CO2. Earth Planet. Sci. Lett. 286, 479–491 (2009). Global Volcanism Program, 2013. Volcanoes of the World, v. 4.8.7. 10.5479/si.GVP.VOTW4-2013. (2013). Bryson, R. U., Bryson, R. A. & Ruter, A. A calibrated radiocarbon database of late Quaternary volcanic eruptions. EEarth Discuss 1, 123–134 (2006). Watt, S. F. L., Pyle, D. M. & Mather, T. A. The volcanic response to deglaciation: Evidence from glaciated arcs and a reassessment of global eruption records. Earth-Sci. Rev. 122, 77–102 (2013). Crosweller, H. S. et al. Global database on large magnitude explosive volcanic eruptions (LaMEVE). J. Appl. Volcanol. 1, 4 (2012). Cameron, C. E., Snedigar, S. F. & Nye, C. J. Alaska Volcano Observatory geochemical database. DDS 8 http://www.dggs.alaska.gov/pubs/id/29120 (2014) doi:10.14509/29120. Praetorius, S. et al. Interaction between climate, volcanism, and isostatic rebound in Southeast Alaska during the last deglaciation. Earth Planet. Sci. Lett. 452, 79–89 (2016). Wilcox, P. S. et al. A new set of basaltic tephras from Southeast Alaska represent key stratigraphic markers for the late Pleistocene. Quat. Res. 92, 246–256 (2019). Davies, L. J., Jensen, B. J. L., Froese, D. G. & Wallace, K. L. Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: Key beds and chronologies over the past 30,000 years. Quat. Sci. Rev. 146, 28–53 (2016). GIA models: Roy, K. & Peltier, W. R. Relative sea level in the Western Mediterranean basin: A regional test of the ICE-7G_NA (VM7) model and a constraint on late Holocene Antarctic deglaciation. Quat. Sci. Rev. 183, 76–87 (2018). Lambeck, K., Purcell, A. & Zhao, S. The North American Late Wisconsin ice sheet and mantle viscosity from glacial rebound analyses. Quat. Sci. Rev. 158, 172–210 (2017). PISM sensitivity experiments and temperature forcing: Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M. & Kleman, J. Numerical simulations of the Cordilleran ice sheet through the last glacial cycle. The Cryosphere 10, 639–664 (2016). Seguinot. (2020). Cordilleran ice sheet glacial cycle simulations continuous variables [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3606536 Dansgaard, W. et al. Evidence for general instability of past climate from a 250-kyr ice-core record. Nature 364, 218–220 (1993). Andersen, K. K. et al. High-resolution record of Northern Hemisphere climate extending into the last interglacial period. Nature 431, 147–151 (2004). Jouzel, J. et al. Orbital and Millennial Antarctic Climate Variability over the Past 800,000 Years. Science 317, 793–796 (2007). Petit, J. R. et al. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436 (1999). Herbert, T. D. et al. Collapse of the California Current During Glacial Maxima Linked to Climate Change on Land. Science 293, 71–76 (2001). Be10 data compilation: Lesnek, A. J., Briner, J. P., Baichtal, J. F. & Lyles, A. S. New constraints on the last deglaciation of the Cordilleran Ice Sheet in coastal Southeast Alaska. Quat. Res. 96, 140–160 (2020). Haeussler, P. J. et al. Late Quaternary deglaciation of Prince William Sound, Alaska. Quat. Res. 1–20 (2021) doi:10.1017/qua.2021.33. Walcott, C. K., Briner, J. P., Baichtal, J. F., Lesnek, A. J. & Licciardi, J. M. Cosmogenic ages indicate no MIS 2 refugia in the Alexander Archipelago, Alaska. Geochronology 4, 191–211 (2022). Briner, J. P. et al. The last deglaciation of Alaska. Cuad. Investig. Geográfica 43, 429–448 (2017
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".