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6 results for “paleoclimate reconstruction”
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>
Models and Datasets for "Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium"
<p><strong>Associated publication:</strong> <em>Karamperidou, C., Extracting Paleoweather from Paleoclimate: A Deep Learning Reconstruction of Northern Hemisphere Summertime Atmospheric Blocking over the Last Millennium, Nature Communications Earth & Environment, (2024)</em></p> <p> </p> <p><strong>This repository contains:</strong></p> <ul> <li>the architecture and weights of PaleoBlockNet v1.0</li> <li>the following ensemble DL reconstructions of JJA frequency of blocked days inferred by PaleoBlockNet: <ol> <li>the 10-member NTREND-based DL reconstruction; uses as input the NTREND DA N.Hemisphere MJJA surface temperature anomaly by King et al. (2021)</li> <li>the 100-member PHYDA-based DL reconstruction; uses as input the PHYDA JJA surface temperature anomaly by Steiger et al. (2018)</li> <li>the 12-member LME-based DL reconstruction; uses as input the CESM-LME surface temperature anomaly; this is a sensitivity experiment (see publication for details).</li> </ol> </li> <li>Integrated Gradients that assign importance to the input features for PaleoblockNet's blocking inferences </li> <li>train-validate-test samples to use with sample scripts from the Gituhub repo github/ckaramp-research/paleoblocknet</li> </ul> <p> </p> <p><strong>If you use this dataset, please cite the associated publication and the present repository.</strong></p> <p>To <strong>interactively explore</strong> the datasets, a web interface has been developed and can be accessed at <a href="https://www2.hawaii.edu/~ckaramp/paleoblocknet">https://www2.hawaii.edu/~ckaramp/paleoblocknet</a></p> <p>Contact the author Christina Karamperidou (<a title="Karamperidou Research Group" href="https://www2.hawaii.edu/~ckaramp" target="_blank" rel="noopener">https://www2.hawaii.edu/~ckaramp</a>) for more information about the details of these datasets.</p>
Terrestrial paleoclimate reconstruction of the UK Neogene (?Langhian to Piacenzian) comparing CREST, CRACLE and the Co-existence Approach
<p><strong>Abstract </strong></p> <p>The first detailed reconstruction of the terrestrial paleoclimate development of the UK Neogene (?Langhian to Piacenzian) is presented. The paleoclimate data are derived from the paleobotanical record using two probability-based reconstruction techniques CREST (Climate REconstruction SofTware) (Chevalier et al. 2014) and CRACLE (Climate Reconstruction Analysis using Coexistence Likelihood Estimation) (Harbert & Nixon 2015) that use Bayesian and likelihood estimation probability respectively. The results of these reconstructions are presented alongside reconstructions using the widely-applied Co-existence Approach (CA) (Utescher et al. 2014) for comparison. While all three techniques use the climate requirements of their Nearest Living Relatives as the basis of their reconstruction, they use different database observations. CREST and CRACLE use the GBIF (Global Biodiverstiy Information Facility) (GBIF, 2021) as well as WorldClim inputs for the 19 bioclimate variables used by BIOCLIM (<a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>). Meanwhile, the CA uses the Palaeoflora database, meaning the input for the three models is different. The reconstructions for the UK Neogene palaeoclimate come from 4 localities (12 samples total) spanning the Middle Miocene (Langhian) to Pliocene (Piacenzian): Trwyn y Parc, Anglesey (Middle Miocene), Brassington Formation, Derbyshire (Serravallian-Tortonian), Coralline Crag Formation (latest Zanclean-earliest Piacenzian) and Red Crag Formation (Piacenzian-Gelasian) of southeast England. We present CREST and CRACLE reconstructions of Mean Annual Temperature (MAT), Mean Temperature of Warmest Quarter (MTWQ), Mean Temperature of Coldest Quarter (MTCQ), Mean Annual Precipitation (MAP) and precipitation seasonality (CoV ×100). The CA does not reconstruct MTWQ, MTCQ or precipitation seasonality. Instead, the CA reconstructs Warmest Month Mean Temperature (WMMT) and Coldest Month Mean Temperature (CMMT). The proportion of rainfall falling in the wettest months of the year (RMPwet(%)) was used as a proxy for precipitation seasonality following the methodology of Jacques et al. (2011) and Utescher et al. (2015). The CREST R-code output provides 0.5 and 0.95 (2-σ) uncertainties as well as an optimum and mean for each variable. The CRACLE R-code output provides both parametric and non-parametric joint likelihoods (P-CRACLE and N-CRACLE) with 0.95 (2-σ) uncertainties and a mean that is based on P-CRACLE. The CA generates a minimum and maximum likelihood which together comprise the coexistence interval. The Neogene climate reconstruction of the UK shows a cooling trend from the Langhian to the Pliocene-Pleistocene boundary. CREST and CRACLE produce trends and values consistent with Co-existence Approach data with 0.95 uncertainties overlapping with the CA coexistence interval.</p> <p><strong>File Descriptions </strong></p> <p>Table S1 displays the complete reconstruction for the UK Neogene using CREST, CRACLE and the Co-existence Approach.<br> Table S2 displays detailed site information including: modern and paleo latitude and longitude, dating technique, modern climatology and fossil assemblage diversity (number of fossil taxa versus number of NLRs used for climate reconstruction). Modern climatology has been included to serve as a comparison to the reconstructed Neogene climate. This data has been extracted from WorldClim 2.1 (Fick & Hijmans, 2017).<br> Data Set S1 contains the list of fossil spore and pollen taxa per site and associated Nearest Living Relatives (NLRs), where identifiable, used as the input for CREST, CRACLE and the Co-existence Approach. Relic taxa are included and highlighted in red.<br> Data Set S2 is included to show the effect relic taxa have on paleoclimate reconstructions. The relic taxa are removed following the protocol of Utescher et al. (2014) whereby known relic taxa are removed from analyses to avoid biased reconstructions. Relic taxa removed from analyses include <em>Cathaya</em>, <em>Cryptomeria</em>, <em>Pinus sylvestris</em> and <em>Sciadopitys </em>when present.<br> Data Set S3 is included to show the effects of removing family-level identifications in CRACLE reconstructions. Removing families is shown to generate a less informative reconstruction. Including both genera- and family-level classifications of NLR (Nearest Living Relative) is recommended, however we suggest identifying NLRs (Nearest Living Relatives) to genera-level wherever possible.</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>
Pronghorn (Antilocapra americana) enamel phosphate δ18O values reflect climate seasonality: implications for paleoclimate reconstruction
Open the record for dataset details and reuse information.
Neglected effect of continental circulation on paleoclimate reconstruction
<p>Three drilling sediment cores (YC03, YC08, and YC10) and 26 surface grab samples were collected during summer of 2016 in western area of the North Yellow Sea, and another 26 surface samples were supplemented during summer of 2020. The cores were cut length-wise, described, and photographed before subsampling in the laboratory. Sediment cores were sampled at 2 cm intervals for grain size. The samples were soaked in 0.05 mol L<sup>-1</sup> of (NaPO<sub>3</sub>)<sub>6</sub> for 24 h before measurement in a laser particle analyzer (Mastersizer 2000, Malvern Panalytical, UK).</p>
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