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2 results for “WG1”
AR6 WG1 Plots and Processing
<p>Repository reproducing plots and processing used in AR6 WG1 made by Zebedee Nicholls, Malte Meinshausen and Jared Lewis.<br> <br> For questions and comments, please contact Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org), Jared Lewis (jared.lewis@climate-resource.com) and Malte Meinshausen (malte.meinshausen@unimelb.edu.au). For full details, please see https://gitlab.com/magicc/ar6-wg1-plots-and-processing.</p>
IPCC Working Group 1 (WG1) Sixth Assessment Report (AR6) Annex III Extended Data
<p>Extended data relating to atmospheric abundences and effective radiative forcing from historical and future projections. Data is presented in abridged form in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) Working Group 1 (WG1) Annex 3. </p> <p>In this dataset, data is provided for all years, and includes additional scenarios not included in the published tables.</p> <p><strong>Contents</strong></p> <ul> <li>table A3.1: historical observed greenhouse gas (GHG) abundances. All subtables a-f in the printed report are combined into one CSV file.</li> <li>table A3.2: future projections (2020-2500) of GHG abundances for nine SSP scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP3-7.0-lowNTCF, SSP4-3.4, SSP4-6.0, SSP5-3.4-over, SSP5-8.5). Orignal data is from Meinshausen et al. (2020): https://doi.org/10.5194/gmd-2019-222 </li> <li>table A3.3: historical effective radiative forcing (ERF) for 1750-2019 (unit is W m<sup>-2</sup>) <ul> <li>best estimate</li> <li>5th percentile</li> <li>95th percentile</li> <li>100000 member Monte Carlo ensemble (HDF file)</li> </ul> </li> <li>table A3.4: future projections of ERF from 1750-2500 (including historical to 2014, projections starting from 2015). Unit is W m<sup>-2</sup>. <ul> <li>table A3.4a: SSP1-1.9 (best estimate, 5th and 95th percentile)</li> <li>table A3.4b: SSP1-2.6 (best estimate, 5th and 95th percentile)</li> <li>table A3.4c: SSP2-4.5 (best estimate, 5th and 95th percentile)</li> <li>table A3.4d: SSP3-7.0 (best estimate, 5th and 95th percentile)</li> <li>table A3.4e: SSP5-8.5 (best estimate, 5th and 95th percentile)</li> <li>table A3.4f: breakdown of minor greenhouse gases, and aggregated categories, for the five Tier 1 SSP scenarios in tables A3.4a to A3.4e (best estimate)</li> <li>tables A3.4x: tables A3.4a to A3.4f for Tier 2 SSP scenarios: <ul> <li>SSP3-7.0-lowNTCF</li> <li>SSP3-7.0-lowNTCFCH4</li> <li>SSP4-3.4</li> <li>SSP4-6.0</li> <li>SSP5-3.4-over</li> </ul> </li> </ul> </li> <li>table A3.5: projections of ERF from 1750-2500 from RCP2.6, RCP4.5, RCP6.0 and RCP8.5 using AR6 assessment (best estimate, 5th and 95th percentile, breakdown of minor gases; unit is W m<sup>-2</sup>)</li> </ul> <p><strong>Citation</strong></p> <p>IPCC, 2021: Annex III: Tables of historical and projected well-mixed greenhouse gas mixing ratios and effective radiative forcing of all climate forcers [Dentener F.J., B. Hall, C. Smith (eds.)]. In <em>Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change</em> [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.