Creation year

2022

870 record(s)
 
Type of resources
Topics
Keywords
Contact for the resource
Provided by
Years
Formats
Representation types
Update frequencies
Service types
Resolution
From 1 - 10 / 870
  • Data for Figure 3.2 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.2 shows changes in surface temperature for different paleoclimates. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [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, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has three subpanels, the data provided for all panels in subdirectories named panel_a, panel_b, panel_c --------------------------------------------------- List of data provided --------------------------------------------------- For panel (a): - PMIP3 global temperature anomalies over continents and oceans reconstruction sites - PMIP4 CMIP6 global temperature anomalies over continents and oceans reconstruction sites - PMIP4 non-CMIP6 global temperature anomalies over continents and oceans reconstruction sites - Tierney 2020 reconstructions of marine temperature - Cleator 2020 reconstructions of continental temperature For panel (b): - CMIP5 temperature data for paleoclimate periods - CMIP6 temperature data for paleoclimate periods - non-CMIP temperature data for paleoclimate periods - Instrumental observational and observations from reconstructions For panel (c): - Volcanic forcing from TS17, CU12, GRA08 - CMIP6 GMST anomaly with respect to 1850-1900 modelled with TS17 volcanic forcing - CMIP5 GMST anomaly with respect to 1850-1900 modelled with CU12 volcanic forcing - CMIP5 GMST anomaly with respect to 1850-1900 modelled with GRA08 volcanic forcing --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - panel_a/temperature_anomalies_scatter_points.csv relates to the scatter points and their standard deviation for panel (a) - For panel (b) the datasets are stored as following panel_b/temperature_{color}_{marker}_{period}_{model_group}_{additional_info}.csv and relates to the scatter points for panel (b). - For panel (c) the data is stored in panel_c/gmst_changes_paleo_volcanic_forcings.csv and relates to red, green, blue and black lines on the panel as well as grey shadings. Additional information about data provided in relation to figure in files headers. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. PMIP4 is the Paleoclimate Modelling Intercomparison Project phase 4 PMIP3 is the Paleoclimate Modelling Intercomparison Project phase 3 --------------------------------------------------- Temporal Range of Paleoclimate Data --------------------------------------------------- This dataset covers a paleoclimate timespan from 3.3Ma to 6ka (3.3 million years ago to 6 thousand years ago). --------------------------------------------------- Notes on reproducing the figure from the provided data. --------------------------------------------------- For panel (a) the error bar should be plotted as anomalies from columns 2/4 +/- standard deviation. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

  • Data for CCB 10.4 Figure 1 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). CCB10.4 Figure 1 shows historical annual-mean surface air temperature linear trend (°C per decade) and its attribution over the Hindu Kush Himalaya (HKH) region. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[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, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has four subpanels. Data for all subpanels is provided. --------------------------------------------------- List of data provided --------------------------------------------------- The data is annual means for: - Observed and modelled trends over 1961-2014 - Anomalies 1961-2014 with respect to 1961-1980 average for the HKH region mean - Trends 1961-2014 for the HKH region mean --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel (a): - Data files: Fig_10_CCB-4_1_panel-a_mapplot_tas_trend_BerkeleyEarth_single_trend.nc, Fig_10_CCB-4_1_panel-a_mapplot_tas_trend_CRU_single_trend.nc, Fig_10_CCB-4_1_panel-a_mapplot_tas_trend_APHRO-MA_single_trend.nc, Fig_10_CCB-4_1_panel-a_mapplot_tas_trend_JRA-55_single_trend.nc; Observed and reanalysis surface air temperature OLS linear trends over 1961-2014 over the HKH region, from left to right Berkeley Earth, CRU TS, APHRO-MA, JRA-55 Panel (b): - Data files: Fig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_min_single-MultiModelMean_trend-min-median-max.nc, Fig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_MultiModelMedian_single-MultiModelMean_trend-min-median-max.nc, Fig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_max_single-MultiModelMean_trend-min-median-max.nc; Modelled surface air temperature OLS linear trends over 1961-2014 over the Hindu Kush Himalaya region, from left to right (CMIP6 models with min (coldest), median and max (warmest) trends) Panel (c): - Data file: Fig_10_CCB-4_1_panel-c_timeseries.csv; Surface air temperature anomalies 1961-2014 in respect to 1961-1980 average for the Hindu Kush Himalaya (HKH) region mean: means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue), Berkeley Earth (dark blue), CRU TS (brown), APHRO-MA (light green) and JRA-55 (dark green). Panel (d): - Data file: Fig_10_CCB-4_1_panel-d_trends.csv; Surface air temperature OLS linear trends 1961-2014 for the Hindu Kush Himalaya (HKH) region mean: observed and reanalysis data (Berkeley Earth, CRU TS, APHRO-MA, JRA-55: black crosses), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading) Acronyms: CRU TS- Climatic Research Unit Time Series, CMIP - Coupled Model Intercomparison Project, JRA - Japanese 55year Reanalysis, DAMIP - Detection and Attribution Model Intercomparison Project, GHG - Greenhouse Gas, SMILEs - Single model initial-condition large ensembles, MIROC - Model for Interdisciplinary Research on Climate, CSIRO -Commonwealth Scientific and Industrial Research Organisation, MPI - Max-Planck-Institut für Meteorologie, ESM - Earth System Model, d4PDF - database for policy decision-making for future climate changes, OLS - ordinary least squares regression. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The code for ESMValTool is provided. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 10) - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11 - Link to the code for the figure, archived on Zenodo.

  • Data for Figure 3.7 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.7 shows regression coefficients and corresponding attributable warming estimates for individual CMIP6 models. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [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, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has four panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c and panel_d. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains information on global temperature attributable warming (2010-2019 relative to 1850-1900) from CMIP6 models:  - Regression coefficients for two way regression (2010-2019 relative to 1850-1900) - Regression coefficients for three way regression (2010-2019 relative to 1850-1900) - Attributable warming for two way regression (2010-2019 relative to 1850-1900) - Attributable warming for three way regression (2010-2019 relative to 1850-1900) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - panel_a/regression_coeff_two_way_regression.csv has data for brown and green crosses - panel_b/regression_coeff_three_way_regression.csv has data for grey, green and blue crosses - panel_c/attributable_warming_two_way_regression.csv has data for brown and green crosses - panel_d/attributable_warming_three_way_regression.csv has data for grey, green and blue crosses Details about the data provided in relation to the figure in the header of every file. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

  • Daily concatenated files of ceilometer cloud base height and aerosol profile data from Finnish Meteorological Institution (FMI)'s Vaisala CL31 deployed at Pirkkala Tampere Pirkkala Lentoasema, Finland. These data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide. The site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-246-0-101118. See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. EUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.

  • Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for Arctic Cold Air Outbreak (ACAO) FAAM Project project.

  • Daily concatenated files of ceilometer cloud base height and aerosol profile data from Deutscher Wetterdienst (DWD)'s Lufft CHM15k "Nimbus" deployed at Goettingen, Germany. These data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide. The site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-10444. See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool. EUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.

  • This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from the Along-Track Scanning Radiometer (ATSR-2) on European Remote-sensing Satellite 2 (ERS-2). Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water. Daytime and nighttime temperatures are provided in separate files corresponding to the morning and evening ERS-2 equator crossing times which are 10:30 and 22:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class. The dataset coverage is near global over the land surface. Small regions were not covered due to downlinking constraints (most noticeably a track extending southwards across central Asia through India – further details can be found on the ATSR project webpages at http://www.atsr.rl.ac.uk/dataproducts/availability/coverage/atsr-2/index.shtml. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. ATSR-2 achieves full Earth coverage in 3 days so the daily files have gaps where the surface is not covered by the satellite swath on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface. Dataset coverage starts on 1st August 1995 and ends on 22nd June 2003. There are two gaps of several months in the dataset: no data were acquired from ATSR-2 between 23 December 1995 and 30 June 1996 due to a scan mirror anomaly; and the ERS-2 gyro failed in January 2001, data quality was less good between 17th Jan 2001 and 5th July 2001 and are not used in this dataset. There are minor interruptions (1-2 days) during satellite/instrument maintenance periods. The dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain. The dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.

  • "Three spreadsheet tables of biomarker data, element ratios, and accumulation rates. Sediment samples from IODP Expedition 341, Site U1419 in the Gulf of Alaska. Site U1419 is located in 721 m water depth on the continental slope above the Khitrov basin. Extracted lipids were analysed by HPLC-MS. Two independent methods for bulk element geochemistry analyses were applied to the samples. More details available in the paper Zindorf et al., 2020 https://doi.org/10.1016/j.chemgeo.2020.119864"

  • Spreadsheet containing three tabs of biomarker concentration data for three Mediterranean sapropel records. Core LC21 was collected at 1522m water depth in the Aegean Sea by the R/V Marion Dufresne. An S5 sapropel (core 64PE406-E1) at a water depth of 1760m in the eastern basin aboard the R/V Pelagia. Site 967 of ODP Leg 160 was located at a water depth of 2560 m, south of Cyprus on the lower northern slope of Eratosthenes Seamount, in the eastern Levantine Basin Three core records were extracted for lipid biomarkers and analysed using HPLC and UHPLC-MS. The data and the interpretation of it is available in the published paper; Rush et al., 2019 https://bg.copernicus.org/articles/16/2467/2019/

  • PRIMAVERA Project data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ECMWF-IFS-LR model output for the "coupled control with fixed 1950's forcing (HighResMIP equivalent of pre-industrial control)" (control-1950) experiment. These are available at the following frequencies: Prim6hrPt, PrimOday, PrimOmon, PrimSIday and Primday. The runs included the ensemble member: r1i1p1f1. PRIMAVERA was a European Union Horizon2020 (grant agreement 641727) project.