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2022

865 record(s)
 
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  • This dataset contains PM10 concentration and composition measurements taken in Lak Si, Bangkok, Thailand. Particulate matter under 10 micrometres diametre in size (PM10) was collected in 24 hour and 72 hour samples in a rooftop site in Lak Si, Bangkok, Thailand using a Sven Leckel LVS3 PM10 sampler. Samples were weighed and analysed for concentrations of the following elements: Mg, Al, Ca, V, Cr, Mn, Fe, Co, Ni, Cu. Zn, As, Se, Mo, Cd, Sb, Ba and Pb. The data covered three seasons in Bangkok, hot, cool and rainy, from 5th March 2018 until 15th November 2018. Meteorological data was measured in the same location using a Gill Maximet 501 and Gill Maximet 100 weather station. Weather parameters measured included rainfall, wind speed, wind direction, pressure, relative humidity, temperature, dew point and solar radiation. Measurements were taken by the staff of the Toxicology group in the Chulabhorn Research Institute, Thailand and the Atmospheric Chemistry Research Group in the University of Bristol, UK as part of the NERC grant Ultrafine and Submicron Particles in the Urban Environment in Thailand.

  • This dataset consists of daily total column water vapour (TCWV) over land, at a 0.05 degree resolution, observed by various satellite instruments. It has been produced by the European Space Agency Water Vapour Climate Change Initiative (Water_Vapour_cci), and forms part of their TCVW over land Climate Data Record -1 (TCWV-land (CDR-1). This version of the data is v3.2. This is an updated dataset, which fixes an issue with the filtering of the v3.1 data.

  • PRIMAVERA Project data from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-HM 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: Prim1hr, Prim3hr, Prim3hrPt, Prim6hr, Prim6hrPt, PrimOday, PrimOmon, PrimSIday, Primday, PrimdayPt and PrimmonZ. The runs included the ensemble member: r1i1p1f1. PRIMAVERA was a European Union Horizon2020 (grant agreement 641727) project.

  • Data for Figure 3.22 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.22 shows time series of Northern Hemisphere March-April mean snow cover extent (SCE) from observations, CMIP5 and CMIP6 simulations. --------------------------------------------------- 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 --------------------------------------------------- There are technically two panels top and bottom (CMIP5 and CMIP6), however, the data is stored in the parent directory. --------------------------------------------------- List of data provided --------------------------------------------------- The data is for the Northern Hemisphere snow cover extent anomalies (SCEA) from models and observations: - The SCEA observational data from GLDAS-NOAH (1948-2012), Brown-NOAA (1923-2017), Mudryk et al 2020 (1968-2017) - The SCEA modelled by CMIP5 historical-rcp45 experiment (1923-2017) - The SCEA modelled by CMIP5 historicalNat experiment (1923-2012) - The SCEA modelled by CMIP6 historical-ssp245 experiment (1923-2017) - The SCEA modelled by CMIP6 hist-nat experiment (1923-2017) - The SCEA modelled by CMIP5 and CMIP6 piControl experiments --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- snow_cover_extent_cmip5_obs.csv is the data for the green and brown lines and shadings in the upper panel and grey lines (1923-2017) snow_cover_extent_cmip6_obs.csv is the data for the green and brown lines and shadings in the lower panel and grey lines (1923-2017) snow_cover_extent_piControl.csv for the blue error bars in the both panels Additional details of data provided in relation to figure in the file header (BADC-CSV file) CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. 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.

  • The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Chinese Academy of Sciences (CAS) FGOALS-g3 model output for the "mid-Holocene" (midHolocene) experiment. These are available at the following frequencies: Amon, Omon, SImon, day and fx. The runs included the ensemble members: r1i1p1f1, r2i1p1f1 and r3i1p1f1. CMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6). The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.

  • PRIMAVERA Project data from the the CNRM-CERFACS team CNRM-CM6-1-HR model output for the "forced atmosphere experiment for 2015-2050 using SST/sea-ice derived from CMIP5 RCP8.5 simulations and a scenario as close to RCP8.5 as possible within CMIP6" (highresSST-future) experiment. These are available at the following frequencies: Prim1hr, Prim3hr, Prim3hrPt, Prim6hr, Prim6hrPt, Primday, PrimdayPt, Primmon and PrimmonZ. The runs included the ensemble member: r1i1p1f2. PRIMAVERA was a European Union Horizon2020 (grant agreement 641727) project. The the CNRM-CERFACS team team consisted of the following agencies: Centre National de Recherches Météorologiques (CNRM) and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CERFACS).

  • Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for BIOARC project.

  • Data for Figure 10.12 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 10.12 shows Southeastern South America positive mean precipitation trend and its drivers during 1951-2014. --------------------------------------------------- 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 4 subpanels. Data for 3 subpanels (b-d) is provided. Subpanel (a) is a schematic. --------------------------------------------------- List of data provided --------------------------------------------------- The data is annual December-Jannuary (DJF) precipitation means for: - Observed and model relative anomalies over 1951-2014 with respect to 1995-2014 average over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W) - Observed precipitation trends 1951-2014 South America - Trends in precipitation over 1951-2014 over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel (b): - Data file: Fig_10_12_panel-b_timeseries.csv; Observed (CRU TS, black line, and CRU TS no-running mean (bars)) and Model (MPI-ESM runs with min (brown) and max (green) trends) precipitation rate relative anomalies over 1951-2014 with respect to 1995-2014 average over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W) Panel (c): - Data files: Fig_10_12_panel-c_mapplot_pr_trend_CRU_single_trend.nc, Fig_10_12_panel-c_mapplot_pr_trend_GPCC_single_trend.nc; Observed precipitation OLS linear trends 1951-2014 over South America Panel (d): - Data file: Fig_10_12_panel-d_trends.csv; OLS linear trends in precipitation over 1951-2014 over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W): observed data (GPCC, CRU TS: black crosses), individual members of CMIP6 historical (red circles), 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, 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 Change, 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.

  • This dataset contains monthly-averaged land surface temperatures (LSTs) and their uncertainty estimates from multiple Infra-Red (IR) instruments on satellites in Geostationary Earth Orbit (GEO) and Low Earth Orbiting (LEO) sun-synchronous (a.k.a. polar orbiting) satellites. 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. LST fields are provided at 3 hourly intervals each day (00:00 UTC, 03:00 UTC, 06:00 UTC, 09:00 UTC, 12:00 UTC, 15:00 UTC, 18:00 UTC and 21:00 UTC). 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 the solar geometry angles. The product is based on merging of available GEO data and infilling with available LEO data outside of the GEO discs. Inter-instrument biases are accounted for by cross-calibration with the IASI instruments on METOP and LSTs are retrieved using a Generalised Split Window algorithm from all instruments. As data towards the edge of the GEO disc is known to have greater uncertainty, any datum with a satellite zenith angle of more than 60 degrees is discarded. All LSTs included have an observation time that lies within +/- 30 minutes of the file nominal Universal Time. Data from the following instruments is included in the dataset: geostationary, Imagers on Geostationary Operational Environmental Satellite (GOES) 12 and GOES 13, Advanced Baseline Imager (ABI) on GOES 16, Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on Meteosat Second Generation (MSG) 1, MSG 2, MSG 3, and MSG 4, Japanese Advanced Meteorological Imager (JAMI) on Multifunctional Transport Satellite MTSAT) 1, and MTSAT 2; and polar, Advanced Along-Track Scanning Radiometer (AATSR) on Environmental Satellite (Envisat), Moderate-resolution Imaging Spectroradiometer (MODIS) on Earth Observation System (EOS) - Aqua and EOS - Terra, Sea and Land Surface Temperature Radiometer SLSTR on Sentinel-3A and Sentinel-3B. However, it should be noted that which instruments contribute to a particular product file depends on depends on mission start and end dates and instrument downtimes. Dataset coverage starts on 1st January 2009 and ends on 31st December 2020. LSTs are provided on a global equal angle grid at a resolution of 0.05° longitude and 0.05° latitude. The dataset coverage is nominally global over the land surface but varies depending on satellite and instrument availability and coverage. 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. The dataset was produced by the University of Leicester (UoL) and data were processed in the UoL processing chain. The Geostationary data were produced by the Instituto Português do Mar e da Atmosfera (IPMA) before being merged into the final dataset. 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.

  • Daily concatenated files of ceilometer cloud base height and aerosol profile data from Deutscher Wetterdienst (DWD)'s Lufft CHM15k "Nimbus" deployed at Freiburg, 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: 0-20000-0-10803. 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.