Format

Data are netCDF formatted

313 record(s)
 
Type of resources
Topics
Keywords
Contact for the resource
Provided by
Years
Formats
Representation types
Update frequencies
From 1 - 10 / 313
  • Quaternary QUEST was led by Dr Tim Lenton at UEA, with a team of 10 co-investigators at the Universities of Cambridge, Oxford, Reading, Leeds, Bristol, Southampton and at UEA. This dataset contains FAMOUS (FAst Met Office/UK Universities Simulator) glacial cycle model data from 150,000 years ago to present. The project team aimed to compile a synthesis of palaeodata from sediments and ice cores, improve the synchronization of these records with each other, and use this greater understanding of the Earth’s ancient atmosphere to improve Earth system models simulating climate over very long timescales. A combined long-term data synthesis and modelling approach has helped to constrain some key mechanisms responsible for glacial-interglacial CO2 change, and Quaternary QUEST have narrowed the field of ocean processes that could have caused glacial CO2 drawdown.

  • This dataset contains nitrate and nitric acid simulation data to explore the sensitivity of atmospheric nitrate concentrations to nitric acid uptake rate using the Met Office’s Unified Model. The files are seperated into directories by simulation name - 1. A control simulation with no nitrate aerosol (CNTL); 2. A simulation with NH4·NO3 reaching equilibrium instantaneously (INSTANT); 3. A simulation with the HNO3 uptake rate set to 0.193 (FAST); and 4. A simulation with the HNO3 uptake rate 0.001 (SLOW). All simulations are performed with the Met Office Unified Model (UM or MetUM) in an N96L85 resolution.

  • Data for Figure 3.38 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.38 shows model evaluation of ENSO teleconnection for 2m-temperature and precipitation in boreal winter (December-January-February). --------------------------------------------------- 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 --------------------------------------------------- Data provided for all panels in one single directory --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains observed global patterns for: - temperature from the Berkeley Earth dataset over land - temperature from ERSSTv5 over ocean - precipitation from GPCC over land (shading, mm day–1) - precipitation from GPCP worldwide (contours, period: 1979-2014) and distributions of regression coefficients in IPCC regions for: - temperature - precipitation --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- maps: - reg_tas_NINO34_BEST_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over land) - reg_sst_NINO34_ERSSTv5_ERSSTv5_1901_2018_DJF.nc (var = 'rc', upper map over ocean) - reg_precip_NINO34_GPCP_ERSST5_1979_2018_DJF.nc (var = 'rc', lower map, contours) - reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc (var = 'rc', lower map, shading) histograms: - tas_enso_regression_pdf_v4_no_cosweight_DJF.nc . upper grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist' . MME (black line): var = 'region_ave_hist' . Observations (blue lines): var = 'region_obs' - tas_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dashed line): var = 'region_ave_amip_hist' => Fields correspond to regions numbers with labels in the plot, namely for temperature: 'EAU/RFE/RAR/NWN/NCA/ENA/NSA/MED/NWS/ESAF' (see variable region_info with attributes making the association between the region index and the acronym/name). - pr_enso_regression_pdf_v4_no_cosweight_DJF.nc . lower grey histograms: var = 'region_pdfx_hist' and 'region_pdfy_hist' . MME (black line): var = 'region_ave_hist' . Observations (blue lines): var = 'region_obs' - pr_amip_hist_enso_regression_pdf_v4_no_cosweight_DJF.nc (orange dahsed line): var = 'region_ave_amip_hist' => Fields correspond to regions numbers with labels in the plot, namely for precipitation: 'EAS/SEA/EAU/WNA/NCA/SES/NSA/ESAF/SEAF/MED' (see variable info_region with attributes making the association between the region index and the acronym/name). ENSO is the El Niño Southern Oscillation. GPCC is the Global Precipitation Climatology Centre. GPCP is the Global Precipitation Climatology Project. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Data provided in reg_pr_NINO34_GPCC_ERSSTv5_1901_2016_DJF.nc are in mm/month. Values should be divided by 30 for plotting in mm/day. --------------------------------------------------- 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

  • Data for FAQ 3.2, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). FAQ 3.2, Figure 1 shows annual, decadal and multi-decadal variations in average global surface temperature.  --------------------------------------------------- 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 technically has three panels, but they are not labelled. So the datasets are stored just in the main figure folder.  --------------------------------------------------- List of data provided --------------------------------------------------- Dataset contains modelled GSAT anomalies from MPI-ESM grand ensemble (1950-2019): - On annual scale - On decadal scale - On multi-decadal scale --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - annual_gsat_anomalies_mpi_esm_grand_ens.csv has data for the left panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble (black, light green, light marsh green, light dark green lines) - decadal_gsat_anomalies_mpi_esm_grand_ens.csv  has data for the middle panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble  (black, light green, light marsh green, light dark green lines) - multi_decadal_gsat_anomalies_mpi_esm_grand_ens.csv has data for the right panel, GSAT anomalies from 1950 to 2019 from MPI-ESM grand ensemble  (black, light green, light marsh green, light dark green lines) GSAT stands for Global Surface Air Temperature. MPI-ESM is a comprehensive Earth-System Model, consisting of component models for the ocean, the atmosphere and the land surface. --------------------------------------------------- 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 Cloud and Water Vapour Experiment (CWAVE) was a measurement campaign at the CCLRC-Chilbolton Observatory; it was supporting associated with two EC FP5 projects, CLOUDMAP2 and CLOUDNET. A wide range of satellite and ground based instruments measured a variety of atmospheric properties ranging from cloud parameters to water vapour. In addition, the measurements coincided with the results from a reduced resolution Unified Model (UM) run by the Met Office.

  • The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) was organized under the auspices of Atmospheric Chemistry and Climate (AC&C), a project of International Global Atmospheric Chemistry (IGAC) and Stratospheric Processes And their Role in Climate (SPARC) under International Geosphere Bisosphere Programme (IGBP) and World Climate Research Programme (WCRP). The Atmospheric Chemistry and Climate Model Intercomparison Project (ACC-MIP) consists of several sets of simulations that have were designed to facilitate useful evaluation and comparison of the AR5 (Intergovernmental Committee on Climate Change Assessment Report 5) transient climate model simulations. This dataset contains measurements from climate simulations of the 20th century and the future projections, which output feedback between dynamics, chemistry and radiation in every model time step. The data are collected from running the latest set of ozone precursor emissions scenarios, which output tropospheric ozone changes from 1850 to 2100.

  • Large data sets used to study the impact of anthropogenic climate change on the 2013/14 floods in the UK are provided. Data consists of perturbed initial conditions simulations using the Weather@Home regional climate modelling framework. Two different base conditions, Actual, including atmospheric conditions (anthropogenic greenhouse gases and human induced aerosols) as at present and Natural, with these forcings all removed are available. The data set is made up of 13 different ensembles (2 actual and 11 natural) with each having more than 7500 members. The data is available as NetCDF V3 files with their content representing an individual month of simulation. Data within that includes diagnostics written at daily, weekly and monthly within the period of interest (1st Dec 2013 to 15th February 2014) for both a specified European region at a 50km horizontal resolution and globally at N96 resolution. The data were generated in support of the European FP7 project - EUropean CLimate and weather Events: Interpretation and Attribution (EUCLEIA). Full details are available within Sparrow et al 2017, Nature Scientific Data.

  • The Coastal Air Pollution (CAP) field campaigns in 2009 and 2010 (CAP-2009 and CAP-2010 respectively) sought to investigate the impact of local meteorology on coastal air quality and the structure and evolution of the coastal boundary layer. This dataset contains vertical profiles of horizontal and vertical wind components as well as signal-to-noise (SNR) and spectal width measurements which were collected at the Weybourne Atmospheric Observatory, Norfolk, between September 2009 and April 2010. These data were collected by the Facility for Ground-based Atmospheric Measurements' (FGAM) 1290 MHz Mobile Wind Profiler, owned and operated by the University of Manchester and formerly known as the aber-radar-1290mhz. The data are available at 15 minute intervals as netCDF files to all registered British Atmospheric Data Centre (BADC) users.

  • Cloud base and backscatter data from the Met Office's Wattisham Cl31 ceilometer located at Wattisham, Suffolk. The Met Office's laser cloud base recorders network (LCBRs), or ceilometers, returns a range of products for use in forecasting and hazard detection. The backscatter profiles can allow detection of aerosol species such as volcanic ash where suitable instrumentation is deployed.

  • "To what extent was the Little Ice Age a result of a change in the thermohaline circulation?" project. This was a Natural Environment Research Council (NERC) RAPID Climate Change Research Programme project (Joint International Round - NE/C509507/1 - Duration 1 Aug 2005 - 31 Jul 2008) led by Dr Tim Osborn of the University of East Anglia, with co-investigators at the University of East Anglia and Royal Netherlands Meteorology Institute. The dataset contains fresh water hosing model output from the LAR experiment run by the HadCM3 model. The freshwater was added to the North Atlantic basin to a larger area north of the CMIP (between latitudes 50°N and 70°N) area.