This dataset contains scan data from the National Centre for Atmospheric Science's (NCAS) mobile X-band radar collected at the Davidstow Airfield, Cornwall, between June and August 2013 as part of the MICROphysicS of COnvective PrEcipitation (MICROSCOPE) project. The X-band radar is operated as part of the NCAS Atmospheric Measurement Facility's (AMF).

Data from the CSIRO Mark 3.0 simulations

Data were collected by the Chilbolton Facility for Atmospheric and Radio Research (CFARR) Radiometrics Radiometer from the 23rd of August 2007 to the present at Chilbolton, Hampshire. The dataset contains measurements of the total liquid water at zenith, together with the vertical profile of water vapour density. Accuracy of integrated water vapour (IWV) retrieval: ~1 – 2 mm Accuracy of total liquid water path (LWP) retrieval: ~15% in non-precipitating conditions.

The Reading Assimilated Atmospheric Satellite Data presents an analyses of stratospheric and tropospheric temperature, ozone and water vapour incorporating data from research satellites and operational observations, assimilated with the Hadley Centre Atmospheric Model (HADAM3) configuration of the Unified Model (UM). This dataset includes 3-D global fields for selected periods of time in the 1990s and is produced as part of the Assimilation of Remote-sensed Data for Applications in the Atmospheric and Oceanographic Sciences (ARDAAOS) Natural Environment Research Council (NERC) thematic programme.

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.

Cloud base and backscatter data from the Met Office's Leeming Cl31 ceilometer located at Leeming, Basingstoke. 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.

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 from MeteoFrance 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.

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.

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 - Link to the figure on the IPCC AR6 website

The DIAMET project aimed to better the understanding and prediction of mesoscale structures in synoptic-scale storms. Such structures include fronts, rain bands, secondary cyclones, sting jets etc, and are important because much of the extreme weather we experience (e.g. strong winds, heavy rain) comes from such regions. Weather forecasting models are able to capture some of this activity correctly, but there is much still to learn. By a combination of measurements and modelling, mainly using the Met Office Unified Model (UM), the project worked to better understand how mesoscale processes in cyclones give rise to severe weather and how they can be better represented in models and better forecast. This dataset contains NWP output from MetUM vn 7.3 with diabatic theta and PV tracers including mean sea level pressure, 3D atmospheric fields, precipitation-related surface field, diabatic theta tracers, and diabatic pv tracers.