This dataset contains model output for experiments using the UKESM1-CH4 emissions-driven configuration of the UK Earth System model, based on the science version (Sellar et al.,2011, DOI:10.1029/2019MS001739) coupled with the United Kingdom Chemistry and Aerosol scheme (Archibald et al., DOI:10.5194/gmd-13-1223-2020). The simulation follows the experimental design of CMIP6-sponsored ScenarioMIP (O'Neill et al., DOI:10.5194/gmd-9-3461-2016) SSP3-7.0 and SSP1-2.6 Shared Socioeconomic Pathways. These simulation span 2015-2050 and use a coupled atmosphere-ocean climate model. To assess climate model variability, three ensemble members are archived which differ only in the initial conditions for the experiment. The data comprise annual mean output over the experimental period for temperature, ozone, methane, hydroxyl radical, precipitation and provided diagnostic air mass and diagnostics suitable for the calculation of the methane lifetime and budget. We archive data from this methane emissions-driven configuration as NZAME/ensemble experiment as by186(ensemble 1), bz146(ensemble 2) and bz473(ensemble 3). To improve reproducibility the UKESM1 jobids are included as identifiers. For comparison, emissions-driven experiments are included as {Scenario}/JOBID: SSP1-2.6 (bo812), and three ensemble members for SSP3-7.0: bo797(ens 1), ca723(ens 2) and cb039(ens3).

Global model data has been generated for COVID-19 (Coronavirus Disease 2019) simulations. The model used was the United Kingdom Earth System Model 1.0 (UKESM1.0), in an atmosphere-only nudged configuration, with Met Office Unified Model version 11.5. The data is on a global N96 grid (192 x 144 points), and covers the years 2012, 2013, and 2014. These data were used to study the effect of COVID-19 lockdowns (simulated scenarios) on atmospheric composition and radiative forcing. The dataset includes data used in the paper submitted to Geophysical Research Letters (GRL) August 2020 with title 'Minimal climate impacts from short-lived climate forcers following emission reductions related to the COVID-19 pandemic'. See Details/Docs tab for a link to this. For this purpose, there are four experimental integrations (a1, a2, a3, a4), and a control (con) for each year. The files are labelled using variable codes such as m01s34i001 to determine the model variable field contained. A full description of what these are can be found in the included docs/file variable_codes.txt. The data are in NetCDF format, and were generated from the following suites: u-bt034, u-bt090, u-bt091, u-bt092, u-bt637, u-bt341, u-bt342, u-bt343, u-bt344, u-bt926, u-bt375, u-bt376, u-bt377, u-bt378, u-bt927. This is a NERC funded project.

A-CURE was a NERC funded project that tackled one of the most challenging and persistent problems in atmospheric science – understanding and quantifying how changes in aerosol particles caused by anthropogenic activities affect climate. The data here are monthly mean variable data from a large perturbed parameter ensemble of UKESM1 simulations, nudged to horizontal winds above around 2km. Each variable has 220 or 221 members, as indicated in file names. Some months have one fewer member because a model variant repeatedly did not run to completion due to combined model parameter values. The 221 members are model variants that combine the effects of 54 aerosol and physical atmosphere parameters. Variable data in this ensemble span the uncertainty in UKESM1 from these parametric sources.

The Gridded daily Agricultural Burning Emission Inventory of Eastern China dataset contains a unique high Spatio-temporal resolution agricultural burning inventory for eastern China for the years 2012-2015. The data was generated using twice daily fire radiative power (FRP) observations from the ‘small fire optimised’ VIIRS-IM FRP product, and combined with fire diurnal cycle information taken from the geostationary Himawari-8 satellite. This dataset was designed to fully take into account small fires well below the MODIS burned area or active fire detection limit, focusing on dry matter burned (DMB) and emissions of CO2, CO, PM2.5 and black carbon. The fuel for these fires is waste straw and other agricultural residues. Information from a crop rotation map to classify the type of agricultural residue being burned at each observed location and time, in addition to an agricultural area land map was also incorporated in consideration of this.

Data for Figure SPM.2 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure SPM.2 relates to assessed contributions to observed warming. --------------------------------------------------- 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: IPCC, 2021: Summary for Policymakers. 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. 3−32, doi:10.1017/9781009157896.001. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has three panels, with data provided for all panels in subdirectories named panel_a, panel_b and panel_c. --------------------------------------------------- List of data provided --------------------------------------------------- This data set contains: - Observed warming (2010-2019 relative to 1850-1900) - Aggregated contributions to 2010-2019 warming relative 1850 -1900, assessed from attribution studies - Contributions to 2010-2019 warming relative to 1850-1900, assessed from radiative studies --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - Data file: panel_a/SPM2a.csv (Observed warming). Mean value is used for the bar plot and top and bottom values are used for the error bars and they represent borders of the very likely range. Panel b: - Data file: panel_b/SPM2b.csv (Aggregated contributions assessed from attribution studies). Mean values are used for the bar plot and top and bottom values are used for the error bars and represent the borders of the very likely range Panel c: - Data file: panel_c/SPM2c_data.csv (Contributions assessed from radiative studies). Total global surface air temperature (GSAT) effect values are used for the bar plots and 5% and 95% very likely limit values are used for the error bars. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblink is provided in the Related Documents section of this catalogue record: - Link to the report webpage, which includes the report component containing the figure (Summary for Policymakers) and the Supplementary Material for Chapters 3, 6 and 7, which contain details on the input data used in Tables 3.SM.1 (Figure 3.8), 6.SM.1 (Figure 6.12) and 7.SM.14 (Figure 7.7).