This dataset contains humidity and temperature profiles from the NCAS Humidity And Temperature PROfilers (HATPRO) scanning radiometer on board the Alliance research vessel for the Iceland Greenland seas Project (IGP). The Iceland Greenland seas Project (IGP) was an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)

Data for Figure 3.41 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.41 is a summary figure showing simulated and observed changes in key large-scale indicators of climate change across the climate system, for continental, ocean basin and larger scales.  --------------------------------------------------- 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 data of each panel is provided in a single file. --------------------------------------------------- List of data provided --------------------------------------------------- This datasets contains global and regional anomaly time-series for: - near-surface air temperature (1850-2020) - precipitation (1950-2014) - sea ice extent (1979-2014) - ocean heat content (1850-2014) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- near-surface air temperature (tas) -fig_3_41_tas_global.nc, fig_3_41_tas_land.nc, fig_3_41_tas_north_america.nc, fig_3_41_tas_central_south_america.nc, fig_3_41_tas_europe_north_africa.nc, fig_3_41_tas_africa.nc, fig_3_41_tas_asia.nc, fig_3_41_tas_australasia.nc, fig_3_41_tas_antarctic.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 4, stat = 0 (mean) ocean heat content (ohc) -fig_3_41_ohc_global.nc: brown line: ncl5 = 0, ncl6 = 0 (mean); shaded region: ncl6 = 1 (5th percentile) and 2 (95th percentile) green line: ncl5 = 1, ncl6 = 0 (mean); shaded region: ncl6 = 1 (5th percentile) and 2 (95th percentile) black line: ncl5 = 2, ncl6 = 0 (mean) precipitation (pr) -fig_3_41_pr_60N_90N.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 2, stat = 0 (mean) sea ice extent (siconc) -fig_3_41_siconc_nh.nc, fig_3_41_siconc_sh.nc: brown line: exp = 0, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) green line: exp = 1, stat = 0 (mean); shaded region: stat = 1 (5th percentile) and 2 (95th percentile) black line: exp = 2, stat = 0 (mean) The ensemble spread (shaded regions) of CMIP6 data shown in figure 3.41 are the mean, 5th and 95th percentiles. The in-file metadata labels the same ensemble spread with mean, min and max. --------------------------------------------------- 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 - Link to the figure on the IPCC AR6 website

Data from the University of Leeds UMSLIMCAT model, part of the International Global Atmospheric Chemistry (IGAC)/ Stratosphere-troposphere Processes and their Role in Climate (SPARC) Chemistry-Climate Model Initiative (CCMI-1). CCMI-1 is a global chemistry climate model intercomparison project, coordinated by the University of Reading on behalf of the World Climate Research Programme (WCRP). The dataset includes data for the following CCMI-1 experiments: Reference experiments ref-C1, ref-C1SD and ref-C2. Sensitivity experiments senC1SSI, senC2fGHG, senC2fODS, senC2fODS2000, senC2rcp45 and senC2rcp85. ref-C1: Using state-of-knowledge historic forcings and observed sea surface conditions, the models simulate the recent past (1960–2010). ref-C1SD: Similar to ref-C1 but the models are nudged towards reanalysis datasets, and correspondingly the simulations only cover 1980–2010. (“SD” stands for specified dynamics.) ref-C2: Simulations spanning the period 1960–2100. The experiments follow the WMO (2011) A1 baseline scenario for ozone depleting substances and the RCP 6.0 (Meinshausen et al., 2011) for other greenhouse gases, tropospheric ozone (O3) precursors, and aerosol and aerosol precursor emissions. senC1SSI: The same as ref-C1 but using a solar forcing dataset with increased UV intensity. senC2fGHG: Similar to ref-C2 but with greenhouse gasses (GHGs) fixed at their 1960 levels, and sea surface and sea ice conditions prescribed as the 1955–1964 average (where these conditions are imposed). senC2fODS: The same as ref-C2 but with ozone-depleting (halogenated) substances (ODSs) fixed at their 1960 levels. senC2fODS2000: The same as ref-C2 but with ODSs fixed at their year 2000 levels. senC2rcp45: The same as ref-C2, but with the GHG scenario changed to RCP 4.5 (Meinshausen et al., 2011). senC2rcp85: The same as ref-C2, but with the GHG scenario changed to RCP 8.5 (Meinshausen et al., 2011).

Model simulations undertaken by the Quantifying variability of the El Nino Southern Oscillation on adaptation-relevant time scales using a novel palaeodata-modelling approach (QPENSO) project. These are coupled ocean-atmosphere experiments with a modified version of the HadCM3 (UM version 4.5) climate model. The model has been modified to include stable isotopes of oxygen in both the ocean and atmosphere sub-models, after Tindall et al., 2009. The simulations are grouped into two experiments: 1) 'picontrol', comprising a single 750 year duration unforced pre-industrial boundary condition simulation; 2) 'forced', comprising a suite of six historical simulations of the interval 1160-1360 AD and including changes in solar, volcanic and greenhouse gas forcing. The six simulations represent an initial-condition ensemble over this interval.

Data from the ETH-PMOD (Swiss Federal Institute of Technology Zurich and the Physical-Meteorology Observatory Davos) SOCOL3 model, part of the International Global Atmospheric Chemistry (IGAC)/Stratosphere-troposphere Processes and their Role in Climate (SPARC) Chemistry-Climate Model Initiative (CCMI-1). CCMI-1 is a global chemistry climate model intercomparison project, coordinated by the University of Reading on behalf of the World Climate Research Program (WCRP). The dataset includes data for the following CCMI-1 experiments: Reference experiments: ref-C1 and ref-C2. Sensitivity experiments: senC2fCH4, senC2CH4rcp85, senC2fEmis, senC2fN2O, senC2rcp26, senC2rcp45, senC2rcp85. ref-C1: Using state-of-knowledge historic forcings and observed sea surface conditions, the models simulate the recent past (1960–2010). ref-C2: Simulations spanning the period 1960–2100. The experiments follow the WMO (2011) A1 baseline scenario for ozone depleting substances and the RCP 6.0 (Meinshausen et al., 2011) for other greenhouse gases (GHGs), tropospheric ozone (O3) precursors, and aerosol and aerosol precursor emissions. senC2CH4rcp85: Similar to ref-C2 but the methane surface-mixing ratio follows the RCP 8.5 scenario (Meinshausen et al., 2011), all other GHGs and forcings follow RCP 6.0. senC2fCH4: Similar to ref-C2 but the methane surface-mixing ratio is fixed to its 1960 value. senC2fEmis: Similar to ref-C2 but with surface and aircraft emissions fixed to their respective 1960 levels. senC2fN2O: Similar to ref-C2 but the nitrous oxide surface-mixing ratio is fixed to its 1960 value. senC2rcp26: The same as ref-C2, but with the GHG scenario changed to RCP 2.6 (Meinshausen et al., 2011). senC2rcp45: The same as ref-C2, but with the GHG scenario changed to RCP 4.5 (Meinshausen et al., 2011). senC2rcp85: The same as ref-C2, but with the GHG scenario changed to RCP 8.5 (Meinshausen et al., 2011).

Vegetation and meteorological observations (snow and radiation) were collected by various ground instruments in an area of forest near Abisko (Sweden) and Sodankylä (Finland) during measurement campaigns in March 2011 and March 2012. This dataset contains the radiation data collected at Abisko site in March 2011. Above-canopy radiation: An open area was selected at each study site (“plot O”) for measurements assumed to be representative of incoming radiation above the nearby forest canopy. A Delta-T Devices BF3 sunshine sensor and a Kipp & Zonen CGR3 pyrgeometer were connected to a Campbell Scientific CR1000 data logger recording 5-minute averages of measurements made every 5 seconds. The BF3 measures total and diffuse incoming shortwave radiation, and the CGR3 measures thermal longwave radiation. Below-canopy radiation: In the forest plots, two arrays of ten Kipp & Zonen CM3 shortwave pyranometers and four Kipp & Zonen CGR3 longwave pyrgeometers were connected to AM16/32B multiplexers and Campbell Scientific CR1000 data loggers recording 5-minute averages of measurements made every 5 seconds. One array was set up in a “continuity plot” C for the entire duration of each field campaign, while the other array was moved between four “roving plots” R1 to R4, providing at least 5 complete days of data at each plot. All radiometers were placed on small plywood platforms on the snow surface and were levelled and cleared of snow every morning. Radiometer positions were recorded using differential GPS at Abisko and averages of repeated handheld GPS measurements at Sodankylä. This was a NERC funded project.

Cirrus clouds play an important role in determining the radiation budget of the earth, but many of their properties remain uncertain, particularly their response to aerosol variations and to warming. Part of the reason for this uncertainty is the dependence of cirrus cloud properties on the cloud formation mechanism, which itself is strongly dependent on the local meteorological conditions. This classification system is designed to identify cirrus clouds by the cloud formation mechanism. Using re-analysis and satellite data, cirrus clouds are separated in four main types: orographic, frontal, convective and synoptic. Comparisons with convection-permitting model simulations and back-trajectory based analysis have shown that this classification can provide useful information on the cloud scale updraughts and the frequency of occurrence of liquid-origin ice, with the convective regime having higher updraughts and a greater occurrence of liquid-origin ice compared to the synoptic regimes (see description paper). This classification is designed to be easily implemented in global climate models - the observational classification results are made available make this comparison easier. The classification has been generated globally for the years 2003-2013 inclusive. Making use of the moderate resolution imaging spectrometer (MODIS) on-board the Aqua satellite, the classification exists only at 13:30 local solar time each day. The regimes used within this classification are defined as follows (further details are given in the description paper) Orographic - proximity to regions of large-scale topography variation Frontal - satellite detected cirrus clouds that intersect to atmospheric fronts determined from reanalysis data Convective - satellite detected cirrus clouds in regions of large scale ascent determined from reanalysis data Synoptic - Not assigned as one of the other regimes. Data are gridded NetCDF V4 files, provided on a regular longitude-latitude grid at a 1 by 1 degree resolution across the whole globe. The files provide the classification at 13:30 local solar time (the satellite overpass time) and are at a daily resolution, within a folder defining the year. The filename structure is: {year}/IC-CIR.{year}.{day_of_year}.v1.nc where {year} is the year of the data and {doy of year} starts with 001 on the first of January. Further details about the data, including comparisons to convection-resolving model simulations can be found in the description paper (Gryspeerdt et al., ACP, 2018).

This dataset includes the Met Office GloSea5 model output prepared for SPECS seasonal (1992-2012). These data were prepared by the Met Office Hadley Centre, as part of the SPECS project. Model id is GloSea5 (GloSea5: HadGEM3 v3.0 (2014); atmosphere: UM (GA3.0) ; ocean: NEMO (v2, ORCA0.25) ; coupler: OASIS3 (v3.3); sea ice: CICE), frequency is daily and monthly. Daily Atmospheric variables are: pr psl rls rlut tas tasmax tasmin Monthly atmos variables: pr psl ta tas zg Monthly seaIce variables: sic sit snd Ocean variables: so thetao tos uo vo

This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the Beta version of their Climate Research Data Package (CRDP v0). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v0 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m) for the Northern Hemisphere (north of 30°) for the period 2003-2017.

Daily global cloud droplet number concentrations (Nd) have been calculated at 1x1 degree resolution from pixel-level MODIS (MODerate Imaging Spectroradiometer) Collection 5.1 Joint Level-2 (Aqua satellite) optical depth (tau) and the 3.7 micron effective radius (reff) data (and other supporting data) using the adiabatic cloud assumption (liquid water content increases linearly with height, Nd is constant throughout the cloud depth and the ratio of the volumne mean radius to the effective radius is assumed constant). The Nd data is contained in separate NetCDF files for each year for the period 2003-2015. Nd is contained in the "Nd" variable and has units of cm^{-3}. This is a 360x180xNdays (lon x lat x Ndays) sized array, where Ndays is the number of days in the year. The lon x lat grid is a regular 1x1 degree grid. The time is provided as both a 1D array of size Ndays ("time") with units of days since 1st Jan, 1970 and an array of size Ndays x 3 ("time_vec") that contains numbers for the year month and day for each of the Ndays entries. A number of filters have been applied to the data in order to remove retrievals that are likely to be problematic, or to violate the adiabatic cloud assumptions. Data is only included if: 1) Pixels are determined to be liquid pixels by MODIS. 2) The 1x1 degree mean cloud top height (calculated using the MODIS cloud top temperature and the sea surface temperature) is below 3.2km. 3) The 1x1 degree liquid cloud fraction was larger than 80%. 4) The 1x1 degree mean solar zenith angle was 65 degrees or less to avoid biases at high angles (Grosvenor and Wood, 2014). Note, that the filtering is different to that described in Grosvenor, AMTD, 2018 in the following ways :- 1) 1km resolution tau and reff are used to calculate Nd, which is then aggregated to 1x1 degree resolution (rather than using 1x1 degree tau and reff). 2) Only Nd based on the 3.7 micron reff retrieval is provided here. 3) No filtering for the presence of sea-ice is done here - it is recommended that this is done if using for high latitudes. 4) The data here is not restricted to tau>5. Also note that the vertical penetration bias correction described in Grosvenor, AMTD, 2018 is NOT applied here. In addition, as described in the latter paper, further pixel-level screening is performed in order to select high quality data. Details on the reasons for restricting to low solar zenith angles can be found in Grosvenor and Wood, ACP, 2014. Information on the pixel level filtering applied can be found in Grosvenor et al., AMTD, 2018 (noting the differences explained above). A comparison of this dataset with others can be found in Grosvenor et al., Reviews of Geophysics, 2018. This dataset calculates a product that is not provided as standard by MODIS. It uses improved optical depth and effective radius data compared to the standard MODIS Level-3 data since situations (e.g., high solar zenith angles, broken clouds) that have been shown to cause retrieval issues have been filtered out at the Level-2 stage before being averaged into Level-3 droplet concentration data.