The ACTIVE (Aerosol and chemical transport in tropical convection) Natural Environment Research Council (NERC) funded consortium project, combined field measurements and a range of modelling tools at different scales to address questions related to the composition of the tropical tropopause layer (TTL). ACTIVE utilised the Australian Egrett aircraft operated by Airborne Research Australia (ARA) and the NERC Dornier 228 operated by the British Airborne Research and Survey Facility (ARSF) to measure chemical species and aerosol in the inflow and outflow of tropical storms. Cloud-scale and large-scale modelling studies assisted in the interpretation of the measurements to distinguish the different contributions to the TTL composition. The dataset contains the Egrett aircraft core instruments measurements from ARA Grob G520T Egrett aircraft.

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.

Cloud base and backscatter data from the Met Éireann's Shannon Cl31 ceilometer located at Shannon, South WestIreland. The Met Éireann'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 ACTIVE (Aerosol and chemical transport in tropical convection) Natural Environment Research Council (NERC) funded consortium project, combined field measurements and a range of modelling tools at different scales to address questions related to the composition of the tropical tropopause layer (TTL). ACTIVE utilised the Australian Egrett aircraft operated by Airborne Research Australia (ARA) and the NERC Dornier 228 operated by the British Airborne Research and Survey Facility (ARSF) to measure chemical species and aerosol in the inflow and outflow of tropical storms. Cloud-scale and large-scale modelling studies assisted in the interpretation of the measurements to distinguish the different contributions to the TTL composition. The dataset contains ozonesondes measurements at Darwin, Australia.

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

Input data for Figure 2.16 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 2.16 provides global precipitation minus evaporation trend maps and time series from a variety of data sources --------------------------------------------------- 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: Gulev, S.K., P.W. Thorne, J. Ahn, F.J. Dentener, C.M. Domingues, S. Gerland, D. Gong, D.S. Kaufman, H.C. Nnamchi, J. Quaas, J.A. Rivera, S. Sathyendranath, S.L. Smith, B. Trewin, K. von Schuckmann, and R.S. Vose, 2021: Changing State of 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. 287–422, doi:10.1017/9781009157896.004. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has four panels, with input data provided for all panels in the main directory --------------------------------------------------- List of data provided --------------------------------------------------- The datasets contains: - Global precipitation and evaporation data from ERA5 reanalysis - Time series of global, land-only and ocean-only average annual P–E (mm day–1) from the following reanalysis products: 20CRv3, ERA5, ERA20CM, MERRA, CFSR, ERA20C, JRA55 and MERRA2. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - Data files: IntermediateData_era5_evap_2.nc and era5_tp_2.nc Panel b: - Data file: GPME2.csv and GPME2.mat Panel c: - Data file: LPME2.csv and LPME2.mat Panel d: - Data file: OPME2.csv and OPME2.mat For panels b to d: I.     Column 2: orange solid line II.    Column 3: cyan solid line III.   Column 4: black solid line IV.   Column 5: grey solid line V.    Column 6: blue solid line VI.   Column 7: dark green solid line VII.  Column 8: brown solid line VIII. Column 9: green solid line 20CRv3 is the NOAA-CIRES-DOE Twentieth Century Reanalysis Version 3. ERA5 is a reanalysis of the global climate from 1950 to present, developed by ECMWF. ERA20CM is a twentieth century atmospheric model ensemble developed by ECMWF. MERRA stands for Modern-Era Retrospective analysis for Research and Applications. CFSR stands for Climate Forecast System Reanalysis. ERA20C is the first atmospheric reanalysis of the 20th century, from 1900-2010, developed by ECMWF. JRA55 stands for Japanese 55-year Reanalysis. MERRA2 stands for Modern-Era Retrospective analysis for Research and Applications, version 2. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Additional information to correctly reproduce the figure in the corresponding readme files for code archived on Zenodo (see the link to code provided in the Related Documents section of this catalogue record). --------------------------------------------------- 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 2) - Link to the Supplementary Material for Chapter 2, which contains details on the input data used in Table 2.SM.1 - Link to the code for the figure, archived on Zenodo.

The GBS (Global Broadcast Service) dataset is a series of radio attenuation measurements made at three sites in the UK: Chilbolton and Sparsholt, both in southern UK, and Dundee in Scotland. The aim of the experiment was to make long term measurements of the signal strength received from a 20.7GHz beacon on the US Department of Defense satellite UFO-9 at multiple sites, in order to determine whether the use of site diversity as a fade mitigation technique would be effective. The dataset spans a period of 3 years, from August 2003 to August 2006 with signal attenuation sampled once per second. This dataset is cited in: S. A. Callaghan, J. Waight, J.L.Agnew, C. J. Walden, C.L.Wrench , S. Ventouras “The GBS dataset: measurements of satellite site diversity at 20.7 GHz in the UK”, Geoscience Data Journal, 17 March 2013, DOI: 10.1002/gdj3.2

Data for Figure 3.40 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.40 shows the observed and simulated Atlantic Multidecadal Variability (AMV). --------------------------------------------------- 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 six panels. Files are not separated according to the panels. --------------------------------------------------- List of data provided --------------------------------------------------- amv.obs.nc contains - Observed SST anomalies associated with the AMV pattern - Observed AMV index time series (unfiltered) - Observed AMV index time series (low-pass filtered) - Taylor statistics of the observed AMV patterns amv.hist.cmip6.nc contains - Statistical significance of the observed SST anomalies associated with the AMV pattern - Simulated SST anomalies associated with the AMV pattern - Simulated AMV index time series (unfiltered) - Simulated AMV index time series (low-pass filtered) - Taylor statistics of the simulated AMV patterns based on CMIP6 historical simulations. amv.hist.cmip5.nc contains - Simulated SST anomalies associated with the AMV pattern - Simulated AMV index time series (unfiltered) - Simulated AMV index time series (low-pass filtered) - Taylor statistics of the simulated AMV patterns based on CMIP5 historical simulations. amv.piControl.cmip6.nc contains - Simulated SST anomalies associated with the AMV pattern - Simulated AMV index time series (unfiltered) - Simulated AMV index time series (low-pass filtered) - Taylor statistics of the simulated AMV patterns based on CMIP6 piControl simulations. amv.piControl.cmip5.nc contains - Simulated SST anomalies associated with the AMV pattern - Simulated AMV index time series (unfiltered) - Simulated AMV index time series (low-pass filtered) - Taylor statistics of the simulated AMV patterns based on CMIP5 piControl simulations. --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel a: - amv_pattern_obs_ref in amv.obs.nc: shading - amv_pattern_obs_signif (dataset = 1) in amv.obs.nc: cross markers Panel b: - Multimodel ensemble mean of amv_pattern in amv.hist.cmip6.nc: shading, with their sign agreement for hatching Panel c: - tay_stats (stat = 0, 1) in amv.obs.nc: black dots - tay_stats (stat = 0, 1) in amv.hist.cmip6.nc: red crosses, and their multimodel ensemble mean for the red dot - tay_stats (stat = 0, 1) in amv.hist.cmip5.nc: blue crosses, and their multimodel ensemble mean for the blue dot Panel d: - Lag-1 autocorrelation of amv_timeseries_raw in amv.obs.nc: black horizontal lines in left . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.piControl.cmip5.nc: blue open box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.piControl.cmip6.nc: red open box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.hist.cmip5.nc: blue filled box-whisker in the left - Multimodel ensemble mean and percentiles of lag-1 autocorrelation of amv_timeseries_raw in amv.hist.cmip6.nc: red filled box-whisker in the left - Lag-10 autocorrelation of amv_timeseries in amv.obs.nc: black horizontal lines in right . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.piControl.cmip5.nc: blue open box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.piControl.cmip6.nc: red open box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.hist.cmip5.nc: blue filled box-whisker in the right - Multimodel ensemble mean and percentiles of lag-10 autocorrelation of amv_timeseries in amv.hist.cmip6.nc: red filled box-whisker in the right Panel e: - Standard deviation of amv_timeseries_raw in amv.obs.nc: black horizontal lines in left . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.piControl.cmip5.nc: blue open box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.piControl.cmip6.nc: red open box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.hist.cmip5.nc: blue filled box-whisker in the left - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries_raw in amv.hist.cmip6.nc: red filled box-whisker in the left - Standard deviation of amv_timeseries in amv.obs.nc: black horizontal lines in right . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.piControl.cmip5.nc: blue open box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.piControl.cmip6.nc: red open box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.hist.cmip5.nc: blue filled box-whisker in the right - Multimodel ensemble mean and percentiles of standard deviation of amv_timeseries in amv.hist.cmip6.nc: red filled box-whisker in the right Panel f: - amv_timeseries in amv.obs.nc: black curves . ERSSTv5: dataset = 1 . HadISST: dataset = 2 . COBE-SST2: dataset = 3 - amv_timeseries in amv.hist.cmip6.nc: 5th-95th percentiles in red shading, multimodel ensemble mean and its 5-95% confidence interval for red curves - amv_timeseries in amv.hist.cmip5.nc: 5th-95th percentiles in blue shading, multimodel ensemble mean for blue curve CMIP5 is the fifth phase of the Coupled Model Intercomparison Project. CMIP6 is the sixth phase of the Coupled Model Intercomparison Project. SST stands for Sea Surface Temperature. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- Multimodel ensemble means and percentiles of historical simulations of CMIP5 and CMIP6 are calculated after weighting individual members with the inverse of the ensemble size of the same model. ensemble_assign in each file provides the model number to which each ensemble member belongs. This weighting does not apply to the sign agreement calculation. piControl simulations from CMIP5 and CMIP6 consist of a single member from each model, so the weighting is not applied. Multimodel ensemble means of the pattern correlation in Taylor statistics in (c) and the autocorrelation of the index in (d) are calculated via Fisher z-transformation and back transformation. --------------------------------------------------- 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 were collected from the 4th of April 2002 to the present by the Ultra-violet Raman lidar at Chilbolton Observatory, Hampshire. The dataset contains measurements of attenuated backscatter coefficients of aerosols within the atmosphere, and a full Doppler spectrum, and moments Z, v, and w.

Along-Track Scanning Radiometer (ATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR). This dataset contains the Along-Track Scanning Radiometer on ESA ERS-1 satellite (ATSR-1) Browse Product. These data are 3 band colour composite, quick-look images at coarse resolution. The browse product for the ATSR-1 and the ATSR-2 missions were introduced during the third reprocessing of data products. This product was derived from the Level 1B product and auxiliary data.