This data set consist of a single file which contains a set of optimised global surface fluxes of methane (CH4), produced through variational inverse methods using the TOMCAT chemical transport model, and the INVICAT inverse transport model. These surface fluxes are produced as monthly mean values on the (approximately) 5.6-degree horizontal model grid. The associated uncertainty for the flux from each grid cell is also included. The fluxes and uncertainties are global and cover the period Jan 2010 - Dec 2018. The emissions from fossil fuels are labelled FF_FLUX, whilst the uncertainties are labelled FF_ERROR. The emissions from natural, agricultural and biomass burning sources are labelled NAT_FLUX, whilst the uncertainties are labelled NAT_ERROR. These two sectors (fossil fuel and non-fossil fuel) are solved for separately in the inversion. Flux and uncertainty units are kg(CH4)/m2/s, and time units are days since January 1st 2010. These emissions show improved performance relative to independent observations when included in the TOMCAT model. Further details about the data can be found in Wilson et al. (2020) in the documentation section.

These climate projections for the North-West European Shelf Seas update the shelf seas component of UKCP09 Marine Report (Lowe et al, 2009) and were funded by the MINERVA project. This dataset contains three ensemble exemplars for model output based on the QUMP (Quantifying Uncertainties in Model Projections) ensemble of HadCM3 (Hadley Centre Coupled Model version 3) runs downscaled with the POLCOMS (Proudman Oceanographic Laboratory Coastal Ocean Modelling System) under SRES A1B (Special Report on Emissons Scenarios - A1B business-as-usual with medium emissions) conditions, from 1952-2098 for which 30-year means anomalies have been calculated from monthly mean data for each of the 12 months. A Perturbed Physics Ensemble (PPE) of HadCM3 has been downscaled with the shelf seas model POLCOMS. Each of the 11 ensemble members has been downscaled as transient simulations (from 1952-2098) under the SRES A1B emissions scenario. The PPE (QUMP) was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. POLCOMS was run at 12 km resolution, with 32 vertical levels using s-coordinates over the NW European Shelf Seas domain (-18.3 to 14 degrees East, 43 to 63.56 degrees North). Monthly statistics of the model results were recorded. Further details can be found in Tinker et al (2015).

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 is an in-situ dataset of estimates of particulate organic carbon (POC) based on all the current (2021-09-22) profiles of optical backscattering (BBP) collected by (BGC)Biogeochemical-Argo floats. The dataset spans from 2010-06-01 to 2021-09-22 and covers the upper 2000 dbars of the water column (continuous profiles have been binned in 41 vertical bins). The dataset was produced by first devising a new set of automatic tests to quality control the large BBP dataset available (>31M records over >130k profiles). The QCed BBP was then converted into POC using an empirical algorithm (average of the POC: BBP slopes of Cetinic et al., 2012)

This dataset contains v4.0 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 third version of their Climate Research Data Package (CRDP v3). It is derived from a thermal model driven and constrained by satellite data. CRDPv3 covers the years from 1997 to 2021. Grid products of CDRP v3 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). Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2021 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2021 using a pixel-specific statistics for each day of the year.

Data for each figure presented in the paper 'The impact of stratospheric ozone feedbacks on climate sensitivity estimates', as appeared in Journal of Geophysical Research: Atmospheres in the year 2018. - The temporal resolution ('temporalResolution'): depends on the variable: annual means or multi-annual-means. - 'timeslice' climate model simulations using the HadGEM3-AO model from the UK Met Office, coupled to the interactive atmospheric chemistry scheme UKCA. References to model descriptions can be found in the publication. The simulations consist of a pre-industrial control run (A) and several abrupt4xCO2 simulations carried out with different treatments of atmospheric chemistry (B, D1, D2). - 'umid' is the ID of the simulation, to be read from the stitching table. - 'variable' names: 'temp': temperature, 'olr': Outgoing longwave radiation at the top of the atmosphere (TOA), 'csolr': same as olr but under clear sky conditions, 'field207': upward clear sky shortwave flux at the TOA, 'field201': Outgoing SW Flux at the TOA, 'tracer1': ozone mass mixing ratios, 'field1426': frozen cloud fraction in each grid cell, 'q': specific humidity, 'u': zonal wind component, 'ht': tropopause height in km following the WMO lapse rate definition.

Co-Ordinated Regional Downscaling Experiment (CORDEX) data for the South America Domain (SAM-44). The data is produced by the MetOffice Hadley Centre regional model HadRM3P running at 0.44 degree resolution over the South America CORDEX domain (SAM-44). HadRM3P is a regional climate model based on the HadCM3 Coupled Climate Model. The HadRM3P model is driven by European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis data to run the CORDEX Evaluation experiment, representative of the period from 1990 to 2011. The model outputs are interpolated to a common latitude-longitude grid. The collection includes monthly averages and seasonal means. The CORDEX program is sponsored by the World Climate Research Program (WCRP) to organise an internationally coordinated framework to produce improved regional climate change projections for all land regions world-wide. The CORDEX-results will serve as input for climate change impact and adaptation studies.

Data from the MOHC (Met Office Hadley Centre) HadGEM3-ES model, part of the International Global Atmospheric Chemistry (IGAC)/ Stratosphere-troposphere Processes and their Role in Climate (SPARC) Chemistry-Climate Model Initiative phase 1 (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 CCMI-1 reference experiments: ref-C1 and ref-C2. 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, tropospheric ozone (O3) precursors, and aerosol and aerosol precursor emissions.

The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provides 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data. The dataset has global coverage and spans 1998-2018 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information.

These climate projections for the North-West European Shelf Seas update the shelf seas component of UKCP09 Marine Report (Lowe et al, 2009) and were funded by the MINERVA project. This dataset contains ensemble statistics for model output based on the QUMP (Quantifying Uncertainties in Model Projections) ensemble of HadCM3 (Hadley Centre Coupled Model version 3) runs downscaled with the POLCOMS (Proudman Oceanographic Laboratory Coastal Ocean Modelling System) under SRES A1B (Special Report on Emissons Scenarios - A1B business-as-usual with medium emissions) conditions, from 1952-2098 for which 30-year means anomalies have been calculated from monthly mean data for each of the 12 months. A Perturbed Physics Ensemble (PPE) of HadCM3 has been downscaled with the shelf seas model POLCOMS. Each of the 11 ensemble members has been downscaled as transient simulations (from 1952-2098) under the SRES A1B emissions scenario. The PPE (QUMP) was designed to span the range of uncertainty associated with model parameter uncertainty in the atmosphere of the driving global climate model. POLCOMS was run at 12 km resolution, with 32 vertical levels using s-coordinates over the NW European Shelf Seas domain (-18.3 to 14 degrees East, 43 to 63.56 degrees North). Monthly statistics of the model results were recorded. Further details can be found in Tinker et al (2015).