Procedure Computation List
Get a list of ProcedureComputation objects. ProcedureComputations have a 1:1 mapping with Observations.
### Available end points:
- `/ProcedureComputations/` - Will list all ProcedureComputations in the database
- `/ProcedureComputations.json` - Will return all ProcedureComputations in json format
- `/ProcedureComputations/<object_id>/` - Returns ProcedureComputations object with that id
### Available Methods:
- `GET`
- `HEAD`
### Available filters:
- `uuid`
- `title`
- `keywords`
- `abstract`
### How to use filters:
These filters can be used like django query filters using __ for related model relationships.
- `/computations/?uuid=d594d53df2612bbd89c2e0e770b5c1a0`
- `/computations/?title__startswith!=DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE`
- `/computations/?abstract__contains=HadCM3 model`
GET /api/v2/computations/?format=api&offset=3500
{ "count": 3949, "next": "https://api.catalogue.ceda.ac.uk/api/v2/computations/?format=api&limit=100&offset=3600", "previous": "https://api.catalogue.ceda.ac.uk/api/v2/computations/?format=api&limit=100&offset=3400", "results": [ { "ob_id": 38946, "uuid": "ed27975d51a948fb97afe7ced48d3c76", "title": "Max Planck Institute for Meteorology (MPI-M) running: experiment dcppc-amv-neg using the MPI-ESM1-2-HR model.", "abstract": "Max Planck Institute for Meteorology (MPI-M) running the \"dcppc-amv-neg\" experiment using the MPI-ESM1-2-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, MPI-M, MPI-ESM1-2-HR, dcppc-amv-neg, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 38949, "uuid": "fa07902c28d74ce782c8c258813ea20f", "title": "Max Planck Institute for Meteorology (MPI-M) running: experiment dcppc-amv-pos using the MPI-ESM1-2-HR model.", "abstract": "Max Planck Institute for Meteorology (MPI-M) running the \"dcppc-amv-pos\" experiment using the MPI-ESM1-2-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, MPI-M, MPI-ESM1-2-HR, dcppc-amv-pos, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 38953, "uuid": "6a63dedf966b43b8be3d8330b1df4126", "title": "Max Planck Institute for Meteorology (MPI-M) running: experiment dcppc-amv-neg using the MPI-ESM1-2-XR model.", "abstract": "Max Planck Institute for Meteorology (MPI-M) running the \"dcppc-amv-neg\" experiment using the MPI-ESM1-2-XR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, MPI-M, MPI-ESM1-2-XR, dcppc-amv-neg, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 38956, "uuid": "601e5ee5c74c465f80f3ef20ae9f727a", "title": "Max Planck Institute for Meteorology (MPI-M) running: experiment dcppc-amv-pos using the MPI-ESM1-2-XR model.", "abstract": "Max Planck Institute for Meteorology (MPI-M) running the \"dcppc-amv-pos\" experiment using the MPI-ESM1-2-XR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, MPI-M, MPI-ESM1-2-XR, dcppc-amv-pos, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39035, "uuid": "91f6a2805edf48dd9eaae4f6ba45d3b2", "title": "the EC-Earth-Consortium team running: experiment spinup-1950 using the EC-Earth3P-HR model.", "abstract": "The the EC-Earth-Consortium team team consisted of the following agencies: La Agencia Estatal de Meteorología (AEMET), Barcelona Supercomputing Centre (BSC), Institute of Atmospheric Sciences and Climate (CNR-ISAC), Danish Meteorological Institute (DMI), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Finnish Meteorological Institute (FMI), Helmholtz Centre for Ocean Research Kiel (Geomar), Irish Centre for High-End Computing (ICHEC), International Centre for Theoretical Physics (ICTP), Instituto Dom Luiz (IDL), Institute for Marine and Atmospheric research Utrecht (IMAU), Portuguese Institute for Sea and Atmosphere (IPMA), KIT Karlsruhe Institute of Technology, Royal Netherlands Meteorological Institute (KNMI), Lund University, Met Éireann, The Netherlands eScience Center (NLeSC), Norwegian University of Science and Technology (NTNU), University of Oxford, SURFsara, Swedish Meteorological and Hydrological Institute (SMHI), Stockholm University, Unite ASTR, University College Dublin, University of Bergen, University of Copenhagen, University of Helsinki, University of Santiago de Compostela, Uppsala University, University of Utrecht, Vrije Universiteit Amsterdam and Wageningen University.the EC-Earth-Consortium team running the \"coupled spinup with fixed 1950s forcings from 1950 initial conditions (with ocean at rest) to provide initial condition for control-1950 and hist-1950\" (spinup-1950) experiment using the EC-Earth3P-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, EC-Earth-Consortium, EC-Earth3P-HR, spinup-1950, 3hr, 6hrPlev, 6hrPlevPt, Amon, CFday, E3hr, Eday, Emon, LImon, Lmon, Oday, Omon, Prim3hr, Prim6hr, Prim6hrPt, PrimOday, PrimOmon, Primday, PrimdayPt, Primmon, SIday, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39046, "uuid": "f8156af1d27d423f894510b5c311ba0f", "title": "European Centre for Medium-Range Weather Forecasts (ECMWF) running: experiment hist-1950 using the ECMWF-IFS-HR model.", "abstract": "European Centre for Medium-Range Weather Forecasts (ECMWF) running the \"coupled historical 1950-2014\" (hist-1950) experiment using the ECMWF-IFS-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, ECMWF, ECMWF-IFS-HR, hist-1950, Prim6hr, Prim6hrPt, PrimOday, PrimOmon, PrimSIday, Primday", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39056, "uuid": "c2f38005b45f43c288b4788aa974c13d", "title": "European Centre for Medium-Range Weather Forecasts (ECMWF) running: experiment hist-1950 using the ECMWF-IFS-LR model.", "abstract": "European Centre for Medium-Range Weather Forecasts (ECMWF) running the \"coupled historical 1950-2014\" (hist-1950) experiment using the ECMWF-IFS-LR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, ECMWF, ECMWF-IFS-LR, hist-1950, Prim6hr, Prim6hrPt, PrimOday, PrimOmon, PrimSIday, Primday", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39144, "uuid": "650461e45d6344488fa45cfd54e964c8", "title": "the CNRM-CERFACS team running: experiment primWP5-amv-neg using the CNRM-CM6-1 model.", "abstract": "The the CNRM-CERFACS team team consisted of the following agencies: Centre National de Recherches Météorologiques (CNRM) and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CERFACS).the CNRM-CERFACS team running the \"primWP5-amv-neg\" experiment using the CNRM-CM6-1 model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, CNRM-CERFACS, CNRM-CM6-1, primWP5-amv-neg, 3hr, 6hrPlev, 6hrPlevPt, AERday, AERmon, Amon, CFday, Eday, Efx, Emon, LImon, Lmon, Oday, Ofx, Omon, Prim6hr, Primday, SIday, SImon, day, fx", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39147, "uuid": "f807cd714bff4b9eb22ede39bb52bda4", "title": "the CNRM-CERFACS team running: experiment primWP5-amv-pos using the CNRM-CM6-1 model.", "abstract": "The the CNRM-CERFACS team team consisted of the following agencies: Centre National de Recherches Météorologiques (CNRM) and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CERFACS).the CNRM-CERFACS team running the \"primWP5-amv-pos\" experiment using the CNRM-CM6-1 model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, CNRM-CERFACS, CNRM-CM6-1, primWP5-amv-pos, 3hr, 6hrPlev, 6hrPlevPt, AERday, AERmon, Amon, CFday, Eday, Efx, Emon, LImon, Lmon, Oday, Ofx, Omon, Prim6hr, Primday, SIday, SImon, day, fx", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39150, "uuid": "a410f72f56e348449d092a8e724d0bb6", "title": "the EC-Earth-Consortium team running: experiment primWP5-amv-neg using the EC-Earth3P-HR model.", "abstract": "The the EC-Earth-Consortium team team consisted of the following agencies: La Agencia Estatal de Meteorología (AEMET), Barcelona Supercomputing Centre (BSC), Institute of Atmospheric Sciences and Climate (CNR-ISAC), Danish Meteorological Institute (DMI), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Finnish Meteorological Institute (FMI), Helmholtz Centre for Ocean Research Kiel (Geomar), Irish Centre for High-End Computing (ICHEC), International Centre for Theoretical Physics (ICTP), Instituto Dom Luiz (IDL), Institute for Marine and Atmospheric research Utrecht (IMAU), Portuguese Institute for Sea and Atmosphere (IPMA), KIT Karlsruhe Institute of Technology, Royal Netherlands Meteorological Institute (KNMI), Lund University, Met Éireann, The Netherlands eScience Center (NLeSC), Norwegian University of Science and Technology (NTNU), University of Oxford, SURFsara, Swedish Meteorological and Hydrological Institute (SMHI), Stockholm University, Unite ASTR, University College Dublin, University of Bergen, University of Copenhagen, University of Helsinki, University of Santiago de Compostela, Uppsala University, University of Utrecht, Vrije Universiteit Amsterdam and Wageningen University.the EC-Earth-Consortium team running the \"primWP5-amv-neg\" experiment using the EC-Earth3P-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, EC-Earth-Consortium, EC-Earth3P-HR, primWP5-amv-neg, 6hrPlevPt, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39153, "uuid": "940800ea24a1446c94c71019f90e8566", "title": "the EC-Earth-Consortium team running: experiment primWP5-amv-pos using the EC-Earth3P-HR model.", "abstract": "The the EC-Earth-Consortium team team consisted of the following agencies: La Agencia Estatal de Meteorología (AEMET), Barcelona Supercomputing Centre (BSC), Institute of Atmospheric Sciences and Climate (CNR-ISAC), Danish Meteorological Institute (DMI), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Finnish Meteorological Institute (FMI), Helmholtz Centre for Ocean Research Kiel (Geomar), Irish Centre for High-End Computing (ICHEC), International Centre for Theoretical Physics (ICTP), Instituto Dom Luiz (IDL), Institute for Marine and Atmospheric research Utrecht (IMAU), Portuguese Institute for Sea and Atmosphere (IPMA), KIT Karlsruhe Institute of Technology, Royal Netherlands Meteorological Institute (KNMI), Lund University, Met Éireann, The Netherlands eScience Center (NLeSC), Norwegian University of Science and Technology (NTNU), University of Oxford, SURFsara, Swedish Meteorological and Hydrological Institute (SMHI), Stockholm University, Unite ASTR, University College Dublin, University of Bergen, University of Copenhagen, University of Helsinki, University of Santiago de Compostela, Uppsala University, University of Utrecht, Vrije Universiteit Amsterdam and Wageningen University.the EC-Earth-Consortium team running the \"primWP5-amv-pos\" experiment using the EC-Earth3P-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, EC-Earth-Consortium, EC-Earth3P-HR, primWP5-amv-pos, 6hrPlevPt, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39156, "uuid": "a13fa970f3964513965d961574a7ae68", "title": "the EC-Earth-Consortium team running: experiment primWP5-amv-neg using the EC-Earth3P model.", "abstract": "The the EC-Earth-Consortium team team consisted of the following agencies: La Agencia Estatal de Meteorología (AEMET), Barcelona Supercomputing Centre (BSC), Institute of Atmospheric Sciences and Climate (CNR-ISAC), Danish Meteorological Institute (DMI), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Finnish Meteorological Institute (FMI), Helmholtz Centre for Ocean Research Kiel (Geomar), Irish Centre for High-End Computing (ICHEC), International Centre for Theoretical Physics (ICTP), Instituto Dom Luiz (IDL), Institute for Marine and Atmospheric research Utrecht (IMAU), Portuguese Institute for Sea and Atmosphere (IPMA), KIT Karlsruhe Institute of Technology, Royal Netherlands Meteorological Institute (KNMI), Lund University, Met Éireann, The Netherlands eScience Center (NLeSC), Norwegian University of Science and Technology (NTNU), University of Oxford, SURFsara, Swedish Meteorological and Hydrological Institute (SMHI), Stockholm University, Unite ASTR, University College Dublin, University of Bergen, University of Copenhagen, University of Helsinki, University of Santiago de Compostela, Uppsala University, University of Utrecht, Vrije Universiteit Amsterdam and Wageningen University.the EC-Earth-Consortium team running the \"primWP5-amv-neg\" experiment using the EC-Earth3P model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, EC-Earth-Consortium, EC-Earth3P, primWP5-amv-neg, 6hrPlev, 6hrPlevPt, Amon, LImon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39159, "uuid": "58ac91b9aa52499f95455e340b86d460", "title": "the EC-Earth-Consortium team running: experiment primWP5-amv-pos using the EC-Earth3P model.", "abstract": "The the EC-Earth-Consortium team team consisted of the following agencies: La Agencia Estatal de Meteorología (AEMET), Barcelona Supercomputing Centre (BSC), Institute of Atmospheric Sciences and Climate (CNR-ISAC), Danish Meteorological Institute (DMI), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Finnish Meteorological Institute (FMI), Helmholtz Centre for Ocean Research Kiel (Geomar), Irish Centre for High-End Computing (ICHEC), International Centre for Theoretical Physics (ICTP), Instituto Dom Luiz (IDL), Institute for Marine and Atmospheric research Utrecht (IMAU), Portuguese Institute for Sea and Atmosphere (IPMA), KIT Karlsruhe Institute of Technology, Royal Netherlands Meteorological Institute (KNMI), Lund University, Met Éireann, The Netherlands eScience Center (NLeSC), Norwegian University of Science and Technology (NTNU), University of Oxford, SURFsara, Swedish Meteorological and Hydrological Institute (SMHI), Stockholm University, Unite ASTR, University College Dublin, University of Bergen, University of Copenhagen, University of Helsinki, University of Santiago de Compostela, Uppsala University, University of Utrecht, Vrije Universiteit Amsterdam and Wageningen University.the EC-Earth-Consortium team running the \"primWP5-amv-pos\" experiment using the EC-Earth3P model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, EC-Earth-Consortium, EC-Earth3P, primWP5-amv-pos, 6hrPlev, 6hrPlevPt, Amon, LImon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39162, "uuid": "73badac1137b476c8de0fe05826d790d", "title": "European Centre for Medium-Range Weather Forecasts (ECMWF) running: experiment primWP5-amv-neg using the ECMWF-IFS-HR model.", "abstract": "European Centre for Medium-Range Weather Forecasts (ECMWF) running the \"primWP5-amv-neg\" experiment using the ECMWF-IFS-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, ECMWF, ECMWF-IFS-HR, primWP5-amv-neg, 6hrPlevPt, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39165, "uuid": "eda08a6235bc4cdc9232471079b21afb", "title": "European Centre for Medium-Range Weather Forecasts (ECMWF) running: experiment primWP5-amv-pos using the ECMWF-IFS-HR model.", "abstract": "European Centre for Medium-Range Weather Forecasts (ECMWF) running the \"primWP5-amv-pos\" experiment using the ECMWF-IFS-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, ECMWF, ECMWF-IFS-HR, primWP5-amv-pos, 6hrPlevPt, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39168, "uuid": "8da2af5e222f48678f130d9d7ebcb147", "title": "European Centre for Medium-Range Weather Forecasts (ECMWF) running: experiment primWP5-amv-neg using the ECMWF-IFS-LR model.", "abstract": "European Centre for Medium-Range Weather Forecasts (ECMWF) running the \"primWP5-amv-neg\" experiment using the ECMWF-IFS-LR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, ECMWF, ECMWF-IFS-LR, primWP5-amv-neg, 6hrPlevPt, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39171, "uuid": "90244bde62c64071b6b8ed00270d5fc4", "title": "European Centre for Medium-Range Weather Forecasts (ECMWF) running: experiment primWP5-amv-pos using the ECMWF-IFS-LR model.", "abstract": "European Centre for Medium-Range Weather Forecasts (ECMWF) running the \"primWP5-amv-pos\" experiment using the ECMWF-IFS-LR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, ECMWF, ECMWF-IFS-LR, primWP5-amv-pos, 6hrPlevPt, Amon, LImon, Lmon, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39176, "uuid": "bc5331b829b547bbbe7f590cfeb79fba", "title": "National Centre for Atmospheric Science (NCAS) running: experiment primWP5-amv-neg using the MetUM-GOML2-HR model.", "abstract": "National Centre for Atmospheric Science (NCAS) running the \"primWP5-amv-neg\" experiment using the MetUM-GOML2-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, NCAS, MetUM-GOML2-HR, primWP5-amv-neg, 6hrPlevPt, Amon, LImon, Lmon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39179, "uuid": "e575d8904396456b8322fa3597306406", "title": "National Centre for Atmospheric Science (NCAS) running: experiment primWP5-amv-pos using the MetUM-GOML2-HR model.", "abstract": "National Centre for Atmospheric Science (NCAS) running the \"primWP5-amv-pos\" experiment using the MetUM-GOML2-HR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, NCAS, MetUM-GOML2-HR, primWP5-amv-pos, 6hrPlevPt, Amon, LImon, Lmon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39183, "uuid": "79381a20901b44c58b3e9ad94b843f83", "title": "National Centre for Atmospheric Science (NCAS) running: experiment primWP5-amv-neg using the MetUM-GOML2-LR model.", "abstract": "National Centre for Atmospheric Science (NCAS) running the \"primWP5-amv-neg\" experiment using the MetUM-GOML2-LR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, NCAS, MetUM-GOML2-LR, primWP5-amv-neg, 6hrPlevPt, Amon, LImon, Lmon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39186, "uuid": "31cc85af3f8d472daeb14a7f050eea6c", "title": "National Centre for Atmospheric Science (NCAS) running: experiment primWP5-amv-pos using the MetUM-GOML2-LR model.", "abstract": "National Centre for Atmospheric Science (NCAS) running the \"primWP5-amv-pos\" experiment using the MetUM-GOML2-LR model. See linked documentation for available information for each component.", "keywords": "PRIMAVERA, HighResMIP, climate change, NCAS, MetUM-GOML2-LR, primWP5-amv-pos, 6hrPlevPt, Amon, LImon, Lmon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39191, "uuid": "c5efe20099744ea3847b70c1568b7f32", "title": "Caption for Figure 2.38 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Indices of multi-decadal climate variability from 1854–2019 based upon several sea surface temperature data products. Shown are the indices of the AMV and PDV based on area averages for the regions indicated in Annex IV. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39207, "uuid": "a3aa8304674943f788a95d442155520e", "title": "Derivation of the CH4_S5P_WFMD v1.5 product from the WFM-DOAS Retrieval algorithm", "abstract": "The Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS) algorithm is a least-squares retrieval method based on scaling (or shifting) pre-selected atmospheric vertical profiles. The column-averaged dry air mole fractions of methane (denoted XCH4) are derived from the vertical column amounts of methane by normalising with the dry air column, which is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). The corresponding vertical columns of CH4 are retrieved from the measured sun-normalised radiance using spectral fitting windows in the SWIR spectral region (2311-2315.5 nm and 2320-2338 nm).\r\n\r\nFor further details see the documentation section.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39225, "uuid": "ec11710ba1924a4e8f0a1793921633fa", "title": "CCSR-NIES MIROC3.2 model deployed at NIES", "abstract": "CCSR-NIES MIROC3.2 model deployed at NIES", "keywords": "CCMI-2022, CCSR-NIES MIROC3.2, NIES", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39239, "uuid": "621f313c747c4c8ab57c88f8214debb3", "title": "the E3SM-Project team running: experiment abrupt-4xCO2 using the E3SM-2-0 model.", "abstract": "The the E3SM-Project team team consisted of the following agencies: Lawrence Livermore National Laboratory (NCAR LLNL), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratories (SNL).the E3SM-Project team running the \"abrupt quadrupling of CO2\" (abrupt-4xCO2) experiment using the E3SM-2-0 model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, E3SM-Project, E3SM-2-0, abrupt-4xCO2, Amon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39242, "uuid": "1860f9bd6a0f492ba65c0f5fa0508103", "title": "the E3SM-Project team running: experiment piControl using the E3SM-2-0 model.", "abstract": "The the E3SM-Project team team consisted of the following agencies: Lawrence Livermore National Laboratory (NCAR LLNL), Argonne National Laboratory (ANL), Brookhaven National Laboratory (BNL), Los Alamos National Laboratory (LANL), Lawrence Berkeley National Laboratory (LBNL), Oak Ridge National Laboratory (ORNL), Pacific Northwest National Laboratory (PNNL) and Sandia National Laboratories (SNL).the E3SM-Project team running the \"pre-industrial control\" (piControl) experiment using the E3SM-2-0 model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, E3SM-Project, E3SM-2-0, piControl, Amon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39246, "uuid": "4af3c57d67ec4eac99d2a4037310378b", "title": "the EC-Earth-Consortium team running: experiment dcppA-hindcast using the EC-Earth3-HR model.", "abstract": "The the EC-Earth-Consortium team team consisted of the following agencies: La Agencia Estatal de Meteorología (AEMET), Barcelona Supercomputing Centre (BSC), Institute of Atmospheric Sciences and Climate (CNR-ISAC), Danish Meteorological Institute (DMI), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Finnish Meteorological Institute (FMI), Helmholtz Centre for Ocean Research Kiel (Geomar), Irish Centre for High-End Computing (ICHEC), International Centre for Theoretical Physics (ICTP), Instituto Dom Luiz (IDL), Institute for Marine and Atmospheric research Utrecht (IMAU), Portuguese Institute for Sea and Atmosphere (IPMA), KIT Karlsruhe Institute of Technology, Royal Netherlands Meteorological Institute (KNMI), Lund University, Met Éireann, The Netherlands eScience Center (NLeSC), Norwegian University of Science and Technology (NTNU), University of Oxford, SURFsara, Swedish Meteorological and Hydrological Institute (SMHI), Stockholm University, Unite ASTR, University College Dublin, University of Bergen, University of Copenhagen, University of Helsinki, University of Santiago de Compostela, Uppsala University, University of Utrecht, Vrije Universiteit Amsterdam and Wageningen University.the EC-Earth-Consortium team running the \"hindcast initialized based on observations and using historical forcing\" (dcppA-hindcast) experiment using the EC-Earth3-HR model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, EC-Earth-Consortium, EC-Earth3-HR, dcppA-hindcast, Amon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39253, "uuid": "5eb0ad7a75404cbda0b9fbc453dd17f2", "title": "Met Office Hadley Centre (MOHC) running: experiment dcppA-hindcast using the HadGEM3-GC31-MM model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"hindcast initialized based on observations and using historical forcing\" (dcppA-hindcast) experiment using the HadGEM3-GC31-MM model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MOHC, HadGEM3-GC31-MM, dcppA-hindcast, AERday, Amon, Eday, Omon, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39256, "uuid": "70c362a667a04175bcaf9812b0718b20", "title": "the MIROC team running: experiment ssp370-ssp126Lu using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"SSP3-7.0 with SSP1-2.6 land use\" (ssp370-ssp126Lu) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, ssp370-ssp126Lu, Omon, fx", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39259, "uuid": "3a950d72758d4db09154cebf2ff6f780", "title": "the MIROC team running: experiment esm-1pct-brch-1000PgC using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"zero emissions simulation branched from 1% run after 1000 PgC cumulative emission\" (esm-1pct-brch-1000PgC) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, esm-1pct-brch-1000PgC, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39262, "uuid": "285b6888adca4a3aa3d8f9a5fc28e43b", "title": "the MIROC team running: experiment esm-1pct-brch-2000PgC using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"zero emissions simulation branched from 1% run after 2000 PgC cumulative emission\" (esm-1pct-brch-2000PgC) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, esm-1pct-brch-2000PgC, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39265, "uuid": "97a8655ac9264339bbf95b41df1e1979", "title": "the MIROC team running: experiment esm-1pctCO2 using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"emissions driven 1% run\" (esm-1pctCO2) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, esm-1pctCO2, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39268, "uuid": "cb592f91a72442958c0c0bef7445f861", "title": "the MIROC team running: experiment esm-pi-CO2pulse using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"pulse addition of 100 Gt carbon to pre-industrial atmosphere\" (esm-pi-CO2pulse) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, esm-pi-CO2pulse, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39273, "uuid": "fff4ee60773f4139b3c45e5aad5b150f", "title": "Met Office Hadley Centre (MOHC) running: experiment ssp585 using the UKESM1-0-LL model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"update of RCP8.5 based on SSP5\" (ssp585) experiment using the UKESM1-0-LL model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MOHC, UKESM1-0-LL, ssp585, 3hr, 6hrLev, 6hrPlevPt, AERday, AERhr, AERmon, AERmonZ, Amon, CF3hr, CFday, CFmon, E3hr, Eday, EdayZ, Emon, EmonZ, LImon, Lmon, Oday, Omon, SIday, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39276, "uuid": "3117b4a37def49c5b81b4209cb7456b9", "title": "the MIROC team running: experiment volc-pinatubo-full using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"Pinatubo experiment\" (volc-pinatubo-full) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, volc-pinatubo-full, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39284, "uuid": "7c1623e287274decb2292ee9f7a9c6a6", "title": "Met Office Hadley Centre (MOHC) running: experiment ssp370 using the UKESM1-0-LL model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"gap-filling scenario reaching 7.0 based on SSP3\" (ssp370) experiment using the UKESM1-0-LL model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MOHC, UKESM1-0-LL, ssp370, 3hr, 6hrLev, AERday, AERhr, AERmon, AERmonZ, Amon, CF3hr, CFday, CFmon, E3hr, Eday, EdayZ, Emon, EmonZ, LImon, Lmon, Oday, Omon, SIday, SImon, day", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39310, "uuid": "bd62b6fb07c74d92acde45000b99dbbb", "title": "UKESM 1.0 on Met Office Cray XC40 HPC", "abstract": "UKESM 1.0 on Met Office Cray XC40 HPC", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39311, "uuid": "a0dd77615c424b50b314d6c30e17a0bf", "title": "BRDF/Albedo Inversion Model.", "abstract": "Computation for the Globalbedo BRDF products.\r\nFor more information, please see the ATBD document in the related docs tab.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39319, "uuid": "28cb5e24fb464a338d7271a1c9995585", "title": "Technical Summary of the Working Group I Contribution to the IPCC Sixth Assessment Report - notes on reproducing Figure TS.1 v20221110", "abstract": "Notes on reproducing Figure TS.1 from the provided data.\r\n\r\n\r\nTop row of figure (CO2 concentrations) is related to:\r\n\r\n-- Paleo data are from Figure 2.3 and 2.4 (section 2.2.3):\r\n. Paleo 60–1 million years data are from Figure 2.3 (section 2.2.3), compiled data provided in this dataset,\r\n. Paleo and direct measurements 800 thousand years to 1980 CE data are from Figure 2.4 (section 2.2.3), input data references in the input data table 2.SM.1 of the Supplementary Material for Chapter 2.\r\n\r\n-- 2300 emissions scenarios are described in section 4.7.1:\r\n . Data link provided in Related Documents section of this catalogue record (https://zenodo.org/record/6386979#.YnAJPy-cb-E).\r\n\r\n\r\nBottom row of figure (global mean surface temperature) is related to:\r\n\r\n-- Paleo data are from Cross-Chapter Box 2.1, Figure 1:\r\n . 60 –1 million years from Hansen et al., (2013)\r\n . 800 to 0 thousand years from Snyder, (2016).\r\n . Data are archived at CEDA and link provided in Related Records section of this catalogue record (https://catalogue.ceda.ac.uk/uuid/0f05c2fb8f814d60ac2d657a70e9a7f5).\r\n\r\n-- Direct measurements are from Figure 2.11 (section 2.3.1):\r\n . Data are archived at CEDA and link provided in Related Records section of this catalogue record (https://catalogue.ceda.ac.uk/uuid/f3515388768344bfb2be0521f82388be).\r\n\r\n-- 2300 projections are from Figure 4.40a (section 4.7.1):\r\n . Data link provided in Related Documents section of this catalogue record (https://zenodo.org/record/6386979#.YnAJPy-cb-E).\r\n\r\n\r\nMaps of surface air temperature are related to:\r\n\r\n-- Early Eocene and mid-Pliocene are from Figure 7.13, data are archived at CEDA and link provided in Related Records section of this catalogue record.\r\n-- 2020 in this dataset\r\n-- 2100 and 2300 projections in this dataset.\r\n\r\n\r\nEarly Eocene:\r\n The background map (model) is the same as Figure 7.13a, and the data points (proxies) are the same as Figure 7.13a (circles) and Figure 7.13j (squares). The data for the model map comes from Lunt et al (2021). The model output is in the supp info of that paper, https://cp.copernicus.org/articles/17/203/2021/cp-17-203-2021-supplement.zip; . We only include those simulations which were carried out with CO2 in the IPCC assessed range, that is: \r\n\r\n\r\nN=5: CESM1.2_CAM5-deepmip_stand_6xCO2 , COSMOS-landveg_r2413-deepmip_sens_4xCO2 , GFDL_CM2.1-deepmip_stand_6xCO2 , GFDL_CM2.1-deepmip_sens_4xCO2 , INM-CM4-8-deepmip_stand_6xCO2 \r\n\r\nNote that an anomaly is shown, relative to the zonal mean of the ensemble mean preindustrial control (also available from the same source). \r\n\r\nThe proxies are from Hollis et al (2019), but only those sites that were deemed to not be affected by diagenesis. Data and metadata for these sites are in Inglis et al (2020). \r\n\r\n\r\nMid-Pliocene: \r\nThe background map (model) is the same as Figure 7.13b, and the data points (proxies) are the same as Figure 7.13b (circles) and Figure 7.13k (squares). The data for the model map comes from Haywood et al. (2020). The model output is in the PlioMIP database (for details see Chapter 2 Supplementary Material Table 2.SM.1, Cross-Chapter Box 2.4). In addition we also included one model published after Haywood et al., described in Williams et al. (2021), which is available on the ESGF. The simulations are:\r\n\r\nN=17: CCSM4 , CCSM4-UoT . CCSM4-Utrecht , CESM1.2 , CESM2.0 , COSMOS , EC-Earth3.3 , GISS-E2-1-G , HadCM3 , HadGEM3 , IPSL-CM6A-LR , IPSLCM5A , IPSLCM5A2 , MIROC4m , MRI-CGCM2.3 , NorESM-L , NorESM1-F \r\n\r\nNote that an anomaly is shown, relative to the ensemble mean preindustrial control (also available from the same sources). \r\n\r\nThe proxies are from McClymont et al (2020; squares) and Salzmann et al (2013; circles; only those sites that fall within the time window of the Km5c PlioMIP time slice).\r\n\r\nSSPs:\r\n\r\n\r\nThe SSP map plots were produced in the following way:\r\n\r\n- For 2300, the means plotted are Years 2281-2300 minus 1850-1900.\r\n - For 2100, the means plotted are Years 2081-2100 minus 1850-1900.\r\n\r\n\r\nThe SSPs are 5-8.5 and 1-2.6. \r\n\r\nThe multi-model ensemble mean is based on models with 2300 simulations of both these SSPs at the time of making the plot: \r\n\r\nCanESM5 , IPSL-CM6A-LR , MRI-ESM2-0 , CESM2-WACCM , UKESM1-0-LL.\r\n\r\nAll available from the ESGF – SSP simulations and historical simulations.\r\n\r\n2020: \r\n The methodology for this maps is in Hawkins et al (2020), and is based on regression of local temperatures on the global mean.\r\n\r\nReferences :\r\n\r\nHansen, J., M. Sato, G. Russell, and P. Kharecha, 2013: Climate sensitivity, sea level and atmospheric carbon dioxide. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 371(2001), 20120294, doi:10.1098/rsta.2012.0294.\r\n\r\nHawkins, E., Frame, D., Harrington, L., Joshi, M., King, A., Rojas, M., and Sutton, R, 2020: Observed emergence of the climate change signal: From the familiar to the unknown. Geophysical Research Letters, 47, e2019GL086259. doi:10.1029/2019GL086259.\r\n\r\nHaywood, A. M., Tindall, J. C., Dowsett, H. J., Dolan, A. M., Foley, K. M., Hunter, S. J., Hill, D. J., Chan, W.-L., Abe-Ouchi, A., Stepanek, C., Lohmann, G., Chandan, D., Peltier, W. R., Tan, N., Contoux, C., Ramstein, G., Li, X., Zhang, Z., Guo, C., Nisancioglu, K. H., Zhang, Q., Li, Q., Kamae, Y., Chandler, M. A., Sohl, L. E., Otto-Bliesner, B. L., Feng, R., Brady, E. C., von der Heydt, A. S., Baatsen, M. L. J., and Lunt, D. J., 2020: The Pliocene Model Intercomparison Project Phase 2: large-scale climate features and climate sensitivity. Climate of the Past, 16, 2095-2123. doi:10.5194/cp-16-2095-2020. \r\n\r\nHollis, C. J., Dunkley Jones, T., Anagnostou, E., Bijl, P. K., Cramwinckel, M. J., Cui, Y., Dickens, G. R., Edgar, K. M., Eley, Y., Evans, D., Foster, G. L., Frieling, J., Inglis, G. N., Kennedy, E. M., Kozdon, R., Lauretano, V., Lear, C. H., Littler, K., Lourens, L., Meckler, A. N., Naafs, B. D. A., Pälike, H., Pancost, R. D., Pearson, P. N., Röhl, U., Royer, D. L., Salzmann, U., Schubert, B. A., Seebeck, H., Sluijs, A., Speijer, R. P., Stassen, P., Tierney, J., Tripati, A., Wade, B., Westerhold, T., Witkowski, C., Zachos, J. C., Zhang, Y. G., Huber, M., and Lunt, D. J., 2019: The DeepMIP contribution to PMIP4: methodologies for selection, compilation and analysis of latest Paleocene and early Eocene climate proxy data, incorporating version 0.1 of the DeepMIP database, Geoscience Model Development, 12, 3149-3206, doi:10.5194/gmd-12-3149-2019. \r\n\r\nInglis, G. N., Bragg, F., Burls, N. J., Cramwinckel, M. J., Evans, D., Foster, G. L., Huber, M., Lunt, D. J., Siler, N., Steinig, S., Tierney, J. E., Wilkinson, R., Anagnostou, E., de Boer, A. M., Dunkley Jones, T., Edgar, K. M., Hollis, C. J., Hutchinson, D. K., and Pancost, R. D., 2020: Global mean surface temperature and climate sensitivity of the early Eocene Climatic Optimum (EECO), Paleocene-Eocene Thermal Maximum (PETM), and latest Paleocene. Climate of the Past, 16, 1953-1968. doi:10.5194/cp-16-1953-2020. \r\n\r\nLunt, D. J., Bragg, F., Chan, W.-L., Hutchinson, D. K., Ladant, J.-B., Morozova, P., Niezgodzki, I., Steinig, S., Zhang, Z., Zhu, J., Abe-Ouchi, A., Anagnostou, E., de Boer, A. M., Coxall, H. K., Donnadieu, Y., Foster, G., Inglis, G. N., Knorr, G., Langebroek, P. M., Lear, C. H., Lohmann, G., Poulsen, C. J., Sepulchre, P., Tierney, J. E., Valdes, P. J., Volodin, E. M., Dunkley Jones, T., Hollis, C. J., Huber, M., and Otto-Bliesner, B. L., 2021: DeepMIP: model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data. Climate of the Past, 17, 203-227, doi: 10.5194/cp-17-203-2021. \r\n\r\nMcClymont, E. L., Ford, H. L., Ho, S. L., Tindall, J. C., Haywood, A. M., Alonso-Garcia, M., Bailey, I., Berke, M. A., Littler, K., Patterson, M. O., Petrick, B., Peterse, F., Ravelo, A. C., Risebrobakken, B., De Schepper, S., Swann, G. E. A., Thirumalai, K., Tierney, J. E., van der Weijst, C., White, S., Abe-Ouchi, A., Baatsen, M. L. J., Brady, E. C., Chan, W.-L., Chandan, D., Feng, R., Guo, C., von der Heydt, A. S., Hunter, S., Li, X., Lohmann, G., Nisancioglu, K. H., Otto-Bliesner, B. L., Peltier, W. R., Stepanek, C., and Zhang, Z., 2020: Lessons from a high-CO2 world: an ocean view from ∼ 3 million years ago. Climate of the Past, 16, 1599–1615. doi:10.5194/cp-16-1599-2020.\r\n\r\nSalzmann, U., Dolan, A., Haywood, A., Chan, W.-L., Voss, J., Hill, D., Abe-Ouchi, A., Otto-Bliesner, B., Brag, F., Chandler, M., Contoux, C., Dowsett, H., Jost, A., Kamae, Y., Lohmann, G., Lunt, D., Pickering, s., Pound, M., Ramstein, G., Rosenbloom, N., Soh, L., Stepanek, C., Ueda, H., and Zhang, Z., 2013: Challenges in quantifying Pliocene terrestrial warming revealed by data–model discord. Nature Climate Change, 3, 969–974. doi:10.1038/nclimate2008.\r\n\r\nSnyder, C.W., 2016: Evolution of global temperature over the past two million years. Nature, 538, 226, doi:10.1038/nature19798.\r\n\r\nWilliams, C. J. R., Sellar, A. A., Ren, X., Haywood, A. M., Hopcroft, P., Hunter, S. J., Roberts, W. H. G., Smith, R. S., Stone, E. J., Tindall, J. C.,and Lunt, D. J., 2021: Simulation of the mid-Pliocene Warm Period using HadGEM3: experimental design and results from modelmodel and modeldata comparison. Climate of the Past, 17, 21392163. doi:10.5194/cp-17-2139-2021.", "keywords": "Cenozoic global surface temperature, Cenozoic atmospheric CO2 concentration, CO2 and surface temperature projections, IPCC-DDC, IPCC, AR6, WG1, WGI, Sixth Assessment Report, Working Group 1, Physical Science Basis", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39322, "uuid": "b82bf09141a641029c94bba72f25b54a", "title": "Computation for Mesoscale convective system tracks", "abstract": "Mesoscale convective systems tracks and their summary statistics were produced using the UK Met Office (UKMO) Unified Model and ETH-Zurich (ETHZ) COSMO 2.2km Europe-wide convection permitting model simulations. The simulations are described in Berthou et al (2020) and Brogli et al (2022); they include two ECMWF Interim hindcast simulations (one from each model), two GCM-driven simulations (UM-only, present-climate and end-of-century RCP8.5), and one pseudo-global warming simulation (COSMO-only, end-of-century RCP8.5).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39328, "uuid": "b352843aed184a06887e19053d9cb4d7", "title": "the MIROC team running: experiment deforest-globe using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"idealized transient global deforestation\" (deforest-globe) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, deforest-globe, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39331, "uuid": "e0ae0692c8de48d48638c28f52d22e2c", "title": "the MIROC team running: experiment esm-ssp585-ssp126Lu using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"emissions-driven SSP5-8.5 with SSP1-2.6 land use\" (esm-ssp585-ssp126Lu) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, esm-ssp585-ssp126Lu, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39334, "uuid": "8a4cd24e69264188b97e30ac4d1d6ddb", "title": "the MIROC team running: experiment hist-noLu using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"historical with no land-use change\" (hist-noLu) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, hist-noLu, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39337, "uuid": "1b5b9ff5fd4b443da07401e35232be6f", "title": "the MIROC team running: experiment past1000 using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"last millennium\" (past1000) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, past1000, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39340, "uuid": "0e87c00f30a240d593fad9fd9bcba2fe", "title": "the MIROC team running: experiment volc-pinatubo-strat using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"Pinatubo experiment with partial radiative forcing, includes only stratospheric warming\" (volc-pinatubo-strat) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, volc-pinatubo-strat, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39343, "uuid": "ef8b359105314c21b73ef7277a60703e", "title": "the MIROC team running: experiment volc-pinatubo-surf using the MIROC-ES2L model.", "abstract": "The the MIROC team team consisted of the following agencies: Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI).the MIROC team running the \"Pinatubo experiment with partial radiative forcing, solar radiation scattering only\" (volc-pinatubo-surf) experiment using the MIROC-ES2L model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MIROC, MIROC-ES2L, volc-pinatubo-surf, Amon, Lmon, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39348, "uuid": "15731dd5e693496480a0874d2da60398", "title": "UKESM1-StratTrop model deployed at NCAS-CAMBRIDGE", "abstract": "One simulation is a UKESM1.0 configuration at UM version 11.5 with additional diagnostics added and \"nudged\" to ERA-Interim reanalyses for 1981-2014 (u-bv711 and u-bw316) and continued using SSP3-7.0 forcings to 2019 (u-by117).\r\n\r\nOne simulation is a UKESM1.0 configuration at UM version 11.5 with additional diagnostics added and \"nudged\" to ERA-5 reanalyses for 1982-2014 (u-bw784) and continued using SSP3-7.0 forcings to 2020 (u-by803). The bugfix documented in Ranjithkumar et al. 2021 has been applied (DOI:10.5194/acp-21-4979-2021).\r\n\r\nOne simulation is a UKESM-StratTrop configuration with stratospheric ozone improvements at UM version 11.5 with additional diagnostics and \"nudged\" to ERA-5 reanalyses for 1982-2014 (u-bv828 and u-bx320) and continued using SSP3-7.0 forcings to 2020 (u-by808). The bugfix documented in Ranjithkumar et al. 2021 has been applied (DOI:10.5194/acp-21-4979-2021).", "keywords": "CCMI-2022, UKESM1-StratTrop, NCAS-CAMBRIDGE", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39351, "uuid": "97c7359a058341a6992965389227e4f4", "title": "GEOSCCM model deployed at NASA-GSFC", "abstract": "GEOSCCM model deployed at NASA-GSFC", "keywords": "CCMI-2022, GEOSCCM, NASA-GSFC", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39354, "uuid": "41c2b350586e47d396137c78b95e0735", "title": "IPSL-CM6A-ATM-LR-REPROBUS model deployed at IPSL", "abstract": "IPSL-CM6A-ATM-LR-REPROBUS model deployed at IPSL", "keywords": "CCMI-2022, UKESM1-StratTrop, NCAS-CAMBRIDGE", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39357, "uuid": "a9c5ee7a72e84337ab1a713904bd1a3c", "title": "CMAM model deployed at CCCma", "abstract": "Canadian Middle Atmosphere Model (CMAM) is a well-established high-top chemistry-climate model deployed at Canadian Centre for Climate Modelling and Analysis (CCCma).", "keywords": "CCMI-2022, CMAM, CCCma", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39362, "uuid": "08c4600003e8496fb8180cec243b321d", "title": "EMAC-CCMI2 model deployed by the MESSy-Consortium", "abstract": "EMAC-CCMI2 model deployed by the MESSy-Consortium", "keywords": "CCMI-2022, CMAM, CCCma", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39368, "uuid": "f13d79567f5f427b800a474bd9b34619", "title": "CNRM-MOCAGE model deployed at CNRM-CERFACS", "abstract": "CNRM-MOCAGE model deployed at CNRM-CERFACS", "keywords": "CCMI-2022, CNRM-MOCAGE, CNRM-CERFACS", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39386, "uuid": "69cbb2961d6b4ee9a16c65169d7735e0", "title": "ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM) deployed on ARCHER2 computer", "abstract": "ICOsahedral Nonhydrostatic (ICON) Atmospheric Global Circulation Model (GCM) deployed on ARCHER2 computer", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39497, "uuid": "ec38cf000e0e401d82ed041c75bbc6e4", "title": "Computation for High-resolution daily global climate dataset of BCCAQ statistically downscaled CMIP6 models for the EVOFLOOD project", "abstract": "A novel statistical downscaling model, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), is used to downscale daily precipitation, air temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6).", "keywords": "CMIP6", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39503, "uuid": "a08cc459df1945b29f1bd5a67eaee9ca", "title": "DTU Space ESMR NIMBUS-5 algorithm", "abstract": "Sea ice concentration is obtained from ESMR passive microwave satellite data over the polar regions. The processing chain features: 1) dynamic tuning of tie-points and algorithms, 2) correction of atmospheric noise using a Radiative Transfer Model, 3) computation of per-pixel uncertainties, and 4) one channel sea ice concentration algorithm.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39506, "uuid": "98b47ef7b7de48d8bc769c96a75bf424", "title": "Caption for Figure 6.25 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effect of dedicated air pollution or climate policy on population-weighted PM2.5 concentrations (µg m–3) and share of population (%) exposed to different PM2.5 levels across selected world regions. Thresholds of 10 µg m–3 and 35 µg m–3 represent the WHO air quality guideline and the WHO interim target 1, respectively; WHO (2017). Results are compared for SSP3-7.0 (no major improvement of current legislation is assumed), SSP3-lowSLCF (strong air pollution controls are assumed), and a climate change mitigation scenario SSP3-3.4; details of scenario assumptions are discussed in Riahi et al. (2017) and Rao et al. (2017). Analysis performed with the TM5-FASST model (Van Dingenen et al., 2018) using emission projections from the Shared economic Pathway (SSP) database (Riahi et al., 2017; Rogelj et al., 2018a; Gidden et al., 2019). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39512, "uuid": "27b781b39d224ce08f00e45c4ba35179", "title": "Caption for Figure 6.26 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effect of dedicated air pollution or climate policy on population-weighted ozone concentrations (SOMO0; ppb) and share of population (%) exposed to chosen ozone levels across across 10 world regions. Results are compared for SSP3-7.0 (no major improvement of current legislation is assumed), SSP3-low NTCF (strong air pollution controls are assumed), and a climate change mitigation scenario (SSP3-3.4); details of scenario assumptions are discussed in Riahi et al. (2017) and Rao et al. (2017). Analysis performed with the TM5-FASST model (Van Dingenen et al., 2018) using emission projections from the Socio-economic Pathway (SSP) database (https://tntcat.iiasa.ac.at/SspDb/dsd (Riahi et al., 2017; Rogelj et al., 2018a; Gidden et al., 2019). Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39515, "uuid": "685156be91b14dcfa41c4116f6c3fbf8", "title": "Caption for Box TS.7, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effects of short-lived climate forcers (SLCFs) on global surface temperature and air pollution across the WGI core set of Shared Socio-economic Pathways (SSPs). The intent of this figure is to show the climate and air quality (surface ozone and particulate matter smaller than 2.5 microns in diameter, or PM2.5) response to SLCFs in the SSP scenarios for the near and long-term. Effects of net aerosols, tropospheric ozone, hydrofluorocarbons (HFCs; with lifetimes less than 50 years), and methane (CH4) are compared with those of total anthropogenic forcing for 2040 and 2100 relative to year 2019. The global surface temperature changes are based on historical and future evolution of effective radiative forcing (ERF) as assessed in Chapter 7 of this Report. The temperature responses to the ERFs are calculated with a common impulse response function (RT) for the climate response, consistent with the metric calculations in Chapter 7 (Box 7.1). The RT has an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 concentration (feedback parameter of –1.31 W m–2°C–1). The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes). Uncertainties are 5–95% ranges. The global changes in air pollutant concentrations (ozone and PM2.5) are based on multimodel Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and represent changes in five-year mean surface continental concentrations for 2040 and 2098 relative to 2019. Uncertainty bars represent inter-model ±1 standard deviation. {6.7.2, 6.7.3, Figure 6.24}", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39523, "uuid": "42c34fdcaa514bea986b06a47f06e7a3", "title": "Caption for Figure 2.4 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Atmospheric well-mixed greenhouse gas (WMGHG) concentrations from ice cores. (a) Records during the last 800 kyr with the Last Glacial Maximum (LGM) to Holocene transition as inset. (b) Multiple high-resolution records over the CE. The horizontal black bars in panel (a) inset indicate LGM and Last Deglacial Termination (LDT) respectively. The red and blue lines in (b) are 100-year running averages for CO2 and N2O concentrations, respectively. The numbers with vertical arrows in (b) are instrumentally measured concentrations in 2019. Further details on data sources and processing are available in the chapter data table (Table 2.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39525, "uuid": "28a24e2f4a1e4b50962b3378afabc980", "title": "Computation Component: Level 2 Land processing algorithm applied to Sentinel 3 SRAL raw data.", "abstract": "There are three main steps in the Level-2 processing chain:\r\n\r\n1. Compute time-derived geophysical/environmental parameters.\r\n2. Perform re-tracking and compute physical parameters.\r\n3. Compute Level-2 altimeter/radiometer geophysical processing. \r\n\r\nComputing time-derived geophysical parameters involves:\r\n\r\n* re-computing altitude, orbital altitude rate, location and Doppler correction, accounting for updated orbit data\r\n* computing ionospheric corrections\r\n*computing non-equilibrium and equilibrium ocean tide heights, tidal loading, solid earth tide height, equilibrium long period ocean tide height and pole tide height (using pole locations)\r\n*computing the height of the mean sea surface above the reference ellipsoid\r\n*computing the mean dynamic topography, the height of the geoid and the ocean depth/land elevation.\r\n\r\nPerforming retracking and computing physical parameters (both SAR and LRM modes) involves:\r\n\r\n*discriminating echoes (ocean/lead, sea-ice, ice sheet margin or ocean/coastal)\r\n*performing retracking (ocean/lead, sea-ice, ice sheet margin or ocean/coastal)\r\n*computing physical parameters\r\n*merging snow depth (ocean/lead and sea-ice only)\r\n*performing a short-arc, along track ocean interpolation (ocean/lead and sea-ice only)\r\n*estimating freeboards (ocean/lead and sea-ice only)\r\n*performing a latitude limit check (ocean/lead and sea-ice only)\r\n\r\n(For SAR mode) performing modified slope correction (ice sheet margin and ocean/coastal only).\r\nLevel-2 altimeter/radiometer geophysical processing involves:\r\n\r\n*inputting and checking Level-1 MWR products\r\n*computing and correcting physical parameters according to platform data\r\n*interpolating MWR data and computing MWR geophysical parameters\r\n*computing altimeter wind speed and rain/ice flags\r\n*computing wind, tropospheric corrections and inverted barometer according to meteorological data\r\n*computing HF fluctuations of the atmospheric effect on the surface (dynamic atmosphere correction)\r\n*computing sea state bias\r\n*computing dual frequency ionospheric corrections\r\n*building and checking Level-2 SRAL/MWR products.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39526, "uuid": "0dae9a32133941daa422df4e1c8598db", "title": "TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset [1991-2021] constructed using machine-learning", "abstract": "TOMCAT simulation is performed at T64L32 resolution that is similar to the one used in Dhomse et al., (2021, 2022) for 1991-2021 time period. Model profile are sample at ACE-FTS (2004-present) measurement collocation, so that we get model output at nearest lat/lon and time. Then collocated N2O profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Corrections for overlapping latitude are averaged to ensure that mean correction terms do not have sharp edges\r\n\r\nInitially, differences are calculated for each zonal bins for 51 height levels (10km to 60km). Then separate XGBoost regression models are trained for the N2O differences between TOMCAT and measurements at each level for a given latitude bin. Same model is used for all day/night time (2 X11323 days) TOMCAT output sampled at 1.30 am and 1.30 pm local time at the equator. Bias corrections for a given model grid are calculated using XGBoost and are added to the original TOMCAT day and night time profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39527, "uuid": "7bb74f45412947aeb044e59d3bd653f4", "title": "TOMCAT CTM and Occultation Measurements based daily zonal stratospheric methane profile dataset (1991-2021) constructed using machine-learning", "abstract": "TOMCAT simulation is performed at T64L32 resolution that is similar to the one used in Dhomse et al., (2021, 2022) for 1991-2021 time period. Collocated methane profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, differences are calculated for each zonal bins for 46 height levels (15km to 60km). Then separate XGBoost regression models are trained for the methane differences between TOMCAT and measurements at each level for a given latitude bin. Same model is used for all day/night time (2 X11323 days) TOMCAT output sampled at 1.30 am and 1.30 pm local time at the equator. This way we get bias corrections for a given model grid that are added to the original TOMCAT day and night time profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation.\r\n\r\nDataset also includes two files containing daily mean zonal mean methane profiles on height (15-60 km) and pressure (300-0.1 hPa) levels", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39535, "uuid": "8b24f4f225634d2cb58ea982f3139fae", "title": "Igor 7.0 computation for MAQS Aerosol Chemical Species Monitor (ACSM) at Manchester Air Quality Site", "abstract": "Igor 7.0 is used in conjunction with the ACSM Local Software Version 1.6.1.0 to process the data with correct calibrations", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39540, "uuid": "160ca5a2ae134a6e985d49d412bfb602", "title": "EuroCORDEX-UK: Regional climate projections for the UK domain at 12 km Resolution for 1980-2080", "abstract": "Ten regional climate models were forced by outputs from six different general circulation models to 0.11 degree resolution (EUR-11) to produce an ensemble of 64 regional climate model runs. This data was conservatively regridded onto the 12 km OSGB grid, with land cells regridded only to land cell and sea cells regridded only to sea cells.", "keywords": "UK, Climate, Projections, UKCP18, Regional, Europe, Simulation, Model, Runs, CORDEX, EuroCORDEX", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39544, "uuid": "e7122dee4a894ff483f1bc311a931e17", "title": "EuroCORDEX-UK: Regional climate projections for the UK by Administrative Regions for 1980-2080", "abstract": "Ten regional climate models were forced by outputs from six different general circulation models to produce an ensemble of 64 regional climate model runs at 0.11 degree resolution (EUR-11). Land surface cells were aggregated into administrative regions by assigning each cell to the region containing its centre, and averaging over each region.", "keywords": "UK, Climate, Projections, UKCP18, Regional, Europe, Simulation, Model, Runs, CORDEX, EuroCORDEX", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39545, "uuid": "0f00fdf2d07a4e519614d075a81b76b8", "title": "EuroCORDEX-UK: Regional climate projections for the UK by Countries for 1980-2080", "abstract": "Ten regional climate models were forced by outputs from six different general circulation models to produce an ensemble of 64 regional climate model runs at 0.11 degree resolution (EUR-11). Land surface cells were aggregated into countries by assigning each cell to the country containing its centre, and averaging over each country.", "keywords": "UK, Climate, Projections, UKCP18, Regional, Europe, Simulation, Model, Runs, CORDEX, EuroCORDEX", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39546, "uuid": "57f6483cfb2b4be9a2b1eece0c9b7c75", "title": "EuroCORDEX-UK: Regional climate projections for the UK by River Basins for 1980-2080", "abstract": "Ten regional climate models were forced by outputs from six different general circulation models to produce an ensemble of 64 regional climate model runs at 0.11 degree resolution (EUR-11). Land surface cells were aggregated into river basins by assigning each cell to the basin containing its centre, and averaging over each basin.", "keywords": "UK, Climate, Projections, UKCP18, Regional, Europe, Simulation, Model, Runs, CORDEX, EuroCORDEX", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39563, "uuid": "6d7a2e491ec94cbc958b89c72b3e61fb", "title": "WaveWatch III", "abstract": "WAVEWATCH III® (Tolman 1997, 1999a, 2009) is a third generation wave model developed at NOAA/NCEP in the spirit of the WAM model (WAMDIG 1988, Komen et al. 1994). It is a further development of the model WAVEWATCH, as developed at Delft University of Technology (Tolman 1989, 1991a) and WAVEWATCH II, developed at NASA, Goddard Space Flight Center (e.g., Tolman 1992). WAVEWATCH III®, however, differs from its predecessors in many important points such as the governing equations, the model structure, the numerical methods and the physical parameterizations. Furthermore, with model version 3.14, WAVEWATCH III® is evolving from a wave model into a wave modeling framework, which allows for easy development of additional physical and numerical approaches to wave modeling. WAVEWATCH III® solves the random phase spectral action density balance equation for wavenumber-direction spectra. The implicit assumption of this equation is that properties of medium (water depth and current) as well as the wave field itself vary on time and space scales that are much larger than the variation scales of a single wave. With version 3.14 some source term options for extremely shallow water (surf zone) have been included, as well as wetting and drying of grid points. Whereas the surf-zone physics implemented so far are still fairly rudimentary, it does imply that the wave model can now be applied to arbitrary shallow water.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39577, "uuid": "94f26e92d6e9469a8828bdd83eedafcc", "title": "Caption for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models (Thornhill et al. , 2021b). ERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6. Error bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). ‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ Section 7.3.3). For GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2°C–1. Contributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39578, "uuid": "33ffe137cbed458994d2c0cd88e049d3", "title": "Caption for Figure 6.22 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-Economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2°C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39579, "uuid": "848b8a79298f48d3a55c4c313ff603fa", "title": "Caption for Figure 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), are compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2°C–1; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs and their uncertainties. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39580, "uuid": "c7624ed5d9f1410b86820236c4346ac6", "title": "Caption for Figure 6.12 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Contribution to effective radiative forcing (ERF) (a) and global mean surface air temperature (GSAT) change (b) from component emissions between 1750 to 2019 based on CMIP6 models (Thornhill et al. , 2021b). ERFs for the direct effect of well-mixed greenhouse gases (WMGHGs) are from the analytical formulae in section 7.3.2, H2O (strat) is from Table 7.8. ERFs for other components are multi-model means from Thornhill et al. (2021b) and are based on ESM simulations in which emissions of one species at a time are increased from 1850 to 2014 levels. The derived emissions-based ERFs are rescaled to match the concentration-based ERFs in Figure 7.6. Error bars are 5–95% and for the ERF account for uncertainty in radiative efficiencies and multi-model error in the means. ERFs due to aerosol–radiation (ERFari) and cloud effects are calculated from separate radiation calls for clear-sky and aerosol-free conditions (Ghan, 2013; Thornhill et al., 2021b). ‘Cloud’ includes cloud adjustments (semi-direct effect) and ERF from indirect aerosol-cloud to –0.22 W m–2 for ERFari and –0.84 W m–2 interactions (ERFaci). The aerosol components (SO2, organic carbon and black carbon) are scaled to sum to –0.22 W m–2 for ERFari and –0.84 W m–2 for ‘cloud’ Section 7.3.3). For GSAT estimates, time series (1750–2019) for the ERFs have been estimated by scaling with concentrations for WMGHGs and with historical emissions for SLCFs. The time variation of ERFaci for aerosols is from Chapter 7. The global mean temperature response is calculated from the ERF time series using an impulse response function (Cross-Chapter Box 7.1) with a climate feedback parameter of –1.31 W m–2°C–1. Contributions to ERF and GSAT change from contrails and light-absorbing particles on snow and ice are not represented, but their estimates can be seen on Figure 7.6 and 7.7, respectively. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39581, "uuid": "2e38597edadc49e4a124c00bc51d88ec", "title": "Caption for Figure 7.3 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Anomalies in global mean all-sky top-of-atmosphere (TOA) fluxes from CERES-EBAF Ed4.0 (solid black lines) and various CMIP6 climate models (coloured lines) in terms of (a) reflected solar, (b) emitted thermal and (c) net TOA fluxes. The multi-model means are additionally depicted as solid red lines. Model fluxes stem from simulations driven with prescribed sea surface temperatures (SSTs) and all known anthropogenic and natural forcings. Shown are anomalies of 12-month running means. All flux anomalies are defined as positive downwards, consistent with the sign convention used throughout this chapter. The correlations between the multi-model means (solid red lines) and the CERES records (solid black lines) for 12-month running means are: 0.85 for the global mean reflected solar; 0.73 for outgoing thermal radiation; and 0.81 for net TOA radiation. Figure adapted from Loeb et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39582, "uuid": "3dcec0b720fc464a97cf59130e23a5a0", "title": "Caption for Figure 7.4 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Radiative adjustments at top of atmosphere for seven different climate drivers as a proportion of forcing. Tropospheric temperature (orange), stratospheric temperature (yellow), water vapour (blue), surface albedo (green), clouds (grey) and the total adjustment (black) is shown. For the greenhouse gases (carbon dioxide, methane, nitrous oxide and CFC-12) the adjustments are expressed as a percentage of stratospheric-temperature-adjusted radiative forcing (SARF), whereas for aerosol, solar and volcanic forcing they are expressed as a percentage of instantaneous radiative forcing (IRF). Land surface temperature response (outline red bar) is shown, but included in the definition of forcing. Data from Smith et al. (2018b) for carbon dioxide and methane; Smith et al. (2018b) and Gray et al. (2009) for solar; Hodnebrog et al. (2020b) for nitrous oxide and CFC-12; Smith et al. (2020b) for aerosol, and Marshall et al. (2020) for volcanic. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39583, "uuid": "2740dacb1116498ba481751b4298e660", "title": "Caption for Figure 7.5 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Net aerosol effective radiative forcing (ERF) from different lines of evidence. The headline AR6 assessment of –1.3 [–2.0 to –0.6] W m–2 is highlighted in purple for 1750–2014 and compared to the AR5 assessment of –0.9 [–1.9 to –0.1] W m–2 for 1750–2011. The evidence comprising the AR6 assessment is shown below this: energy balance constraints [–2 to 0 W m–2 with no best estimate]; observational evidence from satellite retrievals of –1.4 [–2.2 to –0.6] W m–2; and climate model-based evidence of –1.25 [–2.1 to –0.4] W m–2. Estimates from individual CMIP5 (Zelinka et al., 2014) and CMIP6 (Smith et al., 2020b and Table 7.6) models are depicted by blue and red crosses respectively. For each line of evidence the assessed best-estimate contributions from ERFari and ERFaci are shown with darker and paler shading respectively. The observational assessment for ERFari is taken from the IRFari. Uncertainty ranges are represented by black bars for the total aerosol ERF and depictvery likely ranges. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39584, "uuid": "bd5e0c0d1b044d57a70dcf1e7e96ac49", "title": "Caption for Figure 7.6 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Change in effective radiative forcing (ERF) from 1750 to 2019 by contributing forcing agents (carbon dioxide, other well-mixed greenhouse gases (WMGHGs), ozone, stratospheric water vapour, surface albedo, contrails and aviation-induced cirrus, aerosols, anthropogenic total, and solar). Solid bars represent best estimates, and very likely (5–95%) ranges are given by error bars. Non-CO2 WMGHGs are further broken down into contributions from methane (CH4), nitrous oxide (N2O) and halogenated compounds. Surface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. Aerosols are broken down into contributions from aerosol–cloud interactions (ERFaci) and aerosol–radiation interactions (ERFari). For aerosols and solar, the 2019 single-year values are given (Table 7.8), which differ from the headline assessments in both cases. Volcanic forcing is not shown due to the episodic nature of volcanic eruptions. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39585, "uuid": "a860fe2510ff4d02aa574f152f5ef035", "title": "Caption for Figure 7.7 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "The contribution of forcing agents to 2019 temperature change relative to 1750 produced using the two-layer emulator (Supplementary Material 7.SM.2), constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1. The results are from a 2237-member ensemble. Temperature contributions are expressed for carbon dioxide, other well-mixed greenhouse gases (WMGHGs), ozone, stratospheric water vapour, surface albedo, contrails and aviation-induced cirrus, aerosols, solar, volcanic, and total. Solid bars represent best estimates, and very likely (5–95%) ranges are given by error bars. Dashed error bars show the contribution of forcing uncertainty alone, using best estimates of ECS (3.0°C), TCR (1.8°C) and two-layer model parameters representing the CMIP6 multi-model mean. Solid error bars show the combined effects of forcing and climate response uncertainty using the distribution of ECS and TCR from Tables 7.13 and 7.14, and the distribution of calibrated model parameters from 44 CMIP6 models. Non-CO2WMGHGs are further broken down into contributions from methane (CH4), nitrous oxide (N2O) and halogenated compounds. Surface albedo is broken down into land-use changes and light-absorbing particles on snow and ice. Aerosols are broken down into contributions from aerosol–cloud interactions (ERFaci) and aerosol–radiation interactions (ERFari). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39586, "uuid": "2d96254dc3974cfb9ffbdf82c36e3f5a", "title": "Caption for Figure 7.8 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Attributed global surface air temperature change (GSAT) from 1750 to 2019 produced using the two-layer emulator (Supplementary Material 7.SM.2), forced with ERF derived in this chapter (displayed in Figure 2.10) and climate response constrained to assessed ranges for key climate metrics described in Cross-Chapter Box 7.1. The results shown are the medians from a 2237-member ensemble that encompasses uncertainty in forcing and climate response (year-2019 best estimates and uncertainties are shown in Figure 7.7 for several components). Temperature contributions are expressed for carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), other well-mixed greenhouse gases (WMGHGs), ozone (O3), aerosols, and other anthropogenic forcings, as well as total anthropogenic, solar, volcanic, and total forcing. Shaded uncertainty bands showvery likely (5–95%)ranges. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39587, "uuid": "8a368690755e4d34989bdc2e76910935", "title": "Caption for Figure 7.10 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean climate feedbacks estimated in abrupt 4xCO2 simulations of 29 CMIP5 models (light blue) and 49 CMIP6 models (orange), compared with those assessed in this Report (red). Individual feedbacks for CMIP models are averaged across six radiative kernels as computed in Zelinka et al. (2020). The white line, black box and vertical line indicate the mean, 66% and 90% ranges, respectively. The shading represents the probability distribution across the full range of GCM/ESM values and for the 2.5–97.5 percentile range of the AR6 normal distribution. The unit is W m–2°C–1. Feedbacks associated with biogeophysical and non-CO2 biogeochemical processes are assessed in AR6, but they are not explicitly estimated from general circulation models (GCMs)/Earth system models (ESMs) in CMIP5 and CMIP6. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39588, "uuid": "b82ba89a28024df294edc22f55c6a967", "title": "Caption for Figure 7.11 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Feedback parameter, α (W m–2°C–1), as a function of global mean surface air temperature anomaly relative to pre-industrial, for ESM simulations (red circles and lines) (Caballero and Huber, 2013; Jonko et al., 2013; Meraner et al., 2013; Good et al., 2015; Duan et al., 2019; Mauritsen et al., 2019; Stolpe et al., 2019; Zhu et al., 2019a), and derived from paleoclimate proxies (grey squares and lines) (von der Heydt et al., 2014; Anagnostou et al., 2016, 2020; Friedrich et al., 2016; Royer, 2016; Shaffer et al., 2016; Köhler et al., 2017; Snyder, 2019; Stap et al., 2019). For the ESM simulations, the value on The x-axis refers to the average of the temperature before and after the system has equilibrated to a forcing (in most cases a CO2 doubling), and is expressed as an anomaly relative to an associated pre-industrial global mean temperature from that model. The light blue shaded square extends across the assessed range of α (Table 7.10) on The y-axis, and on The x-axis extends across the approximate temperature range over which the assessment of α is based (taken as from zero to the assessed central value of ECS; see Table 7.13). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39589, "uuid": "7ad2a4488c2f4b9ba7cc13e7a7d56850", "title": "Caption for Figure 7.13 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Polar amplification in paleo proxies and models of the Early Eocene Climatic Optimum (EECO), the Mid-Pliocene Warm Period (MPWP) and the Last Glacial Maximum (LGM).\r\nTemperature anomalies compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’) are shown for the high-CO2 EECO and MPWP time periods, and for the low-CO2 LGM (expressed as pre-industrial minus LGM). (a), (b) and (c) Modelled near-surface air temperature anomalies for ensemble-mean simulations of the (a) EECO (Lunt et al., 2021); (b) MPWP (Haywood et al., 2020; Zhang et al., 2021); and (c) LGM (Kageyama et al., 2021; Zhu et al., 2021). Also shown are proxy near-surface air temperature anomalies (coloured circles). (d), (e) and (f) Proxy near-surface air temperature anomalies (grey circles), including published uncertainties (grey vertical bars), model ensemble mean zonal mean anomaly (solid red line) for the same model ensembles as in (a–c), light-red lines show the modelled temperature anomaly for the individual models that make up each ensemble (LGM, N=9; MPWP, N=17; EECO, N=5). Black dashed lines show the average of the proxy values in each latitude band: 90°S–30°S, 30°S–30°N, and 30°N–90°N. Red dashed lines show the same banded average in the model ensemble mean, calculated from the same locations as the proxies. Black and red dashed lines are only shown if there are five or more proxy points in that band. Mean differences between the 90°S/N to 30°S/N and 30°S to 30°N bands are quantified for the models and proxies in each plot. Panels (g), (h) and (i) are like panels (d–f) but for sea surface temperature (SST) instead of near-surface air temperature. Panels (j), (k) and (l) are like panels (a–c) but for SST instead of near-surface air temperature. For the EECO maps – (a) and (j) – the anomalies are relative to the zonal mean of the pre-industrial, due to the different continental configuration. Proxy datasets are: (a) and (d) Hollis et al. (2019); (b) and (e) Salzmann et al. (2013); Vieira et al. (2018), (c) and (f) Cleator et al. (2020) at the sites defined in Bartlein et al. (2011); (g) and (j) Hollis et al. (2019); (h) and (k) McClymont et al. (2020); (i) and (l) Tierney et al. (2020b). Where there are multiple proxy estimations at a single site, a mean is taken. Model ensembles are (a), (d), (g) and (j) DeepMIP (only model simulations carried out with a mantle-frame paleogeography, and carried out under CO2 concentrations within the range assessed in Table 2.2, are shown); (b), (e), (h) and (k) PlioMIP; and (c), (f), (i) and (l) PMIP4. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39590, "uuid": "803a22aa5e924b0da10b15eefb35701c", "title": "Caption for Figure 7.16 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Probability distributions of ERF to CO2doubling (ΔF2×CO2; top) and the net climate feedback ( α ; right), derived from process-based assessments in Sections 7.3.2 and 7.4.2. Central panel shows the joint probability density function calculated on a two-dimensional plane ofΔF2×CO2 and α (red), on which the 90% range shown by an ellipse is imposed to the background theoretical values of ECS (colour shading). The white dot, and thick and thin curves inside the ellipse represent the mean, likely and very likely ranges of ECS. An alternative estimation of the ECS range (pink) is calculated by assuming thatΔF2×CO2 and α have a covariance. The assumption about the co-dependence betweenΔF2×CO2 and α does not alter the mean estimate of ECS but affects its uncertainty. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39591, "uuid": "172ca1ad5306422fbe26c9ae1982e3fc", "title": "Caption for Figure 7.17 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "(a) Time evolution of the effective radiative forcing (ERF) to the CO2 concentration increased by 1% per year until year 70 (equal to the time of doubling) and kept fixed afterwards (white line). The likely and very likely ranges of ERF indicated by light and dark orange have been assessed in Section 7.3.2.1. (b) Surface temperature response to the CO2 forcing calculated using the emulator with a given value of ECS, considering uncertainty in ΔF2×CO2, α , and κ associated with the ocean heat uptake and efficacy (white line). The likely and very likely ranges are indicated by cyan and blue, respectively. For comparison, the temperature response to abrupt doubling of the CO2 concentration is displayed by a grey curve. The mean, likely and very likely ranges of ECS and TCR are shown at the right (the values of TCR also presented in the panel). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39592, "uuid": "91c4618b962f48ed8618d77582f99f5e", "title": "Caption for Figure 7.18 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Summary of the equilibrium climate sensitivity (ECS panel (a)) and transient climate response (TCR panel (b)) assessments using different lines of evidence. Assessed ranges are taken from Tables 7.13 and 7.14 for ECS and TCR respectively. Note that for the ECS assessment based on both the instrumental record and paleoclimates, limits (i.e., one-sided distributions) are given, which have twice the probability of being outside the maximum/minimum value at a given end, compared to ranges (i.e., two-tailed distributions) which are given for the other lines of evidence. For example, the extremely likely limit of greater than 95% probability corresponds to one side of the very likely (5–95%) range. Best estimates are given as either a single number or by a range represented by a grey box. CMIP6 model values are not directly used as a line of evidence but presented on the Figure for comparison. ECS values are taken from Schlund et al. (2020) and TCR values from Meehl et al. (2020); see Supplementary Material 7.SM.4. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39593, "uuid": "54f0c9b48e7245dc82351297c7e8475e", "title": "Caption for Figure 7.19 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean temperature anomaly in models and observations from five time periods. (a) Historical (CMIP6 models); (b) post-1975 (CMIP6 models); (c) Last Glacial Maximum (LGM; Cross-Chapter Box 2.1; PMIP4 models; Kageyama et al., 2021; Zhu et al., 2021); (d) mid-Pliocene Warm Period (MPWP; Cross-Chapter Box 2.4; PlioMIP models; Haywood et al., 2020; Zhang et al., 2021); (e) Early Eocene Climatic Optimum (EECO; Cross-Chapter Box 2.1; DeepMIP models; Zhu et al., 2020; Lunt et al., 2021). Grey circles show models with ECS in the assessed very likely range; models in red have an ECS greater than the assessed very likely range (>5°C); models in blue have an ECS lower than the assessed very likely range (<2°C). Black ranges show the assessed temperature anomaly derived from observations Section 2.3). The historical anomaly in models and observations is calculated as the difference between 2005–2014 and 1850–1900, and the post-1975 anomaly is calculated as the difference between 2005–2014 and 1975–1984. For the LGM, MPWP and EECO, temperature anomalies are compared with pre-industrial (equivalent to CMIP6 simulation ‘piControl’). All model simulations of the MPWP and LGM were carried out with atmospheric CO2 concentrations of 400 and 190 ppm respectively. However, CO2 during the EECO is relatively more uncertain, and model simulations were carried out at either 1120ppm or 1680 ppm (except for the one high-ECS EECO simulation which was carried out at 840 ppm; Zhu et al., 2020). The one low-ECS EECO simulation was carried out at 1680 ppm. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39595, "uuid": "57a4499101764268aba05a585e8b25e7", "title": "Caption for Figure 7.21 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Emissions metrics for two short-lived greenhouse gases: HFC-32 and methane (CH4; lifetimes of 5.4 and 11.8 years). The temperature response function comes from Supplementary Material 7.SM.5.2. Values for non-CO2 species include the carbon cycle response Section 7.6.1.3). Results for HFC-32 have been divided by 100 to show on the same scale. (a) Temperature response to a step change in short-lived greenhouse gas emissions. (b) Temperature response to a pulse CO2 emission. (c) Conventional GTP metrics (pulse vs pulse). (d) Combined GTP metric (step versus pulse). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39596, "uuid": "6b339fc064524688aed6bb3e359b8c39", "title": "Caption for Figure 7.22 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Explores how cumulative carbon dioxide equivalent emissions estimated for methane vary under different emissions metric choices and how estimates of the global surface air temperature (GSAT) change deduced from these cumulative emissions compare to the actual temperature response computed with the two-layer emulator (solid black lines). Panels (a) and (b) show the SSP4-6.0 and SSP1-2.6 scenarios respectively. The panels show annual methane emissions as the dotted lines (left axis) from 1750 to 2100. The solid lines can be read as either estimates of GSAT change or estimates of the cumulative carbon dioxide equivalent emissions. This is because they are related by a constant factor, the TCRE. Thus, values can be read using either of the right-hand axes. Emissions metric values are taken from Table 7.15. The GWP* calculation is given in Section 7.6.1.4. The two-layer emulator has been calibrated to the central values of the Report’s assessment (see Supplementary Material 7.SM.5.2). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39597, "uuid": "544d5c2f3b1840a9880ac8b2a2ee12d6", "title": "Caption for Figure 7.22 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Explores how cumulative carbon dioxide equivalent emissions estimated for methane vary under different emissions metric choices and how estimates of the global surface air temperature (GSAT) change deduced from these cumulative emissions compare to the actual temperature response computed with the two-layer emulator (solid black lines). Panels (a) and (b) show the SSP4-6.0 and SSP1-2.6 scenarios respectively. The panels show annual methane emissions as the dotted lines (left axis) from 1750 to 2100. The solid lines can be read as either estimates of GSAT change or estimates of the cumulative carbon dioxide equivalent emissions. This is because they are related by a constant factor, the TCRE. Thus, values can be read using either of the right-hand axes. Emissions metric values are taken from Table 7.15. The GWP* calculation is given in Section 7.6.1.4. The two-layer emulator has been calibrated to the central values of the Report’s assessment (see Supplementary Material 7.SM.5.2). Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39598, "uuid": "db032a49ac10480c84f3844656f8f7f2", "title": "Caption for FAQ 7.3, Figure 1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Equilibrium climate sensitivity and future warming. (left) Equilibrium climate sensitivities for the current generation (Coupled Model Intercomparison Project Phase 6, CMIP6) climate models, and the previous (CMIP5) generation. The assessed range in this Report (AR6) is also shown. (right) Climate projections of CMIP5, CMIP6 and AR6 for the very high-emissions scenarios RCP8.5, and SSP5-8.5, respectively. The thick horizontal lines represent the multi-model average and the thin horizontal lines represent the results of individual models. The boxes represent the model ranges for CMIP5 and CMIP6 and the range assessed in AR6.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39599, "uuid": "e3c7a62ce26846a68b86f854286b5eb8", "title": "Caption for Figure 7.SM.1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Total effective radiative forcing from SSP scenarios with respect to 1750 for 2000-2500, 14 showing best estimate and 5–95% uncertainty range.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39600, "uuid": "1a4e8f4fb6974644aa5dd4da7e1c8bca", "title": "Caption for Box 7.2, Figure 1 from Chapter 7 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Estimates of the net cumulative energy change (ZJ = 1021Joules) for the period 1971–2018 associated with: (a) observations of changes in the global energy inventory; (b) integrated radiative forcing; and (c) integrated radiative response. Black dotted lines indicate the central estimate with likely and very likely ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated pre-industrial Earth energy imbalance of 0.2 W m–2 (a), and an illustration of an assumed pattern effect of –0.5 W m–2°C–1 (c). Background grey lines indicate equivalent heating rates in W m–2 per unit area of Earth’s surface. Panels (d) and (e) show the breakdown of components, as indicated in the legend, for the global energy inventory and integrated radiative forcing, respectively. Panel (f) shows the global energy budget assessed for the period 1971–2018, that is, the consistency between the change in the global energy inventory relative to pre-industrial and the implied energy change from integrated radiative forcing plus integrated radiative response under a number of different assumptions, as indicated in the legend, including assumptions of correlated and uncorrelated uncertainties in forcing plus response. Shading represents the very likely range for observed energy change relative to pre-industrial levels and likely range for all other quantities. Forcing and response time series are expressed relative to a baseline period of 1850–1900. Further details on data sources and processing are available in the chapter data table (Table 7.SM.14).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39602, "uuid": "a02af9e7251847148b22bef51a4d0add", "title": "Derivation of the ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Plant Function Types (PFT) Dataset", "abstract": "The plant functional type (PFT) distribution was created by combining auxiliary data products with the CCI Medium Resolution Land Cover (MRLC) map series. The land cover (LC) classification provides the broad characteristics of the 300 m pixel, including the expected vegetation form(s) (tree, shrub, grass) and/or abiotic land type(s) (water, bare area, snow and ice, built-up) in the pixel. For some classes, the class legend specifies an expected range for the fractional covers of the contributing PFTs and broadly differentiates between natural and cultivated vegetation. We used a quantitative, globally consistent method that fuses the 300-metre MRLC product with a suite of existing high-resolution datasets to develop spatially explicit annual maps of PFT fractional composition at 300 metres. The new PFT product exhibits intraclass spatial variability in PFT fractional cover at the 300-metre pixel level and is complementary to the MRLC maps since the derived PFT fractions maintain consistency with the original LC class legend. \r\n\r\nThis dataset was generated to reduce the cross-walking component of uncertainty by adding spatial variability to the PFT composition within a LC class. This work moved beyond fine-tuning the cross-walking approach for specific LC classes or regions and, instead, separately quantifies the PFT fractional composition for each 300 m pixel globally. The result is a dataset representing the cover fractions of 14 PFTs at 300 m for each year within the time range, consistent with the CCI MRLC LC maps for the corresponding year.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39609, "uuid": "aaa20cc705654b399a314bf2fa15c294", "title": "BRDF/Albedo Inversion Model computation for QA4ECV broadband albedo.", "abstract": "More information needed", "keywords": "QA4ECV", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39610, "uuid": "f2958997cd8345759467257dcf78f62e", "title": "Caption for Figure 9.3 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Sea surface temperature (SST) and its changes with time. (a) Time series of global mean SST anomaly relative to 1950–1980 climatology. Shown are paleoclimate reconstructions and PMIP models, observational reanalyses (HadISST) and multi-model means from the Coupled Model Intercomparison Project (CMIP) historical simulations, CMIP projections, and HighResMIP experiment. (b) Map of observed SST (1995–2014 climatology HadISST). (c) Historical SST changes from observations. (d) CMIP 2005–2100 SST change rate. (e) Bias of CMIP. (f) CMIP change rate. (g) 2005–2050 change rate for SSP5-8.5 for the CMIP ensemble. (h) Bias of HighResMIP (bottom left) over 1995–2014. (i) HighResMIP change rate for 1950–2014. (j) 2005–2050 change rate for SSP5-8.5 for the HighResMIP ensemble. No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39611, "uuid": "e57907dd153348b48f0e75a74498a75e", "title": "Caption for Figure 9.4 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global maps of observed mean fluxes (a, d, g), the observed trends in these fluxes (b, e, h) and the projected rate of change in these fluxes from SSP5-8.5 (c, f, i). Shown are the freshwater flux (a–c), net heat flux (d–f), and momentum flux or wind stress magnitude (g–i), with positive numbers indicating ocean freshening, warming, and accelerating respectively. The means and observed trends are calculated between 1995–2014 (freshwater and wind stress) or 2001–2014 (heat). The SSP5-8.5 projected rates are between 1995–2100 using 20-year averages at each end of the time period. Observations show objective interpolation from Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) v4 (Kato et al., 2018), Objectively Analyzed air–sea Fluxes-High Resolution (OAFlux-HR) (Yu, 2019), and Global Precipitation Climatology Project (GPCP) (Adler et al., 2003) of fluxes and flux trends (b, e, h). Observed trends with no overlay indicate regions where the trends are significant at p = 0.34 level. Crosses indicate regions where trends are not significant. For (c, f, i) projections, no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39612, "uuid": "270817e8fee640bb94777de50bd4491a", "title": "Caption for Figure 9.5 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Mixed-layer depth in (a–d) winter and (e–h) summer. (a, e) Observed climatological mean mixed-layer depth (based on density threshold) from the Argo Mixed Layer Depth Climatology (Holte et al., 2017) usingobservations for 2000–2019. (b, f) Bias between the observation-based estimate (2000–2019) and the 1995–2014 Coupled Model Intercomparison Project Phase 6 (CMIP6) climatological mean mixed-layer depth. (c, d, g, h) Projected mixed-layer depth (MLD) change from 1995–2014 to 2081–2100 under (c, g) SSP1-2.6 and (d, h) SSP5-8.5 scenarios. The (a–d) winter row shows December–January–February (DJF) in the Northern Hemisphere and June–July–August (JJA) in the Southern Hemisphere; The (e–h) summer row shows JJA in the Northern Hemisphere and DJF in the Southern Hemisphere. The mixed-layer depth is the depth where the potential density is 0.03 kg m–3denser than at 10 m. No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39613, "uuid": "81e91e9e0bfc44f0a7003c735989e621", "title": "Caption for Figure 9.6 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Ocean heat content (OHC) and its changes with time. (a) Time series of global OHC anomaly relative to a 2005–2014 climatology in the upper 2000 m of the ocean. Shown are observations (Ishii et al., 2017; Baggenstos et al., 2019; Shackleton et al., 2020), model-observation hybrids (Cheng et al., 2019; Zanna et al., 2019), and multi-model means from the Coupled Model Intercomparison Project Phase 6 (CMIP6) historical (29 models) and Shared Socio-economic Pathway (SSP) scenarios (label subscripts indicate number of models per SSP). (b–g) Maps of OHC across different time periods, in different layers, and from different datasets/experiments. Maps show the CMIP6 ensemble bias and observed (Ishii et al., 2017) trends of OHC for (b, c) 0–700 m for the period 1971–2014, and (e, f) 0–2000 m for the period 2005–2017. CMIP6 ensemble mean maps show projected rate of change 2015–2100 for (d) SSP5-8.5 and (g) SSP1-2.6 scenarios. Also shown are the projected change in 0–700 m OHC for (d) SSP1-2.6 and (g) SSP5-8.5 in the CMIP6 ensembles, for the period 2091–2100 versus 2005–2014. No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39614, "uuid": "6622d59f1a84422084f5890fb68664b6", "title": "Caption for Figure 9.7 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Meridional-depth profiles of zonal-mean potential temperature in the ocean and its rate of change in the upper 2000 m of the Global, Pacific, Atlantic and Indian oceans. Shown are (a, e, i, m) observed temperature (Argo climatology 2005–2014), (b, f, j, n) bias of the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble over this period, and future changes under (c, g, k, o) SSP1-2.6 and (d, h, l, p) SSP5-8.5. No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39615, "uuid": "8d9cb1477e5f42159a1adddbb69a04f6", "title": "Caption for Figure 9.9 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term trends of ocean heat content (OHC) and surface temperature. (a, b) Ice-core rare gas estimates of past mean OHC (ZJ), scaled to global mean ocean temperature (°C), and to steric global mean sea level (GMSL) (m) per CCB-2 (red dashed line), compared to surface temperatures (black solid line, gold solid line; °C rightmost axis). Southern Ocean sea surface temperature (SST) from multiple proxies in 11 sediment cores and from ice core deuterium excess (Uemura et al., 2018). (a) Penultimate glacial interval to last interglacial, 150,000–100,000 yr B2K (before 2000) (Shackleton et al., 2020). (b) Last glacial interval to modern interglacial, 40,000–0 yr B2K (Baggenstos et al., 2019; Shackleton et al., 2019). Changes in OHC (dashed lines) track changes in Southern Ocean SST (solid lines). (c) Long-term projected (2000 to 12000 CE) changes of OHC (dashed lines) in response to four greenhouse gas emissions scenarios (Clark et al., 2016) scale similarly to large-scale paleo changes but lag projected global mean SST (solid lines). (d) model simulated 1500–1999 OHC (Gregory et al., 2006) and 1955–2019 observations (Levitus et al., 2012) updated by NOAA NODC. All data expressed as anomalies relative to pre-industrial time. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39616, "uuid": "c835cba84f8d42a0ba6b940f1a46be4b", "title": "Caption for Figure 9.10 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Atlantic Meridional Overturning Circulation (AMOC) strength in simulations and sensitivity to resolution and forcing. (Top left) AMOC magnitude (units: Sverdrup (Sv) = 109kgs–1) in Paleoclimate Modelling Intercomparison Project (PMIP) experiments. (Top right) Time series of AMOC from Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) based on (Menary et al., 2020b). (Bottom left) Percent change in AMOC strength per year at different resolutions over the 1950–2050 period with colours for model families (Roberts et al., 2020). (Bottom right) A compilation of percentage changes in the simulated AMOC after applying an additional freshwater flux in the subpolar North Atlantic at the surface for a limited time (de Vries and Weber, 2005; Stouffer et al., 2006; Yin and Stouffer, 2007; Jackson, 2013; Liu and Liu, 2013; Jackson and Wood, 2018; Haskins et al., 2019). Symbols indicate whether the AMOC recovers within 200 years (circles), is starting to recover (upwards arrow), or does not recover within 200 years (downwards arrow). Symbol size indicates rate of freshwater input. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ] }