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=3200
{ "count": 3949, "next": "https://api.catalogue.ceda.ac.uk/api/v2/computations/?format=api&limit=100&offset=3300", "previous": "https://api.catalogue.ceda.ac.uk/api/v2/computations/?format=api&limit=100&offset=3100", "results": [ { "ob_id": 34634, "uuid": "47a6cbe7ec024640889e1a379a744a72", "title": "Caption for Figure 10.6 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Illustration of some model biases in simulations performed with dynamical models. (a) Top row: Mean summer (June to August) near-surface air temperature (in °C) over the Mediterranean area in Berkeley Earth and respective mean bias for five multi-model historical experiments with global models (CMIP5, CMIP6 and HighResMIP) and regional climate models (CORDEX EUR-44 and EUR-11) averaged between 1986–2005. Bottom row: Box-and-whisker plot shows spread of the 20 annual mean summer surface air temperature averaged over land areas in the western Mediterranean region (33°N–45°N, 10°W–10°E, black quadrilateral in the first panel of the top row) for a set of references and single model runs of the five multi-model experiments (one simulation per model) between 1986–2005. Additional observation and reanalysis data included in the bottom row are CRU TS, HadCRUT4, HadCRUT5, E-OBS, WFDE5, ERA5, ERA-Interim, CERA-20C, JRA-25, JRA-55, CFSR, MERRA2, MERRA. Berkeley Earth is shown in the first box to the left. (b) As (a) but for precipitation rate (mm day–1) and showing CRU TS in the first panel of the top row. Biases of the five multi-model experiments are shown with respect to CRU TS. Additional observation and reanalysis data included in the bottom row are GPCC, REGEN, E-OBS, GHCN, WFDE5, CFSR, ERA-Interim, ERA5, JRA-55, MERRA2, MERRA. CRU TS is shown in the first box to the left. All box-and-whisker plots show the median (line), and the interquartile range (IQR = Q3–Q1, box), with top whiskers extending to the last data less than Q3 + 1.5 × IQR and analogously for bottom whiskers. Data outside the whiskers range appear as flyers (circles). Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34636, "uuid": "513cc318b4c44c81a575f3f19b70caeb", "title": "Caption for CCB 10.4 Figure 1 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Historical annual-mean surface air temperature linear trend (°C per decade) and its attribution over the Hindu Kush Himalaya (HKH) region. (a) Observed trends from Berkeley Earth (also showing the HKH outline), CRU TS (also showing the AR6 Tibetan Plateau (TIB) outline, for ease of comparison to the Interactive Atlas), APHRO-MA and JRA-55 datasets over 1961–2014. (b) Models showing the coldest, median and warmest HKH temperature linear trends among the CMIP6 historical ensemble over 1961–2014. (c) Low-pass-filtered time series of annual-mean surface air temperature anomalies (°C, baseline 1961–1980) over the HKH region as outlined in panel (a), showing means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue). Observed datasets are Berkeley Earth (dark blue), CRU (brown), APHRO-MA (light green) and JRA-55 (dark green). The filter is the same as that used in Figure 10.10. (d) Distribution of annual mean surface air temperature trends (°C per decade) over the HKH region from 1961 to 2014 for ensemble means, the aforementioned observed and reanalysis data (black crosses), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs used throughout Chapter 10 (grey shading). Ensemble means are also shown. All trends are estimated using ordinary least-squares regression and box-and-whisker plots follow the methodology used in Figure 10.6. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34642, "uuid": "bcd546e64c2b4c83941119f1f909cb42", "title": "Caption for Figure 11.16 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in annual maximum daily precipitation at (a) 1.5°C, (b) 2°C, and (c) 4°C of global warming compared to the 1850–1900 baseline. Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers on the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: 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. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in Rx1day are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34645, "uuid": "86649a472cb346bd9dfee5e0c6aad196", "title": "Caption for Figure 11.11 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in (a–c) annual maximum temperature (TXx) and (d–f) annual minimum temperature (TNn) at 1.5°C, 2°C, and 4°C of global warming compared to the 1850–1900 baseline. Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathways (SSPs) SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers in the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: 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. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in TXx and TNn are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34651, "uuid": "61f80d1ceea540c48fcda2546200b57b", "title": "Anomalies of Biogeochemical Parameters in the Tropical Region (TROPBGC)", "abstract": "Anomalies were computated using these tools:\\n\\nHumberto L. Varona. (2021). mNC: A tool for Oceanographers and Meteorologists to easily create their NetCDF files using Matlab (1.0). Zenodo. https://doi.org/10.5281/zenodo.5572749\\n\\nHumberto L. Varona. (2021). CalcPlotAnomaly: Matlab function set for the calculation and plotting of anomalies (1.01). Zenodo. https://doi.org/10.5281/zenodo.5576889", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34656, "uuid": "8360662a470d427a8f52431d96c01d5f", "title": "Caption for Figure 2.6 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean atmospheric mixing ratios of select ozone-depleting substances and other greenhouse gases. Data shown are based on the CMIP6 historical dataset and data from NOAA and AGAGE global networks. PFCs include CF4, C2F6, and C3F8, and c-C4F8; Halons include halon-1211, halon-1301, and halon-2402; other HFCs include HFC-23, HFC-32, HFC-125, HFC-143a, HFC-152a, HFC-227ea, HFC-236fa, HFC-245fa, and HFC-365mfc, and HFC-43-10mee. Note that the y-axis range is different for (a), (b) and (c) and a 25 parts per trillion (ppt) yardstick is given next to each panel to aid interpretation. Further data are in Annex III and 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": 34659, "uuid": "da4655b6164e4cbcbf52b543283a5830", "title": "Caption for Figure 2.25 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in permafrost temperature. Average departures of permafrost temperature (measured in the upper 20–30 m) from a baseline established during International Polar Year (2007–2009) for Arctic regions. 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": 34662, "uuid": "7fe0b4bae54047a19017cd76d95376c7", "title": "Caption for Figure 3.6 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated internal variability of global surface air temperature (GSAT) versus observed changes. (a) Time series of five-year running mean GSAT anomalies in 45 CMIP6 pre-industrial control (unforced) simulations. The 10 most variable models in terms of five-year running mean GSAT are coloured according to the legend on Figure 3.4. (b) Histograms of GSAT changes in CMIP6 historical simulations (extended by using SSP2-4.5 simulations) from 1850–1900 to 2010–2019 are shown by pink shading in (c), and GSAT changes between the average of the first 51 years and the average of the last 20 years of 170-year overlapping segments of the pre-industrial control simulations shown in (a) are shown by blue shading. GMST changes in observational datasets for the same period are indicated by black vertical lines. (c) Observed GMST anomaly time series relative to the 1850–1900 average. Black lines represent the five-year running means while grey lines show unfiltered annual time series. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34666, "uuid": "45354bf51d2a475d9b7abfdd04bcbcab", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Along-Track Scanning Radiometer 2 (ATSR-2) level 3 collated (L3C) global product (1995-2003), version 3.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34690, "uuid": "27a2d430de2e4f36bbb341eee55e9502", "title": "Post processing of station data at the University of Iceland and the University of Bergen", "abstract": "The vapour measurements averaged to 15 min time intervals. In total, the instrument operated on 60 days, with a total of 51.7 days of ambient air measurements. Uncertainty after calibration is 0.4, 2.0 and 1.8 for δO18, δD and the d-excess, respectively. The vapour isotope data are joined with the meteorological data from nearby automatic weather stations with a 15 min averaging time using the processing tool isofuse (naming: IGP2018_SNOWPACE_Husavik_station_data.nc; format: netcdf). Weather station data for the 7 closest automatic weather stations near the sampling transects are joined in a common data file at the original 10 min time resolution (naming: IGP2018_vedur_met_stations.nc; format: netcdf). Results from precipitation and surface snow sample analysis for water isotopes from the Iceland and southern Norway are available in one datafile (naming:IGP2018_SNOWPACE_water_isotope_samples_stations.csv, format: csv).\\n\\n\\nPost processing of the vapour isotope data was done by Jean-Lionel Lacour (UoI). Post processing of the discrete sample data was done by Harald Sodemann (UiB), who also acts as data contact.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34696, "uuid": "99163507794a465e9261bfe7ac3c5af6", "title": "Post processing of ship data at University of Bergen", "abstract": "Data files recorded by the analyzer in *.dat format are converted to netCDF format using a python routine. The raw data are then processed using the calibration routines FaVaCal, in use at FARLAB, University of Bergen, Norway. Calibration periods are identified and removed for separate processing with plots and quality evaluation. Water vapour isotope measurements are corrected for the humidity-isotope ratio dependency, as documented by Weng et al., 2020. The complete data processing is described in more detail in the data report for stable water isotope measurements from MASIN aircraft during IGP. The vapour isotope data are joined with the meteorological data from the R/V Alliance obtained during IGP at a 60 s averaging time using the processing tool isofuse. Output from this conversion is stored as one single datafiles (naming: IGP2018_SNOWPACE_Alliance_cruise_data_V3.1_60s.nc; format: netcdf). Results from liquid sample analysis for water isotopes from the R/V Alliance are available in one datafile (naming:IGP2018_SNOWPACE_water_isotope_samples_Alliance.csv, format: csv). Post processing was done by Harald Sodemann (UiB), who also acts as data contact.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34702, "uuid": "80df33b9f15c479b8786ab10c95202a1", "title": "Post processing of aircraft data at University of Bergen", "abstract": "Data files recorded by the analyzer in *.dat format are converted to netCDF format using apython routine. The raw data are then processed using the calibration routines FaVaCal, in use at FARLAB, University of Bergen, Norway. Calibration periods are identified and removed for separate processing with plots and quality evaluation. Water vapour isotope measurements are corrected for the humidity-isotope ratio dependency, as documented by Weng et al., 2020. The complete data processing is described in more detail in the data report for stable water isotope measurements from aircraft during IGP. The vapour isotope data are joined with the meteorological data from the MASIN aircraft obtained during IGP at a 2s, 10s, 30s and 60 s averaging time using the processing tool isofuse. Output from this conversion is stored as in separate datafiles for each averaging time and flight (naming: MASIN_isotopes_IGP2018_V3.3_20180304_f295_02s_final.nc; folder: IGP2018_SNOWPACE_MASIN_flight_data_V3.3/02s; format: netcdf). Results from liquid sample analysis for water isotopes from MASIN are available in one datafile (naming:IGP2018_SNOWPACE_water_isotope_samples_MASIN.csv, format: csv).Post processing was done by Harald Sodemann (UiB), who also acts as data contact.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34705, "uuid": "a3299e95950e4934802b93455cb25150", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Aqua level 3 collated (L3C) global product (2002-2018), version 3.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34709, "uuid": "915609dc14234791b9dd64792e32c2c1", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Moderate resolution Infra-red Spectroradiometer (MODIS) on Terra level 3 collated (L3C) global product (2000-2018), version 3.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34726, "uuid": "1c8ebc22681d48f8a67008e900ece8ef", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Advanced Along-Track Scanning Radiometer (AATSR) level 3 collated (L3C) global product (2002-2012), version 3.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34734, "uuid": "e77e5d3dafb44414abdf34b273f73b33", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3A level 3 collated (L3C) global product (2016-2020), version 3.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34738, "uuid": "fb22d4204c5943ccbc0a22d293a5aa01", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) on Sentinel 3B level 3 collated (L3C) global product (2018-2020), version 3.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34741, "uuid": "950e57621baf4a2dbeecc0ac69f63c48", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) Land surface temperature (LST) time series level 3 supercollated (L3S) global product (1995-2020), version 2.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34743, "uuid": "37c5516b18df4545b3a271c639e3db63", "title": "Derivation of the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multisensor Infra-Red (IR) Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) Land surface temperature (LST) level 3 supercollated (L3S) global product (2009-2020), version 1.00", "abstract": "For more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 34902, "uuid": "7688b3764f4e47dca47b3246f19b8924", "title": "HadGEM3, Global Atmosphere (GA) 7.0", "abstract": "The atmosphere component of HadGEM3, Global Atmosphere (GA) 7.0, was run for three different scenarios. Based on QBOi experiments 2,3,4, these force the atmosphere model with year 2002 conditions (e.g. of solar radiation and sea surface temperatures) every year for 21 years. The first scenario has no modifications (as a control), the second has doubled CO2 concentrations and sea surface temperatures (SSTs) are increased by 2K, and the same again where CO2 concentrations are quadrupled and SSTs are increased by 4K. Simulations were allowed 10 years to stabilise to their modified forcing conditions and the final 11 years were analysed further.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 35032, "uuid": "b1d7a82ac2844548a41b98df77007b52", "title": "ESA Snow Climate Change Initiative (snow_cci): SWE, v2", "abstract": "The snow_cci SWE product has been based on the ESA GlobSnow SWE retrieval approach (Takala et al. 2011). The retrieval is based on passive microwave radiometer (PMR) data considering the change of brightness temperature due to different snow depth, snow density, grain size and more. The retrieval algorithm handles data from the sensors SMMR, SSM/I, SSMIS, AMSR-E and AMSR-2. The retrieval methodology combines the satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include the utilisation of an advanced emission model with an improved forest transmissivity module and treatment of sub-grid lake ice. Because of the importance of the weather station snow-depth observations on the SWE retrieval, there is improved screening for consistency through the time series.\r\n\r\nThe version 2 dataset has some notable differences compared to the v1 data. In v2, passive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data-sets/) the grid spacing is reduced from 25 km to 12.5 km, and spatially and temporally varying snow density fields are used to adjust SWE retrievals in post processing. The output grid spacing is reduced from 0.25-degree to 0.10-degree WGS84 latitude / longitude to be compatible with other Snow_cci products. The time series has been extended by two years with data from 2018 to 2020 added.\r\n\r\nSWE products are based on SMMR, SSM/I and SSMIS passive microwave radiometer data for non-alpine regions of the Northern Hemisphere.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37070, "uuid": "c12c62a5b3c445789fa0289b4b3dd9fc", "title": "dummy", "abstract": "none", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37142, "uuid": "7564e6b5035341a29c17e4ab73996509", "title": "Simcenter STAR-CCM+", "abstract": "The dispersion model hs been developed using a commercial CFD software STAR-CCM+. \n\n\nSimcenter STAR-CCM+ is a multiphysics computational fluid dynamics (CFD) software for the simulation of products operating under real-world conditions. Simcenter STAR-CCM+ uniquely brings automated design exploration and optimization to the CFD simulation toolkit of every engineer.\n\nThe single integrated environment includes everything from CAD, automated meshing, multiphysics CFD, sophisticated postprocessing, and design exploration. This allows engineers to efficiently explore the entire design space to make better design decisions faster.\n\n https://www.plm.automation.siemens.com/global/en/products/simcenter/STAR-CCM.html\n", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37147, "uuid": "35a31aaeea234553921cfe181fa84701", "title": "Derivation of the ESA Fire Climate Change Initiative (Fire_cci): Sentinel-3 SYN Burned Area products, v1.0", "abstract": "For more information see the documentation at https://climate.esa.int/projects/fire/key_documents", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37272, "uuid": "fcb7fdc77242484293f59a8b39c15adc", "title": "Computation for Global ocean-ice and pan-Arctic sea ice simulations with different sea ice physics and atmospheric forcing data sets", "abstract": "6 forced ocean-ice simulations and 2 stand-alone ice simulations to document the impact of sea ice physics and\\ \\ atmospheric forcing data on the Arctic sea ice evolution. All of them use the\\ \\ same sea ice model CICE configuration GSI8.1 (Ridley et al., 2018) and the ocean-ice\\ \\ ones the same ocean model NEMO GO6.0 (Storkey et al., 2018) as HadGEM3. Three\\ \\ different atmospheric forcing data set are applied: NCEP Reanalysis-2 (NCEP2)\\ \\ data (Kanamitsu et al., 2002, updated 2020), CORE II surface data (Large & Yeager,\\ \\ 2009) and the atmospheric forcing data set DFS5.2 (Dussin et al., 2016). Regarding\\ \\ the sea ice component, we use the default CICE setup as in HadGEM3 (CICE-default)\\ \\ and an advanced setup (CICE-best) in which a new process is added (snow loss due\\ \\ to drifting snow) and some adjustments have been made to model physics and parameters.\\ \\ \\n\\nThe simulations were performed by the Centre of Polar Observation and Modelling\\ \\ (CPOM) at University of Reading under the ACSIS project", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37275, "uuid": "bb2ca27b31884089a13c10730d9448cc", "title": "Computation for the velocity and strain rate fields of the Northeast Tibetan Plateau", "abstract": "The interferograms are processed from Sentinel-1 Level 1 (L1) Synthetic Aperture Radar (SAR) imagery using the Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) routine. The average line-of-sight (LOS) velocities and associated uncertainties are derived from frame-based five-year time series, which are inverted from networks of short temporal baseline interferograms using the New Small Baseline Subset (NSBAS) method. The scaled uncertainties are the LOS uncertainties with referencing effects corrected by fitting a spherical model to the scatter points between uncertainty and distance from the reference. The stitched LOS velocities in the reference frame of the Global Navigational Satellite System (GNSS) velocities are the results of mosaicking frame-sized LOS velocities into tracks by adding a planar ramp per frame to close the differences between overlapping pixels in consecutive LOS frames and between InSAR and GNSS LOS velocities. The stitched LOS velocities in two line-of-sights were then decomposed into Cartesian velocities in two steps, first into an east component and a combination of the north and vertical components, and then resolving the vertical component from the combination component using an interpolated north component from the GNSS velocities. The strain rate fields are calculated from the horizontal gradients of the filtered InSAR east velocities and interpolated GNSS north velocities.", "keywords": "LiCSAR, NSBAS, GMT, Python", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37277, "uuid": "b28242c2531f4186a2189b9cecba8e50", "title": "Derivation of the ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data (CSR RL06), derived by DTU Space, v2.2", "abstract": "Estimates of mass change have been derived based on inversion methods developped at DTU Space.\r\n\r\nThe underlying L2 monthly gravity field solutions used in the derivation were generated by the Center for Space Research (University of Texas at Austin) primarily using K-Band ranging, accelerometer and GPS observations acquired by the GRACE and GRACE-FO twin-satellite missions.\r\n\r\n For more information see the linked documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37298, "uuid": "20b36b2a0e5e4924be10b7d78ecf66c3", "title": "GEOS-Chem deployed on Viking the University of York's research computing cluster", "abstract": "GEOS-Chem deployed on Viking the University of York's research computing cluster", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37310, "uuid": "d5f94f321ea7430c91ea6cb5606aa7ba", "title": "Caption for Figure 2.11 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Earth’s surface temperature history with key findings annotated within each panel. (a) GMST over the Holocene divided into three time scales: (i) 12 kyr–1 kyr in 100-year time steps; (ii) 1000–1900 CE, 10-year smooth; and (iii) 1900–2020 CE (from panel (c)). Median of the multi-method reconstruction (bold lines), with 5th and 95th percentiles of the ensemble members (thin lines). Vertical bars are the assessed medium confidence ranges of GMST for the Last Interglacial and mid-Holocene (Section 2.3.1.1). The last decade value and very likely range arises from Section 2.3.1.1.3. (b) Spatially resolved trends (C per decade) for HadCRUTv5 over (upper map) 1900–1980, and (lower map) 1981–2020.Significance is assessed following AR(1) adjustment after Santer et al. (2008), ‘x’ marks denote non-significant trends. (c) Temperature from instrumental data for 1850–2020, including (upper panel) multi-product mean annual timeseries assessed in Section 2.3.1.1.3 for temperature over the oceans (blue line) and temperature over the land (red line) and indicating the warming to the most recent 10 years; and annually (middle panel) and decadally (bottom panel) resolved averages for the GMST datasets assessed in Section 2.3.1.1.3. The grey shading in each panel shows the uncertainty associated with the HadCRUT5 estimate (Morice et al., 2021). All temperatures relative to the 1850–1900 reference period. 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": 37317, "uuid": "01d2d0615b5346c6a3a7ec198d8e7a98", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Geostationary Operational Environmental Satellite (GOES) level 3 (L3U) product (2009-2020), version 1.00", "abstract": "For information on the retrieval algorithm used see the documentation on the LST CCI webpage", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37343, "uuid": "d65febad7d8140c593de1268b91cb13d", "title": "ACCESS-CM2-Chem model deployed at CSIRO, ARCCSS", "abstract": "ACCESS-CM2-Chem model deployed at CSIRO, ARCCSS", "keywords": "CCMI-2022, ACCESS-CM2-Chem", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37346, "uuid": "d903a8f24ba940c899704e3219e54808", "title": "Derivation of Invisible Tracks: Collocation of wind-advected ship locations and shipping emissions with data from the MODIS cloud product", "abstract": "The dataset contains data from the MODIS cloud product, collocated to wind-advected ship locations and shipping emissions. It is the product of three data sources: AIS data giving ship locations, ERA5 winds used to advect the emissions up to the time of the Aqua and Terra overpasses, as well as the level-2 cloud product MOD06.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37367, "uuid": "6556dc638815454799f669eae3c7e0b7", "title": "MetUM vn.10.6 using the nested suite for IGP.", "abstract": "The UK Met Office Unified Model (MetUM) version 10.6 with a regional nested domain was used to carry out a suite of simulations of the atmosphere over the NE North Atlantic region. This set up of the MetUM used the Global Atmosphere 6 and Global Land 6 (GA6/GL6) configurations including the ENDGame dynamical core (Walters et al. 2017). One modification to the standard GA6/GL6 configuration was to include form drag in surface momentum exchange over sea ice, based on Lüpkes et al. (2012) and Elvidge et al. (2016), and now part of the GL8 configuration. This new scheme has recently been implemented in the operational forecasting suite following evidence of significant improvements in simulated fluxes of momentum and heat and consequently improvements to the representation of wind speeds and temperatures over-and-downwind of the marginal-ice-zone during Arctic CAOs (Renfrew et al. 2019a). In our set up the MetUM was run globally with an N320 longitude-latitude grid (0.56° x 0.375°, equivalent to 60 km by 42 km at the equator) and 70 vertical levels up to a height of 40 km. The Iceland and Greenland Seas nested domain was 200 x 210 grid points with a spacing of 0.072° x 0.072° (equivalent to 8 km by 8 km) centred on 70.8°N, 14.0°W.\r\n\r\nThe MetUM was run in atmosphere-only mode with SST and sea-ice fields prescribed at the lower boundary for both the global and regional nested domains. The SST and sea-ice data were taken from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system (Donlon et al. 2012; Roberts-Jones et al. 2012) and re-gridded to match the respective resolutions of the global model and the nested domain. The lower boundary conditions were updated daily. Within this set up, the global model was re-initialised daily at 00 UTC by ERA-Interim reanalysis (Dee et al. 2011). After initialisation on the first day of the simulation, the nested domain was only forced at the lateral boundaries by the global model. This means the nested domain is able to spin up and maintain mesoscale structures, within a regional atmospheric circulation environment that is nudged towards reality on a daily basis. The nested domain is relatively small, so is strongly influenced by the lateral boundary conditions. All simulations were run across an extended winter period, 1st November to 30th April, for 20 seasons from winter 1990/91 to 2009/10.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37375, "uuid": "b32ca49d33644065b52401a6119774b1", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Spinning Enhanced Visible and Infrared Imager (SEVIRI) on MSG level 3 (L3U) product (2004-2020), version 3.00", "abstract": "For information on the retrieval algorithm used see the documentation on the LST CCI webpage", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37380, "uuid": "94eae3a57e984e2ba1920f5f775511d3", "title": "Derivation of ESA Land Surface Temperature Climate Change Initiative (LST_cci): Multi-Functional Transport Satellite (MTSAT) level 3 (L3U) product (2009-2015), version 1.00", "abstract": "For information on the retrieval algorithm used see the documentation on the LST CCI webpage", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37387, "uuid": "684e9e0ffc054f4a9a62e95687c764ea", "title": "Caption for Figure 3.2 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": ". (a) Comparison of reconstructed and modelled surface temperature anomalies for the Last Glacial Maximum over land and ocean in the Tropics (30°N–30°S). Land-based reconstructions are from Cleator et al. (2020). Ocean-based reconstructions are from Tierney et al. (2020b). Model points are calculated as the difference between Last Glacial Maximum and pre-industrial control simulations of the PMIP3 and PMIP4 ensembles, sampled at the reconstruction data points. (b) Land–sea contrast in global mean surface temperature change for different paleoclimates. Crosses show individual model simulations from the CMIP5 and CMIP6 ensembles. Filled symbols show ensemble means and assessed values. Acronyms are Last Glacial Maximum (LGM), Last Inter Glacial (LIG), mid-Pliocene Warm Period (MPWP), Early Eocene Climatic Optimum (EECO). (c) Upper panel shows time series of volcanic radiative forcing, in W m−2, as used in the CMIP5 (Gao et al., 2008; Crowley and Unterman, 2013; see also Schmidt et al., 2011) and CMIP6 (850 BCE to 1900 CE from Toohey and Sigl (2017), 1850-2015 from Luo (2018)). The forcing was calculated from the stratospheric aerosol optical depth at 550 nm shown in Figure 2.2. Lower panel shows time series of global mean surface temperature anomalies, in °C, with respect to 1850–1900 for the CMIP5 and CMIP6 past 1000 simulations and their historical continuation simulations. Simulations are coloured according to the volcanic radiative forcing dataset they used. The median reconstruction of temperature from PAGES 2k Consortium (2019) is shown in black, the 5–95% confidence interval is shown by grey lines and the grey envelopes show the 1st, 5th, 15th, 25th, 35th, 45th, 55th, 65th, 75th, 85th, 95th, and 99th percentiles. All data in both panels are band-passed, where frequencies longer than 20 years have been retained. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37393, "uuid": "90766ddc290e4692a5ef9ed8cd9159d0", "title": "Caption for Figure 3.7 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regression coefficients and corresponding attributable warming estimates for individual CMIP6 models. Upper panels show regression coefficients based on a two-way regression (left) and three-way regression (right), of observed five-year mean, globally averaged, masked and blended surface temperature (HadCRUT4) onto individual model response patterns, and a multi-model mean, labelled ‘Multi’. Anthropogenic, natural, greenhouse gas, and other anthropogenic (aerosols, ozone, land-use change) regression coefficients are shown. Regression coefficients are the scaling factors by which the model responses must be multiplied to best match observations. Regression coefficients consistent with one indicate a consistent magnitude response in observations and models, and regression coefficients inconsistent with zero indicate a detectable response to the forcing concerned. Lower panels show corresponding observationally-constrained estimates of attributable warming in globally-complete GSAT for the period 2010–2019, relative to 1850–1900, and the horizontal black line shows an estimate of observed warming in GSAT for this period. Figure is adapted from Gillett et al. (2021), their Extended Data Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37396, "uuid": "f9c77dd6554f403bb648520d05219978", "title": "Caption for Figure 3.8 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Assessed contributions to observed warming, and supporting lines of evidence. Shaded bands show assessed likely ranges of temperature change in GSAT, 2010-2019 relative to 1850-1900, attributable to net human influence, well-mixed greenhouse gases, other human forcings (aerosols, ozone, and land-use change), natural forcings, and internal variability, and the 5-95% range of observed warming. Bars show 5-95% ranges based on (left to right) Haustein et al. (2017), Gillett et al. (2021) and Ribes et al. (2021), and crosses show the associated best estimates. No 5-95% ranges were provided for the Haustein et al. (2017) greenhouse gas or other human forcings contributions. The Ribes et al. (2021) results were updated using a revised natural forcing time series, and the Haustein et al. (2017) results were updated using HadCRUT5. The Chapter 7 best estimates and ranges were derived using assessed forcing time series and a two-layer energy balance model as described in Section 7.3.5.3. Coloured symbols show the simulated responses to the forcings concerned in each of the models indicated. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37507, "uuid": "ed6fc23ca6f745a398bcacd3da747175", "title": "Caption for Figure 3.21 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Seasonal evolution of observed and simulated Arctic (left) and Antarctic (right) sea ice area (SIA) over 1979–2017. SIA anomalies relative to the 1979–2000 means from observations (OBS from OSISAF, NASA Team, and Bootstrap, top) and historical (ALL, middle) and natural only (NAT, bottom) simulations from CMIP5 and CMIP6 multi-models. These anomalies were obtained by computing non-overlapping three-year mean SIA anomalies for March (February for Antarctic SIA), June, September, and December separately. CMIP5 historical simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 historical simulations are extended by using SSP2-4.5 scenario simulations after 2014. CMIP5 NAT simulations end in 2012. Numbers in brackets represent the number of models used. The multi-model mean is obtained by taking the ensemble mean for each model first and then averaging over models. Grey dots indicate multi-model mean anomalies stronger than inter-model spread (beyond ± 1 standard deviation). Results remain very similar when based on sea ice extent (SIE – not shown). Units: 106 km2. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37510, "uuid": "388488ad642c4071bd76357010fd0995", "title": "Caption for Figure 3.22 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Time series of Northern Hemisphere March–April mean snow cover extent (SCE) from observations, CMIP5 and CMIP6 simulations. The observations (grey lines) are updated Brown-NOAA (Brown and Robinson, 2011), Mudryk et al. (2020), and GLDAS2. CMIP5 (top) and CMIP6 (bottom) simulations of the response to natural plus anthropogenic forcing are shown in orange, natural forcing only in green, and the pre-industrial control simulation range is presented in blue. Five-year mean anomalies are shown for the 1923–2017 period with the x-axis representing the centre years of each five-year mean. CMIP5 all forcing simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 all forcing simulations are extended by using SSP2-4.5 scenario simulations after 2014. Shading indicates 5th–95th percentile ranges for CMIP5 and CMIP6 all and natural forcings simulations, and solid lines are ensemble means, based on all available ensemble members with equal weight given to each model (Section 3.2). The blue vertical bar indicates the mean 5th–95th percentile range of pre-industrial control simulation anomalies, based on non-overlapping segments. The numbers in brackets indicate the number of models used. Anomalies are relative to the average over 1971–2000. For models, SCE is restricted to gridcells with land fraction ≥50%. Greenland is excluded from the total area summation. Figure is modified from Paik et al. (2020a), their Figure 1. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37513, "uuid": "ddfcacd0ee474e8182ff88c546c32d92", "title": "Caption for Figure 3.20 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Mean (x-axis) and trend (y-axis) in Arctic sea ice area (SIA) in September (left) and Antarctic SIA in February (right) for 1979–2017 from CMIP5 (upper) and CMIP6 (lower) models. All individual models (ensemble means) and the multi-model mean values are compared with the observations (OSISAF, NASA Team, and Bootstrap). Solid line indicates a linear regression slope with corresponding correlation coefficient (r) and p-value provided. Note the different scales used on the y-axis for Arctic and Antarctic SIA. Results remain essentially the same when using sea ice extent (SIE; not shown). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37516, "uuid": "3464cd9f09f2465eac27fbb68000aedf", "title": "Caption for Figure 3.24 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Biases in zonal mean and equatorial sea surface temperature (SST) in CMIP5 and CMIP6 models. CMIP6 (red), CMIP5 (blue) and HighResMIP (green) multi-model mean (a) zonally averaged SST bias; (b) equatorial SST bias; and (c) equatorial SST compared to observed mean SST (black line) for 1979–1999. The inter-model 5th and 95th percentiles are depicted by the respective shaded range. Model climatologies are derived from the 1979–1999 mean of the historical simulations, using one simulation per model. The Hadley Centre Sea Ice and Sea Surface Temperature version 1 (HadISST) (Rayner et al., 2003) observational climatology for 1979–1999 is used as the reference for the error calculation in (a) and (b); and for observations in (c). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37519, "uuid": "18c19d847b114b3fababeb3f73778d59", "title": "Caption for Figure 3.25 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "CMIP6 potential temperature and salinity biases for the global ocean, Atlantic, Pacific and Indian Oceans. Shown in colour are the time-mean differences between the CMIP6 historical multi-model climatological mean and observations, zonally averaged for each basin (excluding marginal and regional seas). The observed climatological values are obtained from the World Ocean Atlas 2018 (WOA18, 1981-–2010; Prepared by the Ocean Climate Laboratory, National Oceanographic Data Center, Silver Spring, MD, USA), and are shown as labelled black contours for each of the basins. The simulated annual mean climatologies for 1981 to 2010 are calculated from available CMIP6 historical simulations, and the WOA18 climatology utilized synthesized observed data from 1981 to 2010. A total of 30 available CMIP6 models have contributed to the temperature panels (left column) and 28 models to the salinity panels (right column). Potential temperature units are °C and salinity units are the Practical Salinity Scale 1978 [PSS-78]. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37522, "uuid": "063505b8b9c54ef49d9600ab96d0e544", "title": "Caption for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. Units are mm yr–1. In the right-hand column, three observed maps of 0 to 2000 m halosteric sea level trends: top (D&W) from Durack and Wijffels (2010), 1950–2019, updated; upper-middle (EN4) from Good et al. (2013), 1950–2019, updated; and lower-middle (Ishii) from Ishii et al. (2017), 1955–2019, updated. Bottom-right: the CMIP6 historical multi-model mean (1950–2014). Red and orange colours show a halosteric contraction (enhanced salinity) and blue and green a halosteric expansion (reduced salinity). In the left-hand column, basin-integrated halosteric (top) and thermosteric (bottom) trends for the Atlantic and Pacific, the two largest ocean basins, where Pacific anomalies are presented on the x-axis and Atlantic on the y-axis. Observational estimates are presented in black, CMIP6 historical (all forcings) simulations are shown in orange squares, with the multi-model mean shown as a dark orange diamond with a black bounding box. CMIP6 hist-nat (historical natural forcings only) simulations are shown in green squares with the multi-model mean as a dark green diamond with a black bounding box. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37525, "uuid": "2b6efdfc0f8844f2beecaa3402236315", "title": "Caption for Figure 3.30 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Observed and CMIP6 simulated AMOC mean state, variability and long-term trends. (a) AMOC meridional stream function profiles at 26.5°N from the historical CMIP5 (1860–2004) and CMIP6 (1860–2014) simulations compared with the mean maximum overturning depth (horizontal grey line) and magnitude (vertical grey line) from the RAPID observations (2004–2018). The distributions of model ranges of AMOC maximum magnitude and depth are respectively displayed on the x- and y-axis. (b) Distributions of overlapping eight-year AMOC trends from individual CMIP6 historical simulations (pink box plots) are plotted along with the combined distributions of all available CMIP5 (blue boxplot) and CMIP6 (red boxplot) models. For reference, the observed eight-year trend calculated between 2004–2012 is also shown as a horizontal grey line (following Roberts et al., 2014). (c) Distributions of interannual AMOC variability from individual CMIP6 model historical simulations, along with the combined distributions of all available CMIP5 and CMIP6 models. Interannual variability in models and observations are estimated as annual mean (April–March) differences, and the horizontal grey line is the observed value for 2009/2010 minus 2008/2009 (following Roberts et al., 2014). (d–f) Distributions of linear AMOC trends calculated over various time periods (see panel titles) in CMIP6 simulations forced with: greenhouse gas forcing only (GHG), natural forcing only (NAT), anthropogenic aerosol forcing only (AER) and all forcing combined (Historical; HIST). (a–f) Boxes indicate the 25th to 75th percentile range, whiskers indicate 1st and 99th percentiles, and dots indicate outliers, while the horizontal black line is the multi-model mean trend. In (d–f) the multi-model mean trend is also written above each distribution. The multi-model distributions in (a–c) were produced with one historical ensemble member per model for which the AMOC variable was available (listed), while those in (d–f) were produced with the AMOC detection and attribution simulation datasets utilized by Menary et al. (2020). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37528, "uuid": "43abaed7d0994ecab6ab4d97eb291925", "title": "Caption for Figure 3.35 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Southern Annular Mode (SAM) indices in the last millennium. (a) Annual SAM reconstructions by Abram et al. (2014) and Dätwyler et al. (2018). (b) The annual-mean SAM index defined by Gong and Wang (1999) in CMIP5 and CMIP6 last millennium simulations extended by historical simulations. All indices are normalized with respect to 1961–1990 means and standard deviations. Thin lines and thick lines show seven-year and 70-year moving averages, respectively. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37531, "uuid": "d0efa8b19231476ea3f974a39a15f294", "title": "Caption for Figure 3.39 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Model evaluation of the Pacific Decadal Variability (PDV). (a, b) Sea surface temperature (SST) anomalies (ºC) regressed onto the Tripole Index (TPI; Henley et al., 2015) for 1900–2014 in (a) ERSST version 5 and (b) CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of the model ensemble size. A 10-year low-pass filter was applied beforehand. Cross marks in (a) represent regions where the anomalies are not significant at the 10% level based on t-test. Diagonal lines in (b) indicate regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the PDV pattern in CMIP5 (each a cross in light blue, and the weighted multi-mode mean as a dot in dark blue), CMIP6 (each ensemble member is shown as a cross in red, weighted multi-model mean as a dot in orange) and observations over 40ºS–60ºN and 110ºE–70ºW. The reference pattern is taken from ERSST version 5 and black dots indicate other observational products, Hadley Centre Sea Ice and Sea Surface Temperature data set version 1 (HadISST version 1) and Centennial in situ Observation-Based Estimates of Sea Surface Temperature version 2 (COBE-SST2). (d) Autocorrelation of unfiltered annual TPI at lag one year and 10-year low-pass filtered TPI at lag 10 years for observations over 1900–2014 (horizontal lines) and 115-year chunks of pre-industrial control simulations (open boxes) and individual historical simulations over 1900–2014 (filled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but standard deviation of the unfiltered and filtered TPI (ºC). Boxes and whiskers show weighted multi-model mean, interquartile ranges and 5th and 95th percentiles. (f) Time series of the 10-year low-pass filtered TPI (ºC) in ERSST version 5, HadISST version 1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are the weighted multi-model mean for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile range across ensemble members. The 5–95% confidence interval for the CMIP6 multi-model mean is given in thin dashed lines. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37534, "uuid": "fa024534389041a0b2c82b3fb7c70f6d", "title": "Caption for Figure 3.40 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Model evaluation of Atlantic Multi-decadal Variability (AMV). (a, b) Sea surface temperature (SST) anomalies (ºC) regressed onto the AMV index defined as the 10-year low-pass filtered North Atlantic (0º–60°N, 80°W–0°E) area-weighted SST* anomalies over 1900–2014 in (a) ERSST version 5 and (b) the CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of each model’s ensemble size. The asterisk denotes that the global mean SST anomaly has been removed at each time step of the computation. Cross marks in (a) represent regions where the anomalies are not significant at the 10% level based on a t-test. Diagonal lines in (b) show regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the AMV pattern in CMIP5 (each member is shown as a cross in light blue, and the weighted multi-model mean is shown as a dot in dark blue), CMIP6 (each member is shown as a cross in red, and the weighted multi-model mean is shown as a dot in orange) and observations over [0º–60°N, 80°W–0°E]. The reference pattern is taken from ERSST version 5 and black dots indicate other observational products (HadISST version 1 and COBE-SST2). (d) Autocorrelation of unfiltered annual AMV index at lag one year and 10-year low-pass filtered AMV index at lag 10 years for observations over 1900–2014 (horizontal lines) and 115-year chunks of pre-industrial control simulations (open boxes) and individual historical simulations over 1900–2014 (filled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but showing standard deviation of the unfiltered and filtered AMV indices (ºC). Boxes and whiskers show the weighted multi-model mean, interquartile ranges and 5th and 95th percentiles. (f) Time series of the AMV index (ºC) in ERSST version 5, HadISST version 1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are the weighted multi-model mean for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile range obtained from all ensemble members. The 5–95% confidence interval for the CMIP6 multi-model mean is shown by the thin dashed line. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37545, "uuid": "398b4cd024b74d81ab4d31376fabe3d5", "title": "Caption for CCB 3.1, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "15-year trends of surface global warming for 1998–2012 and 2012–2026. (a, b) GSAT and GMST trends for 1998–2012 (a) and 2012–2026 (b). Histograms are based on GSAT in historical simulations of CMIP6 (red shading, extended by SSP2-4.5) and CMIP5 (grey shading; extended by RCP4.5). Filled and open diamonds at the top represent multi-model ensemble means of GSAT and GMST trends, respectively. Diagonal lines show histograms of HadCRUT5.0.1.0. Triangles at the top of (a) represent GMST trends of Berkeley Earth, GISTEMP, Kadow et al. (2020) and NOAAGlobalTemp-Interim, and the GSAT trend of ERA5. Selected CMIP6 members whose 1998–2012 trends are lower than the HadCRUT5.0.1.0 mean trend are indicated by purple shading (a) and (b). In (a), model GMST and GSAT, and ERA5 GSAT are masked to match HadCRUT data coverage. (c–d) Trend maps of annual near-surface temperature for 1998–2012 based on HadCRUT5.0.1.0 mean (c) and composited surface air temperature trends of subsampled CMIP6 simulations (d) that are included in purple shading area in (a). In (c), cross marks indicate trends that are not significant at the 10% level based on t-tests with serial correlation taken into account. Ensemble size used for each of the histograms and the trend composite is indicated at the top right of each of panels (a, b, d). Model ensemble members are weighted with the inverse of the ensemble size of the same model, so that individual models are equally weighted. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37548, "uuid": "21e430cc321841e88595d14a45264ff9", "title": "Caption for Cross-Chapter Box 3.2, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Comparison of observed and simulated changes in global mean temperature and precipitation extremes. Time series of globally averaged five-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation (Rx1day as standardized probability index in %) during 1953–2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with natural and human forcing (top) and natural forcing only (bottom). For CMIP5, historical simulations for 1953–2005 are combined with corresponding RCP4.5 scenario runs for 2006–2017. For CMIP6, historical simulations for 1953–2014 are combined with SSP2-4.5 scenario simulations for 2015–2017. Numbers in brackets represents the number of models used. The time-fixed observational mask has been applied to model data throughout the whole period. Gridcells with more than 70% data availability during 1953–2017 plus data for at least three years during 2013–2017 are used. Coloured lines indicate multi-model means, while shading represents 5th–95th percentile ranges, based on all available ensemble members with equal weight given to each model (Section 3.2). Anomalies are relative to 1961–1990 means. Figure is updated from Seong et al. (2021), their Figure 3 and Paik et al. (2020b), their Figure 3. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37551, "uuid": "5ef06524f5f3426880b4408ae60743d3", "title": "Caption for FAQ 3.1, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Observed warming (1850–2019) is only reproduced in simulations including human influence. Global surface temperature changes in observations, compared to climate model simulations of the response to all human and natural forcings (grey band), greenhouse gases only (red band), aerosols and other human drivers only (blue band) and natural forcings only (green band). Solid coloured lines show the multi-model mean, and coloured bands show the 5–95% range of individual simulations. ", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37554, "uuid": "50547c2a3077454a832d5e614768841d", "title": "Caption for FAQ 3.2, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Annual (left), decadal (middle) and multi-decadal (right) variations in average global surface temperature. The thick black line is an estimate of the human contribution to temperature changes, based on climate models, whereas the green lines show the combined effect of natural variations and human-induced warming, different shadings of green represent different simulations, which can be viewed as showing a range of potential pasts. The influence of natural variability is shown by the green bars, and it decreases with longer time scales. The data is sourced from the CESM1 large ensemble.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37557, "uuid": "d3e73c74a0264a6bb1b7cde31c8c06b0", "title": "Caption for FAQ 3.3, Figure 1 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Pattern correlations between models and observations of three different variables: surface air temperature, precipitation and sea level pressure. Results are shown for the three most recent generations of models, from the Coupled Model Intercomparison Project (CMIP): CMIP3 (orange), CMIP5 (blue) and CMIP6 (purple). Individual model results are shown as short lines, along with the corresponding ensemble average (long line). For the correlations the yearly averages of the models are compared with the reference observations for the period 1980-1999, with 1 representing perfect similarity between the models and observations. CMIP3 simulations performed in 2004-2008 were assessed in the IPCC Fourth Assessment, CMIP5 simulations performed in 2011-2013 were assessed in the IPCC Fifth Assessment, and CMIP6 simulations performed in 2018-–2021 are assessed in this Report. ", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37560, "uuid": "fc6c3c4e3da34bb697dd37452a452716", "title": "Caption for Figure 3.38 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Model evaluation of ENSO teleconnection for 2 metre temperature and precipitation in boreal winter (December–January–February). Teleconnections are identified by linear regression with the Niño 3.4 SST index based on Extended Reconstructed Sea Surface Temperature (ERSST) version 5 during the period 1958–2014. Maps show observed patterns for temperature from the Berkeley Earth dataset over land and from ERSST version 5 over ocean (ºC, top) and for precipitation from GPCC over land (shading, mm day–1) and GPCP worldwide (contours, period: 1979–2014). Distributions of regression coefficients (grey histograms) are provided for a subset of AR6 reference regions defined in Atlas.1.3 for temperature (top) and precipitation (bottom). All fields are linearly detrended prior to computation. Multi-model multi-member ensemble means are indicated by thick vertical black lines. Blue vertical lines show three observational estimates of temperature, based on Berkeley Earth, GISTEMP and CRUTS datasets, and two observational estimates of precipitation, based on GPCC and CRUTS datasets. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37563, "uuid": "659a77e7056c4f40a128d14c9969bdbf", "title": "Caption for Figure 3.26 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global ocean heat content in CMIP6 simulations and observations. Time series of observed (black) and simulated (red) global ocean heat content anomalies with respect to 1995–2014 for the full ocean depth (left-hand panel); upper layer: 0–700 m (top right-hand panel); intermediate layer: 700–2000 m (middle right-hand panel); and the abyssal ocean: >2000 m (bottom right-hand panel). The best estimate observations (black solid line) for the period of 1971–2018, along with very likely ranges (black shading) are from Section 2.3.3.1. For the models (1860–2014), ensemble members from 15 CMIP6 models are used to calculate the multi-model mean values (red solid line) after averaging across simulations for each independent model. The very likely ranges in the simulations are shown in red shading. Simulation drift has been removed from all CMIP6 historical runs using a contemporaneous portion of the linear fit to each corresponding pre-industrial control run (Gleckler et al., 2012). Units are zettajoules (ZJ; 1021 joule). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37569, "uuid": "b916f422ff904d9bbbde04cef5443516", "title": "Caption for Figure 3.27 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Maps of multi-decadal salinity trends for the near-surface ocean. Units are Practical Salinity Scale 1978 [PSS-78] per decade. (Top) The best estimate (Section 2.3.3.2) observed trend (Durack and Wijffels, 2010). (Bottom) Simulated trend from the CMIP6 historical experiment multi-model mean (1950–2014). Black contours show the climatological mean salinity in increments of 0.5 PSS-78 (thick lines 1 PSS-78). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37572, "uuid": "dac576df690c47ec851c681f3f22128a", "title": "Caption for Figure 3.29 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated and observed global mean sea level change due to thermal expansion for CMIP6 models and observations relative to the baseline period 1850–1900. Historical simulations are shown in brown, natural only in green, greenhouse gas only in grey, and aerosol only in blue (multi-model means shown as thick lines, and shaded ranges between the 5th and 95th percentile). The best estimate observations (black solid line) for the period of 1971–2018, along with very likely ranges (black shading) are from Section 2.3.3.1 and are shifted to match the multi-model mean of the historical simulations for the 1995–2014 period. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37574, "uuid": "8f7400954c7f4946afdda0ac202211ce", "title": "Derivation of the dataset: Cloud droplet number concentration, calculated from the MODIS (Moderate resolution imaging spectroradiometer) cloud optical properties retrieval and gridded using different sampling strategies", "abstract": "For more information see Gryspeerdt et al., The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data. Atmos. Meas. Tech. 2022.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37583, "uuid": "fd3c87fad3974fce92c5c2469305fbb7", "title": "Caption for Figure 3.36 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Life cycle of (left) El Niño and (right) La Niña events in observations (black) and historical simulations from CMIP5 (blue; extended with RCP4.5) and CMIP6 (red). An event is detected when the December ENSO index value in year zero exceeds 0.75 times its standard deviation for 1951–2010. (a, b) Composites of the ENSO index (ºC). The horizontal axis represents month relative to the reference December (the grey vertical bar), with numbers in parentheses indicating relative years. Shading and lines represent 5th–95th percentiles and multi-model ensemble means, respectively. (c, d) Mean durations (months) of El Niño and La Niña events defined as number of months in individual events for which the ENSO index exceeds 0.5 times its December standard deviation. Each dot represents an ensemble member from the model indicated on the vertical axis. The boxes and whiskers represent multi-model ensemble mean, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6. The CMIP5 and CMIP6 multi-model ensemble means and observational values are indicated at the top right of each panel. The multi-model ensemble means and percentile values are evaluated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their ensemble sizes. The ENSO index is defined as the SST anomaly averaged over the Niño 3.4 region (5ºS–5ºN, 170ºW–120ºW). All results are based on five-month running mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37586, "uuid": "c78e806f67624e9c8d4f0f8a70888cd8", "title": "Caption for Figure 3.37 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "ENSO seasonality in observations (black) and historical simulations from CMIP5 (blue; extended with RCP4.5) and CMIP6 (red) for 1951–2010. (a) Climatological standard deviation of the monthly ENSO index (SST anomaly averaged over the Niño 3.4 region; °C). Shading and lines represent 5th–95th percentiles and multi-model ensemble means, respectively. (b) Seasonality metric, which is defined for each model and each ensemble member as the ratio of the ENSO index climatological standard deviation in November–January (NDJ) to that in March–May (MAM). Each dot represents an ensemble member from the model indicated on the vertical axis. The boxes and whiskers represent the multi-model ensemble mean, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6 individually. The CMIP5 and CMIP6 multi-model ensemble means and observational values are indicated at the top right of the panel. The multi-model ensemble means and percentile values are evaluated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their ensemble sizes. All results are based on five-month running mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37610, "uuid": "35e0af4b45ec43cdb28b3e0b21986d7b", "title": "Caption for Figure 5.33 from Chapter 5 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Carbon sink response in a scenario with net carbon dioxide (CO2) removal from the atmosphere. Shown are CO2 flux components from concentration-driven Earth system model (ESM) simulations during different emissions stages of SSP1-2.6 and its long-term extension: (a) Large net positive CO2 emissions; (b) small net positive CO2 emissions; (c), (d) net negative CO2 emissions; (e) net zero CO2 emissions. Positive flux components act to raise the atmospheric CO2 concentration, whereas negative components act to lower the CO2 concentration. Net CO2 emissions, land and ocean CO2 fluxes represent the multi-model mean and standard deviation (error bar) of four ESMs (CanESM5, UKESM1, CESM2-WACCM, IPSL-CM6a-LR) and one Earth system model of intermediate complexity (UVic ESCM; Mengis et al., 2020). Net CO2 emissions are calculated from concentration-driven ESM simulations as the residual from the rate of increase in atmospheric CO2 and land and ocean CO2 fluxes. Fluxes are accumulated over each 50-year period and converted to concentration units (ppm). Further details on data sources and processing are available in the chapter data table (Table 5.SM.6).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37614, "uuid": "df2cebce3aa24f0187bc9151ee0c8a24", "title": "Caption for Figure 12.4 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Median projected changes in selected climatic impact-driver indices based on CMIP6 models. (a–c) Mean number of days per year with maximum temperature exceeding 35°C; (d–f) mean number of days per year with the NOAA Heat Index (HI) exceeding 41°C; (g–i) number of negative precipitation anomaly events per decade using the six-month Standardized Precipitation Index; (j–l) mean soil moisture (%) and (m–o) mean wind speed (%). (p–r) shows change in extreme sea level (1-in-100-year return period total water level from Vousdoukas et al. (2018)’s CMIP5 based dataset; metres). Left-hand column is for SSP1-2.6, 2081–2100; middle column is for SSP5-8.5 2041–2060; and right-hand column SSP5-8.5, 2081–2100, all expressed as changes relative to 1995–2014. Exception is extreme total water level which is for (p) RCP4.5 2100, (q) RCP8.5 2050 and (r) RCP8.5 2100, each relative to 1980–2014. Bias correction is applied to daily maximum temperature and HI data (Annex VI and Atlas.1.4.5). Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign (direction) of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. See Annex VI for details of indices. Figures 12. SM.1–12.SM.6 show regionally averaged values of these indices for the AR6 WGI Reference Regions for various model ensembles, scenarios, time horizons and global warming levels.Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37619, "uuid": "5f4ba5fffd24495c8e04d15f71379351", "title": "Caption for Figure 2.29 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Low-latitude surface ocean pH over the last 65 million years (65 Myr). (a) Low-latitude (30°N–30°S) surface ocean pH over the last 65 Myr, reconstructed using boron isotopes in foraminifera. (b) as (a) but for the last 3.5 Myr. Double headed arrow shows the approximate magnitude of glacial-interglacial pH changes. (c) Multisite composite of surface pH. In (a, b, c) uncertainty is shown at 95% confidence as a shaded band. Relevant paleoclimate reference periods (CCB2.1) have been labelled. Period windows for succeeding panels are shown as horizontal black lines in (a) and (b). (d) Estimated low-latitude surface pH from direct observations (BATS, HOT) and global mean pH (65°S–65°N) from two indirect estimates (CMEMS, OCEAN-SODA). 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": 37625, "uuid": "8fd4daeead734530aa9a119d5c56d306", "title": "Coastal and Regional Ocean Community model", "abstract": "Ocean (lateral) forcing (1993-2018 climatology): CMEMS Global Ocean Physics Reanalysis (https://doi.org/10.48670/moi-00021) Atmospheric (surface) forcing (1993-2018 climatology): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate (http://doi.org/10.1002/qj.3803) Tidal forcing: TPXO9-atlas-v2a (https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2) Climatological river fluxes: Dai and Trenberth (2002) Estimates of Freshwater Discharge from Continents: Latitudinal and Seasonal Variations (https://doi.org/10.1175/1525-7541(2002)003%3C0660:EOFDFC%3E2.0.CO;2)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37649, "uuid": "f26066a14fbe42728e1144ce138a40d5", "title": "Caption for FAQ 11.1, figure 1 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global maps of future changes in surface temperature (top panels) and precipitation (bottom panels) for long-term average (left) and extreme conditions (right). All changes were estimated using the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble median for a scenario with a global warming of 4°C relative to 1850–1900 temperatures. Average surface temperatures refers to the warmest three-month season (summer in mid- to high latitudes) and extreme temperature refer to the hottest day in a year. Precipitation changes, which can include both rainfall and snowfall changes, are normalized by 1850–1900 values and shown in percentage; extreme precipitation refers to the largest daily precipitation in a year.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37652, "uuid": "cf0a50cd069243d580024a0467f8d178", "title": "Caption for Figure 11.3 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and the global land area (except Antarctica), against changes in global mean surface air temperature (GSAT) as simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) models under different Shared Socio-economic Pathway (SSP) forcing scenarios, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Changes in TXx and GSAT are relative to the 1850–1900 baseline, and changes in GSAT are expressed as global warming level. (a) Individual models from the CMIP6 ensemble (grey), the multi-model median under three selected SSPs (colours), and the multi-model median (black); (b) to (l) Multi-model median for the pooled data for individual AR6 regions. Numbers in parentheses indicate the linear scaling between regional TXx and GSAT. The black line indicates the 1:1 reference scaling between TXx and GSAT. See Atlas.1.3.2 for the definition of regions. Changes in TXx are also displayed in the Interactive Atlas. For details on the methods, see Supplementary Material 11.SM.2.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37655, "uuid": "b82482de8e144324bf34599b3f6333b4", "title": "Caption for Figure 11.19 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in (a–c) the number of consecutive dry days (CDD), (d–f) annual mean soil moisture over the total column, and (g–l) the frequency and intensity of 1-in-10-year soil moisture drought for the June-to-August and December-to-February seasons at 1.5°C, 2°C, and 4°C of global warming compared to the 1850–1900 baseline. The unit for soil moisture change is the standard deviation of interannual variability in soil moisture during 1850–1900. Standard deviation is a widely used metric in characterizing drought severity. A projected reduction in mean soil moisture by one standard deviation corresponds to soil moisture conditions typical of about 1-in-6-year droughts during 1850–1900 becoming the norm in the future. Results are based on simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model ensemble under the Shared Socio-economic Pathway (SSP), SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers in the top right indicate the number of simulations included. Uncertainty is represented using the simple approach: 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. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Changes in CDDs are also displayed in the Interactive Atlas. Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37658, "uuid": "6402b9fe3c6d48ddb5e4c186900ca077", "title": "Caption for Figure 11.A.1 from Chapter 11 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Figure 11.A.1:\tAs Figure 11.3 but for the annual minimum temperature (TNn).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37671, "uuid": "09f9e20e45f94feb80baf94928e26d1d", "title": "ARC36 stand-alone SI3 Arctic configuration", "abstract": "This is a configuration of the NEMO community ocean model based on the ORCA2_SAS_ICE reference configuration. The NEMO code is available from https://forge.nemo-ocean.eu/nemo/nemo. This configuration has a resolution of 1/36 degree and is a cut-out of the global 1/36 configuration: https://github.com/immerse-project/ORCA36-demonstrator. The code base is a pre-4.2.0 NEMO version, the model source code can be found in the file src_tar. Model setup: Follow the instructions on https://sites.nemo-ocean.io/user-guide/index.html to download and install the NEMO model version 4.2.0. Swap the src directory for the one in the tar file src_tar. Compile the ORCA2_SAS_ICE reference configuration. Put the rest of the files in this zenodo archive in the EXP00 directory, except the namelist_cfg_for_DOMAINcfg file which goes into tools/DOMAINcfg along with the grid files to be downloaded later. The files provided include example configuration namelist files namelist_cfg and namelist_ice_cfg. The atmospheric forcing used is the Drakkar forcing set (DFS) version 5.2, year 2008. The atmospheric forcing is interpolated on-the-fly, using the weights files. The weights were calculated using the nemo WEIGHTS tool. For the ocean (bottom) boundary the World Ocean Atlas 2018 multidecadal monthly averages are used. The data is already interpolated onto the ARC36 grid. Interpolation was done using the SOSIE tool. Files provided are monthly averages of sea surface salinity and temperature. Finally, the model grid domain_cfg.nc needs to be created. Download the ORCA36 files from ftp://ftp.mercator-ocean.fr/download/users/cbricaud/BENCH-ORCA36-INPUT.tar.gz, see the ORCA36 demonstrator github page. The necessary files are the coordinates and bathymetry files. To cut out the Arctic domain use ncks -F -d y,7000,,1 in.nc out.nc. Put in tools/DOMAINcfg and use the DOMAINcfg NEMO tool to create the domain_cfg.nc file using the file namelist_cfg_for_DOMAINcfg as namelist_cfg. The resulting file is large (122GB) therefore executing in parallel mode is required. The individual processor files need to be merged into one, use the REBUILD_NEMO tool. Put the resulting domain_cfg.nc file into EXP00 and run NEMO following the instructions. The ARC36 configuration was set up and run on ARCHER2 using 594 NEMO processors and 12 XIOS processors.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37679, "uuid": "d129f860b47b40fdafd57a5b69457926", "title": "shiptrack_semantic_segmentation_v1", "abstract": "A convolutional neural network with a Unet architecture, with a RESNET-152 backbone, trained to segment shiptrack clouds from enhanced day_microphysics imagery from AQUA MODIS Input Description AQUA MODIS level 1B day microphysics composite granules, enhanced with histogram stretch. Output Description Netcdf files with a single variable 'shiptracks' that contains shiptrack inference values and shares the coordinates of the original AQUA MODIS granule from which they are derived. Post-processing is required to extract contours and filter them by brightness temperature to obtain final results used in publication. Software Reference https://github.com/duncanwp/shiptrack-detection", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37683, "uuid": "b0c4de065765470aaecdcea2791cb434", "title": "Caption for Figure 2.12 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Temperature trends in the upper air. (a) Zonal cross-section of temperature anomaly trends (2007–2016 baseline) for 2002–2019 in the upper troposphere and lower stratosphere region. The climatological tropopause altitude is marked as a grey line. Significance is not indicated due to the short period over which trends are shown, and because the assessment findings associated to this figure relate to difference between trends at different heights, not the absolute trends. (b, c) Trends in temperature at various atmospheric heights for 1980–2019 and 2002–2019 for the near-global (70°N–70°S) domain. (d, e) as for (b, c) but for the tropical (20°N–20°S) region. 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": 37686, "uuid": "3c5838a8bd4847cc97a8445fef27f2a2", "title": "Caption for Figure 2.16 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in precipitation minus evaporation. (a) Trends in precipitation minus evaporation (P–E) between 1980 and 2019. Trends are calculated using OLS regression with significance assessed following AR(1) adjustment after Santer et al. (2008) (‘x’ marks denote non-significant trends). Time series of (b) global, (c) land-only and (d) ocean-only average annual P–E (mm day–1). 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": 37690, "uuid": "17a17e2d8cae429eb89fcdf75e5cccc1", "title": "Caption for Figure 2.23 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Mountain glacier advance and annual mass change. (a) Number of a finite selection of surveyed glaciers that advanced during the past 2000 years. (b) Annual and decadal global glacier mass change (Gt yr–1) from 1961 until 2018. In addition, mass change mean estimates are shown. Ranges show the 90% confidence interval. 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": 37709, "uuid": "1092d97568364adfb4ed37293aee99ab", "title": "Caption for Figure 2.12 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Temperature trends in the upper air. (a) Zonal cross-section of temperature anomaly trends (2007–2016 baseline) for 2002–2019 in the upper troposphere and lower stratosphere region. The climatological tropopause altitude is marked as a grey line. Significance is not indicated due to the short period over which trends are shown, and because the assessment findings associated to this figure relate to difference between trends at different heights, not the absolute trends. (b, c) Trends in temperature at various atmospheric heights for 1980–2019 and 2002–2019 for the near-global (70°N–70°S) domain. (d, e) as for (b, c) but for the tropical (20°N–20°S) region. 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": 37711, "uuid": "d3d568f134634dda9bb06b9556756b5b", "title": "Level 2 (L2) cloud retrieval algorithm applied to Suomi National Polar-orbiting Partnership (SNPP) Visible Infra-red Imaging Radiometer Suite (VIIRS) instrument: level 2 Suomi-NPP VIIRS raw cloud data.", "abstract": "The computation process for the transformation of cloud data from the VIIRS instrument on board the JPSS (Joint Polar Satellite System) operational meterological satellite prototype SNPP, to \r\nTo view the retrieval algorithms for these products, visit the following Sentinel/Copernicus page: \r\nhttps://sentinels.copernicus.eu/documents/247904/2476257/Sentinel-5P-NPP-ATBD-NPP-Clouds\r\n\r\nThe S5P-NPP Cloud processor is a stand-alone code separate from other TROPOMI-related computations. The retrieval algorithm requires the following input fields:\r\n• Semi-major and semi-minor axis, and eccentricity, of the WGS'84 ellipsoid\r\n• Time window of tolerance to identify relevant NPP files for a given S5P scan line\r\n• Number of VIIRS cloud classifcations, defined S5P FOVs, and VIIRS moderate resolution channels used in the S5P-NPP product\r\n• Maximum and minimum values of transformed coordinates (y & z) which define a specific S5P FOV.\r\n• Instantaneous spatial response function\r\n• Latitude and longitude of S5P pixel corner, centre or sensor, and altitude of sensor\r\n• View zenith angle of S5P pixel and line-of-sight zenith angle of a VIIRS pixel\r\n• Sensing time of S5P scan line and a VIIRS scan line\r\n• Sensing start and stop time of S5P L1 file\r\n• Latitude and longitude of VIIRS pixel\r\n• Sensing start and stop time of NPP L1 file\r\n• Sensing start and stop time of NPP L1 granule\r\n• Sensing start and stop centre-scan latitude and longitude of NPP L1 granule\r\n• VIIRS pixel level geolocation, radiance and cloud mask data quality flags\r\n• VIIRS band averaged spectral radiance\r\n• VIIRS pixel cloud mask\r\n• Ratio of nominal FOV extent to that of L1GPC in across- and along-track directions\r\n• Spatial response function (SRF), including effects of satellite motion\r\n\r\nThe retrieval is performed in 3 steps:\r\n1) S5P L1 file is ingested, containing the geolocation of each scene for which the S5P-NPP-Cloud information is required.\r\n2) The time difference, ∆tsat, between S5P centre-swath observations and those of spatially co-located VIIRS measurements is calculated to an accuracy of ≈1 minute using metadata in the VIIRS geololcation file.\r\n3) S5P scan lines are looped over: a) VIIRS files which contain information relevant to these scan lines are identified b) The relevant information from these VIIRS files is read and stored in memory c) S5P across-track pixels within the current scan line are looped over.\r\n \r\nThe NPP-Cloud product is then the re-gridding of the VIIRS L1 and cloud mask products from the SNPP to the resolution acquired by the TROPOMI instrument on the Sentinel 5P satellite.\r\n\r\nFor more information on the processing algorithm please look at the ATBD (Algorithm Theoretical Baseline Document) on the Sentinel Copernicus webpage, URL linked in the documentation.", "keywords": "Sentinel, ESA, VIIRS", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37713, "uuid": "83951253e83b4d4dadc4b07a79a76b0e", "title": "Derivation of ESA Water Vapour Climate Change Initiative (Water_Vapour_cci): Vertically resolved water vapour - stratosphere (CCI WV-strato, CDR-3), v3.3", "abstract": "CCI WV-strato is based on multiple limb satellite sounders (SAGE II, UARS-MLS, HALOE, POAM III, SMR, SAGE III, SCIAMACHY, MIPAS, ACE-FTS, ACE-MAESTRO, Aura-MLS and SAGE III/ISS) merged using a chemistry-climate model in specified dynamics mode as transfer function following the methodology established in Hegglin et al. (2014).", "keywords": "", "inputDescription": 23, "outputDescription": null, "softwareReference": 38318, "identifier_set": [] }, { "ob_id": 37741, "uuid": "883b1cb68d414c93b851de3a4a1f77a6", "title": "UK Global Ocean GO8p7 configuration, based on version 4.0.4 of the NEMO (Nucleus for European Modelling of the Ocean) ocean and sea-ice model", "abstract": "GO8p7 configuration was developed under JMMP collaborative programme, based on NEMO v4.0.4", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37755, "uuid": "d015125d92c14211a83804cbf48738d9", "title": "NAME Model computation for MOYA Bolivia BAS Twin Otter flights with modelling", "abstract": "NAME model simulation which ran using the Met Office NAME model at of 0.14° × 0.09° and temporal resolution of 3 hourly. A footprint was simulated for each minute of aircraft sampling to capture the Llanos de Moxos wetlands in Bolivia 2019-03-08 to 2019-03-09\r\nThe NAME model inversion was carried out using footprints simulated from the NAME Lagrangian particle dispersion model and a hierarchical Bayesian Markov chain Monte Carlo (MCMC) framework", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37756, "uuid": "f29a2dce4ec74723a75c85220f98c6cc", "title": "GEOSChem model simulation for MOYA BAS flights in Bolivia", "abstract": "A nested GEOS-Chem simulation for the MOYA Bolivia flights 2019-03-08 to 2019-03-09.\r\n\r\nThe GEOS-Chem inverse modeling methodology followed a Bayesian synthesis inversion framework (4). The state vector included 100 elements, 99 corresponding to emissions and one describing the baseline mole fraction. Measurements were averaged into 1-minute means. Model-measurement uncertainties included the standard deviation of measurements with in each one-minute period and a fixed 8 ppb model uncertainty. A flat prior emissions distribution was used within the Llanos de Moxos basin with emissions of 48 mg CH4/m2/day. A nested GEOS-Chem simulation at 0.25° x 0.3125° was used to map the relationship between emissions and aircraft measurements in a regional domain bounded by 24 - 0 °S and 75 – 55 °W. Initial boundary conditions for the nested domain were created by a global GEOS-Chem simulation at 2° x 2.5°.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37760, "uuid": "72c2436b889842eca00ec230276de8ce", "title": "Caption for Box 8.2, figure 1 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term changes in precipitation seasonality. Global maps of projected changes in precipitation seasonality (simply defined as the sum of the absolute deviations of mean monthly rainfalls from the overall monthly mean, divided by the mean annual rainfall as in Walsh and Lawler, 1981) averaged across 31 to 33 CMIP6 models in the SSP1-2.6 (b), SSP2-4.5 (c) and SSP5-8.5 (d) scenario respectively. The simulated 1995–2014 climatology is shown in panel (a). All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. 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. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37763, "uuid": "ac6b2a9b2517438cb37081d9c3278e6f", "title": "Caption for Figure 8.14 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term relative changes in seasonal mean precipitation. Global maps of projected relative changes (%) in seasonal mean of precipitation averaged across 29 CMIP6 models in the SSP2-4.5 scenario. All changes are estimated for 2081–2100 relative to the 1995–2014 base period. Uncertainty is represented using the simple approach. 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. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37766, "uuid": "44765428d93547b3a7bc3f77d411f8db", "title": "Caption for Figure 8.15 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term relative changes in daily precipitation statistics. Global maps of projected seasonal mean relative changes (%) in the number of dry days (for examplei.e., days with less than 1 mm of rain) and daily precipitation intensity (in mm/ day–1, estimated as the mean daily precipitation amount at wet days –- for examplei.e., days with intensity above 1 mm/ day–1) averaged across CMIP6 models in the SSP1-2.6 (a, b), SSP2-4.5 (c, d) and SSP5-8.5 (e, f) scenario respectively. Uncertainty is represented using the simple approach.: No overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change.; Ddiagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37769, "uuid": "a86f91aeb6804a7f81ae8ee5e43b36b3", "title": "Caption for Figure 8.16 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Rate of change in mean and variability across increasing global warming levels. Relative change (%) in seasonal mean total precipitable water (grey line), precipitation (red dashed lines), runoff (blue dashed lines), as well as in standard deviation of precipitation (red dashed lines) and runoff (blue dashed lines) averaged over extratropical land in (c) summer and (d) winter, and tropical land in (a) June–July–August (JJA) and (b) December–January–February (DJF) as a function of global mean surface temperature for the CMIP6 multi-model mean across the SSP5-8.5 scenario. Extratropical winter refers to DJF for Northern Hemisphere and JJA for Southern Hemisphere (and the reverse for extratropical summer). Each marker indicates a 21-year period centred on consecutive decades between 2015 and 2085 relative to the 1995–2014 base period. Precipitation and runoff variability are estimated by their standard deviation after removing linear trends from each time series. Error bars show the 5–95% confidence interval for the warmest 5°C global warming level. Figure adapted fromPendergrass et al. (2017) and updated with CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37772, "uuid": "8466f565e78142d9a94d99e99920dce1", "title": "Caption for Figure 8.17 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term relative changes in seasonal mean evapotranspiration. Global maps of projected relative changes (%) in seasonal mean of surface evapotranspiration for December–January–February (DJF; left panels) and June–July–August (JJA; right panels) averaged across 29 or 30 CMIP6 models for SSP1.2-6 (a, b) SSP2-4.5 (c, d) and SSP5-8.5 (e, f) scenario respectively. All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. 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. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37775, "uuid": "df421c953c5746e3a8a7ff6bc14ccaab", "title": "Caption for Figure 8.13 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Zonal and annual-mean projected long-term changes in the atmospheric water budget. Zonal and annual mean projected changes (mm day–1) in P (precipitation, left column), E (evaporation, middle column), and P–E (right column) over both land and ocean areas (thick line) and over land only (dashed line) averaged across 36 to 38 CMIP6 models in the SSP1-2.6 (top row), SSP2-4.5 (middle row) and SSP5-8.5 (bottom row) scenario, respectively. Shading denotes confidence intervals estimated from the CMIP6 ensemble under a normal distribution hypothesis. Colour shading denotes changes over both land and ocean. Grey shading represents internal variability derived from the pre-industrial control simulations. All changes are estimated for 2081–2100 relative to the 1995–2014 base period. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37778, "uuid": "2ffd8a8f55fb45bfbf48572bbeba23ab", "title": "Caption for Figure 8.18 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected long-term relative changes in seasonal mean runoff. Global maps of projected relative change (%) in runoff seasonal mean for December–January–February (DJF; left panels) and June–July–August (JJA; right panels) averaged across CMIP6 models SSP1.2-6 (a, b), SSP2-4.5 (c, d) and SSP5-8.5 (e, f) scenario respectively. All changes are estimated in 2081–2100 relative to 1995–2014. Uncertainty is represented using the simple approach. 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. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37781, "uuid": "01545bad7c4e48cf9958ec6654eeb143", "title": "Caption for Figure 8.21 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Schematic depicting large-scale circulation changes and impacts on the regional water cycle. The central figures show precipitation minus evaporation (P–E) changes at 3°C or global warming relative to an 1850–1900 base period (mean of 23 CMIP6 SSP5-8.5 simulations). Annual mean changes (large map) include contours depicting control climate P–E = 0 lines with the solid contour enclosing the tropical rain belt region and dashed lines representing the edges of subtropical regions. Confidence levels assess understanding of how large-scale circulation change affect the regional water.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37784, "uuid": "356ce94601d54026bf0062866822a893", "title": "Caption for Figure 8.25 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Effect of first versus second 2°C of global warming relative to the 1850-1900 base period on seasonal mean precipitation (mm day–1). CMIP6 multi-model ensemble mean December–January–February (left panels) and June–July–August (right panels) precipitation difference for (a, b) SSP5-8.5 at +2°C (c, d) SSP5-8.5 at +4°C minus SSP5-8.5 at +2°C (second 2°C warming); (e, f) second minus first 2°C fast warming (c–a and d–b). Only models reaching the +4°C warming levels in SSP5-8.5 are considered. Differences are computed based on 21-year time windows centred on the first year reaching or exceeding the selected global warming level using a 21-year running mean global surface atmospheric temperature criterion. Uncertainty is represented using the simple approach. 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. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37787, "uuid": "9e2b84be5e154462934ff6af2b7afcf6", "title": "Caption for Figure 8.26 from Chapter 8 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Rate of change in basin-scale annual mean runoff with increasing global warming levels. Relative changes (%) in basin-averaged annual mean runoff estimated as multi-model ensemble median from a variable subset of CMIP6 models for each SSP over nine major river basins: (a) Mississippi, (b) Danube, (c) Lena, (d) Amazon, (e) Euphrates, (f) Yangtze, (g) Niger, (h) Indus, and (i) Murray. The basin averages have been estimated after a first-order conservative remapping of the model outputs on the 0.5° by 0.5° river network of Decharme et al. (2019). The shaded area indicates the 5–95% confidence interval of the ensemble values across all SSPs. Note that the y-axis range differs across basins and is particularly large for Niger and Murray (panels g and i). The number of models considered is specified for each scenario in the legend located inside panel b. Further details on data sources and processing are available in the chapter data table (Table 8.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37835, "uuid": "0a76ab1544d843ef9b87ff24fc9f861d", "title": "Transient simulation with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) version 2.0, part of the Community Earth System Model (CESM) release 2.1.0, from 1950 to 2015", "abstract": "The data were generated with the Whole Atmosphere Community Climate Model eXtension (WACCM-X) version 2.0 (Liu et al., 2018), part of the Community Earth System Model (CESM) release 2.1.0 (http://www.cesm.ucar.edu/models/cesm2) WACCM-X is a global model of the atmosphere from the surface up to ~500 km altitude and was run in free-running mode with a horizontal resolution of 1.9degrees latitude and 2.5 degrees longitude (giving 96 latitude points and 144 longitude points) and 126 vertical levels from Jan 2015 to 2070. This is an extension of the simulation by Cnossen (2020) from 1950 to 2015. For 2015-2070, lower boundary forcings and chemical emissions followed Shared Socio-economic Pathway (SSP) 2-4.5, a moderate scenario, comparable to Representative Concentration Pathway (RCP) 4.5 (O'Neill et al., 2016). The main magnetic field was specified according to the prediction by Aubert (2015). Solar radiative and particle forcings were specified according to the reference scenario of the Climate Model Intercomparison Project (CMIP) 6 recommendation by Matthes et al. (2017). The simulation therefore includes all known drivers of long-term change in the upper atmosphere and for all of these realistic long-term variations were adopted, offering a plausible, realistic estimate of the future climate of the upper atmosphere. For further details see Cnossen (2022).\r\nThe WACCM-X simulation was run on the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk) in 2019-2020 by Ingrid Cnossen.\r\n\r\n\r\n\r\nReferences: See related documents", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37851, "uuid": "5cf4987fc61b4974a5f1be25e4b8f238", "title": "Caption for Figure 12.5 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for Africa. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX models for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (GWLs, defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37854, "uuid": "e969d6921e6c49628bb79295c510e3eb", "title": "Caption for Figure 12.7 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for Australasia. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX models for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional mean change estimates and bars the 5–95th percentiles ranges of associated uncertainty. Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37857, "uuid": "723befe56178420f8d8b531a824c0263", "title": "Caption for Figure 12.9 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for Europe. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2), and (b) median change in the number of days with snow water equivalent (SWE) over 100 mm (from November to March), from EURO-CORDEX models for 2041–2060 relative to 1995–2014 and RCP8.5. Diagonal lines indicate where less than 80% of models agree on the sign (direction )of change. (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) As for (c) but showing absolute values for number of days with SWE > 100mm, masked to grid cells with at least 14 such days in the recent past. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37860, "uuid": "6374eccfd34d4f9ebd8cdfbbe51f375b", "title": "Caption for Figure 12.10 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for North America. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2), and (b) median change in the number of days with snow water equivalent (SWE) over 100 mm (from November to March), from CORDEX-North America models for 2041–2060 relative to 1995–2014 and RCP8.5. Diagonal lines indicate where less than 80% of models agree on the sign (direction) of change. (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) As for (c) but showing absolute values for number of days with SWE > 100 mm, masked to grid cells with at least 14 such days in the recent past. See Technical Annex VI for details of indices. A Caribbean (CAR) Q100 bar plot is included here but assessed in the Small Islands section (Section 12.4.7). Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37863, "uuid": "51d905b64408486fbed837dea056cae9", "title": "Caption for Figure 12.6 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected changes in selected climatic impact-driver indices for Asia. (a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX models for 2041–2060 relative to 1995–2014 for RCP8.5. (b) Shoreline position change along sandy coasts by the year 2100 relative to 2010 (metres; negative values indicate shoreline retreat) from the CMIP5-based dataset presented by Vousdoukas et al. (2020b). (c) Bar plots for Q100 (m3 s–1 km–2) averaged over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The left-hand column within each panel (associated with the left-hand y-axis) shows the ‘recent past’ (1995–2014) Q100 absolute values in grey shades. The other columns (associated with the right-hand y-axis) show the Q100 changes relative to the recent past values for two time periods (‘mid’ 2041–2060 and ‘long’ 2081–2100) and for three global warming levels (defined relative to the pre-industrial period 1850–1900): 1.5°C (purple), 2°C (yellow) and 4°C (brown). The bars show the median (dots) and the 10–90th percentile range of model ensemble values across each model ensemble. CMIP6 is shown by the darkest colours, CMIP5 by medium, and CORDEX by light. SSP5-8.5/RCP8.5 is shown in red and SSP1-2.6/RCP2.6 in blue. (d) Bar plots for shoreline position change show CMIP5-based projections of shoreline position change along sandy coasts for 2050 and 2100 relative to 2010 for RCP8.5 (red) and RCP4.5 (blue) from Vousdoukas et al. (2020b). Dots indicate regional mean change estimates and bars show the 5–95th percentile range of associated uncertainty. Note that these shoreline position change projections assume that there are no additional sediment sinks/sources or any physical barriers to shoreline retreat. See Technical Annex VI for details of indices. Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37891, "uuid": "0a16f7c82f3040bd87b2488e0368aa05", "title": "Caption for Figure 12.4 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Median projected changes in selected climatic impact-driver indices based on CMIP6 models. (a–c) Mean number of days per year with maximum temperature exceeding 35°C; (d–f) mean number of days per year with the NOAA Heat Index (HI) exceeding 41°C; (g–i) number of negative precipitation anomaly events per decade using the six-month Standardized Precipitation Index; (j–l) mean soil moisture (%) and (m–o) mean wind speed (%). (p–r) shows change in extreme sea level (1-in-100-year return period total water level from Vousdoukas et al. (2018)’s CMIP5 based dataset; metres). Left-hand column is for SSP1-2.6, 2081–2100; middle column is for SSP5-8.5 2041–2060; and right-hand column SSP5-8.5, 2081–2100, all expressed as changes relative to 1995–2014. Exception is extreme total water level which is for (p) RCP4.5 2100, (q) RCP8.5 2050 and (r) RCP8.5 2100, each relative to 1980–2014. Bias correction is applied to daily maximum temperature and HI data (Annex VI and Atlas.1.4.5). Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on the sign (direction) of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1. See Annex VI for details of indices. Figures 12. SM.1–12.SM.6 show regionally averaged values of these indices for the AR6 WGI Reference Regions for various model ensembles, scenarios, time horizons and global warming levels.Further details on data sources and processing are available in the chapter data table (Table 12.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 37896, "uuid": "3b5fa422fe5344699cd0e223e127da64", "title": "Ocean Parcels", "abstract": "The OceanParcels project develops Parcels (Probably A Really Computationally Efficient Lagrangian Simulator), a set of Python classes and methods to create customisable particle tracking simulations using output from Ocean Circulation models. Parcels can be used to track passive and active particulates such as water, plankton, plastic and fish. The code from the OceanParcels project is licensed under an open source MIT license and can be downloaded from github.com/OceanParcels/parcels or installed via anaconda.org/conda-forge/parcels.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 38095, "uuid": "cd0ab0a5aa3447138a63c6178280bbdd", "title": "Climate models inputs and outputs introduced in Rome et al. 2022 (DOI:10.1002/essoar.10511015.1)", "abstract": "Seven simulations outputs were uploaded under the names XOUPA, TFGBD_XOUPD, XOUPH, TFGBR_XOUPL, TFGBI, XOUPF and TFGBJ. The simulations were created using the general circulation model HadCM3 over the entire globe under last glacial maximum (21,000 years ago) conditions and forced with snapshots of meltwater derived from the early deglaciation (21,500 to 17,800 years ago). The outputs consists of sea-ice concentration, ocean overturning circulation, mixed layer depth, sea surface salinity, sea surface temperature, precipitation and surface air temperature. For reproducibility, we also uploaded the meltwater input files used for the forcing (lgm_inputs), the model input files (mw_inputs), the pre-industrial climatologies (pi_climate) and the crash diagnostics (crash_data), all discussed in the article. \"", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 38102, "uuid": "99820bff0b994aa29f1a5557f5f14d86", "title": "Computation for SASSO Australian Wildfires", "abstract": "A combination based on CALIOP and OMPS-LP satellite retrievals that have been zonally averaged and placed into subsets and then converted into the products required as input by the UKESM1 model by the project team.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 38130, "uuid": "cd7d7823afce4a089ba78fc65c0ebcc2", "title": "Caption for Figure TS.15 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Contrbution to ERF and b) global surface temperature change from component emissions between 1750 to 2019 based on CMIP6 models and c) net aerosol effective radiative forcing (ERF) from different lines of evidence. The intent of the figure is to show advances since AR5 in the understanding of a) aerosol ERF from different lines of evidence as assessed in Chapter 7, b) emissions-based ERF and c) global surface temperature response for SLCFs as estimated in Chapter 6. In panel a), ERFs for well-mixed greenhouse gases (WMGHGs) are from the analytical formulae. ERFs for other components are multi-model means based on ESM simulations that quantify the effect of individual components. The derived emission-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. In panel b), the global mean temperature response is calculated from the ERF time series using an impulse response function. In panel c), the AR6 assessment is based on energy balance constraints, observational evidence from satellite retrievals, and climate model-based evidence. For each line of evidence, the assessed best-estimate contributions from ERF due to ERFari and ERFaci are shown with darker and paler shading, respectively. Estimates from individual CMIP5 and CMIP6 models are depicted by blue and red crosses, respectively. The observational assessment for ERFari is taken from the instantaneous forcing due to aerosol-radiation interactions (IRFari). Uncertainty ranges are given in black bars for the total aerosol ERF and depict very likely ranges. {Sections 7.3.3, 6.4.2, Cross-Chapter Box 7.1, Figures 6.12, 7.5 ; Table 7.8}", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] } ] }