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=3600
{ "count": 3949, "next": "https://api.catalogue.ceda.ac.uk/api/v2/computations/?format=api&limit=100&offset=3700", "previous": "https://api.catalogue.ceda.ac.uk/api/v2/computations/?format=api&limit=100&offset=3500", "results": [ { "ob_id": 39617, "uuid": "bdd989d212244327ace15c68499b7114", "title": "Caption for Figure 9.11 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated barotropic streamfunction, surface speed and major current transport in Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6). (a) Mean barotropic streamfunction (unit: 109kgs–1; 1995–2014) and projected barotropic streamfunction change (109kgs–1; 2018–2100 vs 1995–2014) under (b) SSP5-8.5. (d) Mean surface (0–100 m) speed (m s–1) and projected surface speed change (m s–1, 2081–2100) versus 1995–2014 under (e) SSP5-8.5. (c, f) Median and likely range of 1995–2014 and 2081–2100 transport of three currents with the largest transport change and four with the largest fractional change (Sen Gupta et al., 2016). (c) Deep currents: Agulhas Extension (ACx), Gulf Stream (GS), Gulf Stream Extension (GSx), Tasman Leakage (TASL), East Australia Current Extension (EACx), Indonesian Throughflow (ITF), and Brazil Current (BC). (f) Shallow currents: as for deep but with New Guinea Current (NGC), and without ACx. No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39620, "uuid": "4257ffdb796e49a7b46edeb9db5350c6", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV AATSR v1 product.", "abstract": "The retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39622, "uuid": "7e310b21fb4e45eea77f188dd85b06df", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG ATSR-2 v1 product.", "abstract": "The retrieval method of the snow_cci SCFG product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39626, "uuid": "f4d9f525a03f4db7b6eea9f2f003068b", "title": "ESA Snow Climate Change Initiative: Derivation of SCFG AATSR v1 product.", "abstract": "The retrieval method of the snow_cci SCFG product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nImprovements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39629, "uuid": "51ce66d2c6804ed6a14c768e4768e2e8", "title": "ESA Snow Climate Change Initiative: Derivation of SCFV ATSR-2 v1 product.", "abstract": "The retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. \r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39631, "uuid": "08fddd2bb51440b58583938b715dd6d0", "title": "Caption for Figure 6.22 and 6.24 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Figure 6.22 | Time evolution of the effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-Economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), and the sum of these, relative to the year 2019 and to the year 1750. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2°C–1, see Cross-Chapter Box 7.1). The vertical bars to the right in each panel show the uncertainties (5–95% ranges) for the GSAT change between 2019 and 2100. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).\r\n\r\nFigure 6.24 | Effects of changes in short-lived climate forcers (SLCFs) and hydrofluorocarbons (HFCs) on global surface air temperature (GSAT) across the WGI core set of Shared Socio-economic Pathways (SSPs). Effects of net aerosols, methane, tropospheric ozone and hydrofluorocarbons (HFCs; with lifetimes <50years), are compared with those of total anthropogenic forcing for 2040 and 2100 relative to the year 2019. The GSAT changes are based on the assessed historic and future evolution of effective radiative forcing (ERF; Section 7.3.5). The temperature responses to the ERFs are calculated with an impulse response function with an equilibrium climate sensitivity of 3.0°C for a doubling of atmospheric CO2 (feedback parameter of –1.31 W m–2°C–1; Cross-Chapter Box 7.1). Uncertainties are 5–95% ranges. The scenario total (grey bar) includes all anthropogenic forcings (long- and short-lived climate forcers, and land-use changes) whereas the white diamonds and bars show the net effects of SLCFs and HFCs and their uncertainties. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39641, "uuid": "ddb402f172ed4bdd87d8fef1f703be30", "title": "Caption for Figure 9.12 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "(a–f) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean projected change contributions to relative sea level change in (a, d) steric sea level anomaly, (b, e) thermosteric sea level anomaly, and (c, f) halosteric sea level anomaly between 1995–2014 and 2081–2100 using a method that does not require a reference level (Landerer et al., 2007). Global mean change has been removed from these figures, consistent with the methods in Sections 9.6.3 and 9.SM.4 and the definitions of Gregory et al. (2019). (Gregory et al., 2019). See Figure 9.27 for global mean sea level (GMSL). (g–i) Standard deviation of ocean dynamic sea level change from (g) Aviso observations (10-day high-pass filter); (h) five-day mean of high-resolution Ocean Model Intercomparison Project phase 2 (OMIP-2) models forced with observed fluxes; and (i) five-day mean of low-resolution OMIP-2 models which are comparable in resolution to the models in (a–f). No overlay indicates regions with high model agreement, where ≥80% of models agree on the sign of change. Diagonal lines indicate regions with low model agreement, where <80% of models agree on the sign of change (see Cross-Chapter Box Atlas.1 for more information). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39644, "uuid": "9004f18f721a4ee3b6ebd216a3478fd4", "title": "Caption for Figure 4.12 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Near-term change of seasonal mean surface temperature. Displayed are projected spatial patterns of CMIP6 multi-model mean change (°C) in (top) December–January–February (DJF) and (bottom) June–July–August (JJA) near-surface air temperature for 2021–2040 from SSP1-2.6 and SSP3-7.0 relative to 1995–2014. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39647, "uuid": "1b9151cce8ed46268790734353b6f3db", "title": "Caption for Figure 4.13 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Near-term change of seasonal mean precipitation. Displayed are projected spatial patterns of CMIP6 multi-model mean change (%) in (top) December–January–February (DJF) and (bottom) June–July–August (JJA) precipitation from SSP1-2.6 and SSP3-7.0 in 2021–2040 relative to 1995–2014. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39650, "uuid": "c43ee0357d1744a4bc64198ec9a29e02", "title": "Caption for Figure 4.19 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Mid- and long-term change of annual mean surface temperature. Displayed are projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2041–2060 and 2081–2100 relative to 1995–2014 for (top) SSP1-2.6 and (bottom) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (see Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39653, "uuid": "16e53ff760d8460b9e4852e9dd829206", "title": "Caption for Figure 4.22 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term change of annual and zonal mean atmospheric temperature. Displayed are multi-model mean change in annual and zonal mean atmospheric temperature (°C) in 2081–2100 relative to 1995–2014 for (left) SSP1-2.6 and (right) SSP5-8.5. The number of models used is indicated in the top right of the maps. Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay where 80% or more of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39656, "uuid": "9ebdbf6975474b48a8e7c119d800ecf7", "title": "Caption for Figure 4.23 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term changes in seasonal mean relative humidity. Displayed are projected spatial patterns of multi-model mean change (%) in seasonal (top) December–January–February (DJF) and (bottom) June–July–August (JJA) mean near-surface relative humidity in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39664, "uuid": "9361e0dce73c47d387de7828ce4ffb5f", "title": "Met Office operational Unified Model (UM) deployed on the Monsoon2 system, a collaborative facility supplied under the Joint Weather and Climate Research Programme - a strategic partnership between the Met Office and the Natural Environment Research Council. This work also used JASMIN, the UK collaborative data analysis facility, to post-process model data.", "abstract": "", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39669, "uuid": "cf1fa3494af64adaaea970887f6c6330", "title": "Met Office operational Unified Model (UM) deployed on the Monsoon2 system, a collaborative facility supplied under the Joint Weather and Climate Research Programme - a strategic partnership between the Met Office and the Natural Environment Research Council. This work also used JASMIN, the UK collaborative data analysis facility, to post-process model data.", "abstract": "", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39673, "uuid": "fcad1bd2fab348dd966bfe4ae052c2dd", "title": "ECMWF Integrated Forecasting System output generated by ECMWF. This work also used JASMIN, the UK collaborative data analysis facility, to post-process model data.", "abstract": "", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39677, "uuid": "afd18789acab4d6a9c9c5280a0b2748d", "title": "Met Office operational Unified Model (UM) deployed on the Monsoon2 system, a collaborative facility supplied under the Joint Weather and Climate Research Programme - a strategic partnership between the Met Office and the Natural Environment Research Council. This work also used JASMIN, the UK collaborative data analysis facility, to post-process model data.", "abstract": "", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39678, "uuid": "26daf647d5ff4e87b1acb1912bd90fbc", "title": "Caption for Figure 9.15 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Antarctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. (left) Absolute anomaly of observed monthly mean Antarctic sea ice area during the period 1979–2019 relative to the average monthly mean Antarctic sea ice area during the period 1979–2008. (right) Sea ice coverage in the Antarctic as given by the average of the three most widely used satellite-based estimates for September and February, which usually are the months of maximum and minimum sea ice coverage, respectively. First column: Mean sea ice coverage during the decade 1979–1988. Second column: Mean sea ice coverage during the decade 2010–2019. Third column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15% for the decade 2045–2054. The average observational record of sea ice area is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39679, "uuid": "da64337eab4940e5a44825dd08726be2", "title": "Caption for Figure 9.14 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Monthly mean March (a–d) and September (e–h) sea ice area as a function of global surface air temperature (GSAT) anomaly (a, e); cumulative anthropogenic CO2 emissions (b, f); year (c, g) in Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations (shading, ensemble mean as bold line) and in observations (black dots). Panels (d) and (h) show the sensitivity of sea ice loss to anthropogenic CO2 emissions as a function of the modelled sensitivity of GSAT to anthropogenic CO2 emissions. In panels (d) and (h), the black dot denotes the observed sensitivity, while the shading around it denotes internal variability as inferred from CMIP6 simulations (after Notz and SIMIP Community, 2020). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39680, "uuid": "3c19a763a0564aa599355e87acee95fa", "title": "Caption for Figure 9.22 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated versus observed permafrost extent and volume change by warming level. (a) Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) models, from the first ensemble member of the historical coupled run, and for CMIP6 Atmospheric Model Intercomparison Project (AMIP) (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. Estimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: Obu et al. (2018); star: Zhang et al. (1999); circle: central value and associated range from Gruber (2012). (b) Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39681, "uuid": "643bfa33ef0549ddb1ec7dcf2ac2b036", "title": "Caption for Figure 9.24 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Simulated Coupled Model Intercomparison Project Phase 6 (CMIP6) and observed snow cover extent (SCE). (a) Simulated CMIP6 and observed (Mudryk et al., 2020) SCE (in millions of km2) for 1981–2014. Boxes and whiskers with outliers represent monthly mean values for the individual CMIP6 models averaged over 1981–2014, with the red bar indicating the median of the CMIP6 multi-model ensemble for that period. The observed interannual distribution over the period is represented in green, with the yellow bar indicating the median. (b) Spring (March to May) Northern Hemisphere SCE against global surface air temperature (GSAT) (relative to the 1995–2014 average) for the CMIP6 Tier 1 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), with linear regressions. Each data point is the mean for one CMIP6 simulation (first ensemble member for each available model) in the corresponding temperature bin. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39682, "uuid": "2103ebc78e85483d86e2fcf74832a137", "title": "Caption for Figure 9.26 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Median global mean and regional relative sea level projections (m) by contribution for the SSP1-2.6 and SSP5-8.5 scenarios. Upper time series: Global mean contributions to sea level change as a function of time, relative to 1995–2014. Lower maps: Regional projections of the sea level contributions in 2100 relative to 1995–2014 for SSP5-8.5 and SSP1-2.6. Vertical land motion is common to both Shared Socio-economic Pathways (SSPs). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39684, "uuid": "4cb88a7da37742318975dc2e5f0566ff", "title": "Caption for Figure 9.28 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional sea level change at 2100 for different scenarios (with respect to 1995–2014). Median regional relative sea level change from 1995–2014 up to 2100 for: (a) SSP1-1.9; (b) SSP1-2.6; (c) SSP2-4.5; (d) SSP3-7.0; (e) SSP5-8.5; and (f) width of the likely range for SSP3-7.0. The high uncertainty in projections around Alaska and the Aleutian Islands arises from the tectonic contribution to vertical land motion, which varies greatly over short distances in this region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39687, "uuid": "847ea81176f84e648d101d79cb6b94b1", "title": "Caption for Figure 9.29 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Timing of when global mean sea level (GMSL) thresholds of 0.5, 1.0, 1.5 and 2.0 m are exceeded, based on four different ice-sheet projection methods informing post-2100 projections. Methods are labelled based on their treatment of ice sheets. ‘No acceleration’ assumes constant rates of mass change after 2100. ‘Assessed ice sheet’ models post-2100 ice-sheet losses using a parametric fit (Supplementary Material 9.SM.4) extending to 2300 based on a multi-model assessment of contributions under RCP2.6 and RCP8.5 at 2300. Structured expert judgement (SEJ) employs ice-sheet projections from Bamber et al. (2019). Marine ice-cliff instability (MICI) combines the parametric fit (Supplementary Material 9.SM3.4) for Greenland with Antarctic projections based on DeConto et al. (2021). Circles, thick bars and thin bars represent the 50th, 17th–83rd and 5th–95th percentiles of the exceedance timing for the indicated projection method. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39688, "uuid": "1b5f43f3e12e414796c9ad2971cacccf", "title": "Caption for Figure 9.30 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean sea level (GMSL) commitment as a function of peak global surface air temperature. From models (Clark et al., 2016; DeConto and Pollard, 2016; Garbe et al., 2020; Van Breedam et al., 2020) and paleo data on 2000-year (lower row) and 10,000 year (upper row) time scales. Columns indicate different contributors to GMSL rise (from left to right: total GMSL change, Antarctic Ice Sheet, Greenland Ice Sheet, global mean thermosteric sea level rise, and glaciers). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39689, "uuid": "90d27d3e388843499d26e994d59b59be", "title": "Caption for Figure 9.32 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected median frequency amplification factors for the 1% average annual probability extreme still water level in 2050 (a, c, e) and 2100 (b, d, f). Based on a peak-over-threshold (99.7%) method applied to the historical extreme still water levels of Global Extreme Sea Level Analysis version 2 (GESLA2) following Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) and additionally fitting a Gumbel distribution between Mean Higher High Water (MHHW) and the threshold following Buchanan et al. (2016), using the regional sea level projections of Section 9.6.3.3 for (a, b) SSP5-8.5, (c, d) SSP2-4.5 and (e, f) SSP1-2.6. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39696, "uuid": "2a45b4e5a3674115899120f56747eb0c", "title": "CNRM-CM 6.1 atmosphere-ocean general circulation model deployed on Météo-France computing facilities.", "abstract": "CNRM-CM 6.1 atmosphere-ocean general circulation model deployed on Météo-France computing facilities. The model consists of the ARPEGE-Climat 6.3 atmospheric model, the NEMO 3.6 ocean model, the GELATO 6 sea ice model and the ISBA-CTRIP land surface model.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39705, "uuid": "22beeb711b9d4f979a9a1f77f2de2869", "title": "Caption for Figure 9.13 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Arctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. (left) Absolute anomaly of monthly-mean Arctic sea ice area during the period 1979 to 2019 relative to the average monthly-mean Arctic sea ice area during the period 1979 to 2008. (right) Sea ice concentration in the Arctic for March and September, which usually are the months of maximum and minimum sea ice area, respectively. First column: Satellite-retrieved mean sea ice concentration during the decade 1979–1988. Second column: Satellite-retrieved mean sea ice concentration during the decade 2010–2019. Third column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15 % for the decade 2045–2054. The average observational record of sea ice area is derived from the UHH sea ice area product (Doerr et al., 2021), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) (Lavergne et al., 2019), NASA Team (version 1, 1979–2019) (Cavalieri et al., 1996) and Bootstrap (version 3, 1979–2019) (Comiso, 2017) that is also used for the figure panels showing observed sea ice concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39706, "uuid": "13477dbb98d4424a94d308b710944fee", "title": "ESA Snow Climate Change Initiative: Derivation of Fractional Snow Cover in CryoClim, v1.0", "abstract": "The snow_cci CryoClim FSC products are based on fundamental climate data records (FCDRs) developed by EUMETSAT CM SAF for AVHRR (Karlson et al. 2020) and PMR (Fenning et al. 2017). These have been used to assure having the best possible temporal quality of input data. The FCDRs are based on optical data from AVHRR sensors aboard the satellites NOAA-7, -9, -11, -14, -16, -18, -19, and PMR data from SMMR, SSM/I and SSMIS sensors aboard the Nimbus-7, DMSP F8, DMSP F11, DMSP F13 and DMSP F17 satellites, respectively. \r\n\r\nThe snow_cci CryoClim FSC processing chain includes retrieval of fractional snow cover per grid cell for all grid cells. Permanent snow and ice areas as well as water bodies are masked in the FSC products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All FSC products are prepared according to the CCI data standards.\r\n\r\nThe processing chain was developed by Norwegian Computing Center (Norsk Regnesentral, NR) and Norwegian Meteorological Institute (MET Norway), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39713, "uuid": "7e7faf2001424594816ba1df878b712a", "title": "GEOS-Chem chemical transport model (CTM) deployed on University of York Viking research computing cluster\n", "abstract": "GEOS-Chem chemical transport model (CTM) deployed on University of York Viking research computing cluster\n", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39717, "uuid": "8fa6daf7618b48dcb7015f6209c507e6", "title": "Caption for Figure 6.7 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Distribution of PM2.5 composition mass concentration (in μg m–3) for the major PM2.5 aerosol components. Those aerosol components are sulphate, nitrate, ammonium, sodium, chloride, organic carbon and elemental carbon. The central world map depicts the intermediate-level regional breakdown of observations (10 regions) following the IPCC Sixth Assessment Report Working Group III (AR6 WGIII). Monthly averaged PM2.5 aerosol component measurements are from: (i) the Environmental Protection Agency (EPA) network which include 211 monitor sites primarily in urban areas of North America during 2000–2018 (Solomon et al., 2014), (ii) the Interagency Monitoring of Protected Visual Environments (IMPROVE) network during 2000–2018 over 198 monitoring sites representative of the regional haze conditions over North America, (iii) the European Monitoring and Evaluation Programme (EMEP) network over 70 monitoring in Europe and (eastern) Eurasia during 2000–2018, (iv) the Acid Deposition Monitoring Network in Eastern Asia (EANET) network with 39 (18 remote, 10 rural, 11 urban) sites in Eurasia, Eastern Asia, South East Asia and Developing Pacific, and Asia-Pacific Developed during 2001–2017, (v) the global Surface Particulate Matter Network (SPARTAN) during 2013–2019 with sites primarily in highly populated regions around the world (i.e., North America, Latin America and Caribbean, Africa, Middle East, Southern Asia, Eastern Asia, South East Asia and Developing Pacific; Snider et al., 2015, 2016), and (vii) individual observational field campaign averages over Latin America and Caribbean, Africa, Europe, Eastern Asia, and Asia-Pacific Developed (Celis et al., 2004; Feng et al., 2006; Bourotte et al., 2007; Fuzzi et al., 2007; Mariani and de Mello, 2007; Molina et al., 2007, 2010; Favez et al., 2008; Mkoma, 2008; Aggarwal and Kawamura, 2009; Mkoma et al., 2009; de Souza et al., 2010; Li et al., 2010; Martin et al., 2010; Radhi et al., 2010; Weinstein et al., 2010; Batmunkh et al., 2011; Gioda et al., 2011; Pathak et al., 2011; F. Zhang et al., 2012; Cho and Park, 2013; Zhao et al., 2013; Wang et al., 2019; Kuzu et al., 2020). Further details on data sources and processing are available in the chapter data table (Table 6.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39720, "uuid": "31584495b6b44402a6a95449bc324ab3", "title": "Caption for Figure 6.8 from Chapter 6 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Time evolution of changes in global mean aerosol optical depth (AOD) at 550 nm. The year of reference is 1850. Data shown from individual Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations. Each time series corresponds to the ensemble mean of realizations done by each model. Simulation results from years including major volcanic eruptions (e.g., Novarupta, 1912; Pinatubo, 1991), are excluded from the analysis for models encompassing the contribution of stratospheric volcanic aerosols to total AOD. Further details on data sources and processing are available in the chapter data table (Table 6.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39723, "uuid": "4aaea88dbbc8476fb970c44caca89908", "title": "Caption for Figure 4.24 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term change of seasonal mean precipitation. Displayed are projected spatial patterns of multi-model mean change (%) in (top) December–January–February (DJF) and (bottom) June–July–August (JJA) mean precipitation in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No map overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39729, "uuid": "2473deaec7c842689b58f851c5ee93de", "title": "Caption for Figure 4.25 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term change of seasonal-mean sea level pressure. Displayed are projected spatial patterns of multi-model mean change in (top) December–January–February (DJF) and (bottom) June–July–August (JJA) mean sea level pressure (hPa) in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39732, "uuid": "02fc2f12f737468eab597e4164b67f84", "title": "Caption for Figure 4.41 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "High-warming storylines for changes in annual mean temperature. (a, b) Changes in 2081–2100 relative to 1995–2014 consistent with the assessed best global surface air temperature (GSAT) estimate (0.9°C and 3.5°C relative to 1995–2014 for SSP1-2.6 and SSP5-8.5, respectively). The CMIP6 multi-model mean is linearly pattern-scaled to the best GSAT estimate. (c–h) Annual mean warming above the best estimate, relative to panels (a) and (b) respectively; note the different colour bar in a high and very high-warming storyline for 2081–2100. (c, d) Multi-model mean warming pattern scaled to very high GSAT level corresponding to the upper bound of the assessed very likely range (4.8°C for SSP5-8.5 and 1.5°C for SSP1-2.6; see Section 4.3.4). (e, f) Average of five models with high GSAT warming nearest to the upper estimate of the very likely range (CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR, EC-Earth3 for SSP1-2.6 and ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HRfor SSP5-8.5); (g, h) Average of four and five models, respectively (ACCESS-CM2, HadGEM3-GC31-LL, HadGEM3-GC31-MM, UKESM1-0-LL for SSP1-2.6 and CanESM5, CanESM5-CanOE, HadGEM3-GC31-LL: HadGEM3-GC31-MM, UKESM1-0-LL for SSP5-8.5) projecting very high GSAT warming exceeding the very likely range. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39735, "uuid": "155ff0aee5fc41f4b14e4dbe9c3f0aac", "title": "Caption for Figure 4.42 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "High-warming storylines for changes in annual mean precipitation. (a) Estimates for annual mean precipitation changes in 2081–2100, relative to 1995–2014, consistent with the best global surface air temperature (GSAT) estimate derived by linearly scaling the CMIP6 multi-model mean changes to a GSAT change of 3.5°C. (b, c) Estimates for annual mean precipitation changes in 2081–2100 relative 1995–2014 in a storyline representing a physically plausible high-global-warming level. (b) Multi-model-mean precipitation scaled to high-global-warming level (corresponding to 4.8°C, the upper bound of the very likely range; see Section 4.3.4). (c) Average of five models with GSAT warming nearest to the high level of warming (ACCESS-CM2, CESM2, CESM2-WACCM, CNRM-CM6-1, CNRM-CM6-1-HR) (d) Annual mean precipitation changes in four of the five individual model simulations averaged in (c). (e, f) Local upper estimate (95% quantile across models) and lower estimate (5% quantile across models) at each grid point. Information at individual grid points comes from different model simulations and illustrates local uncertainty range but should not be interpreted as a pattern. (g) Area fraction of changes in annual mean precipitation 2081–2100 relative to 1995–2014 for (i) all CMIP6 model simulations (thin black lines), (ii) models shown in (c) (red lines), and (iii) models showing very high warming above the models shown in (c) (dark red lines). The grey range illustrates the 5–95% range across CMIP6 models and the solid black line the area fraction of the multi-model mean pattern shown in (a). Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39748, "uuid": "fcc644708ec94e5f9eeb26504b87fb3c", "title": "Tracer inverse model", "abstract": "The tracer inverse model, an inverse model with optional use of secondary trace gases for source attribution was developed by Alice Ramsden and the Atmospheric Chemistry Research Group at the University of Bristol. The inverse model produces most-likely posterior estimates of CH4 surface fluxes by considering the likelihood of emissions in relation to amtospheric mole fraction observations of methane and the probability of emissions in relation to an a priori flux estimate.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39749, "uuid": "a614274eb9514c9f85a9c090d1a4d08c", "title": "Caption for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global Energy Inventory and Sea Level Budget. (a) Observed changes in the global energy inventory for 1971–2018 (shaded time series) with component contributions as indicated in the figure legend. Earth System Heating for the whole period and associated uncertainty is indicated to the right of the plot (red bar = central estimate; shading =very likely range); (b) Observed changes in components of global mean sea level for 1971–2018 (shaded time series) as indicated in the figure legend. Observed global mean sea level change from tide gauge reconstructions (1971–1993) and satellite altimeter measurements (1993–2018) is shown for comparison (dashed line) as a three-year running mean to reduce sampling noise. Closure of the global sea level budget for the whole period is indicated to the right of the plot (red bar = component sum central estimate; red shading =very likely range; black bar = total sea level central estimate; grey shading =very likely range). Full details of the datasets and methods used are available in Annex I. Further details on energy and sea level components are reported in Table 7.1 and Table 9.5.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39754, "uuid": "0fbb2006d84446d5acd495c08167a7fd", "title": "Caption for Cross- Section Box TS.1, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Earth’s surface temperature history and future with key findings annotated within each panel. The intent of this figure is to show global surface temperature observed changes from the Holocene to now, and projected changes. (a) Global surface temperature over the Holocene divided into three time scales: (i) 12,000 to 1000 years ago (10,000 BCE to 1000 CE) in 100-year time steps, (ii) 1000 to 1900 CE, 10-year smooth, and (iii) 1900 to 2020 CE (mean of four datasets in panel c). Bold lines show the median of the multi-method reconstruction, with 5% and 95% percentiles of the ensemble members (thin lines). Vertical bars are 5–95th percentile ranges of estimated global surface temperature for the Last Interglacial and mid-Holocene (medium confidence) (Section 2.3.1.1). All temperatures are relative to 1850–1900. (b) Spatially resolved trends (°C per decade) for (upper map) HadCRUTv5 over 1981–2020, and (lower map, total change) multi-model mean projected changes from 1995–2014 to 2081–2100 in the SST3-7.0 scenario. Observed trends have been calculated where data are present in both the first and last decade and for at least 70% of all years within the period using ordinary least squares. Significance is assessed with autoregressive AR(1) model correction and denoted by stippling. Hatched areas in the lower map show areas of conflicting model evidence on significance of changes. (c) Temperature from instrumental data for 1850–2020, including annually resolved averages for the four global surface temperature datasets assessed in Section 2.3.1.1.3 (see text for references). The grey shading shows the uncertainty associated with the HadCRUTv5 estimate. All temperatures are relative to the 1850–1900 reference period. (d) Recent past and 2015–2050 evolution of annual mean global surface temperature change relative to 1850–1900, from HadCRUTv5 (black), Coupled Model Intercomparison Project Phase 6 (CMIP6) historical simulations (up to 2014, in grey, ensemble mean solid, 5% and 95% percentiles dashed, individual models thin), and CMIP6 projections under scenario SSP2-4.5, from four models that have an equilibrium climate sensitivity near the assessed central value (thick yellow). Solid thin coloured lines show the assessed central estimate of 20-year change in global surface temperature for 2015–2050 under three scenarios, and dashed thin coloured lines the corresponding 5% and 95% quantiles. (e) Assessed projected change in 20-year running mean global surface temperature for five scenarios (central estimate solid, very likely range shaded for SSP1-2.6 and SSP3-7.0), relative to 1995–2014 (left y-axis) and 1850–1900 (right y-axis). The y-axis on the right-hand side is shifted upward by 0.85°C, the central estimate of the observed warming for 1995–2014, relative to 1850–1900. The right y-axis in (e) is the same as the y-axis in (d). {2.3, 4.3, 4.4}", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39757, "uuid": "34301be6d9f4452ebb18b1da83d079dd", "title": "Derivation of the ESA Fire Climate Change Initiative (Fire_cci): Beta Merged Burned Area Grid product, version 0.1", "abstract": "The BETA MERGED Fire_cci Burned Area (BA) product v0.1 (also called FireCCIM01 for short) was obtained using machine learning techniques to obtain a FireCCI51-like (https://catalogue.ceda.ac.uk/uuid/3628cb2fdba443588155e15dee8e5352) burned area product based on the FireCCILT11 (https://catalogue.ceda.ac.uk/uuid/62866635ab074e07b93f17fbf87a2c1a) grid product.\r\n\r\nFor more details see the Fire CCI documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39768, "uuid": "2a7d2d2f8a6c40598620fdfdb1a4a41c", "title": "Computation for the 3D displacements and strain from the 2023 February Turkey Earthquakes", "abstract": "The Sentinel-1 azimuth and range offsets came from the COMET-LiCS portal (https://comet.nerc.ac.uk/comet-lics-portal/) and are processed using Gamma package from Level 1 (L1) data from the European Space Agency (https://scihub.copernicus.eu/dhus/#/home) The offsets are from 4 tracks of interferometric pairs which are provided in links in the docs tab. These are results of processing by GAMMA command offset_pwr_tracking, cross-correlating 128x64 pixel windows (range x azimuth) over 2x oversampled deramped low-pass filtered intensity data, selecting data of cross-corr. coeff. over 0.1. Upsampled by linear interpolation. The optical pixel tracking east and north offsets are derived from the Sentinel-2 data using the ENVI COSI-Corr plugin and are also provided in the docs tab. Images: L1C 25th Jan and 9th Feb. Parameters: window size of 64 down to 32 with step of 4. 2 iterations, 0.9 mask threshold, no resampling and gridded output. All the offset data are referenced to a distribution of dummy zero points away from the coseismic ruptures by removing a planar ramp. The uncertainties of the offset data are empirically estimated from the offset data themselves by evaluating the absolute deviation of each pixel value from its local mean averaged across a 4x4 pixel window, assuming nan-values are zeros. These uncertainties are used to weight the 3D motion inversion and are propagated to the uncertainties of the decomposed displacements through a model covariance matrix. The motion magnitude field is a vector combination of the east and north motion fields, each masked by respective uncertainties. The arrows represent average east and north motions from 30 km windows evaluated at 0.2 degree intervals. The second invariant of horizontal strain is calculated from the horizontal displacement gradients of east and north motions after median filter with 30 km windows at 0.01 degree intervals.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39771, "uuid": "9b8646a130504b09bf2efeded8a1e2a2", "title": "Caption for Figure TS.12 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Land-related changes relative to the 1850-1900 as a function of global warming levels. The intent of this figure is to show that extremes and mean land variables change consistently with warming levels and to show the changes with global warming levels of water cycle indicators (i.e., precipitation and runoff) over tropical and extratropical land in terms of mean and interannual variability (interannual variability increases at a faster rate than the mean). (a) Changes in the frequency (left scale) and intensity (in °C, right scale) of daily hot extremes occurring every 10 and 50 years. (b) as (a), but for daily heavy precipitation extremes, with intensity change in %. (c) Changes in 10-year droughts aggregated over drought-prone regions (WNA, CNA, NCA, SCA, NSA, NES, SAM, SWS, SSA, WCE, MED, WSAF, ESAF, MDG, SAU, and EAU; for definitions of these regions, see Figure Atlas.2), with drought intensity (right scale) represented by the change of annual mean soil moisture, normalized with respect to interannual variability. Limits of the 5%−95% confidence interval are shown in panels (a–c). (d) Changes in Northern Hemisphere spring (March–April–May) snow cover extent relative to 1850–1900; (e,f) Relative change (%) in annual mean of total precipitable water (grey line), precipitation (red solid lines), runoff (blue solid lines) and in standard deviation (i.e., variability) of precipitation (red dashed lines) and runoff (blue dashed lines) averaged over (e) tropical and (f) extratropical land as function of global warming levels. Coupled Model Intercomparison Project Phase 6 (CMIP6) models that reached a 5°C warming level above the 1850–1900 average in the 21st century in SSP5-8.5 have been used. Precipitation and runoff variability are estimated by respective standard deviation after removing linear trends. Error bars show the 17–83% confidence interval for the warmest +5°C global warming level. {Figures 8.16, 9.24, 11.6, 11.7, 11.12, 11.15, 11.18 and Atlas.2}", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39777, "uuid": "b8ebfb65a98746deaeec33a6fc69c00a", "title": "Caption for CCB 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global Energy Inventory and Sea Level Budget. (a) Observed changes in the global energy inventory for 1971–2018 (shaded time series) with component contributions as indicated in the figure legend. Earth System Heating for the whole period and associated uncertainty is indicated to the right of the plot (red bar = central estimate; shading =very likely range); (b) Observed changes in components of global mean sea level for 1971–2018 (shaded time series) as indicated in the figure legend. Observed global mean sea level change from tide gauge reconstructions (1971–1993) and satellite altimeter measurements (1993–2018) is shown for comparison (dashed line) as a three-year running mean to reduce sampling noise. Closure of the global sea level budget for the whole period is indicated to the right of the plot (red bar = component sum central estimate; red shading =very likely range; black bar = total sea level central estimate; grey shading =very likely range). Full details of the datasets and methods used are available in Annex I. Further details on energy and sea level components are reported in Table 7.1 and Table 9.5.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39780, "uuid": "e903925c8ea24c8499df3bfb236b84e6", "title": "Met Office Hadley Centre (MOHC) running: experiment dcppA-assim using the HadGEM3-GC31-MM model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"Assimilation run paralleling the historical simulation, which may be used to generate hindcast initial conditions\" (dcppA-assim) experiment using the HadGEM3-GC31-MM model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, MOHC, HadGEM3-GC31-MM, dcppA-assim, Omon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39800, "uuid": "b226420753504b3ca1f8149a5d0634d9", "title": "Uncertainty sampling procedure", "abstract": "The starting point for the creation of this dataset was HadCRUT.4.5.0.0, which is a 100-member ensemble of gridded temperature 'observations'. This was then infilled (i.e. expanded to give data everywhere) using multi-resolution lattice krigging with the uncertainty in the statistical model also sample. The R package used for the infilling and sampling was 'fields' (https://doi.org/10.5065/D6W957CT)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39808, "uuid": "4a2d0f1a95b4454889d91cc4fa878855", "title": "Computation for the ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover and Land Cover Change Maps for Africa at 30m spatial resolution, 1990-2019, v1", "abstract": "For information on the derivation of the ESA CCI High Resolution Land Cover and Land Cover Change Maps for Africa at 30m spatial resolution, 1990-2019, v1 product see the linked project documentation on the CCI website.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39811, "uuid": "ac560d881750438fad472505ab0ed74e", "title": "Computation for the ESA High Resolution Land Cover Climate Change Initiative (High_Resolution_Land_Cover_cci): High Resolution Land Cover Map for Africa at 10m spatial resolution for 2019, v1", "abstract": "For information on the derivation of the ESA CCI High Resolution Land Cover Map for Africa at 10m spatial resolution for 2019, v1 product see the linked project documentation on the CCI website.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39816, "uuid": "091b007a16ca45dca0a23f00f349b687", "title": "Met Office Unified Model coupled with the United Kingdom Chemistry and Aerosol Scheme (UM-UKCA) Vn.11.2 deployed on ARCHER", "abstract": "Met Office Unified Model coupled with the United Kingdom Chemistry and Aerosol Scheme (UM-UKCA) Vn.11.2 deployed on ARCHER", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39847, "uuid": "752cd95d7ebe460db5f86ce90d7de935", "title": "Statistical downscaling of daily SST for the global coral reef area", "abstract": "HighResCoralStress was generated by statistical downscaling of sea surface temperature (SST) projected by CMIP6 models for the global coral reef area. The global coral reef area was determined by extracting the latitude and longitude of 1 km global coral reef pixels from the UNEP World Conservation Monitoring Centre dataset (UNEP-WCMC, WorldFish Centre, WRI, TNC, 2010). The 1 km global coral reef pixels were split into 12 coral reef regions (McWilliam et al., 2018). Observed SST used in the statistical downscaling was a combination of the JPL MUR SST Analysis (Chin et al., 2017; 1 km) and ESA CCI SST Analysis (Merchant et al., 2016; 5 km) resulting in a single 1 km resolution dataset for each global coral reef pixel (01/01/1985 - 31/12/2019) - see S1 Appendix in Dixon et al. (2022) for more information. Data for reef pixels where CCI uses a climatology, likely due to missing data, and MUR does not were replaced with NOAA CRW CoralTemp SST (NOAA Coral Reef Watch, 2018; 5 km) - again see S1 Appendix in Dixon et al. (2022) for more information. CMIP6 model 'tos' output for 14 models and four Shared Socioeconomic Pathways (SSP) was interpolated longitudinally to fill missing data points, converted to 1 km resolution by bilinear interpolation and data extracted for each 1 km global coral reef pixel - see S2 Appendix in Dixon et al. (2022) for more information. For the statistical downscaling, linear regression models were generated for four seasonal periods for each global coral reef pixel using the ranked observed and simulated 1 km SST for a model training period (even years). Different combinations of polynomial trends (1st, 2nd and 3rd order) were removed from the observed and simulated SST datasets prior to downscaling and added back in after to maintain the long-term warming trend simulated by the model - see S3 Appendix in Dixon et al. (2022) for more information. The statistically downscaled SST for the historical period (1985-2019) was evaluated relative to observed SST for a model testing period (odd years) by calculating the root mean square error - see S3 Appendix in Dixon et al. (2022). The 'best' combination of polynomial trends was selected by finding the lowest root mean square error - see S3 Appendix in Dixon et al. (2022). Finally, the seasonal linear regression models were generated using all years in the historical time period (1985-2019) to statistically downscale the simulated SST (1985-2100). The method is described in more detail in Dixon et al. (2022).", "keywords": "CMIP6, coral reefs, sea surface temperature, statistical downscaling", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39924, "uuid": "9f41866f44224fd0b260bec8f4d81309", "title": "Research Center for Environmental Changes (AS-RCEC) running: experiment amip-piForcing using the TaiESM1 model.", "abstract": "Research Center for Environmental Changes (AS-RCEC) running the \"AMIP SSTs with pre-industrial anthropogenic and natural forcing\" (amip-piForcing) experiment using the TaiESM1 model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, AS-RCEC, TaiESM1, amip-piForcing, Amon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39927, "uuid": "69884c867e8b4caa91819406cb52249e", "title": "the CNRM-CERFACS team running: experiment amip-piForcing using the CNRM-CM6-1 model.", "abstract": "The the CNRM-CERFACS team team consisted of the following agencies: Centre National de Recherches Météorologiques (CNRM) and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CERFACS).the CNRM-CERFACS team running the \"AMIP SSTs with pre-industrial anthropogenic and natural forcing\" (amip-piForcing) experiment using the CNRM-CM6-1 model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, CNRM-CERFACS, CNRM-CM6-1, amip-piForcing, Amon, fx", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39930, "uuid": "d0eb7546e04a4eafb2b620c84901319b", "title": "NASA Goddard Institute for Space Studies (NASA GISS) running: experiment amip-piForcing using the GISS-E2-1-G model.", "abstract": "NASA Goddard Institute for Space Studies (NASA GISS) running the \"AMIP SSTs with pre-industrial anthropogenic and natural forcing\" (amip-piForcing) experiment using the GISS-E2-1-G model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, NASA-GISS, GISS-E2-1-G, amip-piForcing, Amon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39939, "uuid": "fc61a060aace4d10b06f5b4ae1b91866", "title": "Caption for Figure 12.8 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "(a) Mean change in 1-in-100-year river discharge per unit catchment area (Q100, m3 s–1 km–2) from CORDEX-South and Central America 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 for RCP8.5 (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": 39950, "uuid": "9aba59ecdfd04a24a67a6ad2a9ef93b8", "title": "Derivation of Ocean Colour v6 data from the ESA Climate Change Initiative Ocean Colour project (Ocean_Colour_cci)", "abstract": "The ocean colour CCI has calculated ocean colour Essential Climate Variable data, using input data from various satellite instruments, as part of the ESA Climate Change Initiative program.\r\n\r\nFor more information see the Ocean Colour v6 Product User Guide.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39988, "uuid": "c04a7f3c061d4c99ada6ca255a56a536", "title": "Derivation of the Copernicus Climate Change Service Dataset: L4 Sea Surface Temperature Analysis Integrated Climate Data Record (ICDR), version 2", "abstract": "The L4 Sea Surface Temperature Analysis data contains daily, spatially complete estimated daily SST data, derived using the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) processing system. This creates the L4 data from the SLSTR and AVHRR Level 2 and Level 3 data sets also produced in the Copernicus Climate Change project.\r\n\r\nFor further information please see the associated documentation.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39993, "uuid": "ce22aec236b44835acdc9914fecb930a", "title": "UKESM1 deployed on UK supercomputing platform MONSooN", "abstract": "UKESM1 Earth System Model described in Sellar et al. (2019) (DOI:10.1029/2019MS001739) at N96 horizontal resolution over global domain run on UK supercomputing platform MONSooN.", "keywords": "UKESM1, N96, MONSooN", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 39999, "uuid": "0ea3c51424374e1b8de4c59d295ff6a6", "title": "CCI+ Phase 1 SIC: N90LIN and pan-sharpening", "abstract": "The CCI+ Phase 1 Sea Ice Concentration (SIC) production has two streams: N90LIN, and pan-sharpening. The N90LIN step prepares SICs relying mostly on the near-90 GHz channels (higher resolution, higher noise). The pan-sharpening step prepares SICs using the results of both the OSI SAF “19/37 GHz” algorithm (SICCI3LF) and N90LIN (higher resolution, higher noise). The result of the pan-sharpening step (named “resolution enhanced” SICCI3LF, reSICCI3LF) is a main SIC outcome from CCI+ Phase 1, while N90LIN is more seen as a by-product for expert users (because of the increased noise).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40006, "uuid": "d2bc57f26d58470d8ec94dc0616c367d", "title": "MIXCRA: the mixed-phase cloud property retrieval algorithm", "abstract": "mixcra2.pro,v 1.12 2022/03/16 00:32:15 dave.turner", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40012, "uuid": "9d5496e08abc416aaeec18630613fa59", "title": "NEMO Shelf Coastal Ocean Model 9 (CO9) based on NEMO4.0.4", "abstract": "The shelf seas model used in these climate projections is available on github:\r\nhttps://github.com/hadjt/NEMO_4.0.4_CO9_shelf_climate", "keywords": "CO9, NEMO4.0.4", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40014, "uuid": "b752b12bc7a4462b8e9afc00ea184bc5", "title": "NEMO Shelf Coastal Ocean Model 6 (CO6) based on NEMO3.6", "abstract": "The shelf seas model used in these climate projections is available on github:\r\nhttps://github.com/hadjt/NEMO_3.6_CO6_shelf_climate", "keywords": "CO6, NEMO3.6", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40023, "uuid": "bd277710c77d441cb3bb6251f7d30ec4", "title": "Caption for Figure 12.SM.1 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for the number of days per year with maximum temperature exceeding 35°C for different scenarios, time horizons and global warming levels. The bar plots show projections from CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles, for RCP8.5/SSP5-8.5 (red) and CP2.6/SSP1-2.6 (blue), for the mid-term (2041–2060), long term (2081–2100) and the recent past (grey, 1995–2014). Results for global warming levels (defined relative to the pre-industrial period 1850–1900) are shown in purple for 1.5°C, yellow for 2°C and brown for 4°C. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). Bias adjustment is applied (see Atlas.1.4.5). The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB). 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": 40025, "uuid": "c96f38f9239f4f0b9a9b56f4a096af3e", "title": "Caption for Figure 12.SM.2 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for the number of days per year with the NOAA Heat Index exceeding 41°C for different scenarios, time horizons and global warming levels. 41°C corresponds to conditions that the US National Weather Service classifies into the category of ‘Danger’ (Blazejczyk et al., 2012). The bar plots show projections from CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles, for RCP8.5/SSP5-8.5 (red) and RCP2.6/SSP1-2.6 (blue), for the mid-term (2041–2060), long term (2081–2100) and the recent past (grey, 1995–2014). Results for global warming levels (defined relative to the pre-industrial period 1850–1900) are shown in purple for 1.5°C, yellow for 2°C and brown for 4°C. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). Bias adjustment is applied. The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB). See Technical Annex VI for details of indices and bias adjustment. 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": 40029, "uuid": "a71d72717a5a4d2498bbfcff8f4c4567", "title": "Simulating Waves Nearshore (SWAN)", "abstract": "SWAN propagates offshore wave conditions to the nearshore, and can account for: Wave propagation in time and space, shoaling, refraction due to current and depth, frequency shifting due to currents and non-stationary depth. Wave generation by wind. Three- and four-wave interactions. Whitecapping, bottom friction and depth-induced breaking. Dissipation due to aquatic vegetation, turbulent flow and viscous fluid mud. Wave-induced set-up. Propagation from laboratory up to global scales. Transmission through and reflection (specular and diffuse) against obstacles. Diffraction.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40030, "uuid": "1c33edd56c064616bcf3f82f264eae89", "title": "Caption for Figure 12.SM.5 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for changes in mean wind speed for different scenarios, time horizons and global warming levels. The bar plots show projections of wind speed as percentage changes relative to the \r\nrecent past (1994–2015) for the mid-term (2041–2060) and long term (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), using CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles. RCP8.5/SSP5-8.5 is shown in red and RCP2.6/SSP1-2.6 in blue. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB). 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": 40031, "uuid": "ae34548d28714b96a5e2bedd3c9e04bb", "title": "Caption for Figure 12.SM.5 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections of extreme sea level (1-in-100-year return period Extreme Total Water Level (ETWL). The bar plots show projections of regionally averaged ETWL from the CMIP5-based datasets presented in Vousdoukas et al. (2018b; filled circles, ‘V’ in legend), and Kirezci et al. (2020; open circles, ‘K’ in legend), for the AR6 WGI Reference Regions, for RCP8.5 (red) and RCP4.5 (blue). Dots represent the median estimate and bars the 5th–95th percentiles representing the uncertainty associated with the projections for the mid-term (2050), long term (2100) and the recent past (black, 1979/1980–2014). Units are metres. See Technical Annex VI for details about the index. 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": 40051, "uuid": "ddc470549f214868ab49fc440e0f9be3", "title": "Norwegian Climate Centre (NCC) running: experiment amip-p4K using the NorESM2-LM model.", "abstract": "Norwegian Climate Centre (NCC) running the \"AMIP with uniform 4K SST increase\" (amip-p4K) experiment using the NorESM2-LM model. See linked documentation for available information for each component.", "keywords": "CMIP6, WCRP, climate change, NCC, NorESM2-LM, amip-p4K, Amon", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40084, "uuid": "c4cb80ae80cd45d492ae8c27f606d878", "title": "Computation for Hydro-JULES: Global Drought Indices", "abstract": "Data were generated using Standardized Precipitation-Evapotranspiration Index (SPEI) at 5km horizontal resolution over the domain 180W-180E, 55S-85N", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40091, "uuid": "2c4f8aeee2c6459c9545ed207d6e9b7d", "title": "Caption for Cross-Chapter Box 9.1, Figure 1 from Chapter 9 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global Energy Inventory and Sea Level Budget. (a) Observed changes in the global energy inventory for 1971–2018 (shaded time series) with component contributions as indicated in the figure legend. Earth System Heating for the whole period and associated uncertainty is indicated to the right of the plot (red bar = central estimate; shading =very likely range); (b) Observed changes in components of global mean sea level for 1971–2018 (shaded time series) as indicated in the figure legend. Observed global mean sea level change from tide gauge reconstructions (1971–1993) and satellite altimeter measurements (1993–2018) is shown for comparison (dashed line) as a three-year running mean to reduce sampling noise. Closure of the global sea level budget for the whole period is indicated to the right of the plot (red bar = component sum central estimate; red shading =very likely range; black bar = total sea level central estimate; grey shading =very likely range). Full details of the datasets and methods used are available in Annex I. Further details on energy and sea level components are reported in Table 7.1 and Table 9.5.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40094, "uuid": "89ce14fbdeee4793b4992ab1674d6f76", "title": "Caption for Figure 28 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 global mean sea level. (a) Reconstruction of sea-level from ice core oxygen isotope analysis for the last 800 kyr. For target paleo periods (CCB2.1) and MIS11 the estimates based upon a broader range of sources are given as box whiskers. Note the much broader axis range (200 m) than for later panels (tenths of metres). (b) Reconstructions for the last 2500 years based upon a range of proxy sources with direct instrumental records superposed since the late 19th century. (c) Tide-gauge and, more latterly, altimeter-based estimates since 1850. The consensus estimate used in various calculations in Chapters 7 and 9 is shown in black. (d) The most recent period of record from tide-gauge and altimeter-based records. 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": 40096, "uuid": "83c9b0009a6b496497d92c138b7b9def", "title": "Caption for Figure 2.26 from Chapter 2 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Figure 2.26 | Changes in ocean heat content (OHC). Changes are shown over (a) full depth of the ocean from 1871–2019 from a selection of indirect and direct measurement methods. The series from Table 2.7 is shown in solid black in both (a) and (b) (see Table 2.7 caption for details). (b) as (a) but for 0–2000 m depths only and reflecting the broad range of available estimates over this period. For further details see chapter data table (Table 2.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40098, "uuid": "8c8a2e21234b4ec3b2ad9ffccde6035c", "title": "Caption for Figure TS.13 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Estimates of the net cumulative energy change (ZJ = 1021 Joules) for the period 1971–2018 associated with: (a) observations of changes in the Global Energy Inventory (b) Integrated Radiative Forcing; (c) Integrated Radiative Response. Black dotted lines indicate the central estimate with likely and very likely ranges as indicated in the legend. The grey dotted lines indicate the energy change associated with an estimated 1850-1900 Earth energy imbalance of 0.2 W m-2 (panel a) and an illustration of an assumed pattern effect of –0.5 W m–2°C–1 (panel c). Background grey lines indicate equivalent heating rates in W m–2 per unit area of Earth’s surface. Panels (d) and (e) show the breakdown of components, as indicated in the legend, for the Global Energy Inventory and Integrated Radiative Forcing, respectively. Panel (f) shows the Global Energy Budget assessed for the period 1971–2018, i.e. the consistency between the change in the Global Energy Inventory relative to 1850-1900 and the implied energy change from Integrated Radiative Forcing plus Integrated Radiative Response under a number of different assumptions, as indicated in the figure legend, including assumptions of correlated and uncorrelated uncertainties in Forcing plus Response. Shading represents the very likely range for observed energy change relative to 1850-1900 and likely range for all other quantities. Forcing and Response timeseries are expressed relative to a baseline period of 1850–1900. {Box 7.2 Figure 1}", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40103, "uuid": "bace7ed22c804ae2850b1e63dfb76304", "title": "DETAILS NEEDED - COMPUTATION CREATED FOR SATELLITE COMPOSITE.", "abstract": "Computation for ozone from GOME-2 deployed on Metop-A.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40112, "uuid": "a763e258f5a8426d93cf0af3fe5f1acf", "title": "Caption for Figure 4.26 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Long-term change of zonal-mean, zonal wind. Displayed are multi-model mean changes in (left) boreal winter (December–January–February, DJF) and (right) austral winter (June–July–August, JJA) zonal mean, zonal wind (m s–1) in 2081–2100 for (top) SSP1-2.6 and (right) SSP3-7.0 relative to 1995–2014. The 1995–2014 climatology is shown in contours with spacing 10 m s–1. Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40115, "uuid": "31da838d60ee4360bd31c099679f08a6", "title": "Caption for Figure 4.31 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected spatial patterns of change in annual average near-surface temperature (°C) at different levels of global warming. Displayed are (a–d) spatial patterns of change in annual average near-surface temperature at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900 and (e–g) spatial patterns of differences in temperature change at 2°C, 3°C, and 4°C of global warming compared to 1.5°C of global warming. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and likely emerges from internal variability. That is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40118, "uuid": "d2ba5efc9ca54d0c9bb1b204502a9061", "title": "Caption for Figure 4.32 from Chapter 4 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected spatial patterns of change in annual average precipitation (expressed as a percentage change) at different levels of global warming. Displayed are (a–d) spatial patterns of change in annual precipitation at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900. No map overlay indicates regions where the change is robust and likely emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold (Section 4.2.6) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40122, "uuid": "bcc6df7ca83544de8e352f4b290deed8", "title": "Caption for Figure Atlas.2 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "WGI reference regions used in the (a) AR5 and (b) AR6 reports (Iturbide et al., 2020). Asterisks indicate regions that extend across both sides of the map. The latter includes both land and ocean regions and it is used as the standard for the regional analysis of atmospheric variables in the Atlas chapter and the Interactive Atlas. The codes used in the Interactive Atlas are included in the figure. The full description of the regions (grouped by continents) is as follows. North America: NWN (North-Western North America), NEN (North-Eastern North America), WNA (Western North America), CNA (Central North America), ENA (Eastern North America); Central America: NCA (Northern Central America), SCA (Southern Central America), CAR (Caribbean); South America: NWS (North-Western South America), NSA (Northern South America), NES (North-Eastern South America), SAM (South American Monsoon), SWS (South-Western South America), SES (South-Eastern South America), SSA (Southern South America); Europe: GIC (Greenland/Iceland), NEU (Northern Europe), WCE (Western and Central Europe), EEU (Eastern Europe), MED (Mediterranean); Africa: MED (Mediterranean), SAH (Sahara), WAF (Western Africa), CAF (Central Africa), NEAF (North Eastern Africa), SEAF (South Eastern Africa), WSAF (West Southern Africa), ESAF (East Southern Africa), MDG (Madagascar); Asia: RAR (Russian Arctic), WSB (West Siberia), ESB (East Siberia), RFE (Russian Far East), WCA (West Central Asia), ECA (East Central Asia), TIB (Tibetan Plateau), EAS (East Asia), ARP (Arabian Peninsula), SAS (South Asia), SEA (South East Asia); Australasia: NAU (Northern Australia), CAU (Central Australia), EAU (Eastern Australia), SAU (Southern Australia), NZ (New Zealand); Antarctica: WAN (Western Antarctica), EAS (Eastern Antarctica). The definition of the regions and companion notebooks and scripts are available at the Atlas repository (Iturbide et al., 2021). Figure from Iturbide et al. (2020).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40134, "uuid": "11036297016e4688993cf27498f985cc", "title": "Caption for Figure TS.24 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Projected change in the mean number of days per year with maximum temperature exceeding 35°C for Coupled Model Intercomparison Project Phase 5 (CMIP5; first column), Phase 6 (CMIP6; second column) and Coordinated Regional Climate Downscaling Experiment (CORDEX; third column) ensembles. The intent of this figure is to show that there is a consistent message about the patterns of projected change in extreme daily temperatures from the CMIP5, CMIP6 and CORDEX ensembles. The map shows the median change in the number of days per year between the mid-century (2041–2060) or end-century (2081–2100) and historical (1995–2014) periods for the CMIP5 and CORDEX RCP8.5 and RCP2.6 scenario ensembles and the CMIP6 SSP5-8.5 and SSP1-2.6 scenario ensembles. Hatching indicates areas where less than 80% of the models agree on the sign of change. {Interactive Atlas}", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40142, "uuid": "8210545e1f564114a80d30d99ec1d73b", "title": "Caption for Box TS4, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global mean sea level (GMSL) change on different time scales and under different scenarios. The intent of this figure is to (i) show the century-scale GMSL projections in the context of the 20th century observations, (ii) illustrate ‘deep uncertainty’ in projections by considering the timing of GMSL rise milestones, and (iii) show the long-term commitment associated with different warming levels, including the paleo evidence to support this. (a) GMSL change from 1900 to 2150, observed (1900–2018) and projected under the SSP scenarios (2000–2150), relative to a 1995–2014 baseline. Solid lines show median projections. Shaded regions showlikely ranges for SSP1-2.6 and SSP3-7.0. Dotted and dashed lines show respectively the 83rd and 95th percentilelow confidence projections for SSP5-8.5. Bars at right showlikely ranges for SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 in 2150. Lightly shaded thick/thin bars show 17th–83rd/5th–95th percentile low-confidence ranges in 2150 for SSP1-2.6 and SSP5-8.5, based upon projection methods incorporating structured expert judgement and marine ice cliff instability. Low confidence range for SSP5-8.5 in 2150 extends to 4.8/5.4 m at the 83rd/95th percentile. (b) GMSL change on 100- (blue), 2000- (green) and 10,000-year (magenta) time scales as a function of global surface temperature, relative to 1850–1900. For 100-year projections, GMSL is projected for the year 2100, relative to a 1995–2014 baseline, and temperature anomalies are average values over 2081–2100. For longer-term commitments, warming is indexed by peak warming above 1850–1900 reached after cessation of emissions. Shaded regions show paleo-constraints on global surface temperature and GMSL for the Last Interglacial and mid-Pliocene Warm Period. Lightly shaded thick/thin blue bars show 17th–83rd/5th–95th percentile low confidence ranges for SSP1-2.6 and SSP5-8.5 in 2100, plotted at 2°C and 5°C. (c) Timing of exceedance of GMSL thresholds of 0.5, 1.0, 1.5 and 2.0 m, under different SSPs. Lightly shaded thick/thin bars show 17th–83rd/5th–95th percentile low-confidence ranges for SSP1-2.6 and SSP5-8.5. {4.3.2, 9.6.1, 9.6.2, 9.6.3, Box 9.4}", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40145, "uuid": "276c038366ab4c9d9a822dc8cfa815a4", "title": "Caption for Box TS.13, Figure 1 from the Technical Summary of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Global and regional monsoons: past trends and projected changes. The intent of this figure is to show changes in precipitation over regional monsoon domains in terms of observed past trends, how greenhouse gases and aerosols relate to these changes, and in terms of future projections in one intermediate emissions scenario in the near, medium and long term. (a) Global (black contour) and regional monsoons (colour shaded) domains. The global monsoon (GM) is defined as the area with local summer-minus-winter precipitation rate exceeding 2.5 mm day–1 (see Annex V). The regional monsoon domains are defined based on published literature and expert judgement (see Annex V) and accounting for the fact that the climatological summer monsoon rainy season varies across the individual regions. Assessed regional monsoons are South and South East Asia (SAsiaM, Jun–July–August–September), East Asia (EAsiaM, June–July–August), West Africa (WAfriM, June–July–August–September), North America (NAmerM, July–August–-September), South America (SAmerM, December–January–February), Australia and Maritime Continent Monsoon (AusMCM, December–January–February). Equatorial South America (EqSAmer) and South Africa (SAfri)regions are also shown, as they receive unimodal summer seasonal rainfall although their qualification as monsoons is subject to discussion. (b) Global and regional monsoons precipitation trends based on DAMIP CMIP6 simulations with both natural and anthropogenic (ALL), greenhouse gas only (GHG), aerosols only (AER) and natural only (NAT) radiative forcing. Weighted ensemble means are based on nine Coupled model Intercomparison Project Phase 6 (CMIP6) models contributing to the MIP (with at least three members). Observed trends computed from CRU, GPCP and APHRO (only forSAsiaM and EAsiaM) datasets are shown as well. (c) Percentage change in projected seasonal mean precipitation over global and regional monsoons domain in the near term (2021–2040), mid-term (2041–2060), and long term (2081–2100) under SSP2-4.5 based on 24 CMIP6 models. {Figures 8.11 and 8.22}", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40149, "uuid": "bc00289ce807473da5a54a9914f93fcd", "title": "Derivation of the combined high resolution global TCWV product from microwave and near infrared imagers - COMBI", "abstract": "Details of the retrieval are described in Andersson et al. (2010) and ATBD HOAPS for the microwave imagers as well as in Lindstrot et al. (2012), Diedrich et al. (2015) and ABTD NIR Level 2 for the near-infrared imagers. The water vapour of the atmosphere is vertically integrated over the full column and given in units of kg/m². The microwave and near-infrared data streams are processed independently and combined afterwards by not changing the individual TCWV values and their uncertainties.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40170, "uuid": "62c89c91587c4bc9adcf20b2fe7677fd", "title": "HadISDH.extremes: gridded global monthly land surface wet bulb and dry bulb temperature extremes index dataset produced by the Met Office Hadley Centre", "abstract": "HadISDH.extremes utilises simultaneous sub-daily dry bulb and wet bulb temperature (calculated from dry bulb and dew point temperature) data from over 4000 quality controlled HadISD stations that have sufficiently long records. After checking for sufficient completeness at the daily, monthly, annual, climatological and whole record scale, monthly indices are created from the maximum and minimum of the available daily values. Note that these likely underestimate the true extremes. Climatological averages are calculated over 1991-2020 and monthly climate anomalies obtained. These anomalies (in addition to climatological mean and standard deviation, actual values) are then averaged over 5° by 5° gridboxes centred on -177.5°W and -87.5°S to 177.5°E and 87.5°N. Each gridbox month has an associated homogeneity score obtained from the homogenisation information from HadISDH.landT and HadISDH.landTw. Users can filter the data to remove those gridboxes likely affected by large inhomogeneity. While unlikely to be perfect, this process does help remove large errors from the data an improve robustness of long-term climate monitoring. For greater detail please see: \r\n\r\n\r\n\r\nWillett, K, 2023: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, 40, 1952–1967, doi: 10.1007/s00376-023-2347-8. https://link.springer.com/article/10.1007/s00376-023-2347-8. \r\n\r\nWillett, K. 2023: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, 40, 1968–1985, doi: 10.1007/s00376-023-2348-7. https://link.springer.com/article/10.1007/s00376-023-2348-7.\r\n\r\nSee the documentation links in the online resources section of this record for links to both these publications.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40177, "uuid": "027a8c62ddbf42c681c7cd73b08313ad", "title": "Computation for BICEP/NCEO: Monthly global particulate inorganic carbon (PIC) for between 1997-2021 at 9 km spatial resolution (derived from the Ocean Colour Climate Change Initiative version 5.0 dataset)", "abstract": "Computation of the particulate inorganic carbon (PIC) were generated using a random forest approach that incorporates the following key input variables: remote sensing reflectances (Rrs) at 560 and 665 nm, chlorophyll-a concentration, colour index, and maximum waterclass values. The Rrs(560), Rrs(665), and chlorophyll-a concentration data were obtained directly from the Ocean Colour Climate Change Initiative (OC-CCI) version 5.0. The colour index values were estimated using Mitchell et al. (2017) algorithm: Rrs(560) minus Rrs(665). The maximum waterclass values were estimated using fourteen optical waterclasses obtained from the OC-CCI version 5.0. The PIC data are provided as netCDF files containing global, month PIC concentration at 9 km spatial resolution (1997-2021). For more details on the algorithm and its validation, please see the BICEP algorithm theoretical basline document (https://bicep-project.org/Home)", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40178, "uuid": "1c81595aa99c4a9b814f7666c82405de", "title": "Caption for Figure 12.SM.3 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for the number of negative precipitation anomaly events per decade using the six-month Standardized Precipitation Index for different scenarios, time horizons and global warming levels. The bar plots show projections from CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles, for RCP8.5/SSP5-8.5 (red) and RCP2.6/SSP1-2.6 (blue), for the mid-term (2041–2060), long term (2081–2100) and the recent past (grey, 1995–2014). Results for global warming levels (defined relative to the pre-industrial period 1850–1900) are shown in purple for 1.5°C, yellow for 2°C and brown for 4°C. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). Units are events per decade. The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB). 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": 40179, "uuid": "3c27822f6442450999a593c1ff6895f5", "title": "Caption for Figure 12.SM.4 from Chapter 12 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections for changes in soil moisture for different scenarios, time horizons and global warming levels. The bar plots show projections of soil moisture as percentage changes relative to the recent past (1994–2015) for the mid-term (2041–2060) and long term (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), using CMIP6 (darkest colours), CMIP5 (medium colours) and CORDEX (lightest colours) ensembles. RCP8.5/SSP5-8.5 is shown in red and RCP2.6/SSP1-2.6 in blue. The median (dots) and the 10th–90th percentile range of model ensemble values across each model ensemble and each time period are shown for the regional mean over land areas for the AR6 WGI Reference Regions (defined in Chapter 1). The CORDEX ensemble is missing in regions that are not fully covered by the CORDEX domain (EEU, ESB, RAR, RFE and WSB) or because less than five simulations were available (NWN, NEN, WNA, CAN, ENA and NCA). 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": 40180, "uuid": "fbdb0f9a175b4fb5be47cb4e25a5430c", "title": "Computation for Large ensemble of global mean temperatures: 6-hourly HadAM4 model run data using the Climateprediction.net platform", "abstract": "The global HadAM4 atmosphere and land surface model includes prognostic cloud, convection and gravity-wave drag parameterisation schemes, a radiation scheme that treats water vapour and ice crystals separately, and a land surface scheme, like its predecessor, HadAM3. The updates in HadAM4 include a mixed-phase precipitation scheme, parameterisation of ice cloud particle size and the radiative effects of non-spherical ice particles, and a revised boundary layer scheme. The version used here incorporates an upgrade to the spatial resolution, which matches the horizontal resolution of the HadGEM3-GC3.05 simulations analysed here. HadAM4 has 38 vertical levels.", "keywords": "HadAM4", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40181, "uuid": "6d9ffdeb02374eaf83a808bf9bb7224d", "title": "Caption for Figure Atlas.13 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Changes in annual mean surface air temperature and precipitation averaged over the global land–sea (left) and global land areas (right) in each horizontal pair of panels. The top-left two panels show the median (dots) and 10th–90th percentile range across each model ensemble for temperature change, for two datasets (CMIP5 and CMIP6) and two scenarios (SSP1-2.6/RCP2.6 and SSP5-8.5/RCP8.5). The first 12 bars represent the projected changes over three time periods (near-term 2021–2040, mid-term 2041–2060 and long-term 2081–2100) compared to the baseline period of 1995–2014, and the remaining four bars represent the additional warming projected relative to the same baseline to reach four global warming levels (GWLs; 1.5°C, 2°C, 3°C and 4°C). The top-right two panels show scatter diagrams of temperature against precipitation changes, displaying the median (dots) and 10th–90th percentile ranges for the same four GWLs, again representing the additional changes for the global temperature to reach the respective GWL from the baseline period of 1995–2014. In all panels the dark (light) grey lines or dots represent the CMIP6 (CMIP5) simulated changes in temperature and precipitation between the 1850–1900 baseline used for calculating GWLs and the recent-past baseline of 1995–2014 used to calculate the changes in the bar diagrams and scatter plots. Changes are absolute for temperature and relative for precipitation. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40182, "uuid": "51cacfa1ba1043a88978b9010cbda4d8", "title": "Caption for Figure Atlas.16 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional changes over land in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Africa (warming since the 1850–1900 pre-industrial baseline is also provided as an offset). Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February–March (DJFM; middle panel) and June–July–August–September (JJAS; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See Atlas.1.3 for more details on reference regions (Iturbide et al., 2020) and Atlas.1.4 for details on model data selection and processing. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40183, "uuid": "e21afd1dab3947138db38bbd05ceef8c", "title": "Caption for Figure Atlas.17 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional changes over land in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Asia (warming since the 1850–1900 pre-industrial baseline is also provided as an offset). Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See Atlas.1.3 for more details on reference regions (Iturbide et al., 2020) and Atlas.1.4 for details on model data selection and processing. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40184, "uuid": "01f5d04b6e48449c9993530acdde1334", "title": "Caption for Figure Atlas.21 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional changes over land in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Australasia (warming since the 1850–1900 pre-industrial baseline is also provided as an offset). Bar plots in theleft panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See Atlas.1.3 for more details on reference regions (Iturbide et al., 2020) and Atlas.1.4 for details on model data selection and processing. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40185, "uuid": "5ad3595016774f6aa49c3403d6983009", "title": "Caption for Figure Atlas.22 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional changes over land in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Central America, the Caribbean and South America (warming since the 1850–1900 pre-industrial baseline is also provided as an offset). Barplots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWL: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See Atlas.1.3 for more details on reference regions (Iturbide et al., 2020) and Atlas.1.4 for details on model data selection and processing. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40186, "uuid": "b506372c354f4721a604e32b9e5a0495", "title": "Caption for Figure Atlas.24 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional changes over land in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Europe (warming since the 1850–1900 pre-industrial baseline is also provided as an offset). Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See Atlas.1.3 for more details on reference regions (Iturbide et al., 2020) and Atlas.1.4 for details on model data selection and processing. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40187, "uuid": "1a15e72326ce44a192ab7be3d00a718f", "title": "Caption for Figure Atlas.26 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional changes over land in annual mean surface air temperature andprecipitation relative to the 1995–2014 baseline for the reference regions in North America (warming since the 1850–1900 pre-industrial baseline is also provided as an offset). Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See Atlas.1.3 for more details on reference regions (Iturbide et al., 2020) and Atlas.1.4 for details on model data selection and processing. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40188, "uuid": "d2522502f69d4c94bcbfbee6d33603eb", "title": "Caption for Figure Atlas.28 from Chapter Atlas 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 mean surface air temperature, precipitation and sea level rise relative to the 1995–2014 baseline for the reference regions in the Small Islands (warming since the 1850–1900 pre-industrial baseline is also provided as an offset). Maps on the top show global June–July–August (JJA) precipitation changes (%, relative to 1995–2014) projected for 2081–2100 under RCP8.5 (left) and SSP5-8.5 (right) for the CMIP5 and CMIP6 ensembles, respectively. Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). Bar plots in the right panel show the median (dots) and 5th–95th percentile range (bars) sea level rise from the CMIP6 ensemble (see Chapter 9 for details) for the same time periods and scenarios. The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See Atlas.1.3 for more details on reference regions (Iturbide et al., 2020) and Atlas.1.4 for details on model data selection and processing. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40189, "uuid": "9ac8166782b74891a805667477371a49", "title": "Caption for Figure Atlas.29 from Chapter Atlas of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional changes over land (except for ARO) in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Arctic and Antarctica (warming since the 1850–1900 pre-industrial baseline is also provided as an offset). Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See Atlas.1.3 for more details on reference regions (Iturbide et al., 2020) and Atlas.1.4 for details on model data selection and processing. The script used to generate this figure is available online (Iturbide et al., 2021) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40193, "uuid": "4b08c47fd0314dc78648c54ee515401b", "title": "MIROC-ES2H model based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan).", "abstract": "MIROC-ES2H model based on a global climate model MIROC (Model for Interdisciplinary Research on Climate) which has been cooperatively developed by JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan).", "keywords": "CCMI-2022, MIROC-ES2H, MIROC, JAMSTEC, AORI, NIES, R-CCS", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40195, "uuid": "2c1df78e252d4953a3c32534b7d8776d", "title": "GEM-NEMO model", "abstract": "This data was produced by the GEM-NEMO model run by scientists at Environment and Climate Change Canada (ECCC) for the SNAPSI project.", "keywords": "GEM-NEMO, ECCC, SNAPSI", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40204, "uuid": "d46157a80d9d4f7b8cfc8d0897e56c30", "title": "GLOBO model run by scientists at CNR-ISAC", "abstract": "This data was produced by the GLOBO model run by scientists at CNR-ISAC (Institute of Atmospheric Sciences and Climate, Bologna, Italy) for the SNAPSI project.", "keywords": "GLOBO, CNR-ISAC, SNAPSI", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40212, "uuid": "b09b68bead9e4278aed895e7ff907e84", "title": "Global/Regional Integrated Model System (GRIMs) deployed on KISTI NURION", "abstract": "Global/Regional Integrated Model System (GRIMs) deployed on KISTI NURION. The GRIMs model is an atmospheric general circulation model (AGCM) using Optimum Interpolation Sea Surface Temperature (OISST) dataset as ocean boundary conditions and climatological ozone. All data in this dataset are regridded to 1.5x1.5 degree resolution.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40220, "uuid": "02e928af2efb42d6856750b518bd6ea4", "title": "Global Seasonal Forecasting System version 6 (GloSea6) deployed on the Korea Meteorological Administration's 5th supercomputer.", "abstract": "Global Seasonal Forecasting System version 6 (GloSea6) deployed on KMA's 5th supercomputer at N216 (432x324) horizontal and L85 vertical resolutions. The GloSea6 model is a coupled Global Climate Model (CGCM) consisting of UM11.5, NEMO3.6, CICE5.1.2, JULES5.6 for atmosphere, ocean, sea ice, and land models, respectively. All data in this dataset are regridded to 1.5x1.5 degree resolution.", "keywords": "", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40227, "uuid": "c94db763fc6d4c8daca317928af8ad73", "title": "GloSea6 model run by scientists at UKMO", "abstract": "This data was produced by the GloSea6 model run by scientists at the UK Met Office for the SNAPSI project. GloSea6 is an ensemble prediction system built around the high resolution version of the Met Office climate prediction model: HadGEM3 family.", "keywords": "GloSea6, UKMO, SNAPSI", "inputDescription": null, "outputDescription": null, "softwareReference": null, "identifier_set": [] }, { "ob_id": 40235, "uuid": "4d909eabbd2446ca84f4d749b66b810c", "title": "Caption for Figure 12.SM.6 from Chapter 12 Supplementary Material of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6)", "abstract": "Regional projections of extreme sea level (1-in-100-year return period Extreme Total Water Level (ETWL). The bar plots show projections of regionally averaged ETWL from the CMIP5-based datasets presented in Vousdoukas et al. (2018b; filled circles, ‘V’ in legend), and Kirezci et al. (2020; open circles, ‘K’ in legend), for the AR6 WGI Reference Regions, for RCP8.5 (red) and RCP4.5 (blue). Dots represent the median estimate and bars the 5th–95th percentiles representing the uncertainty associated with the projections for the mid-term (2050), long term (2100) and the recent past (black, 1979/1980–2014). Units are metres. See Technical Annex VI for details about the index. 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": [] } ] }