The bias-corrected d4PDF dataset offers daily data of 10 climatic variables over the globe from 1951 to 2010. Data from the historical experiment and non-warming counterfactual simulation are available (at this moment, there is no plan to conduct bias-correction of data from the +4 degC experiment). See Shiogama et al. (2016), Mizuta et al. (2017) and Imada et al. (2017) for details on the original d4PDF database. For each simulation, data for 100-member ensemble are available. The data over the sea and Antarctica are not bias-corrected (i.e., the raw data of the MRI-AGCM3.2 (Mizuta et al., 2012) were used), whereas those over the land are bias-corrected using S14FD meteorological forecing dataset (doi:10.20783/DIAS.523) as the baseline. Variables include daily mean 2m air temperature (tave2m, °C), daily maximum 2m air temperature (tmax2m, °C), daily minimum 2m air temperature (tmin2m, °C), daily total precipitation (precsfc, mm d-1), daily mean downward shortwave radiation flux (dswrfsfc, W m-2), daily mean downward longwave radiation flux (dlwrfsfc, W m-2), daily mean 2m relative humidity (rh2m, %), daily mean 2m specific humidity (spfh2m, kg kg-1), daily mean 10m wind speed (wind10m, m s-1) and daily mean surface pressure (pressfc, hPa).

(1) This is the dataset simulated by high resolution atmospheric model of which horizontal resolution is 60km-mesh over the globe (GCM), and 20km over Japan and surroundings (RCM), respetively. The climate of the latter half of the 20th century is simulated for 6000 years (3000 years for the Japan area), and the climates 1.5 K(*2), 2 K (*1) and 4 K warmer than the pre-industrial climate are simulated for 1566, 3240 and 5400 years, respectivley, to see the effect of global warming. (2) Huge number of ensembles enable not only with statistics but also with high accuracy to estimate the future change of extreme events such as typoons and localized torrential downpours. In addtion, this dataset provides the highly reliable information on the impact of natural disasters due to climate change on future societies. (3) This dataset provides the climate projections which adaptations against global warming are based on in various fields, for example, disaster prevention, urban planning, environmetal protection, and so on. It would realize the global warming adaptations consistent not only among issues but also among regions. (4) Total size of this dataset is 3 PB (3 × the 15th power of 10 bytes).

(*1) Datasets of the climates 2K warmer than the pre-industorial climate (d4PDF 2K) is available on 10th August, 2018. (*2) Datasets of the climates 1.5K warmer than the pre-industorial climate (d4PDF 1.5K) is available on 8th February, 2022.

The d4PDF-WaveHs dataset represents the first single model initial-condition large ensemble (SMILE, 100-member) of historical significant ocean wave height (Hs) at a global scale. It was produced using an advanced statistical model with predictors derived from Japan's database for policy decision-making for future climate change (d4PDF) ensemble of historical simulations of sea level pressure. d4PDF-WaveHs provides 100 realizations of Hs for the period 1951-2010 (hence 6,000 years of data) on a 1° x 1° latitude-longitude grid. In addition, this dataset contains 14 statistics (including extreme indices) calculated on monthly, seasonal, and annual scales. d4PDF-WaveHs provides unique data to understand better the poorly known role of internal climate variability in ocean wave climate. For example, it can better distinguish climate variability from trend signals. It also provides a better sampling of the entire probability distribution, including the tails where extreme events occur. This is crucial to properly assess wave-driven impacts, such as extreme sea levels on low-lying (and densely) populated coastal areas. This dataset may interest a variety of researchers, engineers, and stakeholders, including those in the fields of climate science, oceanography, coastal management, offshore engineering, and energy resource development.

Data for Figure 10.13 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 10.13 shows attribution of the southwestern North America precipitation decline during the 1983-2014 period. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has 3 subpanels. Data for all subpanels is provided. --------------------------------------------------- List of data provided --------------------------------------------------- The data is annual October-September (water year) precipitation means for: - Observed and modelled trends over 1983-2014 - Observed and modelled relative anomalies with respect to 1971-2000 averages over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N) - Trends in relative precipitation anomalies between 1983-2014 (baseline 1983-2014) over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N) --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel (a): Observed and Model (MPI-ESM and d4PDF runs with min and max trends as well as mean trends) OLS linear trends in precipitation between 1983 and 2014 over North America: - Data files: Fig_10_13_panel-a_mapplot_pr_trend_CRU_single_trend.nc, Fig_10_13_panel-a_mapplot_pr_trend_REGEN_single_trend.nc, Fig_10_13_panel-a_mapplot_pr_trend_GPCC_single_trend.nc, Fig_10_13_panel-a_mapplot_pr_trend_GPCP_single_trend.nc, Fig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_max_single-MultiModelMean_trend-min-mean-max.nc, Fig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_min_single-MultiModelMean_trend-min-mean-max.nc, Fig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_MultiModelMean_single-MultiModelMean_trend-min-mean-max.nc, Fig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_max_single-MultiModelMean_trend-min-mean-max.nc, Fig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_min_single-MultiModelMean_trend-min-mean-max.nc, F ig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_MultiModelMean_single-MultiModelMean_trend-min-mean-max.nc Panel (b): Observed (CRU TS, black) and Model (d4PDF runs with min (brown) and max (green) trends) timeseries relative precipitation anomalies in respect to 1971-2000 averages over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N): - Data file: Fig_10_13_panel-b_timeseries.csv Panel (c): OLS linear trends in relative precipitation anomalies between 1983-2014 (baseline 1983-2014) over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N): observed data (CRU TS, REGEN, GPCC and GPCP, black crosses), individual members of CMIP6 historical (red circles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading): - Data file: Fig_10_13_panel-c_trends.csv Acronyms: CMIP - Coupled Model Intercomparison Project, HighResMIP - High Resolution Model Intercomparison Project, Cordex – Coordinated Regional Climate Downscaling Experiment, CRU TS- Climatic Research Unit Time Series, GPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, GPCP - Global Precipitation Climatology Project, d4PDF - Database for Policy Decision-Making for Future Climate Change, MPI GE - Max-Planck-Institut für Meteorologie Grand Ensemble, ESM - Earth System Model, SMILEs -single model initial-condition large ensembles, MIROC - Model for Interdisciplinary Research on Climate, CSIRO - Commonwealth Scientific and Industrial Research Organisation, REGEN -Rainfall Estimates on a Gridded Network, OLS - ordinary least squares regression. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The code for ESMValTool is provided. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 10) - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11 - Link to the code for the figure, archived on Zenodo.

Data for Figure 10.13 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 10.13 shows attribution of the southwestern North America precipitation decline during the 1983-2014 period.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012.

Figure subpanels

The figure has 3 subpanels. Data for all subpanels is provided.

List of data provided

The data is annual October-September (water year) precipitation means for:

• Observed and modelled trends over 1983-2014
• Observed and modelled relative anomalies with respect to 1971-2000 averages over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N)
• Trends in relative precipitation anomalies between 1983-2014 (baseline 1983-2014) over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N)

Data provided in relation to figure

Panel (a): Observed and Model (MPI-ESM and d4PDF runs with min and max trends as well as mean trends) OLS linear trends in precipitation between 1983 and 2014 over North America: - Data files: Fig_10_13_panel-a_mapplot_pr_trend_CRU_single_trend.nc, Fig_10_13_panel-a_mapplot_pr_trend_REGEN_single_trend.nc, Fig_10_13_panel-a_mapplot_pr_trend_GPCC_single_trend.nc, Fig_10_13_panel-a_mapplot_pr_trend_GPCP_single_trend.nc, Fig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_max_single-MultiModelMean_trend-min-mean-max.nc, Fig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_min_single-MultiModelMean_trend-min-mean-max.nc, Fig_10_13_panel-a_mapplot_pr_trend_d4pdf_d4PDF_MultiModelMean_single-MultiModelMean_trend-min-mean-max.nc, Fig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_max_single-MultiModelMean_trend-min-mean-max.nc, Fig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_min_single-MultiModelMean_trend-min-mean-max.nc, F ig_10_13_panel-a_mapplot_pr_trend_mpige_MPI-GE_MultiModelMean_single-MultiModelMean_trend-min-mean-max.nc

Panel (b): Observed (CRU TS, black) and Model (d4PDF runs with min (brown) and max (green) trends) timeseries relative precipitation anomalies in respect to 1971-2000 averages over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N): - Data file: Fig_10_13_panel-b_timeseries.csv

Panel (c): OLS linear trends in relative precipitation anomalies between 1983-2014 (baseline 1983-2014) over southwestern North America (lon: 240°E-255°E, lat: 28°N-40°N): observed data (CRU TS, REGEN, GPCC and GPCP, black crosses), individual members of CMIP6 historical (red circles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading): - Data file:
Fig_10_13_panel-c_trends.csv

Acronyms: CMIP - Coupled Model Intercomparison Project, HighResMIP - High Resolution Model Intercomparison Project, Cordex – Coordinated Regional Climate Downscaling Experiment, CRU TS- Climatic Research Unit Time Series, GPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, GPCP - Global Precipitation Climatology Project,
d4PDF - Database for Policy Decision-Making for Future Climate Change, MPI GE - Max-Planck-Institut für Meteorologie Grand Ensemble, ESM - Earth System Model,
SMILEs -single model initial-condition large ensembles, MIROC - Model for Interdisciplinary Research on Climate, CSIRO - Commonwealth Scientific and Industrial Research Organisation, REGEN -Rainfall Estimates on a Gridded Network, OLS - ordinary least squares regression.

---------------------------------... For full abstract see: https://catalogue.ceda.ac.uk/uuid/5d64c2103c534f83b8ec11a2a4cab10d.

Data for Figure 10.20 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 10.20 shows aspects of Mediterranean summer warming.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012.

Figure subpanels

The figure has 7 subpanels. Data for subpanels d, e, f and g is provided.

List of data provided

The data is annual summer (JJA) means for:

• Observed trends over 1960-2014
• Anomalies 1960-2014 with respect to 1995-2014 average for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N)
• Trends 1960-2014 for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N)
• Modelled trend differences to the observed over 1960-2014

Data provided in relation to figure

Panel (d): - Data file: Fig_10_20_panel-d_mapplot_tas_obs_trend_single_single_trend.nc; JJA Berkeley Earth surface air temperature OLS linear trends over 1960-2014 over the Mediterranean (lon: 10°W-40°E, lat: 25°N-50°N)

Panel (e): - Data file: Fig_10_20_panel-e_timeseries.csv; Observed and modelled JJA surface air temperature anomalies 1960-2014 (baseline 1995-2014) for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N): CMIP5 (blue), CMIP6 (red), HighResMIP (orange), CORDEX EUR-44 (light blue), CORDEX EUR-11 (green), Berkeley Earth (dark blue), CRU TS (brown), HadCRUT5 (cyan)

Panel (f): - Data file: Fig_10_20_panel-f_trends.csv; JJA OLS linear trends in surface air temperature 1960-2014 for the Mediterranean mean (lon: 10°W-40°E, lat: 25°N-50°N) of observations (Berkeley Earth, CRU TS, HadCRUT5: black crosses) and models (CMIP5 (blue circles), CMIP6 (red circles), HighResMIP (orange circles), CORDEX EUR-44 (light blue circles), CORDEX EUR-11 (green circles)) and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading)

Panel (g): - Data files: Fig_10_20_panel-g_mapplot_tas_cmip5_mean_trend_bias_tas_cmip5_maps_trend_MultiModelMean_trend-bias.nc, Fig_10_20_panel-g_mapplot_tas_cmip6_mean_trend_bias_tas_cmip6_maps_trend_MultiModelMean_trend-bias.nc, Fig_10_20_panel-g_mapplot_tas_cordex_11_mean_trend_bias_tas_cordex_11_maps_trend_MultiModelMean_trend-bias.nc, Fig_10_20_panel-g_mapplot_tas_cordex_44_mean_trend_bias_tas_cordex_44_maps_trend_MultiModelMean_trend-bias.nc, Fig_10_20_panel-g_mapplot_tas_hrmip_mean_trend_bias_tas_hrmip_maps_trend_MultiModelMean_trend-bias.nc; Modelled OLS linear surface air temperature trend differences to the observed trend (Berkeley Earth) over 1960-2014 of CMIP5, CMIP6, HighResMIP, CORDEX EUR-44, and CORDEX EUR-11 ensemble means

Acronyms: CMIP - Coupled Model Intercomparison Project, Cordex – Coordinated Regional Climate Downscaling Experiment, CRU TS- Climatic Research Unit Time Series, CSIRO - Commonwealth Scientific and Industrial Research Organisation, MIROC - Model for Interdisciplinary Research on Climate, SMILEs - single model initial-condition large ensembles, d4PDF - Database for Policy Decision-Making for Future Climate Change, OLS - ordinary least squares regression.

Notes on reproducing the figure from the provided data

The code for ESMValTool is provided.

Sources of additional information

The following weblinks are provided... For full abstract see: https://catalogue.ceda.ac.uk/uuid/19ec340e6f2d47479ddb483961b0c1bb.

Data for Figure 10.12 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 10.12 shows Southeastern South America positive mean precipitation trend and its drivers during 1951-2014.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012.

Figure subpanels

The figure has 4 subpanels. Data for 3 subpanels (b-d) is provided. Subpanel (a) is a schematic.

List of data provided

The data is annual December-Jannuary (DJF) precipitation means for:

• Observed and model relative anomalies over 1951-2014 with respect to 1995-2014 average over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W)
• Observed precipitation trends 1951-2014 South America
• Trends in precipitation over 1951-2014 over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W)

Data provided in relation to figure

Panel (b): Observed (CRU TS, black line, and CRU TS no-running mean (bars)) and Model (MPI-ESM runs with min (brown) and max (green) trends) precipitation rate relative anomalies over 1951-2014 with respect to 1995-2014 average over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W): - Data file: Fig_10_12_panel-b_timeseries.csv

Panel (c): Observed precipitation OLS linear trends 1951-2014 over South America: - Data files: Fig_10_12_panel-c_mapplot_pr_trend_CRU_single_trend.nc, Fig_10_12_panel-c_mapplot_pr_trend_GPCC_single_trend.nc

Panel (d): OLS linear trends in precipitation over 1951-2014 over south-eastern South America (26.25°S–38.75°S, 56.25°W–66.25°W): observed data (GPCC, CRU TS: black crosses), individual members of CMIP6 historical (red circles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading): - Data file: Fig_10_12_panel-d_trends.csv

Acronyms:
CRU TS- Climatic Research Unit Time Series, CMIP - Coupled Model Intercomparison Project,
SMILEs -single model initial-condition large ensembles, MIROC - Model for Interdisciplinary Research on Climate, CSIRO - Commonwealth Scientific and Industrial Research Organisation, MPI - Max-Planck-Institut für Meteorologie, ESM - Earth System Model,
d4PDF - Database for Policy Decision-Making for Future Climate Change, OLS - ordinary least squares regression.

Notes on reproducing the figure from the provided data

The code for ESMValTool is provided.

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 10) - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11 - Link to the code for the figure, archived on Zenodo.

Downscaling data from d4PDF with 5km regional climate model (JMA/MRI NHRCM)

This data set contains the 100-member ensembles of monthly leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) estimated using GIMMS3g LAI and FAPAR and d4PDF air temperature by the method described in Sasai et al. (2016) for historical and non-warming climates in 1951-2010/2011. Data is 0.5625-degree (640×320) 4-byte binary (.raw). Undefined value is -9999.

For more details, please check the ReadmeVeg.pdf

If you questions, please contact Irina Melnikova (irina.melnikova.russia@gmail.com)

Data for Figure 10.11 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure. 10.11 shows attribution of historic precipitation change in the Sahelian West African monsoon during June to September.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012.

Figure subpanels

The figure has 5 subpanels. Data for all subpanels is provided.

List of data provided

The data is annual June-September (JJAS) precipitation means for:

• Observed anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N)
• Model anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N)
• Observed precipitation difference 1980-1990 mean - 1950-1960 mean
• Model differences between 1.5x and 0.2x aerosol scalings over 1955-1984
• Trends in relative precipitation anomalies (baseline 1955-1984) over decline (1955-1984) and recovery (1985-2014) period over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N)

Data provided in relation to figure

Panel (a): Observed (CRU TS) timeseries anomalies over 1920-2018 in respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N): - Data file: Fig_10_11_panel-a_timeseries_obs.csv

Panel (b): Observed (CRU TS) precipitation difference 1980-1990 mean - 1950-1960 mean: - Data file: Fig_10_11_panel-b_mapplot_pr_change_CRU_single_mean.nc

Panel (c): Model differences between 1.5x and 0.2x aerosol scalings over 1955-1984: - Data file: Fig_10_11_panel-c_mapplot_pr_diff_SMURPHS_single_mean.nc

Panel (d): Model timeseries anomalies over 1920-2018 respect to 1955-1984 average over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N) for CMIP6 hist all-forcings (red), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), CMIP6 hist-aer (grey) and CMIP6 hist-GHG (pale blue): - Data file: Fig_10_11_panel-d_timeseries_cmip6.csv

Panel (e): Observed and modelled OLS linear trends in relative precipitation anomalies (baseline 1955-1984) over decline (1955-1984) and recovery (1985-2014) period over the Sahel (lon: 20°W-30°E, lat: 10°N-20°N): observed data (GPCC, CRU TS: black crosses), 34 CMIP5 models (dark blue circles), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading): - Data file: Fig_10_11_panel-e_trends.csv;

Acronyms: CMIP - Coupled Model Intercomparison Project, CRU TS- Climatic Research Unit Time Series, SMURPHS - Securing Multidisciplinary UndeRstanding and Prediction of Hiatus and Surge events, DAMIP - Detection and Attribution Model Intercomparison Project, GHG - Greenhouse Gases, GPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, SMILEs -single model initial-condition large ensembles, CSIRO - Commonwealth Scientific and Industrial Research Organisation, MIROC - Model for Interdisciplinary Research on Climate, MPI - Max-Planck-Institut für Meteorologie,
d4PDF - Database for Policy Decision-Making for Future Climate Change, OLS - ordinary least squares regression.

Notes on reproducing the figure from the provided data -------------------------------------------------... For full abstract see: https://catalogue.ceda.ac.uk/uuid/970847e5690c4f9e8c4ad455641bd558.

Data for Figure 10.19 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 10.19 shows changes in the Indian summer monsoon in the historical and future periods. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has 6 subpanels. Data for all subpanels is provided. --------------------------------------------------- List of data provided --------------------------------------------------- The dataset contains: APHRODITE station density for June-September (JJAS) 1956 Precipitation June-September (JJAS): - Model mean bias 1985-2010 - Observed and modelled trends: CRU TS 1950-2000, CMIP6 hist-GHG & hist-aer 1950-2000, and CMIP6 SSP5-8.5 2015-2100 trends - Observed and model relative anomalies over 1950-2100 with respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N) - Modelled change until 2081‒2100 with respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N) - Trends in relative precipitation anomalies (baseline 1995-2014) over past (1950-2000) and future (2015-2100) period over central India (lon: 76°E-87°E, lat: 20°N-28°N). - Trend difference between the 3 MPI-ESM runs with the lowest and the 3 MPI-ESM runs with the highest trend --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- Panel (a): APHRODITE station density for JJAS 1956: - Data file: Fig_10_19_panel-a_mapplot_APHRODITE_stationdensity_single_mean.nc Panel (b): CMIP6 mean precipitation bias June-September mean 1985-2010 mean with respect to CRU TS: - Data file: Fig_10_19_panel-b_mapplot_pr_cmip6_bias_pr_cmip6_maps_past_bias_MultiModelMean_bias.nc Panel (c): OLS linear precipitation for June-September mean trend of CRU TS 1950-2000 (top left), CMIP6 hist-GHG (bottom left) & hist-aer (bottom right) 1950-2000, and CMIP6 SSP5-8.5 2015-2100 (top right): - Data files: Fig_10_19_panel-c_mapplot_pr_cmip6_mean_trend_future_pr_cmip6_maps_trend_future_MultiModelMean_trend.nc, Fig_10_19_panel-c_mapplot_pr_histaer_mean_trend_past_pr_aer_maps_trend_past_MultiModelMean_trend.nc, Fig_10_19_panel-c_mapplot_pr_histghg_mean_trend_past_pr_ghg_maps_trend_past_MultiModelMean_trend.nc, Fig_10_19_panel-c_mapplot_pr_obs_mean_trend_past_CRU_single_trend.nc; Panel (d): Observed and model relative precipitation June-September mean anomalies over 1950-2100 in respect to 1995-2014 averages over central India (lon: 76°E-87°E, lat: 20°N-28°N) (CRU TS (brown), GPCC (dark blue), REGEN (green), APHRO-MA (light brown), IITM all-India rainfall (light blue), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), CMIP6 hist-aer (grey), hist-GHG (light blue) CMIP6 historical/SSP5-8.5 (dark red) and CMIP5 historical/RCP8.5 (dark blue) and Modelled change until 2081‒2100 in respect to 1995-2014 averages over central India (CMIP6 SSP5-8.5 (dark red) and CMIP5 historical/RCP8.5 (dark blue)): - Data files: Fig_10_19_panel-d_timeseries.csv, Fig_10_19_panel-d_boxplot.csv Panel (e): OLS linear trends in relative precipitation June-September mean anomalies (baseline 1995-2014) over past (1950-2000) and future (2015-2100) period over central India (lon: 76°E-87°E, lat: 20°N-28°N) of observations (GPCC, CRU TS, REGEN and APRHO-MA: black crosses) and models (individual members of CMIP5 historical-RCP8.5 (blue), CMIP6 historical-SSP5-8.5 (dark red), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles)), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading): - Data file: Fig_10_19_panel-e_trends.csv Panel (f): June-September mean 2016-2045 OLS linear trend difference in precipitation between the 3 MPI-ESM runs with the lowest and the 3 MPI-ESM runs with the highest trend: - Data file: Fig_10_19_panel-f_mapplot_pr_mpige_mean_trend_future_spread_single_trend-difference-min3-max3.nc Acronyms: CMIP - Coupled Model Intercomparison Project, APHRODITE - ASIAN PRECIPITATION - HIGHLY-RESOLVED OBSERVATIONAL DATA INTEGRATION TOWARDS EVALUATION OF WATER RESOURCES, CRU TS- Climatic Research Unit Time Series, GHG - Greenhouse gas, IITM - Indian Institute of Technology Madras, RCP - Representative Concentration Pathway, DAIMP - Detection and Attribution Model Intercomparison Project, SSP - Shared Socioeconomic Pathways, GPCC - GLOBAL PRECIPITATION CLIMATOLOGY CENTRE, REGEN - Rainfall Estimates on a Gridded Network, S MILEs -single model initial-condition large ensembles, d4PDF - Database for Policy Decision-Making for Future Climate Change, MIROC - Model for Interdisciplinary Research on Climate, MPI - Max-Planck-Institut für Meteorologie, ESM - Earth System Model, Cordex – Coordinated Regional Climate Downscaling Experiment, OLS - ordinary least squares regression. --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The code for ESMValTool is provided. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the figure on the IPCC AR6 website - Link to the report component containing the figure (Chapter 10) - Link to the Supplementary Material for Chapter 10, which contains details on the input data used in Table 10.SM.11 - Link to the code for the figure, archived on Zenodo.

This study assesses the effects of internal climate variability on wave height trend assessment using the d4PDF-WaveHs, the first single model initial-condition large ensemble (100-member) of significant wave height (Hs) simulations for the 1951–2010 period, which was produced using sea level pressure taken from Japan’s d4PDF ensemble of historical climate simulations. Here, the focus is on assessing trends in annual mean and maximum Hs. The result is compared with other model simulations that account for other sources of uncertainty, and with modern wave reanalyses. It is shown that the trend variability arising from internal climate variability is comparable to the variability caused by other factors, such as climate model uncertainty. This study also assesses the likelihood to mis-estimate trends when using only one ensemble member and therefore one possible realization of the climate system. Using single member failed to detect the statistically significant notable positive trend shown in the ensemble in some areas of the Southern Ocean. The North Atlantic Ocean is found to have large internal climate variability, where different ensemble-members can show trends of the opposite signs for the same area. The minimum ensemble size necessary to effectively reduce the risk of mis-assessing Hs trends is estimated to be 10; but this largely depends on the specific wave statistic and the region of interest, with larger ensembles being required to assess extremes. The results also show that wave reanalyses are not suitable for analyzing Hs trends due to temporal inhomogeneities therein, in agreement with recent studies.

Data for CCB 10.4 Figure 1 from Chapter 10 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

CCB10.4 Figure 1 shows historical annual-mean surface air temperature linear trend (°C per decade) and its attribution over the Hindu Kush Himalaya (HKH) region.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Doblas-Reyes, F.J., A.A. Sörensson, M. Almazroui, A. Dosio, W.J. Gutowski, R. Haarsma, R. Hamdi, B. Hewitson, W.-T. Kwon, B.L. Lamptey, D. Maraun, T.S. Stephenson, I. Takayabu, L. Terray, A. Turner, and Z. Zuo, 2021: Linking Global to Regional Climate Change. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1363–1512, doi:10.1017/9781009157896.012.

Figure subpanels

The figure has four subpanels. Data for all subpanels is provided.

List of data provided

The data is annual means for:

• Observed and modelled trends over 1961-2014
• Anomalies 1961-2014 with respect to 1961-1980 average for the HKH region mean
• Trends 1961-2014 for the HKH region mean

Data provided in relation to figure

Panel (a): - Data files: Fig_10_CCB-4_1_panel-a_mapplot_tas_trend_BerkeleyEarth_single_trend.nc, Fig_10_CCB-4_1_panel-a_mapplot_tas_trend_CRU_single_trend.nc, Fig_10_CCB-4_1_panel-a_mapplot_tas_trend_APHRO-MA_single_trend.nc, Fig_10_CCB-4_1_panel-a_mapplot_tas_trend_JRA-55_single_trend.nc; Observed and reanalysis surface air temperature OLS linear trends over 1961-2014 over the HKH region, from left to right Berkeley Earth, CRU TS, APHRO-MA, JRA-55

Panel (b): - Data files: Fig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_min_single-MultiModelMean_trend-min-median-max.nc, Fig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_MultiModelMedian_single-MultiModelMean_trend-min-median-max.nc, Fig_10_CCB-4_1_panel-b_mapplot_tas_trend_cmip6_CMIP6_max_single-MultiModelMean_trend-min-median-max.nc; Modelled surface air temperature OLS linear trends over 1961-2014 over the Hindu Kush Himalaya region, from left to right (CMIP6 models with min (coldest), median and max (warmest) trends)

Panel (c): - Data file: Fig_10_CCB-4_1_panel-c_timeseries.csv; Surface air temperature anomalies 1961-2014 in respect to 1961-1980 average for the Hindu Kush Himalaya (HKH) region mean: means of CMIP6 hist all-forcings (red), and the CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink), for hist-aer (grey) and hist-GHG (pale blue), Berkeley Earth (dark blue), CRU TS (brown), APHRO-MA (light green) and JRA-55 (dark green).

Panel (d): - Data file: Fig_10_CCB-4_1_panel-d_trends.csv; Surface air temperature OLS linear trends 1961-2014 for the Hindu Kush Himalaya (HKH) region mean: observed and reanalysis data (Berkeley Earth, CRU TS, APHRO-MA, JRA-55: black crosses), individual members of CMIP6 hist all-forcings (red circles), CMIP6 hist all-forcings sample corresponding to DAMIP experiments (pink circles), CMIP6 hist-GHG (blue triangles), CMIP6 hist-aer (grey triangles), and box-and-whisker plots for the SMILEs: MIROC6, CSIRO-Mk3-6-0, MPI-ESM, d4PDF (grey shading)

Acronyms: CRU TS- Climatic Research Unit Time Series, CMIP - Coupled Model Intercomparison Project, JRA - Japanese 55year Reanalysis, DAMIP - Detection and Attribution Model Intercomparison Project, GHG - Greenhouse Gas, SMILEs - Single model initial-condition large ensembles, MIROC - Model for Interdisciplinary Research on Climate, CSIRO -Commonwealth Scientific and Industrial Research Organisation, MPI - Max-Planck-Institut für Meteorologie, ESM - Earth System Model, d4PDF - database for policy decision-making for future climate changes, OLS - ordinary least squares regression.

Notes on reproducing the figure from the provided data --... For full abstract see: https://catalogue.ceda.ac.uk/uuid/e4416a7d02ed4eeb9a971a7d3c2f4e42.

Calculation results of freeze-thaw hillslope weathering potential before and after climate change for each mesh of 5km average spatial resolution d4PDF data in the Pekerebetsu River basin, Hokkaido, Japan. This dataset includes both cases with and without snow cover.

This data set contains the 100-member ensembles of monthly gross primary production (GPP) estimated using the biosphere model BEAMS and d4PDF data for historical and non-warming climates in 1951-2010/2011, 100-member ensembles of yearly GPP of the historical sensitivity experiment for 7 input variables in 1951-2010, yearly GPP of the extended CO2 sensitivity experiment using four RCP scenarios in 1951-2300. Data is 0.5625-degree (640×320) 4-byte binary (.raw). Undefined value is -9999.

For more details, please check the ReadmeGPP.pdf

If you questions, please contact Irina Melnikova (irina.melnikova.russia@gmail.com)

The daily precipitation and daily mean temperature data from the nationwide d4PDF downscaling data have been bias-corrected using the Dual-Window method, starting from the observation point (however, this is limited to within and near the basins of 109 first-class river systems nationwide).

日本全国を対象に、d4PDFの過去実験・４度上昇実験を気象研究所非静力学地域気候モデル（NHRCM）により5kmにダウンスケーリングしたデータセット。

① 全世界および日本周辺領域について、それぞれ60km、20kmメッシュの高解像度大気モデルを使用した高精度モデル実験出力です。過去6000年分(日本周辺域は3000年分)、将来については、全球平均気温が産業革命以降 1.5℃（注2）、2℃ （注1）および 4℃ 上昇した未来の気候状態について、それぞれ1566年分、3240年分と5400年分のモデル実験を行いました。これらを用いることにより、未来の気候状態と現在の気候状態との比較ができます。 ② 多数の実験例 (アンサンブル) を活用することで、台風や集中豪雨などの極端現象の将来変化を、確率的に、かつ高精度に評価することができます。また、気候変化よる自然災害がもたらす未来社会への影響についても確度の高い結論を導くことができます。 ③ 防災、都市計画、環境保全等に関わる様々な地球温暖化対策のために、その基礎となる気候予測データを提供します。共通の予測データを用いることで、諸問題間および地域間で整合した温暖化対策の実現が期待できます。 ④ 総データ量は 3 ペタバイト (3 x 10の15 バイト) です。

（注1）2度昇温実験データは2018年8月10日から公開しています。 （注2）1.5度昇温実験データは2022年2月8日から公開しています。