The COAPEC (Coupled Ocean-Atmosphere Processes and European Climate) programme was a 5 year NERC thematic programme designed to examine the variability of the Earth's climate. Interactions between the oceans and the atmosphere play a major role in governing this variability. The goal of COAPEC was to determine the impact on climate, especially European climate, of the coupling between the Atlantic Ocean and the atmosphere, including the influence of ENSO on this coupling. To aid researchers within the COAPEC programme, datasets have been retrieved from a variety of coupled models. * 100 years (2079 - 2178) monthly means of all atmospheric and oceanic fields derived from the control run of the Hadley Centre HadCM3 model. * 1000 years (1849-2849) of monthly means of selected parameters from the HadCM3 control run. * 50 years (1950-2000) of MOM (GFDL Modular Ocean Model) data. * Output from the 100 year HadCM3 control integration produced using UM4.5 on the BADC Beowulf Cluster. * Surface flux climatology data from SOC If using the 100 year dataset from the Hadley Centre, please be aware that the run was restarted part of the way through. This means that there is a difference in the indicated date of origin in the data files, and can cause a discontinuity if not corrected for during analysis. The 1000 year HadCM3 dataset has been extracted from the Met Office and these data have been added to the archive. The data from a 500 year HadCM3 control integration performed on a linux Beowulf cluster using UM version 4.5 at the BADC has been included in the archive. Please see the README.txt for more information.

The VOCALS [VAMOS (Variability of the American Monsoon System) Ocean Cloud Atmosphere Land Study Regional Experiment] campaign was a large multi-national field campaign that has been established to investigate the coupled processes that control the climate of the South-East Pacific region. This includes the variety of interactions between the ocean surface, the overlying atmosphere and the neighbouring land. A particular focus for the Facility for Airborne Atmospheric Measurements (FAAM) aircraft studies was the sources of natural and anthropogenic aerosol and an understanding of their physical and chemical properties and a study of the interactions of this aerosol with the persistent stratocumulus cloud in the maritime atmospheric boundary layer. The campaign made use of instruments on board the FAAM BAe-146 aircraft to determine the strength and temperature dependence.

The National Centre for Earth Observation (NCEO): Monthly global Particulate Organic Carbon (POC) dataset contains POC concentrations gridded on both sinusoidal (SIN) and geographic (GEO) grid projections at 4 km spatial resolution for 1997-2020. The POC dataset has been produced using the Ocean Colour Climate Change Initiative Remote Sensing Reflectance (Rrs) products, Version 4.2. The dataset includes the Rrs at 443 nm and 555 nm with pixel-by-pixel uncertainty estimates for each wavelength. For more details on the algorithm and its validation, please see papers by Stramski et al. (2008) and Evers-King et al. (2017). Please note that the validation of the POC algorithm is a continuing process. To increase the accuracy of POC algorithms, further in situ POC data need to be collected with high spatial and temporal resolution.

The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v5 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). The POC datasets have been produced by using a modified empirical band ratio algorithm by Stramski et al. (2008): 292*Rrs(490)/Rrs(560)^-1.49. Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Remote Sensing Reflectance (Rrs) at 490 nm and 560 nm obtained from the ESA Ocean Colour Climate Change Initiative version 5 dataset (OC-CCI v5). For more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home). A related dataset based on the ESA Ocean Colour Climate Change Initiative v4.2 data is also available (see link in the related records section).

The BICEP/NCEO: Monthly global Particulate Organic Carbon (POC) v4.2 datasets contain POC concentrations (mg m^-3) with per pixel uncertainties estimates gridded on both geographic and sinusoidal projections at 4 km spatial resolution for the period of 1997 to 2020. The POC products were generated as part of the European Space Agency (ESA) Biological Pump and Carbon Exchange Processes (BICEP) project with support from the National Centre of Earth Observation (NCEO). The POC concentrations were estimated using an empirical Remote Sensing Reflectance (Rrs) band ratio algorithm by Stramski et al. (2008): 203.2*Rrs(443)/Rrs(555)^-1.034. This algorithm has shown a relatively good performance in the recent global inter-comparison study conducted by Evers-King et al. (2017). Additional variables that were used for the calculation of the POC products are also provided in the datasets, including the Rrs at 443 nm and 555 nm obtained from the ESA Ocean Colour Climate Change Initiative version 4.2 dataset (OC-CCI v4.2)(Sathyendranath et al., 2020). In addition to the papers by Stramski et al. (2008) and Evers-king et al. (2017), for more details on the algorithm and its validation, please see the BICEP Algorithm Theoretical Basis Document (ATBD) and validation report (https://bicep-project.org/Home). This version of the dataset is an updated version of the previous 'NCEO: Monthly global Particulate Organic Carbon (POC) (produced from the Ocean Colour Climate Change Initiative, Version 4.2 dataset)'. A related product based on the Ocean Colour Climate Change Initiative v5.0 data is also available (see the link in the related records section).

This dataset collection contains global spatially predicted sea-surface iodide concentrations at a monthly resolution for the year 1970. It was developed as part of the NERC project Iodide in the ocean:distribution and impact on iodine flux and ozone loss (NE/N009983/1), which aimed to quantify the dominant controls on the sea surface iodide distribution and improve parameterisation of the sea-to-air iodine flux and of ozone deposition. As new observations are made, this global data product will be continually added to and updated through a "living data" model. The datasets follows semantic versioning (https://semver.org/) and holds different versions of this. Please refer to the paper referenced for the current version number and information on this (see related documentation).

This dataset contains global spatially predicted sea-surface iodide concentrations at a monthly resolution for the year 1970. It was developed as part of the NERC project Iodide in the ocean:distribution and impact on iodine flux and ozone loss (NE/N009983/1), which aimed to quantify the dominant controls on the sea surface iodide distribution and improve parameterisation of the sea-to-air iodine flux and of ozone deposition. This dataset is the output used in the published paper 'A machine learning based global sea-surface iodide distribution' ( https://doi.org/10.5194/essd-2019-40). The main ensemble prediction ("Ensemble_Monthly_mean ") is provided in a NetCDF file as a single variable (1). A second file (2) is provided which includes all of the predictions and the standard deviation on the prediction. (1) predicted_iodide_0.125x0.125_Ns_Just_Ensemble.nc (2) predicted_iodide_0.125x0.125_Ns_All_Ensemble_members.nc For ease of use, this output has been re-gridded to various commonly used atmosphere and ocean model resolutions (see table SI table A5 in paper). These re-gridded files are included in the folder titled "regridded_data". Additionally, a further file (3) is provided including the prediction made included data from the Skagerak dataset. As stated in the paper referenced above, it is recommended to use the use the core files (1,2) or their re-gridded equivalents. (3) predicted_iodide_0.125x0.125_All_Ensemble_members.nc As new observations are made, this global data product will be updated through a "living data" model. The dataset versions follow semantic versioning (https://semver.org/). This dataset contains the first publicly released version v0.0.1 and supersedes the pre-review dataset named v0.0.0, Please refer to the paper referenced above for the current version number and information on this. Updates for v0.0.1 vs. v0.0.0 - Additional files included of the core data re-gridded for 0.5x0.5 degree and 0.25x0.25 degree horizontal resolution. - Minor updates were applied to all metadata in NetCDF files. - Updates were made to coordinate grids used for regriding files from 1x1 degree to 4x5 degree.

This dataset contains global spatially predicted sea-surface iodide concentrations at a monthly resolution. This dataset was developed as part of the NERC project Iodide in the ocean:distribution and impact on iodine flux and ozone loss (NE/N009983/1), which aimed to quantify the dominant controls on the sea surface iodide distribution and improve parameterisation of the sea-to-air iodine flux and of ozone deposition. The main ensemble prediction ("Ensemble Monthly mean ") is provided in a NetCDF (1) file as a single variable. A second file (2) is provided which includes all of the predictions and the standard deviation on the prediction. (1) predicted_iodide_0.125x0.125_Ns_Just_Ensemble.nc (2) predicted_iodide_0.125x0.125_Ns_All_Ensemble_members.nc This is the output of the paper 'A machine learning based global sea-surface iodide distribution' (see related documentation). For ease of use, this output has been re-gridded to various commonly used atmosphere and ocean model resolutions (see table SI table A5 in paper). These re-gridded files are included in the folder titled "regridded_data". Additionally, a file (3) is provided including the prediction made included data from the Skagerak dataset. As stated in the paper referenced above, it is recommended to use the use the core files (1,2) or their re-gridded equivalents. (3) predicted_iodide_0.125x0.125_All_Ensemble_members.nc As new observations are made, we will update the global dataset through a "living data" model. The dataset versions archived here follow semantic versioning (https://semver.org/). The pre-review dataset is achieved in the folder named v0.0.0, with the with publically released versions numbered starting from v1.0.0. Please refer to the referenced paper (see related documentation) for the current version number and information on this.

Input Data for Figure 3.28 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.28 shows long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. --------------------------------------------------- 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: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. 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. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The data is used in left upper and left lower panels (scatter panels), as well as right upper panels (D&W, EN4, Ishii)  --------------------------------------------------- List of data provided --------------------------------------------------- 210127_DurackandWijffels_V1.0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc is input data for D&W. The variables steric_height_halo_anom_depthInterp and steric_height_thermo_anom_depthInterp are used. 210201_EN4.2.1.g10_annual_steric_1950-2019_5-5350m.nc  is input data for EN4 210201_Ishii17_v7.3_annual_steric_1955-2019_0-3000m.nc is input data for Ishii --------------------------------------------------- Notes on reproducing the figure from the provided data. --------------------------------------------------- This data is an input observational data for the Figure 3.28. It is used for scatter plots and contour maps. In addition, shapefiles are required to calculate the regional boundaries: Pacific.shp, Atlantic.shp. These regions should be standarised throught AR6. The following changes to filenames were made to archive the data (due to filenaming restrictions). To use the data with any associated figure code, the filenames should be reverted. 210127_DurackandWijffels_V1_0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc -> 210127_DurackandWijffels_V1.0_70yr_steric_1950-2019_0-2000db_210122-205355_beta.nc 210201_EN4_2_1_g10_annual_steric_1950-2019_5-5350m.nc -> 210201_EN4.2.1.g10_annual_steric_1950-2019_5-5350m.nc 210201_Ishii17_v7_3_annual_steric_1955-2019_0-3000m.nc -> 210201_Ishii17_v7.3_annual_steric_1955-2019_0-3000m.nc --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3 - Link to the code for the figure, archived on Zenodo - Link to the figure on the IPCC AR6 website