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In total, there are 100 unique ensemble members across four large ensembles (CanESM2,\u00a0CESM-LENS,\u00a0GFDL,\u00a0MPI-GE).\u00a0 \u00a0Testbed data\u00a0can be downloaded from\u00a0\u200bhttps:\/\/figshare.com\/collections\/Large_ensemble_pCO2_testbed\/4568555.\u00a0<\/a> The large ensemble testbed data are a collection of randomly selected ensemble members from 4 the following large ensemble projects:<\/p>\n - CanESM2 (http:\/\/data.ec.gc.ca\/data\/climate\/scientificknowledge\/the-eccc-climate-model-datasets-for-climate-science-and-impacts-research\/the-canadian-earth-system-model-large-ensembles\/<\/a>)<\/p>\n - CESM-LENS (http:\/\/www.cesm.ucar.edu\/projects\/community-projects\/LENS\/<\/a>)<\/p>\n - GFDL ( http:\/\/poseidon.princeton.edu<\/a>)<\/p>\n - MPI-GE (https:\/\/mpimet.mpg.de\/en\/grand-ensemble\/<\/a>)<\/p>\n \u00a0Analysis scripts in python are provided to produce from these model output the figures of Gloege et al. (2021).\u00a0 See \"Data Files\" for access to the analysis scripts. The \u201cNotebooks\u201d directory includes notebooks to create each figure, and are labeled by figure. The \u201cScripts\u201d and \u201cProcessing_scripts\u201d directories include auxiliary scripts.<\/p>\n Each ensemble member was interpolated from its native grid to a 1x1 degree lat\/lon grid. The variables are monthly over the 1982-2017 time frame and sampled as the SOCATv5 data product. Historical atmospheric CO2 is used up to 2005 with RCP8.5 after 2005.\u00a0<\/p>\n Gap-filling techniques can be evaluated across 100 unique climate states.<\/p>\n The methodology is an earth system model (ESM) large ensemble. The ESM is initialized with small perturbations such that the climate state evolves along a different phase of internal variability in each ensemble member. The forcing scenario is historical through 2005, then RCP8.5 after.<\/p>\n For more methodology details refer to the following publications.<\/p>\n MPI<\/strong>\u00a0ocean pCO2 testbed:<\/strong> GFDL ocean pCO2 testbed:<\/strong> CESM ocean pCO2 testbed:\u00a0<\/strong> CanESM2 ocean pCO2 testbed:\u00a0<\/strong> BCO-DMO Data Manager processing notes:
\n\u00a0<\/p>\n
\nN. Maher,\u00a0et al.<\/em>, Th e Max Planck Institute Grand Ensemble-Enabling the Exploration of Climate System Variability.\u00a0Journal of Advances in Modeling Earth Systems\u00a0<\/em>11<\/strong>, 2050\u20132069 (2019).<\/p>\n
\nK. B. Rodgers, J. Lin, T. L. Fr\u00f6licher, Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model.\u00a0Biogeosciences\u00a0<\/em>12<\/strong>, 3301\u20133320 (2015).<\/p>\n
\nJ. E. Kay,\u00a0et al.<\/em>, The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability.\u00a0Bulletin of the American Meteorological Society\u00a0<\/em>96<\/strong>, 1333\u20131349 (2015).<\/p>\n
\nJ. C. Fyfe, et al., Large near-term projected snowpack loss over the western United States. Nature communications 8, 14996 (2017).\u00a0<\/p><\/div>","@type":"rdf:HTML"}],"http:\/\/www.w3.org\/2000\/01\/rdf-schema#label":[{"@value":"Large Ensemble pCO2 Testbed","@type":"xsd:string"}],"http:\/\/ocean-data.org\/schema\/hasProcessingDescription":[{"@value":"
\n*\u00a0GitHub repository https:\/\/github.com\/lgloege\/large_ensemble_testbed<\/a> forked was forked to BCODMO organization https:\/\/github.com\/BCODMO\/large_ensemble_testbed.\u00a0BCO-DMO<\/a> forks github repositories submitted to us for curatorial purposes.\u00a0The original github repository may continue to be updated, or may be taken down\u00a0the future.
\n* We made a\u00a0release of the repository that corresponds with this version of the dataset https:\/\/github.com\/BCODMO\/large_ensemble_testbed\/releases<\/a>.
\n* A\u00a0copy of the release zip file was attached to this dataset and the zip file containing the release is archived and DOI'ed along with this dataset.<\/p><\/div>","@type":"rdf:HTML"}],"http:\/\/purl.org\/dc\/terms\/identifier":[{"@value":"840334","@type":"xsd:int"}],"http:\/\/purl.org\/dc\/terms\/title":[{"@value":"Large Ensemble pCO2 Testbed"}],"http:\/\/purl.org\/dc\/terms\/date":[{"@value":"2021-02-09T16:00:15-05:00","@type":"xsd:dateTime"}],"http:\/\/purl.org\/dc\/terms\/created":[{"@value":"2021-02-09T16:00:15-05:00","@type":"xsd:dateTime"}],"http:\/\/purl.org\/dc\/terms\/modified":[{"@value":"2021-05-12T15:49:13-04:00","@type":"xsd:dateTime"}],"http:\/\/rdfs.org\/ns\/void#inDataset":[{"@id":"http:\/\/www.bco-dmo.org\/"}],"http:\/\/ocean-data.org\/schema\/namedGraph":[{"@value":"urn:bcodmo:dataset:840334","@type":"xsd:token"}],"http:\/\/ocean-data.org\/schema\/osprey_page":[{"@id":"https:\/\/www.bco-dmo.org\/dataset\/840334"}],"http:\/\/ocean-data.org\/schema\/identifier":[{"@value":"_:Identifier840334"}],"http:\/\/ocean-data.org\/schema\/datasetTitle":[{"@value":"Large Ensemble pCO2 Testbed from 3D climate models interpolated to 1x1 spatial grid over time period 1982-2017","@language":"en-US"}],"http:\/\/ocean-data.org\/schema\/abstract":[{"@value":"These Large Ensemble Testbed (LET) data come from 3D climate models. The intention of this dataset was to evaluate ocean pCO2 gap-filling techniques. See Gloege et al. (2021) for further details about the Earth System Models run for the Large Ensembles. For more information about the large ensembles see https:\/\/www.cesm.ucar.edu\/projects\/community-projects\/MMLEA.\r\n\r\nThe methodology was an earth system model (ESM) large ensemble initialized with small perturbations such that the climate state evolved along a different phase of internal variability in each ensemble member. 25 monthly averaged large ensemble members were interpolated to 1x1 spatial grid over time period 1982-2017. The intention of this dataset was to evaluate ocean pCO2 gap-filling techniques. \r\n\r\nReducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from sparse pCO2 observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions\u2019 ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO2 fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real- world observations. The power of a testbed is that the perfect reconstruction is known for each of the 100 original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a commonly used neural-network approach can skillfully reconstruct air-sea CO2 fluxes when and where it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 39% [15%:58%, interquartile range] overestimation of amplitude, and phasing is only moderately correlated with known truth (r=0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% [3%:34%]. Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink.","@language":"en-US"}],"http:\/\/purl.org\/dc\/terms\/rights":[{"@id":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"http:\/\/ocean-data.org\/schema\/deprecated":[{"@value":"false","@type":"xsd:boolean"}],"http:\/\/ocean-data.org\/schema\/temporalExtent":[{"@value":"_:temporalExtent840334"}],"http:\/\/ocean-data.org\/schema\/spatialCoverage":[{"@value":"_:spatialCoverage840334"}],"http:\/\/purl.org\/dc\/terms\/bibliographicCitation":[{"@value":"McKinley, G. A. (2021) Large Ensemble pCO2 Testbed from 3D climate models interpolated to 1x1 spatial grid over time period 1982-2017. Biological and Chemical Oceanography Data Management Office (BCO-DMO). 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