http://lod.bco-dmo.org/id/dataset/840334
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2021-02-09
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Large Ensemble pCO2 Testbed from 3D climate models interpolated to 1x1 spatial grid over time period 1982-2017
2021-02-09
publication
2021-02-09
revision
BCO-DMO Linked Data URI
2021-02-09
creation
http://lod.bco-dmo.org/id/dataset/840334
Galen A. McKinley
Lamont-Doherty Earth Observatory
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: 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). (Version 1) Version Date 2021-02-09 [if applicable, indicate subset used]. http://lod.bco-dmo.org/id/dataset/840334 [access date]
Methods and Sampling: <p>In total, there are 100 unique ensemble members across four large ensembles (CanESM2,&nbsp;CESM-LENS,&nbsp;GFDL,&nbsp;MPI-GE).&nbsp; &nbsp;Testbed data&nbsp;can be downloaded from&nbsp;https://figshare.com/collections/Large_ensemble_pCO2_testbed/4568555.&nbsp;<br />
&nbsp;</p>
<div>The large ensemble testbed data are a collection of randomly selected ensemble members from 4 the following large ensemble projects:</div>
<div>- 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/)</div>
<div>- CESM-LENS (http://www.cesm.ucar.edu/projects/community-projects/LENS/)</div>
<div>- GFDL ( http://poseidon.princeton.edu)</div>
<div>- MPI-GE (https://mpimet.mpg.de/en/grand-ensemble/)</div>
<p>&nbsp;Analysis scripts in python are provided to produce from these model output the figures of Gloege et al. (2021).&nbsp; See "Data Files" for access to the analysis scripts. The “Notebooks” directory includes notebooks to create each figure, and are labeled by figure. The “Scripts” and “Processing_scripts” directories include auxiliary scripts.</p>
<p>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.&nbsp;<br />
<br />
Gap-filling techniques can be evaluated across 100 unique climate states.<br />
<br />
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.<br />
<br />
For more methodology details refer to the following publications.</p>
<p><strong>MPI</strong><strong>&nbsp;ocean pCO2 testbed:</strong><br />
N. Maher,&nbsp;<em>et al.</em>, Th e Max Planck Institute Grand Ensemble-Enabling the Exploration of Climate System Variability.&nbsp;<em>Journal of Advances in Modeling Earth Systems&nbsp;</em><strong>11</strong>, 2050–2069 (2019).<br />
<br />
<strong>GFDL ocean pCO2 testbed:</strong><br />
K. B. Rodgers, J. Lin, T. L. Frölicher, Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model.&nbsp;<em>Biogeosciences&nbsp;</em><strong>12</strong>, 3301–3320 (2015).</p>
<p><strong>CESM ocean pCO2 testbed:&nbsp;</strong><br />
J. E. Kay,&nbsp;<em>et 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.&nbsp;<em>Bulletin of the American Meteorological Society&nbsp;</em><strong>96</strong>, 1333–1349 (2015).<br />
<br />
<strong>CanESM2 ocean pCO2 testbed:&nbsp;</strong><br />
J. C. Fyfe, et al., Large near-term projected snowpack loss over the western United States. Nature communications 8, 14996 (2017).&nbsp;</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1558225 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1558225
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1558258 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1558258
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1818501 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1818501
completed
Galen A. McKinley
Lamont-Doherty Earth Observatory
845-365-8585
61 Route 9W / P.O. Box 1000 Comer 429, Mailbox 47
Palisades
NY
10964-1707
USA
mckinley@ldeo.columbia.edu
pointOfContact
asNeeded
Dataset Version: 1
Unknown
otherRestrictions
otherRestrictions
Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Ocean Carbon and Biogeochemistry
http://us-ocb.org/
Ocean Carbon and Biogeochemistry
The Ocean Carbon and Biogeochemistry (OCB) program focuses on the ocean's role as a component of the global Earth system, bringing together research in geochemistry, ocean physics, and ecology that inform on and advance our understanding of ocean biogeochemistry. The overall program goals are to promote, plan, and coordinate collaborative, multidisciplinary research opportunities within the U.S. research community and with international partners. Important OCB-related activities currently include: the Ocean Carbon and Climate Change (OCCC) and the North American Carbon Program (NACP); U.S. contributions to IMBER, SOLAS, CARBOOCEAN; and numerous U.S. single-investigator and medium-size research projects funded by U.S. federal agencies including NASA, NOAA, and NSF.
The scientific mission of OCB is to study the evolving role of the ocean in the global carbon cycle, in the face of environmental variability and change through studies of marine biogeochemical cycles and associated ecosystems.
The overarching OCB science themes include improved understanding and prediction of: 1) oceanic uptake and release of atmospheric CO2 and other greenhouse gases and 2) environmental sensitivities of biogeochemical cycles, marine ecosystems, and interactions between the two.
The OCB Research Priorities (updated January 2012) include: ocean acidification; terrestrial/coastal carbon fluxes and exchanges; climate sensitivities of and change in ecosystem structure and associated impacts on biogeochemical cycles; mesopelagic ecological and biogeochemical interactions; benthic-pelagic feedbacks on biogeochemical cycles; ocean carbon uptake and storage; and expanding low-oxygen conditions in the coastal and open oceans.
OCB
largerWorkCitation
program
Collaborative Research: Uncertainty in Predictions of 21st Century Ocean Biogeochemical Change
https://osprey.bco-dmo.org/project/822990
Collaborative Research: Uncertainty in Predictions of 21st Century Ocean Biogeochemical Change
<p><em>NSF Award Abstract:</em><br />
The biogeochemistry of the oceans is undergoing large-scale changes due to anthropogenic climate change. Recent research suggests these changes are occurring significantly on regional scales, but due to model uncertainties, it is difficult to constrain the difference between anthropogenic and natural influences. In studying climate change and its effect on ocean biogeochemistry in the future, it is crucial to be able to distinguish between these influences; therefore, it is critical to identify and quantify the uncertainty in Earth System Models (ESMs). The researchers will use output from Community Earth System Model (CESM) and models participating in the Fifth Coupled Model Intercomparison Project (CMIP5) to isolate prediction uncertainty due to 1) internal variability, 2) model structure, and 3) emission scenario. This research will bridge an existing gap between Earth System Models and observational studies to assess how climate change will influence ocean biogeochemistry. Additionally, this project will support an early-career scientist and a graduate student, and the researchers are dedicated to mentoring undergraduate students through various programs at Colorado University - Boulder, National Center for Atmospheric Research, and the University of Wisconsin.</p>
<p>Earth System Model (ESM) simulations used to predict future changes in ocean biogeochemistry attributed to either natural or anthropogenic influences suffer from uncertainties, particularly on regional scales. This is problematic because, as the ocean continues to undergo large-scale change under the current climate, it is crucial to have an accurate predictor of the future and to be able to delineate between natural and anthropogenic forcing. This research aims to quantify the uncertainty on three levels: uncertainty due to internal variability, model structure, and emission scenario. Using output from the Community Earth System Model (CESM) and models in the Fifth Coupled Model Intercomparison Project (CMIP5), this study will evaluate the degree to which uncertainty has changed with newer models. Additionally, observations from global databased, satellites, and time-series sites will be used to compare models and assess the varying levels of skill in predicting the biogeochemistry of a region. The researchers also plan to break down the various components of the driving mechanisms behind prediction uncertainty, so that future models can begin to take these factors into account.</p>
ESM Uncertainty
largerWorkCitation
project
eng; USA
oceans
-180
180
-90
90
1982-01-01
2017-01-01
0
BCO-DMO catalogue of parameters from 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)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
13078183
https://datadocs.bco-dmo.org/docs/305/ESM_Uncertainty/840334/1/data_docs/large_ensemble_testbed-code_840334_v1.zip
Large Ensemble Testbed Analysis Scripts
Analysis scripts in python to produce from Large Ensemble Testbed model output the figures in :
Gloege, L., G.A. McKinley, P. Landschützer, A. Fay, T. Frölicher, J.C. Fyfe, T. Illyina, S.D. Jones, N.S. Lovenduski, C. Rödenbeck, K.B. Rodgers, S. Schlunegger and Y. Takano: Quantifying errors in observationally-based estimates of ocean carbon sink variability, in review 2020. https://doi.org/10.1002/essoar.10502036.2
The “Notebooks” directory includes notebooks to create each figure, and are labeled by figure. The “Scripts” and “Processing_scripts” directories include auxiliary scripts.
The files in this .zip package were forked from original github repository https://github.com/lgloege/large_ensemble_testbed to BCO-DMO for archival purposes.
Commit forked from https://github.com/lgloege/large_ensemble_testbed was d177ae3 by
Lucas Gloege committed on Sep 2, 2020.
download
https://osprey.bco-dmo.org/dataset/840334/data/download
download
onLine
dataset
<p>In total, there are 100 unique ensemble members across four large ensembles (CanESM2,&nbsp;CESM-LENS,&nbsp;GFDL,&nbsp;MPI-GE).&nbsp; &nbsp;Testbed data&nbsp;can be downloaded from&nbsp;https://figshare.com/collections/Large_ensemble_pCO2_testbed/4568555.&nbsp;<br />
&nbsp;</p>
<div>The large ensemble testbed data are a collection of randomly selected ensemble members from 4 the following large ensemble projects:</div>
<div>- 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/)</div>
<div>- CESM-LENS (http://www.cesm.ucar.edu/projects/community-projects/LENS/)</div>
<div>- GFDL ( http://poseidon.princeton.edu)</div>
<div>- MPI-GE (https://mpimet.mpg.de/en/grand-ensemble/)</div>
<p>&nbsp;Analysis scripts in python are provided to produce from these model output the figures of Gloege et al. (2021).&nbsp; See "Data Files" for access to the analysis scripts. The “Notebooks” directory includes notebooks to create each figure, and are labeled by figure. The “Scripts” and “Processing_scripts” directories include auxiliary scripts.</p>
<p>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.&nbsp;<br />
<br />
Gap-filling techniques can be evaluated across 100 unique climate states.<br />
<br />
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.<br />
<br />
For more methodology details refer to the following publications.</p>
<p><strong>MPI</strong><strong>&nbsp;ocean pCO2 testbed:</strong><br />
N. Maher,&nbsp;<em>et al.</em>, Th e Max Planck Institute Grand Ensemble-Enabling the Exploration of Climate System Variability.&nbsp;<em>Journal of Advances in Modeling Earth Systems&nbsp;</em><strong>11</strong>, 2050–2069 (2019).<br />
<br />
<strong>GFDL ocean pCO2 testbed:</strong><br />
K. B. Rodgers, J. Lin, T. L. Frölicher, Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model.&nbsp;<em>Biogeosciences&nbsp;</em><strong>12</strong>, 3301–3320 (2015).</p>
<p><strong>CESM ocean pCO2 testbed:&nbsp;</strong><br />
J. E. Kay,&nbsp;<em>et 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.&nbsp;<em>Bulletin of the American Meteorological Society&nbsp;</em><strong>96</strong>, 1333–1349 (2015).<br />
<br />
<strong>CanESM2 ocean pCO2 testbed:&nbsp;</strong><br />
J. C. Fyfe, et al., Large near-term projected snowpack loss over the western United States. Nature communications 8, 14996 (2017).&nbsp;</p>
Specified by the Principal Investigator(s)
<p>BCO-DMO Data Manager processing notes:<br />
*&nbsp;GitHub repository https://github.com/lgloege/large_ensemble_testbed forked was forked to BCODMO organization https://github.com/BCODMO/large_ensemble_testbed.&nbsp;BCO-DMO forks github repositories submitted to us for curatorial purposes.&nbsp;The original github repository may continue to be updated, or may be taken down&nbsp;the future.<br />
* We made a&nbsp;release of the repository that corresponds with this version of the dataset https://github.com/BCODMO/large_ensemble_testbed/releases.<br />
* A&nbsp;copy 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>
Specified by the Principal Investigator(s)
asNeeded
7.x-1.1
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact