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17 results for “Multi-model ensemble”
Local explanation SHAP approach applied to MIROC5,RCP8.5-forced multi-model ensemble study of GrIS future sea-level contributions
<p>The repository contains materials for analysing the results of the Local explanation named SHAP-CTREE (Redelmeier et al., 2020) approach applied to the MIROC5,RCP8.5-forced multi-model ensemble study of GrIS future sea-level contributions from Goelzer et al. (2020).</p> <p>The available files are:<br> - run_SupplMat.R: the main R script to perform the diagnostics and the different analyses (levels 1 - 3)<br> - utilsPLOT.R: functions for plotting<br> - Diagnostics.zip: the zip file with the png figures, named 'GrIS_CaseXXX_yYYY.png', that depict the diagnostic for case XXX for prediction time YYY<br> - SupplementaryMaterials.zip<br> - RData files for each prediction time YYY "Shapley_yYYY" with:<br> S: matrix N=55 cases x d+1: SHAP values for the d inputs (+ average sea level value at time YYY)<br> YHAT: ML-based predictions of the sea level for the 55 cases<br> YTRUE: true values for the 55 cases<br> mae: mean absolute error<br> - RData file containing the design of experiments "DOE_GrIS_MIROC5-RCP85.RData"<br> doe: matrix with values of the d=9 inputs</p> <p>These constitute the supplementary materials of Rohmer et al. (2022, The Cryosphere). All technical details are provided in this reference.</p>
AgMIP-Wheat multi-model ensemble simulations on climate change impact and adaptation for 60 representative global locations
<p>This is model output from the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat) dataset of multi-model ensemble simulations for 60 representative global locations under different climate scenarios.</p> <p>The data have been generated following the modeling protocol of Asseng et al. (2019) and Liu et al. (2019).</p> <p>References</p> <p>Asseng, S. et al. (2019). Climate change impact and adaptation for wheat protein. Glob Chang Biol 25, 155-173, doi:10.1111/gcb.14481</p> <p>Liu, B. et al. (2019). Global wheat production with 1.5 and 2.0°C above pre-industrial warming. Global Change Biol 25, 1428-1444, doi:10.1111/gcb.14542</p> <p> </p>
Multi-model Ensemble for Robust Verification of hydrological modeling in Japan (MERV-Jp)
<p>MERV-Jp is the dataset of meteorological forcing and multi-model runoff simulation in 135 (ver1.1) / 87 (ver2.0) Japanese basins, and contributes to carrying out a large sample rainfall-runoff simulation in Japan. In addition, MERV-Jp can be used as a benchmark to evaluate user's hydrological modeling. <br> The detailed description of MERV-Jp can be found at "Y. Sawada, S. Okugawa and T. Kimizuka (2022): Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins, Hydrological Research Letters, 16, 73-79" (https://doi.org/10.3178/hrl.16.73).</p>
Dataset for "The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6"
<p>This data set provides processed model output of ISMIP6 Greenland projections as documented and analysed in the following publication:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>About the data:<br> - The results are based on model output regridded conservatively to a 5x5 km regular ISMIP6 grid unless this is already the native grid. <br> - The results are calculated over the ice-covered area of Greenland, map projection error corrected, ice sheet model specific densities taken into account.<br> - The contribution of peripheral glaciers and ice caps has been removed, by considering their area-coverage in each grid cell.<br> - The results for the projections 'exp*' are all calculated as differences to the control experiment ctrl_proj (suffix cr in filename for control removed).<br> - Results for ctrl_proj and historical are un-corrected (no suffix cr in filename).</p> <p><br> Directory structure:<br> versionid<br> groupname1<br> modelname1<br> expid<br> scalars_mm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_rm_cr_GIS_groupname1_modelname1_expid.nc<br> scalars_zm_cr_GIS_groupname1_modelname1_expid.nc<br> ...</p> <p>Variables per file:</p> <p>scalars_mm_cr_GIS ----------------- Greenland wide numbers </p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> iarea - Fraction of grid cell covered by land ice [1]<br> iareagr - Fraction of grid cell covered by grounded ice sheet<br> iareafl - Fraction of grid cell covered by ice sheet flowing over seawater</p> <p>ivol - ice volume [m3]<br> ivolgr - grounded ice volume [m3]<br> ivolfl - floating ice volume [m3]<br> ivaf - ice volume above flotation [m3]</p> <p>lim - ice mass [kg]<br> limgr - grounded ice mass [kg]<br> limfl - floating ice mass [kg]<br> limaf - ice mass above flotation [kg]</p> <p>sle - sea-level equivalent mass [m] !! decreases with mass loss !! <br> smb - spatially integrated surface mass balance anomaly [kg s-1]</p> <p><br> scalars_rm_cr_GIS ----------------- IMBIE2-Rignot basins xx=[no,ne,se,sw,cw,nw]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p><br> scalars_zm_cr_GIS ----------------- IMBIE2-Zwally basins xx=[z11,z12,z13,z14,z21,z22,z31,z32,z33,z41,z42,z43,z50,z61,z62,z71,z72,z81,z82]</p> <p>oarea - assumed ocean area [m2]<br> rhof - model specific freshwater density [kg m-3]<br> rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, typically in days since X<br> ivaf_xx - ice volume above flotation [m3]<br> smb_xx - spatially integrated surface mass balance anomaly [kg s-1]<br> limaf_xx - ice mass above flotation [kg]<br> sle_xx - sea-level equivalent mass [m] !! decreases with mass loss !! </p> <p> </p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p>
North Atlantic average sea-surface temperature in a CMIP6 multi-model ensemble of historical and ssp585/ssp245 simulations
<p>This dataset contains North Atlantic average sea-surface temperatures and derived indices calculated for a multi-model ensemble of historical and future scenario (ssp585, ssp245) simulations contributing to the Coupled Model Intercomparison Project phase 6. A detailed description of the dataset is provided by Zanchettin, D., and Rubino, A., Accelerated North Atlantic surface warming reshapes the Atlantic Multidecadal Variability, Communications Earth & Environment, 2024, doi:10.1038/s43247-024-01804-x.</p> <p><br>The data are provided as netcdf files.</p> <p>The name of each file is structured as {model}_r{realization}_historical_{scenario}.nc where {model} is the model name, {realization} is a number corresponding to the historical realization, and {scenario} is either of the two scenarios considered (ssp585 and ssp245).</p> <p>Each file contains data for the following one dimensional variables:</p> <ul> <li>year: the sequence of years for which the data are provided</li> <li>NASST: annual-average spatially averaged North Atlantic sea-surface temperature</li> <li>state: slowly variable component of NASST obtained from a dlm decomposition of NASST</li> <li>strend: stochastic trend of NASST obtained from a dlm decomposition of NASST</li> <li>AMV: Atlantic Multidecadal Varibility index obtained as difference between NASST and state</li> </ul> <p> </p>
Results of ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century", published in The Cryosphere, <a href="https://tc.copernicus.org/articles/14/3033/2020/">https://tc.copernicus.org/articles/14/3033/2020/</a></p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Contact: Helene Seroussi, Helene.seroussi@jpl.nasa.gov</p> <p>Further information on ISMIP6 and ISMIP6 Antarctica Projections can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica</p> <p>Users should cite the original publication when using all or part of the data. <br> In order to document CMIP6’s scientific impact and enable ongoing support of CMIP, users are also obligated to acknowledge CMIP6, ISMIP6 and the participating modeling groups.</p> <p>About the dataset:</p> <p>- The results are based on model output computed from the ISMIP6 native grids that vary between models. <br> - The results are calculated over the ice-covered area of Antarctica, corrected for map projection errors, ice sheet model specific densities taken into account.<br> - Results for the experiments 'exp*' are provided both as raw results and calculated as differences to the control experiment (ctrl_proj_open or ctrl_proj_std depending on the experiment). The later files are named with "minus_ctrl_proj" to indicate that the control run is substracted.<br> - Results for ctrl_proj_open, ctrl_proj_std, hist_open and hist_std are not corrected to remove the control run.</p> <p><br> ------------------------------------------------</p> <p>Directory structure:</p> <p>groupname1<br> modelname1<br> expid<br> computed_iareafl_AIS_groupname1_modelname1_expid.nc<br> computed_iareafl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_AIS_groupname1_modelname1_expid.nc<br> computed_iareagr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_AIS_groupname1_modelname1_expid.nc<br> computed_icearea_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_AIS_groupname1_modelname1_expid.nc<br> computed_ivol_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_AIS_groupname1_modelname1_expid.nc<br> computed_ivaf_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smb_AIS_groupname1_modelname1_expid.nc<br> computed_smb_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_AIS_groupname1_modelname1_expid.nc<br> computed_smbgr_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_AIS_groupname1_modelname1_expid.nc<br> computed_bmbfl_minus_ctrl_proj_AIS_groupname1_modelname1_expid.nc<br> ...</p> <p>-------------------------------------------------</p> <p><br> Description of variables:</p> <p>icearea - ice area [m^2]<br> iareafl - floating ice area [m^2]<br> iareagr - grounded ice area [m^2]<br> ivol - ice volume [m^3]<br> ivaf - ice volume above floatation [m^3]<br> smb - spatially integrated surface mass balance [kg/s]<br> smbgr - spatially integrated surface mass balance over grounded ice [kg/s]<br> bmbfl - spatially integrated basal melt rate under floating ice (negative for melting ice) [kg/s]</p> <p>Variables per file:</p> <p>rhoi - model specific ice density [kg m-3]<br> rhow - model specific ocean water density [kg m-3]</p> <p>time - time, in years</p> <p>[variable] - global variable integrated over the Antarctica ice sheet<br> [variable]_region_1 - variable integrated over West Antarctica<br> [variable]_region_2 - variable integrated over East Antarctica<br> [variable]_region_3 - variable integrated over the Antarctic Peninsula<br> [variable]_sector_X - variable integrated over the X sector of the Antarctic ice sheet (18 sectors, from 1 to 18)</p> <p>--------------------------------------------------</p> <p><br> Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below.</p> <p>"We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>Seroussi, H., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: ISMIP6 Antarctica: a multi-model ensemble of the Antarctic ice sheet evolution over the 21st century, The Cryosphere, 14, 3033–3070, https://doi.org/10.5194/tc-14-3033-2020, 2020.</p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Alexander, P., Asay-Davis, X. S., Barthel, A., Bracegirdle, T. J., Cullather, R., Felikson, D., Fettweis, X., Gregory, J. M., Hattermann, T., Jourdain, N. C., Kuipers Munneke, P., Larour, E., Little, C. M., Morlighem, M., Nias, I., Shepherd, A., Simon, E., Slater, D., Smith, R. S., Straneo, F., Trusel, L. D., van den Broeke, M. R., and van de Wal, R.: Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models, The Cryosphere, 14, 2331–2368, https://doi.org/10.5194/tc-14-2331-2020, 2020.</p>
Results of ISMIP6 CMIP6 forced simulations: a multi-model ensemble of the Greenland and Antarctic ice sheet evolution over the 21st century
<p>This archive provides the ice sheet model outputs produced as part of the publication "Payne et al. 2021 Future sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets", published in GRL</p> <p>Contact: Tony Payne a.j.payne@bristol.ac.uk, Sophie Nowicki sophien@buffalo.edu, ismip6@gmail.com </p> <p><br> Further information on ISMIP6 can be found here:<br> http://www.climate-cryosphere.org/activities/targeted/ismip6<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Antarctica<br> http://www.climate-cryosphere.org/wiki/index.php?title=ISMIP6-Projections-Greenland</p> <p>Data usage notice:<br> If you use any of these results, please acknowledge the work of the people involved in the process producing this data set. Acknowledgements should have language similar to the below (if you only use CMIP5 forcing, remove CMIP6 and vice versa).</p> <p>“We thank the Climate and Cryosphere (CliC) effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoted ISMIP6. We acknowledge the World Climate Research Programme, which, through it's Working Group on Coupled Modelling, coordinated and promoted CMIP5 and CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the CMIP data and providing access, the University at Buffalo for ISMIP6 data distribution and upload, and the multiple funding agencies who support CMIP5 and CMIP6 and ESGF. We thank the ISMIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6."</p> <p>You should also refer to and cite the following papers:</p> <p>For Greenland datasets </p> <p>Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andy Shepherd, Erika Simon, Cecile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald Slater, Robin Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke: The future sea-level contribution of the Greenland ice sheet: a multi-model ensemble study of ISMIP6 , The Cryosphere, 2020. doi:10.5194/tc-2019-319</p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., Fettweis, X., and Nowicki, S.: Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution , The Cryosphere, 14, 985–1008, https://doi.org/10.5194/tc-14-985-2020, 2020.</p> <p>Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: <br> Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p>For Antarctica datasets</p> <p>Seroussi, H., Nowicki, S., Simon, E., Abe-Ouchi, A., Albrecht, T., Brondex, J., Cornford, S., Dumas, C., Gillet-Chaulet, F., Goelzer, H., Golledge, N. R., Gregory, J. M., Greve, R., Hoffman, M. J., Humbert, A., Huybrechts, P., Kleiner, T., Larour, E., Leguy, G., Lipscomb, W. H., Lowry, D., Mengel, M., Morlighem, M., Pattyn, F., Payne, A. J., Pollard, D., Price, S. F., Quiquet, A., Reerink, T. J., Reese, R., Rodehacke, C. B., Schlegel, N.-J., Shepherd, A., Sun, S., Sutter, J., Van Breedam, J., van de Wal, R. S. W., Winkelmann, R., and Zhang, T.: initMIP-Antarctica: an ice sheet model initialization experiment of ISMIP6, The Cryosphere, 13, 1441–1471, https://doi.org/10.5194/tc-13-1441-2019, 2019.</p> <p>Jourdain, N. C., Asay-Davis, X., Hattermann, T., Straneo, F., Seroussi, H., Little, C. M., and Nowicki, S.: A protocol for calculating basal melt rates in the ISMIP6 Antarctic ice sheet projections, The Cryosphere, 14, 3111–3134, https://doi.org/10.5194/tc-14-3111-2020, 2020.</p> <p><br> Sophie Nowicki, Antony Payne, Heiko Goelzer, Helene Seroussi, William Lipscomb, Ayako Abe-Ouchi, Cecile Agosta, Patrick Alexander, Xylar Asay-Davis, Alice Barthel, Thomas Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan Gregory, Tore Hatterman, Nicolas Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher Little, Mathieu Morlinghem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin Smith, Fiammetta Straneo, Luke Trusel, Michiel van den Broeke, and Roderik van de Wal: Experimental protocol for sea level projections from ISMIP6 standalone ice sheet models, The Cryosphere, doi:10.5194/tc-2019-322, 2020.</p> <p> </p>
Downscaled North American Multi-Model Ensemble Forecast for the Pacific Northwest USA
<h1>Downscaled North American Multi-Model Ensemble Forecast of Meteorological Variables for the Pacific Northwest</h1> <p>Monthly retrospective hindcasts (1982-2010) and forecasts (2011-2020) of temperature and precipitation are acquired for the Pacific Northwest region of the United States from five models (CFSv2, NASA GEOS5v2, CanCM4i, GEM-NEMO, and NCAR-CCSM) participating in the North American Multi-Model Ensemble project <a href="https://www.zotero.org/google-docs/?WlFE7n">(Kirtman et al., 2014)</a>. These models, detailed in Table 1 with more recent information available in <a href="https://www.zotero.org/google-docs/?PjZZHw">(Becker et al., 2022)</a>, are initialized monthly to provide a forecast of 0-9 months at a 1.0̊ × 1.0̊ spatial resolution. The multi-model ensemble mean (ENSMEAN) is then generated for each initialization by simply averaging all considered models and their ensemble members. Monthly ENSMEAN forecast is bias-corrected and spatially downscaled to 1/24th degree using the methodology described in <a href="https://www.zotero.org/google-docs/?jwPLzP">Wood et al. (2002)</a> and <a href="https://www.zotero.org/google-docs/?2b3lkj">Barbero et al. (2017)</a> using historical meteorological data <a href="https://www.zotero.org/google-docs/?zN8gKh">(gridMET; Abatzoglou, 2013)</a> as the baseline. Then, the downscaled ENSMEAN data are temporally disaggregated to daily timescales using an analog approach. The closest analog month for the ENSMEAN forecast is found from the gridMET dataset by minimizing the root mean square error (RMSE) of monthly gridMET and forecast precipitation (excluding gridMET data for the target month). Other daily meteorological variables (such as maximum and minimum temperature, maximum and minimum relative humidity, wind speed, and specific humidity) are extracted from the same analog month to use as input for the coupled crop-hydrology model. As a last step to the analog approach, the process corrects the bias between the forecast and analog month to ensure that monthly mean temperature and accumulated precipitation match those of the original forecast. </p> <p>Table 1. List of NMME models used to create Ensemble Mean.</p> <div> <table> <tbody> <tr> <td>Model </td> <td>Model Expansion </td> <td>Ensemble Size </td> <td>References</td> </tr> <tr> <td>NCEP- CFSv2 </td> <td>Climate Forecast System, version 2 </td> <td>24 </td> <td><a href="https://www.zotero.org/google-docs/?XTr15U">(Saha et al., 2014)</a></td> </tr> <tr> <td>NASA GEOS5v2</td> <td>Goddard Earth Observing System, version 5 </td> <td>4</td> <td><a href="https://www.zotero.org/google-docs/?fQzQZL">(Molod et al., 2020)</a></td> </tr> <tr> <td>CanCM4i </td> <td>Fourth Generation Canadian Coupled Global Climate Model </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?AoXCO8">(Merryfield et al., 2013)</a></td> </tr> <tr> <td>GEM - NEMO </td> <td>Global Environmental Multiscale Model – Nucleus for European Modelling of the Ocean </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?N4JwM9">(Lin et al., 2020)</a></td> </tr> <tr> <td>NCAR - CCSM </td> <td>Community Climate System Model </td> <td>10 </td> <td><a href="https://www.zotero.org/google-docs/?NMkImR">(Kirtman & Min, 2009)</a></td> </tr> </tbody> </table> </div> <p>The dataset has *.mat files which are MATLAB data files. </p> <h3>References</h3> <ol> <li> <p>Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. <a href="https://doi.org/10.1002/joc.3413">https://doi.org/10.1002/joc.3413</a></p> </li> <li> <p>Barbero, R., Abatzoglou, J. T., & Hegewisch, K. C. (2017). Evaluation of Statistical Downscaling of North American Multimodel Ensemble Forecasts over the Western United States. Weather and Forecasting, 32(1), 327–341. https://doi.org/10.1175/WAF-D-16-0117.1</p> </li> <li> <p>Becker, E. J., Kirtman, B. P., L’Heureux, M., Muñoz, Á. G., & Pegion, K. (2022). A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions. Bulletin of the American Meteorological Society, 103(3), E973–E995. <a href="https://doi.org/10.1175/BAMS-D-20-0327.1">https://doi.org/10.1175/BAMS-D-20-0327.1</a></p> </li> <li> <p>Kirtman, B. P., & Min, D. (2009). Multimodel Ensemble ENSO Prediction with CCSM and CFS. Monthly Weather Review, 137(9), 2908–2930. https://doi.org/10.1175/2009MWR2672.1</p> </li> <li> <p>Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., Dool, H. van den, Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D. G., Tippett, M. K., Barnston, A. G., Li, S., Rosati, A., Schubert, S. D., … Wood, E. F. (2014). The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction. Bulletin of the American Meteorological Society, 95(4), 585–601. <a href="https://doi.org/10.1175/BAMS-D-12-00050.1">https://doi.org/10.1175/BAMS-D-12-00050.1</a></p> </li> <li> <p>Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., & Erfani, A. (2020). The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2). Weather and Forecasting, 35(4), 1317–1343. <a href="https://doi.org/10.1175/WAF-D-19-0259.1">https://doi.org/10.1175/WAF-D-19-0259.1</a></p> </li> <li> <p>Merryfield, W. J., Lee, W.-S., Boer, G. J., Kharin, V. V., Scinocca, J. F., Flato, G. M., Ajayamohan, R. S., Fyfe, J. C., Tang, Y., & Polavarapu, S. (2013). The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization. Monthly Weather Review, 141(8), 2910–2945. <a href="https://doi.org/10.1175/MWR-D-12-00216.1">https://doi.org/10.1175/MWR-D-12-00216.1</a></p> </li> <li> <p>Molod, A., Hackert, E., Vikhliaev, Y., Zhao, B., Barahona, D., Vernieres, G., Borovikov, A., Kovach, R. M., Marshak, J., Schubert, S., Li, Z., Lim, Y.-K., Andrews, L. C., Cullather, R., Koster, R., Achuthavarier, D., Carton, J., Coy, L., Friere, J. L. M., … Pawson, S. (2020). GEOS-S2S Version 2: The GMAO High-Resolution Coupled Model and Assimilation System for Seasonal Prediction. Journal of Geophysical Research: Atmospheres, 125(5), e2019JD031767. https://doi.org/10.1029/2019JD031767</p> </li> <li> <p>Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., Dool, H. van den, Zhang, Q., Wang, W., Chen, M., & Becker, E. (2014). The NCEP Climate Forecast System Version 2. Journal of Climate, 27(6), 2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1</p> </li> <li> <p>Wood, A. W., Maurer, E. P., Kumar, A., & Lettenmaier, D. P. (2002). Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research: Atmospheres, 107(D20), ACL 6-1-ACL 6-15. https://doi.org/10.1029/2001JD000659</p> </li> </ol>
A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Falling Creek Reservoir multi-model ensemble (MME) forecasting work using the FLARE (Forecasting Lake And Reservoir Ecosystems) system and baseline models: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations, forecasts.zip contains forecast parquet files generated from the MME workflow (FLARE & baseline models), and scores.zip contains forecast skill metrics required for analysis.</p>
Accelerating future mass loss of Svalbard glaciers from a multi-model ensemble
<p>This dataset contains the data behind the figures 2-13 and A1-A4 in the article "Accelerating future mass loss of Svalbard glaciers from a multi-model ensemble", which will be published in Journal of Glaciology.</p>
FY-4A/AGRI Infrared Brightness Temperature Estimation of Precipitation based on Multi-model Ensemble Learning
Open the record for dataset details and reuse information.
Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>The tar file contains two directories: data and models. Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data). The data in these directories are unnormalized. The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file. These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively. To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths. The other arguments should be as follows:</p><p>--uniformize=1</p><p>--predictor_norm_type_string="z_score"</p><p>--vector_target_norm_type_string=""</p><p>--scalar_target_norm_type_string=""</p><p> </p><p>Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data. To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
A multi-model ensemble for the Lake Victoria Basin (East-Africa)
<p>Data produced by all CORDEX FPS - <strong>ELVIC</strong> models (COSMO-CLM, HCLIM38, WRF, RegCM, UKMO), covering the larger region surrounding Lake Victoria - East-Africa. This data accompanies ELVIC's evaluation paper (van Lipzig et al., 2022, submitted). More information about ELVIC ("climate Extremes in the Lake Victoria basin") is available via <a href="https://ees.kuleuven.be/en/elvic/">https://ees.kuleuven.be/en/elvic/</a>.</p> <p>In this dataset, zipped files are available per variable (precipitation, top-of-atmosphere short- and longwave radiation and lake surface temperature) for both convection-parametrized (10 km-scale resolution, "PAR") and convection-permitting (km-scale resolution, "CP") simulations. Simulation 10y-period covers the years 2006-2015. Each netcdf file contains 120 monthly diurnal cycle data at 1h or 3h temporal resolution. The full dataset is available by contacting the first author.</p>
Multi-model ensemble bias-corrected precipitation dataset for historical and future climate (1961–2099) in China
<p>本文基于耦合模式比较项目第六阶段(CMIP6)的27个全球气候模式(GCM),采用随机森林(RF)模型和EQM方法整合27个大气监测模型的降水模拟数据,进一步修正中国综合月降水数据。修正后的降水资料在月降水量和极端降水量方面均明显优于原GCM降水资料。数据以 GeoTIFF 格式,其中嵌入了具有 1° 空间分辨率的地理配准信息。LST在GeoTIFF中的单位是mm。压缩文件被命名为历史文件.zip、SSP126.zip、SSP245.zip 和 SSP585.zip。压缩文件中的每个文件都命名为“yyyymm.tif”,其中“yyyy”和“mm”分别表示年份和月份。例如,文件“196101.tif”存储了 1961 年 1 月中国每月降水量。</p>
Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years
<p>1 Introduction</p> <p>The data archive provides the reconstructed dataset capturing the annual runoff across Europe, partitioned into a grid format and preserved in NetCDFv4 (.nc) format for enhanced geospatial information.</p> <p>1.1 Coordinate system and spatial resolution</p> <p>Each grid cell in the dataset corresponds to a 0.5-degree spatial resolution, using the World Geodetic System 1984 (WGS84) as the standard coordinate frame.</p> <p>1.2 Temporal resolution</p> <p>The data encapsulates a yearly temporal resolution, offering a comprehensive outlook from 1500 to 1999. For example data for 1500 are represented by the layer 01/01/1500.</p> <p>1.3 Units</p> <p>Runoff measurements are quantified in millimeters per year (mm/year), providing hydrological data throughout the noted time frame.</p> <p>1.4 Example</p> <p>library(terra)<br> library(raster)</p> <p>> dt_cc<-rast("HEMMF_ERUN_1500_1999.nc")<br> > dt_cc<br> class : SpatRaster <br> dimensions : 70, 104, 500 (nrow, ncol, nlyr)<br> resolution : 0.5, 0.5 (x, y)<br> extent : -12, 40, 35, 70 (xmin, xmax, ymin, ymax)<br> coord. ref. : lon/lat WGS 84 (EPSG:4326) <br> source : HEMMF_ERUN_1500_1999.nc <br> varname : runoff <br> names : runoff_1, runoff_2, runoff_3, runoff_4, runoff_5, runoff_6, ... <br> unit : mm/year, mm/year, mm/year, mm/year, mm/year, mm/year, ... <br> time (days) : 1500-01-01 to 1999-01-01 </p> <p> </p> <p>1.5 Citation</p> <p>The specific data file, named ’HEMMF ERUN 1500 1998.nc,’ is conveniently structured to facilitate easy handling and interpretation of the information. Please ensure to attribute the correct citation when utilizing this dataset, adhering to the subsequent reference: [Singh et al., 2023] References Ujjwal Singh, Petr Maca, Martin Hanel, Yannis Markonis, Rama Rao Nidamanuri, Sadaf Nasreen, Johanna Ruth Bl¨ocher, Filip Strnad, Jiri Vorel, Lubomir Riha, and Akhilesh Singh Raghubanshi. Hybrid multi-model ensemble learning for reconstructing gridded runoff of europe for 500 years. Information Fusion, 97:101807, 2023. ISSN 1566-2535. doi: https://doi.org/10.1016/j.inffus. 2023.101807. URL https://www.sciencedirect.com/science/article/pii/S1566253523001161#d1e5346.</p>
A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble
<p>Supporting information for manuscript titled "A model-data comparison of the hydrological response to Miocene warmth: leveraging the MioMIP1 opportunistic multi-model ensemble"</p><p>Datasets S1. Early to Middle Miocene NetCDF files: E2MMIO280.nc, E2MMIO400.nc, E2MMIO560.nc, E2MMIO850.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S2 Middle to Late Miocene NetCDF files: M2LMIO280.nc, M2LMIO400.nc, M2LMIO560.nc contains MioMIP1 climate variables used to make manuscript figures. </p><p>Datasets S3 Preindustrial NetCDF files: PI contains MioMIP1 climate variables used to make manuscript figures.</p><p>Dataset S4 CSV file MioMIP_MAP_compilation contains newly revised miocene reconstructed mean annual precipitation from proxies. </p>
Enhancing Streamflow Prediction through Multi-model Ensemble Framework and Machine Learning Techniques
<p>This file contains python code used in this study and data used to plot figures. </p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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OpenNeuro
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