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opencc-zeroMay 2021View details →

Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates

This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021). In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force. This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice with waves component is 564 GB. See below for further details. See the related works section for the corresponding datasets.

opencc-zeroMay 2021View details →

Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates

This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021). In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force. This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice without waves component is 484 GB. See below for further details. See the related works section for the corresponding datasets.

zenodo40/100

Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios

Antarctic Ice Sheet simulations driven by CMIP6 climate models under historical and SSP5-8.5 scenarios This dataset contains output ice sheet model runs forced by climate boundary conditions provided by CMIP6 climate model output. Each experiment set is archived in separate compressed tar.gz files.  Description of the experiment sets, including the model setup, key parameters, climate forcings, and their main objectives are documented in Table 1 of Li, DeConto, Pollard (2023) Climate model differences contribute deep uncertainty in future Antarctic ice loss, Science Advances. Two kinds of output are included in each ice sheet run: fort.22 files contain time series of several key variables for the Antarctic Ice Sheet (area, volume, sea-level equivalent, etc.); fort.92.nc files contain 2D and 3D fields such as ice thickness and velocity at specific time slices.

opencc-by-4.0Dec 2022View details →

zenodo40/100

CESM2 simulation output used in the study "On the links between ice nucleation, cloud phase, and climate sensitivity in CESM2"

Provided is all CESM2 model output used to generate figures in the study, for which a preprint is at 'https://doi.org/10.22541/essoar.167214452.25853014/v1'. File names indicate the experiment names used in the study. For each model experiment, there is one file containing variables in a present-day (PD) simulation, plus a second file containing cloud feedbacks calculated by the Zelinka et al 2012 kernel method (comparing PD to PD with 4K warming uniformly added to sea surface temperatures).

opencc-by-4.0Dec 2022View details →

publicMay 2021View details →

Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates

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publicMay 2021View details →

Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates

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publicMay 2021View details →

Ocean and ice spin-up data for role of surface gravity waves in aquaplanet ocean climates

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opencc-by-4.0Dec 2020View details →

Data Release for Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model

CESM2 and CISM2 data files for figures in "Retreat and Regrowth of the Greenland Ice Sheet During the Last Interglacial as Simulated by the CESM2-CISM2 Coupled Climate–Ice Sheet Model" (Sommers et al., 2021, Paleoceanography and Paleoclimatology)

opencc-by-4.0Nov 2023View details →

Main output data used in "Coupling the regional climate MAR model with the ice sheet model PISM mitigates the melt-elevation positive feedback" (Delhasse et al., 2024)

Outputs used in: Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024. MAR-PISM coupling experiments outputs over 1991-2200. The main experiments are: <ul> <li>MAPI-2w: 2-way coupling, consideration online of the melt-elevation feedback (evolving topography in MAR).</li> <li>MAPI-1w: 1-way coupling, consideration of the melt-elevation feedback only with the offline correction (Franco et al., 2012) of the MAR outputs (fixed topography in MAR).</li> <li>MAPI-0w:  0-way coupling, no consideration of the melt-elevation feedback (fixed topography in MAR and no correction during interpolation).</li> </ul> MAR files contain yearly SMB (surface mass balance) and ST (surface temperature) interpolated (with correction) on the PISM-4.5km grid. Gradients used for the correction of the melt-elevation feedback are also given for both variables. SMB and ST are the two required MAR fields to couple MAR with PISM.  PISM files contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM for each of the three experiments.  The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3# (last access: 23 January 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2" target="_blank" rel="noopener noreferrer">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 23 January 2024). Other coupling scripts are also available upon request by email (<a href="mailto:alison.delhasse@uliege.be" target="_blank" rel="noopener noreferrer">alison.delhasse@uliege.be</a>). If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be, johanna.beckmann@monash.edu) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs. Data usage notice: If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications.  "We thank A. Delhasse and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. " You should also refer to and cite the following paper in its latest version: Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024. Reference Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695–711, https://doi.org/10.5194/tc-6-695-2012, 2012. MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3" target="_blank" rel="noopener">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 28 May 2022), 2021.

Dataset for "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", Part II

This is Part II of the output dataset from coupled ice sheet-climate model simulations that investigate the interactions between ice sheet freshwater flux and the warming climate. Description of models, coupling scheme, and design of these simulations is provided in a paper titled "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", which is currently under peer review. More information will be updated when available.

Dataset for "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", Part I

This is Part I of the output dataset from coupled ice sheet-climate model simulations that investigate the interactions between ice sheet freshwater flux and the warming climate. Description of models, coupling scheme, and design of these simulations is provided in a paper titled "Competing climate feedbacks of ice sheet freshwater discharge in a warming world", which is currently under peer review. More information will be updated when available.

opencc-by-4.0Jan 2022View details →

Code for APJAS publication - Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate

In this repository, we include the source codes for WRF microphysics parameterization used in the APJAS publication "Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate" There are three WDM6 codes for simulations. The original WDM6 code (ORG) using parameter defined by Hong et al (2004), the revised WDM6 code (NEW) those revised by removing the numerical errors, and the additional WDM6 code (SEN) for sensitivity experiment adopting the column-shaped parameters. In supplement, several cloud-ice characteristics presented in the paper were induced in detail and compared with Hong et al (2004) and this study.

opencc-by-4.0Jun 2022View details →

Relative importance of meridional and zonal sea surface temperature gradients for the onset of the ice ages and Pliocene-Pleistocene climate evolution

Climatologies from the 3 different model simulations performed for the paper published in Paleoceanography (2010, v25, issue 2, <a href="https://doi.org/10.1029/2009PA001809">https://doi.org/10.1029/2009PA001809</a>). This table shows how the names of the simulations provided here relate to the names in the paper: <table align="center"> <caption>Simulation names for cross-referencing</caption> <thead> <tr> <th scope="col">Name of Files</th> <th scope="col">Name in Article</th> </tr> </thead> <tbody> <tr> <td>EPSST_T_85.*.nc</td> <td>Early Pliocene Simulation</td> </tr> <tr> <td>New_MZSST_T_85.*.nc</td> <td>Modern Zonal Simulation</td> </tr> <tr> <td>ctl_85_Kerry.*.nc</td> <td>Modern Control Simulation</td> </tr> </tbody> </table> Additionally the NCL script originally used to create all the figures is included. It is called paleoc_onsetNHG_rev.ncl. The abstract of the paper is below: "During the early Pliocene (roughly 4 Myr ago), the ocean warm water pool extended over most of the tropics. Subsequently, the warm pool gradually contracted toward the equator, while midlatitudes and subpolar regions cooled, establishing a meridional sea surface temperature (SST) gradient comparable to the modern about 2 Myr ago (as estimated on the eastern side of the Pacific). The zonal SST gradient along the equator, virtually nonexistent in the early Pliocene, reached modern values between 1 and 2 Myr ago. Here, we use an atmospheric general circulation model to investigate the relative roles of the changes in the meridional and zonal temperature gradients for the onset of glacial cycles and for Pliocene-Pleistocene climate evolution in general. We show that the increase in the meridional SST gradient reduces air temperature and increases snowfall over most of North America, both factors favorable to ice sheet inception. The impacts of changes in the zonal gradient, while also important over North America, are somewhat weaker than those caused by meridional temperature variations. The establishment of the modern meridional and zonal SST distributions leads to roughly 3.2°C and 0.6°C decreases in global mean temperature, respectively. Changes in the two gradients also have large regional consequences, including aridification of Africa (both gradients) and strengthening of the Indian monsoon (zonal gradient). Ultimately, this study suggests that the growth of Northern Hemisphere ice sheets is a result of the global cooling of Earth's climate since 4 Myr rather than its initial cause. Thus, reproducing the correct changes in the SST distribution is critical for a model to simulate the transition from the warm early Pliocene to a colder Pleistocene climate."

opencc-by-nc-4.0Sep 2022View details →

Geodetic mass balance of Mýrdalsjökull ice cap, 1999−2021: DEM processing and climate analysis

This repository gathers the data I used and produced during my master thesis at the University of Iceland from April to September 2022 with the financial support of the Landsvirkjun. The geodetic mass of Mýrdalsjökull, the fourth largest Icelandic ice cap, was investigated over the period 1999-2021. The untapped SPOT5 archive (2002−2015), the lidar data, the Pléiades imagery (2011−present), aerial photographs from 1999 [1] and the ArcticDEM dataset (2010−2019) [2] were used to create Digital Elevation Models (DEMs) of the ice cap. A pre-processing of the DEMs was first performed: co-registration, filtering and interpolation. Then, applying a Gaussian Process regression (GP) [3], a state-of-the-art method in DEM processing, a spatially and temporally continuous DEM dataset was created, in 15 x 15 m resolution and 1-month interval from 1999 to 2021. Volume and mass changes based on the synthetic GP-generated DEMs were computed and analyzed in 5-year and annual intervals between 1999 and 2019. A local analysis of three glacierized catchments of Mýrdalsjökull (southern catchment, northern catchment and Kötlujökull outlet) was also performed. Errors were estimated using the method from [4]. Tools from the following repositories were used: <ul> <li> demcoreg (<a href="https://doi.org/10.5281/zenodo.5733347">https://doi.org/10.5281/zenodo.5733347</a>): DEM co-registration </li> <li> xdem (<a href="https://doi.org/10.5281/zenodo.4809698">https://doi.org/10.5281/zenodo.4809698</a>): uncertainties computation and DEM manipulation  </li> <li> pyddem (<a href="https://pypi.org/project/pyddem/">https://pypi.org/project/pyddem/</a>): Gaussian Process regression </li> </ul> The complete master thesis can be accessed at :   The repository contains the following data: 1 – DEM_coregistered All DEMs have been coregistered considering the Islandsdem v1.0 as a reference (atlas.lmi.is/dem) The DEM naming works as follow:  Glaciername_DEM_date_sensor_resolution_zmae_projection_otherinformation.tif The files ending with *_filtered.tif (SPOT5, AerialPhotographs) have been filtered using the filtering combination described in 3.1.3. The files ending with *_mosaic.tif (SPOT5, Pléiades) are the result of the mosaicking of several DEMs.   2 – Shapefiles Outlines of Mýrdalsjökull in 1999 [1], 2003, 2010 and 2019 [6] Outlines of the three catchments (South, North and Kötlujökull) in 1999. Reference buffer around Mýrdalsjökull used to crop all DEMs to the same extent.  Equilibrium Line Altitude (ELA) from 2004-10-05.   3 – Gaussian_Process_regression 2 netcdf files: the stack of DEMs (*_DEMstack_*) and the result of the Gaussian Process regression (*_GPregression_*). Both files were obtained thanks to pyddem tools. The Gaussian Process regression was run at a spatial resolution of 15 x 15m and a temporal resolution of 1 month, starting in January 1999 and ending in December 2022.   4 – Mass_balance_results 1 csv file containing mass balance results: <ul> <li> Annual mass balance for the ice cap & the 3 catchments </li> <li> 4-year mass balance for the ice cap & the 3 catchments </li> <li> Results from the comparison with survey dates mass balance (Fig 11(a)) </li> <li> Results from the comparison with [3] (Fig 11(b)) </li> <li> Mass balance overview (Fig 11(c)) </li> </ul>   References [1] Belart, J., Magnússon, E., Berthier, E., Gunnlaugsson, Á. Þ., Pálsson, F., Aðalgeirsdóttir, G., Jóhannesson, T., Thorsteinsson, T., and Björnsson, H. (2020). Mass balance of 14 Icelandic glaciers, 19452017: spatial variations and links with climate. Frontiers in Earth Science, page 163. [2] Porter, C., Morin, P., Howat, I., Noh, M., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., et al. (2018). ArcticDEM. Harvard Dataverse, 1. [3] Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., et al. (2021). Accelerated global glacier mass loss in the early twenty-first century. Nature, 592(7856):726731. [4] Hugonnet, R., Brun, F., Berthier, E., Dehecq, A., Mannerfelt, E. S., Eckert, N., and Farinotti, D. (2022). Uncertainty analysis of digital elevation models by spatial inference from stable terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. [5] Hannesdóttir, H., Sigurðsson, O., Þrastarson, R. H., Guðmundsson, S., Belart, J. M., Pálsson, F., Magnússon, E., Víkingsson, S., Kaldal, I., and Jóhannesson, T. (2020). A national glacier inventory and variations in glacier extent in Iceland from the Little Ice Age maximum to 2019. Jökull 2020: 1, 34.   Acknowledgements Pléiades images were acquired at research price thanks to the CNES ISIS programme (http://www.isiscnes.fr). This study uses the lidar mapping of the glaciers in Iceland, funded by the Icelandic Research Fund, the Landsvirkjun research fund, the Icelandic Road Administration, the Reykjavík Energy Environmental and Energy Research Fund, the Klima- og Luftgruppen research fund of the Nordic Council of Ministers, the Vatnajökull National Park, the organization Friends of Vatnajökull, LMÍ, IMO, and the UI research fund.   Dataset Attribution  This dataset is licensed under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0 International License</a> (Attribution-NonCommercial).

DEEPICE Stories Videos series - Insight into Ice & Climate - Videos with Italian subtitles

DEEPICE Stories – Insights into Ice & Climate is a series of 15 videos created in collaboration with the 15 PhD students of the European research project <a href="https://deepice.cnrs.fr/">DEEPICE</a>. These 3-minute educational video clips give an overview of scientific research on ice cores. <a href="https://deepice.cnrs.fr">https://deepice.cnrs.fr</a> Credits Writing & presentation: Geunwoo Lee, Hanne Notø, Eirini Malegiannaki, Piers Larkman, Miguel Angel Sanchez Moreno, Lison Soussaintjean, Florian Painer, Niklas Kappelt, Lisa Ardoin, Inès Ollivier, Romilly Harris Stuart, Fyntan Shaw, Qinggang Gao, Ailsa Chung, Daniel Gunning Coordination : Marie Kazeroni (LSCE-CNRS) Direction: Dorothée Adam-Mazard (Inuaprod) & Marie Kazeroni (LSCE-CNRS) Editing: Thomas d’Aram Motion Design: Pauline Fuchs Co-production: DEEPICE & Inuaprod _____________________________________________ DEEPICE Stories - Insights into Ice & Climate è una serie di 15 video creati in collaborazione con i 15 dottorandi del progetto di ricerca europeo <a href="https://deepice.cnrs.fr/">DEEPICE</a>. Queste video didattici di 3 minuti, offrono una sintesi della ricerca scientifica sulle carote di ghiaccio. <a href="https://deepice.cnrs.fr">https://deepice.cnrs.fr</a> Scrittura e presentazione : Geunwoo Lee, Hanne Notø, Eirini Malegiannaki, Piers Larkman, Miguel Angel Sanchez Moreno, Lison Soussaintjean, Florian Painer, Niklas Kappelt, Lisa Ardoin, Inès Ollivier, Romilly Harris Stuart, Fyntan Shaw, Qinggang Gao, Ailsa Chung, Daniel Gunning Coordinamento : Marie Kazeroni (LSCE-CNRS) Direzione : Dorothée Adam-Mazard (Inuaprod) & Marie Kazeroni (LSCE-CNRS) Montaggio : Thomas d’Aram Motion Design : Pauline Fuchs Coproduzione : DEEPICE & Inuaprod

DEEPICE Stories Videos series - Insight into Ice & Climate - Videos with Spanish subtitles

DEEPICE Stories - Insights into Ice & Climate es una serie de 15 vídeos creados en colaboración con los 15 estudiantes de doctorado del proyecto de ciencia europeo <a href="https://deepice.cnrs.fr/">DEEPICE</a>. Estos videoclips educativos de 3 minutos ofrecen una visión general de la ciencia sobre los testigos de hielo. <a href="https://deepice.cnrs.fr">https://deepice.cnrs.fr</a> Presentación: Geunwoo Lee, Hanne Notø, Eirini Malegiannaki, Piers Larkman, Miguel Angel Sanchez Moreno, Lison Soussaintjean, Florian Painer, Niklas Kappelt, Lisa Ardoin, Inès Ollivier, Romilly Harris Stuart, Fyntan Shaw, Qinggang Gao, Ailsa Chung, Daniel Gunning Coordinación: Marie Kazeroni (LSCE-CNRS) Dirección: Dorothée Adam-Mazard (Inuaprod) & Marie Kazeroni (LSCE-CNRS) Montaje y edición : Thomas d’Aram Motion Design: Pauline Fuchs Coproducción: DEEPICE & Inuaprod

opencc-by-4.0May 2024View details →

Climate and ice sheet dynamics in Patagonia throughout Marine Isotope Stages 3 and 2

This dataset contains the modelled ice thickness over Patagonia during the global Last Glacial Maximum at 4 km resolution by using SICOPOLIS forced by the results from PMIP models. It also contains the modelled output of the transient simulations performed at 8 km resolution forced by MPI-ESM1-2-LR and different cores.

opencc-by-4.0Jun 2024View details →

Investigating similarities and differences of the penultimate and last glacial terminations with a coupled ice sheet - climate model

This archive provides the iLOVECLIM-GRISLI outputs as part of the manuscript "Investigating similarities and differences of the penultimate and last glacial terminations with a coupled ice sheet - climate model". Contact: aurelien.quiquet@lsce.ipsl.fr