Find research datasets worth reusing

opencc-by-4.0May 2020View details →

Supplement to the article "Predicting the morphology of ice particles in deep convection using the super-droplet method" (Shima et al., 2020, GMD)

This is a supplement to the article "Predicting the morphology of ice particles in deep convection using the super-droplet method" authored by Shin-ichiro Shima, Yousuke Sato, Akihiro Hashimoto, and Ryohei Misumi, published in Geosci. Model Dev., 2020. Typical realization of CTRL, simulated by SCALE-SDM 0.2.5-2.2.0 <ul> <li>Movie01.QHYD_TYP-CTRL_2.2.0.gif: Spatial structure of the cumulonimbus</li> <li>Movie02.M-D_TYP-CTRL_2.2.0.gif: Mass-dimension relationship of the ice particles</li> <li>Movie03.phi-D_TYP-CTRL_2.2.0.gif : Aspect ratio–dimension relationship of the ice particles</li> <li>Movie04.rho-D_TYP-CTRL_2.2.0.gif : Apparent density–dimension relationship of the ice particles</li> <li>Movie05.V-D_TYP-CTRL_2.2.0.gif : Velocity-dimension relationship of the ice particles</li> </ul> One realization of DX/2, simulated by SCALE-SDM 0.2.5-2.2.0 <ul> <li>Movie06.QHYD_DXx0.5_2.2.0.gif: Spatial structure of the cumulonimbus</li> </ul> The same setup as Moves 1-5 (typical realization of CTRL) is used, but simulated by SCALE-SDM 0.2.5-2.2.1 <ul> <li>Movie07.QHYD_TYP-CTRL.2.2.1.gif: Spatial structure of the cumulonimbus</li> <li>Movie08.M-D_TYP-CTRL_2.2.1.gif: Mass-dimension relationship of the ice particles</li> <li>Movie09.phi-D_TYP-CTRL_2.2.1.gif: Aspect ratio–dimension relationship of the ice particles</li> <li>Movie10.rho-D_TYP-CTRL_2.2.1.gif: Apparent density–dimension relationship of the ice particles</li> <li>Movie11.V-D_TYP-CTRL_2.2.1.gif: Velocity-dimension relationship of the ice particles</li> </ul> The same setup as Moves 1-5 (typical realization of CTRL) is used, but simulated by SCALE-SDM 0.2.5-2.2.2 <ul> <li>Movie12.QHYD_TYP-CTRL.2.2.2.gif: Spatial structure of the cumulonimbus</li> <li>Movie13.M-D_TYP-CTRL_2.2.2.gif: Mass-dimension relationship of the ice particles</li> <li>Movie14.phi-D_TYP-CTRL_2.2.2.gif: Aspect ratio–dimension relationship of the ice particles</li> <li>Movie15.rho-D_TYP-CTRL_2.2.2.gif: Apparent density–dimension relationship of the ice particles</li> <li>Movie16.V-D_TYP-CTRL_2.2.2.gif: Velocity-dimension relationship of the ice particles</li> </ul>

opencc-zeroApr 2024View details →

Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert

Deserts play an important role in the climate system, which is closely associated with the emission and transport of dust aerosols. Based on the intensive observation experiment in the Taklimakan Desert, the potential physical processes between the deep convective boundary layer (CBL) and dust emission are revealed in this study. Deep CBL enables the formation of clouds in the late afternoon, leading to significant cooling of surface. Large-scale buoyant coherent structures thereby transform into the mechanical coherent structures confined near the surface. The responses promote the earlier occurrence of low-level jet (LLJ) than in cloudless conditions, which allows the downward transport of LLJ momentum and substantially increases surface wind. Therefore, dust emission is initiated by strong wind at dusk and lasts for several hours. The results are useful to predict dust emissions and improve our understanding of distinctive boundary-layer processes in desert regions.

opencc-zeroJul 2024View details →

Data from: Moist heatwaves intensified by entrainment of dry air that limits deep convection

Moist heatwaves in the tropics and subtropics pose substantial risks to society, yet the dynamics governing their intensity are not fully understood. The onset of deep convection arising from hot, moist near-surface air has been thought to limit the magnitude of moist heatwaves. Here, we use reanalysis data, and output from the Coupled Model Intercomparison Project Phase 6 and model entrainment perturbation experiments, to show that entrainment of unsaturated air in the lower-free troposphere (roughly 1--3 km above the surface) limits deep convection, thereby allowing much higher near-surface moist heat. Regions with large-scale subsidence and a dry lower-free troposphere, such as coastal areas adjacent to hot and arid land, are thus particularly susceptible to moist heat waves. Even in convective regions such as the northern Indian Plain, southeast Asia, and interior South America, the lower-free tropospheric dryness strongly affects the maximum surface wet-bulb temperature. As the climate warms, the dryness (relative to saturation) of the lower-free tropospheric air increases; this allows for a larger increase of extreme moist heat, further elevating the likelihood of moist heatwaves.

opencc-by-4.0Jan 2023View details →

Dataset of "Divergent convective outflow in ICON deep convection permitting and parameterised deep convection simulations": sample simulations

Please find the full README file for the code and ICON-PER ellipse datasets by extracting  the archive " PER_archive_data.tar.gz ". The other two archives contain example simulation output for two simulations - one with parameterised convection and one with convection-permitting set-up in ICON. The download_all.sh bash script allows one to select the download of the full set of simulations in this dataset. They can also be selected individually, by altering the sequence of ensemble members over which the download-loop ("wget") runs (PER-simulations!), or by removing lines from the download_all.sh-script (for removing simulations from "PAR"-dataset).

opencc-by-4.0Mar 2023View details →

Wind gust during tropical cyclone Ida, with and without deep convection parametrization from 1.4km global simulation with ECMWF IFS

Animations of wind gust during tropical cyclone Ida, from global TCo7999L137 (1.4km horizontal grid-spacing) simulation with hydrostatic IFS from INCITE2022 project.  One animation shows simulation with deep convection parametrization on and the other with off.

opencc-by-4.0Aug 2023View details →

LES data presented in "The role of passive cloud volumes in the transition from shallow to deep atmospheric convection"

This repository contains the LES data presented in the manuscript entitled "The role of passive cloud volumes in the transition from shallow to deep atmospheric convection" by C.V. Vraciu, I.L. Kruse, J.O. Haerter, submitted to the Geophysical Research Letters.

publicJul 2024View details →

Data from: Moist heatwaves intensified by entrainment of dry air that limits deep convection

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publicApr 2024View details →

Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert

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publicMar 2025View details →

Data from: Turbulence characteristics of ice-free radiatively driven convection in a deep, unstratified lake

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opencc-by-4.0Dec 2020View details →

Aquaplanet simulations using CAM5.4-MPAS4 with two different deep convection schemes

This dataset includes model output from aquaplanet simulations using the Communitiy Atmosphere Model (CAM) version 5.4 with the nonhydrostatic Model for Prediction Across Scales (MPAS) version 4 dynamical core. The aquaplanet simulations has been performed with a global quasi-uniform resolution mesh with ~120 km grid spacing and a variable resolution mesh employing a circular refined region with ~30 km grid spacing centering at the equator and ~120 km elsewhere. Specifically, these simulations are produced for evaluating the Grell-Freitas deep convection scheme (GF), which has been implemented in the CAM5.4 recently, for the quasi-uniform resolution and the variable resolution meshes, compared with the Zhang-McFarlane scheme deep convection scheme (ZM) in CAM. The four simulations are run for three years, but the datasets are only for the last 30 months because the first six months are regarded as spinup. The model output was interpolated to a 1-degree by 1-degree latitude-longitude grid. Details of the simulations will be documented in a manuscript submitted to the Journal of Advances in Modeling Earth Systems (JAMES). The dataset will be shared with readers without restrictions by the journal's data policy.

opencc-by-4.0Oct 2024View details →

Supporting data for: "Weakening of the AMOC and Strengthening of Labrador Sea Deep Convection in Response to External Freshwater Forcing"

This repository contains the key supporting data (in the netcdf format) for the following paper: Wei, X., Zhang, R. Weakening of the AMOC and strengthening of Labrador Sea deep convection in response to external freshwater forcing. Nat Commun 15, 10357 (2024). https://doi.org/10.1038/s41467-024-54756-3 In this study, control and water hosing ensembles are conducted using a coupled climate model (GFDL CM4) with an eddy-permitting ocean component. The anomaly is defined as the difference between the water hosing and control ensembles (anomaly = water hosing - control). This paper investigates mechanisms of the AMOC weakening and its subsequent impact on the Labrador Sea open-ocean deep convection in response to external freshwater forcing. Descriptions of data files in this repository: Main figures: 1. The anomalies of the OSNAP AMOC and the Labrador Sea March mixed layer depth (MLD), as shown in Fig. 1 in the paper.  The anomalies of the maximum AMOC and the AMOC at a relatively dense level around sigma0=27.84 kg/m3 across the entire OSNAP section in density space: Anomaly_AMOC_max_OSNAP.nc Anomaly_AMOC_denser_OSNAP.nc The anomalies of the maximum AMOC across OSNAP West and OSANP East in density space: Anomaly_AMOC_max_OSNAP_WEST.nc Anomaly_AMOC_max_OSNAP_EAST.nc The March mixed layer depth (MLD) in the Labrador Sea: Anomaly_MLD_003_March_Labrador.nc The spatial map of March MLD climatology and anomaly: Control_MLD_003_March_spatial.nc Anomaly_MLD_003_March_spatial.nc 2. The climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC streamfunction across the OSNAP section, in density-space and depth-space, as shown in Fig. 2 in the paper. OSNAP West: Control_moc_sigma0_OSNAP_WEST.nc Control_moc_z_OSNAP_WEST.nc WaterHosing_moc_sigma0_OSNAP_WEST.nc WaterHosing_moc_z_OSNAP_WEST.nc Anomaly_moc_sigma0_OSNAP_WEST.nc Anomaly_moc_z_OSNAP_WEST.nc OSNAP East: Control_moc_sigma0_OSNAP_EAST.nc Control_moc_z_OSNAP_EAST.nc WaterHosing_moc_sigma0_OSNAP_EAST.nc WaterHosing_moc_z_OSNAP_EAST.nc Anomaly_moc_sigma0_OSNAP_EAST.nc Anomaly_moc_z_OSNAP_EAST.nc Entire OSNAP section: Control_moc_sigma0_OSNAP.nc Control_moc_z_OSNAP.nc WaterHosing_moc_sigma0_OSNAP.nc WaterHosing_moc_z_OSNAP.nc Anomaly_moc_sigma0_OSNAP.nc Anomaly_moc_z_OSNAP.nc 3. The climatological mean (from the control ensemble) and anomalies of salinity, potential temperature, and potential density across the OSNAP section, as shown in Fig. 3 in the paper. Control_salinity_OSNAP.nc Control_temperature_OSNAP.nc Control_sigma0_OSNAP.nc Anomaly_salinity_OSNAP.nc Anomaly_temperature_OSNAP.nc Anomaly_sigma0_OSNAP.nc 4. The sigma-z diagram of climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC transport across OSNAP East, i.e. integrated volume transport across OSNAP East over each potential density bin and depth bin, as shown in Fig. 4 in the paper. Control_SigmaZ_OSNAP_EAST.nc WaterHosing_SigmaZ_OSNAP_EAST.nc Anomaly_SigmaZ_OSNAP_EAST.nc 5.The deep ocean potential density anomalies along with its thermal and haline components over the west boundary and eastern regions of the OSNAP East subsection, and the AMOC anomalies at a relatively dense level around sigma0=27.84 kg/m3 across OSNAP East in density space, as shown in Fig. 5 in the paper. Anomaly_sigma_west.nc Anomaly_sigmaS_west.nc Anomaly_sigmaT_west.nc Anomaly_sigma_east.nc Anomaly_sigmaS_east.nc Anomaly_sigmaT_east.nc Anomaly_sigma_diff.nc Anomaly_sigmaS_diff.nc Anomaly_sigmaT_diff.nc Anomaly_AMOC_denser_OSANP_EAST.nc 6.The transient salt-based FWF anomalies, dye-based FWF anomalies and their difference at the upper ocean (413m), as shown in Fig. 6 in the paper, and at the deep ocean (2250m), as shown in Fig. 7 in the paper. Anomaly_FWF_salt_413m_10yr.nc Anomaly_FWF_dye_413m_10yr.nc Anomaly_FWF_diff_413m_10yr.nc Anomaly_FWF_salt_413m_20yr.nc Anomaly_FWF_dye_413m_20yr.nc Anomaly_FWF_diff_413m_20yr.nc Anomaly_FWF_salt_413m_30yr.nc Anomaly_FWF_dye_413m_30yr.nc Anomaly_FWF_diff_413m_30yr.nc Anomaly_FWF_salt_413m_40yr.nc Anomaly_FWF_dye_413m_40yr.nc Anomaly_FWF_diff_413m_40yr.nc Anomaly_FWF_salt_413m_50yr.nc Anomaly_FWF_dye_413m_50yr.nc Anomaly_FWF_diff_413m_50yr.nc Anomaly_FWF_salt_2250m_10yr.nc Anomaly_FWF_dye_2250m_10yr.nc Anomaly_FWF_diff_2250m_10yr.nc Anomaly_FWF_salt_2250m_20yr.nc Anomaly_FWF_dye_2250m_20yr.nc Anomaly_FWF_diff_2250m_20yr.nc Anomaly_FWF_salt_2250m_30yr.nc Anomaly_FWF_dye_2250m_30yr.nc Anomaly_FWF_diff_2250m_30yr.nc Anomaly_FWF_salt_2250m_40yr.nc Anomaly_FWF_dye_2250m_40yr.nc Anomaly_FWF_diff_2250m_40yr.nc Anomaly_FWF_salt_2250m_50yr.nc Anomaly_FWF_dye_2250m_50yr.nc Anomaly_FWF_diff_2250m_50yr.nc 7.Climatological mean (from the control ensemble) and anomalies of salinity, potential temperature, potential density and zonal velocity along the Iceland-Scotland Overflow pathway, as shown in Fig. 8 in the paper. Control_salinity_ISOW.nc Control_temperature_ISOW.nc Control_sigma_ISOW.nc Control_u_ISOW.nc Anomaly_salinity_ISOW.nc Anomaly_temperature_ISOW.nc Anomaly_sigma_ISOW.nc Anomaly_u_ISOW.nc 8.The salt-based FWF anomalies, dye-based FWF anomalies and their difference across the Iceland-Scotland Overflow section, as shown in Fig. 9 in the paper. Anomaly_FWF_salt_ISOW.nc Anomaly_FWF_dye_ISOW.nc Anomaly_FWF_diff_ISOW.nc Supplementary figures: S1. The anomalies of the density-space AMOC streamfunction as shown in Supplementary Fig. 1 in the paper. SuppFig1.nc S2. The extra-tropical North Atlantic subsurface (413m) temperature anomalies as shown in Supplementary Fig. 2 in the paper. SuppFig2.nc S3. The climatological mean (from the control and water hosing ensembles) and anomalies of the AMOC streamfunction, surface forced water mass transformation (WMTS) and interior mixing forced water mass transformation (WMTM), as shown in Supplementary Fig. 3 in the paper. SuppFig3_streamfunction.nc SuppFig3_surfaceWMT.nc SuppFig3_interiorWMT.nc SuppFig3_mask_section.nc S4. The climatological mean of the velocity across the OSNAP section in the control ensemble, as shown in Supplementary Fig. 4 in the paper. SuppFig4.nc S5. The transient salinity anomalies, potential temperature anomalies and potential density anomalies across the OSNAP section, as shown in Supplementary Fig. 5 in the paper. SuppFig5_11_20yr.nc SuppFig5_21_30yr.nc SuppFig5_31_40yr.nc SuppFig5_41_50yr.nc S6. The transient salt-based FWF anomalies, dye-based FWF anomalies and their difference at 625m, as shown in Supplementary Fig. 6 in the paper. SuppFig6_10yr.nc SuppFig6_20yr.nc SuppFig6_30yr.nc SuppFig6_40yr.nc SuppFig6_50yr.nc S7. The salt-based FWF anomalies, dye-based FWF anomalies and their difference across the OSNAP section, as shown in Supplementary Fig. 7 in the paper. SuppFig7.nc S8. The anomalies of March Labrador Sea mixed layer depth (MLD), vertical potential density difference, surface and deep ocean potential density, as shown in Supplementary Fig. 8 in the paper. SuppFig8.nc S9. The climatological mean salinity difference between the ISOW-associated NEADW layer and the core Labrador Sea Water layer, and the inverse horizontal grid resolution of GFDL CM4 (this study) and CMIP6 models as shown in Supplementary Fig. 9 in the paper. The CMIP6 model data were downloaded from https://aims2.llnl.gov/search/cmip6/. The climatological mean salinity difference between the ISOW-associated NEADW layer and the core Labrador Sea Water layer in WOA18 as shown in Supplementary Fig. 9 in the paper. The WOA18 data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.ncei.noaa.gov/products/world-ocean-atlas/. SuppFig9.nc S10. The supplementary Fig. 10 shares the same data of climatological mean of the AMOC streamfunction across the OSNAP section in density-space in the control ensemble with Figure 2 in the paper. The OSNAP observation data were downloaded from www.o-snap.org. Control_moc_sigma0_OSNAP_WEST.nc Control_moc_sigma0_OSNAP_EAST.nc Control_moc_sigma0_OSNAP.nc Acknowledgments We acknowledge the use of the following datasets and model code in this study: The World Ocean Atlas 2018 (WOA18) data were downloaded from the NOAA National Centers for Environmental Information (formerly the National Oceanographic Data) https://www.ncei.noaa.gov/products/world-ocean-atlas/. The Data from the OSNAP (Overturning in the Subpolar North Atlantic Program) array were downloaded from https://www.o-snap.org/. OSNAP data were collected and made freely available by the OSNAP project and all the national programs that contribute to it (www.o-snap.org). The CMIP6 (Coupled Model Intercomparison Project Phase 6) model data were downloaded from https://aims2.llnl.gov/search/cmip6/. The source code of the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model version 4 (CM4) is publicly available at https://doi.org/10.5281/zenodo.3339397. The surface forced water mass transformation (WMTS) is calculated using the source code developed by Drake et al. 2024 at https://github.com/hdrake/xwmt. The relevant citations for the above datasets and model code are listed in Wei and Zhang 2024.

opencc-by-4.0Sep 2024View details →

Fortran/Python Interface in ARP-GEM1: Online Test of Neural Network Deep Convection

Manuscript under review in AIES. Supporting Code and Dataset.  Abstract. In this study, we present the integration of a neural network-based parameterization into the global atmospheric model ARP-GEM1, leveraging the Python interface of the OASIS coupler. This approach facilitates the exchange of fields between the Fortran-based ARP-GEM1 model and a Python component responsible for neural network inference. As a proof-of-concept experiment, we trained a neural network to emulate the deep convection parameterization of ARP-GEM1. Using the flexible Fortran/Python interface, we have successfully replaced ARP-GEM1's deep convection scheme with a neural network emulator. To assess the performance of the neural network deep convection scheme, we have run a 5-years ARP-GEM1 simulation where the neural network replaced ARP-GEM1's deep convection parameterization. The evaluation of averaged fields showed good agreement with output from an ARP-GEM1 simulation using the physics-based deep convection scheme. The Python component was deployed on a separate partition from the general circulation model, using GPUs to increase inference speed of the neural network.

opencc-by-4.0Oct 2019View details →

Deep convective cloud air parcels

The dataset consists of 500 files describing the idealized deep convective cloud air parcels. Values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.

opencc-by-4.0Jan 2022View details →

Postprocessed data for "Tracing the rain formation pathways in numerical simulations of deep convection"

This dataset is associated with the journal paper "Tracing the rain formation pathways in numerical simulations of deep convection". There are six .npz files containing data for the 5 simulations (CTRL, CTRLrfix, K13, CTRL800, K13800) and one npz file containing data for constructing the rain pdfs of simulation CTRL (Figure 6). This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52‐07NA27344 IM Release number LLNL-MI-843623

opencc-by-4.0Jun 2022View details →

Air parcel trajectories dataset based on modeling of 16 deep convective clouds in the Amazon.

The dataset contains trajectories of 16 deep convective cloud air parcels simulated with MIMICA code based on soundings retrieved over Manaus (Latitude: -3.1, Longitude: -60.0) during the wet season from April 1 until April 14, 2020 at 00 and 12 UTC.   Parcel data contains values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.