diff --git a/_quarto.yml b/_quarto.yml
index 95319b76..bdbf0053 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -276,6 +276,8 @@ website:
contents:
- text: "Using VDS Starter Notebook"
href: notebooks/Advanced_cloud/using_vds_starter.ipynb
+ - text: "Opening an Icechunk Store"
+ href: notebooks/Advanced_cloud/open_icechunk.ipynb
- text: "Gulf of Tehuantepec Upwelling Example"
href: notebooks/Advanced_cloud/GulfofTehuantepec_Ocean_Response.ipynb
- text: "Virtualizarr Recipes"
diff --git a/notebooks/Advanced_cloud/open_icechunk.ipynb b/notebooks/Advanced_cloud/open_icechunk.ipynb
new file mode 100644
index 00000000..7aa3dcd9
--- /dev/null
+++ b/notebooks/Advanced_cloud/open_icechunk.ipynb
@@ -0,0 +1,12104 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "ce676c6b-db54-449a-9952-a73a9a93bf39",
+ "metadata": {},
+ "source": [
+ "## Example of Opening an Icechunk Store In-Cloud\n",
+ "\n",
+ "#### *Author: Dean Henze, PO.DAAC*\n",
+ "\n",
+ "This notebook demonstrates opening a virtual dataset (VDS) stored as an Icechunk store (note this technology is currently experimental, although it is gaining momentum). For a general overview of VDS's see the [Using Virutal Datasets chapter](https://podaac.github.io/tutorials/quarto_text/UsingVirtualDatasets.html) on the PO.DAAC Cookbook.\n",
+ "\n",
+ "The example dataset(s) used here are from the SASSIE ECCO project (the [User Guide](https://doi.org/10.5067/SEL1D-DUG11) has a project overview). The ECCO model (Estimating the Circulation and Climate of the Ocean) was run over the arctic region over a seven year period in support of the SASSIE field experiment (Salinity and Stratification at the Sea Ice Edge). The SASSIE ECCO data are traditionally archived and available in netCDF format - one file per day(month) for the daily-mean(monthly-snapshot) datasets. The model output variables (totalling ~50 TBs) are large enough that they are spread out over 18 different netCDF datasets for the daily-means, and over 3 different datasets for the monthly-snapshots. One novel functionality of VDS's being tested here is the ability to combine the data across e.g. the 18 daily-mean datasets so users can interact with the data as if all variables were contained in a single dataset. Computing performance is TBD.\n",
+ "\n",
+ "The AWS S3 paths to the SASSIE ECCO icechunk stores are\n",
+ "\n",
+ "**Daily averages:**\n",
+ "\n",
+ "s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_DAILY_V1R1_virtual_s3.icechunk/\n",
+ "\n",
+ "**Monthly snapshots:**\n",
+ "\n",
+ "s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_SNAPSHOT_V1R1_virtual_s3.icechunk/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81464144-bf17-4fe0-861b-b97d01aed169",
+ "metadata": {},
+ "source": [
+ "## Requirements and Python environment\n",
+ "\n",
+ "* Earthdata login account: An Earthdata Login account is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account.\n",
+ "\n",
+ "* Compute environment: This notebook is meant to be run in the cloud (AWS instance running in us-west-2).\n",
+ "\n",
+ "* The minimal working installation for Python 3.13 environment is\n",
+ "\n",
+ "```\n",
+ "earthaccess==0.16.0\n",
+ "icechunk==1.1.19\n",
+ "xarray==2025.6.1\n",
+ "zarr==3.1.1\n",
+ "matplotlib\n",
+ "jupyterlab\n",
+ "```\n",
+ "\n",
+ "***Note that the icechunk=1.1.19 dependency is important! Do not use a more recent version.***"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "0a074463-73c5-4284-85d5-9679eb64158b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import icechunk\n",
+ "import xarray as xr\n",
+ "import earthaccess"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ede75846-f591-408b-9421-ac73dee84312",
+ "metadata": {},
+ "source": [
+ "### 1. Define a function to access the icechunk store\n",
+ "\n",
+ "This function does not need any modification. It defines some access keywords and a mapper to the VDS. The mapper will contain all the required access credentials and can be passed directly to xarray. In the future this step will likely be built into to earthaccess but for now we must define it in the notebook. The only inputs to the function are:\n",
+ "\n",
+ "1. Your EDS credentials\n",
+ "2. The link to the VDS reference file (in the header of this notebook and in the next section)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "db744b69-c703-4234-8fab-b5b19aaffdaf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def open_readonly_icechunkstore_s3(\n",
+ " s3path_store, creds_store, creds_data_chunks, \n",
+ " bucket_data_chunks = \"s3://podaac-ops-cumulus-protected/\"):\n",
+ " \"\"\"\n",
+ " Opens and returns an icechunk store with read-only capabilities.\n",
+ "\n",
+ " Inputs\n",
+ " ------\n",
+ " s3path_store: str or path\n",
+ " AWS S3 path to the icechunk store, e.g. \"s3://podaac-ops-cumulus-public/virtual_collections/...\".\n",
+ " creds_store: dict\n",
+ " Credentials to access the icechunk store (e.g. EDL creds for NASA Earthdata). Expected\n",
+ " dictionary keys are \"accessKeyId\", \"secretAccessKey\", and \"sessionToken\".\n",
+ " creds_data_chunks: dict\n",
+ " Credentials to access the science data that the icechunk store points to (e.g. EDL creds \n",
+ " for NASA Earthdata). Expected dictionary keys are \"accessKeyId\", \"secretAccessKey\",\n",
+ " and \"sessionToken\".\n",
+ " bucket_data_chunks: str\n",
+ " Name of bucket containing the science data that the icechunk store points to. E.g. for\n",
+ " PO.DAAC data this might be \"s3://podaac-ops-cumulus-protected/\". Note you need\n",
+ " the \"s3://\" prefix.\n",
+ " \"\"\"\n",
+ " \n",
+ " \n",
+ " # 1. Create the raw static credentials object for virtual chunks\n",
+ " virtualchunk_static_creds = icechunk.s3_static_credentials(\n",
+ " access_key_id = creds_data_chunks[\"accessKeyId\"],\n",
+ " secret_access_key = creds_data_chunks[\"secretAccessKey\"],\n",
+ " session_token = creds_data_chunks[\"sessionToken\"]\n",
+ " )\n",
+ " auth_map = icechunk.containers_credentials({\n",
+ " bucket_data_chunks: virtualchunk_static_creds\n",
+ " })\n",
+ " \n",
+ " # 2. Config for the store / metadata repository\n",
+ " s3path_split = s3path_store.split(\"/\")\n",
+ " bucket_store = \"/\".join(s3path_split[2:3])\n",
+ " prefix_store = \"/\".join(s3path_split[3:])\n",
+ "\n",
+ " storage = icechunk.s3_storage(\n",
+ " bucket = bucket_store,\n",
+ " prefix = prefix_store,\n",
+ " access_key_id = creds_store['accessKeyId'],\n",
+ " secret_access_key = creds_store['secretAccessKey'],\n",
+ " session_token = creds_store['sessionToken']\n",
+ " )\n",
+ " \n",
+ " # 3. Open the repository, passing the compiled auth_map!\n",
+ " repo = icechunk.Repository.open(\n",
+ " storage,\n",
+ " authorize_virtual_chunk_access=auth_map\n",
+ " )\n",
+ " \n",
+ " # 4. Return the store\n",
+ " session = repo.readonly_session(\"main\")\n",
+ " return session.store"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf5ba5ce-e274-4e10-9932-2624b30ab698",
+ "metadata": {},
+ "source": [
+ "### 2. Access store and open data with Xarray\n",
+ "\n",
+ "Steps are to:\n",
+ "\n",
+ "1. Login to NASA EDL.\n",
+ "2. Use the above function to get the icechunk store mapper.\n",
+ "3. Pass the mapper into Xarray to open the dataset.\n",
+ "4. Perform some sample computations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8bfe1ba3-9cf2-4c56-8f92-ade97b98f9d9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "Enter your Earthdata Login username: deanh808\n",
+ "Enter your Earthdata password: ········\n"
+ ]
+ }
+ ],
+ "source": [
+ "# 1. Authenticate with NASA Earthdata and dynamically fetch S3 credentials ------------------------------------------\n",
+ "earthaccess.login() # Uses your ~/.netrc file or environment variables if configured\n",
+ "ea_creds = earthaccess.get_s3_credentials(daac=\"PODAAC\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1517fc94-f258-4920-b3e1-91269aea2ef6",
+ "metadata": {},
+ "source": [
+ "The AWS S3 paths to the SASSIE ECCO icechunk stores are\n",
+ "\n",
+ "**Daily averages:**\n",
+ "\n",
+ "s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_DAILY_V1R1_virtual_s3.icechunk/\n",
+ "\n",
+ "**Monthly snapshots:**\n",
+ "\n",
+ "s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_SNAPSHOT_V1R1_virtual_s3.icechunk/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a487df69-0502-4cec-930c-297b017f998a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## 2. Get the icechunk store mapper ----------------------------------------------------------------------------------\n",
+ "s3path_store = \"s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_DAILY_V1R1_virtual_s3.icechunk/\"\n",
+ "store = open_readonly_icechunkstore_s3(\n",
+ " s3path_store, \n",
+ " ea_creds, ea_creds, \n",
+ " bucket_data_chunks = \"s3://podaac-ops-cumulus-protected/\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "307180bf-277d-447a-b2d7-6c42fde93097",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/coiled/env/lib/python3.14/site-packages/zarr/codecs/numcodecs/_codecs.py:141: ZarrUserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n",
+ " super().__init__(**codec_config)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 828 ms, sys: 163 ms, total: 991 ms\n",
+ "Wall time: 1.58 s\n"
+ ]
+ },
+ {
+ "data": {
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long_name :
grid index in z corresponding to the bottom face of tracer grid cells ('w' locations)
Vertical advective flux of salinity (SALT) in the +z direction through the top 'w' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, vertical flux quantities are staggered relative to the tracer cells with indexing such that +ADVr_SLT(i,j,k_l) corresponds to upward +z fluxes through the top 'w' face of the tracer cell at (i,j,k). Salinity defined using CF convention 'Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term 'salinity' is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.' see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html
Vertical advective flux of potential temperature (THETA) in the +z direction through the top 'w' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, vertical flux quantities are staggered relative to the tracer cells with indexing such that +ADVr_TH(i,j,k_l) corresponds to upward +z fluxes through the top 'w' face of the tracer cell at (i,j,k)
Lateral advective flux of sea-ice thickness in the model +x direction
units :
m3 s-1
comment :
Lateral advective flux of grid cell mean sea-ice thickness (HEFF) in the +x direction through the 'u' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVxHEFF(i_g,j) corresponds to +x fluxes through the 'u' face of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Lateral advective flux of salinity in the model +x direction
units :
1e-3 m3 s-1
comment :
Lateral advective flux of salinity (SALT) in the +x direction through the 'u' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVx_SLT(i_g,j,k) corresponds to +x fluxes through the 'u' face of the tracer cell at (i,j,k). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. Salinity defined using CF convention 'Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term 'salinity' is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.' see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html
Lateral advective flux of snow thickness in the model +x direction
units :
m3 s-1
comment :
Lateral advective flux of grid cell mean snow thickness (HSNOW) in the +x direction through the 'u' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVxSNOW(i_g,j) corresponds to +x fluxes through the 'u' face of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Lateral advective flux of potential temperature in the model +x direction
units :
degree_C m3 s-1
comment :
Lateral advective flux of potential temperature (THETA) in the +x direction through the 'u' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVx_TH(i_g,j,k) corresponds to +x fluxes through the 'u' face of the tracer cell at (i,j,k). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's lat-lon-cap (llc) curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Lateral advective flux of sea-ice thickness in the model +y direction
units :
m3 s-1
comment :
Lateral advective flux of grid cell mean sea-ice thickness (HEFF) in the +y direction through the 'v' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVyHEFF(i,j_g) corresponds to +y fluxes through the 'v' face of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Lateral advective flux of potential temperature in the model +y direction
units :
degree_C m3 s-1
comment :
Lateral advective flux of potential temperature (THETA) in the +y direction through the 'v' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVy_TH(i,j_g,k) corresponds to +y fluxes through the 'v' face of the tracer cell at (i,j,k). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Lateral advective flux of salinity in the model +y direction
units :
1e-3 m3 s-1
comment :
Lateral advective flux of salinity (SALT) in the +y direction through the 'v' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVy_SLT(i,j_g,k) corresponds to +y fluxes through the 'v' face of the tracer cell at (i,j,k). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. Salinity defined using CF convention 'Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term 'salinity' is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.' see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html
Vertical diffusive flux of potential temperature (implicit term)
units :
degree_C m3 s-1
comment :
The implicit term of the vertical diffusive flux of potential temperature (THETA) in the +z direction through the top 'w' face of the tracer cell on the native model grid. There is no explicit term of the vertical diffusive flux in the SASSIE ECCO model. Note: in the Arakawa-C grid, vertical flux quantities are staggered relative to the tracer cells with indexing such that +DFrI_TH(i,j,k_l) corresponds to upward +z fluxes through the top 'w' face of the tracer cell at (i,j,k)
Model sea level anomaly WITHOUT corrections for global mean density (steric) changes, inverted barometer effect, or volume displacement due to submerged sea-ice and snow. . Note: ETAN should NOT be used for comparisons with altimetry data products because ETAN is NOT corrected for (a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH) nor (b) sea level displacement due to submerged sea-ice and snow (see sIceLoad). The model has no atmospheric pressure forcing so there is no need to make an inverted barometer correction.
Lateral advective flux of snow thickness in the model +y direction
units :
m3 s-1
comment :
Lateral advective flux of grid cell mean snow thickness (HSNOW) in the +y direction through the 'v' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVySNOW(i,j_g) corresponds to +y fluxes through the 'v' face of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Air-sea latent heat flux per unit area of open water (not covered by sea-ice). Note: calculated from the bulk formula following Large and Yeager (2004) NCAR/TN-460+STR.
Open ocean net surface freshwater flux from precipitation, evaporation, and runoff
units :
m s-1
comment :
Net surface freshwater flux from precipitation, evaporation, and runoff per unit area in open water (not covered by sea-ice). Excludes freshwater fluxes involving sea-ice and snow. Note: calculated as EXFevap-EXFpreci-EXFroff.
Net longwave radiative flux per unit area of open water (not covered by sea-ice). Note: net longwave radiation over open water calculated from downward longwave radiation (EXFlwdn) and upward longwave radiation from ocean and sea-ice thermal emission (Stefan-Boltzman law).
Vertical diffusive flux of salinity (implicit term)
units :
1e-3 m3 s-1
comment :
The implicit term of the vertical diffusive flux of salinity (SALT) in the +z direction through the top 'w' face of the tracer cell on the native model grid. There is no explicit term of the vertical diffusive flux in the SASSIE ECCO model. Note: in the Arakawa-C grid, vertical flux quantities are staggered relative to the tracer cells with indexing such that +DFrI_SLT(i,j,k_l) corresponds to upward +z fluxes through the top face 'w' of the tracer cell at (i,j,k). Salinity defined using CF convention 'Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term 'salinity' is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.' see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html
>0 increases horizontal velocity in the +x direction (UVEL)
long_name :
Wind stress in the model +x direction
standard_name :
surface_downward_x_stress
units :
N m-2
comment :
Wind stress in the +x direction at the tracer cell on the native model grid. Note: EXFtaux is the stress applied to the ice-free ocean surface and sea-ice covered surface. When sea-ice is present, the total stress applied to the ocean surface in the +x direction is NOT EXFtaux, but a combination of EXFtaux wind stress in the open water fraction and a stress from sea-ice in the ice-covered fraction (see oceTAUX).
Air-sea sensible heat flux per unit area of open water (not covered by sea-ice). Note: calculated from the bulk formula following Large and Yeager (2004) NCAR/TN-460+STR.
Net air-sea heat flux (turbulent and radiative) per unit area of open water (not covered by sea-ice). Note: net upward heat flux over open water, calculated as EXFlwnet+EXFswnet-EXFlh-EXFhs.
Net shortwave radiative flux per unit area of open water (not covered by sea-ice). Note: net shortwave radiation over open water calculated from downward shortwave flux (EXFswdn) and ocean surface albdeo.
Evaporation rate per unit area of open water (not covered by sea-ice). Note: calculated using the bulk formula following Large and Yeager (2004) NCAR/TN-460+STR.
>0 increases horizontal velocity in the +y direction (VVEL)
long_name :
Wind stress in the model +y direction
standard_name :
surface_downward_y_stress
units :
N m-2
comment :
Wind stress in the +y direction at the tracer cell on the native model grid. Note: EXFtauy is the stress applied to the ice-free ocean surface and sea-ice covered surface. When sea-ice is present, the total stress applied to the ocean surface in the +y direction is NOT EXFtauy, but a combination of EXFtauy wind stress in the open water fraction and a stress from sea-ice in the ice-covered fraction (see oceTAUY).
Depth of the ocean surface boundary layer (h) diagnosed by the KPP bulk Richardson number criterion. Represents the thickness of the layer influenced by surface buoyancy and momentum forcing.
Wind speed at 10m in the +x direction at the tracer cell on the native model grid. Note: ECCO is forced with wind stress (see EXFtaux) not vector winds converted to wind stress using bulk formulae. EXFuwind is calculated by converting wind stress to vector wind using bulk formulae.
Wind speed at 10m in the +y direction at the tracer cell on the native model grid. Note: ECCO is forced with wind stress (see EXFtauy) not vector winds converted to wind stress using bulk formulae. EXFvwind is calculated by converting wind stress to vector wind using bulk formulae.
Vertical diffusion coefficient for salt and tracers
units :
m2 s-1
comment :
Vertical eddy diffusivity for salinity and passive tracers from the KPP scheme. Controls the strength of turbulent vertical mixing of scalars in the ocean boundary layer and interior.
Vertical eddy viscosity coefficient computed by the K-Profile Parameterization (KPP) scheme. Represents the turbulent vertical momentum mixing within the ocean surface boundary layer and interior. . Higher values indicate enhanced vertical mixing of momentum due to shear instability, surface forcing, or boundary layer processes.
Net freshwater flux into the open ocean, sea-ice, and snow
standard_name :
surface_downward_water_flux
units :
kg m-2 s-1
comment :
Net freshwater flux into the combined liquid ocean, sea-ice, and snow reservoirs from the atmosphere and runoff. Note: freshwater fluxes BETWEEN the liquid ocean and sea-ice or snow reservoirs do not contribute to SIatmFW. SIatmFW counts all fluxes to/from the atmosphere that change the TOTAL freshwater stored in the combined liquid ocean, sea-ice, and snow reservoirs.
PHIHYD = p(k) / rhoConst - g z*(k,t), where p = hydrostatic ocean pressure at depth level k, rhoConst = reference density (1027.5 kg m-3) and g is acceleration due to gravity (9.81 m s-2). Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: Quantity referred to in some contexts as hydrostatic pressure anomaly. PHIHYD is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction).
PHIBOT = p_b / rhoConst - g H(t), where p_b = hydrostatic ocean bottom pressure, rhoConst = reference density (1027.5 kg m-3), g is acceleration due to gravity (9.81 m s-2), and H(t) is model depth at time t. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: PHIBOT is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction), and therefore should NOT be used for comparisons with ocean bottom pressure data.
Rate of change of total ocean salinity per m2 accounting for mass fluxes.
units :
g m-2 s-1
comment :
The rate of change of total ocean salinity due to freshwater fluxes across the liquid surface and the addition or removal of mass. Note: the global area integral of SFLUX matches the time-derivative of total ocean salinity (psu s-1). Unlike oceFWflx, SFLUX includes the contribution to the total ocean salinity from changing ocean mass (e.g. from the addition or removal of freshwater in oceFWflx).
Defined using CF convention 'Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term 'salinity' is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.' see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html
Fraction of ocean grid cell covered with sea-ice [0 to 1]. CF Standard Name Table v73: 'Area fraction' is the fraction of a grid cell's horizontal area that has some characteristic of interest. It is evaluated as the area of interest divided by the grid cell area. It may be expressed as a fraction, a percentage, or any other dimensionless representation of a fraction. Sea ice area fraction is area of the sea surface occupied by sea ice. It is also called 'sea ice concentration'. 'Sea ice' means all ice floating in the sea which has formed from freezing sea water, rather than by other processes such as calving of land ice to form icebergs. https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html. Defined using CF Standard Name Table v73: 'Area fraction' is the fraction of a grid cell's horizontal area that has some characteristic of interest. It is evaluated as the area of interest divided by the grid cell area. It may be expressed as a fraction, a percentage, or any other dimensionless representation of a fraction. Sea ice area fraction is area of the sea surface occupied by sea ice. It is also called 'sea ice concentration'. 'Sea ice' means all ice floating in the sea which has formed from freezing sea water and precipitation, rather than by other processes such as calving of land ice to form icebergs. https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html
Sea-ice thickness averaged over the entire model grid cell, including open water where sea-ice thickness is zero. Note: sea-ice thickness over the ICE-COVERED fraction of the grid cell is SIheff/SIarea
Snow thickness averaged over the entire model grid cell, including open water where snow thickness is zero. Note: snow thickness over the ICE-COVERED fraction of the grid cell is SIhsnow/SIarea
Horizontal sea-ice velocity in the +x direction at the 'u' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +SIuice(i_g,j) corresponds to +x fluxes through the 'u' face of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Horizontal sea-ice velocity in the +y direction at the 'v' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +SIvice(i,j_g) corresponds to +y fluxes through the 'v' face of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Dynamic sea surface height anomaly above the geoid. Note: SSH is calculated by correcting model sea level anomaly ETAN for two effects: a) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad), and b) the linear trend in ETAN. The linear trend is removed because the open boundary conditions have net zero inflow and outflow by model construction, so there is an accumulation of volume. This term can be compared with detrended altimetry data that has the inverse barometer (IB) correction.
Sea water potential temperature is the temperature a parcel of sea water would have if moved adiabatically to sea level pressure. Note: the equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g \r",
+ "ho_{0} z$.
Horizontal velocity in the model +x direction per unit area of the grid cell 'u' face
units :
m s-1
comment :
Horizontal velocity in the model +x direction multiplied by the open water fraction (hFacW) of the grid cell 'u' face on the native model grid ('u' grid cell face area = drF dyG). Accounts for partial cells (hFacW < 1). Volume flux in +x = UVELMASS drF dyG. Although UVELMASS is expressed in units of [m s-1], it is important to note that it is a normalized transport: volume flux [m3 s-1] per square meter. UVELMASS(t) = UVEL(t) x (drF x dyG) x hFacW / (drF x dyG). Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +UVELMASS(i,j,k) corresponds to +x fluxes through the 'u' face of the tracer cell at (i,j,k). UVELMASS can be used for volume flux calculations. Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. See VVELMASS and WVELMASS
Horizontal velocity in the +y direction at the 'v' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +VVEL(i,j_g,k) corresponds to +y fluxes through the 'v' face of the tracer cell at (i,j,k). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Horizontal velocity in the model +y direction per unit area of the grid cell 'v' face
units :
m s-1
comment :
Horizontal velocity in the model +y direction multiplied by the open water fraction (hFacS) of the grid cell 'v' face on the native model grid ('v' grid cell face area = drF dxG). Accounts for partial cells (hFacS < 1). Volume flux in +y = VVELMASS drF dxG. Although VVELMASS is expressed in units of [m s-1], it is important to note that it is a normalized transport: volume flux [m3 s-1] per square meter. VVELMASS(t) = VVEL(t) x (drF x dxG) x hFacS / (drF x dxG). Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +VVELMASS(i,j,k) corresponds to +y fluxes through the 'v' face of the tracer cell at (i,j,k). VVELMASS can be used for volume flux calculations. Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. See UVELMASS and WVELMASS.
Horizontal velocity in the +x direction at the 'u' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +UVEL(i_g,j,k) corresponds to +x fluxes through the 'u' face of the tracer cell at (i,j,k). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Rate of change of ocean heat content per m2 accounting for mass fluxes.
units :
W m-2
comment :
The rate of change of ocean heat content due to heat fluxes across the liquid surface and the addition or removal of mass. . Note: the global area integral of TFLUX and geothermal flux (geothermalFlux.bin) matches the time-derivative of ocean heat content (J/s). Unlike oceQnet, TFLUX includes the contribution to the ocean heat content from changing ocean mass (e.g. from oceFWflx).
Vertical velocity in the +z direction at the top 'w' face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, vertical velocities are staggered relative to the tracer cells with indexing such that +WVEL(i,j,k_l) corresponds to upward +z motion through the top 'w' face of the tracer cell at (i,j,k). WVEL is identical to WVELMASS.
Ocean surface potential temperature flux correction for linear free surface
units :
degree_C m s-1
comment :
Surface flux correction of potential temperature associated with a linear free-surface configuration; applied as a mass flux term at the ocean surface.
Grid cell face-averaged vertical velocity in the model +z direction.
standard_name :
upward_sea_water_velocity
units :
m s-1
comment :
Vertical velocity in the +z direction at the top 'w' face of the tracer cell on the native model grid. Volume flux in +z = WVELMASS drA. Note: in the Arakawa-C grid, vertical velocities are staggered relative to the tracer cells with indexing such that +WVELMASS(i,j,k) corresponds to upward +z motion through the top 'w' face of the tracer cell at (i,j,k). Unlike UVELMASS and VVELMASS, WVELMASS is not scaled by a time-varying open water fraction because the open water fraction of the 'w' face is always 1, thus WVELMASS is identical to WVEL.
Rate of change of ocean heat content per m2 accounting for mass fluxes, including sea ice
units :
W m-2
comment :
Rate of change of ocean heat content per unit area including effects of surface mass fluxes (precipitation, evaporation, sea ice); positive values warm the ocean (increase THETA).
>0 increases horizontal velocity in the +x direction (UVEL)
long_name :
Ocean surface stress in the model +x direction
standard_name :
downward_x_stress_at_sea_water_surface
units :
N m-2
comment :
Ocean surface stress due to wind and sea-ice in the +x direction centered over the 'u' side of the the native model grid. Note: in the Arakawa-C grid, wind stress acts on horizontal velocities which are staggered relative to the tracer cells with indexing such that +oceTAUX(i_g,j) corresponds to +x momentum fluxes at 'u' edge of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Total mass of sea-ice and snow in a model grid cell averaged over model grid cell area. Note: sIceLoad is used to correct model sea level anomaly, ETAN, to calculate dynamic sea surface height, SSH. In the model, sea-ice is treated as floating above the sea level with ETAN tracing the location of the ocean-ice interface. Consequently, sea-ice growth in the model lowers ETAN and sea-ice melting raises ETAN. Dynamic sea surface height is obtained by correcting ETAN by the weight of ice and snow directly above following Archimedes’ principle.
Net freshwater flux into the ocean including contributions from runoff, evaporation, precipitation, and mass exchange with sea-ice due to melting and freezing and snow melting. Note: oceFWflx does NOT include freshwater fluxes between the atmosphere and sea-ice and snow. The variable 'SIatmFW' accounts for freshwater fluxes out of the combined ocean+sea-ice+snow reservoir.
>0 increases horizontal velocity in the +y direction (VVEL)
long_name :
Ocean surface stress in the model +y direction
standard_name :
downward_y_stress_at_sea_water_surface
units :
N m-2
comment :
Ocean surface stress due to wind and sea-ice in the +y direction centered over the 'v' side of the the native model grid. Note: in the Arakawa-C grid, wind stress acts on horizontal velocities which are staggered relative to the tracer cells with indexing such that +oceTAUY(i_g,j) corresponds to +y momentum fluxes at 'v' edge of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model's curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.
Net heat flux into the ocean surface from all processes: air-sea turbulent and radiative fluxes and turbulent and conductive fluxes between the ocean and sea-ice and snow. Note: oceQnet does not include the change in ocean heat content due to changing ocean ocean mass (oceFWflx). Mass fluxes from evaporation, precipitation, and runoff (EXFempmr) happen at the same temperature as the ocean surface temperature. Consequently, EmPmR does not change ocean surface temperature. Conversely, mass fluxes due to sea-ice thickening/thinning and snow melt in the model are assumed to happen at a fixed 0C. Consequently, mass fluxes due to phase changes between seawater and sea-ice and snow induce a heat flux when the ocean surface temperaure is not 0C. The variable TFLUX does include the change in ocean heat content due to changing ocean mass.
This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.
author :
Marie Zahn, Mike Wood, Ian Fenty, and Severine Fournier
cdm_data_type :
Grid
Conventions :
CF-1.8, ACDD-1.3
creator_email :
marie.j.zahn@jpl.nasa.gov
creator_institution :
NASA Jet Propulsion Laboratory (JPL)
creator_name :
Marie Zahn
creator_type :
Person
creator_url :
https://salinity.oceansciences.org/sassie.htm
geospatial_lat_max :
90.0
geospatial_lat_min :
48.70000076293945
geospatial_lat_units :
degrees_north
geospatial_lon_max :
180.0
geospatial_lon_min :
-180.0
geospatial_lon_units :
degrees_east
geospatial_bounds_crs :
EPSG:4326
history :
Virtual layer on top of the initial release of the ECCO N1 SASSIE Ocean-Sea Ice Simulation data, for all monthly snapshot collections.
The SASSIE ocean model simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells. The ECCO global solution provided initial and boundary conditions and atmospheric forcing.
standard_name_vocabulary :
NetCDF Climate and Forecast (CF) Metadata Convention
time_coverage_end :
2021-02-08T00:00:00Z
time_coverage_start :
2014-01-15T00:00:00Z
comment :
SASSIE llc1080 V1R1 fields are consolidated onto a single curvilinear grid face focusing on the Arctic domain using fields from the 5 faces of the lat-lon-cap 1080 (llc1080) native grid used in the original simulation. This is a virtual layer on top of the original data, for most of the daily mean datasets - it allows the user to access all the files from those datasets as if they were in a single, composite dataset. The variables contained here are from the following SASSIE ECCO datasets: SASSIE_ECCO_L4_ATM_STATE_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_FRESH_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_HEAT_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OBP_SSH_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_DENS_PRESS_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_SALINITY_ADV_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_SALINITY_DIFF_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_TEMP_ADV_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_TEMP_DIFF_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_VOL_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_VEL_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_SEA_ICE_CONC_THICK_VEL_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_STRESS_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_TEMP_SALINITY_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_KPP_DIAGS_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_KPP_BOUNDARY_LAYER_LLC1080GRID_DAILY_V1R1,SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_TEND_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_FLUX_LLC1080GRID_DAILY_V1R1.
identifier_product_doi_authority :
org.doi
time_coverage_duration :
P1D
time_coverage_resolution :
P1D
geospatial_vertical_max :
0.0
geospatial_vertical_min :
-7000.0
geospatial_vertical_positive :
up
geospatial_vertical_resolution :
variable
geospatial_vertical_units :
meter
identifier_product_doi :
https://doi.org/10.5067/SEL1D-DUG11
date_created :
2026-03-21T00:00:00Z
"
+ ],
+ "text/plain": [
+ " Size: 46TB\n",
+ "Dimensions: (time: 2581, k_l: 90, j: 1080, i: 1800, i_g: 1800, j_g: 1080,\n",
+ " k: 90, k_u: 90, nb: 4, k_p1: 91, nv: 2)\n",
+ "Coordinates: (12/23)\n",
+ " XC (j, i) float32 8MB dask.array\n",
+ " XC_bnds (j, i, nb) float32 31MB dask.array\n",
+ " YC (j, i) float32 8MB dask.array\n",
+ " XU (j, i_g) float32 8MB dask.array\n",
+ " YC_bnds (j, i, nb) float32 31MB dask.array\n",
+ " YU (j, i_g) float32 8MB dask.array\n",
+ " ... ...\n",
+ " * j_g (j_g) int32 4kB 0 1 2 3 4 5 6 ... 1074 1075 1076 1077 1078 1079\n",
+ " * k_p1 (k_p1) int32 364B 0 1 2 3 4 5 6 7 8 ... 83 84 85 86 87 88 89 90\n",
+ " * k_l (k_l) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
+ " time_bnds (time, nv) datetime64[ns] 41kB dask.array\n",
+ " * k_u (k_u) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
+ " * time (time) datetime64[ns] 21kB 2014-01-15T12:00:00 ... 2021-02-07T...\n",
+ "Dimensions without coordinates: nb, nv\n",
+ "Data variables: (12/83)\n",
+ " ADVr_SLT (time, k_l, j, i) float32 2TB dask.array\n",
+ " ADVr_TH (time, k_l, j, i) float32 2TB dask.array\n",
+ " ADVxHEFF (time, j, i_g) float32 20GB dask.array\n",
+ " ADVxAREA (time, j, i_g) float32 20GB dask.array\n",
+ " ADVyAREA (time, j_g, i) float32 20GB dask.array\n",
+ " ADVx_SLT (time, k, j, i_g) float32 2TB dask.array\n",
+ " ... ...\n",
+ " oceQsw (time, j, i) float32 20GB dask.array\n",
+ " oceTAUX (time, j, i_g) float32 20GB dask.array\n",
+ " sIceLoad (time, j, i) float32 20GB dask.array\n",
+ " oceFWflx (time, j, i) float32 20GB dask.array\n",
+ " oceTAUY (time, j_g, i) float32 20GB dask.array\n",
+ " oceQnet (time, j, i) float32 20GB dask.array\n",
+ "Attributes: (12/49)\n",
+ " acknowledgement: This research was carried out by the J...\n",
+ " author: Marie Zahn, Mike Wood, Ian Fenty, and ...\n",
+ " cdm_data_type: Grid\n",
+ " Conventions: CF-1.8, ACDD-1.3\n",
+ " creator_email: marie.j.zahn@jpl.nasa.gov\n",
+ " creator_institution: NASA Jet Propulsion Laboratory (JPL)\n",
+ " ... ...\n",
+ " geospatial_vertical_min: -7000.0\n",
+ " geospatial_vertical_positive: up\n",
+ " geospatial_vertical_resolution: variable\n",
+ " geospatial_vertical_units: meter\n",
+ " identifier_product_doi: https://doi.org/10.5067/SEL1D-DUG11\n",
+ " date_created: 2026-03-21T00:00:00Z"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "\n",
+ "## 3. Open dataset ---------------------------------------------------------------------------------------------------\n",
+ "ds = xr.open_zarr(store, consolidated=False)\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c21297bd-71c8-4153-9aa1-8d34316d6731",
+ "metadata": {},
+ "source": [
+ "#### 4. Sample computations\n",
+ "\n",
+ "#### Map of salinity at a single depth and time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a778944e-5699-4605-90df-d30e6eebf6c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 1.73 s, sys: 341 ms, total: 2.07 s\n",
+ "Wall time: 2.44 s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "ds[\"SALT\"].isel(time=0, k=10).plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ebd5a76-fbc8-449d-8626-ec18dd5e497b",
+ "metadata": {},
+ "source": [
+ "#### Time series of salinity at a single grid point and depth, over the entire record"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "097379df-5141-4cf7-be6e-68a7d5073582",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: user 2min 7s, sys: 21.6 s, total: 2min 29s\n",
+ "Wall time: 3min 27s\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "ds[\"SALT\"].isel(i=600, j=600, k=10).plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "51603a02-b933-46cd-9808-0d9b322ea882",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.13.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/quarto_text/UsingVirtualDatasets.qmd b/quarto_text/UsingVirtualDatasets.qmd
index 7eaad807..cf5c485b 100644
--- a/quarto_text/UsingVirtualDatasets.qmd
+++ b/quarto_text/UsingVirtualDatasets.qmd
@@ -44,7 +44,7 @@ Summarizing the points above, and adding some additional ones, the benefits of V
### Using Virtual Datasets
-To date we have explored accessing VDS’s programmatically via Jupyter notebooks, using the Xarray package for opening data. The quickest way to get up to speed with this method is to check out the [**starter notebook here**](../notebooks/Advanced_cloud/using_vds_starter.ipynb). **As with other access methods, you need an Earthdata Login account**. Other than that, the majority of the code to obtain credentials is boiler-plate and the only action item on the user is to find the VDS link for the dataset they want to use. Currently you can find the link using the table in the [Available Products](./UsingVirtualDatasets.html#available-products) section below. In the future, the links will be advertised on dataset landing pages and searchable in CMR metadata.
+To date we have explored accessing VDS’s programmatically via Jupyter notebooks, using the Xarray package for opening data. The quickest way to get up to speed with this method is to check out the [**starter notebook**](../notebooks/Advanced_cloud/using_vds_starter.ipynb). For those looking to open a VDS in `icechunk` format, see the [**opening icechunk notebook**](../notebooks/Advanced_cloud/open_icechunk.ipynb) (currently in initial tests and only for in-cloud use). **As with other access methods, you need an Earthdata Login account**. Other than that, the majority of the code to obtain credentials is boiler-plate and the only action item on the user is to find the VDS link for the dataset they want to use. Currently you can find the link using the table in the [Available Products](./UsingVirtualDatasets.html#available-products) section below. In the future, the links will be advertised on dataset landing pages and searchable in CMR metadata.
Additionally, the following notebook demonstrates using VDS’s for more involved computations: