diff --git a/notebooks/Dataset_generation/5_postprocess_GDNat.ipynb b/notebooks/Dataset_generation/5_postprocess_GDNat.ipynb new file mode 100644 index 0000000..a4f9da6 --- /dev/null +++ b/notebooks/Dataset_generation/5_postprocess_GDNat.ipynb @@ -0,0 +1,2581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "577ce965-d626-4918-b760-e8215c55f355", + "metadata": {}, + "source": [ + "# Postprocess GDNat dataset\n", + "\n", + "This notebook reads in individual GCM-year GDNat zarr stores and concatenates into one zarr store. It does this in a few steps.\n", + "1. Compile individual GCM-year filepaths\n", + "2. For each GCM, concat all years and save to temporary scratch\n", + "3. Read in all GCM-allyear datasets and concat into one dataset\n", + "4. Save the full dataset to Zarr store\n", + "\n", + "This has been tested and runs to completion.\n", + "\n", + "Once we confirm the attributes and chunks are what we want and validate the output, this should be the dataset to store publicly. \n", + "\n", + "### TODO\n", + "- [x] check this notebook runs through as-is and note environment\n", + "\n", + "- [x] check attributes in final output are what we want\n", + "\n", + "- [x] re-chunk final zarr store - confirm we should chunk all of time together\n", + "\n", + "- [ ] Add validation of the final zarr store (check for nans, max and min vals, etc) - possibly put into a new notebook\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "28dbab27-e2b4-410b-9dca-d288a42ba693", + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import numpy as np\n", + "\n", + "from datetime import datetime\n", + "\n", + "from dask_gateway import GatewayCluster\n", + "import os\n", + "import uuid" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "11dd76c3-e2a1-486a-9f6f-4e9965e24e40", + "metadata": {}, + "outputs": [], + "source": [ + "bp = \"gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted\"\n", + "models = [\"ACCESS-CM2\",\"CanESM5\",\"MIROC6\",\"NorESM2-LM\",\n", + " \"ACCESS-ESM1-5\",\"MRI-ESM2-0\"] # \"FGOALS-g3\", (not using FGOALS)\n", + "var = \"tas\"\n", + "years = np.arange(1979,2021)\n", + "\n", + "uid = str(uuid.uuid4())\n", + "now = datetime.now()\n", + "\n", + "JUPYTER_IMAGE = os.environ['JUPYTER_IMAGE']\n", + "START_TIME = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "f9014915-b337-45fc-8d0f-f3d2158f7d4c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " JUPYTER_IMAGE='pangeo/pangeo-notebook:2024.11.11@sha256:16ac7877726fc5ac25070f863a901945ae678e11a299585f3c404cb3d3a34a24'\n", + " START_TIME='2026-04-21 21:11:58'\n", + " \n" + ] + } + ], + "source": [ + "print(\n", + " f\"\"\"\n", + " {JUPYTER_IMAGE=}\n", + " {START_TIME=}\n", + " \"\"\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c1f4f20-0bf0-4ce1-a2a8-25cf16d88cf7", + "metadata": {}, + "outputs": [], + "source": [ + "savebase = \"gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted\"\n", + "# filename for the full dataset\n", + "savefp = f\"{savebase}/gdnat_{var}_float32_{years[0]}-{years[-1]}_v{now.strftime(\"%Y-%m-%d\")}.zarr\"\n", + "print(savefp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afecc78a", + "metadata": {}, + "outputs": [], + "source": [ + "cluster = GatewayCluster(worker_image=JUPYTER_IMAGE, scheduler_image=JUPYTER_IMAGE)\n", + "client = cluster.get_client()\n", + "print(client.dashboard_link)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c7e4525a", + "metadata": {}, + "outputs": [], + "source": [ + "cluster.scale(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "345982d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ACCESS-CM2': ['gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1979.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1980.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1981.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1982.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1983.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1984.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1985.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1986.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1987.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1988.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1989.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1990.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1991.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1992.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1993.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1994.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1995.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1996.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1997.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1998.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_1999.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2000.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2001.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2002.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2003.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2004.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2005.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2006.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2007.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2008.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2009.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2010.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2011.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2012.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2013.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2014.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2015.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2016.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2017.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2018.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2019.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-CM2/tas_2020.zarr'],\n", + " 'CanESM5': ['gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1979.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1980.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1981.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1982.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1983.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1984.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1985.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1986.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1987.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1988.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1989.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1990.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1991.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1992.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1993.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1994.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1995.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1996.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1997.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1998.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_1999.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2000.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2001.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2002.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2003.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2004.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2005.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2006.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2007.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2008.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2009.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2010.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2011.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2012.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2013.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2014.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2015.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2016.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2017.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2018.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2019.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/CanESM5/tas_2020.zarr'],\n", + " 'MIROC6': ['gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1979.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1980.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1981.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1982.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1983.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1984.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1985.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1986.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1987.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1988.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1989.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1990.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1991.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1992.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1993.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1994.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1995.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1996.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1997.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1998.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_1999.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2000.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2001.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2002.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2003.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2004.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2005.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2006.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2007.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2008.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2009.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2010.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2011.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2012.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2013.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2014.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2015.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2016.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2017.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2018.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2019.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MIROC6/tas_2020.zarr'],\n", + " 'NorESM2-LM': ['gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1979.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1980.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1981.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1982.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1983.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1984.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1985.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1986.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1987.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1988.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1989.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1990.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1991.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1992.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1993.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1994.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1995.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1996.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1997.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1998.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_1999.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2000.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2001.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2002.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2003.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2004.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2005.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2006.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2007.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2008.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2009.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2010.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2011.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2012.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2013.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2014.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2015.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2016.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2017.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2018.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2019.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/NorESM2-LM/tas_2020.zarr'],\n", + " 'ACCESS-ESM1-5': ['gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1979.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1980.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1981.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1982.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1983.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1984.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1985.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1986.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1987.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1988.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1989.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1990.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1991.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1992.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1993.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1994.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1995.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1996.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1997.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1998.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_1999.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2000.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2001.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2002.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2003.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2004.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2005.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2006.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2007.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2008.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2009.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2010.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2011.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2012.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2013.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2014.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2015.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2016.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2017.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2018.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2019.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/ACCESS-ESM1-5/tas_2020.zarr'],\n", + " 'MRI-ESM2-0': ['gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1979.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1980.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1981.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1982.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1983.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1984.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1985.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1986.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1987.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1988.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1989.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1990.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1991.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1992.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1993.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1994.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1995.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1996.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1997.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1998.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_1999.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2000.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2001.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2002.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2003.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2004.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2005.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2006.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2007.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2008.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2009.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2010.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2011.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2012.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2013.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2014.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2015.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2016.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2017.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2018.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2019.zarr',\n", + " 'gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/MRI-ESM2-0/tas_2020.zarr']}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# For each model, create a list off all the annual Zarr Store URIs to append.\n", + "input_url_model_map: dict[str, list[str]] = {}\n", + "for model in models:\n", + " uris = [f\"{bp}/{model}/{var}_{yr}.zarr\" for yr in years]\n", + " input_url_model_map[model] = uris\n", + "\n", + "input_url_model_map" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bd497ac7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['gs://impactlab-data-scratch/kemccusker/24a8eb00-f92f-4fb7-8f2a-fe62695d21ac/tmp_ACCESS-CM2_allyears.zarr',\n", + " 'gs://impactlab-data-scratch/kemccusker/24a8eb00-f92f-4fb7-8f2a-fe62695d21ac/tmp_CanESM5_allyears.zarr',\n", + " 'gs://impactlab-data-scratch/kemccusker/24a8eb00-f92f-4fb7-8f2a-fe62695d21ac/tmp_MIROC6_allyears.zarr',\n", + " 'gs://impactlab-data-scratch/kemccusker/24a8eb00-f92f-4fb7-8f2a-fe62695d21ac/tmp_NorESM2-LM_allyears.zarr',\n", + " 'gs://impactlab-data-scratch/kemccusker/24a8eb00-f92f-4fb7-8f2a-fe62695d21ac/tmp_ACCESS-ESM1-5_allyears.zarr',\n", + " 'gs://impactlab-data-scratch/kemccusker/24a8eb00-f92f-4fb7-8f2a-fe62695d21ac/tmp_MRI-ESM2-0_allyears.zarr']" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Combine all years together for each model and stash in scratch storage.\n", + "save_files = False\n", + "\n", + "model_uris: list[str] = []\n", + "for model, uris in input_url_model_map.items():\n", + " tmp_zarr_uri = f\"{os.environ['CIL_SCRATCH_PREFIX']}/{os.environ['JUPYTERHUB_USER']}/{uid}/tmp_{model}_allyears.zarr\"\n", + "\n", + " if save_files:\n", + " tmp_joined_years = (\n", + " xr.open_mfdataset(\n", + " uris, \n", + " coords=\"minimal\",\n", + " engine=\"zarr\", \n", + " chunks={}\n", + " )\n", + " .expand_dims({\"model\": [model,]})\n", + " .chunk({\"model\": 6, \"time\": -1, \"lat\": 30, \"lon\": 30})\n", + " )\n", + " # Above ^, expand dims and rechunk to avoid work and large graph size in later steps.\n", + "\n", + " \n", + " tmp_joined_years.to_zarr(tmp_zarr_uri, mode=\"w\", compute=True)\n", + " print(f\"Tmp data written to {tmp_zarr_uri}\")\n", + " model_uris.append(tmp_zarr_uri)\n", + "\n", + "model_uris" + ] + }, + { + "cell_type": "markdown", + "id": "2270a072-37a9-45c1-aac3-965051ce6746", + "metadata": {}, + "source": [ + "Next, compile all the per-GCM datasets into one dataset.\n", + "- save the full dataset\n", + "- save a subset that is one GCM for all space and time\n", + "- save a subset that is one region for all GCMs and time" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "613af6d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "size of output 381.459596232\n", + "size of region subset 16.500970632\n", + "size of one gcm subset 63.576715972\n" + ] + }, + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 381GB\n",
+       "Dimensions:  (model: 6, time: 15330, lat: 720, lon: 1440)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 6kB -90.0 -89.75 -89.5 -89.25 ... 89.25 89.5 89.75\n",
+       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * model    (model) <U13 312B 'ACCESS-CM2' 'ACCESS-ESM1-5' ... 'NorESM2-LM'\n",
+       "  * time     (time) object 123kB 1979-01-01 00:00:00 ... 2020-12-31 00:00:00\n",
+       "Data variables:\n",
+       "    tas      (model, time, lat, lon) float32 381GB dask.array<chunksize=(1, 15330, 30, 30), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Description:    GDNat: a global, daily, high-resolution natural-forcing o...\n",
+       "    Method:         Use Quantile Delta Mapping to apply quantile-by-quantile ...\n",
+       "    Created by:     Robert Fofrich <robertfofrich@ucla.edu>\n",
+       "    Creation date:  Compiled ZARR store created and saved 2026-04-21 21:11:58...\n",
+       "    Source files:   gs://impactlab-data/climate/attribution/outputs/hist-nat-...\n",
+       "    Repo:           https://github.com/ClimateImpactLab/gdnat
" + ], + "text/plain": [ + " Size: 381GB\n", + "Dimensions: (model: 6, time: 15330, lat: 720, lon: 1440)\n", + "Coordinates:\n", + " * lat (lat) float64 6kB -90.0 -89.75 -89.5 -89.25 ... 89.25 89.5 89.75\n", + " * lon (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * model (model) \n", + "Attributes:\n", + " Description: GDNat: a global, daily, high-resolution natural-forcing o...\n", + " Method: Use Quantile Delta Mapping to apply quantile-by-quantile ...\n", + " Created by: Robert Fofrich \n", + " Creation date: Compiled ZARR store created and saved 2026-04-21 21:11:58...\n", + " Source files: gs://impactlab-data/climate/attribution/outputs/hist-nat-...\n", + " Repo: https://github.com/ClimateImpactLab/gdnat" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "allds = xr.open_mfdataset(model_uris, coords=\"minimal\", engine=\"zarr\", chunks={})\n", + "allds = allds.drop_vars(\"height\")\n", + "\n", + "# Work around https://github.com/pydata/xarray/issues/3476\n", + "allds[\"model\"] = allds[\"model\"].astype(\"unicode\")\n", + "\n", + "\n", + "print(\"size of output\", allds.nbytes*1e-9)\n", + "\n", + "# Format the date and time as a string\n", + "formatted_date = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n", + "\n", + "attrs = {\n", + " \"Description\": \"GDNat: a global, daily, high-resolution natural-forcing only temperature data set for attribution research\",\n", + " \"Method\": \"Use Quantile Delta Mapping to apply quantile-by-quantile adjustment factors between `historical` and `historical-nat` simulations to ERA5 on a grid cell, daily basis.\",\n", + " \"Created by\": \"Robert Fofrich \",\n", + " \"Creation date\": f\"Compiled ZARR store created and saved {formatted_date}. Compiled by Kelly McCusker \",\n", + " \"Source files\": \"gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/\",\n", + " \"Repo\": \"https://github.com/ClimateImpactLab/gdnat\",\n", + "}\n", + "varattrs = {\n", + " \"Description\": \"Surface air temperature at the reference height (typically 2m) stored as float32 data.\",\n", + " \"Variable names\": \"originally 2m_temperature in ERA5, tas in CMIP6 earth system model output\",\n", + " \"Units\": \"K\"\n", + "}\n", + "\n", + "allds.attrs = attrs\n", + "allds.tas.attrs.update(varattrs)\n", + "\n", + "# select a region in Eurasia\n", + "regionds = allds.sel(lat=slice(35,72))\n", + "regionds = regionds.sel(lon=slice(0, 75)).dropna(dim=\"lon\")\n", + "print(\"size of region subset\", regionds.nbytes*1e-9)\n", + "\n", + "# select just one GCM\n", + "onemodelds = allds.sel(model=\"MIROC6\")\n", + "print(\"size of one gcm subset\", onemodelds.nbytes*1e-9)\n", + "\n", + "allds" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "9ddb12ad-ec51-47af-941d-65758e606849", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 17GB\n",
+       "Dimensions:  (model: 6, time: 15330, lat: 149, lon: 301)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 1kB 35.0 35.25 35.5 35.75 ... 71.25 71.5 71.75 72.0\n",
+       "  * lon      (lon) float64 2kB 0.0 0.25 0.5 0.75 1.0 ... 74.25 74.5 74.75 75.0\n",
+       "  * model    (model) <U13 312B 'ACCESS-CM2' 'ACCESS-ESM1-5' ... 'NorESM2-LM'\n",
+       "  * time     (time) object 123kB 1979-01-01 00:00:00 ... 2020-12-31 00:00:00\n",
+       "Data variables:\n",
+       "    tas      (model, time, lat, lon) float32 17GB dask.array<chunksize=(1, 15330, 10, 30), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Description:    GDNat: a global, daily, high-resolution natural-forcing o...\n",
+       "    Method:         Use Quantile Delta Mapping to apply quantile-by-quantile ...\n",
+       "    Created by:     Robert Fofrich <robertfofrich@ucla.edu>\n",
+       "    Creation date:  Compiled ZARR store created and saved 2026-04-21 21:11:58...\n",
+       "    Source files:   gs://impactlab-data/climate/attribution/outputs/hist-nat-...\n",
+       "    Repo:           https://github.com/ClimateImpactLab/gdnat
" + ], + "text/plain": [ + " Size: 17GB\n", + "Dimensions: (model: 6, time: 15330, lat: 149, lon: 301)\n", + "Coordinates:\n", + " * lat (lat) float64 1kB 35.0 35.25 35.5 35.75 ... 71.25 71.5 71.75 72.0\n", + " * lon (lon) float64 2kB 0.0 0.25 0.5 0.75 1.0 ... 74.25 74.5 74.75 75.0\n", + " * model (model) \n", + "Attributes:\n", + " Description: GDNat: a global, daily, high-resolution natural-forcing o...\n", + " Method: Use Quantile Delta Mapping to apply quantile-by-quantile ...\n", + " Created by: Robert Fofrich \n", + " Creation date: Compiled ZARR store created and saved 2026-04-21 21:11:58...\n", + " Source files: gs://impactlab-data/climate/attribution/outputs/hist-nat-...\n", + " Repo: https://github.com/ClimateImpactLab/gdnat" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regionds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14999747-fa6d-4afc-b451-d8abf49c7f54", + "metadata": {}, + "outputs": [], + "source": [ + "regionds_chunked = regionds.chunk({\"model\": 6, \"time\": -1, \"lat\": 30, \"lon\": 30})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "6a856443-0752-43f0-8318-b0504b5906e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cf \n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig = plt.figure(figsize=(8, 8))\n", + "ax = plt.axes(projection=ccrs.PlateCarree())\n", + "regionds.isel(time=50,model=2).tas.plot(ax=ax)\n", + "ax.add_feature(cf.COASTLINE) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "a1472998-0985-4e71-a953-d7845af99026", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/gdnat_tas_float32_region_1979-2020_v2026-04-21.zarr\n", + "Chunked data written to gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/gdnat_tas_float32_region_1979-2020_v2026-04-21.zarr\n" + ] + } + ], + "source": [ + "# FOR PAPER REVIEW, save one region for all GCMs and time\n", + "regsavefp = f\"{savebase}/gdnat_{var}_float32_region_{years[0]}-{years[-1]}_v{now.strftime(\"%Y-%m-%d\")}.zarr\"\n", + "print(regsavefp)\n", + "\n", + "regionds_chunked.to_zarr(regsavefp, mode=\"w\")\n", + "\n", + "print(f\"Chunked data written to {regsavefp}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2d8bdcac-a3db-4d47-9795-fcc3ca6ede2c", + "metadata": {}, + "outputs": [], + "source": [ + "onemodelds_chunked = onemodelds.chunk({\"time\": -1, \"lat\": 30, \"lon\": 30})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "14fe04f3-7481-4160-a34f-402d22db3932", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/gdnat_tas_float32_MIROC6_1979-2020_v2026-04-21.zarr\n", + "Chunked data written to gs://impactlab-data/climate/attribution/outputs/hist-nat-adjusted/gdnat_tas_float32_MIROC6_1979-2020_v2026-04-21.zarr\n" + ] + } + ], + "source": [ + "# FOR PAPER REVIEW: save one GCM, all space and time.\n", + "\n", + "onesavefp = f\"{savebase}/gdnat_{var}_float32_MIROC6_{years[0]}-{years[-1]}_v{now.strftime(\"%Y-%m-%d\")}.zarr\"\n", + "print(onesavefp)\n", + "\n", + "onemodelds_chunked.to_zarr(onesavefp, mode=\"w\")\n", + "\n", + "print(f\"Chunked data written to {onesavefp}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "26648714", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 381GB\n",
+       "Dimensions:  (model: 6, time: 15330, lat: 720, lon: 1440)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 6kB -90.0 -89.75 -89.5 -89.25 ... 89.25 89.5 89.75\n",
+       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * model    (model) <U13 312B 'ACCESS-CM2' 'ACCESS-ESM1-5' ... 'NorESM2-LM'\n",
+       "  * time     (time) object 123kB 1979-01-01 00:00:00 ... 2020-12-31 00:00:00\n",
+       "Data variables:\n",
+       "    tas      (model, time, lat, lon) float32 381GB dask.array<chunksize=(6, 15330, 30, 30), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Description:    GDNat: a global, daily, high-resolution natural-forcing o...\n",
+       "    Method:         Use Quantile Delta Mapping to apply quantile-by-quantile ...\n",
+       "    Created by:     Robert Fofrich <robertfofrich@ucla.edu>\n",
+       "    Creation date:  Compiled ZARR store created and saved 2025-07-31 21:15:15...\n",
+       "    Source files:   gs://impactlab-data/climate/attribution/outputs/hist-nat-...\n",
+       "    Repo:           https://github.com/ClimateImpactLab/gdnat
" + ], + "text/plain": [ + " Size: 381GB\n", + "Dimensions: (model: 6, time: 15330, lat: 720, lon: 1440)\n", + "Coordinates:\n", + " * lat (lat) float64 6kB -90.0 -89.75 -89.5 -89.25 ... 89.25 89.5 89.75\n", + " * lon (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * model (model) \n", + "Attributes:\n", + " Description: GDNat: a global, daily, high-resolution natural-forcing o...\n", + " Method: Use Quantile Delta Mapping to apply quantile-by-quantile ...\n", + " Created by: Robert Fofrich \n", + " Creation date: Compiled ZARR store created and saved 2025-07-31 21:15:15...\n", + " Source files: gs://impactlab-data/climate/attribution/outputs/hist-nat-...\n", + " Repo: https://github.com/ClimateImpactLab/gdnat" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "allds_chunked = allds.chunk({\"model\": 6, \"time\": -1, \"lat\": 30, \"lon\": 30})\n", + "allds_chunked" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccfed964", + "metadata": {}, + "outputs": [], + "source": [ + "allds_chunked.to_zarr(savefp, mode=\"w\")\n", + "\n", + "print(f\"Chunked data written to {savefp}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b534567-ca01-437f-a9f6-11856a08ed26", + "metadata": {}, + "outputs": [], + "source": [ + "cluster.scale(0)\n", + "cluster.shutdown() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95c98a43-4256-4bf3-8718-a7fef14c4baa", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}