{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Accessing CMIP6 output with `intake`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "A large swath of CMIP6 output is hosted by the [AWS Open Data Sponsorship Program](https://aws.amazon.com/opendata/public-datasets/) and by Google as part of [Google Cloud Public Datasets](https://cloud.google.com/public-datasets). This notebook will demonstrate how to access, query and request data of interest from a particular source's CMIP6 holdings using [intake](https://github.com/intake/intake-esm). Additionally, we will look at how to concatenate output that may not be on the same calendar.\n", "\n", "1. Browse the CMIP6 catalog\n", "1. Select CMIP6 output of interest\n", "1. Concatenate experiments in time (including converting calendars when required)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prerequisites\n", "| Concepts | Importance | Notes |\n", "| --- | --- | --- |\n", "| Xarray | Helpful | |\n", "| CMIP6 | Helpful |Familiarity with metadata structure |\n", "\n", "- **Time to learn**: 15 min\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict, Counter\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "import intake\n", "import xarray as xr\n", "import nc_time_axis\n", "import pyleoclim as pyleo\n", "import cftime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CMIP6 Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's worth taking a minute to explore the catalog and how it's organized. Never fear, opening the datastore doesn't load all holdings into memory, which is good because at the time of writing this there were over 520k files available. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

pangeo-cmip6 catalog with 7674 dataset(s) from 514818 asset(s):

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unique
activity_id18
institution_id36
source_id88
experiment_id170
member_id657
table_id37
variable_id700
grid_label10
zstore514818
dcpp_init_year60
version736
derived_variable_id0
\n", "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# for Google Cloud:\n", "col = intake.open_esm_datastore(\"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\")\n", "\n", "# for AWS S3:\n", "# col = intake.open_esm_datastore(\"https://cmip6-pds.s3.amazonaws.com/pangeo-cmip6.json\")\n", "col" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, we could look at the catalog as a pandas dataframe. While `intake` isn't setup to allow you to pick and choose which datasets you load by filtering the dataframe, the form factor can still be useful for arriving at search criteria." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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activity_idinstitution_idsource_idexperiment_idmember_idtable_idvariable_idgrid_labelzstoredcpp_init_yearversion
0HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
1HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrsdsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
2HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrlusgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
3HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonrldsgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
4HighResMIPCMCCCMCC-CM2-HR4highresSST-presentr1i1p1f1Amonpslgngs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/...NaN20170706
\n", "
" ], "text/plain": [ " activity_id institution_id source_id experiment_id member_id \n", "0 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \\\n", "1 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", "2 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", "3 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", "4 HighResMIP CMCC CMCC-CM2-HR4 highresSST-present r1i1p1f1 \n", "\n", " table_id variable_id grid_label \n", "0 Amon ps gn \\\n", "1 Amon rsds gn \n", "2 Amon rlus gn \n", "3 Amon rlds gn \n", "4 Amon psl gn \n", "\n", " zstore dcpp_init_year version \n", "0 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", "1 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", "2 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", "3 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 \n", "4 gs://cmip6/CMIP6/HighResMIP/CMCC/CMCC-CM2-HR4/... NaN 20170706 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "col.df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can quickly print a list of the values associated with `experiment_id` (or any other column). Nothing ground breaking, but handy when you want to quickly see what experiment output is available. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of experiments: 170\n" ] }, { "data": { "text/plain": [ "array(['highresSST-present', 'piControl', 'control-1950', 'hist-1950',\n", " 'historical', 'amip', 'abrupt-4xCO2', 'abrupt-2xCO2',\n", " 'abrupt-0p5xCO2', '1pctCO2', 'ssp585', 'esm-piControl', 'esm-hist',\n", " 'hist-piAer', 'histSST-1950HC', 'ssp245', 'hist-1950HC', 'histSST',\n", " 'piClim-2xVOC', 'piClim-2xNOx'], dtype=object)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sample of experiments\n", "print('number of experiments: {}'.format(len(col.df.experiment_id.unique())))\n", "col.df.experiment_id.unique()[:20]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Identifying potential data of interest\n", "It can be useful to do some rearranging to see how many files are available for each of the most commonly reported variables for each experiment (labeled for convenience by institution and source)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "experiments = ['historical', 'past1000', 'midHolocene', 'hist-volc','lgm']\n", "experiment_subset = col.df[col.df.experiment_id.isin(experiments)]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "sources = [grp[0] for grp in experiment_subset.groupby('source_id') if len(grp[1].experiment_id.unique())>2]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [] }, "outputs": [], "source": [ "source_sub = experiment_subset[experiment_subset.source_id.isin(sources)]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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prtatasuatotal
institution_idexperiment_idsource_id
AWIhistoricalAWI-ESM-1-1-LR2.03.03.05.013.0
lgmAWI-ESM-1-1-LR1.01.01.00.03.0
midHoloceneAWI-ESM-1-1-LR0.01.00.01.02.0
INMhistoricalINM-CM4-82.02.02.03.09.0
lgmINM-CM4-81.00.01.00.02.0
midHoloceneINM-CM4-80.01.00.01.02.0
MIROChistoricalMIROC-ES2L41.031.042.033.0147.0
lgmMIROC-ES2L1.01.01.01.04.0
past1000MIROC-ES2L1.01.00.00.02.0
MPI-MhistoricalMPI-ESM1-2-LR20.023.030.028.0101.0
lgmMPI-ESM1-2-LR1.01.01.01.04.0
midHoloceneMPI-ESM1-2-LR0.01.00.01.02.0
MRIhist-volcMRI-ESM2-00.00.00.00.00.0
historicalMRI-ESM2-023.017.018.021.079.0
midHoloceneMRI-ESM2-00.01.00.01.02.0
past1000MRI-ESM2-01.01.01.00.03.0
\n", "
" ], "text/plain": [ " pr ta tas ua total\n", "institution_id experiment_id source_id \n", "AWI historical AWI-ESM-1-1-LR 2.0 3.0 3.0 5.0 13.0\n", " lgm AWI-ESM-1-1-LR 1.0 1.0 1.0 0.0 3.0\n", " midHolocene AWI-ESM-1-1-LR 0.0 1.0 0.0 1.0 2.0\n", "INM historical INM-CM4-8 2.0 2.0 2.0 3.0 9.0\n", " lgm INM-CM4-8 1.0 0.0 1.0 0.0 2.0\n", " midHolocene INM-CM4-8 0.0 1.0 0.0 1.0 2.0\n", "MIROC historical MIROC-ES2L 41.0 31.0 42.0 33.0 147.0\n", " lgm MIROC-ES2L 1.0 1.0 1.0 1.0 4.0\n", " past1000 MIROC-ES2L 1.0 1.0 0.0 0.0 2.0\n", "MPI-M historical MPI-ESM1-2-LR 20.0 23.0 30.0 28.0 101.0\n", " lgm MPI-ESM1-2-LR 1.0 1.0 1.0 1.0 4.0\n", " midHolocene MPI-ESM1-2-LR 0.0 1.0 0.0 1.0 2.0\n", "MRI hist-volc MRI-ESM2-0 0.0 0.0 0.0 0.0 0.0\n", " historical MRI-ESM2-0 23.0 17.0 18.0 21.0 79.0\n", " midHolocene MRI-ESM2-0 0.0 1.0 0.0 1.0 2.0\n", " past1000 MRI-ESM2-0 1.0 1.0 1.0 0.0 3.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table = pd.pivot_table(experiment_subset[experiment_subset.source_id.isin(sources)],\n", " values='member_id', index=['institution_id','experiment_id','source_id'],\n", " columns=['variable_id'], aggfunc='count')\n", "\n", "table = table.dropna(axis='columns', thresh=int(.65*len(table))).fillna(value=0)\n", "pd.concat([table, table.aggregate('sum', axis=\"columns\").rename('total')], axis=1)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pulling data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Forming a query dictionary\n", "From here we need to write a dictionary with information `intake` can use to filter the collection. Sometimes hard coding the dictionary by copying and pasting particular values, is the most efficient way to go, but below it is assembled based on the sources and experiments used to filter above, and the variables most commonly reported." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['pr', 'ta', 'tas', 'ua'], dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table.columns.values" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "query_d = dict(source_id=sources, \n", " experiment_id=experiments, \n", " grid_label='gn', \n", " variable_id=table.columns.values.tolist(), \n", " table_id='Amon'\n", " )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ " search_res = col.search(**query_d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additionally, there is at least one, but often multiple members. For our purposes, it would be best to have the same member configurations across experiments so let's identify which members are common and refine our query accordingly" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "members = list(set([grp[0][2] for grp in search_res.df.groupby(['source_id', 'variable_id', 'member_id']) \n", " if len(grp[1].experiment_id.unique())>1]))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "query_d['member_id'] = members" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "search_res = col.search(**query_d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Requesting from the cloud" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "--> The keys in the returned dictionary of datasets are constructed as follows:\n", "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", " \n", " 100.00% [9/9 00:14<00:00]\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "dict_keys(['PMIP.MIROC.MIROC-ES2L.past1000.Amon.gn', 'CMIP.AWI.AWI-ESM-1-1-LR.historical.Amon.gn', 'CMIP.MIROC.MIROC-ES2L.historical.Amon.gn', 'PMIP.MIROC.MIROC-ES2L.lgm.Amon.gn', 'CMIP.MPI-M.MPI-ESM1-2-LR.historical.Amon.gn', 'PMIP.AWI.AWI-ESM-1-1-LR.lgm.Amon.gn', 'PMIP.MRI.MRI-ESM2-0.past1000.Amon.gn', 'PMIP.MPI-M.MPI-ESM1-2-LR.lgm.Amon.gn', 'CMIP.MRI.MRI-ESM2-0.historical.Amon.gn'])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sometimes you have to run this cell twice\n", "_esm_data_d = search_res.to_dataset_dict(require_all_on=['source_id', 'grid_label', 'table_id', 'variant_label'],#['source_id', 'experiment_id'], \n", " xarray_open_kwargs={'consolidated': True,'use_cftime':True, 'chunks':{}},\n", " storage_options={'token': 'anon'})\n", "_esm_data_d.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The returned dictionary has one level. Let's rearrange a bit so that the `institution_id` is top level, then the `source_id`, and then the `experiment_id`. This will make it a bit easier to grab experiments from the same source. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "esm_data_d = {}\n", "for key in _esm_data_d.keys():\n", " parts = key.split('.', 4)\n", " if parts[1] not in esm_data_d.keys():\n", " esm_data_d[parts[1]]= defaultdict(dict)\n", " esm_data_d[parts[1]][parts[2]][parts[3]] = _esm_data_d[key]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MIROC\n", "\tMIROC-ES2L: dict_keys(['past1000', 'historical', 'lgm'])\n", "AWI\n", "\tAWI-ESM-1-1-LR: dict_keys(['historical', 'lgm'])\n", "MPI-M\n", "\tMPI-ESM1-2-LR: dict_keys(['historical', 'lgm'])\n", "MRI\n", "\tMRI-ESM2-0: dict_keys(['past1000', 'historical'])\n" ] } ], "source": [ "for key in esm_data_d.keys():\n", " print(key)\n", " for key2 in esm_data_d[key].keys():\n", " if type(esm_data_d[key][key2])==dict:\n", " print('\\t{}: {}'.format(key2, esm_data_d[key][key2].keys()))\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making a single time series\n", "Let's concatenate the `past1000` and `historical` output into a single timeseries." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "tags": [] }, "outputs": [], "source": [ "exp_list= [esm_data_d['MRI']['MRI-ESM2-0'][key] for key in ['past1000', 'historical']]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:         (lat: 160, bnds: 2, lon: 320, member_id: 1,\n",
       "                     dcpp_init_year: 1, time: 13980, plev: 19)\n",
       "Coordinates:\n",
       "  * lat             (lat) float64 -89.14 -88.03 -86.91 ... 86.91 88.03 89.14\n",
       "    lat_bnds        (lat, bnds) float64 -90.0 -88.59 -88.59 ... 88.59 88.59 90.0\n",
       "  * lon             (lon) float64 0.0 1.125 2.25 3.375 ... 356.6 357.8 358.9\n",
       "    lon_bnds        (lon, bnds) float64 -0.5625 0.5625 0.5625 ... 358.3 359.4\n",
       "  * time            (time) object 0850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bnds       (time, bnds) object dask.array<chunksize=(12000, 2), meta=np.ndarray>\n",
       "  * member_id       (member_id) object 'r1i1p1f1'\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
       "  * plev            (plev) float64 1e+05 9.25e+04 8.5e+04 ... 1e+03 500.0 100.0\n",
       "    height          float64 2.0\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    pr              (member_id, dcpp_init_year, time, lat, lon) float32 dask.array<chunksize=(1, 1, 308, 160, 320), meta=np.ndarray>\n",
       "    ta              (member_id, dcpp_init_year, time, plev, lat, lon) float32 dask.array<chunksize=(1, 1, 26, 19, 160, 320), meta=np.ndarray>\n",
       "    tas             (member_id, dcpp_init_year, time, lat, lon) float32 dask.array<chunksize=(1, 1, 439, 160, 320), meta=np.ndarray>\n",
       "    ua              (member_id, dcpp_init_year, time, plev, lat, lon) float32 dask.array<chunksize=(1, 1, 35, 19, 160, 320), meta=np.ndarray>\n",
       "Attributes: (12/44)\n",
       "    Conventions:                      CF-1.7 CMIP-6.2\n",
       "    activity_id:                      PMIP\n",
       "    branch_method:                    no parent\n",
       "    cmor_version:                     3.5.0\n",
       "    data_specs_version:               01.00.31\n",
       "    experiment:                       last millennium\n",
       "    ...                               ...\n",
       "    intake_esm_attrs:member_id:       r1i1p1f1\n",
       "    intake_esm_attrs:table_id:        Amon\n",
       "    intake_esm_attrs:grid_label:      gn\n",
       "    intake_esm_attrs:version:         20200120\n",
       "    intake_esm_attrs:_data_format_:   zarr\n",
       "    intake_esm_dataset_key:           PMIP.MRI.MRI-ESM2-0.past1000.Amon.gn
" ], "text/plain": [ "\n", "Dimensions: (lat: 160, bnds: 2, lon: 320, member_id: 1,\n", " dcpp_init_year: 1, time: 13980, plev: 19)\n", "Coordinates:\n", " * lat (lat) float64 -89.14 -88.03 -86.91 ... 86.91 88.03 89.14\n", " lat_bnds (lat, bnds) float64 -90.0 -88.59 -88.59 ... 88.59 88.59 90.0\n", " * lon (lon) float64 0.0 1.125 2.25 3.375 ... 356.6 357.8 358.9\n", " lon_bnds (lon, bnds) float64 -0.5625 0.5625 0.5625 ... 358.3 359.4\n", " * time (time) object 0850-01-16 12:00:00 ... 2014-12-16 12:00:00\n", " time_bnds (time, bnds) object dask.array\n", " * member_id (member_id) object 'r1i1p1f1'\n", " * dcpp_init_year (dcpp_init_year) float64 nan\n", " * plev (plev) float64 1e+05 9.25e+04 8.5e+04 ... 1e+03 500.0 100.0\n", " height float64 2.0\n", "Dimensions without coordinates: bnds\n", "Data variables:\n", " pr (member_id, dcpp_init_year, time, lat, lon) float32 dask.array\n", " ta (member_id, dcpp_init_year, time, plev, lat, lon) float32 dask.array\n", " tas (member_id, dcpp_init_year, time, lat, lon) float32 dask.array\n", " ua (member_id, dcpp_init_year, time, plev, lat, lon) float32 dask.array\n", "Attributes: (12/44)\n", " Conventions: CF-1.7 CMIP-6.2\n", " activity_id: PMIP\n", " branch_method: no parent\n", " cmor_version: 3.5.0\n", " data_specs_version: 01.00.31\n", " experiment: last millennium\n", " ... ...\n", " intake_esm_attrs:member_id: r1i1p1f1\n", " intake_esm_attrs:table_id: Amon\n", " intake_esm_attrs:grid_label: gn\n", " intake_esm_attrs:version: 20200120\n", " intake_esm_attrs:_data_format_: zarr\n", " intake_esm_dataset_key: PMIP.MRI.MRI-ESM2-0.past1000.Amon.gn" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_ds = xr.concat(exp_list, dim='time')\n", "_ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to visualize the timeseries we just carefully assembled, we need to focus in on a specific location (we'll save the production of an animation showing the time evolution of a spatial distribution for a future notebook). In the cell below, we'll specify a dictionary containing a `lat`, `lon` pair. By using the coordinate names used for latitude and longitude in the xarray dataset, we can select from the dataset simply by passing the dictionary. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's focus in on -10N, 110E, which happens to be a location downwind of Tambora, a volcano in Indonesia that erupted enthusiastically in 1816. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "lat, lon = -10, 110" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "tags": [] }, "outputs": [], "source": [ "loc = {'lat':lat, 'lon':lon}\n", "da = _ds.sel(loc, method=\"nearest\")['tas']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that `method` is set to `\"nearest\"`. It's useful that Xarray will return the nearest data to the search criteria. Other options include: `None, \"nearest\", \"pad\", \"ffill\", \"backfill\", \"bfill\"`. Details about what these options entail can be found in the [xarray documentation](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.sel.html).\n", "\n", "Looking into the details of the returned DataArray, we can see that the nearest location was -9.533N, 110.2E. Very near, indeed. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'tas' (member_id: 1, dcpp_init_year: 1, time: 13980)>\n",
       "dask.array<getitem, shape=(1, 1, 13980), dtype=float32, chunksize=(1, 1, 600), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    lat             float64 -9.533\n",
       "    lon             float64 110.2\n",
       "  * time            (time) object 0850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * member_id       (member_id) object 'r1i1p1f1'\n",
       "  * dcpp_init_year  (dcpp_init_year) float64 nan\n",
       "    height          float64 2.0\n",
       "Attributes:\n",
       "    cell_measures:  area: areacella\n",
       "    cell_methods:   area: time: mean\n",
       "    comment:        near-surface (usually, 2 meter) air temperature\n",
       "    history:        2020-01-04T03:36:44Z altered by CMOR: Treated scalar dime...\n",
       "    long_name:      Near-Surface Air Temperature\n",
       "    original_name:  TA\n",
       "    standard_name:  air_temperature\n",
       "    units:          K
" ], "text/plain": [ "\n", "dask.array\n", "Coordinates:\n", " lat float64 -9.533\n", " lon float64 110.2\n", " * time (time) object 0850-01-16 12:00:00 ... 2014-12-16 12:00:00\n", " * member_id (member_id) object 'r1i1p1f1'\n", " * dcpp_init_year (dcpp_init_year) float64 nan\n", " height float64 2.0\n", "Attributes:\n", " cell_measures: area: areacella\n", " cell_methods: area: time: mean\n", " comment: near-surface (usually, 2 meter) air temperature\n", " history: 2020-01-04T03:36:44Z altered by CMOR: Treated scalar dime...\n", " long_name: Near-Surface Air Temperature\n", " original_name: TA\n", " standard_name: air_temperature\n", " units: K" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "da" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to build a Pyleoclim `Series`, we need to convert the time units into fractional years. To do this, we'll use a package called `cftime` that has a slurry of methods to help convert between different descriptions of time. In the cell below, let's write a short function that converts the time coordinate of a DataArray first to `'days since 00-01-01'` (specifying that `has_year_zero`=`True`) and then fractional years by dividing by 365 (assuming no leap year)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def time_to_float(da):\n", " float_time = [cftime.date2num(da.time.data[ik],\n", " units='days since 00-01-01',\n", " calendar='noleap', \n", " has_year_zero=True)/365 \n", " for ik in range(len(da.time))]\n", " return float_time" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 5 µs, sys: 2 µs, total: 7 µs\n", "Wall time: 11 µs\n" ] }, { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%time\n", "model ='MRI-ESM2-0'\n", "ts = pyleo.Series(time=time_to_float(da), value=da.squeeze().data, \n", " time_unit='years', clean_ts=False, \n", " value_name='Surface Air Temp (K)',\n", " verbose=False)\n", "ts.plot(xlabel='year', \n", " title='{}; lat: {}N, lon: {}E'.format(model, loc['lat'], loc['lon']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Converting Calendars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "

Warning

\n", " Experiments do not necessarily fall on the same calendar, even those carried out using the same model. \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If two different datasets do not use the same calendar, it will be difficult to use the concatenated data. For example, `past1000` is of type `cftime.DatetimeProlepticGregorian` while `historical` is of type `cftime.DatetimeGregorian`, despite both being produced with MIROC-ES2L." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "tags": [] }, "outputs": [], "source": [ "exp_list= [esm_data_d['MIROC']['MIROC-ES2L'][key] for key in ['past1000', 'historical']]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array(cftime.DatetimeProlepticGregorian(850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n", " dtype=object),\n", " array(cftime.DatetimeGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=False),\n", " dtype=object))" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp_list[0].time[0].data, exp_list[1].time[0].data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, xarray can help! If we pass a target calendar (e.g. 'proleptic_gregorian') to `convert_calendar()` (a method attached to our dataset), xarray will do its best to make the conversion. Note: specify `use_cftime=True` if you prefer time to be of type `cftime` as xarray returns in type `datetime64[ns]` by default." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "exp_list[1] = exp_list[1].convert_calendar('proleptic_gregorian', \n", " use_cftime=True)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array(cftime.DatetimeProlepticGregorian(850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n", " dtype=object),\n", " array(cftime.DatetimeProlepticGregorian(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n", " dtype=object))" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "exp_list[0].time[0].data, exp_list[1].time[0].data" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "_ds = xr.concat(exp_list, dim='time')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "tags": [] }, "outputs": [], "source": [ "loc['plev']=100000\n", "da = _ds.sel(loc, method=\"nearest\")['ta']" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
,\n", " )" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model ='MIROC-ES2L'\n", "ts = pyleo.Series(time_to_float(da), da.squeeze().data, \n", " time_unit='years', clean_ts=False, \n", " value_name='Air Temp (K) at 1000hPa',\n", " verbose=False)\n", "ts.plot(xlabel='year', \n", " title='{}; lat: {}N, lon: {}E'.format(model, loc['lat'], loc['lon']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, the two experiments are visualized together without trouble, so by no means is it always necessary to formally convert calendars and concatenate. However, it can be convenient for resampling, or other subsequent data processing." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "loc = {'lat':lat, 'lon':lon, 'plev':100000}\n", "model ='MIROC-ES2L'\n", "segs = []\n", "for key in ['past1000', 'historical']:\n", " da = esm_data_d['MIROC']['MIROC-ES2L'][key].sel(loc, method=\"nearest\")['ta']\n", " segs.append(pyleo.Series(time_to_float(da), da.squeeze().data, \n", " time_unit='years', \n", " clean_ts=False,\n", " value_name='Air Temp (K) at 1000hPa', \n", " label = '_'.join([model, key]),\n", " verbose=False))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = segs[0].plot(xlabel='year', title='{}; lat: {}N, lon: {}E'.format(model, loc['lat'], loc['lon']))\n", "segs[1].plot(ax = ax[1], xlabel='year', title='{}; lat: {}N, lon: {}E'.format(model, loc['lat'], loc['lon']))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary\n", "Accessing CMIP6 data via `intake` takes a little getting used to, but is a very efficient strategy for leveraging a mammoth body of data without having to store it locally. Working with output from different sources/experiments comes with processing overhead like aligning calendars, but the community (particularly `xarray`) is rich with strategies for smoothing these wrinkles. \n", "\n", "## What's next?\n", "Check out our science bit about [CMIP6 and LMR](../science_bits/CMIP6_LMR.ipynb) to see how to compare CMIP6 model output to climate reconstructions!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resources and references\n", "\n", "| _For details, see_|\n", "|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n", "| Kageyama, M., Braconnot, P., Harrison, S. P., Haywood, A. M., Jungclaus, J. H., Otto-Bliesner, B. L., Peterschmitt, J.-Y., Abe-Ouchi, A., Albani, S., Bartlein, P. J., Brierley, C., Crucifix, M., Dolan, A., Fernandez-Donado, L., Fischer, H., Hopcroft, P. O., Ivanovic, R. F., Lambert, F., Lunt, D. J., Mahowald, N. M., Peltier, W. R., Phipps, S. J., Roche, D. M., Schmidt, G. A., Tarasov, L., Valdes, P. J., Zhang, Q., and Zhou, T.: The PMIP4 contribution to CMIP6 – Part 1: Overview and over-arching analysis plan, Geosci. Model Dev., 11, 1033–1057, https://doi.org/10.5194/gmd-11-1033-2018, 2018.|" ] } ], "metadata": { "kernelspec": { "display_name": "conda-env-paleobook-dev-py", "language": "python", "name": "conda-env-paleobook-dev-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.10" }, "nbdime-conflicts": { "local_diff": [ { "diff": [ { "diff": [ { "key": 0, "op": "addrange", "valuelist": [ "Python 3" ] }, { "key": 0, "length": 1, "op": "removerange" } ], "key": "display_name", "op": "patch" } ], "key": "kernelspec", "op": "patch" } ], "remote_diff": [ { "diff": [ { "diff": [ { "key": 0, "op": "addrange", "valuelist": [ "Python3" ] }, { "key": 0, "length": 1, "op": "removerange" } ], "key": "display_name", "op": "patch" } ], "key": "kernelspec", "op": "patch" } ] }, "toc-autonumbering": false }, "nbformat": 4, "nbformat_minor": 4 }