Running your first recipe

Overview

Teaching: 15 min
Exercises: 15 min
Compatibility: ESMValTool v2.12.0

Questions

How to run a recipe?

What happens when I run a recipe?

Objectives

Run an existing ESMValTool recipe

Examine the log information

Navigate the output created by ESMValTool

Make small adjustments to an existing recipe

This episode describes how ESMValTool recipes work, how to run a recipe and how to explore the recipe output. By the end of this episode, you should be able to run your first recipe, look at the recipe output, and make small modifications.

Running an existing recipe

The recipe format has briefly been introduced in the Introduction episode. To see all the recipes that are shipped with ESMValTool, type

esmvaltool recipes list

We will start by running examples/recipe_python.yml

esmvaltool run examples/recipe_python.yml

or if you have the user configuration file in your current directory then

esmvaltool run --config_dir . examples/recipe_python.yml

If everything is okay, you should see that ESMValTool is printing a lot of output to the command line. The final message should be “Run was successful”. The exact output varies depending on your machine, but it should look something like the example log output on terminal below.

INFO [3966381] ________________________ ___ __ __ __ __ _ ___ _ | __/ || \/ \ \ / / | | |__ __ | | | | ___ | |\/| |\ \ / / _` | | | |/ _ \ / _ | | | | ) | | | | \ V / (| | | | | () | () | | ||/|| || _/ _,|| ||_/ _/|| ________________________

Earth System Model Evaluation Tool

A community tool for the evaluation of Earth system models.

https://esmvaltool.org

The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth System Models (ESMs) that allows for routine comparison of single or multiple models, either against predecessor versions or against observations.

Tutorial: https://tutorial.esmvaltool.org Documentation: https://docs.esmvaltool.org Contact: esmvaltool-dev@listserv.dfn.de

If you find this software useful for your research, please cite it using https://doi.org/10.5281/zenodo.3387139 for ESMValCore or https://doi.org/10.5281/zenodo.3401363 for ESMValTool or any of the reference papers listed at https://esmvaltool.org/references/.

Have fun!

INFO [3966381] Package versions INFO [3966381] —————- INFO [3966381] ESMValCore: 2.12.0 INFO [3966381] ESMValTool: 2.12.0 INFO [3966381] —————- INFO [3966381] Reading configuration files from: /home/user/username/ESMValTool/ESMValCore/esmvalcore/config/configurations/defaults (defaults) /home/user/username/.config/esmvaltool (default user configuration directory) INFO [3966381] Writing program log files to: /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/main_log.txt /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/main_log_debug.txt /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/cmor_log.txt INFO [3966381] Starting the Earth System Model Evaluation Tool at time: 2025-06-03 12:01:59 UTC INFO [3966381] ———————————————————————- INFO [3966381] RECIPE = /home/user/username/ESMValTool/ESMValTool/esmvaltool/recipes/examples/recipe_python.yml INFO [3966381] RUNDIR = /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run INFO [3966381] WORKDIR = /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/work INFO [3966381] PREPROCDIR = /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/preproc INFO [3966381] PLOTDIR = /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/plots INFO [3966381] ———————————————————————- INFO [3966381] Running tasks using at most 256 processes INFO [3966381] If your system hangs during execution, it may not have enough memory for keeping this number of tasks in memory. INFO [3966381] If you experience memory problems, try reducing ‘max_parallel_tasks’ in your configuration. INFO [3966381] Creating tasks from recipe INFO [3966381] Creating tasks for diagnostic map INFO [3966381] Creating diagnostic task map/script1 INFO [3966381] Creating preprocessor task map/tas INFO [3966381] Creating preprocessor ‘to_degrees_c’ task for variable ‘tas’ INFO [3966381] Found input files for Dataset: tas, Amon, CMIP6, BCC-ESM1, CMIP, historical, r1i1p1f1, gn, v20181214 INFO [3966381] Found input files for Dataset: tas, Amon, CMIP5, bcc-csm1-1, historical, r1i1p1, v1 INFO [3966381] PreprocessingTask map/tas created. INFO [3966381] Creating tasks for diagnostic timeseries INFO [3966381] Creating diagnostic task timeseries/script1 INFO [3966381] Creating preprocessor task timeseries/tas_amsterdam INFO [3966381] Creating preprocessor ‘annual_mean_amsterdam’ task for variable ‘tas_amsterdam’ INFO [3966381] Found input files for Dataset: tas, Amon, CMIP6, BCC-ESM1, CMIP, historical, r1i1p1f1, gn, v20181214 INFO [3966381] Found input files for Dataset: tas, Amon, CMIP5, bcc-csm1-1, historical, r1i1p1, v1 INFO [3966381] PreprocessingTask timeseries/tas_amsterdam created. INFO [3966381] Creating preprocessor task timeseries/tas_global INFO [3966381] Creating preprocessor ‘annual_mean_global’ task for variable ‘tas_global’ INFO [3966381] Found input files for Dataset: tas, Amon, CMIP6, BCC-ESM1, CMIP, historical, r1i1p1f1, gn, v20181214, supplementaries: areacella, fx, 1pctCO2, v20190613 INFO [3966381] Found input files for Dataset: tas, Amon, CMIP5, bcc-csm1-1, historical, r1i1p1, v1, supplementaries: areacella, fx, r0i0p0 INFO [3966381] PreprocessingTask timeseries/tas_global created. INFO [3966381] These tasks will be executed: timeseries/script1, timeseries/tas_amsterdam, timeseries/tas_global, map/tas, map/script1 INFO [3966381] Wrote recipe with version numbers and wildcards to: file:///scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/recipe_python_filled.yml WARNING [3966381] /home/user/username/ESMValTool/ESMValCore/esmvalcore/config/_dask.py:160: ESMValCoreDeprecationWarning: Usage of Dask configuration file ~/.esmvaltool/dask.yml has been deprecated in ESMValCore version 2.12.0 and is scheduled for removal in version 2.14.0. Please use the configuration option dask instead (see https://docs.esmvaltool.org/projects/ESMValCore/en/latest/quickstart/configure.html#dask-configuration for details). Ignoring all existing dask configuration options for this run. To enable the new dask configuration options, delete or move the file ~/.esmvaltool/dask.yml or set the environment variable ESMVALTOOL_USE_NEW_DASK_CONFIG=1. warn_if_old_dask_config_exists()

INFO [3966381] Using Dask distributed scheduler (address: tcp://10.128.10.130:41935, dashboard link: http://10.128.10.130:8787/status) INFO [3966381] Running 5 tasks using 5 processes INFO [3966381] Progress: 3 tasks running, 2 tasks waiting for ancestors, 0/5 done INFO [3967058] Starting task map/tas in process [3967058] INFO [3967062] Starting task timeseries/tas_amsterdam in process [3967062] INFO [3967064] Starting task timeseries/tas_global in process [3967064] INFO [3967062] Extracting data for Amsterdam, Noord-Holland, Nederland (52.3730796 °N, 4.8924534 °E) INFO [3967062] Extracting data for Amsterdam, Noord-Holland, Nederland (52.3730796 °N, 4.8924534 °E) INFO [3967062] Generated PreprocessorFile: /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/preproc/timeseries/tas_amsterdam/MultiModelMean_historical_Amon_tas_1850-2000.nc INFO [3967062] Computing and saving data for preprocessing task timeseries/tas_amsterdam INFO [3967062] timeseries/tas_amsterdam [ ] | 0% Completed | 10.18 s INFO [3967062] timeseries/tas_amsterdam [ ] | 0% Completed | 20.24 s INFO [3967062] timeseries/tas_amsterdam [ ] | 0% Completed | 30.31 s INFO [3967062] timeseries/tas_amsterdam [ ] | 0% Completed | 40.38 s INFO [3967062] timeseries/tas_amsterdam [ ] | 0% Completed | 50.42 s INFO [3967062] timeseries/tas_amsterdam [ ] | 0% Completed | 60.47 s INFO [3967062] timeseries/tas_amsterdam [####### ] | 18% Completed | 70.54 s INFO [3967062] timeseries/tas_amsterdam [####### ] | 18% Completed | 80.55 s INFO [3967062] timeseries/tas_amsterdam [########################################] | 100% Completed | 90.56 s INFO [3967058] Computing and saving data for preprocessing task map/tas INFO [3967062] Successfully completed task timeseries/tas_amsterdam (priority 3) in 0:01:33.741823 INFO [3966381] Progress: 2 tasks running, 2 tasks waiting for ancestors, 1/5 done INFO [3967058] map/tas [########################################] | 100% Completed | 2.24 s INFO [3967064] Computing and saving data for preprocessing task timeseries/tas_global INFO [3967058] Successfully completed task map/tas (priority 1) in 0:01:36.040206 INFO [3966381] Progress: 1 tasks running, 2 tasks waiting for ancestors, 2/5 done INFO [3967066] Starting task map/script1 in process [3967066] INFO [3967066] Running command [‘/work/bd0854/b309059/utils/mambaforge/envs/esmvaltool/bin/python3.12’, ‘/home/user/username/ESMValTool/ESMValTool/esmvaltool/diag_scripts/examples/diagnostic.py’, ‘/scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/map/script1/settings.yml’] INFO [3967066] Writing output to /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/work/map/script1 INFO [3967066] Writing plots to /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/plots/map/script1 INFO [3967066] Writing log to /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/map/script1/log.txt INFO [3967066] To re-run this diagnostic script, run: cd /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/map/script1; MPLBACKEND=”Agg” /work/bd0854/b309059/utils/mambaforge/envs/esmvaltool/bin/python3.12 /home/user/username/ESMValTool/ESMValTool/esmvaltool/diag_scripts/examples/diagnostic.py /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/map/script1/settings.yml INFO [3966381] Progress: 2 tasks running, 1 tasks waiting for ancestors, 2/5 done INFO [3967064] timeseries/tas_global [########################################] | 100% Completed | 2.08 s INFO [3967064] Successfully completed task timeseries/tas_global (priority 4) in 0:01:38.392573 INFO [3966381] Progress: 1 tasks running, 1 tasks waiting for ancestors, 3/5 done INFO [3967068] Starting task timeseries/script1 in process [3967068] INFO [3967068] Running command [‘/work/bd0854/b309059/utils/mambaforge/envs/esmvaltool/bin/python3.12’, ‘/home/user/username/ESMValTool/ESMValTool/esmvaltool/diag_scripts/examples/diagnostic.py’, ‘/scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/timeseries/script1/settings.yml’] INFO [3967068] Writing output to /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/work/timeseries/script1 INFO [3967068] Writing plots to /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/plots/timeseries/script1 INFO [3967068] Writing log to /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/timeseries/script1/log.txt INFO [3967068] To re-run this diagnostic script, run: cd /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/timeseries/script1; MPLBACKEND=”Agg” /work/bd0854/b309059/utils/mambaforge/envs/esmvaltool/bin/python3.12 /home/user/username/ESMValTool/ESMValTool/esmvaltool/diag_scripts/examples/diagnostic.py /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/timeseries/script1/settings.yml INFO [3966381] Progress: 2 tasks running, 0 tasks waiting for ancestors, 3/5 done INFO [3967066] Maximum memory used (estimate): 0.3 GB INFO [3967066] Sampled every second. It may be inaccurate if short but high spikes in memory consumption occur. INFO [3967068] Maximum memory used (estimate): 0.3 GB INFO [3967068] Sampled every second. It may be inaccurate if short but high spikes in memory consumption occur. INFO [3967066] Successfully completed task map/script1 (priority 0) in 0:00:28.351139 INFO [3966381] Progress: 1 tasks running, 0 tasks waiting for ancestors, 4/5 done INFO [3967068] Successfully completed task timeseries/script1 (priority 2) in 0:00:26.597442 INFO [3966381] Progress: 0 tasks running, 0 tasks waiting for ancestors, 5/5 done INFO [3966381] Successfully completed all tasks. INFO [3966381] Wrote recipe with version numbers and wildcards to: file:///scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/recipe_python_filled.yml INFO [3966381] Wrote recipe output to: file:///scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/index.html INFO [3966381] It looks like you are connected to a remote machine via SSH. To show the output html file, you can try the following command on your local machine: server=136.172.124.1 && port=31415 && ssh -t -L ${port}:localhost:${port} b309059@${server} /work/bd0854/b309059/utils/mambaforge/envs/esmvaltool/bin/python3.12 -m http.server ${port} -d /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145 Then visit http://localhost:31415 in your browser INFO [3966381] If the port 31415 is already in use, you can replace it with any other free one (e.g., 12789). If you are connected through a jump host, replace the server IP address 136.172.124.1 with your SSH server name INFO [3966381] Ending the Earth System Model Evaluation Tool at time: 2025-06-03 12:04:12 UTC INFO [3966381] Time for running the recipe was: 0:02:12.411071 INFO [3966381] Maximum memory used (estimate): 2.2 GB INFO [3966381] Sampled every second. It may be inaccurate if short but high spikes in memory consumption occur. WARNING [3966381] Input data is not (fully) CMOR-compliant, see /scratch/user/username/esmvaltool_output/recipe_python_20250603_120145/run/cmor_log.txt for details INFO [3966381] Run was successful WARNING [3966381] /home/user/username/ESMValTool/ESMValCore/esmvalcore/_main.py:441: ESMValCoreDeprecationWarning: Usage of Dask configuration file ~/.esmvaltool/dask.yml has been deprecated in ESMValCore version 2.12.0 and is scheduled for removal in version 2.14.0. Please use the configuration option dask instead (see https://docs.esmvaltool.org/projects/ESMValCore/en/latest/quickstart/configure.html#dask-configuration for details). Ignoring all existing dask configuration options for this run. To enable the new dask configuration options, delete or move the file ~/.esmvaltool/dask.yml or set the environment variable ESMVALTOOL_USE_NEW_DASK_CONFIG=1.

Pro tip: ESMValTool search paths

You might wonder how ESMValTool was able find the recipe file, even though it’s not in your working directory. All the recipe paths printed from esmvaltool recipes list are relative to ESMValTool’s installation location. This is where ESMValTool will look if it cannot find the file by following the path from your working directory.

Investigating the log messages

Let’s dissect what’s happening here.

Output files and directories

After the banner and general information, the output starts with some important locations.

Did ESMValTool use the right config file?

What is the path to the example recipe?

What is the main output folder generated by ESMValTool?

Can you guess what the different output directories are for?

ESMValTool creates two log files. What is the difference?

Answers

The config file should be the one we edited in the previous episode, something like /home/<username>/.config/esmvaltool/config-user.yml or ~/esmvaltool_tutorial/config-user.yml.

ESMValTool found the recipe in its installation directory, something like /home/users/username/mambaforge/envs/esmvaltool/bin/esmvaltool/recipes/examples/ or if you are using a pre-installed module on a server, something like /apps/jasmin/community/esmvaltool/ESMValTool_<version> /esmvaltool/recipes/examples/recipe_python.yml, where <version> is the latest release.

ESMValTool creates a time-stamped output directory for every run. In this case, it should be something like recipe_python_YYYYMMDD_HHMMSS. This folder is made inside the output directory specified in the previous episode: ~/esmvaltool_tutorial/esmvaltool_output.

There should be four output folders:

plots/: this is where output figures are stored.

preproc/: this is where pre-processed data are stored.

run/: this is where esmvaltool stores general information about the run, such as log messages and a copy of the recipe file.

work/: this is where output files (not figures) are stored.

The log files are:

main_log.txt is a copy of the command-line output

main_log_debug.txt contains more detailed information that may be useful for debugging.

Debugging: No ‘preproc’ directory?

If you’re missing the preproc directory, then your config-user.yml file has the value remove_preproc_dir set to true (this is used to save disk space). Please set this value to false and run the recipe again.

After the output locations, there are two main sections that can be distinguished in the log messages:

Creating tasks
Executing tasks

Analyse the tasks

List all the tasks that ESMValTool is executing for this recipe. Can you guess what this recipe does?
Answer

Just after all the ‘creating tasks’ and before ‘executing tasks’, we find the following line in the output:
INFO    [3966381] These tasks will be executed: timeseries/script1,
timeseries/tas_amsterdam, timeseries/tas_global, map/tas, map/script1
So there are three tasks related to timeseries: global temperature, Amsterdam temperature, and a script (tas: near-surface air temperature). And then there are two tasks related to a map: something with temperature, and again a script.

Examining the recipe file

To get more insight into what is happening, we will have a look at the recipe file itself. Use the following command to copy the recipe to your working directory

esmvaltool recipes get examples/recipe_python.yml

Now you should see the recipe file in your working directory (type ls to verify). Use the nano editor to open this file:

nano recipe_python.yml

For reference, you can also view the recipe by unfolding the box below.

recipe_python.yml

# ESMValTool
# recipe_python.yml
#
# See https://docs.esmvaltool.org/en/latest/recipes/recipe_examples.html
# for a description of this recipe.
#
# See https://docs.esmvaltool.org/projects/esmvalcore/en/latest/recipe/overview.html
# for a description of the recipe format.
---
documentation:
 description: |
   Example recipe that plots a map and timeseries of temperature.

 title: Recipe that runs an example diagnostic written in Python.

 authors:
   - andela_bouwe
   - righi_mattia

 maintainer:
   - schlund_manuel

 references:
   - acknow_project

 projects:
   - esmval
   - c3s-magic

datasets:
 - {dataset: BCC-ESM1, project: CMIP6, exp: historical, ensemble: r1i1p1f1, grid: gn}
 - {dataset: bcc-csm1-1, project: CMIP5, exp: historical, ensemble: r1i1p1}

preprocessors:
 # See https://docs.esmvaltool.org/projects/esmvalcore/en/latest/recipe/preprocessor.html
 # for a description of the preprocessor functions.

 to_degrees_c:
   convert_units:
     units: degrees_C

 annual_mean_amsterdam:
   extract_location:
     location: Amsterdam
     scheme: linear
   annual_statistics:
     operator: mean
   multi_model_statistics:
     statistics:
       - mean
     span: overlap
   convert_units:
     units: degrees_C

 annual_mean_global:
   area_statistics:
     operator: mean
   annual_statistics:
     operator: mean
   convert_units:
     units: degrees_C

diagnostics:

 map:
   description: Global map of temperature in January 2000.
   themes:
     - phys
   realms:
     - atmos
   variables:
     tas:
       mip: Amon
       preprocessor: to_degrees_c
       timerange: 2000/P1M
       caption: |
         Global map of {long_name} in January 2000 according to {dataset}.
   scripts:
     script1:
       script: examples/diagnostic.py
       quickplot:
         plot_type: pcolormesh
         cmap: Reds

 timeseries:
   description: Annual mean temperature in Amsterdam and global mean since 1850.
   themes:
     - phys
   realms:
     - atmos
   variables:
     tas_amsterdam:
       short_name: tas
       mip: Amon
       preprocessor: annual_mean_amsterdam
       timerange: 1850/2000
       caption: Annual mean {long_name} in Amsterdam according to {dataset}.
     tas_global:
       short_name: tas
       mip: Amon
       preprocessor: annual_mean_global
       timerange: 1850/2000
       caption: Annual global mean {long_name} according to {dataset}.
   scripts:
     script1:
       script: examples/diagnostic.py
       quickplot:
         plot_type: plot

Do you recognize the basic recipe structure that was introduced in episode 1?

Documentation with relevant (citation) information
Datasets that should be analysed
Preprocessors groups of common preprocessing steps
Diagnostics scripts performing more specific evaluation steps

Analyse the recipe

Try to answer the following questions:

Who wrote this recipe?

Who should be approached if there is a problem with this recipe?

How many datasets are analyzed?

What does the preprocessor called annual_mean_global do?

Which script is applied for the diagnostic called map?

Can you link specific lines in the recipe to the tasks that we saw before?

How is the location of the city specified?

How is the temporal range of the data specified?

Answers

The example recipe is written by Bouwe Andela and Mattia Righi.

Manuel Schlund is listed as the maintainer of this recipe.

Two datasets are analysed:

CMIP6 data from the model BCC-ESM1

CMIP5 data from the model bcc-csm1-1

The preprocessor annual_mean_global computes an area mean as well as annual means

The diagnostic called map executes a script referred to as script1. This is a python script named examples/diagnostic.py

There are two diagnostics: map and timeseries. Under the diagnostic map we find two tasks:

a preprocessor task called tas, applying the preprocessor called to_degrees_c to the variable tas.

a diagnostic task called script1, applying the script examples/diagnostic.py to the preprocessed data (map/tas).

Under the diagnostic timeseries we find three tasks:

a preprocessor task called tas_amsterdam, applying the preprocessor called annual_mean_amsterdam to the variable tas.

a preprocessor task called tas_global, applying the preprocessor called annual_mean_global to the variable tas.

a diagnostic task called script1, applying the script examples/diagnostic.py to the preprocessed data (timeseries/tas_global and timeseries/tas_amsterdam).

The extract_location preprocessor is used to get data for a specific location here. ESMValTool interpolates to the location based on the chosen scheme. Can you tell the scheme used here? For more ways to extract areas, see the Area operations page.

The timerange tag is used to extract data from a specific time period here. The start time is 01/01/2000 and the span of time to calculate means is 1 Month given by P1M. For more options on how to specify time ranges, see the timerange documentation.

Pro tip: short names and variable groups

The preprocessor tasks in ESMValTool are called ‘variable groups’. For the diagnostic timeseries, we have two variable groups: tas_amsterdam and tas_global. Both of them operate on the variable tas (as indicated by the short_name), but they apply different preprocessors. For the diagnostic map the variable group itself is named tas, and you’ll notice that we do not explicitly provide the short_name. This is a shorthand built into ESMValTool.

Output files

Have another look at the output directory created by the ESMValTool run.

Which files/folders are created by each task?

Answer

map/tas: creates /preproc/map/tas, which contains preprocessed data for each of the input datasets, a file called metadata.yml describing the contents of these datasets and provenance information in the form of .xml files.

timeseries/tas_global: creates /preproc/timeseries/tas_global, which contains preprocessed data for each of the input datasets, a metadata.yml file and provenance information in the form of .xml files.

timeseries/tas_amsterdam: creates /preproc/timeseries/tas_amsterdam, which contains preprocessed data for each of the input datasets, plus a combined MultiModelMean, a metadata.yml file and provenance files.

map/script1: creates /run/map/script1 with general information and a log of the diagnostic script run. It also creates /plots/map/script1/ and /work/map/script1, which contain output figures and output datasets, respectively. For each output file, there is also corresponding provenance information in the form of .xml, .bibtex and .txt files.

timeseries/script1: creates /run/timeseries/script1 with general information and a log of the diagnostic script run. It also creates /plots/timeseries/script1 and /work/timeseries/script1, which contain output figures and output datasets, respectively. For each output file, there is also corresponding provenance information in the form of .xml, .bibtex and .txt files.

Pro tip: diagnostic logs

When you run ESMValTool, any log messages from the diagnostic script are not printed on the terminal. But they are written to the log.txt files in the folder /run/<diag_name>/log.txt.

ESMValTool does print a command that can be used to re-run a diagnostic script. When you use this the output will be printed to the command line.

Modifying the example recipe

Let’s make a small modification to the example recipe. Notice that now that you have copied and edited the recipe, you can use

esmvaltool run recipe_python.yml

to refer to your local file rather than the default version shipped with ESMValTool.

Change your location

Modify and run the recipe to analyse the temperature for your own location.
Solution

In principle, you only have to modify the location in the preprocessor called annual_mean_amsterdam. However, it is good practice to also replace all instances of amsterdam with the correct name of your location. Otherwise the log messages and output will be confusing. You are free to modify the names of preprocessors or diagnostics.

In the diff file below you will see the changes we have made to the file. The top 2 lines are the filenames and the lines like @@ -39,9 +39,9 @@ represent the line numbers in the original and modified file, respectively. For more info on this format, see here.
--- recipe_python.yml	
+++ recipe_python_london.yml	
@@ -39,9 +39,9 @@
    convert_units:
      units: degrees_C

-  annual_mean_amsterdam:
+  annual_mean_london:
    extract_location:
-      location: Amsterdam
+      location: London
      scheme: linear
    annual_statistics:
      operator: mean
@@ -83,7 +83,7 @@
          cmap: Reds

  timeseries:
-    description: Annual mean temperature in Amsterdam and global mean since 1850.
+    description: Annual mean temperature in London and global mean since 1850.
    themes:
      - phys
    realms:
@@ -92,9 +92,9 @@
      tas_amsterdam:
        short_name: tas
        mip: Amon
-        preprocessor: annual_mean_amsterdam
+        preprocessor: annual_mean_london
        timerange: 1850/2000
-        caption: Annual mean {long_name} in Amsterdam according to {dataset}.
+        caption: Annual mean {long_name} in London according to {dataset}.
      tas_global:
        short_name: tas
        mip: Amon

Key Points

ESMValTool recipes work ‘out of the box’ (if input data is available)

There are strong links between the recipe, log file, and output folders

Recipes can easily be modified to re-use existing code for your own use case

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Running your first recipe

Overview

Running an existing recipe

Pro tip: ESMValTool search paths

Investigating the log messages

Output files and directories

Answers

Debugging: No ‘preproc’ directory?

Analyse the tasks

Answer

Examining the recipe file

recipe_python.yml

Analyse the recipe

Answers

Pro tip: short names and variable groups

Output files

Answer

Pro tip: diagnostic logs

Modifying the example recipe

Change your location

Solution

Key Points

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