Preprocessor#
In this section, each of the preprocessor modules is described, roughly following the default order in which preprocessor functions are applied:
See Preprocessor functions for implementation details and the exact default order.
Overview#
The ESMValCore preprocessor can be used to perform a broad range of operations on the input data before diagnostics or metrics are applied. The preprocessor performs these operations in a centralized, documented and efficient way, thus reducing the data processing load on the diagnostics side. For an overview of the preprocessor structure see the Recipe section: preprocessors.
Each of the preprocessor operations is written in a dedicated python module and
all of them receive and return an instance of
iris.cube.Cube
, working
sequentially on the data with no interactions between them. The order in which
the preprocessor operations is applied is set by default to minimize
the loss of information due to, for example, temporal and spatial subsetting or
multimodel averaging. Nevertheless, the user is free to change such order to
address specific scientific requirements, but keeping in mind that some
operations must be necessarily performed in a specific order. This is the case,
for instance, for multimodel statistics, which required the model to be on a
common grid and therefore has to be called after the regridding module.
Statistical preprocessors#
Many preprocessors calculate statistics over data.
Those preprocessors typically end with _statistics
, e.g.,
area_statistics()
or
multi_model_statistics()
.
All these preprocessors support the options operator, which directly
correspond to iris.analysis.Aggregator
objects used to perform the
statistical calculations.
In addition, arbitrary keyword arguments can be passed which are directly
passed to the corresponding iris.analysis.Aggregator
object.
Note
The preprocessors multi_model_statistics()
and ensemble_statistics()
support the
computation of multiple statistics at the same time.
In these cases, they are defined by the option statistics (instead of
operator), which takes a list of possible operators.
Each operator can be given as single string or as dictionary.
In the latter case, the dictionary needs the keyword operator
(corresponding to the operator as above).
All other keywords are interpreted as keyword arguments for the given
operator.
Some operators support weights for some preprocessors (see following table), which are used by default. The following operators are currently fully supported; other operators might be supported too if proper keyword arguments are specified:
operator 
Corresponding 
Weighted? [1] 



no 


no 


no 


yes 


no 


no 


no 


no 


yes 


no 


yes 


no 


yes 
Examples#
Calculate the global (weighted) mean:
preprocessors:
global_mean:
area_statistics:
operator: mean
Calculate zonal maximum.
preprocessors:
zonal_max:
zonal_statistics:
operator: max
Calculate the 95% percentile over each month separately (will result in 12 time steps, one for January, one for February, etc.):
preprocessors:
monthly_percentiles:
climate_statistics:
period: monthly
operator: percentile
percent: 95.0
Calculate multimodel median, 5%, and 95% percentiles:
preprocessors:
mm_stats:
multi_model_statistics:
span: overlap
statistics:
 operator: percentile
percent: 5
 operator: median
 operator: percentile
percent: 95
Calculate the global nonweighted root mean square:
preprocessors:
global_mean:
area_statistics:
operator: rms
weighted: false
Warning
The disabling of weights by specifying the keyword argument weights:
False
needs to be used with great care; from a scientific standpoint, we
strongly recommend to not use it!
Variable derivation#
The variable derivation module allows to derive variables which are not in the CMIP standard data request using standard variables as input. The typical use case of this operation is the evaluation of a variable which is only available in an observational dataset but not in the models. In this case a derivation function is provided by the ESMValCore in order to calculate the variable and perform the comparison. For example, several observational datasets deliver total column ozone as observed variable (toz), but CMIP models only provide the ozone 3D field. In this case, a derivation function is provided to vertically integrate the ozone and obtain total column ozone for direct comparison with the observations.
The tool will also look in other mip
tables for the same project
to find
the definition of derived variables. To contribute a completely new derived
variable, it is necessary to define a name for it and to provide the
corresponding CMOR table. This is to guarantee the proper metadata definition
is attached to the derived data. Such custom CMOR tables are collected as part
of the ESMValCore package.
By default, the variable derivation will be applied only if the variable is not
already available in the input data, but the derivation can be forced by
setting the force_derivation
flag.
variables:
toz:
derive: true
force_derivation: false
The required arguments for this module are two boolean switches:
derive
: activate variable derivationforce_derivation
: force variable derivation even if the variable is directly available in the input data.
See also esmvalcore.preprocessor.derive()
. To get an overview on
derivation scripts and how to implement new ones, please go to
Deriving a variable.
CMORization and datasetspecific fixes#
Data checking#
Data preprocessed by ESMValCore is automatically checked against its CMOR definition. To reduce the impact of this check while maintaining it as reliable as possible, it is split in two parts: one will check the metadata and will be done just after loading and concatenating the data and the other one will check the data itself and will be applied after all extracting operations are applied to reduce the amount of data to process.
Checks include, but are not limited to:
Requested coordinates are present and comply with their definition.
Correctness of variable names, units and other metadata.
Compliance with the valid minimum and maximum values allowed if defined.
The most relevant (i.e. a missing coordinate) will raise an error while others (i.e an incorrect long name) will be reported as a warning.
Some of those issues will be fixed automatically by the tool, including the following:
Incorrect standard or long names.
Incorrect units, if they can be converted to the correct ones.
Direction of coordinates.
Automatic clipping of longitude to 0  360 interval.
Minute differences between the required and actual vertical coordinate values
Dataset specific fixes#
Sometimes, the checker will detect errors that it can not fix by itself. ESMValCore deals with those issues by applying specific fixes for those datasets that require them. Fixes are applied at three different preprocessor steps:
fix_file
: apply fixes directly to a copy of the file. Copying the files is costly, so only errors that prevent Iris to load the file are fixed here. Seeesmvalcore.preprocessor.fix_file()
.
fix_metadata
: metadata fixes are done just before concatenating the cubes loaded from different files in the final one. Automatic metadata fixes are also applied at this step. Seeesmvalcore.preprocessor.fix_metadata()
.
fix_data
: data fixes are applied before starting any operation that will alter the data itself. Automatic data fixes are also applied at this step. Seeesmvalcore.preprocessor.fix_data()
.
To get an overview on data fixes and how to implement new ones, please go to Fixing data.
Supplementary variables (ancillary variables and cell measures)#
The following preprocessor functions either require or prefer using an ancillary variable or cell measure to perform their computations. In ESMValCore we call both types of variables “supplementary variables”.
Preprocessor 
Variable short name 
Variable standard name 


cell_area 


land_area_fraction, sea_area_fraction 


land_ice_area_fraction 


ocean_volume 


land_area_fraction, sea_area_fraction 
Only one of the listed variables is required. Supplementary variables can be defined in the recipe as described in Defining supplementary variables (ancillary variables and cell measures). In some cases, preprocessor functions may work without supplementary variables, this is documented case by case in the preprocessor function definition. If a preprocessor function requiring supplementary variables is used without specifying these in the recipe, these will be automatically added. If the automatic selection does not give the desired result, specify the supplementary variables in the recipe as described in Defining supplementary variables (ancillary variables and cell measures).
By default, supplementary variables will be removed from the
variable before saving it to file because they can be as big as the main
variable.
To keep the supplementary variables, disable the preprocessor
function esmvalcore.preprocessor.remove_supplementary_variables()
that
removes them by setting remove_supplementary_variables: false
in the
preprocessor in the recipe.
Examples#
Compute the global mean surface air temperature, while automatically selecting the best matching supplementary dataset:
datasets:
 dataset: BCCESM1
project: CMIP6
ensemble: r1i1p1f1
grid: gn
 dataset: MPIESMMR
project: CMIP5
ensemble: r1i1p1,
preprocessors:
global_mean:
area_statistics:
operator: mean
diagnostics:
example_diagnostic:
description: Global mean temperature.
variables:
tas:
mip: Amon
preprocessor: global_mean
exp: historical
timerange: '1990/2000'
supplementary_variables:
 short_name: areacella
mip: fx
exp: '*'
activity: '*'
ensemble: '*'
scripts: null
Attach the land area fraction as an ancillary variable to surface air temperature and store both in the same file:
datasets:
 dataset: BCCESM1
ensemble: r1i1p1f1
grid: gn
preprocessors:
keep_land_area_fraction:
remove_supplementary_variables: false
diagnostics:
example_diagnostic:
description: Attach land area fraction.
variables:
tas:
mip: Amon
project: CMIP6
preprocessor: keep_land_area_fraction
exp: historical
timerange: '1990/2000'
supplementary_variables:
 short_name: sftlf
mip: fx
exp: 1pctCO2
scripts: null
Automatically define the required ancillary variable (sftlf
in this case)
and cell measure (areacella
), but do not use areacella
for dataset
BCCESM1
:
datasets:
 dataset: BCCESM1
project: CMIP6
ensemble: r1i1p1f1
grid: gn
supplementary_variables:
 short_name: areacella
skip: true
 dataset: MPIESMMR
project: CMIP5
ensemble: r1i1p1
preprocessors:
global_land_mean:
mask_landsea:
mask_out: sea
area_statistics:
operator: mean
diagnostics:
example_diagnostic:
description: Global mean temperature.
variables:
tas:
mip: Amon
preprocessor: global_land_mean
exp: historical
timerange: '1990/2000'
scripts: null
Vertical interpolation#
Vertical level selection is an important aspect of data preprocessing since it
allows the scientist to perform a number of metrics specific to certain levels
(whether it be air pressure or depth, e.g. the QuasiBiennialOscillation (QBO)
u30 is computed at 30 hPa). Dataset native vertical grids may not come with the
desired set of levels, so an interpolation operation will be needed to regrid
the data vertically. ESMValCore can perform this vertical interpolation via the
extract_levels
preprocessor. Level extraction may be done in a number of
ways.
Level extraction can be done at specific values passed to extract_levels
as
levels:
with its value a list of levels (note that the units are
CMORstandard, Pascals (Pa)):
preprocessors:
preproc_select_levels_from_list:
extract_levels:
levels: [100000., 50000., 3000., 1000.]
scheme: linear
It is also possible to extract the CMIPspecific, CMOR levels as they appear in
the CMOR table, e.g. plev10
or plev17
or plev19
etc:
preprocessors:
preproc_select_levels_from_cmip_table:
extract_levels:
levels: {cmor_table: CMIP6, coordinate: plev10}
scheme: nearest
Of good use is also the level extraction with values specific to a certain
dataset, without the user actually polling the dataset of interest to find out
the specific levels: e.g. in the example below we offer two alternatives to
extract the levels and vertically regrid onto the vertical levels of
ERAInterim
:
preprocessors:
preproc_select_levels_from_dataset:
extract_levels:
levels: ERAInterim
# This also works, but allows specifying the pressure coordinate name
# levels: {dataset: ERAInterim, coordinate: air_pressure}
scheme: linear_extrapolate
By default, vertical interpolation is performed in the dimension coordinate of
the z axis. If you want to explicitly declare the z axis coordinate to use
(for example, air_pressure
’ in variables that are provided in model levels
and not pressure levels) you can override that automatic choice by providing
the name of the desired coordinate:
preprocessors:
preproc_select_levels_from_dataset:
extract_levels:
levels: ERAInterim
scheme: linear_extrapolate
coordinate: air_pressure
If coordinate
is specified, pressure levels (if present) can be converted
to height levels and vice versa using the US standard atmosphere. E.g.
coordinate = altitude
will convert existing pressure levels
(air_pressure) to height levels (altitude);
coordinate = air_pressure
will convert existing height levels
(altitude) to pressure levels (air_pressure).
If the requested levels are very close to the values in the input data, the function will just select the available levels instead of interpolating. The meaning of ‘very close’ can be changed by providing the parameters:
rtol
Relative tolerance for comparing the levels in the input data to the requested levels. If the levels are sufficiently close, the requested levels will be assigned to the vertical coordinate and no interpolation will take place. The default value is 10^7.
atol
Absolute tolerance for comparing the levels in the input data to the requested levels. If the levels are sufficiently close, the requested levels will be assigned to the vertical coordinate and no interpolation will take place. By default, atol will be set to 10^7 times the mean value of of the available levels.
Schemes for vertical interpolation and extrapolation#
The vertical interpolation currently supports the following schemes:
linear
: Linear interpolation without extrapolation, i.e., extrapolation points will be masked even if the source data is not a masked array.linear_extrapolate
: Linear interpolation with nearestneighbour extrapolation, i.e., extrapolation points will take their value from the nearest source point.nearest
: Nearestneighbour interpolation without extrapolation, i.e., extrapolation points will be masked even if the source data is not a masked array.nearest_extrapolate
: Nearestneighbour interpolation with nearestneighbour extrapolation, i.e., extrapolation points will take their value from the nearest source point.See also
esmvalcore.preprocessor.extract_levels()
.See also
esmvalcore.preprocessor.get_cmor_levels()
.
Note
Controlling the extrapolation mode allows us to avoid situations where extrapolating values makes little physical sense (e.g. extrapolating beyond the last data point).
Weighting#
Land/sea fraction weighting#
This preprocessor allows weighting of data by land or sea fractions. In other words, this function multiplies the given input field by a fraction in the range 01 to account for the fact that not all grid points are completely land or seacovered.
The application of this preprocessor is very important for most carbon cycle variables (and other land surface outputs), which are e.g. reported in units of \(kgC~m^{2}\). Here, the surface unit actually refers to ‘square meter of land/sea’ and NOT ‘square meter of gridbox’. In order to integrate these globally or regionally one has to weight by both the surface quantity and the land/sea fraction.
For example, to weight an input field with the land fraction, the following preprocessor can be used:
preprocessors:
preproc_weighting:
weighting_landsea_fraction:
area_type: land
exclude: ['CanESM2', 'reference_dataset']
Allowed arguments for the keyword area_type
are land
(fraction is 1
for grid cells with only land surface, 0 for grid cells with only sea surface
and values in between 0 and 1 for coastal regions) and sea
(1 for
sea, 0 for land, in between for coastal regions). The optional argument
exclude
allows to exclude specific datasets from this preprocessor, which
is for example useful for climate models which do not offer land/sea fraction
files. This arguments also accepts the special dataset specifiers
reference_dataset
and alternative_dataset
.
This function requires a land or sea area fraction ancillary variable.
This supplementary variable, either sftlf
or sftof
, should be attached
to the main dataset as described in Defining supplementary variables (ancillary variables and cell measures).
See also esmvalcore.preprocessor.weighting_landsea_fraction()
.
Masking#
Introduction to masking#
Certain metrics and diagnostics need to be computed and performed on specific domains on the globe. The preprocessor supports filtering the input data on continents, oceans/seas and ice. This is achieved by masking the model data and keeping only the values associated with grid points that correspond to, e.g., land, ocean or ice surfaces, as specified by the user. Where possible, the masking is realized using the standard mask files provided together with the model data as part of the CMIP data request (the socalled ancillary variable). In the absence of these files, the Natural Earth masks are used: although these are not modelspecific, they represent a good approximation since they have a much higher resolution than most of the models and they are regularly updated with changing geographical features.
Landsea masking#
To mask out a certain domain (e.g., sea) in the preprocessor,
mask_landsea
can be used:
preprocessors:
preproc_mask:
mask_landsea:
mask_out: sea
and requires only one argument: mask_out
: either land
or sea
.
This function prefers using a land or sea area fraction ancillary variable,
but if it is not available it will compute a mask based on
Natural Earth shapefiles.
This supplementary variable, either sftlf
or sftof
, can be attached
to the main dataset as described in Defining supplementary variables (ancillary variables and cell measures).
If the corresponding ancillary variable is not available (which is the case for some models and almost all observational datasets), the preprocessor attempts to mask the data using Natural Earth mask files (that are vectorized rasters). As mentioned above, the spatial resolution of the the Natural Earth masks are much higher than any typical global model (10m for land and glaciated areas and 50m for ocean masks).
See also esmvalcore.preprocessor.mask_landsea()
.
Ice masking#
For masking out ice sheets, the preprocessor uses a different
function, to ensure that both land and sea or ice can be masked out without
losing generality. To mask ice out, mask_landseaice
can be used:
preprocessors:
preproc_mask:
mask_landseaice:
mask_out: ice
and requires only one argument: mask_out
: either landsea
or ice
.
This function requires a land ice area fraction ancillary variable.
This supplementary variable sftgif
should be attached to the main dataset as
described in Defining supplementary variables (ancillary variables and cell measures).
Glaciated masking#
For masking out glaciated areas a Natural Earth shapefile is used. To mask
glaciated areas out, mask_glaciated
can be used:
preprocessors:
preproc_mask:
mask_glaciated:
mask_out: glaciated
and it requires only one argument: mask_out
: only glaciated
.
Missing values masks#
Missing (masked) values can be a nuisance especially when dealing with multimodel ensembles and having to compute multimodel statistics; different numbers of missing data from dataset to dataset may introduce biases and artificially assign more weight to the datasets that have less missing data. This is handled via the missing values masks: two types of such masks are available, one for the multimodel case and another for the single model case.
The multimodel missing values mask (mask_fillvalues
) is a preprocessor step
that usually comes after all the singlemodel steps (regridding, area selection
etc) have been performed; in a nutshell, it combines missing values masks from
individual models into a multimodel missing values mask; the individual model
masks are built according to common criteria: the user chooses a time window in
which missing data points are counted, and if the number of missing data points
relative to the number of total data points in a window is less than a chosen
fractional threshold, the window is discarded i.e. all the points in the window
are masked (set to missing).
preprocessors:
missing_values_preprocessor:
mask_fillvalues:
threshold_fraction: 0.95
min_value: 19.0
time_window: 10.0
In the example above, the fractional threshold for missing data vs. total data is set to 95% and the time window is set to 10.0 (units of the time coordinate units). Optionally, a minimum value threshold can be applied, in this case it is set to 19.0 (in units of the variable units).
Common mask for multiple models#
To create a combined multimodel mask (all the masks from all the analyzed
datasets combined into a single mask using a logical OR), the preprocessor
mask_multimodel
can be used. In contrast to mask_fillvalues
,
mask_multimodel
does not expect that the datasets have a time
coordinate, but works on datasets with arbitrary (but identical) coordinates.
After mask_multimodel
, all involved datasets have an identical mask.
Minimum, maximum and interval masking#
Thresholding on minimum and maximum accepted data values can also be performed:
masks are constructed based on the results of thresholding; inside and outside
interval thresholding and masking can also be performed. These functions are
mask_above_threshold
, mask_below_threshold
, mask_inside_range
, and
mask_outside_range
.
These functions always take a cube as first argument and either threshold
for threshold masking or the pair minimum
, maximum
for interval masking.
See also esmvalcore.preprocessor.mask_above_threshold()
and related
functions.
Horizontal regridding#
Regridding is necessary when various datasets are available on a variety of latlon grids and they need to be brought together on a common grid (for various statistical operations e.g. multimodel statistics or for e.g. direct intercomparison or comparison with observational datasets). Regridding is conceptually a very similar process to interpolation (in fact, the regridder engine uses interpolation and extrapolation, with various schemes). The primary difference is that interpolation is based on sample data points, while regridding is based on the horizontal grid of another cube (the reference grid). If the horizontal grids of a cube and its reference grid are sufficiently the same, regridding is automatically and silently skipped for performance reasons.
The underlying regridding mechanism in ESMValCore uses
iris.cube.Cube.regrid
from Iris.
The use of the horizontal regridding functionality is flexible depending on what type of reference grid and what interpolation scheme is preferred. Below we show a few examples.
Regridding on a reference dataset grid#
The example below shows how to regrid on the reference dataset
ERAInterim
(observational data, but just as well CMIP, obs4MIPs,
or ana4mips datasets can be used); in this case the scheme is
linear.
preprocessors:
regrid_preprocessor:
regrid:
target_grid: ERAInterim
scheme: linear
Regridding on an MxN
grid specification#
The example below shows how to regrid on a reference grid with a cell
specification of 2.5x2.5
degrees. This is similar to regridding on
reference datasets, but in the previous case the reference dataset grid cell
specifications are not necessarily known a priori. Regridding on an MxN
cell specification is oftentimes used when operating on localized data.
preprocessors:
regrid_preprocessor:
regrid:
target_grid: 2.5x2.5
scheme: nearest
In this case the NearestNeighbour
interpolation scheme is used (see below
for scheme definitions).
When using a MxN
type of grid it is possible to offset the grid cell
centrepoints using the lat_offset and lon_offset
arguments:
lat_offset
: offsets the grid centers of the latitude coordinate w.r.t. the pole by half a grid step;lon_offset
: offsets the grid centers of the longitude coordinate w.r.t. Greenwich meridian by half a grid step.
preprocessors:
regrid_preprocessor:
regrid:
target_grid: 2.5x2.5
lon_offset: True
lat_offset: True
scheme: nearest
Regridding to a regional target grid specification#
This example shows how to regrid to a regional target grid specification.
This is useful if both a regrid
and extract_region
step are necessary.
preprocessors:
regrid_preprocessor:
regrid:
target_grid:
start_longitude: 40
end_longitude: 60
step_longitude: 2
start_latitude: 10
end_latitude: 30
step_latitude: 2
scheme: nearest
This defines a grid ranging from 40° to 60° longitude with 2° steps,
and 10° to 30° latitude with 2° steps. If end_longitude
or end_latitude
do
not fall on the grid (e.g., end_longitude: 61
), it cuts off at the nearest
previous value (e.g. 60
).
The longitude coordinates will wrap around the globe if necessary, i.e.
start_longitude: 350
, end_longitude: 370
is valid input.
The arguments are defined below:
start_latitude
: Latitude value of the first grid cell center (start point). The grid includes this value.end_latitude
: Latitude value of the last grid cell center (end point). The grid includes this value only if it falls on a grid point. Otherwise, it cuts off at the previous value.step_latitude
: Latitude distance between the centers of two neighbouring cells.start_longitude
: Latitude value of the first grid cell center (start point). The grid includes this value.end_longitude
: Longitude value of the last grid cell center (end point). The grid includes this value only if it falls on a grid point. Otherwise, it cuts off at the previous value.step_longitude
: Longitude distance between the centers of two neighbouring cells.
Regridding (interpolation, extrapolation) schemes#
ESMValCore has a number of builtin regridding schemes, which are presented in Builtin regridding schemes. Additionally, it is also possible to use third party regridding schemes designed for use with Iris. This is explained in Generic regridding schemes.
Builtin regridding schemes#
linear
: Bilinear regridding. For source data on a regular grid, usesLinear
with extrapolation_mode=’mask’. For source data on an irregular grid, usesESMPyLinear
. Source data on an unstructured grid is not supported, yet.nearest
: Nearestneighbor regridding. For source data on a regular grid, usesNearest
with extrapolation_mode=’mask’. For source data on an irregular grid, usesESMPyNearest
. For source data on an unstructured grid, usesUnstructuredNearest
.area_weighted
: Firstorder conservative (areaweighted) regridding. For source data on a regular grid, usesAreaWeighted
. For source data on an irregular grid, usesESMPyAreaWeighted
. Source data on an unstructured grid is not supported, yet.
See also esmvalcore.preprocessor.regrid()
Generic regridding schemes#
Iris’ regridding is based around the flexible use of socalled regridding schemes. These are classes that know how to transform a source cube with a given grid into the grid defined by a given target cube. Iris itself provides a number of useful schemes, but they are largely limited to work with simple, regular grids. Other schemes can be provided independently. This is interesting when special regriddingneeds arise or when more involved grids and meshes need to be considered. Furthermore, it may be desirable to have finer control over the parameters of the scheme than is afforded by the builtin schemes described above.
To facilitate this, the regrid()
preprocessor
allows the use of any scheme designed for Iris. The scheme must be installed
and importable. To use this feature, the scheme
key passed to the
preprocessor must be a dictionary instead of a simple string that contains all
necessary information. That includes a reference
to the desired scheme
itself, as well as any arguments that should be passed through to the
scheme. For example, the following shows the use of the builtin scheme
iris.analysis.AreaWeighted
with a custom threshold for missing data
tolerance.
preprocessors:
regrid_preprocessor:
regrid:
target_grid: 2.5x2.5
scheme:
reference: iris.analysis:AreaWeighted
mdtol: 0.7
Another example is bilinear regridding with extrapolation.
This can be achieved with the iris.analysis.Linear
scheme and the
extrapolation_mode
keyword.
Extrapolation points will be calculated by extending the gradient of the
closest two points.
preprocessors:
regrid_preprocessor:
regrid:
target_grid: 2.5x2.5
scheme:
reference: iris.analysis:Linear
extrapolation_mode: extrapolate
Note
Controlling the extrapolation mode allows us to avoid situations where extrapolating values makes little physical sense (e.g. extrapolating beyond the last data point).
The value of the reference
key has two parts that are separated by a
:
with no surrounding spaces. The first part is an importable Python
module, the second refers to the scheme, i.e. some callable that will be called
with the remaining entries of the scheme
dictionary passed as keyword
arguments.
One package that aims to capitalize on the support for unstructured meshes introduced in Iris 3.2 is irisesmfregrid. It aims to provide lazy regridding for structured regular and irregular grids, as well as unstructured meshes. An example of its usage in a preprocessor is:
preprocessors:
regrid_preprocessor:
regrid:
target_grid: 2.5x2.5
scheme:
reference: esmf_regrid.schemes:ESMFAreaWeighted
mdtol: 0.7
Additionally, the use of generic schemes that take source and target grid cubes as
arguments is also supported. The call function for such schemes must be defined as
(src_cube, grid_cube, **kwargs) and they must return iris.cube.Cube objects.
The regrid module will automatically pass the source and grid cubes as inputs
of the scheme. An example of this usage is
the regrid_rectilinear_to_rectilinear()
scheme available in irisesmfregrid:
preprocessors:
regrid_preprocessor:
regrid:
target_grid: 2.5x2.5
scheme:
reference: esmf_regrid.schemes:regrid_rectilinear_to_rectilinear
mdtol: 0.7
Ensemble statistics#
For certain use cases it may be desirable to compute ensemble statistics. For example to prevent models with many ensemble members getting excessive weight in the multimodel statistics functions.
Theoretically, ensemble statistics are a special case (grouped) multimodel statistics. This grouping is performed taking into account the dataset tags project, dataset, experiment, and (if present) sub_experiment. However, they should typically be computed earlier in the workflow. Moreover, because multiple ensemble members of the same model are typically more consistent/homogeneous than datasets from different models, the implementation is more straightforward and can benefit from lazy evaluation and more efficient computation.
The preprocessor takes a list of statistics as input:
preprocessors:
example_preprocessor:
ensemble_statistics:
statistics: [mean, median]
Additional keyword arguments can be given by using a dictionary:
preprocessors:
example_preprocessor:
ensemble_statistics:
statistics:
 operator: percentile
percent: 20
 operator: median
This preprocessor function exposes the iris analysis package, and works with all
(capitalized) statistics from the iris.analysis
package
that can be executed without additional arguments.
See Statistical preprocessors for more details on supported statistics.
Note that ensemble_statistics
will not return the single model and ensemble files,
only the requested ensemble statistics results.
In case of wanting to save both individual ensemble members as well as the statistic results, the preprocessor chains could be defined as:
preprocessors:
everything_else: &everything_else
area_statistics: ...
regrid_time: ...
multimodel:
<<: *everything_else
ensemble_statistics:
variables:
tas_datasets:
short_name: tas
preprocessor: everything_else
...
tas_multimodel:
short_name: tas
preprocessor: multimodel
...
Multimodel statistics#
Computing multimodel statistics is an integral part of model analysis and
evaluation: individual models display a variety of biases depending on model
setup, initial conditions, forcings and implementation; comparing model data to
observational data, these biases have a significantly lower statistical impact
when using a multimodel ensemble. ESMValCore has the capability of computing a
number of multimodel statistical measures: using the preprocessor module
multi_model_statistics
will enable the user for example to ask for either a multimodel
mean
, median
, max
, min
, std_dev
, and / or percentile
with a set of argument parameters passed to multi_model_statistics
.
See Statistical preprocessors for more details on supported statistics.
Percentiles can be specified with additional keyword arguments using the syntax
statistics: [{operator: percentile, percent: xx}]
.
Restrictive computation is also available by excluding any set of models that
the user will not want to include in the statistics (by setting exclude:
[excluded models list]
argument).
Input datasets may have different time coordinates.
Apart from that, all dimensions must match.
Statistics can be computed
across overlapping times only (span: overlap
) or across the full time span
of the combined models (span: full
). The preprocessor sets a common time
coordinate on all datasets. As the number of days in a year may vary between
calendars, (sub)daily data with different calendars are not supported.
The preprocessor saves both the input single model files as well as the multimodel
results. In case you do not want to keep the single model files, set the
parameter keep_input_datasets
to false
(default value is true
).
To remove scalar coordinates before merging input datasets into the
multidataset cube, use the option ignore_scalar_coords: true
.
The resulting multidataset cube will not have scalar coordinates in this case.
This ensures that differences in scalar coordinates in the input datasets are
ignored, which is helpful if you encounter a ValueError: Multimodel
statistics failed to merge input cubes into a single array
with Coordinates
in cube.aux_coords (scalar) differ
.
Some special scalar coordinates which are expected to differ across cubes (p0
and ptop) are always removed.
preprocessors:
multi_model_save_input:
multi_model_statistics:
span: overlap
statistics: [mean, median]
exclude: [NCEPNCARR1]
multi_model_without_saving_input:
multi_model_statistics:
span: overlap
statistics: [mean, median]
exclude: [NCEPNCARR1]
keep_input_datasets: false
ignore_scalar_coords: true
multi_model_percentiles_5_95:
multi_model_statistics:
span: overlap
statistics:
 operator: percentile
percent: 5
 operator: percentile
percent: 95
Multimodel statistics also supports a groupby
argument. You can group by
any dataset key (project
, experiment
, etc.) or a combination of keys in a list. You can
also add an arbitrary tag to a dataset definition and then group by that tag. When
using this preprocessor in conjunction with ensemble statistics preprocessor, you
can group by ensemble_statistics
as well. For example:
datasets:
 {dataset: CanESM2, exp: historical, ensemble: "r(1:2)i1p1"}
 {dataset: CCSM4, exp: historical, ensemble: "r(1:2)i1p1"}
preprocessors:
example_preprocessor:
ensemble_statistics:
statistics: [median, mean]
multi_model_statistics:
span: overlap
statistics: [min, max]
groupby: [ensemble_statistics]
exclude: [NCEPNCARR1]
This will first compute ensemble mean and median, and then compute the multimodel min and max separately for the ensemble means and medians. Note that this combination will not save the individual ensemble members, only the ensemble and multimodel statistics results.
When grouping by a tag not defined in all datasets, the datasets missing the tag will
be grouped together. In the example below, datasets UKESM and ERA5 would belong to the same
group, while the other datasets would belong to either group1
or group2
datasets:
 {dataset: CanESM2, exp: historical, ensemble: "r(1:2)i1p1", tag: 'group1'}
 {dataset: CanESM5, exp: historical, ensemble: "r(1:2)i1p1", tag: 'group2'}
 {dataset: CCSM4, exp: historical, ensemble: "r(1:2)i1p1", tag: 'group2'}
 {dataset: UKESM, exp: historical, ensemble: "r(1:2)i1p1"}
 {dataset: ERA5}
preprocessors:
example_preprocessor:
multi_model_statistics:
span: overlap
statistics: [min, max]
groupby: [tag]
Note that those datasets can be excluded if listed in the exclude
option.
Time manipulation#
The _time.py
module contains the following preprocessor functions:
extract_time: Extract a time range from a cube.
extract_season: Extract only the times that occur within a specific season.
extract_month: Extract only the times that occur within a specific month.
hourly_statistics: Compute intraday statistics
daily_statistics: Compute statistics for each day
monthly_statistics: Compute statistics for each month
seasonal_statistics: Compute statistics for each season
annual_statistics: Compute statistics for each year
decadal_statistics: Compute statistics for each decade
climate_statistics: Compute statistics for the full period
resample_time: Resample data
resample_hours: Convert between Nhourly frequencies by resampling
anomalies: Compute (standardized) anomalies
regrid_time: Aligns the time axis of each dataset to have common time points and calendars.
timeseries_filter: Allows application of a filter to the timeseries data.
local_solar_time: Convert cube with UTC time to local solar time.
Statistics functions are applied by default in the order they appear in the list. For example, the following example applied to hourly data will retrieve the minimum values for the full period (by season) of the monthly mean of the daily maximum of any given variable.
daily_statistics:
operator: max
monthly_statistics:
operator: mean
climate_statistics:
operator: min
period: season
extract_time
#
This function subsets a dataset between two points in times. It removes all times in the dataset before the first time and after the last time point. The required arguments are relatively self explanatory:
start_year
start_month
start_day
end_year
end_month
end_day
These start and end points are set using the datasets native calendar. All six arguments should be given as integers  the named month string will not be accepted.
See also esmvalcore.preprocessor.extract_time()
.
extract_season
#
Extract only the times that occur within a specific season.
This function only has one argument: season
. This is the named season to
extract, i.e. DJF, MAM, JJA, SON, but also all other sequentially correct
combinations, e.g. JJAS.
Note that this function does not change the time resolution. If your original data is in monthly time resolution, then this function will return three monthly datapoints per year.
If you want the seasonal average, then this function needs to be combined with the seasonal_mean function, below.
See also esmvalcore.preprocessor.extract_season()
.
extract_month
#
The function extracts the times that occur within a specific month.
This function only has one argument: month
. This value should be an integer
between 1 and 12 as the named month string will not be accepted.
See also esmvalcore.preprocessor.extract_month()
.
hourly_statistics
#
This function produces statistics at a xhourly frequency.
 Parameters:
hour: Number of hours per period. Must be a divisor of 24, i.e., (1, 2, 3, 4, 6, 8, 12).
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics. Default is mean.
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
daily_statistics
#
This function produces statistics for each day in the dataset.
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics. Default is mean.
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
monthly_statistics
#
This function produces statistics for each month in the dataset.
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics. Default is mean.
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
seasonal_statistics
#
This function produces statistics for each season (default: [DJF, MAM, JJA,
SON]
or custom seasons e.g. [JJAS, ONDJFMAM]
) in the dataset. Note that
this function will not check for missing time points. For instance, if you are
looking at the DJF field, but your datasets starts on January 1st, the first
DJF field will only contain data from January and February.
We recommend using the extract_time to start the dataset from the following December and remove such biased initial data points.
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics. Default is mean.
seasons: Seasons to build statistics. Default is
'[DJF, MAM, JJA, SON]'
.Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
annual_statistics
#
This function produces statistics for each year.
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics. Default is mean.
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
decadal_statistics
#
This function produces statistics for each decade.
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics. Default is mean.
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
climate_statistics
#
This function produces statistics for the whole dataset. It can produce scalars (if the full period is chosen) or hourly, daily, monthly or seasonal statistics.
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics. Default is mean.
period: Define the granularity of the statistics: get values for the full period, for each month, day of year or hour of day. Available periods: full, season, seasonal, monthly, month, mon, daily, day, hourly, hour, hr. Default is full.
seasons: if period ‘seasonal’ or ‘season’ allows to set custom seasons. Default is
'[DJF, MAM, JJA, SON]'
.Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
Note
Some operations are weighted by the time coordinate by default, i.e., the length of the time intervals. See Statistical preprocessors for more details on supported statistics. For sum, the units of the resulting cube are multiplied by the corresponding time units (e.g., days).
 Examples:
Monthly climatology:
climate_statistics: operator: mean period: month
Daily maximum for the full period:
climate_statistics: operator: max period: day
Minimum value in the period:
climate_statistics: operator: min period: full
80% percentile for each month:
climate_statistics: period: month operator: percentile percent: 80
resample_time
#
This function changes the frequency of the data in the cube by extracting the timesteps that meet the criteria. It is important to note that it is mainly meant to be used with instantaneous data.
 Parameters:
month: Extract only timesteps from the given month or do nothing if None. Default is None
day: Extract only timesteps from the given day of month or do nothing if None. Default is None
hour: Extract only timesteps from the given hour or do nothing if None. Default is None
 Examples:
Hourly data to daily:
resample_time: hour: 12
Hourly data to monthly:
resample_time: hour: 12 day: 15
Daily data to monthly:
resample_time: day: 15
See also esmvalcore.preprocessor.resample_time()
.
resample_hours:
resample_hours
#
This function changes the frequency of the data in the cube by extracting the timesteps that belongs to the desired frequency. It is important to note that it is mainly mean to be used with instantaneous data
 Parameters:
interval: New frequency of the data. Must be a divisor of 24
offset: First desired hour. Default 0. Must be lower than the interval
 Examples:
Convert to 12hourly, by getting timesteps at 0:00 and 12:00:
resample_hours: hours: 12
Convert to 12hourly, by getting timesteps at 6:00 and 18:00:
resample_hours: hours: 12 offset: 6
See also esmvalcore.preprocessor.resample_hours()
.
anomalies
#
This function computes the anomalies for the whole dataset. It can compute anomalies from the full, seasonal, monthly, daily and hourly climatologies. Optionally standardized anomalies can be calculated.
 Parameters:
period: define the granularity of the climatology to use: full period, seasonal, monthly, daily or hourly. Available periods: ‘full’, ‘season’, ‘seasonal’, ‘monthly’, ‘month’, ‘mon’, ‘daily’, ‘day’, ‘hourly’, ‘hour’, ‘hr’. Default is ‘full’
reference: Time slice to use as the reference to compute the climatology on. Can be ‘null’ to use the full cube or a dictionary with the parameters from extract_time. Default is null
standardize: if true calculate standardized anomalies (default: false)
seasons: if period ‘seasonal’ or ‘season’ allows to set custom seasons. Default is ‘[DJF, MAM, JJA, SON]’
 Examples:
Anomalies from the full period climatology:
anomalies:
Anomalies from the full period monthly climatology:
anomalies: period: month
Standardized anomalies from the full period climatology:
anomalies: standardized: true
Standardized Anomalies from the 19792000 monthly climatology:
anomalies: period: month reference: start_year: 1979 start_month: 1 start_day: 1 end_year: 2000 end_month: 12 end_day: 31 standardize: true
See also esmvalcore.preprocessor.anomalies()
.
regrid_time
#
This function aligns the time points of each component dataset so that the Iris
cubes from different datasets can be subtracted. The operation makes the
datasets time points common; it also resets the time
bounds and auxiliary coordinates to reflect the artificially shifted time
points. Current implementation for monthly and daily data; the frequency
is
set automatically from the variable CMOR table unless a custom frequency
is
set manually by the user in recipe.
See also esmvalcore.preprocessor.regrid_time()
.
timeseries_filter
#
This function allows the user to apply a filter to the timeseries data. This filter may be
of the user’s choice (currently only the lowpass
Lanczos filter is implemented); the
implementation is inspired by this iris example and uses aggregation via iris.cube.Cube.rolling_window
.
 Parameters:
window: the length of the filter window (in units of cube time coordinate).
span: period (number of months/days, depending on data frequency) on which weights should be computed e.g. for 2yearly: span = 24 (2 x 12 months). Make sure span has the same units as the data cube time coordinate.
filter_type: the type of filter to be applied; default ‘lowpass’. Available types: ‘lowpass’.
filter_stats: the type of statistic to aggregate on the rolling window; default ‘sum’. Available operators: ‘mean’, ‘median’, ‘std_dev’, ‘sum’, ‘min’, ‘max’, ‘rms’.
 Examples:
Lowpass filter with a monthly mean as operator:
timeseries_filter: window: 3 # 3monthly filter window span: 12 # weights computed on the first year filter_type: lowpass # lowpass filter filter_stats: mean # 3monthly mean lowpass filter
local_solar_time
#
Many variables in the Earth system show a strong diurnal cycle. The reason for that is of course Earth’s rotation around its own axis, which leads to a diurnal cycle of the incoming solar radiation. While UTC time is a very good absolute time measure, it is not really suited to analyze diurnal cycles over larger regions. For example, diurnal cycles over Russia and the USA are phaseshifted by ~180° = 12 hr in UTC time.
This is where the local solar time (LST) comes into play: For a given location, 12:00 noon LST is defined as the moment when the sun reaches its highest point in the sky. By using this definition based on the origin of the diurnal cycle (the sun), we can directly compare diurnal cycles across the globe. LST is mainly determined by the longitude of a location, but due to the eccentricity of Earth’s orbit, it also depends on the day of year (see equation of time). However, this correction is at most ~15 min, which is usually smaller than the highest frequency output of CMIP6 models (1 hr) and smaller than the time scale for diurnal evolution of meteorological phenomena (which is in the order of hours, not minutes). Thus, instead, we use the mean LST, which solely depends on longitude:
where the times are given in hours and lon in degrees in the interval [180, 180]. To transform data from UTC to LST, this preprocessor shifts data along the time axis based on the longitude.
This preprocessor does not need any additional parameters.
Example:
calculate_local_solar_time:
local_solar_time:
Area manipulation#
The area manipulation module contains the following preprocessor functions:
extract_coordinate_points: Extract a point with arbitrary coordinates given an interpolation scheme.
extract_region: Extract a region from a cube based on
lat/lon
corners.extract_named_regions: Extract a specific region from in the region coordinate.
extract_shape: Extract a region defined by a shapefile.
extract_point: Extract a single point (with interpolation)
extract_location: Extract a single point by its location (with interpolation)
zonal_statistics: Compute zonal statistics.
meridional_statistics: Compute meridional statistics.
area_statistics: Compute area statistics.
extract_coordinate_points
#
This function extracts points with given coordinates, following either a
linear
or a nearest
interpolation scheme.
The resulting point cube will match the respective coordinates to
those of the input coordinates. If the input coordinate is a scalar,
the dimension will be a scalar in the output cube.
If the point to be extracted has at least one of the coordinate point values outside the interval of the cube’s same coordinate values, then no extrapolation will be performed, and the resulting extracted cube will have fully masked data.
 Examples:
Extract a point from coordinate grid_latitude with given coordinate value 26.0:
extract_coordinate_points: definition: grid_latitude: 26. scheme: nearest
See also esmvalcore.preprocessor.extract_coordinate_points()
.
extract_region
#
This function returns a subset of the data on the rectangular region requested. The boundaries of the region are provided as latitude and longitude coordinates in the arguments:
start_longitude
end_longitude
start_latitude
end_latitude
Note that this function can only be used to extract a rectangular region. Use
extract_shape
to extract any other shaped region from a shapefile.
If the grid is irregular, the returned region retains the original coordinates, but is cropped to a rectangular bounding box defined by the start/end coordinates. The deselected area inside the region is masked.
See also esmvalcore.preprocessor.extract_region()
.
extract_named_regions
#
This function extracts a specific named region from the data. This function
takes the following argument: regions
which is either a string or a list
of strings of named regions. Note that the dataset must have a region
coordinate which includes a list of strings as values. This function then
matches the named regions against the requested string.
extract_shape
#
Extract a shape or a representative point for this shape from the data.
 Parameters:
shapefile
: path to the shapefile containing the geometry of the region to be extracted. If the file contains multiple shapes behaviour depends on thedecomposed
parameter. This path can be relative toauxiliary_data_dir
defined in the User configuration file or relative toesmvalcore/preprocessor/shapefiles
(in that priority order). Alternatively, a string (see “Shapefile name” below) can be given to load one of the following shapefiles that are shipped with ESMValCore:Shapefile name
Description
Reference
ar6
IPCC WG1 reference regions (v4) used in Assessment Report 6
method
: the method to select the region, selecting either all points contained by the shape or a single representative point. Choose either ‘contains’ or ‘representative’. If not a single grid point is contained in the shape, a representative point will be selected.crop
: by default extract_region will be used to crop the data to a minimal rectangular region containing the shape. Set tofalse
to only mask data outside the shape. Data on irregular grids will not be cropped.decomposed
: by defaultfalse
; in this case the union of all the regions in the shapefile is masked out. If set totrue
, the regions in the shapefiles are masked out separately and the output cube will have an additional dimensionshape_id
describing the requested regions.ids
: Shapes to be read from the shapefile. Can be given as:list
: IDs are assigned from the attributesname
,NAME
,Name
,id
, orID
(in that priority order; the first one available is used). If none of these attributes are available in the shapefile, assume that the given ids correspond to the reading order of the individual shapes. So, for example, if a file has bothname
andid
attributes, the ids will be assigned fromname
. If the file only has theid
attribute, it will be taken from it and if noname
norid
attributes are present, an integer ID starting from 0 will be assigned automatically when reading the shapes. We discourage to rely on this last behaviour as we can not assure that the reading order will be the same on different platforms, so we encourage you to specify a custom attribute using adict
(see below) instead. Note: An empty list is interpreted asids=None
(see below).dict
: IDs (dictionary value;list
ofstr
) are assigned from attribute given as dictionary key (str
). Only dictionaries with length 1 are supported. Example:ids={'Acronym': ['GIC', 'WNA']}
.None: select all available regions from the shapefile.
 Examples:
Extract the shape of the river Elbe from a shapefile:
extract_shape: shapefile: Elbe.shp method: contains
Extract the shape of several countries:
extract_shape: shapefile: NaturalEarth/Countries/ne_110m_admin_0_countries.shp decomposed: True method: contains ids:  Spain  France  Italy  United Kingdom  Taiwan
Extract European AR6 regions:
extract_shape: shapefile: ar6 method: contains ids: Acronym:  NEU  WCE  MED
See also esmvalcore.preprocessor.extract_shape()
.
extract_point
#
Extract a single point from the data. This is done using either nearest or linear interpolation.
Returns a cube with the extracted point(s), and with adjusted latitude and longitude coordinates (see below).
Multiple points can also be extracted, by supplying an array of latitude and/or longitude coordinates. The resulting point cube will match the respective latitude and longitude coordinate to those of the input coordinates. If the input coordinate is a scalar, the dimension will be missing in the output cube (that is, it will be a scalar).
If the point to be extracted has at least one of the coordinate point values outside the interval of the cube’s same coordinate values, then no extrapolation will be performed, and the resulting extracted cube will have fully masked data.
 Parameters:
cube
: the input dataset cube.latitude
,longitude
: coordinates (as floating point values) of the point to be extracted. Either (or both) can also be an array of floating point values.scheme
: interpolation scheme: either'linear'
or'nearest'
. There is no default.
See also esmvalcore.preprocessor.extract_point()
.
extract_location
#
Extract a single point using a location name, with interpolation
(either linear or nearest). This preprocessor extracts a single
location point from a cube, according to the given interpolation
scheme scheme
. The function retrieves the coordinates of the
location and then calls the esmvalcore.preprocessor.extract_point()
preprocessor. It can be used to locate cities and villages,
but also mountains or other geographical locations.
Note
Note that this function’s geolocator application needs a working internet connection.
 Parameters:
cube: the input dataset cube to extract a point from.
location: the reference location. Examples: ‘mount everest’, ‘romania’, ‘new york, usa’. Raises ValueError if none supplied.
scheme : interpolation scheme. linear or nearest. There is no default, raises ValueError if none supplied.
zonal_statistics
#
The function calculates the zonal statistics by applying an operator along the longitude coordinate.
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics.
normalize: If given, do not return the statistics cube itself, but rather, the input cube, normalized with the statistics cube. Can either be subtract (statistics cube is subtracted from the input cube) or divide (input cube is divided by the statistics cube).
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
meridional_statistics
#
The function calculates the meridional statistics by applying an operator along the latitude coordinate. This function takes one argument:
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics.
normalize: If given, do not return the statistics cube itself, but rather, the input cube, normalized with the statistics cube. Can either be subtract (statistics cube is subtracted from the input cube) or divide (input cube is divided by the statistics cube).
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
area_statistics
#
This function can be used to apply several different operations in the horizontal plane: for example, mean, sum, standard deviation, median, variance, minimum, maximum and root mean square. Some operations are grid cell area weighted by default. For sums, the units of the resulting cubes are multiplied by m \(^2\). See Statistical preprocessors for more details on supported statistics.
Note that this function is applied over the entire dataset. If only a specific region, depth layer or time period is required, then those regions need to be removed using other preprocessor operations in advance.
For weighted statistics, this function requires a cell area cell measure,
unless the coordinates of the input data are regular 1D latitude and longitude
coordinates so the cell areas can be computed internally.
The required supplementary variable, either areacella
for atmospheric
variables or areacello
for ocean variables, can be attached to the main
dataset as described in Defining supplementary variables (ancillary variables and cell measures).
 Parameters:
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics.
normalize: If given, do not return the statistics cube itself, but rather, the input cube, normalized with the statistics cube. Can either be subtract (statistics cube is subtracted from the input cube) or divide (input cube is divided by the statistics cube).
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
Examples: * Calculate global mean:
area_statistics: operator: mean
Subtract global mean from dataset:
area_statistics: operator: mean normalize: subtract
Volume manipulation#
The _volume.py
module contains the following preprocessor functions:
axis_statistics
: Perform operations along a given axis.extract_volume
: Extract a specific depth range from a cube.volume_statistics
: Calculate the volumeweighted average.depth_integration
: Integrate over the depth dimension.extract_transect
: Extract data along a line of constant latitude or longitude.extract_trajectory
: Extract data along a specified trajectory.
extract_volume
#
Extract a specific range in the zdirection from a cube. The range is given as an interval that can be:
open
(z_min, z_max)
, in which the extracted range does not includez_min
norz_max
.closed
[z_min, z_max]
, in which the extracted includes bothz_min
andz_max
.left closed
[z_min, z_max)
, in which the extracted range includesz_min
but notz_max
.right closed
(z_min, z_max]
, in which the extracted range includesz_max
but notz_min
.
The extraction is performed by applying a constraint on the coordinate values, without any kind of interpolation.
This function takes four arguments:
z_min
to define the minimum value of the range to extract in the zdirection.z_max
to define the maximum value of the range to extract in the zdirection.interval_bounds
to define whether the bounds of the interval areopen
,closed
,left_closed
orright_closed
. Default isopen
.
nearest_value
to extract a range taking into account the values of the zcoordinate thatare closest to
z_min
andz_max
. Default isFalse
.
As the coordinate points are likely to vary depending on the dataset, sometimes it might be
useful to adjust the given z_min
and z_max
values to the values of the coordinate
points before performing an extraction. This behaviour can be achieved by setting the
nearest_value
argument to True
.
For example, in a cube with z_coord = [0., 1.5, 2.6., 3.8., 5.4]
, the preprocessor below:
preprocessors:
extract_volume:
z_min: 1.
z_max: 5.
interval_bounds: 'closed'
would return a cube with a z_coord
defined as z_coord = [1.5, 2.6., 3.8.]
,
since these are the values that strictly fall into the range given by [z_min=1, z_max=5]
.
Whereas setting ǹearest_value: True
:
preprocessors:
extract_volume:
z_min: 1.
z_max: 5.
interval_bounds: 'closed'
nearest_value: True
would return a cube with a z_coord
defined as z_coord = [1.5, 2.6., 3.8., 5.4]
,
since z_max = 5
is closest to the coordinate point z = 5.4
than it is to z = 3.8
.
Note that this preprocessor requires the requested zcoordinate range to be the same sign
as the Iris cube. That is, if the cube has zcoordinate as negative, then
z_min
and z_max
need to be negative numbers.
See also esmvalcore.preprocessor.extract_volume()
.
volume_statistics
#
This function calculates the volumeweighted average across three dimensions, but maintains the time dimension.
By default, the mean operation is weighted by the grid cell volumes.
For weighted statistics, this function requires a cell volume cell measure,
unless the coordinates of the input data are regular 1D latitude and longitude
coordinates so the cell volumes can be computed internally.
The required supplementary variable volcello
can be attached to the main
dataset as described in Defining supplementary variables (ancillary variables and cell measures).
No depth coordinate is required as this is determined by Iris.
 Parameters:
operator: Operation to apply. At the moment, only mean is supported. See Statistical preprocessors for more details on supported statistics.
normalize: If given, do not return the statistics cube itself, but rather, the input cube, normalized with the statistics cube. Can either be subtract (statistics cube is subtracted from the input cube) or divide (input cube is divided by the statistics cube).
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
axis_statistics
#
This function operates over a given axis, and removes it from the output cube.
 Takes arguments:
axis: direction over which the statistics will be performed. Possible values for the axis are x, y, z, t.
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics.
normalize: If given, do not return the statistics cube itself, but rather, the input cube, normalized with the statistics cube. Can either be subtract (statistics cube is subtracted from the input cube) or divide (input cube is divided by the statistics cube).
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
Note
The coordinate associated to the axis over which the operation will be performed must be onedimensional, as multidimensional coordinates are not supported in this preprocessor.
Some operations are weighted by the corresponding coordinate bounds by default. For sums, the units of the resulting cubes are multiplied by the corresponding coordinate units. See Statistical preprocessors for more details on supported statistics.
depth_integration
#
This function integrates over the depth dimension. This function does a weighted sum along the zcoordinate, and removes the z direction of the output cube. This preprocessor takes no arguments. The units of the resulting cube are multiplied by the zcoordinate units.
extract_transect
#
This function extracts data along a line of constant latitude or longitude.
This function takes two arguments, although only one is strictly required.
The two arguments are latitude
and longitude
. One of these arguments
needs to be set to a float, and the other can then be either ignored or set to
a minimum or maximum value.
For example, if we set latitude to 0 N and leave longitude blank, it would
produce a cube along the Equator. On the other hand, if we set latitude to 0
and then set longitude to [40., 100.]
this will produce a transect of the
Equator in the Indian Ocean.
extract_trajectory
#
This function extract data along a specified trajectory.
The three arguments are: latitudes
, longitudes
and number of point
needed for extrapolation number_points
.
If two points are provided, the number_points
argument is used to set a
the number of places to extract between the two end points.
If more than two points are provided, then extract_trajectory
will produce
a cube which has extrapolated the data of the cube to those points, and
number_points
is not needed.
Note that this function uses the expensive interpolate
method from
Iris.analysis.trajectory
, but it may be necessary for irregular grids.
Cycles#
The _cycles.py
module contains the following preprocessor functions:
amplitude
: Extract the peaktopeak amplitude of a cycle aggregated over specified coordinates.
amplitude
#
This function extracts the peaktopeak amplitude (maximum value minus minimum
value) of a field aggregated over specified coordinates. Its only argument is
coords
, which can either be a single coordinate (given as str
) or
multiple coordinates (given as list
of str
). Usually, these
coordinates refer to temporal categorised coordinates
iris.coord_categorisation
like year, month, day of year, etc. For example, to extract the amplitude
of the annual cycle for every single year in the data, use coords: year
; to
extract the amplitude of the diurnal cycle for every single day in the data,
use coords: [year, day_of_year]
.
See also esmvalcore.preprocessor.amplitude()
.
Trend#
The trend module contains the following preprocessor functions:
linear_trend
: Calculate linear trend along a specified coordinate.linear_trend_stderr
: Calculate standard error of linear trend along a specified coordinate.
linear_trend
#
This function calculates the linear trend of a dataset (defined as slope of an
ordinary linear regression) along a specified coordinate. The only argument of
this preprocessor is coordinate
(given as str
; default value is
'time'
).
See also esmvalcore.preprocessor.linear_trend()
.
linear_trend_stderr
#
This function calculates the standard error of the linear trend of a dataset
(defined as the standard error of the slope in an ordinary linear regression)
along a specified coordinate. The only argument of this preprocessor is
coordinate
(given as str
; default value is 'time'
). Note that
the standard error is not identical to a confidence interval.
Detrend#
ESMValCore also supports detrending along any dimension using the preprocessor function ‘detrend’. This function has two parameters:
dimension
: dimension to apply detrend on. Default: “time”method
: It can belinear
orconstant
. Default:linear
If method is linear
, detrend will calculate the linear trend along the
selected axis and subtract it to the data. For example, this can be used to
remove the linear trend caused by climate change on some variables is selected
dimension is time.
If method is constant
, detrend will compute the mean along that dimension
and subtract it from the data
See also esmvalcore.preprocessor.detrend()
.
Rolling window statistics#
One can calculate rolling window statistics using the
preprocessor function rolling_window_statistics
.
This function takes three parameters:
coordinate: Coordinate over which the rollingwindow statistics is calculated.
operator: Operation to apply. See Statistical preprocessors for more details on supported statistics.
window_length: size of the rolling window to use (number of points).
Other parameters are directly passed to the operator as keyword arguments. See Statistical preprocessors for more details.
This example applied on daily precipitation data calculates twoday rolling precipitation sum.
preprocessors:
preproc_rolling_window:
coordinate: time
operator: sum
window_length: 2
See also esmvalcore.preprocessor.rolling_window_statistics()
.
Unit conversion#
convert_units
#
Converting units is also supported. This is particularly useful in cases where different datasets might have different units, for example when comparing CMIP5 and CMIP6 variables where the units have changed or in case of observational datasets that are delivered in different units.
In these cases, having a unit conversion at the end of the processing will guarantee homogeneous input for the diagnostics.
Conversion is only supported between compatible units!
In other words, converting temperature units from degC
to Kelvin
works
fine, while changing units from kg
to m
will not work.
However, there are some welldefined exceptions from this rule in order to
transform one quantity to another (physically related) quantity.
These quantities are identified via their standard_name
and their units
(units convertible to the ones defined are also supported).
For example, this enables conversions between precipitation fluxes measured in
kg m2 s1
and precipitation rates measured in mm day1
(and vice
versa).
Currently, the following special conversions are supported:
precipitation_flux
(kg m2 s1
) –lwe_precipitation_rate
(mm day1
)equivalent_thickness_at_stp_of_atmosphere_ozone_content
(m
) –equivalent_thickness_at_stp_of_atmosphere_ozone_content
(DU
)
Hint
Names in the list correspond to standard_names
of the input data.
Conversions are allowed from each quantity to any other quantity given in a
bullet point.
The corresponding target quantity is inferred from the desired target units.
In addition, any other units convertible to the ones given are also
supported (e.g., instead of mm day1
, m s1
is also supported).
Note
For the transformation between the different precipitation variables, a
water density of 1000 kg m3
is assumed.
See also esmvalcore.preprocessor.convert_units()
.
accumulate_coordinate
#
This function can be used to weight data using the bounds from a given coordinate.
The resulting cube will then have units given by cube_units * coordinate_units
.
For instance, if a variable has units such as X s1
, using accumulate_coordinate
on the time coordinate would result on a cube where the data would be multiplied
by the time bounds and the resulting units for the variable would be converted to X
.
In this case, weighting the data with the time coordinate would allow to cancel
the time units in the variable.
Note
The coordinate used to weight the data must be onedimensional, as multidimensional coordinates are not supported in this preprocessor.
See also esmvalcore.preprocessor.accumulate_coordinate.()
Bias#
The bias module contains the following preprocessor functions:
bias
: Calculate absolute or relative biases with respect to a reference dataset
bias
#
This function calculates biases with respect to a given reference dataset.
For this, exactly one input dataset needs to be declared as
reference_for_bias: true
in the recipe, e.g.,
datasets:
 {dataset: CanESM5, project: CMIP6, ensemble: r1i1p1f1, grid: gn}
 {dataset: CESM2, project: CMIP6, ensemble: r1i1p1f1, grid: gn}
 {dataset: MIROC6, project: CMIP6, ensemble: r1i1p1f1, grid: gn}
 {dataset: ERAInterim, project: OBS6, tier: 3, type: reanaly, version: 1,
reference_for_bias: true}
In the example above, ERAInterim is used as reference dataset for the bias
calculation.
The reference dataset needs to be broadcastable to all other datasets.
This supports iris’ rich broadcasting abilities.
To ensure this, the preprocessors esmvalcore.preprocessor.regrid()
and/or
esmvalcore.preprocessor.regrid_time()
might be helpful.
The bias
preprocessor supports 4 optional arguments in the recipe:
bias_type
(str
, default:'absolute'
): Bias type that is calculated. Can be'absolute'
(i.e., calculate bias for dataset \(X\) and reference \(R\) as \(X  R\)) orrelative
(i.e., calculate bias as \(\frac{X  R}{R}\)).denominator_mask_threshold
(float
, default:1e3
): Threshold to mask values close to zero in the denominator (i.e., the reference dataset) during the calculation of relative biases. All values in the reference dataset with absolute value less than the given threshold are masked out. This setting is ignored whenbias_type
is set to'absolute'
. Please note that for some variables with very small absolute values (e.g., carbon cycle fluxes, which are usually \(< 10^{6}\) kg m \(^{2}\) s \(^{1}\)) it is absolutely essential to change the default value in order to get reasonable results.keep_reference_dataset
(bool
, default:False
): IfTrue
, keep the reference dataset in the output. IfFalse
, drop the reference dataset.exclude
(list
ofstr
): Exclude specific datasets from this preprocessor. Note that this option is only available in the recipe, not when usingesmvalcore.preprocessor.bias()
directly (e.g., in another python script). If the reference dataset has been excluded, an error is raised.
Example:
preprocessors:
preproc_bias:
bias:
bias_type: relative
denominator_mask_threshold: 1e8
keep_reference_dataset: true
exclude: [CanESM2]
See also esmvalcore.preprocessor.bias()
.
Information on maximum memory required#
In the most general case, we can set upper limits on the maximum memory the analysis will require:
Ms = (R + N) x F_eff  F_eff
 when no multimodel analysis is performed;
Mm = (2R + N) x F_eff  2F_eff
 when multimodel analysis is performed;
where
Ms
: maximum memory for nonmultimodel moduleMm
: maximum memory for multimodel moduleR
: computational efficiency of module; R is typically 23N
: number of datasetsF_eff
: average size of data per dataset whereF_eff = e x f x F
wheree
is the factor that describes how lazy the data is (e = 1
for fully realized data) andf
describes how much the data was shrunk by the immediately previous module, e.g. time extraction, area selection or level extraction; note that for fix_dataf
relates only to the time extraction, if data is exact in time (no time selection)f = 1
for fix_data so for cases when we deal with a lot of datasetsR + N \approx N
, data is fully realized, assuming an average size of 1.5GB for 10 years of 3D netCDF data,N
datasets will require:
Ms = 1.5 x (N  1)
GB
Mm = 1.5 x (N  2)
GB
As a rule of thumb, the maximum required memory at a certain time for
multimodel analysis could be estimated by multiplying the number of datasets by
the average file size of all the datasets; this memory intake is high but also
assumes that all data is fully realized in memory; this aspect will gradually
change and the amount of realized data will decrease with the increase of
dask
use.
Other#
Miscellaneous functions that do not belong to any of the other categories.
Clip#
This function clips data values to a certain minimum, maximum or range. The function takes two arguments:
minimum
: Lower bound of range. Default:None
maximum
: Upper bound of range. Default:None
The example below shows how to set all values below zero to zero.
preprocessors:
clip:
minimum: 0
maximum: null