Santander Meteorology Group <http://meteo.unican.es>
2021-09-20
The datasource ontology describes the provenance of climate datasets (observations, operative/retrospective forecasts, reanalysis, climate projections...), and other subsequent operations performed with these data sources producing their transformation, but that do not entail a second dataset (may entail a different subset of the same dataset though; e.g. temporal/spatial aggregation, ensemble mean calculation, spatial interpolation/regridding, calculation of climate indices and anomalies...). It also provides a framework for describing the source code generating the product.
The Climate Data Source Vocabulary from METACLIP
http://www.metaclip.org
https://doi.org/10.1016/j.envsoft.2019.07.005
0.25
Establishes a direct link between the METACLIP vocabulary names and the vocabularies used in the DRS for the CORDEX Bias-Adjusted simulations
http://is-enes-data.github.io/CORDEX_adjust_drs.pdf
Establishes a direct link between the METACLIP vocabulary names and the vocabularies used in the CMIP5 DRS
http://pcmdi.github.io/mips/cmip5/docs/cmip5_data_reference_syntax.pdf
Establishes a direct link between the METACLIP vocabulary names and the vocabularies used in the CMIP6 DRS
https://github.com/WCRP-CMIP/CMIP6_CVs
Establishes a direct link between the METACLIP vocabulary names and the vocabularies used in the CORDEX DRS
http://is-enes-data.github.io/cordex_archive_specifications.pdf
Establishes a direct link between the METACLIP vocabulary names and the vocabularies used in a certain DRS.
The Data Reference Syntax is a naming system to be used within files, directories, metadata and URL's to identify data sets wherever they might be located, for instance within a distributed ESGF archive. A DRS is heavily dependent on controlled vocabularies and CF standard names. This annotation property establishes a link between a certain DRS (e.g. CMIP5 DRS) and the names of entities in the METACLIP vocabularies. Therefore, the different project-specific subproperties should be used
The index type is an attempt to provide a synthetic overview of the aspects described by the Climate Index at hand. "Circulation" indices are synthetic descriptors of circulation patterns, for instance North Atlantic Oscillation (NAO, uses mean sea-level pressure as input) or El Niño Southern Oscillation (ENSO, based on sea-surface temperature anomalies). Indices of "extremes" are used to characterize extreme climate conditions (e.g. the number of icing days, defined as the anual count of days when daily maximum temperature is below 0 degC). "Drought" is used to indicate drought indices. "Other" categories exist, so a fourth miscellaneous category is considered for this property (e.g. heating degree days).
Annotates the input Essential Climate Variable(s) required for index calculation
An active web link providing an authoritative technical description of the resource
referenceURL
The season is a character string used to describe a temporal period as a monthly time slice. For instance, boreal summer is typically designated as 'JJA', boreal winter as 'DJF' etc.
http://metaclip.org/datasource/datasource.owl#
Predictia
2018-03-02T16:16:14Z
This entity is aimed at the description of the types of threshold used for climate index definition. "Absolute" thresholds refer to absolute quantities, while "relative" thresholds are based on percentiles (and thus are relative to a given sample). In case no threshold is used for a certain climate index, the category "none" can optionally be used.
Designation of a dataset dimension along which an operation (e.g. a transformation) is performed
http://www.metaclip.org/datasource/datasource.owl
Along Dimension
Predictia
This property associates a specific :Command (prov:Activity) with a software :Package (prov:SoftwareAgent)
fromPackage
Predictia
2018-04-02T16:44:31Z
An object property to express the derivation of an :Aggregation from a previous :Step
http://www.metaclip.org/datasource/datasource.owl
hadAggregation
2018-04-02T16:52:33Z
An object property to express the derivation of an :Anomaly from a previous :Step
http://www.metaclip.org/datasource/datasource.owl
hadAnomalyCalculation
Predictia
2017-07-01T16:24:14Z
A property representing the :ArgumentValue taken by an :Argument
http://www.metaclip.org/datasource/datasource.owl
hadArgumentValue
An object property to express a :Binding operation of two or more previous :Transformation class objects into a one single :Binding class object
http://www.metaclip.org/datasource/datasource.owl
hadBinding
hadClimateChangeSignalCalculation
An object property to express the derivation of an :ClimateIndex from a previous :Step
http://www.metaclip.org/datasource/datasource.owl
hadClimateIndexCalculation
Climatological difference calculation. Its more specific subproperties should be used
hadClimatologicalDifference
Predictia
2018-04-02T17:00:57Z
An object property to express the derivation of a :Climatology from a previous :Step
http://www.metaclip.org/datasource/datasource.owl
hadClimatology
Object property linking a multi-model Ensemble with a specific combination technique
hadCombinationMethod
Predictia
2018-03-13T09:09:32Z
This property links a fiven :Step within the data workflow with the :Command activity generating it
hadCommandCall
This property links a Dataset with an organization providing the data (DataProvider)
Predictia
2018-04-02T15:40:35Z
This property describes a :DatasetSubset derivation. This can happen from a :Dataset entity to produce a new :DatasetSubset, or from any other :Step within the data workflow from which a specific subsetting operation is performed (e.g., a :ClimateIndex might be temporally or spatially subsetted for further analysis or graphical representation).
http://www.metaclip.org/datasource/datasource.owl
hadDatasetSubset
This property has been defined as an inverse of prov:wasDerivedFrom, in order to preserve the chronological sequence of steps wthin the climate data workflow
http://www.metaclip.org/datasource/datasource.owl
hadDerivation
prov:wasDerivedFrom
Predictia
2018-03-15T17:33:02Z
This property links an :RCM Sofware agent with the driving GCM providing the boundary conditions
http://www.metaclip.org/datasource/datasource.owl
hadDrivingGCM
This property links a :Dataset that was generated by a simulation model (:GCM or :RCM) with an :Experiment
http://www.metaclip.org/datasource/datasource.owl
hadExperiment
2018-04-02T17:04:25Z
An object property to express the derivation of a :Regridding from a previous :Step
http://www.metaclip.org/datasource/datasource.owl
hadInterpolation
This object property related an interpolation :Step with a specific :InterpolationMethod
hadInterpolationMethod
Assign the responsibility of the Dataset to a specific ModellingCenter
Predictia
2018-04-02T17:01:31Z
An object property to express the derivation of a :PCA from a previous :Step
http://www.metaclip.org/datasource/datasource.owl
hadPCA
Predictia
2017-06-30T11:07:19Z
Establishes the Project to which the Dataset is adscribed
hadProject
A data property linking a seasonal forecast dataset (either a hindcast or an operational forecast) with its forecasting system
hadSeasonalForecastingSystem
Predictia
2018-04-02T16:44:19Z
This property describes any :Transformation experienced throughout the data workflow, leading to a derivation from one :Step into another. For instance, a :DatasetSubset may be aggregated or subsequently subsetted. Because :Transformation is a generic :Step, the more specific subproperties should be used when applicable (e.g. :hadRegridding, :hadAnomalyCalculation etc.)
http://www.metaclip.org/datasource/datasource.owl
hadTransformation
Predictia
2018-03-05T12:27:17Z
The numbers of the Niño 1,2,3, and 4 regions correspond with the labels assigned to ship tracks that crossed these regions. Data from these tracks enabled the historic records of El Niño to be carried back in time to 1949, as discussed in a classic study by Rasmusson and Carpenter (1982)
http://www.metaclip.org/datasource/datasource.owl
Horizontal position may be expressed directly in geographic coordinates (latitude and longitude) for global models or in a map projection planar coordinates for regional models.
http://www.metaclip.org/datasource/datasource.owl
hasHorizontalExtent
http://www.metaclip.org/datasource/datasource.owl
Initialization time
http://www.metaclip.org/datasource/datasource.owl
Realizations included in the dataset subset
An object property linking :Interpolation or :DatasetSubset with a specific :SpatialDomain for which they apply. The subproperties defined, either for horizontal or vertical extents, should be asserted when relevant.
hasSpatialExtent
A standard definition of the variable provided by an authoritative entity (CF, ECMWF tables...)
http://www.metaclip.org/datasource/datasource.owl
Standard Definition
Predictia
2017-06-29T12:29:42Z
http://www.metaclip.org/datasource/datasource.owl
Temporal resolution of a Variable
Predictia
Property of a dataset subset defining the time interval encompassed by the validation times.
http://www.metaclip.org/datasource/datasource.owl
http://www.metaclip.org/datasource/datasource.owl
Variable of the :DatasetSubset
http://www.metaclip.org/datasource/datasource.owl
hasVerticalExtent
Object property linking a :Command with its :Argument
usedArgument
This object property is used to indicate the target locations used for interpolation/regridding operations (i.e., the target coordinates onto which the data are interpolated). Its range is a :HorizontalExtent class entity, representing either a regular grid or scattered localities
usedReferenceCoordinates
Predictia
2018-04-05T14:15:53Z
Link a :Dataset or a subsequent :Step derivation with an :Ensemble of which it is member
http://www.metaclip.org/datasource/datasource.owl
wasEnsembleMember
This object property relates Command A, explicitly called by the user, with a second Command B acting internally as a result of Commad A call. Therefore, this object property is designed to account for internal functions whose output is relevant for the description of the climate product generation
http://www.metaclip.org/datasource/datasource.owl
wasInternallyCalledBy
Predictia
2018-04-05T14:15:20Z
This property has been defined as an inverse of prov:hadMember, in order to preserve the chronological sequence of steps within the climate data workflow, so preprocessing steps for each member of the :Ensemble are recorded before forming it.
http://www.metaclip.org/datasource/datasource.owl
wasMemberOf
withClimateIndex
Links a :ClimateIndexCalculation derivation with the speciifc :ClimateIndex entity
A climatology encompassing a reference period used to calculate the 'average climate' or 'baseline'
This is either a reference subset for anomaly calculation or a climatological baseline to compute a delta change
http://www.metaclip.org/datasource/datasource.owl
http://www.metaclip.org/datasource/datasource.owl
Software Package Version property
1
10
11
12
2
3
4
5
6
7
8
9
Month included in the validation period
Multiple validation months are used to define specific seasons
Validation Month
The literal command call triggering the step. 'Lliteral' here means that the :hasLiteralCommandCall string could be directly copied/pasted into a terminal, and it should work provided that the environment contains the necessary elements to run it (installed software, input variables in scope etc.)
hadLiteralCommandCall
When data that is representative of cells can be described by simple statistical methods, those methods can be indicated using the cell_methods attribute
A specific cell method is specified in the case of :Dimension, when this is asserted in the context of an :Aggregation step
http://cfconventions.org/cf-conventions/cf-conventions.html#appendix-cell-methods
hasCellMethod
Horizontal Resolution of X coordinates
hasHorizontalResX
Horizontal resolution of Y coordinates
hasHorizontalResY
Long name of the variable
hasLongName
A URL pointing to the source of the entity
hasMainURL
hasMember
A map projection is a systematic transformation of the latitudes and longitudes of locations from the surface of a sphere or an ellipsoid into locations on a plane
This triad of integers (N, M, L), formatted e.g., "r3i1p21" distinguishes among closely related simulations by a single model. All three are required even if only a single simulation is performed.
The so-called "realization" number (a positive integer value of 'N') is used to distinguish among members of an ensemble typically generated by initializing a set of runs with different, but equally realistic, initial conditions
Models used for forecasts that depend on the initial conditions might be initialized from observations using different methods or different observational datasets. These should be distinguished by assigning different positive integer values of 'M' in the "initialization method indicator".
If there are many closely related model versions, which, as a group, are generally referred to as a perturbed physics ensemble, then these should
be distinguishable by a "perturbed physics" number 'L', where the positive integer value of 'L' is uniquely associated with a particular set of model parameters (e.g., r3i1p78 is a third realization of the seventy-eighth version of the perturbed physics model).
For example the two different ensemble members, r3i1p7 and r3i1p8, should both be initialized from exactly the same initial conditions using the same method (because the "r" and "i" values are identical) although the subsequent evolution of the simulations will presumably differ since they were produced by two different "perturbed physics" versions of the same model.
Ensemble member (r<N>i<M>p<L>)
A model run refers to one complete model calculation. There is usually a well established nomenclature for run definition. Model run is indicated as a xsd:string
https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf
A short name is an abbreviated name for a variable, as internally encoded in the dataset.
hasShortName
A software agent version
The time frame used to compute the anomaly. Possible values are "none" (i.e., the whole period has been averaged and subtracted), "monthly" (i.e., the climatological mean is subtracted month by month) or "daily" (i.e., a daily climatology is subtrated to each day)
Climatological period
The time step is defined according to ISO 8601
https://en.wikipedia.org/wiki/ISO_8601#Durations
Time Step Duration
Vertical Level
hasVerticalLevel
withDataype
Percentage of total variance explained by the retained PCs
withExplainedVariance
Number of principal components/EOFs retained in the transformation step
Number of PCs
bilinear
cubic
nearest
none
The name of the method used for regridding (e.g. 'bilinear')
Regridding method
A description of the simulation domain of a :RCM simulation. A domain is a region for which the regional downscaling is taking place, for example the African domain in the :CORDEX experiment covers the whole of the African continent.
withSimulationDomain
Units of a geophysical variable
withUnits
Version number
withVersionTag
Easternmost gridcell coordinate of the domain. Should correspond to the grid cell centroid.
xmax
Westernmost gridcell coordinate of the domain. Should correspond to the grid cell centroid.
xmin
Northernmost gridcell coordinate of the domain. Should correspond to the grid cell centroid.
ymax
Southernmost gridcell coordinate of the domain. Should correspond to the grid cell centroid.
ymin
An aggregate function is a function where the values of multiple rows (data array dimensions) are grouped together as input on certain criteria to form a single value of more significant meaning. Common aggregate functions include average, count, maximum, minimum, median...
http://www.metaclip.org/datasource/datasource.owl
Aggregation
https://en.wikipedia.org/wiki/Aggregate_function
The deviation of a variable from its value averaged over a reference period.
This class should not be used instead of :ClimateChangeSignal. The latter is used to calculate the difference between a future scenario and a historical baseline.
https://www.ipcc.ch/site/assets/uploads/2018/11/sr15_glossary.pdf
Anomaly
http://glossary.ametsoc.org/wiki/Climate_anomaly
A command argument is an item of information provided to a program when it is started. A program can have many command-line arguments that identify sources or destinations of information, or that alter the operation of the program.
http://www.metaclip.org/datasource/datasource.owl
Argument
https://en.wikipedia.org/wiki/Command-line_interface#Arguments
http://www.metaclip.org/datasource/datasource.owl
ArgumentValue
Bicubic interpolation is an extension of cubic interpolation for interpolating data points on a two-dimensional regular grid. The interpolated surface is smoother than corresponding surfaces obtained by bilinear interpolation or nearest-neighbor interpolation. Bicubic interpolation can be accomplished using either Lagrange polynomials, cubic splines, or cubic convolution algorithm.
BicubicInterpolation
https://en.wikipedia.org/wiki/Bicubic_interpolation
Bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables (e.g., x and y) on a rectilinear 2D grid. The key idea is to perform linear interpolation first in one direction, and then again in the other direction. Although each step is linear in the sampled values and in the position, the interpolation as a whole is not linear but rather quadratic in the sample location
BilinearInterpolation
https://en.wikipedia.org/wiki/Bilinear_interpolation
Binding is a combination multi-dimensional arrays. It takes a sequence of two or more DatasetSubsets that are dimensionally consistent to produce a single DatasetSubset by binding them along the specified dimension.
http://www.metaclip.org/datasource/datasource.owl
Binding
Regions used for the CORDEX regional climate model integrations project which define the RCM interior domain, i.e. the area left once the relaxation zone is excluded.
https://is-enes-data.github.io/cordex_archive_specifications.pdf
The CORDEX domains are defined as rectangles in rotated-pole coordinates. A domain has to lie inside the RCM interior computational domain, i.e. in the area left once the relaxation zone is excluded. The domain acronym has to be ‘domain’-‘resolution’, for example 'domain'-44, 'domain'-22, 'domain'-11, etc. corresponding to the chosen gridspacing. Output simulation data are provided for the CORDEX domain only, i.e. the relaxation zones have to be cut off before the data is delivered.
In addition, a set of "Core variables" have to be provided on a regular geographic latitude/longitude grid. These grids have grid point boundaries (not centers) at whole-number degrees of longitude and latitude, and have roughly the same resolution as the native grid.The domain acronyms for the regular grids are the same as those for the corresponding model grid with the letter 'i' appended to the resolution (e.g. “SAM-44i”).
CORDEX Domain
https://cordex.org/wp-content/uploads/2012/11/CORDEX-domain-description_231015.pdf
The difference (a.k.a. 'delta') between the climatological values for a reference period and a future time period, providing the climate change signal.
In the case of climate model outputs, as it compares time slices of simulated future scenarios relative to a simulated historical scenario of the same model, the physics and parametrizations remain consistent, and therefore the bias adjustment step is usually not needed. The appropriate subclasses should be used for either relative or absolute delta changes. Note the difference with the Anomaly class, that should not be used in place of this one.
https://doi.org/10.1038/nature09388
A climate index is a simple diagnostic quantity that is used to characterize an aspect of a geophysical system such as a circulation pattern
The more specific subclassess should be asserted when relevant. Also, specific individual instances have been defined for some common climate indices widely used
http://www.metaclip.org/datasource/datasource.owl
ClimateIndex
https://climatedataguide.ucar.edu/climate-data/overview-climate-indices
ClimateIndexCalculation
A :ClimateIndexCalculation is a :Transformation subclass entailing the calculation of a climate index. Climate Indices are described by the :ClimateIndex entity
The difference (either an arithmetic difference or a ratio), between two climatological layers. This is a broad class, and therefore the more specific subclasses should be always used.
Climatological difference
http://www.metaclip.org/datasource/datasource.owl
Climatology
http://www.euporias.eu/taxonomy/term/16
The normal state of climate such as a base line over the normal period. Climatology is often taken as the mean value for a given month over, for example, 1961-1990
Combination is a post-processing family of techniques by which different models are combined to produce an :Ensemble. These techniques can range from a direct combination assigning equal weights to each member to more sophisticated combination techniques computing individual model weights according to specific criteria.
CombinationMethod
A Command is a directive to a computer program (prov:SoftwareAgent) acting as an interpreter of some kind, in order to perform a specific task
http://www.metaclip.org/datasource/datasource.owl
Command
https://en.wikipedia.org/wiki/Command_(computing)
In a fully coupled climate model, state variables and heat and water fluxes must be transferred between models periodically, and such fields must be remapped from one component grid to another. Fluxes in particular must be remapped in a conservative manner in order to maintain the energy and water budgets of the coupled climate system. Conservative mapping methods attempt to preserve fluxes (or other integrals) during the interpolation process
ConservativeRemapping
https://doi.org/10.1137/0908037
https://doi.org/10.1175/1520-0493(1999)127%3C2204:FASOCR%3E2.0.CO;2
DataProviders are Agents (institutions or any other type of organizations) maintaining any type of infrastructure for the provision of climate data. These are not necessarily the institutions producing the data (ds:ModellingCenter), but rather those institutions or coordinated infrastructures distributing (or re-distributing) the data (they might be the same, though). This class is envisaged to provide provence information about the primary source of the Dataset in the current application.
http://www.metaclip.org/datasource/datasource.owl
DataProvider
The Dataset constitutes the primary source of the data workflow
http://www.metaclip.org/datasource/datasource.owl
Dataset
A subset of a Dataset
http://www.metaclip.org/datasource/datasource.owl
DatasetSubset
In the difference anomaly, the difference between the target period (pred) and the reference (base) is computed as the difference pred minus base.
DiffAnomaly
The climate change signal is calculated as the arithmetic difference between future time slice and baseline climatology
Difference climate change signal
A Dimension is used to define the array shape of a Variable.
A Dataset Dimension
http://www.metaclip.org/datasource/datasource.owl
Dimension
https://www.unidata.ucar.edu/software/thredds/v4.3/netcdf-java/v4.3/javadoc/ucar/nc2/Dimension.html
Historically “El Niño” referred to the appearance of unusually warm water off the coast of Peru near Christmastime (Niño is Spanish and refers to “the boy Christ child”). Today it describes broader changes that occur across the Pacific basin. Oceanic and atmospheric conditions in the tropical Pacific fluctuate somewhat irregularly between warm El Niño phases and cold phases in which surface waters cool across the tropical Pacific. These cooling events are called “La Niña” (“the girl” in Spanish). The most intense phase of each event typically lasts about a year. El Niño is linked to major changes in the atmosphere known as the Southern Oscillation (SO). The whole phenomenon is known as the El Niño Southern Oscillation (ENSO)
ENSO
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni?qt-climatedatasetmaintabs=1#qt-climatedatasetmaintabs
These are :SpatialExtent class features used to represent specific domains upon which the ENSO phenomenon is characterized
ENSOregion
http://etccdi.pacificclimate.org/indices.shtml
The Expert Team on Climate Change Detection and Indices (ETCCDI) has ellaborated a set of 27 core indices aimed at capturing changes in climate extremes and in selected climate impact indicators deemed relevant to other disciplines, described by the :ETCCDI subclass
http://www.metaclip.org/datasource/datasource.owl
ETCCDI
https://link.springer.com/chapter/10.1007/978-94-015-9265-9_2
The :Ensemble class is aimed at the characterization of groups of datasets that have been joined to form an ensemble prior to the next :Step in the data workflow. This ensemble does not refer to different members from the same dataset (e.g., as typically in :SeasonalHindcast datasets), but to a new class that is a subclass of prov:Collection, formed by two or more distinct :Datasets, or (more often) subsequent :Steps after them, like for instance collections of :DatasetSubsets or transformed :DatasetSubsets.
Specific :CombinationMethod approaches can be used to this aim
http://www.metaclip.org/datasource/datasource.owl
Ensemble
https://en.wikipedia.org/wiki/Climate_ensemble
In the context of climate science, an Ensemble involves slightly different models of the climate system that are used together to use their outcomes jointly. The aim of running an ensemble is usually in order to be able to deal with uncertainties in the system. An ultimate aim may be to produce policy relevant information such as a probability distribution function of different outcomes.
Experiment
An :Experiment is an activity aimed at addressing a specific scientific problem. In the climate context, different datasets (particularly model simulations, but also observations etc...) can be adscribed to specific experiments. Several predefined :Experiment individuals can be instantiated (this is preferred), for instance :RCP45 or :Evaluation experiments.
http://www.metaclip.org/datasource/datasource.owl
Experiment
Global Climate Models or General Circulation Models (GCMs) are global, three-dimensional computer models of the climate system, used to solve a set of mathematical equations describing the laws of physics relevant to the atmospheric and oceanic circulation, the distribution of heat and the interaction between electromagnetic radiation and atmospheric gases and aerosols. They consist of different modules describing the atmosphere, oceans, sea-ice/snow and the land surface, and represent the world in terms of boxes stacked next to and on top of each other. Evolution of the model circulation is computed by time integration of those equations starting from an initial condition.
Model
GCM
https://en.wikipedia.org/wiki/General_circulation_model
Provides the definition of a horizontal spatial extent. The horizontal spatial extent defines a horizontal spatial domain. Further data properties of the spatial domain can be included (e.g., xmin, xmax, ymin, ymax, resX, resY...)
HorizontalExtent
Initialization is the process of entering observation data into the model to generate initial conditions
http://www.metaclip.org/datasource/datasource.owl
Initialization
https://en.wikipedia.org/wiki/Numerical_weather_prediction#Initialization
Regridding is the process of interpolating from one grid resolution to a different grid resolution. This could involve temporal, vertical or spatial ('horizontal') interpolations. However, most commonly, regridding refers to spatial interpolation.
Regridding
https://climatedataguide.ucar.edu/climate-data-tools-and-analysis/regridding-overview
Interpolation is the problem of approximating the value of a function for a non-given point in some space when given the value of that function in points around (neighboring) that point. This class should not be used, but instead its most specific subclasses describing different interpolation methods should be asserted when relevant
InterpolationMethod
https://climatedataguide.ucar.edu/climate-data-tools-and-analysis/regridding-overview
Inverse distance weighting (IDW) is a type of deterministic method for multivariate interpolation with a known scattered set of points. The assigned values to unknown points are calculated with a weighted average of the values available at the known points.
InverseDistanceWeighting
https://en.wikipedia.org/wiki/Inverse_distance_weighting
http://www.metaclip.org/datasource/datasource.owl
Latitude
http://www.metaclip.org/datasource/datasource.owl
Longitude
http://www.metaclip.org/datasource/datasource.owl
Member
https://en.wikipedia.org/wiki/Ensemble_forecasting
Institute
The organization producing the Dataset, either by running and/or delivering the climate model simulations or carrying out any other type of data processing (interpolation, homogenization, assimilation, harmonization, distillation...) leading to a data product that is a primary source described by _ds:Dataset_.
http://www.metaclip.org/datasource/datasource.owl
A Dataset containing multidecadal simulations. These are mainly used in climate change or paleoclimate studies
MultiDecadalSimulation
circulation
sea-level pressure
POLYGON((-90 20, 40 20, 40 80, -90 80. -90 20))
http://www.metaclip.org/datasource/datasource.owl
NAO
Hurrell, J.W., Kushnir, Y., Ottersen, G., Visbeck, M., 2003. An overview of the North Atlantic Oscillation, in: Hurrell, J.W., Kushnir, Y., Ottersen, G., Visbeck, M. (Eds.), Geophysical Monograph Series. American Geophysical Union, Washington, D. C., pp. 1–35.
https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-pc-based
The principal component (PC)-based indices of the North Atlantic Oscillation (NAO) are the time series of the leading Empirical Orthogonal Function (EOF) of SLP anomalies over the Atlantic sector, 20°-80°N, 90°W-40°E
The nearest neighbor algorithm selects the value of the nearest point and does not consider the values of neighboring points at all, yielding a piecewise-constant interpolant. The algorithm is very simple to implement and is widely used
NearestNeighbor
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation
Observational datasets are constructed upon observations of the real world. In some occasions, these observations are post-processed and/or combined with other sources of information to generate the final product, that is used a representation of the "true" climate record (e.g. for validation purposes or for historical reconstructions).
http://www.metaclip.org/datasource/datasource.owl
ObservationalDataset
Regression analysis to determine from a set of independent variables (predictors) those contributing most to the explained variance
http://www.metaclip.org/datasource/datasource.owl
Principal Component Analysis
A package is a distribution of software and data in archive files. Packages contain metadata, such as the software's name, description of its purpose, version number, vendor, checksum, and a list of dependencies necessary for the software to run properly. Upon installation, metadata is stored in a local package database.
Package
https://en.wikipedia.org/wiki/Package_manager
An (intercomparison) Project generating GCM/RCM simulations, observational datasets etc.
Project
This Class aims at recording the necessary information to unequivocally identify a given data simulation with a specific project. For instance, A MultiDecadalSImulationDataset may belong to CMIP5 or CMIP3. Similarly, a SeasonalHindcastDataset may be adscribed to DEMETER.
http://www.metaclip.org/datasource/datasource.owl
Project
A Regional Climate Model. It is a numerical climate prediction model forced by specified lateral and ocean conditions from a general circulation model (GCM) or observation-based dataset (reanalysis) that simulates atmospheric and land surface processes, while accounting for high-resolution topographical data, land-sea contrasts, surface characteristics, and other components of the Earth-system. Since RCMs only cover a limited domain, the values at their boundaries must be specified explicitly, referred to as boundary conditions, by the results from a coarser GCM or reanalysis; RCMs are initialized with the initial conditions and driven along its lateral-atmospheric-boundaries and lower-surface boundaries with time-variable conditions. RCMs thus downscale global reanalysis or GCM runs to simulate climate variability with regional refinements.
RCM
http://glossary.ametsoc.org/wiki/Regional_climate_model
http://www.metaclip.org/datasource/datasource.owl
Realization
Reanalysis is a scientific method for developing a comprehensive record of how weather and climate are changing over time. In it, observations and a numerical model that simulates one or more aspects of the Earth system are combined objectively to generate a synthesized estimate of the state of the system. A reanalysis typically extends over several decades or longer, and covers the entire globe from the Earth’s surface to well above the stratosphere.
Reanalysis
http://reanalyses.org/
Native horizontal 2D grid of gridded datasets. The rectangular/square (or latitude-longitude) grid is the most commonly used grid in gridded climate products and models.
Rectangular Grid
This class is conceived to provide a relatively simple, yet useful definition of a dataset grid. The grid is defined as a 2-D matrix of XY coordinates. At each individual grid point (x,y) the solution to the system of equations solved or algorithm output is represented. Additional object properties provide details on the main grid characteristics (for instance x and y resolution)
http://www.metaclip.org/datasource/datasource.owl
In the relative anomaly, the difference between the target period (pred) and the reference (base) is computed as a ratio 'pred/base'
RelAnomaly
The climate change signal is calculated as the ratio between future time slice and baseline climatology
Relative climate change signal
Numerical weather prediction requires input of meteorological data, collected by satellites and earth observation systems such as automatic and manned stations, aircraft, ships and weather balloons. Assimilation of this data is used to produce an initial state of a computer model of the atmosphere. Seasonal forecasting systems are based on an atmospheric model (GCM) coupled to the ocean, used to produce forecasts of the weather up to 12 months ahead. These forecasts are produced operationally and are typically issued once per month, although their periodicity varies among forecasting systems. The forecast verification relies on an antecedent historical forecast dataset (a 'reforecast' or 'hindcast'), for which historical observations exist.
SeasonalForecastingSystem
A seasonal hindcast (a.k.a. 'reforecast') is a forecast made for a period in the past using only information available before the beginning of the forecast. The hindcast can be used for verification purposes, helping to build trust (or not) on the forecasting system. Hindcasts typically encompass a relatively long period (i.e. 30 years or more) in order to provide more robust skill assessments.
SeasonalHindcast
SeasonalOperationalForecast
ShortRangeForecast
Definition of the spatial boundaries of a :DatasetSubset or :Transformation.
http://www.metaclip.org/datasource/datasource.owl
SpatialExtent
A spline is a function defined piecewise by polynomials. In interpolation problems, spline interpolation is often preferred to polynomial interpolation because it yields similar results, even when using low degree polynomials, while avoiding Runge's phenomenon for higher degrees (i.e., a problem due to oscillation at the edges of an interval).
Splines
https://en.wikipedia.org/wiki/Spline_(mathematics)
A step within the data workflow. Conceived as an Abstract class. The more specific forms of ds:Step (i.e., ds:DatasetSubset, ds:Aggregation etc.) should be asserted if they apply.
http://www.metaclip.org/datasource/datasource.owl
A specialization of prov:Start, most often used to define the start time of an :Initialization
http://www.metaclip.org/datasource/datasource.owl
TemporalInstant
Define a temporal period as the ellapsed time between start and end time stamps. Time periods will be defined as a time interval that is left-closed and right-open. For example, the period 1981-2010 will be usually expressed as [1981-01-01 00:00:00, 2011-01-01 00:00:00)
http://www.metaclip.org/datasource/datasource.owl
TemporalPeriod
Describes the time resolution of the variable
Frequency
http://www.metaclip.org/datasource/datasource.owl
TemporalResolution
A transformation of data implies the application of a deterministic mathematical function to each point in a data set (that is, each data point z_i is replaced with the transformed value y_i = f(z_i), where f is a function. Examples of transformations are scaling and/or centering. In a different sense, a data transformation converts a set of data values from the data format of a source data system into the data format of a destination data system e.g. regridding, data aggregation...
The more specific subclasses of ds:Transformation (i.e. ds:ClimateIndex, ds:Aggregation etc.) should be asserted if they apply
http://www.metaclip.org/datasource/datasource.owl
Transformation
https://en.wikipedia.org/wiki/Data_transformation
https://en.wikipedia.org/wiki/Data_transformation_(statistics)
The validation time is the time at which each of the data points of a climate dataset verify along the time axis
http://www.metaclip.org/datasource/datasource.owl
ValidationTime
Characterization of a Geophysical Variable
Variable
Reference to a Standard Definition from a authoritative convention (e.g.: ECMWF Tables, CF Convention...)
http://www.metaclip.org/datasource/datasource.owl
VariableStandardDefinition
Provides the definition of a vertical spatial extent
VerticalExtent
The Copernicus Climate Change Service (C3S) supports scientists, policy makers and businesses by providing authoritative, quality-assured and up-to-date information about the past, current and future climate across Europe and worldwide.
The service is implemented by the European Centre for Medium-Range Weather Forecasts (:ECMWF) on behalf of the European Commission.
C3S is one of six core thematic services of Copernicus, the European Union's flagship programme for monitoring Earth’s environment based on satellite and in-situ observations as well as modelling information.
C3S
https://climate.copernicus.eu
It is the number of degrees that a day's average temperature is above a certain temperature threshold
Cooling Degree Days
Energy
tas, tasmin, tasmax
https://doi.org/10.1002/joc.3959
Absolute
Symmetrical to the Heating Degree Days (HDD) Index. Energy consumption in hot environments typically depends on the excess of temperature above a given threshold, where cooling within buildings is required.
CDD
Maximum length of dry spell, maximum number of consecutive days with RR < 1mm
extremes
precipitation
absolute
This climate index is a measure of low precipitation, with high values corresponding to long periods of low precipitation and potentially drought-favouring conditions. An increase of this index with time means that the chance of drought conditions will increase.
Expert Team on Climate Change Detection and Indices (ETCCDI)
CDD
http://etccdi.pacificclimate.org/list_27_indices.shtml
Copernicus Climate Data Store
https://cds.climate.copernicus.eu
The CDS is a cloud-based tool that allows to browse and combine online petabytes of raw data. It is developed by the Copernicus Climate Change Service (:C3S) at :ECMWF, the store draws on Earth observation data collected through the European Commission's Copernicus Programme.
CDS
The second version of the NCEP Climate Forecast System (CFSv2) was made operational at :NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system
CFSv2
http://dx.doi.org/10.1175/JCLI-D-12-00823.1
The 10 atmospheric perturbed Initial Conditions (ICs), the 3 land perturbed ICs and the 8 (4 in hindcast mode) are combined to yield 240 (120 in hindcast mode) possible perturbed ICs among which the 50 ICs (40 in hindcast mode) to produce the forecast ensemble are chosen at random
CMCC-SPS3
Model description at the C3S site (private access) <https://software.ecmwf.int/wiki/display/C3SS/Description+of+CMCC+C3S+contribution>
Technical model description:
Sanna, A., A. Borrelli, P. Athanasiadis, S. Materia, A. Storto, S. Tibaldi, S. Gualdi, 2017: CMCC-SPS3: CMCC-SPS3: The CMCC Seasonal Prediction System 3. Centro Euro-Mediterraneo sui Cambiamenti Climatici. CMCC Tech. Rep. RP0285, 61pp. Available at adress: <https://www.cmcc.it/it/publications/rp0285-cmcc-sps3-the-cmcc-seasonal-prediction-system-3>
Cold spell duration index: Annual count of days with at least 6 consecutive days when TN < 10th percentile
extremes
temperature (minimum)
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
CSDI
http://etccdi.pacificclimate.org/list_27_indices.shtml
Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm
extremes
precipitation
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
CWD
http://etccdi.pacificclimate.org/list_27_indices.shtml
Copernicus is the European Union's Earth Observation Programme, looking at our planet and its environment. It offers information services based on satellite Earth Observation and in situ (non-space) data.
The Programme is coordinated and managed by the European Commission. It is implemented in partnership with the Member States, the European Space Agency (ESA), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), the European Centre for Medium-Range Weather Forecasts (ECMWF), EU Agencies and Mercator Océan.
Copernicus
http://www.copernicus.eu/
Drought Frequency. Drought months are calculated from SPI, considering the number of months since SPI falls below −1 until the SPI returns to positive values. This is an absolute frequency (units are number of months).
Drought Frequency
Drought
DF
Daily temperature range: Monthly mean difference between TX and TN
temperature (minimum), temperature (maximum)
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
DTR
http://etccdi.pacificclimate.org/list_27_indices.shtml
Deutscher Wetterdienst
https://www.dwd.de
DWD
E-OBS is a daily gridded observational dataset for precipitation, temperature and sea level pressure in Europe. The datafiles contain gridded data for 5 elements (daily mean temperature TG, daily minimum temperature TN, daily maximum temperature TX, daily precipitation sum RR and daily averaged sea level pressure PP). They cover the area: 25N-75N x 40W-75E. The data files are in compressed NetCDF format. Data is made available on a 0.25 and 0.5 degree regular lat-lon grid, as well as on a 0.22 and 0.44 degree rotated pole grid.
https://www.ecad.eu/download/ensembles/download.php
EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP
E-OBS
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2008JD010201
European Climate Assessment
The European Climate Assessment & Dataset (ECA&D, <www.ecad.eu>) is the result of a collaboration between meteorological institutes and universities throughout Europe and the Meditteranean area, WMO Region VI. One of the partners is the MEDARE initiative which coordinates DARE (Data Rescue projects and initiatives) activities across the Greater Mediterranean Region.
ECA
https://www.ecad.eu/
European Centre for Medium-Range Weather Forecasts
https://www.ecmwf.int/
The European Centre for Medium-Range Weather Forecasts (ECMWF) is an independent intergovernmental organisation supported by 34 states
ECMWF
The ENSEMBLES project (contract number GOCE-CT-2003-505539) is supported by the European Commission's 6th Framework Programme as a 5 year Integrated Project from 2004-2009 under the Thematic Sub-Priority "Global Change and Ecosystems"
ENSEMBLES
http://ensembles-eu.metoffice.com/
Rectangular region used for the computation of the The Niño 1+2 anomalies.
POLYGON((-90 10, -80 -10, -80 0, -90 0, -90 10))
The Niño 1+2 region is the smallest and eastern-most of the Niño SST regions, and corresponds with the region of coastal South America where El Niño was first recognized by the local populations.
ENSO-1+2
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
Rectangular region used for the computation of the The Niño 3 anomalies.
POLYGON((-150 -5, -90 -5, -90 5, -150 5, -150 -5))
This region was once the primary focus for monitoring and predicting El Niño, but researchers later learned that the key region for coupled ocean-atmosphere interactions for ENSO lies further west (Trenberth, 1997). Hence, the Niño 3.4 and ONI became favored for defining El Niño and La Niña events.
ENSO-3
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
Rectangular region used for the computation of the The Niño 3.4 anomalies.
POLYGON((-170 -5, -120 -5, -120 5, -170 5, -170 -5))
The ENSO 3.4 SST anomalies may be thought of as representing the average equatorial SSTs across the Pacific from about the dateline to the South American coast.
ENSO-3.4
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
Rectangular region used for the computation of the The Niño 4 anomalies.
POLYGON((160 -5, -150 -5, -150 5, 160 5, 160 -5))
The Niño 4 index captures SST anomalies in the central equatorial Pacific. This region tends to have less variance than the other Niño regions.
ENSO-4
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
ERA-Interim is a global atmospheric reanalysis from 1979, continuously updated in real time, produced by ds:ECMWF
ERA-Interim
Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Hólm, E.V., Isaksen, L., K\aallberg, P., Köhler, M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.J., Park, B.K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., Vitart, F., 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 553–597. https://doi.org/10.1002/qj.828
https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim
ERA5 provides hourly estimates of a large number of atmospheric, land and oceanic climate variables. The data cover the Earth on a 30km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions
ERA5 will eventually replace the ERA-Interim reanalysis
ERA5
https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era5
Earth System Grid Federation
https://doi.org/10.1016/j.future.2013.07.002
The Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP). Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.
ESGF
https://journals.ametsoc.org/doi/10.1175/BAMS-D-15-00132.1
Conservative remapping in which parameters sensitive to land-sea transitions are dually interpolated, i.e. land-sea separated, and then re-combined in one file. Residual missing values (NaN) in the interior domain are filled with values from a straightforward remap.
This conservative remapping approach has been used in the framework of EURO-CORDEX. The basic remapping operand is conservative remapping as defined in CDO.
1. Variables exposing a large sensitivity to land-sea contrast are remapped with reference to land-sea mask in the native grid and the regular grid. This includes virtually all (near-) surface variables
2. Near-surface temperature parameters (tas, tasmin, tasmax) are corrected with an adiabatic adjustment (6.5K/km) to account for differences in surface height between source grid points in the native domain and target grid points in the regular grids.
3. A common land-sea fraction (sftlf), land-sea mask (lsm), and surface height (orog) describing the basic surface characteristics of the regular reference grids are employed. NetCDF files containing such information at both input/output resolutions have been prepared for that aim.
4. Variables not categorized in (2) are remapped straightforwardly with disregard of land sea mask and model orography.
5. Isolated islands or lakes in the regular grid (typically sized at grid cell mesh) which are not counter-represented in the native grid will not be processed in step (2).
6. To avoid the occurrence of missing values in the interior of the regular grid, those grid cells are filled with the outcome from the straightforward remap according to step (4)
EURO-CORDEX Conservative Remapping
The EWEMBI dataset was compiled to support the bias correction of climate input data for ISIMIP2b and the ISIMIP2a protocol extension. It is a reference data set for bias correction
http://doi.org/10.5880/pik.2019.004
EWEMBI
Number of frost days: Annual count of days when TN (daily minimum temperature) < 0 degC
temperature (minimum)
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
FD
http://etccdi.pacificclimate.org/list_27_indices.shtml
The forcing database for radiative parameters like ozone, aerosol and greenhouse gases is provided by CMIP6 for the historical period up to 2014. Afterwards, values are kept constant as CMIP6 future scenarios were not yet available.
GCFS_2.0
General Information <https://www.dwd.de/EN/ourservices/seasonals_forecasts/start.html>
Model description at the C3S site (private access) <https://software.ecmwf.int/wiki/display/C3SS/Description+of+DWD+C3S+contribution>
Growing degree days (GDD), also called growing degree units (GDUs), are a heuristic tool in phenology. GDD are a measure of heat accumulation used by horticulturists, gardeners, and farmers to predict plant and animal development rates such as the date that a flower will bloom, an insect will emerge from dormancy, or a crop will reach maturity. It is calculated as the cumulative number of degrees above a threshold (often between 0 and 10 degC, depending on species and farming system) during a given growing period. In AR6 Chaper 12 and Atlas, a threshold of 5 degC is used (see e.g. Ruosteenoja et al. 2016).
Growing Degree Days
Agriculture
Mean daily temperature
https://doi.org/10.1002/joc.4535
GDD
Growing season length: Annual (1st Jan to 31st Dec in Northern Hemisphere (NH), 1st July to 30th June in Southern Hemisphere (SH)) count between first span of at least 6 days with daily mean temperature TG > 5 degC and first span after July 1st (Jan 1st in SH) of 6 days with TG < 5 degC
temperature (mean)
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
GSL
http://etccdi.pacificclimate.org/list_27_indices.shtml
The UK Met Office Global Seasonal forecast system version 5 (GloSea5), using the Global Coupled configuration version 2
GloSea5-GC2
<https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.2396> for a description of the forecasting system
<https://www.geosci-model-dev.net/8/1509/2015/gmd-8-1509-2015.html> for a description of the model components making up the coupled configuration
Model description at the C3S site (private access) <https://software.ecmwf.int/wiki/display/C3SS/Description+of+Met+Office+C3S+contribution>
A horizontal spatial domain covering the whole globe
POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))
Heating Degree Days
Energy
tas, tasmin, tasmax
https://doi.org/10.1002/joc.3959
Absolute
Symmetrical to the Cooling Degree Day index, the HDD index is used for illustrating energy demand for heating. It has been used in several studies of impacts of climate change on the energy sector.
HDD
Number of icing days: Annual count of days when TX (daily maximum temperature) < 0 degC
temperature (maximum)
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
ID
http://etccdi.pacificclimate.org/list_27_indices.shtml
ISIMIP offers a framework for consistently projecting the impacts of climate change across affected sectors and spatial scales. An international network of climate-impact modellers contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
The Inter-Sectoral Impact Model Intercomparison Project
https://www.isimip.org/
ISIMIP
https://www.geosci-model-dev.net/10/4321/2017/
Number of days between the last spring frost and first fall frost using 0 degC as a threshold for the daily minimum temperature and adjusting for season between hemispheres: from January to December in the Northern Hemisphere and from July to June in the Southern Hemisphere
Length of frost-free period (days)
Minimum temperature
https://doi.org/10.1002/joc.4315
Absolute
Many ecosystems and crops are sensitive to frost conditions, and can only develop over a frost-free period.
LFFP
Before getting an operational status in July 2016, the forecasts had been produced since January 2015.
The hindcast/forecast ocean initial conditions are calculated with Mercator-Ocean PSI2G3R3 software. Before 1993, the ocean initial conditions come from :ECMWF Nemovar reanalysis.
The forecast uses two start dates:
-The first Wednesday falling between the 12th and the 18th of the previous month (25 members)
-The following Wednesday (26 members)
The hindcast uses only the latter date (15 members)
MF-System5
Model description at the C3S site (private access) <https://confluence.ecmwf.int/pages/viewpage.action?pageId=95453627>
Technical model documentation <http://www.umr-cnrm.fr/IMG/pdf/system5-technical.pdf>
The NCEP/NCAR Reanalysis 1 project is using a state-of-the-art analysis/forecast system to perform data assimilation using past data from 1948 to the present.
NCEP-Reanalysis1
https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
This index is similar to El Niño 3.4 (:Nino3_4), but considers a smaller region of coastal South America where El Niño was first recognized by the local population (:ENSO1plus2)
This index tends to have the largest variance of the Niño SST indices
El Niño 1+2
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
This index is similar to El Niño 3.4 (:Nino3_4), but using a a different region definition (:ENSO3)
El Niño 3
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
The Niño 3.4 anomalies may be thought of as representing the average equatorial SSTs across the Pacific from about the dateline to the South American coast. The Niño 3.4 index typically uses a 5-month running mean, and El Niño or La Niña events are defined when the Niño 3.4 SSTs exceed +/- 0.4C for a period of six months or more
El Niño 3.4
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
Similar to El Niño 3.4 index (:Nino3_4), the Niño 4 index captures SST anomalies in the central equatorial Pacific, considering a particular domain defined by (:ENSO4)
This region tends to have less variance than the other Niño regions.
El Niño 4
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
The ONI uses the same region as the Niño 3.4 index. The ONI uses a 3-month running mean, and to be classified as a full-fledged El Niño or La Niña, the anomalies must exceed +0.5C or -0.5C for at least five consecutive months.
This is the operational definition used by :NOAA
Oceanic Niño Index
https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni
Multimodel combinations based on averaging of PDFs (with equal weights)
PDF average
Multimodel combinations based on averaging of PDFs (with weights proportional to the ensemble mean difference)
PDF average weighted by ensemble mean
Multimodel combinations based on averaging of PDFs (with weights proportional to the leave-one-out CV inverse RMSE)
PDF average weighted by RMSE
Potsdam Institute for Climate Impact Research
https://www.pik-potsdam.de/
PIK
Annual total precipitation in wet days
precipitation
none
Expert Team on Climate Change Detection and Indices (ETCCDI)
PRCPTOT
http://etccdi.pacificclimate.org/list_27_indices.shtml
Multimodel combination of probability forecasts with equal weights
Simple probabilities combination
multimodel combination of probability forecasts with weights proportional to the cross-validatad inverse RPS
Probability combination weighted by RPS
Copernicus Climate Change Project 'C3S_51_Lot3', "Quality Assurance for Seasonal Forecast Products"
Copernicus Project code C3S_51_Lot3
Quality Assurance for Seasonal Forecast Products
QA4Seas
https://climate.copernicus.eu/quality-assurance-multi-model-seasonal-forecast-products
The QA4Seas prototype is a proof-of-concept software for seasonal forecast quality assessment that illustrates the most relevant practices suggested in the C3S EQC strategy. It was developed in the QA4Seas Project, part of the Copernicus Climate Change Initiative.
QA4Seas prototype
Main program call to run the QA4Seas prototype
Included for convenience in the framework of QA4seas project, for which its usage is restricted
QA4Seas.py
Annual count of days when PRCP≥ 10mm
precipitation
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
R10mm
http://etccdi.pacificclimate.org/list_27_indices.shtml
Annual count of days when PRCP ≥ 20mm
precipitation
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
R20mm
http://etccdi.pacificclimate.org/list_27_indices.shtml
Annual total PRCP when RR > 95p
extremes
precipitation
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
R95pTOT
http://etccdi.pacificclimate.org/list_27_indices.shtml
Annual total PRCP when RR > 99p
extremes
precipitation
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
R99pTOT
http://etccdi.pacificclimate.org/list_27_indices.shtml
Randomized multimodel combination, based on resampling of ensemble members with replacement; weights are assigned such that all contributing models are equally important.
Multimodel Resampling Combination
Annual count of days when PRCP≥ nnmm, nn is a user defined threshold
precipitation
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
Rnnmm
http://etccdi.pacificclimate.org/list_27_indices.shtml
Monthly maximum 1-day precipitation
extremes
precipitation
none
Expert Team on Climate Change Detection and Indices (ETCCDI)
Rx1day
http://etccdi.pacificclimate.org/list_27_indices.shtml
Monthly maximum consecutive 5-day precipitation
extremes
precipitation
none
Expert Team on Climate Change Detection and Indices (ETCCDI)
Rx5day
http://etccdi.pacificclimate.org/list_27_indices.shtml
Simple pricipitation intensity index
precipitation
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
SDII
http://etccdi.pacificclimate.org/list_27_indices.shtml
A seasonal forecast system that became operative in November 2011
System4
https://www.ecmwf.int/sites/default/files/elibrary/2011/11209-new-ecmwf-seasonal-forecast-system-system-4.pdf
The fifth generation of the ECMWF seasonal forecasting system, SEAS5, was introduced 1 November 2017, replacing System 4. SEAS5 includes updated versions of the atmospheric (IFS) and interactive ocean (NEMO) models and adds the interactive sea ice model LIM2.
System5
Model description at the C3S site (private access) <https://software.ecmwf.int/wiki/display/C3SS/Description+of+ECMWF+C3S+contribution>
SEAS5 User Guide: <https://www.ecmwf.int/sites/default/files/medialibrary/2017-10/System5_guide.pdf>
https://www.ecmwf.int/en/forecasts/documentation-and-support/long-range
The SPEI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character (as SPI) with the capacity to include the effects of temperature variability on drought assessment.
Standardized Precipitation-Evapotranspiration Index
Drought
Precipitation, temperature
https://doi.org/10.1175/2009JCLI2909.1
SPEI
Standardized Precipitation Index
Drought
Precipitation
The SPI is based on the probability of precipitation for any time scale. The probability of observed precipitation is then transformed into an index. It is being used in research or operational mode in more than 70 countries.
SPI
McKee, T.B., Doesken, N.J., Kleist, J., others, 1993. The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society Boston, MA, pp. 179–183.
Number of summer days: Annual count of days when TX (daily maximum temperature) > 25 degC
temperature (maximum)
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
SU
http://etccdi.pacificclimate.org/list_27_indices.shtml
Simple multimodel combination: the model predictions are joined together to construct the multimodel ensemble assigning equal weights and without any further transformation
Simple
Number of days with mean temperature above 21.5 degC
Mean daily temperature
absolute
T21.5
Percentage of days when TN < 10th percentile
extremes
temperature (minimum)
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
TN10p
http://etccdi.pacificclimate.org/list_27_indices.shtml
Percentage of days when TN > 90th percentile
extremes
temperature (minimum)
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
TN90p
http://etccdi.pacificclimate.org/list_27_indices.shtml
Monthly minimum value of daily minimum temperature
extremes
temperature (minimum)
none
Expert Team on Climate Change Detection and Indices (ETCCDI)
TNn
http://etccdi.pacificclimate.org/list_27_indices.shtml
Monthly maximum value of daily minimum temperature
extremes
temperature (minimum)
none
Expert Team on Climate Change Detection and Indices (ETCCDI)
TNx
http://etccdi.pacificclimate.org/list_27_indices.shtml
Number of tropical nights: Annual count of days when TN (daily minimum temperature) > 20 degC
temperature (minimum)
absolute
Expert Team on Climate Change Detection and Indices (ETCCDI)
TR
http://etccdi.pacificclimate.org/list_27_indices.shtml
Percentage of days when TX < 10th percentile
extremes
temperature (maximum)
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
TX10p
http://etccdi.pacificclimate.org/list_27_indices.shtml
Number of days with maximum temperature above 35 degC
Extremes
Maximum temperature (daily)
absolute
TX35
Number of days with maximum temperature above 40 degC
Extremes
Maximum temperature (daily)
absolute
TX40
Percentage of days when TX > 90th percentile:
extremes
temperature (maximum)
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
TX90p
http://etccdi.pacificclimate.org/list_27_indices.shtml
Monthly minimum value of daily maximum temperature
extremes
temperature (maximum)
none
Expert Team on Climate Change Detection and Indices (ETCCDI)
TXn
http://etccdi.pacificclimate.org/list_27_indices.shtml
Monthly maximum value of daily maximum temperature
extremes
temperature (maximum)
none
Expert Team on Climate Change Detection and Indices (ETCCDI)
TXx
http://etccdi.pacificclimate.org/list_27_indices.shtml
The User Data Gateway (UDG) is a climate data service hosted by University of Cantabria (<http://meteo.unican.es/udg-wiki>) consisting on two main components: (1) A THREDDS Data Server (TDS) and (2) the THREDDS Access Portal (TAP), which provide standard services for data access (e.g. OPeNDAP or the NetCDF Subset Service –NCSS–) and user management and authentication (based on data policies associated with virtual datasets), respectively. The UDG provides harmonized access to a variety of common datasets typically used in sectoral applications, including state-of-the-art global and regional climate projections such as those from the Coupled Model Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2011a) and the COordinated Regional climate Downscaling EXperiment (CORDEX; Giorgi and Gutowski, 2015).
User Data Gateway
https://doi.org/10.1016/j.envsoft.2018.09.009
UDG
http://dx.doi.org/10.1016/j.cliser.2017.07.001
The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). Version 1.0 of the W5E5 dataset covers the entire globe at 0.5deg horizontal and daily temporal resolution from 1979 to 2016. Data sources of W5E5 are version 1.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2019, 2020), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003).
WFDE5 over land merged with ERA5 over the ocean
https://doi.org/10.24381/cds.20d54e34
W5E5
https://doi.org/10.5880/pik.2019.023
The EU-WATCH Project
WATCH
https://journals.ametsoc.org/doi/abs/10.1175/JHM-D-11-024.1
The WFDE5 dataset has been generated using the WATCH Forcing Data (WFD) methodology applied to surface meteorological variables from the ERA5 reanalysis. The WFDEI dataset had previously been generated by applying the WFD methodology to ERA-Interim. The WFDE5 is provided at 0.5∘ spatial resolution but has higher temporal resolution (hourly) compared to WFDEI (3-hourly). It also has higher spatial variability since it was generated by aggregation of the higher-resolution ERA5 rather than by interpolation of the lower-resolution ERA-Interim data.
Bias-adjusted ERA5 reanalysis data for impact studies
https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.20d54e34?tab=overview
WFDE5
The WFDEI meteorological forcing data set has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA‐Interim reanalysis data.
WFDEI
WFDEI generation methodology is described by Weedon et al (2014)
<https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014WR015638>
Warm spell duration index: Annual count of days with at least 6 consecutive days when TX > 90th percentile
extremes
temperature (maximum)
relative
Expert Team on Climate Change Detection and Indices (ETCCDI)
WSDI
http://etccdi.pacificclimate.org/list_27_indices.shtml
This R package is a wrapper of the climdex.pcic package implementing the 27 core ETCCDI Climate Indices <http://etccdi.pacificclimate.org/list_27_indices.shtml> in the framework of climate4R
https://github.com/SantanderMetGroup/climate4R.climdex
http://www.meteo.unican.es/climate4R
An R package for empirical-statistical downscaling focusing on daily data and covering the most popular approaches (bias correction, Model Output Statistics, Perfect Prognosis) and techniques.
https://github.com/SantanderMetGroup/downscaleR
http://www.meteo.unican.es/climate4R
Set of tools to simplify application of atomic forecast verification metrics for (comparative) verification of ensemble forecasts to large data sets. The forecast metrics are imported from the 'SpecsVerification' package, and additional forecast metrics are provided with this package. Alternatively, new user-defined forecast scores can be implemented using the example scores provided and applied using the functionality of this package.
https://github.com/cran/easyVerification
loadeR is an R package enabling transparent, user-friendly access to local and remote climate datasets compliant with Unidata's Common Data Model, through the exploitation of the NetCDF-Java API utilities
https://github.com/SantanderMetGroup/loadeR
loadeR
http://www.meteo.unican.es/climate4R
An extension of the R package loadeR for accessing seasonal forecast datasets
https://github.com/SantanderMetGroup/loadeR.ECOMS
loadeR.ECOMS
http://meteo.unican.es/ecoms-udg
https://www.sciencedirect.com/science/article/pii/S2405880717300079?via%3Dihub
The pH scale is logarithmic and approximates the negative of the base 10 logarithm of the molar concentration (measured in units of moles per liter) of hydrogen ions in a solution.
pH
pH is defined as the decimal logarithm of the reciprocal of the hydrogen ion activity in a solution.
http://cfconventions.org
Sea-level pressure
Air pressure at sea level is the quantity often abbreviated as MSLP or PMSL. Air pressure is the force per unit area which would be exerted when the moving gas molecules of which the air is composed strike a theoretical surface of any orientation. "Mean sea level" means the time mean of sea surface elevation at a given location over an arbitrary period sufficient to eliminate the tidal signals.
Sea ice concentration is defined as the area fraction occupied by sea ice relative to total sea area at a given location in the ocean. "Area fraction" is the fraction of a grid cell's horizontal area that has some characteristic of interest. It is evaluated as the area of interest divided by the grid cell area. It may be expressed as a fraction, a percentage, or any other dimensionless representation of a fraction. Sea ice area fraction is area of the sea surface occupied by sea ice. It is also called "sea ice concentration". "Sea ice" means all ice floating in the sea which has formed from freezing sea water, rather than by other processes such as calving of land ice to form icebergs.
Sea ice concentration
siconc
Sea surface temperature is usually abbreviated as "SST". It is the temperature of sea water near the surface (including the part under sea-ice, if any). More specific terms, namely sea_surface_skin_temperature, sea_surface_subskin_temperature, and surface_temperature are available for the skin, subskin, and interface temperature respectively. For the temperature of sea water at a particular depth or layer, a data variable of sea_water_temperature with a vertical coordinate axis should be used.
Sea-surface temperature
http://cfconventions.org
sst
Air temperature is the bulk temperature of the air, not the surface (skin) temperature. This can be near-surface air (typically 2m) or any other vertical level, that should be included in the metadata.
Usually, the code 'tas' is used to refer to near-surface air temperature (typically 2-m height).
http://cfconventions.org
ta
Near-surface (typically 2m) air temperature. Air temperature is the bulk temperature of the air, not the surface (skin) temperature.
tas
tas
tas
Maximum near-surface daily temperature
tasmax
tasmax
tasmax
Minimum near-surface daily temperature
tasmin
tasmin
tasmin
Sea-surface temperature
Sea surface temperature is usually abbreviated as "SST". It is the temperature of sea water near the surface (including the part under sea-ice, if any). More specific terms, namely sea_surface_skin_temperature, sea_surface_subskin_temperature, and surface_termperature are available for the skin, subskin, and interface temperature respectively. For the temperature of sea water at a particular depth or layer, a data variable of sea_water_temperature with a vertical coordinate axis should be used.
http://cfconventions.org
tos
This variable is the total precipitation amount. "Amount" in this context means mass per unit area.
This variable is most often referred to as 'pr', although according to CF convention, pr is a flux
http://cfconventions.org
total precip
This variable is the total amount of precipitation in form of snow. "Amount" in this context means mass per unit area.
http://cfconventions.org
Snowfall
An R package for climate data manipulation and transformation, including subsetting, regridding, and data conversion.
https://github.com/SantanderMetGroup/transformeR
transformeR
http://www.meteo.unican.es/climate4R
An R package for climate data visualization, with special focus on ensemble forecasting and uncertainty communication. It includes functions for visualizing climatological, forecast and evaluation products, and combinations of them.
https://github.com/SantanderMetGroup/visualizeR
visualizeR
http://www.meteo.unican.es/climate4R
https://doi.org/10.1016/j.envsoft.2017.09.008
Speed is the magnitude of velocity. Wind is defined as a two-dimensional (horizontal) air velocity vector, with no vertical component. (Vertical motion in the atmosphere has the standard name upward_air_velocity.) The wind speed is the magnitude of the wind velocity.
In particular, 'wss' refers to near-surface wind speed (typically measured at 10-m height).
http://cfconventions.org
Wind speed
Geopotential is the sum of the specific gravitational potential energy relative to the geoid and the specific centripetal potential energy. Geopotential height is the geopotential divided by the standard acceleration due to gravity. It is numerically similar to the altitude (or geometric height) and not to the quantity with standard name height, which is relative to the surface.
Geopotential is specified for a particular isobaric surface pressure level.
http://cfconventions.org
Geopotential height
Polygon((-170 -5, -120 -5, -120 5, -170 5, -170 -5))
rotated_north_pole
Position of the North Pole in a rotated pole grid, in geographical coordinates of the form (lon, lat)
Position of the zero longitude in a rotated pole grid, in geographical coordinates of the form (lon, lat)
rotated_zero_longitude