2026-04-08T18:07:00Z The Climate Data Source Ontology from METACLIP Daniel San-Martin <https://orcid.org/0000-0002-2862-2704> Joaquin Bedia <https://orcid.org/0000-0001-6219-4312> Jose Manuel Gutierrez <https://orcid.org/0000-0002-2766-6297> METACLIP.org <https://github.com/metaclip> The datasource ontology provides a core semantic framework for describing the provenance of climate datasets (e.g., observations, operational and retrospective forecasts, reanalyses, climate projections) and the subsequent processing steps applied to them. It captures transformations performed within a workflow that modify or derive new views of a dataset—such as temporal or spatial aggregation, ensemble summarization, regridding, or the computation of climate indices and anomalies—even when these operations do not produce a distinct dataset but rather a subset or transformed representation of the original. The ontology also supports the description of the source code and computational procedures involved in generating these products. http://www.metaclip.org https://doi.org/10.1016/j.envsoft.2019.07.005 1.0.1 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 cell_measures attribute extra information about the spatial properties of a variable’s grid cells. This metadata is crucial for understanding the specific measurements taken within each cell of a data grid. It provides a definitive description of what the data values found in each variable represent. This ensures that the data can be accurately interpreted and compared with data from other sources. cell_measures http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html#cell-measures cell_measures The dimensions attribute specifies the axes or coordinate values associated with each variable in a dataset. The dimensions attribute typically include: Time: Represents the time dimension for the data, often with a daily/monthly resolution (ds:frequency). Latitude (lat): Represents the latitude dimension, usually with a specific resolution (e.g., 0.5°). Longitude (lon): Represents the longitude dimension, also with a specific resolution (e.g., 0.5°). Height: Represents the height dimension, such as height above ground level For example, a near-surface variable might have dimensions like "longitude, latitude, time, height2m" http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/cf-conventions.html#dimensions dimensions frequency The temporal frequency of a climate variable refers to the rate at which data about the variable is recorded or observed over time. It indicates how often the variable is measured or observed, which could be hourly, daily, monthly, seasonally, annually, or on any other time scale. This frequency is crucial in understanding patterns and trends in climate data over time. frequency frequency 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 The software license property describes the legal agreement that defines the terms of usage and provides the right to one or more copies of software to end users. The license is designed to protect the intellectual property of the software developer and to limit any claims against them that may arise from its use. It also provides legally binding definitions for the distribution and use of the software. The term "modelling realm" refers to the specific area or component of the Earth’s climate system that a climate model is designed to simulate (e.g.: atmosphere, ocean, cryosphere etc.). Model output Variables are produced in the context of the specific modelling realm. modelling_realm modelling_realm An active web link providing an authoritative technical description of the resource referenceURL The release year of a software refers to the year when a particular version of the software was made available to the public The release year property refers to when the software was actually distributed or deployed, not when it was developed. The development process could take place over several years and involves various stages, such as design, coding, testing, and debugging... Release year 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# A standard name refers to a unique identifier associated with each geophysical parameter in a dataset. Each standard name provides a precise description of the physical quantities being represented The use of standard names promotes interoperability among data providers, data users, and data services by providing a clear and unambiguous standard for representing geolocations and times of Earth science data, physical quantities that the data represent, and other ancillary information useful in interpreting the data or comparing it with data from other sources standard_name http://cfconventions.org/ standard_name 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 Inverse of wasDevelopedBy: links a ModellingCenter to the simulation Models (GCMs or RCMs) for which it is institutionally responsible through development, maintenance or design develops model Inverse of hasDrivingGCM: links a Global Climate Model (GCM) to the Regional Climate Models (RCMs) that it drives by providing large-scale boundary conditions for their simulations drives RCM This property associates a specific :Command (prov:Activity) with a software :Package (prov:SoftwareAgent) fromPackage 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 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 This property links a fiven :Step within the data workflow with the :Command activity generating it hadCommandCall Deprecated in datasource.owl version 1.0. Replaced by ds:hasDataProvider after a revision of provenance-related attributions. Not equivalent due to semantic and hierarchical updates. true Deprecated in datasource.owl version 1.0. The property structure for dataset derivation was revised, and ds:hasDatasetSubset supersedes this name. Semantic changes prevent declaring equivalence. true 2018-04-02T17:04:25Z An object property to express the derivation of a :Regridding from a previous :Step hadInterpolation This object property related an interpolation :Step with a specific :InterpolationMethod hadInterpolationMethod Deprecated in datasource.owl version 1.0. This property was removed after semantic restructuring and replaced by ds:hasModellingCenter. Due to semantic and hierarchical changes, this is NOT an equivalent property. true 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 A data property linking a seasonal forecast dataset (either a hindcast or an operational forecast) with its forecasting system hadSeasonalForecastingSystem Deprecated in datasource.owl version 1.0. This property was removed during a semantic reorganization of model attribution statements, and replaced by ds:hasSimulationModel. Not equivalent due to semantic changes. true 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.) has transformation Links a climate model to its aerosol model component responsible for simulating aerosol microphysics, chemistry, and aerosol–radiation and aerosol–cloud interactions. Editorial note: Introduced as a specific subproperty of hasModelComponent to classify aerosol-related subsystems of climate models. Used to associate an RCM or GCM with the module that represents atmospheric aerosol processes. Typical components include aerosol optical properties, deposition, and aerosol–cloud interactions. has aerosol model component Associates a climate model with its atmospheric chemistry component responsible for gas-phase chemistry, chemical mechanisms, and reactive transport processes. Editorial note: Defined as a specialised model-component relation for atmospheric chemistry modules. Represents the chemical subsystem of an atmospheric model, including oxidation pathways, chemical tracers, and coupling with aerosols or radiation. has atmospheric chemistry model component Links a climate model to the atmospheric dynamical core or physical parameterisation package that simulates atmospheric circulation and thermodynamic processes. Editorial note: Introduced to capture general atmospheric modules before distinguishing specific physics packages. Covers atmospheric dynamics, radiation, convection, microphysics, boundary-layer schemes, and other components of the atmospheric model. has atmospheric model component This property links a Dataset to the Agent (ModellingCenter: or DataProvider:) that provides or supplies it as a primary source has data provider Relates a Dataset to a DatasetSubset that is derived from it through a logical subsetting operation. This represents a specialised form of PROV derivation, where the derived entity captures a restricted portion of the original dataset. has dataset subset Relates a Regional Climate Model (RCM) to the Global Climate Model (GCM) that provides its large-scale boundary conditions. This property represents a structural and scientific dependency: the RCM operates nested within the GCM, which drives or constrains its simulations. It is modeled as a specialisation of prov:wasInfluencedBy, since the driving GCM exerts a defining influence on the RCM but is not its creator or developer. has driving GCM 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 Links a Dataset to the Experiment that defines the scientific protocol, scenario or configuration under which the dataset is produced, analyzed or classified. Experiments represent conceptual specifications (e.g., CMIP experiment IDs such as RCP45, Historical or Evaluation) rather than executable activities. has experiment 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 Links a climate model (e.g., RCM or GCM) to the lake model component responsible for simulating inland water bodies Used to associate a model configuration with its lake model component, which typically represents vertical lake temperature profiles, mixing processes, and lake ice evolution. Examples include FLake, Hostetler, or other prognostic or mixed-layer lake schemes. has lake model component Links a climate model to a land-ice component responsible for simulating glacier dynamics, ice-sheet evolution, and surface mass balance. Editorial note: Added as a dedicated relation for land-ice modelling subsystems. Includes modules that handle ice-flow dynamics, ice-sheet coupling, melt processes, and accumulation on land-based ice bodies. has land-ice model component Associates a climate model with its land-surface module responsible for energy, water, and momentum exchanges between land and atmosphere. Editorial note: Created as a parent relation for LSM subsystems Typically includes soil moisture, vegetation processes, evapotranspiration, snow, and carbon-cycle interactions on land surfaces. has land-surface model component Links a software‑based climate model (e.g., RCM, GCM) to one of its internal model components responsible for representing a specific physical or biogeochemical subsystem, including specific parameterization schemes or prescribed fields. General parent property for all model-component relations. Use specific subproperties (e.g., hasAerosolModelComponent) when available has model component Links a Dataset to the ModellingCenter (typically a climate modelling institution or consortium) to which the dataset is attributed in the PROV sense. This property expresses institutional responsibility for the production, curation or publication of the dataset, as commonly assumed in climate data infrastructures (e.g., CMIP, ESGF, CORDEX). has modelling center Links a climate model to its ocean biogeochemistry component responsible for nutrients, plankton dynamics, biogeochemical cycles, and ocean carbon processes. has ocean biogeochemistry model component Editorial note: Defined to distinguish ocean biogeochemical modules from physical ocean components. Represents ocean internal biogeochemical processes, including nutrient cycles, primary production, remineralisation, and CO2 fluxes. Associates a climate model with its physical ocean component responsible for ocean circulation, mixing, temperature, salinity, and sea-level processes. Editorial note: Introduced to represent physical-ocean components in coupled climate models. Covers large-scale ocean dynamics, boundary processes, vertical mixing, and coupling to sea-ice or biogeochemistry modules. has ocean model component Relates a Dataset to the Project within which it was created, developed or funded. has Project Realizations included in the dataset subset Links a climate model to its sea-ice component responsible for sea-ice concentration, thickness, thermodynamics, and dynamics. Editorial note: Added to identify sea-ice components in coupled modelling systems. Represents sea-ice processes such as freezing/melting, albedo evolution, ridging, and coupling with ocean and atmosphere components. has sea-ice model component Links a Dataset to the simulation Model (GCM or RCM) to which the dataset is attributed in the PROV sense. This property asserts that the model is responsible for the numerical output and climatic patterns present in the dataset, as they arise directly from the model’s formulation and configuration. has simulation model 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 Temporal resolution of a Variable Associates a climate model with its urban land-surface module responsible for simulating urban canopy processes and urban energy balance. Editorial note: Specific subproperty to represent urban LSM components such as UCM, BEP, or TEB. Models urban radiative trapping, heat storage in buildings, anthropogenic heat emissions, modified turbulence, and the urban heat island effect. has urban land-surface model component Property of a dataset subset defining the time interval encompassed by the validation times. Variable of the :DatasetSubset http://www.metaclip.org/datasource/datasource.owl hasVerticalExtent Inverse property linking an aerosol model component to the climate model that incorporates it. Editorial note: Declared as a subproperty of isModelComponentOf to mirror the hierarchy of hasAerosolModelComponent. Allows navigation from an aerosol module to the parent climate model, covering microphysics, aerosol–radiation interactions, deposition, and related aerosol processes. is aerosol model component of Inverse property linking an atmospheric chemistry component to the climate model that employs it. Editorial note: Declared as a subproperty of isModelComponentOf to maintain consistency across component inverses. Represents the relation from a chemistry module to the model that uses it, covering gas-phase chemistry, tracers, and reactive transport. is atmospheric chemistry model component of Inverse property linking an atmospheric model component to the climate model that integrates it. Editorial note: Included to support inverse navigation of hasAtmosModelComponent and preserve the component hierarchy. Connects atmospheric-dynamics or physics modules (radiation, convection, microphysics, boundary-layer schemes) to the employing model. is atmospheric model component of Inverse of hasDataProvider: links a DataProvider: or ModellingCenter: to the Datasets they supply. is data provider of Inverse of hasExperiment: links an Experiment to the Datasets that conform to or are associated with its scientific protocol, scenario or configuration. is experiment of Inverse property linking a lake model component to the climate model that employs it. Identifies the parent climate model (RCM, GCM, or similar) that incorporates the given lake model component in its configuration. is lake model component of Inverse property linking a land-ice component to the climate model that incorporates it. Editorial note: Declared as a subproperty of isModelComponentOf for hierarchical consistency. Used to relate ice-sheet and glacier-dynamics modules back to the parent climate model. is land-ice model component of Inverse property linking a land-surface model component to the climate model that uses it. Editorial note: Declared as a subproperty of isModelComponentOf, matching the hierarchy of hasLandSurfaceModelComponent. Covers soil moisture, vegetation processes, evapotranspiration, land–atmosphere fluxes, snow, and other land-surface processes. is land-surface model component of 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. is member of Inverse property linking a model component to the climate model that incorporates it as part of its internal architecture. Identifies the parent climate model (RCM/GCM...) to which a given component belongs, enabling upward navigation from subsystem modules to composite models. se for component-to-model relationships. Specific inverses (e.g., isUrbanLandSurfaceModelComponentOf) should be subproperties is model component of Inverse of hasModellingCenter: links a ModellingCenter to the Datasets for which it is considered institutionally responsible, typically by generating, maintaining or distributing the data. is modelling center of Inverse property linking an ocean biogeochemistry module to the climate model that employs it. Editorial note: Added as a subproperty of isModelComponentOf to ensure inverse hierarchy alignment. Includes nutrient cycles, plankton dynamics, carbon processes, and other biogeochemical subsystems in ocean modelling. is ocean biogeochemistry model component of Inverse property linking a physical ocean model component to the climate model that incorporates it. Editorial note: Declared as a subproperty of isModelComponentOf to mirror the direct property hierarchy. Covers ocean circulation, mixing, temperature/salinity, and sea-level-related processes. is ocean model component of Inverse of hasProject: links a Project to the Dataset produced or managed within it is project of Inverse property linking a sea-ice component to the climate model that uses it. Editorial note: Declared as a subproperty of isModelComponentOf to ensure a parallel inverse hierarchy. Includes sea-ice thermodynamics, dynamics, ridging, albedo evolution, and interactions with ocean/atmosphere. is sea-ice model component of Inverse of hasSimulationModel: relates a simulation Model (GCM or RCM) to the Datasets that are attributed to it, meaning that the model is responsible for producing the patterns, variables or outputs contained in those datasets is simulation model of Inverse of hasDatasetSubset: relates a DatasetSubset to the original Dataset from which it is derived through a logical subsetting operation is subset of dataset Inverse property linking an urban land-surface model component to the climate model that incorporates it. Editorial note: Declared as a subproperty of both isLandSurfaceModelComponentOf and isModelComponentOf to reflect its dual classification. Models urban canopy processes, radiative trapping, heat storage, anthropogenic heat fluxes, and urban–atmosphere exchange. is urban land-surface model component of 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 Relates a simulation Model (GCM or RCM) to the ModellingCenter responsible for its development, maintenance or technical design. This property asserts institutional attribution in the PROV sense, indicating that the organization is accountable for the model's formulation, implementation and evolution. was developed by Specialised attribution stating that the modelling center developed the climate model (RCM/GCM). 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 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 Software Package Version property Additional free-text information related to the aerosol model component, including climatologies, references, or experiment-specific aerosol settings. Intended for heterogeneous notes that do not fit into other aerosol metadata fields, such as published climatologies, spatial/temporal assumptions, or extra configuration flags. aerosol model miscellaneous information Free-text description of how aerosol processes are parameterized in the aerosol model component. Captures the methodological approach used in the aerosol scheme, including turbulent flux handling, coupling mechanisms, or specific algorithmic choices reported by the modelling group. aerosol parameterization description Mode in which aerosols are represented in the model (e.g., prescribed, interactive, semi-interactive, climatology-based). Stores the high-level approach used for aerosol representation, typically distinguishing between prescribed fields, interactive schemes, climatologies, or other modelling modes. aerosol representation mode Name or identifier of the cumulus convection parameterization used in the atmospheric model. Represents deep/shallow convection schemes (e.g., Kain–Fritsch, Grell–Freitas, Tiedtke), including tuned configurations where applicable. atmospheric convection scheme Name or identifier of the dynamical core used by the atmospheric model. Typical values include hydrostatic/non-hydrostatic cores and specific dynamical frameworks (e.g., spectral, finite-volume, ARW). atmospheric dynamics core Name or identifier of the longwave radiation parameterization used by the atmospheric component. Includes the LW radiative transfer package (e.g., RRTMG-LW) and any relevant options reported by the modelling group. atmospheric longwave radiation scheme Name or identifier of the cloud and precipitation microphysics scheme used by the atmospheric model. Covers single-/double-moment, mixed-phase and ice processes; typical labels include WSM6, Thompson, Morrison, etc. atmospheric microphysics scheme Additional free-text information related to the atmospheric model configuration that does not fall into specific parameterization categories. Intended to capture heterogeneous comments or notes relevant to the atmospheric component, such as unusual configuration flags, namelist options, special tuning choices, references, or experiment-specific considerations. atmospheric model miscellaneous information Name or identifier of the planetary boundary layer (PBL) scheme employed by the atmospheric model. Includes turbulence closure approach and commonly used schemes (e.g., YSU, MYJ, MYNN), with optional stability-dependent variants. atmospheric PBL scheme Name or identifier of the shortwave radiation parameterization used by the atmospheric component. Captures spectral treatment and options; examples include RRTMG-SW or model-specific SW radiation packages. atmospheric shortwave radiation scheme 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 cell method cell_methods 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 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 Free-text description of the spatial grid used by a climate model or model component, including resolution, projection, coordinate system, and any relevant configuration details. Intended to store heterogeneous metadata regarding grid specifications such as grid type (e.g., regular lat-lon, rotated pole, Lambert), horizontal and vertical resolution, staggering, map projection, nesting configuration, or experiment-specific grid notes. grid description 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 Horizontal Resolution of X coordinates hasHorizontalResX Horizontal resolution of Y coordinates hasHorizontalResY Long name of the variable hasLongName long_name 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 A short name is an abbreviated name for a variable, as internally encoded in the dataset. hasShortName A software agent version Time frequency refers to the interval at which data is recorded or output by the model. Climate models simulate various atmospheric, oceanic, and land processes over time, and the time frequency determines how often these simulations are saved for analysis Time frequency frequency Vertical Level hasVerticalLevel Mode in which lakes are represented in the lake model component (e.g., prognostic lake ice, mixed-layer scheme, prescribed lake temperature) Describes whether the lake model predicts vertical temperature profiles, lake ice evolution, mixed-layer behaviour, or uses prescribed or climatological fields. lake representation mode Name or identifier of the land-surface model component or configuration used. Captures labels such as 'TERRA-ML', 'Noah-MP', or descriptive variants (e.g., 'TERRA-ML with modified soil hydrology scheme (Schlemmer et al. 2018)'). land-surface model component name Free-text indication of the number of vertical levels used by the land-surface model. Stores the reported count of land/soil levels (e.g., '10', '10 levels', or strings with additional notes). Values may include units or qualifiers as provided by the modelling group. land-surface model levels Maximum depth of the land/soil column represented by the land-surface model (free text with units). Typical values include strings with units, e.g., '11.5 m'. This property does not normalise units or vertical coordinate conventions; it preserves the original metadata text. land-surface model maximum depth Additional free-text information related to the land-surface model configuration. For heterogeneous notes that do not fit other LSM fields (e.g., 'deepest soil mid-layer level is 11.5 m; dynamic moisture calculation is restricted to upper 8 levels until 2.86 m'). land-surface model miscellaneous information 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. 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 model run https://pcmdi.llnl.gov/mips/cmip5/docs/cmip5_data_reference_syntax.pdf Ensemble member (r<N>i<M>p<L>) The nominal resolution is the approximate resolution of the final output data. The term ‘nominal resolution’ refers to a metric that allows users to discern the relative fineness of the data output by different models. It is important to note that the nominal resolution might be presented to end-users by the data provider, which may not necessarily reflect the resolution of the data as originally produced by the model. Therefore, while it serves as a useful guide for comparison, it should not be solely relied upon for assessing the detailed accuracy of the model’s output. has nominal resolution of Name or bibliographic reference of the ocean model component used in the configuration. Captures identifiers such as 'NEMOMED8 (Beuvier et al., 2010)' or other combinations of official model names, configurations, and citation details as reported by the modelling group. ocean component reference Additional free-text information related to the ocean model component. Intended for heterogeneous notes such as basin extent, grid structure, nesting configuration, vertical level structure, horizontal resolution, or any experiment-specific settings. ocean model miscellaneous information Mode in which the ocean component is represented (e.g., interactive, prescribed, slab ocean, climatology). Stores the high-level configuration describing whether the ocean model is fully coupled, prescribed, slab-only, or driven by climatological fields. ocean representation mode Free-text notes about the simulation or experiment runs (e.g., re-runs, campaign-specific remarks, dataset release clarifications). General miscellaneous information: can be used to distinguish simulation or run versions (e.g., v1/v2/v3 as re-runs) from actual model versioning, and to record experiment-level caveats, naming conventions, or other release notes. The property has no declared domain in order to allow its use with RCMs, GCMs, ModelComponents, and even Datasets. simulation notes Free-text description of the number of vertical levels up to the model top (top of atmosphere) as reported by the modelling group. Used to capture user-provided textual information about the vertical grid, including number of levels, optional notes on layer distribution, and additional details such as model-top height in km or pressure. This property intentionally accepts heterogeneous text as metadata differ across modelling centres. top-of-atmosphere levels Free-text indication of how the urban surface is represented within the land-surface component of a climate model. Captures heterogeneous descriptions of the urban modelling approach, ranging from simplified assumptions (e.g., urban areas treated as rock) to explicit urban land-surface model names or configurations (e.g., CLM4.5, UCM variants, or other scheme-specific labels). urban land-surface model representation String identifier used to record the version of a software agent, dataset, model component, or any other entity represented in the ontology. Intended to store version labels from model documentation or metadata tables, including semantic versioning (e.g., 'v1.0', '3.2.1') or descriptive strings. Intended to store version labels from model documentation or metadata tables, including semantic versioning (e.g., 'v1.0', '3.2.1') or descriptive strings. version tag Version number 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 units 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 aerosol model within a GCM aims to quantify and describe how these particles interact with sunlight and clouds, affecting the Earth’s energy balance and climate. It takes into account various properties of aerosols, including their size distribution, chemical composition, and life cycle, which encompasses their emission, transportation, transformation, and removal processes from the atmosphere. Aerosols are tiny particles or droplets suspended in the air, which can originate from both natural sources, like dust storms and sea spray, and human activities, such as industrial emissions and vehicle exhaust. Aerosol models are crucial for understanding the indirect effects of aerosols on climate, such as their role in cloud formation and precipitation. They help in assessing the aerosol radiative forcing, which is a measure of the influence of aerosols on the balance of incoming and outgoing energy in the Earth’s atmosphere. These models are integrated with other components of GCMs to provide comprehensive simulations of the climate system and to predict future climate scenarios. AerosolModel 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 A chemical transport model (CTM) is a type of computer numerical model which typically simulates atmospheric chemistry and may give air pollution forecasting. The tendency, especially as the cost of computing declines over time, is for GCMs to incorporate CTMs for species of special interest to climate dynamics, especially shorter-lived species such as nitrogen oxides and volatile organic compounds; this allows feedbacks from the CTM to the GCM's radiation calculations, among other applications. Atmospheric Chemistry Model https://en.wikipedia.org/wiki/Chemical_transport_model An atmospheric model is a mathematical model constructed around the full set of primitive, dynamical equations which govern atmospheric motions. It can supplement these equations with parameterizations for turbulent diffusion, radiation, moist processes (clouds and precipitation), heat exchange, soil, vegetation, surface water, the kinematic effects of terrain, and convection Atmospheric Model https://en.wikipedia.org/wiki/Atmospheric_model 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. 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 https://is-enes-data.github.io/cordex_archive_specifications.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 A climate dataset is a collection of data that includes measurements and observations related to climate variables such as temperature, precipitation, humidity, wind speed, and other climatic conditions. These datasets are typically used to analyze climate variability, monitor climate change, and support climate modeling and prediction efforts 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 a prov:Collection grouping multiple Datasets or DatasetSubsets that share a common scientific purpose (e.g., a multimodel ensemble, multi-run ensemble, or perturbed-physics ensemble). Ensembles do not represent new data entities but organizational groupings that support comparative analysis or aggregated interpretation. 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. An :Experiment represents a scientific protocol, configuration or methodological setup under which datasets are produced, analysed or classified. In the climate domain, experiments describe standardized conditions (e.g. forcing pathways, scenarios, evaluation settings) that allow datasets —typically model simulations but also observational or reanalysis products— to be consistently grouped and interpreted. It is recommended to instantiate predefined :Experiment individuals, such as :RCP45, :Historical or :Evaluation, following established controlled vocabularies (e.g. CMIP experiment IDs) http://www.metaclip.org/datasource/datasource.owl Experiment 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. GCM https://en.wikipedia.org/wiki/General_circulation_model Model A GrandEnsemble is a higher-level ensemble that aggregates multiple Ensembles (e.g., regional ensembles from an aggregation of CORDEX domains) to provide a broader category or a global spatial coverage in the case of multiple RCM ensembles. It is a collection of Ensembles and supports analyses that require harmonized multi-domain datasets. Grand Ensemble 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 Model component used to represent inland water bodies (lakes) within a regional or global climate model. Lake models simulate thermal stratification, vertical mixing, heat storage, and lake ice formation/decay. They provide physically-based surface fluxes for inland water surfaces. Examples include FLake, Hostetler, and other prognostic or mixed-layer schemes used in RCMs and ESMs. lake model A land-ice model is a computational tool used to simulate the behavior of ice sheets and glaciers on land. These models are used to understand and predict how ice sheets and glaciers respond to changes in climate and contribute to sea level rise. They take into account various factors such as ice dynamics, surface mass balance, and interactions with the atmosphere and oceans. LandIceModel Land surface models (LSMs) use quantitative methods to simulate the exchange of water and energy fluxes at the Earth surface–atmosphere interface. They are key component of climate models. Over the past two decades, they have evolved from oversimplified schemes, which described the surface boundary conditions for general circulation models (GCMs), to complex models that can be used alone or as part of GCMs to investigate the biogeochemical, hydrological, and energy cycles at the Earth's surface Land-Surface Model https://en.wikipedia.org/wiki/Land_surface_models_(climate) 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 Generic category representing any subsystem or module that forms part of a climate model, responsible for simulating a specific physical, dynamical, or biogeochemical process. Editorial note: introduced as a parent category to organise all model components and support extensible domain and range definitions This class groups all components that constitute the internal architecture of a climate model (e.g., land‑surface components, aerosol modules, radiation schemes, lake models, or convection parameterizations). It is used as the common range of the property hasModelComponent and as the superclass of more specific component types. model component 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 Institute A Dataset containing multidecadal simulations. These are mainly used in climate change or paleoclimate studies MultiDecadalSimulation 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 circulation sea-level pressure 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 An ocean biogeochemical model (ds:OceanBgchmeModel) with a climate model is a complex system that simulates the interactions between the ocean’s physical properties, circulation, and biogeochemical transformations. It describes the ocean’s temperature, salinity distributions, and circulation, which includes wind and density-driven currents, and wind, convective, and eddy-driven mixing. They also describe the transformations of biogeochemical constituents contained in seawater, typically nutrients, functional plankton groups, non-living organic matter, dissolved gases, and parameters of the inorganic carbon system These models can be regional or global in their geographical scope and can be a component of a larger model, such as Earth System Models (ESMs), where the OceanBgchemModel is coupled to a model of the atmosphere and land biosphere. OceanBgchemModel https://doi.org/10.1038/s43586-022-00154-2 Ocean general circulation models (OGCMs) are a particular kind of general circulation model to describe physical and thermodynamical processes in oceans. Ocean Model https://en.wikipedia.org/wiki/Ocean_general_circulation_model 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. 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 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 A sea-Ice model is a computer model that simulates the growth, melt and movement of sea ice. Sea-Ice models often integrate into coupled climate system models as well as global ocean and weather forecasting models and are often used as a tool in Arctic and Southern Ocean research. Much of the world's sea ice is enclosed within the polar ice packs in the Earth's polar regions: the Arctic ice pack of the Arctic Ocean and the Antarctic ice pack of the Southern Ocean. Polar packs undergo a significant yearly cycling in surface extent, a natural process upon which depends the Arctic ecology, including the ocean's ecosystems. Due to the action of winds, currents and temperature fluctuations, sea ice is very dynamic, leading to a wide variety of ice types and features. Sea ice may be contrasted with icebergs, which are chunks of ice shelves or glaciers that calve into the ocean. Depending on location, sea ice expanses may also incorporate icebergs. Sea-Ice Model 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 :Dataset, a :DatasetSubset or a :Transformation. http://www.metaclip.org/datasource/datasource.owl Spatial Extent 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 (ds:Aggregation etc.) should be asserted if they apply. http://www.metaclip.org/datasource/datasource.owl 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 http://www.metaclip.org/datasource/datasource.owl TemporalResolution Frequency 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) Urban land-surface model used to simulate the physical exchanges of momentum, heat, and moisture between the atmosphere and urban areas. It represents key features of the urban canopy—such as buildings, streets, roofs, pavements, and limited vegetation—and accounts for processes including radiative trapping in street canyons, heat storage in construction materials, anthropogenic heat emissions, and the modification of turbulent fluxes. This model improves the representation of urban microclimates and the urban heat island effect within regional climate simulations. Urban land-surface models (ULSMs) extend general land-surface modelling approaches to represent the distinct physical processes occurring in built environments. They simulate the urban canopy layer, including radiative trapping in street canyons, heat storage in construction materials, modified turbulent exchanges, and anthropogenic heat emissions. By providing parameterisations tailored to densely built areas, ULSMs improve the representation of urban energy and water fluxes and the urban heat island effect within regional and global climate models. Urban Model 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 A Variable can be understood as a specific measure or attribute of the climate system that can be observed or calculated. These variables represent key characteristics of the climate system’s components, such as near-surface air temperature, the concentration of CO2 in the atmosphere or the rate of oceanic circulation. Variables are essential elements in climate models and ontologies because they allow us to quantify and track changes in the climate system over time. They can include a wide range of measures, from temperature and precipitation to more complex attributes like greenhouse gas concentrations or sea ice extent. Variable Provides the definition of a vertical spatial extent VerticalExtent Independent non-profit scientific organization focused on the analysis and production of global climate datasets, particularly surface temperature reconstructions, using transparent and reproducible methodologies. Berkeley Earth https://berkeleyearth.org 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 https://doi.org/10.1175/BAMS-D-21-0315.1 This GrandEnsemble compiles an unprecedented collection of CORDEX simulations published through ESGF, covering all 14 CORDEX domains. It addresses heterogeneous availability of CORDEX data by promoting publication in underrepresented domains and performing quality control. The ensemble includes comprehensive documentation of contributing regional models and has been used as a line of evidence in the IPCC Sixth Assessment Report (AR6), particularly in WGI regional chapters and the Interactive Atlas. Its climatic consistency was evaluated through precipitation and temperature change signals across overlapping domains. The dataset is publicly available through the Copernicus Climate Data Store (CDS) A GrandEnsemble aggregating CORDEX regional climate model simulations across the 14 CORDEX continental-scale domains to provide near-global coverage. This ensemble was assembled within the Copernicus Climate Change Service (C3S) to support the IPCC AR6 Working Group I regional assessment and the Interactive Atlas. It integrates heterogeneous CORDEX–ESGF data, performs quality screening, and harmonises simulations to produce a consistent worldwide ensemble suitable for regional climate projections. https://doi.org/10.1175/BAMS-D-22-0111.1 https://doi.org/10.24381/cds.bc91edc3 It is the number of degrees that a day's average temperature is above a certain temperature threshold Cooling Degree Days 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 Energy tas, tasmin, tasmax https://doi.org/10.1002/joc.3959 Absolute Maximum length of dry spell, maximum number of consecutive days with RR < 1mm 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 extremes precipitation absolute Copernicus Climate Data Store 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 https://cds.climate.copernicus.eu 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> CORDEX‑CORE is a WCRP CORDEX initiative designed to provide a globally consistent set of high‑resolution (~25 km) regional climate projections. It employs multiple regional climate models to downscale selected CMIP5 global simulations under RCP2.6 and RCP8.5 across nine CORDEX domains, delivering homogeneous datasets for climate‑change assessment, impacts analysis, and contributions to future IPCC reports. CORDEX-CORE https://doi.org/10.1175/BAMS-D-21-0119.1 NOAA Climate Prediction Center (CPC) Operational climate prediction center of NOAA's National Centers for Environmental Prediction, responsible for producing subseasonal to seasonal climate outlooks, climate monitoring products, and gridded observational and analysis datasets such as CPC Unified Precipitation and CMAP. https://www.cpc.ncep.noaa.gov/ Cold spell duration index: Annual count of days with at least 6 consecutive days when TN < 10th percentile Expert Team on Climate Change Detection and Indices (ETCCDI) CSDI http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (minimum) relative Maximum length of wet spell, maximum number of consecutive days with RR ≥ 1mm Expert Team on Climate Change Detection and Indices (ETCCDI) CWD http://etccdi.pacificclimate.org/list_27_indices.shtml extremes precipitation absolute 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/ 2021-03-23T00:00:00Z Copernicus CMIP6 Climate Projections This catalogue entry provides daily and monthly global climate projections data from a large number of experiments, models and time periods computed in the framework of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Copernicus CMIP6 Creative Commons Attribution-ShareAlike 4.0 International License <https://creativecommons.org/licenses/> https://doi.org/10.24381/cds.c866074c 2024-06-17T00:00:00Z Gridded CORDEX-CMIP5 dataset underpinning the Copernicus Interactive Climate Atlas The Copernicus CORDEX-CMIP5 dataset provides regional climate projections from the CORDEX initiative driven by CMIP5 global models, harmonized on a regular latitude-longitude grid. It includes monthly and annual time series under CMIP5 RCP scenarios. Copernicus CORDEX-CMIP5 Creative Commons Attribution-ShareAlike 4.0 International License <https://creativecommons.org/licenses/> https://doi.org/10.24381/cds.h35hb680 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 DF Drought Daily temperature range: Monthly mean difference between TX and TN Expert Team on Climate Change Detection and Indices (ETCCDI) DTR http://etccdi.pacificclimate.org/list_27_indices.shtml temperature (minimum), temperature (maximum) relative Deutscher Wetterdienst DWD https://www.dwd.de 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 The European Centre for Medium-Range Weather Forecasts (ECMWF) is an independent intergovernmental organisation supported by 34 states ECMWF https://www.ecmwf.int/ 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 European Space Agency (ESA) Intergovernmental European agency coordinating the development and operation of Earth observation missions and space infrastructure. ESA is a key partner in the Copernicus programme and produces and disseminates satellite-based climate data records, including Essential Climate Variables developed under the Climate Change Initiative (CCI). European Space Agency https://www.esa.int/ Earth System Grid Federation 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 https://doi.org/10.1016/j.future.2013.07.002 European domain for downscaling experiments within the CORDEX initiative https://euro-cordex.net/ https://cordex.org/domains/cordex-region-euro-cordex EURO‑CORDEX is the European branch of the WCRP CORDEX initiative, producing coordinated ensembles of regional climate projections for Europe using multiple dynamical and empirical–statistical downscaling models forced by CMIP5 global climate simulations. It provides high‑resolution datasets at 0.44° (EUR‑44) and 0.11° (EUR‑11) for climate‑change assessment, impacts research, and adaptation planning. EURO-CORDEX https://www.euro-cordex.net/index.php.en 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 EWEMBI http://doi.org/10.5880/pik.2019.004 Number of frost days: Annual count of days when TN (daily minimum temperature) < 0 degC Expert Team on Climate Change Detection and Indices (ETCCDI) FD http://etccdi.pacificclimate.org/list_27_indices.shtml temperature (minimum) absolute 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 GDD Agriculture Mean daily temperature https://doi.org/10.1002/joc.4535 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 Expert Team on Climate Change Detection and Indices (ETCCDI) GSL http://etccdi.pacificclimate.org/list_27_indices.shtml temperature (mean) absolute 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 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 Energy tas, tasmin, tasmax https://doi.org/10.1002/joc.3959 Absolute Number of icing days: Annual count of days when TX (daily maximum temperature) < 0 degC Expert Team on Climate Change Detection and Indices (ETCCDI) ID http://etccdi.pacificclimate.org/list_27_indices.shtml temperature (maximum) absolute 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 ISIMIP https://www.geosci-model-dev.net/10/4321/2017/ https://www.isimip.org/ 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) Many ecosystems and crops are sensitive to frost conditions, and can only develop over a frost-free period. LFFP Minimum temperature https://doi.org/10.1002/joc.4315 Absolute 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 PIK https://www.pik-potsdam.de/ Annual total precipitation in wet days Expert Team on Climate Change Detection and Indices (ETCCDI) PRCPTOT http://etccdi.pacificclimate.org/list_27_indices.shtml precipitation none 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 Expert Team on Climate Change Detection and Indices (ETCCDI) R10mm http://etccdi.pacificclimate.org/list_27_indices.shtml precipitation absolute Annual count of days when PRCP ≥ 20mm Expert Team on Climate Change Detection and Indices (ETCCDI) R20mm http://etccdi.pacificclimate.org/list_27_indices.shtml precipitation absolute Annual total PRCP when RR > 95p Expert Team on Climate Change Detection and Indices (ETCCDI) R95pTOT http://etccdi.pacificclimate.org/list_27_indices.shtml extremes precipitation relative Annual total PRCP when RR > 99p Expert Team on Climate Change Detection and Indices (ETCCDI) R99pTOT http://etccdi.pacificclimate.org/list_27_indices.shtml extremes precipitation relative 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 Expert Team on Climate Change Detection and Indices (ETCCDI) Rnnmm http://etccdi.pacificclimate.org/list_27_indices.shtml precipitation absolute Monthly maximum 1-day precipitation Expert Team on Climate Change Detection and Indices (ETCCDI) Rx1day http://etccdi.pacificclimate.org/list_27_indices.shtml extremes precipitation none Monthly maximum consecutive 5-day precipitation Expert Team on Climate Change Detection and Indices (ETCCDI) Rx5day http://etccdi.pacificclimate.org/list_27_indices.shtml extremes precipitation none Simple pricipitation intensity index Expert Team on Climate Change Detection and Indices (ETCCDI) SDII http://etccdi.pacificclimate.org/list_27_indices.shtml precipitation absolute 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 SPEI Drought Precipitation, temperature https://doi.org/10.1175/2009JCLI2909.1 Standardized Precipitation Index 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. Drought Precipitation Number of summer days: Annual count of days when TX (daily maximum temperature) > 25 degC Expert Team on Climate Change Detection and Indices (ETCCDI) SU http://etccdi.pacificclimate.org/list_27_indices.shtml temperature (maximum) absolute 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 T21.5 Mean daily temperature absolute Percentage of days when TN < 10th percentile Expert Team on Climate Change Detection and Indices (ETCCDI) TN10p http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (minimum) relative Percentage of days when TN > 90th percentile Expert Team on Climate Change Detection and Indices (ETCCDI) TN90p http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (minimum) relative Monthly minimum value of daily minimum temperature Expert Team on Climate Change Detection and Indices (ETCCDI) TNn http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (minimum) none Monthly maximum value of daily minimum temperature Expert Team on Climate Change Detection and Indices (ETCCDI) TNx http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (minimum) none Number of tropical nights: Annual count of days when TN (daily minimum temperature) > 20 degC Expert Team on Climate Change Detection and Indices (ETCCDI) TR http://etccdi.pacificclimate.org/list_27_indices.shtml temperature (minimum) absolute Percentage of days when TX < 10th percentile Expert Team on Climate Change Detection and Indices (ETCCDI) TX10p http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (maximum) relative Number of days with maximum temperature above 35 degC TX35 Extremes Maximum temperature (daily) absolute Number of days with maximum temperature above 40 degC TX40 Extremes Maximum temperature (daily) absolute Percentage of days when TX > 90th percentile: Expert Team on Climate Change Detection and Indices (ETCCDI) TX90p http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (maximum) relative Monthly minimum value of daily maximum temperature Expert Team on Climate Change Detection and Indices (ETCCDI) TXn http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (maximum) none Monthly maximum value of daily maximum temperature Expert Team on Climate Change Detection and Indices (ETCCDI) TXx http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (maximum) none 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 UDG http://dx.doi.org/10.1016/j.cliser.2017.07.001 https://doi.org/10.1016/j.envsoft.2018.09.009 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 W5E5 https://doi.org/10.5880/pik.2019.023 https://doi.org/10.24381/cds.20d54e34 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 WFDE5 https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.20d54e34?tab=overview 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 Expert Team on Climate Change Detection and Indices (ETCCDI) WSDI http://etccdi.pacificclimate.org/list_27_indices.shtml extremes temperature (maximum) relative 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))