1. Introduction

Climate change and variability pose an unprecedented challenge to the overall society, which requires mitigation and adaptation responses to reduce the threats and maximise the opportunities presented to organisations of all kinds. The impacts of climate variability and change can take various forms such as physical, social, financial, or political, and as such climate change adaptation has a very broad scope. Both business and public administrations are vulnerable to potentially disruptive risks and are key actors in the creation of a climate-resilient future (ISO 14090:2019; ISO 14091:2021).

Both monitoring and modelling of the Earth system can provide the information and guidance necessary for the policy and decision makers to deal with climate-related challenges. This has led to the establishment of various initiatives designed to better understand the Earth system through an improvement in both observational capabilities and modeling tools. As a result, an increasing amount of environmental data about past, present, and future climate is becoming available. Unfortunately, these data often come with inconsistent or missing metadata, inhomogeneous documentation, and sometimes sparse evidence concerning their uncertainty and validation. A variety of data streams is generated independently and from multiple sources, adhering to different definitions and assumptions, often not standardised across communities, and, at times, with overlapping but disconnected objectives. As a consequence, the users can feel disoriented when it comes to identifying the most appropriate dataset for an intended application (Nightingale et al. 2019).

Given the ever more prominent role that climate products are assuming in decision making, it is unavoidable that the quality of these data will come under increasing scrutiny in the future. Climate services are emerging as the link to narrow down the gap between upstream climate science and downstream users. Climate services form the backbone of the process that translates climate knowledge and data into bespoke products for decision making in diverse sectors of society, ranging from public administrations to private business (Hewitt et al. 2020; Medri et al. 2012). The Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO) stresses the increasing need for robust climate information based on observations and simulations covering future periods, ranging from several months up to centuries for economic, industrial, and political planning. Moreover, climate services play a crucial role in disseminating relevant standards (GFCS WMO) fostering adoption of common data models and formats and with sufficient metadata uniformly stored. The GFCS offers an umbrella for the development of climate services and has identified the quality assessment, along with its use in user guidance, as a key aspect of the service. The services, and the quality assessments in particular, need to be provided to users in a seamless manner and need to respond to user requirements.¹ The ultimate goal is building trust between data providers and users, as well as maximising usage uptake (Callahan et al. 2017; Rfll 2020). Thus, the questions of what type of quality information to provide and how to present it to users are receiving sustained attention.

A relatively young operational climate service is the Copernicus Climate Change Service (C3S, Thépaut et al. 2018), one of the six operational thematic services established by the European Commission within the Copernicus Earth Observation Programme (EC 2020). The C3S, implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF), aims to be an authoritative source of climate information for a wide variety of downstream users, ranging from policy makers to industrial sectors. The backbone of C3S is a cloud-based Climate Data Store (CDS), designed to be a single point of access to a catalogue of climate datasets of different categories, including in-situ and satellite observations, seasonal forecasts, reanalysis, and climate projections. In a progressive commitment to establish relations of trust between data providers and downstream users, as well as providing sufficient guidance for users to address their specific needs, C3S has made significant investments in the development of an Evaluation and Quality Control (EQC) function. By being transparent and characterising data quality attributes in a traceable and reproducible way, C3S is setting the basis for the inclusion of reliable climate data into policies and actions.

The establishment of an EQC function has three main advantages: i) it guides the users into the documentation to understand properly the dataset, it simplifies the comparison across datasets and builds trust in the products available, ii) it helps the data providers to understand which information they need to deliver to be compliant with standards, increases data uptake from the users, and clarifies how to provide standardised dataset quality information, and iii) it triggers actions for the service improvement leading to the provision of the most relevant dataset information and ensuring that published datasets are mature enough to support the authoritative character of C3S. The relation of trust between data providers and downstream users generated by these advantages is a necessary condition to foster a flourishing market for climate services (Zeng et al. 2019).

While the need for a quality assessment of the data available on the CDS is well recognised, the methodologies, the metrics, the assessment framework, and the way to present all this information have never been developed before in an operational service that disseminates all the main climate dataset categories. The proof-of-concept framework described in Nightingale et al. (2019) focused on observations only. Instead, here we implement the operational delivery of homogeneous EQC information across all the CDS climate dataset categories mentioned before. This objective, along with the task of building the underlying technical solutions, posed an unprecedented design challenge and makes the C3S EQC unique. The framework for the operational EQC function of the CDS builds on past and present international research initiatives. A variety of concepts were developed in previous projects (mainly the EU FP7 funded QA4ECV² and the C3S_51 Lots 2-4³), most of these concepts were integrated while moving the EQC function towards its operational phase.

This paper describes the challenges, the development and the implementation of the operational EQC function providing an overarching quality assurance service for the whole CDS. The results of the dataset assessments are publicly available in the CDS Catalogue.⁴ The manuscript focuses on the framework implemented for the CDS datasets only, leaving out other aspects of the EQC function, such as the assessment of the software made available by the CDS to explore the datasets (i.e. Toolbox), the assessment of the CDS infrastructure and the assessment of the user’s satisfaction and requirements.

2. The EQC framework in a nutshell

Commitment to quality is fundamental to build trust among stakeholders and the EQC function is the key element of C3S devoted to reach this goal. The main purpose of the EQC framework is to provide the C3S with a consistent, structured, and pragmatic approach to enhance the dataset reliability and usability. The design of this framework adopts an iterative approach integrating continual learning and improvement.

The EQC function regularly informs and recommends C3S about drawbacks, shortcomings and limitations related to the CDS datasets. These analyses are completed by a continuous user-engagement process to identify the user expectations that need to be addressed. The EQC team provides technical and scientific quality information of the CDS datasets via a set of homogeneous Quality Assurance Reports (QARs), helping to set the minimum requirements and baseline criteria for including new datasets in the CDS Catalogue. The QARs are filled templates called Quality Assurance Templates (QATs). Consistency across the QATs is obtained through the adoption of a vocabulary of homogeneous concepts and common practices.

The general strategy for assessing the CDS datasets consists of five steps:

• designing QATs for all the dataset categories with a consistent terminology and a structure as similar as possible;
• interacting with the data providers, who are the ones with the best knowledge of their datasets, and encouraging them to fill in the QATs or, in case the data provider is not available, fill in the templates using the documentation publicly available;
• evaluating the content collected in the QATs, paying attention to ensure that the content is understandable for the users, the level of detail is similar across datasets, and the type of information is complete, correct and consistent with the template requests;
• performing an independent quality assessment of the dataset, looking at aspects like (meta)data completion and integrity, scientific soundness and other characteristics that illustrate the multi-faceted nature of data quality; and
• publishing the information in the CDS dataset catalogue, once the QAR is approved by the corresponding authority, which in this case is the C3S governance board.

The production of the QARs calls for setting the procedures to initiate, develop and update the QARs (e.g., workflow), developing the software tools to support the assessments (e.g., data checker), and engaging with a wide range of stakeholders to choose the most adequate options for the QARs. These steps lead to the creation of QARs, which provide users with comprehensive information about the technical and scientific quality of the datasets. The different sections of the QARs are made accessible to the users in the CDS web portal through a synthesis table. The synthesis table is devised as a tool to organise and homogenise the EQC information, which is made of atomic elements corresponding to the different entries of this table. These entries contain links leading the user to the respective subsection of the QAR, where the user can find the EQC information of interest.

The overall EQC framework is guided by homogeneity and scalability approaches. The former leads to consistency of the EQC information across the CDS dataset categories, the latter leads to the integration of automatic tools to produce timely and sustainable data assessments in an operational environment. In particular, the EQC framework is driven by:

• a modular and flexible system able to consider new data/information sources and new actors involved;
• as much as possible automation of information acquisition (e.g., variable description, metadata checks) and its update in order to reduce human errors, speed up the QAR production, and make the system sustainable in the long-term;
• an iterative and reproducible approach permeable to the evolving requirements of both users and C3S to ensure continuous improvement;
• a user-friendly presentation of the quality information provided, clustered to facilitate its consultation and uptake to facilitate users in making their own decisions about climate data;
• consistent provision of the CDS dataset quality information, recognising the existence of inherent differences across the dataset categories;
• transparency and traceability of the quality assessments;
• FAIR (Wilkinson et al. 2016) and TRUST (Lin et al. 2020) principles and ISO 19157:2013;
• service management practices to make the EQC activities resilient in an operational environment.

Finally, the guidelines provided in Peng et al. (2021) have been followed when developing the EQC framework, as shown in Table 1.

3. Dataset quality information and its dissemination

The QAT is the tool used to gather information on the most relevant aspects of the CDS datasets, informing the user in a quicker way rather than accessing and reading several documents (e.g., user guides, peer-reviewed papers, dataset descriptions). The QAT includes all the relevant quality information, in a concise and standardised form, with references and links leading to further details.

The general strategy is to provide seamless QATs, which are as homogenous as possible across all dataset categories. The QATs for each dataset category (i.e., satellite and in-situ observations, reanalysis, seasonal forecasts, climate projections) are available as supplementary material. The QATs are regularly reviewed to gradually converge towards harmonisation. Much improvement has been achieved by adopting a common terminology (see section 6) and common minimum requirement fields. The homogenisation of the QATs of different dataset categories ideally tends towards adopting one single QAT for all datasets. However, this goal is not feasible due to the diverse nature of the CDS dataset categories (concepts like ‘processing level’ or ‘quality flag’ are relevant for observational datasets, but not for other categories; along the same lines, the concept of ‘ensemble size of the hindcast’ is mostly relevant for seasonal forecasts). This homogenisation effort was pragmatically addressed by mapping the different QATs, one for each category, onto a general table agnostic of the dataset type.

In practice, all the QAT fields were grouped under main sections and subsections with common names for all dataset categories. An excerpt of the resulting QAT is reported in Figure 1. Having common names for sections and subsections to all the QATs allows to organise and homogenise the EQC information in a general table named synthesis table (Figure 2).

The synthesis table entries contain links leading the user to the respective subsection of the QAR (i.e., filled QAT), where the user can find the EQC information of interest. Therefore, the structure of the synthesis table is agnostic of the dataset category, while the QAT fields, within each subsection (the information that is displayed when clicking on a cell in the synthesis table), depend on the dataset category.

The synthesis table offers an effective approach to guide the users into the documentation and homogenise the access to it. It addresses a typical user requirement: ‘most of the time, the problems with the documentation are not due to the lack of it, but to the difficulty in finding it’ (extracted from the C3S User Requirement Analysis Document 09/2019) or ‘all documentation should be easy-to-access’ (Nightingale et al. 2019). For instance, a non-expert user might not know the meaning of ATBD (Algorithm Theoretical Baseline Document) and the synthesis table overcomes this complication by guiding the user through questions (i.e., QAT fields), answered with high-level information further detailed in the complete document referred (ATBD in this case). Moreover, the synthesis table offers an extra level of assurance through independent assessments and guarantees the user that all the information made available through this table is traceable and quality controlled, because the information given by the provider is double checked by the EQC team and is versioned in the CMS. An extra advantage of the synthesis table is the possibility to track which EQC material the user is interested in by recording the user’s actions in the table. These actions can be analysed in a later stage to steer the future decisions of the EQC function and C3S in general.

The information accessible through the synthesis table may be grouped into two categories:

• Descriptive data information. Documentation has been selected to tackle data provenance, showing the origin, history, and methodology used to create the data. This information is available prominently in the column ‘user documentation’ of the synthesis table (Figure 2), in particular in the scientific methodology part. The documentation is completed by references to more detailed material, such as uncertainty characterisation, license, citation, and the like, for further user queries. Information here is the result of the documentation and accessibility assessments. In general, content is filled in by the data provider and reviewed by the EQC team.
• Independent assessments. An analysis of the dataset quality is performed independently of the provider, with the advantage of using the same metrics and tools nonpartisan of the source where the dataset was generated. This guarantees a uniform and impartial basic evaluation across datasets. Information here is the result of the technical and scientific assessments. See Appendix III for a definition of ‘technical’ and ‘scientific’ in this context.

The table is characterized by fields grouped into columns (Figure 2). The column with the header ‘introduction’ gives a quick overview of the data characteristics (e.g., name, provider, time resolution), as inspired by the WIGOS guide on metadata standards (WMO/WIGOS 2017). The column ‘user documentation’ provides the essential documentation for the effective use and understanding of the dataset (e.g., user guide). The column ‘access’ describes whether the dataset variable can be served by the CDS Toolbox and which are the archiving practices followed for this dataset. Finally, the column ‘independent assessment’, being more articulated, is explained in detail in Appendix I.

4. Development of the technical solution

Building the framework of the EQC requires designing protocols, software tools, QATs, and workflows for the QAR production as well as following common vocabularies and practices (e.g., TRUST Principles for Data Repositories, Lin et al. 2020). Among these, we focus now on the technical solutions underlying the EQC framework. Substantial technical developments have been undertaken during the onset of the operational phase of the EQC and more will be needed while it gets more mature over time:

• a Content Management System (CMS) and its maintenance, more details below;
• a Drupal-based module, inspired by the shiny-app R package,¹⁰ to show dynamic plots resulting from the scientific assessments;
• the integration of the EQC tab into the CDS infrastructure and its synchronisation with the other catalogue elements (e.g., download tab);
• software packages adapted to perform the scientific assessment tailored to the CDS characteristics (e.g., the ESMValTool¹¹ was adapted for climate projections analyses);
• a data-checker software to scrutinise CDS data and metadata;
• compatibility tests to check whether the data variables can be served through the CDS Toolbox; and
• setting up the software infrastructure, such as a network of virtual machines with the right environment and a Git manager for software repository, data flow architecture, and so on.

4.1 Content Management System (CMS)

At the heart of the EQC assessments is the Content Management System (CMS), an application used to manage content stored in a database and displayed in a presentation layer based on a set of templates, i.e. the QATs. Its objective is to ease the collaborative definition of the QAT structure and to facilitate and manage the creation of the QAT content. Creation of the QAT content is partially automated, as detailed in section 5.2.

The CMS facilitates the QAR production following a workflow that involves several roles, described in Table 2, that access the CMS sequentially.

Table 2

Roles involved in the QAR production workflow.

ROLE	RESPONSIBILITY
EQC main contact	One EQC team member who acts as the main contact of a specific dataset category. As QAR production is a multi-actor process, it is important that there is a central person, the EQC main contact, to coordinate the QAR production. This member contacts the data providers to agree on when they are available to fill in the QAT. Once the link with the providers is established, the EQC main contact defines the QAR name, fills in the QAT entries that identify the QAR uniquely and selects the team involved in the QAR production. Eventually, the EQC team member lets the actors involved know where there is a potential issue before it impacts the production.
Data provider	Typically, a member of the team that provided the CDS with the dataset under evaluation. The providers fill in the information requested in the QAT, because they are considered the best source to fully describe their datasets and so are the preferential choice for this task.
Evaluator	An EQC member who vets the QAR content and fills in the independent assessment fields. This role interacts with the provider for guidance about the amount and type of content expected and for any clarification needed.
Reviewer	An EQC member who scrutinises the whole QAR content for completeness and understandability. The reviewer is fundamental, because of her/his work in checking and verifying the correctness and consistency of all information introduced, while interacting with the evaluator to address any issue encountered.
Approver	Role covered by one C3S governance board member, who makes decisions about the publication of the dataset, (also) based on the QAR, and conducts a final check of the QAR before making it public together with the dataset. If the QAR requires further review, it will be sent back to the EQC team, commenting about what is still needed. Otherwise, it will be published in the CDS.

Figure 3 sketches the interaction between the roles involved in the generation of the QARs. The implementation of this workflow into the QAR production needs more consideration; it needs to distinguish between fast and in-depth assessment cycles as well as between common and non-common QAT fields. Details are provided in the next section.

Figure 3 

Sketch showing the basic roles, their interactions, and their responsibilities during the QAR production within the CMS. Note the iteration loop between roles to allow refinement of the content. The sketch gives a grasp of the more complex workflow shown in the next figures.

To complete the list of roles involved in the CMS, two additional roles are considered but they are not directly part of the QAR production workflow:

• QAR support role: can edit any part of published QARs to fix simple issues like typos or broken links or update technical data checks performed on new parts of a dataset regularly extended over time. Depending on the task, this role is covered by either an EQC member or a CMS automatic functionality.
• Observer: can read and comment in the CMS but is not granted the right to insert any content. This role, typically covered by a C3S technical officer, can intervene in case of blocking issues with the provider or whenever some aspects in the QARs need improvement.

5. Workflow and procedures for the QAR production

The trade-off between timely and detailed assessment is tackled by splitting the QAR production workflow into two phases:

• A first phase (the fast assessment), mostly focused on verification of the minimum requirement fields. The dataset stewardship is scrutinised in terms of documentation, accessibility, and compliance with metadata standards.
• If the C3S governance board decides to make the fast assessment part of the QAR public together with the associated dataset, a second phase (the in-depth assessment) starts. During this phase, the complete independent assessment is performed, and the other fields are updated in case of need. The in-depth assessment focuses on the technical, scientific, and maturity data evaluation.

An additional element that makes the QAR production sustainable is the identification of QAT fields associated with common content across several QARs. More details are reported in Appendix II. In the following section, it is shown how these two elements, fast/in-depth assessment and common/non-common fields, come into play during the QAR production.

5.2 Datasets already published

The EQC function has been implemented after many datasets were already published in the CDS. As a consequence, a workflow needed to be envisaged to produce the necessary QARs. In this case, the trigger of the QAR initiation is a QAR release calendar, defined by the EQC team together with the C3S governance board. Once the QARs are triggered, the workflow is managed in the CMS, as shown in Figure 4.

Figure 4 

Sketch showing the interaction across roles during the QAR production within the CMS. Compared to Figure 3, here the distinctions between fast/in-depth assessment cycles and common/non-common fields are explicit and it is clarified at which stage the QAR is published.

Given the same roles identified in section 4, the QAT is filled in the private domain during the fast assessment cycle and then published. Once public, the QAR is completed with the independent assessment during the in-depth cycle and finally updated in the public domain. Each assessment cycle distinguishes between common and non-common fields:

• The part associated with the common fields requires the data provider expertise and does not include the independent assessment, as such this part involves many members, but it does not need to go through the in-depth cycle.
• The part associated with the non-common fields is unique to each QAR, because it is tailored to each variable. During the fast assessment, almost¹² any non-common field (e.g., variable description) can be extracted from precompiled tables validated with the data provider and extracted automatically to fill in the unique QAR. This part of the QAR production is nearly automatic, so it can involve fewer members, the EQC main contact to initiate the QAR and a reviewer to guarantee that the content is meeting expectations. Once the fast assessment cycle is complete, the non-common fields follow the in-depth cycle, where the evaluator includes the independent assessment material.

Once published, one QAR might need to be updated. More details about the procedure in this case are given in Appendix II.

5.3 Datasets ready to be published

In the future, the evolution of EQC will need to consider a workflow for datasets ready to be published. So far, this workflow has not been implemented. Several options are considered based on the lessons learned during the ramp-up phase of EQC. Here we give some recommendations.

A new dataset is a dataset the provider considers ready to be served through the CDS. At this stage, the provider and C3S officers iterate to ingest data information, like documentation or the location where data are stored. Much of this information could be collected in the CMS (or a tool connected with the EQC CMS), which facilitates the completion of part of the QARs shortly after. Instead of EQC asking for similar information again, we can leverage the content already stored in the CMS to streamline the flow of information exchanged among the various authors involved, by introducing a workflow starting with the fast assessment explained in the previous section. Once the fast assessment cycle is complete, the dataset could be either rejected because, for instance, the minimum documentation required is not complete, or accepted for publication. When the dataset and the initial QARs are public, the in-depth assessment cycle starts. The logical flow, illustrated in Figure 5, may be summarised as follows:

• When the dataset is ready to be evaluated, the C3S governance board opens a ticket addressed to the EQC team.
• The EQC team meets regularly to assign the work for the QAR production based on, among other sources, the tickets received.
• Once the related QARs are completed of the fast assessment and approved in the CMS, the CMS closes automatically the ticket considered.
• The C3S governance board decides whether to publish, postpone the publication or discard the dataset using, among other input, the QARs made available.
• Once the dataset is published along with the preliminary QARs, the in-depth assessment to complete the QARs can start, as described in the previous section.

Figure 5 

Scheme of the preliminary workflow about the way EQC may be engaged in the fast assessment of datasets not yet published in the CDS. Once a dataset is published (identified with ‘end’ in the figure), the in-depth assessment cycle starts as usual. The part associated with the EQC is in light blue, while the part associated with the EQC user engagement team is in green.

The independent assessment is part of the in-depth cycle that always starts after the dataset is published with its QAR. However, for new datasets it would be convenient that the data provider performs the technical assessment, that is, data checks, and reports the evidence logs to the EQC team. The EQC team then controls that evidence is available for the entire dataset and performs random checks autonomously on a subset of the entire dataset. The reason for this logical flow is that there are technical limitations that make the data checks timely only when done by the provider. Indeed, the downloading and queuing time and the disk storage requirements are technical limitations that would demand more resources for EQC, while these resources are likely already allocated on the provider’s side. The strategy described would also reduce duplication of efforts and optimise resources, while guaranteeing independent checks.

6. Protocols and practices complementing the implementation of the EQC function

Besides the QAR production, the EQC function for the CDS is completed by additional protocols that make it a solid building block of C3S. Here follows a brief list of the protocols and practices considered.

7. Lessons learned

The evolution of the EQC function would benefit from optimising the protocols, the templates and the workflow implemented so far, while tackling the gaps identified. The main issues encountered and more general considerations follow:

• EQC workflow is a multi-actor process: collaborative iteration is key. Several lessons have been learned while working on the QAR production and made clear that the EQC framework is a multi-actor process that requires collaborative iterations with different stakeholders. Tied collaboration between the C3S contracts and approver, user engagement, and data provider is extremely important for sustaining the delivery of EQC information. Clear responsibilities and timelines have been defined. Perhaps, the most important interaction is with the data providers/producers for filling the QATs. Responses from the providers are sometimes sparse or non-existent. Data providers/producers are considered the best source to fully describe their datasets and are the preferential choice to contribute to the QARs. This gap has been narrowed by:
  • including the officer in charge of the relationship with the data provider contractor (a technical officer in the case of C3S), who facilitates the interaction with the provider and ensures that specialistic knowledge about the data under scrutiny is fully accounted for;
  • ensuring that the EQC takes place earlier in the data ingestion process than now where the EQC work is done on already published data; and
  • making all EQC-related tasks a contractual obligation in the data provider commitments. For brokered datasets (i.e., pre-existing dataset, not subject to the Copernicus license, to which C3S only acquires a license for the purpose of making it available in the CDS), the situation is not so different, because the contact point with EQC is the broker.
• Technical constraints limit the extension of the minimum requirements. The current version of the minimum requirements (MRs) fits the existing technological infrastructure as well as available human resources. The most important constraints to be faced are downloading and queueing time, memory disk space needs, lack of metadata information (that the provider should make available) about valid ranges for some variables, need for (land/sea) mask, and lack of automatic tools for data checks of each dataset category. To favor the development of data checker tools, common standards for data and metadata (formats and conventions) shall be enforced by C3S. Overall, climate services play a crucial role in disseminating relevant standards, in this case metadata standards (GFCS WMO¹⁶). A service aiming at providing seamless products necessarily needs to disseminate data in a common format with files structured similarly and with sufficient metadata uniformly stored. This supports interoperability of data files and archives for automated data processing through improved and extended standards and metadata. As a first step, datasets served in NetCDF should comply with the CF¹⁷ convention, but this covers only units, dimensions, and a few variables’ metadata attributes. It is beneficial to ingest input data following a more constrained and controlled common vocabulary, covering variable naming, file names, grid descriptions, and global attributes. Examples of domain-specific conventions are CMIP¹⁸ and ESA-CCI.¹⁹ For the latter, C3S and ESA are coordinating to homogenise their metadata standards for satellite observation datasets. In addition, the ACDD²⁰ conventions cover the global attributes for easier discovery and interoperability of the data (e.g., spatial and time coverage description, keywords).
• Capacity building both in terms of human resources and technologies. The implementation of the EQC requires cross-disciplinary knowledge (e.g., science, data management, computer engineering) to design protocols, software, and workflows following best practices. A constraint emerged during the work described in this paper is the importance of designing solutions sustainable for a large number of datasets. Otherwise, it is not possible to guarantee the necessary throughput with the available resources. As a consequence, some choices that seem to be straightforward and better than what has been implemented could not be considered because they do not scale for the large number of datasets under scrutiny (e.g., writing individual QARs for each variable). It was necessary to automise as many parts of the workflow as possible to guarantee a timely production of the QARs. Based on our experience, it is also challenging to find a sufficient number of experts willing to regularly review in-depth all data streams. Considering that on demand requests for review do not necessarily guarantee that the same level of expertise could be kept over time, it would be appropriate to identify suitable funding mechanisms and contractual arrangements to keep the experts engaged for a longer period. The EQC framework must be tailored to the service infrastructure to be successful both in terms of human resources, coordination, and technology capabilities. This should translate into an increased effort towards capacity building, the need of which was also highlighted by Hewitt et al. (2020) for climate services in general. Capacity building shall be closely taken into account during the implementation of a sustainable EQC framework.
• Optimise the production of more insightful independent assessments. The scientific assessment needs to expand towards the diagnostics and standard metrics considered most insightful for the users. It would be beneficial to engage with the data providers to identify common baseline metrics to be applied independently. This will help to converge towards stronger provider engagement and satisfaction with the assessments performed and to reduce iterations during the in-depth cycle of the QAR production process. The huge and increasing amount of CDS datasets will need to consider pragmatic approaches to streamline the production of the independent assessment. While the fast cycle of the EQC framework has been made very efficient, more work will be needed to establish sustainable mechanisms for the detailed in-depth cycle of the EQC framework.
• Consistent and shared vocabulary. A common terminology should be shared and kept improving across the various C3S components. This facilitates consistency within the C3S (across the Catalogue entries for instance), it backs the user support desk when answering frequent questions by users about terminology, it gives a reference for the users to consult when jargon or acronyms are found, and it supports the project management to avoid ambiguities across C3S contracts. As such, the common vocabulary is considered a fundamental guidance document to be integrated in the C3S portal to benefit both users and service.
• Benchmarking and cross-service coordination. Consolidation of the EQC function also passes through the investigation of the most recent approaches. Given the challenges and opportunities that arose while implementing the EQC framework of the CDS C3S, exploration of the existing literature and coordination with other Earth data services, and in particular the other Copernicus services, is a key task to build a state-of-art EQC framework (i.e., benchmarking). It is important to investigate the standards and best practices implemented in similar services operating around the world (e.g., Leadbetter et al. 2020, RfII 2020). This helps to assess the applicability of the different approaches to the EQC function.
• Scientific gaps warrant further research. There are clear scientific gaps that hinder smooth development of protocols for data quality assessments. Further research investments shall be considered by major funding bodies (e.g., Horizon Europe) to fill the current scientific gaps. For instance,
  • the system maturity matrix CORE-CLIMAX (EUMETSAT 2014) was identified as a tool for the maturity assessments, but it exhibits limitations of scalability in an operational environment. An example, the in-situ observations GRUAN dataset required three months of work to complete the maturity assessment. In an environment like C3S, with increasing datasets and new versions, this effort is not practical. Scalability requires to detail the guidelines of the assessment (e.g., specify the metadata standards to check against or the source considered for citation to score usage). Moreover, the current scoring rules of the maturity matrix might require a peer-review process, possibly involving the data provider, to reduce subjectivity of the judgments. This poses once more challenges to produce scalable and timely assessments. In addition, the literature does not offer system maturity matrices for climate projections and seasonal forecasts, which opens intriguing questions about the different role maturity and verification play in modeling and observational communities.
  • Another case that would benefit from further research is the development of a metadata standard convention for seasonal forecasts and ERA5 data served in GRIB2 format.
  • Another example is the lack of well-defined ranges of physical plausibility for all the CDS variables, as well as a list of variable names and descriptions consistent across dataset categories. For instance, so far it is not clear what reference to use to name and describe the surface temperature, it ranges from ‘(surface) temperature’ in GCOS²¹ to ‘near-surface air temperature’ in CMIP tables.²²
• User engagement as an integral component of the EQC. User engagement needs to be an integral part of the EQC evolution towards user-endorsed practices. It is inevitable that the users will drive the requirement for the provision of bespoke dataset quality information. The FP7 EUPORIAS project (Buontempo et al. 2018) defined a set of principles for a successful climate service, which are particularly relevant for the design of an EQC framework in a user-driven context. The next phase of EQC would benefit from identifying the type of QAR content considered most useful from the user’s perspective. At present, our understanding of the usage of EQC assessments by users is still limited, but more attention to uncertainty characterisation, dataset stewardship, and ways the information is presented seem good candidates to start with. Thanks to the first QARs made available online, it will be insightful to test the efficacy of the communication strategies to ensure that appropriate and accurate quality assessments reach the users and are interpreted correctly.
• Central repository of information. The tool used for the QAR production (the Content Management System in our case) may need to expand to consider the several data ingestion processes happening in C3S beyond EQC and may need to upgrade enhancing functionalities in relation to data import and synchronisation with the rest of the CDS information. The tool may be better integrated into a single system for data ingestion and repository of information within the service to avoid duplication of effort and duplication of content describing the datasets. Better integration would also make the flow of information smoother across the actors involved in the service. Given the granularity of the datasets and their quality assessments, it may be convenient to make the quality information accessible through a structured API.
• New challenges for the next phase. The next phase of EQC will need to evolve to tackle some new challenges. For instance, it remains to define the details of a workflow dealing with new datasets ready but not yet public in the CDS. Another example is defining how to trigger QAR updates and their maintenance due to dataset new versions, due to datasets extending over time and due to new documentation available. Some practical directions have been suggested, but before adopting them operationally they would need to be assessed and tested. Evolution on how the quality information is disseminated (e.g., synthesis table) and development of an advisory service about dataset robustness (e.g., scoring scheme) will also deserve further exploration. A scoring system can be introduced so that users can quickly see which atomic elements of the EQC information have a good amount of detail. The scoring scheme would not aim at determining whether one dataset is better than another comparable dataset in an absolute sense, but only indicate the amount of quality information available. The scheme has to be simple and can be based on levels of increasing amount of detail/justification provided, as inspired by common practices in the literature (e.g., Nightingale et al. 2018, GEO Label²³). While working on the EQC framework, some ideas have been already put forward, the levels of appraisal may depend on the fulfillment of the minimum requirements described in this paper, making the scoring objective and prone to automation.

8. Summary and conclusions

The current framework developed for the ramp-up operational phase of the Evaluation and Quality control (EQC) for the C3S CDS was presented. The framework considers the tasks, protocols and tools required to ensure that data are reliable and usable. It is inspired by the WMO GFCS guidelines, the ISOs 14090/1 and previous EU FP7/C3S projects. The framework is driven by a holistic approach aiming at homogenising the type of information made available across different climate datasets in a way that is both human and machine readable. It is characterised by a two-tier review system to assure the quality of dataset information released to the public. On the one hand, this approach enables fair and consistent comparison across datasets and facilitates guidance on the best use of data for the intended user’s application; on the other hand, this approach makes the assessments sustainable and maintainable in an operational environment. In doing so, the framework explored optimal mechanisms (e.g., fast vs in-depth assessment and common vs non-common dataset information) for setting up sustaining delivery of EQC information, meeting the guidelines of the European Roadmap for Climate Services (EC 2015) and Peng et al. (2021).

The establishment of a quality management framework demonstrated benefits to the many actors involved:

• the users: easy access and guidance to quality assurance information;
• the data providers: feedback on data quality and incentive for improvement to increase data uptake and usability. This is in line with the good practice to connect the quality information with the dataset before release to reduce data misuse since this may also result in damage to the reputation of the data provider (Peng et al. 2021);
• the service itself: delivery of trusted and authoritative climate information, commitment to a user-driven evolution of the service. The service benefits from an established vehicle that triggers actions to improve the service itself; and
• the funding agencies: to get a measure of how compliant the funded datasets or services are with specific requirements.

As mentioned in the introduction, it is the first time that the methodologies, the metrics, the evaluation framework and the way to present all this information are being developed in an operational service that disseminates the majority of the climate dataset categories (including in-situ and satellite observations, seasonal forecasts, reanalysis, and climate projections). Building the underlying technical solutions makes the C3S EQC unique and requires pragmatic decisions for its implementation. The first part of the EQC framework design focused on ensuring the robustness of the baselines and processes to collect the information required for the QATs, keeping their coherence and their comparability across all the datasets available in the CDS. Having a set of QATs covering consistently all the dataset categories is a unique endeavour. These activities needed continuous improvement to fine-tune the EQC framework, benefitting from the operational assessments (QARs) and user engagement process. During the second part of the EQC framework design, activities focused on defining the level of content required in the QARs and its homogeneity across. Optimisation of the QAR update by means of automation tools and workflow streamlining has also played an increasing role.

The EQC framework was developed and implemented for all datasets published in the CDS at the beginning of 2020. QARs have been generated at the granularity of the variable for each dataset and made available to the users via the CDS web platform. The implementation of the EQC function addressed most of the recommendations that arose during the pre-operational phase (see Nightingale et al. 2019): (i) designing of dataset category specific QATs, (ii) enhancement of the CMS functionalities, (iii) identification of minimum requirements to publish a CDS dataset, (iv) establishment of an overarching consistent EQC vocabulary, (v) creation of guidance documents for evaluators and reviewers to guarantee consistency in the QAR production, (vi) regular benchmarking activities brought into the operational process, (vii) ability to track changes in the QAR content.

Several constructive pieces of feedback from data providers, downstream users and C3S officers made the dissemination of the EQC information more robust over time. Orchestrating the different elements involved requires considerable coordination efforts and a continuous improvement approach to integrate the inputs regularly emerging from stakeholders and technical constraints. A number of lessons learned and science knowledge gaps were identified during the development of the EQC function and are detailed in the paper. These warrant further investment to comprehensively address the quality dimension of climate datasets in an operational environment.

Additional File

The additional file for this article can be found as follows: