Introduction

  • Interoperability ensures that data can be understood, combined, accessed, and reused across tools, institutions, and workflows with minimal manual intervention.

  • Interoperability operates at three complementary layers:structural (how data is encoded and organized),semantic (how data is described and interpreted), and technical (how data is accessed and exchanged).

  • The FAIR interoperability principles I1–I3 primarily address the semantic layer. They provide essential guidance on shared metadata languages, vocabularies, and references, but they do not fully cover structural and technical interoperability.

  • In climate and atmospheric science, all three layers are required for practical reuse. Structural standards (e.g., NetCDF, Zarr), semantic conventions (e.g., CF), and technical mechanisms (e.g., APIs, OPeNDAP, THREDDS) must work together.

  • Many real-world barriers to reuse datasets (unclear metadata, missing units, inconsistent coordinate systems, incompatible file formats, unstable access mechanisms) are failures of one or more interoperability layers.

  • Interoperable research workflows rely on established community formats, standardized metadata conventions, stable access protocols, and scalable cloud-native layouts that allow large heterogeneous datasets to be aligned, streamed, and analysed consistently.

  • Interoperability is essential in climate science because datasets come from diverse sources (models, satellites, sensors, reanalysis) and must be combined into integrated analyses that are reproducible and machine-actionable.

Structural interoperability

  • Structural interoperability concerns how data are organized, not what they mean.
  • Open standards are essential for machine-actionability and long-term reuse.
  • Structural interoperability is enforced by data models, not file extensions.
  • Structural interoperability is enforced by data models.
  • Standards maintained by communities (e.g. Unidata, Pangeo, OGC/WMO) encode shared structural contracts that tools and workflows can reliably depend on.
  • NetCDF exemplifies structural interoperability for multidimensional geoscience data.

Semantic interoperability

  • Semantic interoperability ensures that data variables have shared, machine-actionable scientific meaning, not just readable structure.

  • Structural interoperability is necessary but insufficient for reliable comparison and reuse across datasets.

  • The CF Conventions provide a community-governed semantic layer on top of NetCDF through standard names, units, and coordinate semantics.

  • CF compliance enables automated discovery, comparison, and integration in climate and atmospheric science workflows.

  • Semantic interoperability depends on community-agreed conventions, not on file formats or variable names alone.

Technical interoperability: Streaming protocols

  • OPeNDAP (DAP) is a protocol that enables remote access to subsets of scientific datasets without downloading entire files, exemplifying technical interoperability.
  • Using OPeNDAP allows efficient server-side subsetting and slicing of large NetCDF files, facilitating scalable workflows for large climate datasets.
  • Programmatic access to NetCDF files via OPeNDAP can be achieved using libraries like xarray, enabling efficient data exploration and manipulation.

Technical interoperability: API

  • APIs (Application Programming Interfaces) are interoperable protocols that enable machine-to-machine communication, allowing automated data retrieval, publication, and integration across distributed systems.
  • REST APIs use standard HTTP methods (GET, POST, PUT, DELETE) and JSON serialization to provide predictable and self-describing endpoints, facilitating seamless interaction with data repositories.
  • By adhering to established metadata standards and versioning practices, APIs ensure consistent and reliable access to datasets, supporting scalable and interoperable workflows in climate science.

Cloud-Native Layouts

  • Cloud-native data layouts, such as Zarr and Parquet, are designed for efficient storage and access in cloud environments, enabling scalable and parallel processing of large climate datasets.
  • Cloud-native layouts enhance interoperability by allowing seamless integration with distributed computing frameworks like Dask, Ray, and Spark, facilitating efficient data slicing and analysis.
  • Key technologies for cloud-native data layouts include Zarr for chunked storage, Kerchunk for virtual datasets, and Parquet for tabular data, all of which support scalable and interoperable workflows in climate science.

Interoperable Infrastructure in the AI Era

  • AI-ready data infrastructure requires large-scale multidimensional datasets, consistent CF metadata, chunked cloud-native formats, STAC-like discoverability, and stable APIs for pipeline automation.
  • Interoperability is crucial for AI applications in climate science as it enables efficient data access, reproducibility of results, integrability of diverse datasets, and trust in AI-driven insights.
  • Key elements of an AI-ready interoperable data infrastructure include adherence to community formats, cloud-native layouts, stable APIs, comprehensive data catalogs, and robust versioning and identifier systems.