What makes a variable an Essential Ocean Variable (EOV)

EarthzineEarth Observation, Ocean Decade

Schematic of the ECV concept

An EOV makes a critical contribution to characterizing the state of the world’s oceans and climate system.

14 June, 2026

Christoph Waldmann,
IEEE Life Senior Member,
BEACON Contributing Editor

Observations are fundamental to ocean science, and several strategies have been developed to ensure that they are carried out as efficiently and effectively as possible. A key element of these strategies has been the selection of a core set of variables to be measured systematically and consistently. The primary purpose is to define a minimum, prioritized set of ocean variables that enables the characterization of the global ocean system and the detection of long-term change.

This concept originates from an initiative of the World Meteorological Organization (WMO). Through the Global Climate Observing System (GCOS), WMO introduced the concept of Essential Climate Variables (ECVs), with the term first appearing in GCOS in 2003 [1]. In practice, ECV and Essential Ocean Variable (EOV) datasets partly overlap. They are referred to as “essential” because they represent the minimum set of oceanic and atmospheric observations required to describe, monitor, and understand the state and variability of the ocean and climate. These observations support science, forecasting, climate assessment, ecosystem management, and a wide range of societal applications.

An ECV or EOV is a physical, chemical, or biological variable, or a group of closely related variables, that makes a critical contribution to characterizing the state of the world’s oceans and the Earth’s climate system. The current list of ECVs is specified in GCOS (2010a); all GCOS reports are available at [2].

Schematic of the ECV concept

Figure 1: Schematic of the ECV concept: knowing existing climate relevant observing capabilities, climate datasets, and the level of scientific understanding of the climate system are the foundations (lower-left box) necessary for selecting the ECVs from a pool of climate system variables. In addition, guidance is needed to make practical use of the ECVs (lower-right box): user requirements capture the data quality needs of science, services, and policy; climate specific principles guide the operation of observing systems and infrastructure; and guidelines facilitate the transparent generation of ECV data records [cited from 1].

 

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GOOS (Global Ocean Observing System) describes EOVs as variables selected because they have high impact and can be observed or derived in a globally feasible way [3].

The essential variables “stick out” from other variables in three main ways:

Criterion

What it means

Impact

The variable is important for understanding ocean state, climate, services, hazards, or ocean health.

Feasibility

It can be measured or derived reliably with existing or scientifically understood methods.

Readiness for sustained observing

It can support consistent, repeatable, long-term observations across regions, platforms, and institutions.

Role of the GOOS expert panels in the EOV framework

Figure 2: Adopted from [2], illustrates the role of the GOOS expert panels in the EOV framework

An EOV can be understood as a backbone measurement for the ocean observing system. Variables such as temperature, salinity, currents, oxygen, nutrients, carbon, sea level, ocean color, plankton, and habitat-related parameters are essential because they provide a basis for interpreting many other ocean processes. For EOVs, GOOS provides so-called specification sheets [4], which are currently under revision.

In summary, ECVs primarily relate to the climate system, with many variables describing atmospheric processes, whereas EOVs are specifically aimed at describing ocean processes. Accordingly, the nature of the variables differs, as they address different components and dynamics of the Earth system. The main overlap between ECVs and EOVs occurs in measurements of the ocean surface.

Across scientific disciplines, it is standard practice to report not only measurement results, but also the associated uncertainties. The WMO has adopted the Guide to the Expression of Uncertainty in Measurement — the GUM [5] — as the reference framework for quantifying measurement uncertainty. In ocean science, however, terminology is not always used consistently. Scientists still often refer to concepts such as systematic error, statistical error, or true measurement values, although some of these terms are no longer fully aligned with the GUM framework.

With the increasing use of AI methods, including machine learning, additional terminology is entering the discussion, in particular the distinction between aleatoric and epistemic uncertainty and the type A and B uncertainties defined by GUM.

To clarify the relationship between these different concepts and promote more consistent usage, OES should consider contributing through dedicated workshops or by developing practical guidelines.

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