A Global 3D Chlorophyll Concentration Dataset (1997-2024): 4 km Spatial Resolution, Monthly Scale, 0-220 m.

Yanfeng Wen, Peng Chen, Delu Pan

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In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Yanfeng WenState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Bochubeilu, Hangzhou, 310012, China.

Peng ChenState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Bochubeilu, Hangzhou, 310012, China. chenp@sio.org.cn.ORCID http://orcid.org/0000-0002-0635-9220

Delu PanState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, 36 Bochubeilu, Hangzhou, 310012, China.

Funding

National Key Research and Development Program of China Grant #2022YFB3901703; Grant #2022YFB3902603National Natural Science Foundation of China Grant #42322606; Grant #42276180; Grant #W2521002Zhejiang Provincial Natural Science Foundation Grant # LZ25D060001

6 · The paper itself

Abstract

Oceanic chlorophyll-a (Chl-a) 3D distribution is critical for quantifying marine primary productivity, ecosystem dynamics, and the oceanic carbon cycle. However, existing global 3D Chl-a datasets have coarse spatial resolution (≥0.25°), limiting their ability to resolve mesoscale and sub-mesoscale processes. We present MEGO-C-M (Multi-model Ensemble for Global-Ocean Chlorophyll-a - Monthly Gridded Dataset), a high-resolution global 3D Chl-a dataset on a 0.0416° (~4.6 km) grid with monthly coverage (October 1997-November 2024). Derived via the MEGO-C machine-learning framework (trained on 144,936 quality-controlled BGC-Argo profiles and co-located satellite/physical reanalysis predictors), the model is applied to monthly predictor fields to generate gridded Chl-a across 27 vertical levels. Validation against independent BGC-Argo profiles shows strong agreement (R² = 0.818, RMSE = 0.254 log₁₀(mg·m

Identifiers

PMID42581055

DOI10.1038/s41597-026-07795-y

PMCPMC13463031

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.