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Contrasting biological production trends over land and ocean

Nature Climate Change volume 15, pages 880–888 (2025)Cite this article

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Abstract

Terrestrial and marine ecosystems constitute the primary components of the Earth’s biosphere, yet their photosynthetic productions are typically studied separately, which limits understanding of planetary carbon uptake and biosphere health. Here, using multiple satellite-derived products, we identify contrasting net primary production (NPP) trends between land and ocean, probably reflecting their differential sensitivity to climate warming, especially in tropical regions. Planetary NPP shows an overall increase of 0.11 ± 0.13 PgC yr−1 (P = 0.05) from 2003 to 2021, driven by a significant terrestrial enhancement of 0.20 ± 0.07 PgC yr−1 (P < 0.001) and partially offset by an oceanic decline of −0.12 ± 0.12 PgC yr−1 (P = 0.07). While land contributes to the strong upwards NPP trend, the interannual variability in global NPP is predominantly driven by the ocean, especially during strong El Niño–Southern Oscillation events. Our findings highlight the resilience and potential vulnerability of biosphere primary productivity in a warming climate, calling for integrated land–ocean monitoring and assessment to support climate mitigation initiatives.

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Fig. 1: Annual NPP changes over land and ocean from 2003 to 2021.
Fig. 2: Spatial distributions of global NPP mean and trend from 2003 to 2021.
Fig. 3: Putative environmental controls on annual NPP trends over land and ocean.
Fig. 4: El Niño–Southern Oscillation (ENSO)-driven interannual variability in land and ocean NPP from 2003 to 2021.

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Data availability

All global satellite and geophysical products needed to evaluate the conclusions in this article are publicly available, with their respective access links provided in Supplementary Table 1. The generated global land and ocean means and their annual trends and significance from 2003 to 2021 are available via the Open Science Framework repository73. An interactive tool for the visualization of the key global NPP metrics including multiyear mean, annual trend, interannual variability and specific yearly anomaly is available online74.

Code availability

All codes needed to reproduce the key findings in this article are available via the Open Science Framework repository73.

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Acknowledgements

We gratefully acknowledge the NASA MODIS Ocean and Land Science Teams, M. Behrenfeld (Oregon State University), M. Zhao (NASA Goddard Space Flight Center), K. A. Endsley (University of Montana), S. Liang (University of Maryland), W. Ju (Nanjing University) and J. Chen (University of Toronto) for providing global ocean and land NPP products. We also thank the modelling groups contributing to CMIP6 for their ocean biogeochemistry simulations and the TRENDY project teams for their terrestrial biosphere model outputs, as coordinated through the Global Carbon Budget initiative. In addition, we extend our sincere appreciation to the institutions and researchers listed in Supplementary Table 1 for making the publicly available land and ocean datasets used in this study. Y.Z., W.L. and G.S. are partially supported by the Duke University–USDA Forest Service collaboration (grant no. 23-JV-11330180-119). N.C. is supported by the National Science Foundation (grant no. OCE-2123198). J.X. is supported by the National Science Foundation (Macrosystem Biology and NEON-Enabled Science program: grant no. DEB-2017870). J.M. is supported by the Oak Ridge National Laboratory (ORNL) through the ‘Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computing’ scientific focus area and the ‘Terrestrial Ecosystem Science Scientific’ focus area, funded by the Earth and Environmental Systems Sciences Division of the Biological and Environmental Research Office within the US Department of Energy Office of Science. ORNL is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-00OR22725.

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Authors and Affiliations

  1. Division of Earth and Climate Sciences, Nicholas School of the Environment, Duke University, Durham, NC, USA

    Yulong Zhang, Wenhong Li, Zuchuan Li, Haipeng Zhao, Qianru Zhang, Shineng Hu & Nicolas Cassar

  2. Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest Service, Raleigh, NC, USA

    Yulong Zhang & Ge Sun

  3. Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Yulong Zhang & Jiafu Mao

  4. School of Earth, Environment, and Sustainability, University of Iowa, Iowa City, IA, USA

    Matthew Dannenberg

  5. Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA

    Jingfeng Xiao

  6. Data and Environmental Sciences, Duke Kunshan University, Kushan, China

    Zuchuan Li

  7. Department of Geography and Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Conghe Song

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Contributions

Y.Z. and N.C. conceived the idea and designed the research. Y.Z., N.C., W.L., G.S., M.D., J.M., Z.L. and Q.Z. developed the methodology. Y.Z. and N.C. carried out the investigation. Y.Z. performed the data visualization and wrote the original draft of the paper. All authors, including Y.Z., N.C., W.L., G.S., J.M., M.D., J.X., Z.L., H.Z., Q.Z., S.H. and C.S., discussed the design, methods and results, and contributed to the writing, review, and editing of the paper

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Extended data

Extended Data Fig. 1 Comparison of global ensemble means and interannual variability in land and ocean NPP based on remote sensing products from 2003 to 2021.

a, Ensemble means of land NPP (N = 3), ocean NPP (N = 3) and global total NPP (N = 9). b, Annual land NPP changes from three RS-based products (BEPS, GLASS and MOD17). c, Annual ocean NPP changes from three RS-based products (CAFE, CbPM and EVGPM). In a, error bars represent the standard deviations across three models for both land and ocean NPP, as well as nine annual combinations for global NPP. Annual NPP anomalies in b and c are calculated relative to the multi-year mean (2003–2021) of each modeled NPP.

Extended Data Fig. 2 Annual ocean NPP changes along the latitudinal gradient (1° interval) from 2003 to 2021.

a, Annual NPP anomalies expressed as latitudinal area-weighted mean. b, Annual NPP anomalies expressed as latitudinal area-weighted total. In both panels, anomalies are calculated relative to the multiple year mean within each latitudinal band. The vertical line marks the 2015 breakpoint in the global ocean NPP trend, as shown in Fig. 1b. Two horizontal lines denote the mid- to high-latitude regions, where the decline-to-increase reversal in NPP is mainly observed.

Extended Data Fig. 3 Evaluation of processed-based models in simulating land and ocean NPP with remote sensing (RS)-based models.

a, Annual land NPP from 16 Dynamic Global Vegetation Models (DGVMs) and three RS-based models. b, Annual land NPP from the ensemble means of DGVM (DGVM-Mean) and RS-based models (RS-Mean), with shaded areas representing one standard deviation among each model group. c, Annual ocean NPP from 15 Global Ocean Biogeochemistry Models (GOBMs) and three RS-based models. d, Annual ocean NPP from the ensemble means of GOBMs (GOBM-Mean) and RS-based models (RS-Mean), with shaded areas representing one standard deviation among each model group. In a, b, DGVMs are from TRENDY V10 (2003 to 2020); In c, d, GOBMs are from historic simulations from CMIP6 (2003 to 2014).

Extended Data Fig. 4 Annual NPP trends and multi-year baseline NPP from 2003 to 2021 across land and ocean zones.

a, Land zones; b, Ocean zones. Land and ocean zones are shown in Extended Data Fig. 5. In b, cold oceans includes Southern Ocean and Arctic Oceans. Trends are calculated using Theil–Sen regression, with two-sided statistical significance assessed by the Mann–Kendall test (*P ≤ 0.05; **P ≤ 0.01).

Extended Data Fig. 5 Global zone classifications used in this study.

a, Land zones. b, Ocean zones. In a, terrestrial zones include TAM (Tropical America), TAF (Tropical Africa), TAS (Tropical Asia), AS (Arid/Semi-arid), TEM (Temperate), and BA (Boreal/Alpine). In b, marine zones include TPO (Tropical Pacific Ocean), NTP (Northern Temperate Pacific Ocean), STP (Southern Temperate Pacific Ocean), TAO (Tropical Atlantic Ocean), NTA (Northern Temperate Atlantic Ocean), STA (Southern Temperate Atlantic Ocean), TIO (Tropical Indian Ocean), STI (Southern Temperate Indian Ocean), AO (Arctic Ocean), SO (Southern Ocean), and SW (Shelf Water).

Extended Data Fig. 6 Co-regulation of gross primary production (GPP) and autotrophic respiration (AR) on annual NPP changes from 2003 to 2021.

a, Co-occurring trends of GPP and AR and their associations with NPP trends. Change classes are based on annual Theil-Sen’s slope: + (slope > 0), – (slope < 0). For example, GPP + AR + NPP+ indicates that positive trends in both GPP and AR are associated with an increase in NPP. b, Dominant drivers of NPP trends based on the relative magnitude of GPP and AR slopes. For instance, AR+ indicates that the absolute Theil-Sen’s slope of AR exceeds that of GPP, suggesting AR as the dominant influence on NPP. In a and b, values in parentheses represent the percentage of area relative to the global total vegetated land surface. c, Annual trends of key climate variables over tropical regions, including photosynthetically active radiation (PAR), precipitation, air temperature (TA), and vapor pressure deficit (VPD). d, Rank correlations among TA, VPD, GPP, and NPP across the tropical region.

Extended Data Fig. 7 Structural equation modeling of environmental effects on land NPP trends across different zones.

a-d: tropical, temperate, arid/semi-arid and boreal/alpine zones (shown in Extended Data Fig. 5). Climatic variables and path coefficient descriptions are consistent with those presented in Fig. 3g.

Extended Data Fig. 8 Structural equation modeling of environmental effects on ocean NPP trends across different zones.

a-e: tropical oceans, temperate oceans, Arctic Ocean, Southern Ocean, and Shelf Water (shown in Extended Data Fig. 5). Environmental variables and path coefficient descriptions are consistent with those presented in Fig. 3h.

Extended Data Fig. 9 Comparison of inter-annual variations in land and ocean NPP across different zones.

a. Land zones. b, Ocean zones. Annual NPP anomalies are relative to the multi-year mean from 2003 to 2021. Annual NPP values are detrended, and anomalies are calculated relative to the 2003–2021 mean for each zone. The zone exhibiting the highest absolute anomaly each year is marked with green circles for land NPP (in a) and purple circles for ocean NPP (in b). Zonal NPP values with Z-scores (defined as the ratio of the annual anomaly to the multi-year standard deviation) greater than 1.0 are indicated with an asterisk (*). Land and ocean zones are shown in Extended Data Fig. 5.

Extended Data Fig. 10 Environmental responses during the identified strong El Niño and La Niña events.

a, c, El Niño. b, d, La Niña. In a and b, land temperature represents the multi-year mean air temperature (TA), while ocean temperature refers to the multi-year mean sea surface temperature (SST) during the identified ENSO years. In c and d, land is represented by multi-year mean precipitation (PRE), and ocean is represented by multi-year mean mixed layer depth (MLD) during the identified ENSO years. All data are normalized as Z-scores, calculated by dividing annual anomalies by the multi-year standard deviation (2003–2021).

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Zhang, Y., Li, W., Sun, G. et al. Contrasting biological production trends over land and ocean. Nat. Clim. Chang. 15, 880–888 (2025). https://doi.org/10.1038/s41558-025-02375-1

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