ABSTRACT Low-latitude (LL) oceans account for up to half of global net primary production and export1,2,3,4,5. It has been argued that the Southern Ocean dominates LL primary production and

export6, with implications for the response of global primary production and export to climate change7. Here we applied observational analyses and sensitivity studies to an individual model

to show, instead, that 72% of LL primary production and 55% of export is controlled by local mesopelagic macronutrient cycling. A total of 34% of the LL export is sustained by preformed

macronutrients supplied from the Southern Ocean via a deeper overturning cell, with a shallow preformed northward supply, crossing 30° S through subpolar and thermocline water masses,

also the sign, of LL primary production. Under the stronger SSP5-8.5 forcing, with more substantial upper-ocean warming, the CMIP6 models that account for temperature-dependent

remineralization promoted enhanced LL mesopelagic nutrient retention under warming, with this providing a first-order contribution to stabilizing or increasing, rather than decreasing, LL

production under high emissions and low mitigation. This underscores the importance of a mechanistic understanding of mesopelagic remineralization and its sensitivity to ocean warming for

predicting future ecosystem changes. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access through

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support SIMILAR CONTENT BEING VIEWED BY OTHERS REGIONAL AND GLOBAL IMPACT OF CO2 UPTAKE IN THE BENGUELA UPWELLING SYSTEM THROUGH PREFORMED NUTRIENTS Article Open access 04 May 2023 CLIMATE

CHANGE AND TERRIGENOUS INPUTS DECREASE THE EFFICIENCY OF THE FUTURE ARCTIC OCEAN’S BIOLOGICAL CARBON PUMP Article Open access 06 January 2025 REDUCED CO2 UPTAKE AND GROWING NUTRIENT

SEQUESTRATION FROM SLOWING OVERTURNING CIRCULATION Article 22 December 2022 DATA AVAILABILITY The data that support the findings of this study are available from the corresponding authors

upon reasonable request. CODE AVAILABILITY The code for the PISCES-v2 model is available at Zenodo (https://zenodo.org/records/10554639; https://doi.org/10.5281/zenodo.10554639)43. The model

output and analysis code used in the study is available at Zenodo (https://zenodo.org/records/11617863;https://doi.org/10.5281/zenodo.11617863)44. The code used to analyse the data and

generate figures was based on Ferret and Python. We used an open-license Python package (Cartopy, https://scitools.org.uk/cartopy/docs/latest/index.html) to draw the base maps for Figs. 1

and 2 and Extended Data Figs. 1 and  2. As stated on their website

(https://scitools.org.uk/cartopy/docs/latest/citation.html#data-copyright-table,https://www.naturalearthdata.com/about/terms-of-use/), the map data are in the public domain and can be used

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productivity and export”. Zenodo https://doi.org/10.5281/zenodo.11617863 (2024). Download references ACKNOWLEDGEMENTS We would like to thank J. Sarmiento for his iterative feedback and

constructive comments throughout the development of this project. We would also like to thank L. Kwiatkowski for his feedback and constructive comments. K.B.R. was supported by the World

Premier International Research Center Initiative, MEXT, Japan. D.B. acknowledges support from US NSF grant OCE-1847687. K.T. and M.I. received support from the Japan Meteorological Research

Institute research fund C4 for the study of ocean biogeochemistry and acidification. K.T. was also supported by a grant-in-aid for Transformative Research Areas, under grant no. JP24H02224

from the Ministry of Education, Culture, Sports, Science, and Technology, Japan. L.R. acknowledges funding support from a US NSF career award 2042672 and a Grand Challenge research award

funded by the Princeton University High Meadows Environmental Institute. R.Y. was supported by JSPS KAKENHI grant number JP24H02221. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS *

WPI-Advanced Institute for Marine Ecosystem Change, Tohoku University, Sendai, Japan Keith B. Rodgers * Sorbonne Universités, UPMC, Univ. Paris 06-CNRS-IRD-NHNH, LOCEAN/IPSL, Paris, France

Olivier Aumont * Climate and Geochemistry Research Department, Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan Katsuya Toyama & Masao Ishii * Princeton

University, Department of Geosciences, Princeton, NJ, USA Laure Resplandy * Princeton University High Meadows Environmental Institute, Princeton, NJ, USA Laure Resplandy * Nagasaki Ocean

Academy, NPO Nagasaki Marine Industry Cluster Promotion Association, Nagasaki, Japan Toshiya Nakano * Atmosphere and Ocean Department, Japan Meteorological Agency, Tokyo, Japan Daisuke

Sasano * Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA Daniele Bianchi * Research Institute for Global Change, Japan Agency for

Marine-Earth Science and Technology, Yokosuka, Japan Ryohei Yamaguchi Authors * Keith B. Rodgers View author publications You can also search for this author inPubMed Google Scholar *

Olivier Aumont View author publications You can also search for this author inPubMed Google Scholar * Katsuya Toyama View author publications You can also search for this author inPubMed 

Google Scholar * Laure Resplandy View author publications You can also search for this author inPubMed Google Scholar * Masao Ishii View author publications You can also search for this

author inPubMed Google Scholar * Toshiya Nakano View author publications You can also search for this author inPubMed Google Scholar * Daisuke Sasano View author publications You can also

search for this author inPubMed Google Scholar * Daniele Bianchi View author publications You can also search for this author inPubMed Google Scholar * Ryohei Yamaguchi View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Conceptualization: K.B.R., O.A. Methodology: K.B.R., O.A. Investigation: K.B.R., O.A., K.T., D.S.

Visualization: K.B.R., O.A., K.T., R.Y. Writing—original draft: K.B.R., O.A. Writing—review and editing: K.B.R., O.A., K.T., L.R., M.I., T.N., D.S., R.Y., D.B. CORRESPONDING AUTHORS

Correspondence to Keith B. Rodgers or Olivier Aumont. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature_ thanks

Robert Letscher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with

regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA FIGURES AND TABLES EXTENDED DATA FIG. 1 MODEL-DERIVED SENSITIVITY OF PRIMARY PRODUCTION (PP)

AND EXPORT TO SUPPRESSION OF NUTRIENT REGENERATION OVER THE SOUTHERN OCEAN (PERT_SOUTH) (90°S-30°S). PISCES-v227 sensitivity for primary production (PP) and export to suppression of nutrient

regeneration over the Southern Hemisphere extratropical domain 30°S-90°S. (A) Annual mean PP for control (STD) experiment, (B) annual mean export for STD experiment; (C) PP for PERT_SOUTH

experiment; (D) Export for PERT_SOUTH experiment; (E) difference in PP between PERT_SOUTH and STD; (F) difference in export between PERT_SOUTH and STD. PP is considered for full-depth

integrals, and export is considered at 150 m. Units are in molesC m−2 yr−1. EXTENDED DATA FIG. 2 MODEL-DERIVED SENSITIVITY OF PRIMARY PRODUCTION (PP) AND EXPORT TO SUPPRESSION OF NUTRIENT

REGENERATION OVER THE NORTHERN OCEANS (PERT_NORTH) (30°N-90°N). PISCES-v227 sensitivity for primary production (PP) and export to suppression of nutrient regeneration over the Northern

Hemisphere extratropical domain 30°N-90°N. (A) Annual mean PP for control (STD) experiment, (B) annual mean export for STD experiment; (C) PP for PERT_NORTH experiment; (D) Export for

PERT_NORTH experiment; (E) difference in PP between PERT_NORTH and STD; (F) difference in export between PERT_NORTH and STD. PP is considered for full-depth integrals, and export is

considered at 150 m. Units are in molesC m−2 yr−1. EXTENDED DATA FIG. 3 DISTRIBUTION OF PO43− OVER FULL DEPTH RANGE. Zonally-averaged PO43− simulated by PISCES-v227 over the full ocean depth

range 0–5000 m: (A) For the control simulation STD, (B) for the PERT_TROP simulation; (C) for the PERT_SOUTH simulation; and (D) for the PERT_NORTH simulation. Units are μmol L−1 for all

panels. EXTENDED DATA FIG. 4 DISTRIBUTION OF PO43− AND PERTURBATION ANOMALIES OVER 0–1,800 M. Zonally-averaged PO43− simulated by PISCES-v227 over the ocean depth range 0–1800 m: (A) STD,

(B) perturbation for PERT_TROP (PERT_TROP minus STD); (C) perturbation for PERT_SOUTH (PERT_SOUTH minus STD); (D) perturbation for PERT_NORTH (PERT_NORTH minus STD). Units are μmol L−1 for

all panels. EXTENDED DATA FIG. 5 COARSE-GRAINED VIEW OF MODELLED PO43− FLUXES ABOUT THE LOW-LATITUDE MESOPELAGIC DOMAIN (LLMD). Fluxes are shown from the PISCES-v227 model simulations. The

boundary fluxes and interior sources of PO43− for the domain bounded by 30°S-30°N and 150m–870m for (A) STD, (B) PERT_TROP, (C) PERT_SOUTH, and (D) PERT_NORTH. The domain for each case is

the same as that used in Fig. 3. The lateral exchanges for each case represent net fluxes, and thereby account for the effects of gyre recirculation across 30°S and 30°N. Shown in red are

the transfer efficiency, defined as the ratio of export flux across 870 m to the export flux across 150 m, and the net horizontal convergence (hor. conv.) from the LLMD domain across the

meridional boundaries at 30°S and 30°N, and PP in units of PgCyr−1. Fluxes are for PO43−, but shown in carbon units of PgC yr−1 to be consistent with units typically used for export and PP.

EXTENDED DATA FIG. 6 LARGE-SCALE OCEAN OVERTURNING STRUCTURES THAT PROMOTE PO43− UPWELLING. Meridional overturning streamfunction for ORCA2 configuration of NEMO-LIM27 for (A) the

Indo-Pacific basin and (B) the Atlantic basin. Units are Sverdrups (Sv, or 106 m3 s−1). Contour intervals for both cases are (−30, −25, −20, −15, −12, −10, −8, −6, −4, −2, 0, 2, 4, 6, 8, 10,

12, 15, 20, 25, 30) in order to emphasize both the deep upwelling amplitude and the strength of the Indo-Pacific Subtropical Cells22 within the thermocline. EXTENDED DATA FIG. 7 TRANSIENTS

IN TEMPERATURE STATE OF CMIP6 MODELS UNDER EXTENDED SIMULATIONS TO 2300. Evolution of changes in temperature relative to the respective 1850–1899 mean for (A) globally-averaged 2 m air

temperature, and (B) globally-averaged sea surface temperature for five CMIP6 models: [IPSL-CM6A16, CESM2-WACCM17, UKESM118, ACCESS-ESM1.519, and MIROC-ES2L20] run to 2300 under

historical/SSP5-8.5 forcing21. Changes in zonally averaged potential temperature between a 1990s climatology and a 2290 s climatology for two of the models: (C) IPSL-CM6A and (D)

CESM2-WACCM. The units for all panels are °C. EXTENDED DATA FIG. 8 MERIDIONAL SHIFTS IN SOUTHERN OCEAN LATITUDE OF MAXIMUM WIND SPEED FOR CMIP6 MODELS. The panels show for each ESM listed in

Extended Data Table 1 the zonally-averaged monthly-mean windspeed (variable sfcwind) over 70°S-40°S of the five CMIP6 models are IPSL-CM6A16, CESM2-WACCM17, UKESM118, ACCESS-ESM1.519, and

MIROC-ES2L20 under historical/SSP5-8.5 forcing21, and a 10-year running mean has been applied to filter seasonal and interannual variations. Units are m s−1. RIGHTS AND PERMISSIONS Springer

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CITE THIS ARTICLE Rodgers, K.B., Aumont, O., Toyama, K. _et al._ Low-latitude mesopelagic nutrient recycling controls productivity and export. _Nature_ 632, 802–807 (2024).

https://doi.org/10.1038/s41586-024-07779-1 Download citation * Received: 10 September 2023 * Accepted: 02 July 2024 * Published: 21 August 2024 * Issue Date: 22 August 2024 * DOI:

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