Sea-air CO <sub>2</sub> fluxes along the Brazilian continental margin

Affe, Helen Michelle de Jesus; Rocha, Diogo Souza Bezerra; Piedras, Fernanda Reinhardt; Moser, Gleyci Aparecida Oliveira; Araujo Filho, Moacyr Cunha de; Cunha, Leticia Cotrim da

doi:10.1590/2675-2824071.22051hmdja

Abstract

Measurements of the marine carbonate system on tropical and subtropical continental margins are poorly distributed in space and time, with many uncertainties persisting regarding the role of carbon exchanges at the ocean-atmosphere interface in these areas. To calculate sea-to-air CO ₂ fluxes in Marine Ecoregions along the Brazilian continental margin (4°N to 34°S), we used data from the Surface Ocean CO ₂ Atlas (SOCAT v2020), collected up to 400 km from the coast, at the surface (5 m), between 1991 and 2018, with the aim of investigating the role of ecoregions as potential sinks or sources of atmospheric CO ₂. The temperature and salinity of seawater presented variability in the north-south direction mainly because of the broad latitudinal range, reflecting typical patterns of tropical (T = 27.4°C ±1.49; S = 36.4 ±1.91) and subtropical waters (T = 22.8°C ±3.41; S = 35 ±2.91), in addition to the greater or lesser influence of river inputs in each ecoregion. The pCO ₂ values in the surface waters varied from 121.81 (Amazon) to 478.92 μatm (Eastern), differing significantly between ecoregions and showing an expected decadal increasing trend, both in the atmosphere and in the seawater. The calculated values of CO ₂ fluxes showed non-homogeneous spatio-temporal variations, from -24.37 mmol m-2 d-1 (Rio Grande) to 9.87 mmol m- 2 d-1 (Southeastern). Throughout the analyzed time series, we observed that the Northeast, Amazon and Eastern ecoregions acted predominantly as sources of CO ₂ and the Southeastern ecoregions and, mainly, Rio Grande, acted predominantly as sinks of atmospheric CO ₂.

Descriptors:
antic Ocean; Blue Amazon; Carbonate system; CO ₂ source or sink ; Brazilian marine ecoregions

INTRODUCTION

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Continental margins, although corresponding to a small fraction of the area and volume of the ocean, represent around 25% of global primary production and absorb 0.2 PgC of the total of 2.4 ± 0.5 Pg C assimilated annually by the global oceans, despite strong spatial variability (Ito et al. 2016ITO, R. G., GARCIA, C. A. E. & TAVANO, V. M. 2016. Net sea-air CO2 fluxes and modelled pCO2 in the southwesternsubtropical Atlantic continental shelf during spring 2010 and summer 2011. Continental Shelf Research, 119, 68-84, DOI: https://doi.org/10.1016/j.csr.2016.03.013
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https://doi.org/10.1029/2019GB006239... ). Long-term analyzes of the sea-air pCO ₂ gradient have shown that continental shelves represent a global CO ₂ sink, and some regions display a trend to increase atmospheric carbon dioxide absorption (Laruelle et al., 2018LARUELLE, G. G., CAI, W. J., HU, X., GRUBER, N., MACKENZIE, F. T. & REGNIER, P. 2018. Continental shelves as a variable but increasing global sink for atmospheric carbon dioxide. Nature Communications, 9, 454, DOI: https://doi.org/10.1038/s41467-017-02738-z
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The partial pressure of CO ₂ (pCO ₂- product of the molar fraction of CO ₂ and the total mixing pressure (Libes, 2011LIBES, S. 2011. Susan. Introduction to marine biogeochemistry. San Diego: Academic Press.). High spatial resolution measurements of the pCO ₂ in the surface waters in many global coastal regions are performed on several cruises, including ships of opportunity on commercial routes, leading to an increase in the number of CO ₂ measurements in recent decades (Sabine et al., 2010SABINE, C., DUCKLOW, H. & HOOD, M. 2010. International Carbon Coordination: Roger Revelle’s Legacy in the Intergovernmental Oceanographic Commission. Oceanography, 23, 48-61, DOI: https://doi.org/10.5670/oceanog.2010.23
https://doi.org/10.5670/oceanog.2010.23... ). These data are compiled and made available by the Surface Ocean CO ₂ Atlas – SOCAT (Bakker et al., 2020BAKKER, D. C. E., PFEIL, B., LANDA, C. S., METZL, N., O’BRIEN, K. M., OLSEN, A., SMITH, K., COSCA, C., HARASAWA, S., JONES, S. D., NAKAOKA, S., NOJIRI,Y., SCHUSTER, U., STEINHOFF, T., SWEENEY,C., TAKAHASHI, T., TILBROOK, B., WADA, C., WANNINKHOF, R., ALIN, S. R., BALESTRINI, C. F., BARBERO, L., BATES, N. R., BIANCHI, A. A., BONOU, F., BOUTIN, J., BOZEC, Y., BURGER, E. F., CAI, W. J., CASTLE, R. D., CHEN, L., CHIERICI, M., CURRIE, K., EVANS, W., FEATHERSTONE, C., FEELY, R. A., FRANSSON, A., GOYET, C., GREENWOOD, N., GREGOR, L., HANKIN, S., HARDMAN-MOUNTFORD, N. J., HARLAY, J., HAUCK, J., HOPPEMA, M., HUMPHREYS, M. P., HUNT, C. W., HUSS, B., IBÁNHEZ, J. S. P., JOHANNESSEN, T., KEELING, R., KITIDIS, V., KÖRTZINGER, A., KOZYR, A., KRASAKOPOULOU, E., KUWATA, A., LANDSCHÜTZER, P., LAUVSET, S. K., LEFÈVRE, N., LO MONACO, C., MANKE, A., MATHIS, J. T., MERLIVAT, L., MILLERO, F. J., MONTEIRO, P. M. S., MUNRO, D. R., MURATA, A., NEWBERGER, T., OMAR, A. M., ONO, T., PATERSON, K., PEARCE, D., PIERROT, D., ROBBINS, L. L., SAITO, S., SALISBURY, J., SCHLITZER, R., SCHNEIDER, B., SCHWEITZER, R., SIEGER, R., SKJELVAN, I., SULLIVAN, K.F., SUTHERLAND, S. C., SUTTON, A. J., TADOKORO, K., TELSZEWSKI, M., TUMA, M., VAN HEUVEN, S. M. A. C., VANDEMARK, D., WARD, B., WATSON, A. J. & XU, S. 2020. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data, 8(2), 383-413, DOI: https://doi.org/10.5194/essd-8-383-2016
https://doi.org/10.5194/essd-8-383-2016... ), a database with over million observations for the 1957–2019 peri- od (Gloege et al., 2022GLOEGE, L., YAN, M., ZHENG, T. & MCKINLEY, G. A. 2022. Improved quantification of ocean carbon uptake by using machine learning to merge global models and pCO2 data. Journal of Advances in Modeling Earth Systems, 14(2), e2021MS002620, DOI: https://doi.org/10.1029/2021MS002620
https://doi.org/10.1029/2021MS002620... ), and with around 172,000 intermittent observations, between the years 1991 and 2018, along the limits of the Brazilian conti- nental margin. The SOCAT dataset was used in the present work aiming at calculating the sea-air CO ₂ fluxes along the Brazilian continental margin and investigating the role of the ecoregions as a potential source or sink of atmospheric CO ₂, to understand the dynamic ocean-atmosphere in this large geographical area.

METHODS

Study area

The Brazilian continental margin (10,959 km, latitudes ranging from 4°N to 33°S), the so-called “Blue Amazon” (5.7 million km ²), includes the Territorial Sea (12 miles from the coast) and the largest Exclusive Economic Zone (Brazilian EEZ) in South America (3.5 million km ²), one of the larg- est on the planet (Bauer et al., 2013BAUER, J. E., CAI, W. J., RAYMOND, P. A., BIANCHI, T. S., HOPKINSON, C. S. & REGNIER, P. A. G. 2013. The changing carbon cycle of the coastal ocean. Nature, 504, 61-70, DOI: https://doi.org/10.1038/nature12857
https://doi.org/10.1038/nature12857... ; Gerhardinger et al., 2018GERHARDINGER, L. C., GORRIS, P., GONÇALVES, L. R., HERBST, D. F., VILA-NOVA, D. A., CARVALHO, F. G., GLASER, M., ZONDERVAN, R. & GLAVOVIC, B. C. 2018. Healing Brazil’s Blue Amazon: the role of knowledge networks in nurturing cross-scale transformations at the frontlines of ocean sustainability. Frontiers in Marine Science, 4, 395, DOI: https://doi.org/10.3389/fmars.2017.00395
https://doi.org/10.3389/fmars.2017.00395... ). The main currents along the Brazilian continental shelf are the warm, western boundary Brazil Current, associated with the South Atlantic Subtropical Gyre (Silveira et al., 2000SILVEIRA, I. C. A., FLIERL, G. R. & BROWN, W. S. 2000. Dynamics of separating Western Boundary Currents. Journal of Physical Oceanography, 29(2), 129-144, DOI: https://doi.org/10.1175/1520-0485(1999)0290119:DOSWBC2.0.CO;2
https://doi.org/10.1175/1520-0485(1999)0... ), and the North Brazil Current. Tropical Water is the pre-dominant water mass in the surface, with typically warm (> 18°C) and saline (> 36) waters, due to intense radiation and evaporation (Silveira et al., 2000SILVEIRA, I. C. A., FLIERL, G. R. & BROWN, W. S. 2000. Dynamics of separating Western Boundary Currents. Journal of Physical Oceanography, 29(2), 129-144, DOI: https://doi.org/10.1175/1520-0485(1999)0290119:DOSWBC2.0.CO;2
https://doi.org/10.1175/1520-0485(1999)0... ).

According to the regional classification of Marine Ecoregions of the World (MEOW) (Spalding et al., 2007SPALDING, M. D., FOX, H. E., ALLEN, G. R., DAVIDSON, N., FERDAÑA, Z. A., FINLAYSON, M., HALPERN, B. S., JORGE, M. A., LOMBANA, A., LOURIE, S. A., MARTIN, K. D., MCMANUS, E., MOLNAR, J., RECCHIA, C. A. & ROBERTSON, J. 2007. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience, 57(7), 573-583, DOI: https://doi.org/10.1641/B570707
https://doi.org/10.1641/B570707... ), the Warm Temperate SW Atlantic comprises the sub-regions S and SE Brazil, the Tropical SW Atlantic includes the sub- regions E and NE Brazil, and the North Brazil Shelf includes the sub-region Amazon ( Figure 1). The main coastal upwelling in Brazil is located in the Warm Temperate SW Atlantic. This feature results from a combination of the coastline orientation and NE winds, perpen-dicular to the continent, promoting the upwelling of the South Atlantic Central Water (SACW), a wa-ter mass with temperatures <18° C and salinities <36 (Silveira et al., 2000SILVEIRA, I. C. A., FLIERL, G. R. & BROWN, W. S. 2000. Dynamics of separating Western Boundary Currents. Journal of Physical Oceanography, 29(2), 129-144, DOI: https://doi.org/10.1175/1520-0485(1999)0290119:DOSWBC2.0.CO;2
https://doi.org/10.1175/1520-0485(1999)0... ; Castro et al., 2017CASTRO, B. M., BRANDINI, F. P., DOTTORI, M. & FORTES, J. F. 2017. A Amazônia Azul: recursos e preservação. Revista USP, 113, 7, DOI: https://doi.org/10.11606/issn.2316-9036.v0i113p7-26
https://doi.org/10.11606/issn.2316-9036.... ). The Tropical SW Atlantic is a region under the predomi-nant influence of oligotrophic oceanic waters, sea-sonally influenced by the Intertropical Convergence Zone (ITCZ) and El Niño- Southern Oscillation (Araujo et al., 2019ARAUJO, M., NORIEGA, C., MEDEIROS, C., LEFÈVRE, N., IBÁNHEZ, J. S. P., MONTES, M. F., SILVA, A. C. & SANTOS, M. L., 2019. On the variability in the CO2 system and water productivity in the western tropical Atlantic off North and Northeast Brazil. Journal of Marine Systems, 189, 62-77, DOI: https://doi.org/10.1016/j.jmarsys.2018.09.008
https://doi.org/10.1016/j.jmarsys.2018.0... ; Cotovicz et al., 2020aCOTOVICZ, L. C., VIDAL, L. O., REZENDE, C. E., BERNARDES, M. C., KNOPPERS, B. A., SOBRINHO, R.L., CARDOSO, R. P., MUNIZ, M., ANJOS, R. M., BIEHLER, A. & ABRIL, G. 2020. Carbon dioxide sources and sinks in the delta of the Paraíba do Sul River (Southeastern Brazil) modulated by carbonate thermodynamics, gas exchange and ecosystem metabolism during estuarine mixing. Marine Chemistry, 226, 103869, DOI: https://doi.org/10.1016/j.marchem.2020.103869
https://doi.org/10.1016/j.marchem.2020.1... ). In the North Brazil Shelf, the Amazon River plume may reach areas up to 300 km from the coast, season-ally interfering with surface temperature and salinity patterns in this region , with the river volume varying ca. 50% between the dry and rainy seasons (Silva et al., 2010SILVA, A. C., ARAÚJO, M. & BOURLÈS, B. 2010. Seasonal variability of the Amazon river plume during Revizee program. Tropical Oceanography, 38(1), 76, DOI: https://doi.org/10.5914/tropocean.v38i1.5162
https://doi.org/10.5914/tropocean.v38i1.... ).

Figure 1.
Map of the Brazilian continental margin/Brazilian Exclusive Economic Zone (Blue Amazon) region, considering the boundaries of the ecoregions (Spalding et al., 2007SPALDING, M. D., FOX, H. E., ALLEN, G. R., DAVIDSON, N., FERDAÑA, Z. A., FINLAYSON, M., HALPERN, B. S., JORGE, M. A., LOMBANA, A., LOURIE, S. A., MARTIN, K. D., MCMANUS, E., MOLNAR, J., RECCHIA, C. A. & ROBERTSON, J. 2007. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience, 57(7), 573-583, DOI: https://doi.org/10.1641/B570707
https://doi.org/10.1641/B570707... ) with the overlap of the SOCAT collection points (black dots = 171,499 observations).

The wind regime in the Eastern and North- Eastern coasts is dominated by the south-east- erly and easterly trade winds. Along the eastern coast occurs a zone of divergence between the trade winds, and northeastern winds blow to the south of this zone. On the Brazilian North coast, northeasterly trades prevail and, in the South, the easterly and south-easterly winds blow during fall and winter (April–September) and the north- easterly winds prevail during spring and summer (September– February) (Leão et al., 2010LEÃO, Z., KIKUCHI, R., OLIVEIRA, M. D. & VASCONCELLOS, V. 2010. Status of Eastern Brazilian coral reefs in time of climate changes. Pan-American Journal Aquatic Sciences, 5(2), 224-35.).

Data processing

Carbon system parameters used in this study were obtained from Surface Ocean CO ₂ Atlas (SOCAT 2020SOCAT (Surface Ocean CO2 Atlas). 2020. Welcome to SOCAT. Bergen: SOCAT. Available at: http://www.socat.info. [Accessed: 2020 Out 19].
http://www.socat.info... ), which gathers data on fugacity data (fCO ₂), temperature, salinity, atmospheric pressure, and sea level pressure, from continuous measure- ments in situ, from scientific and opportunity cruises (Bakker et al., 2020BAKKER, D. C. E., PFEIL, B., LANDA, C. S., METZL, N., O’BRIEN, K. M., OLSEN, A., SMITH, K., COSCA, C., HARASAWA, S., JONES, S. D., NAKAOKA, S., NOJIRI,Y., SCHUSTER, U., STEINHOFF, T., SWEENEY,C., TAKAHASHI, T., TILBROOK, B., WADA, C., WANNINKHOF, R., ALIN, S. R., BALESTRINI, C. F., BARBERO, L., BATES, N. R., BIANCHI, A. A., BONOU, F., BOUTIN, J., BOZEC, Y., BURGER, E. F., CAI, W. J., CASTLE, R. D., CHEN, L., CHIERICI, M., CURRIE, K., EVANS, W., FEATHERSTONE, C., FEELY, R. A., FRANSSON, A., GOYET, C., GREENWOOD, N., GREGOR, L., HANKIN, S., HARDMAN-MOUNTFORD, N. J., HARLAY, J., HAUCK, J., HOPPEMA, M., HUMPHREYS, M. P., HUNT, C. W., HUSS, B., IBÁNHEZ, J. S. P., JOHANNESSEN, T., KEELING, R., KITIDIS, V., KÖRTZINGER, A., KOZYR, A., KRASAKOPOULOU, E., KUWATA, A., LANDSCHÜTZER, P., LAUVSET, S. K., LEFÈVRE, N., LO MONACO, C., MANKE, A., MATHIS, J. T., MERLIVAT, L., MILLERO, F. J., MONTEIRO, P. M. S., MUNRO, D. R., MURATA, A., NEWBERGER, T., OMAR, A. M., ONO, T., PATERSON, K., PEARCE, D., PIERROT, D., ROBBINS, L. L., SAITO, S., SALISBURY, J., SCHLITZER, R., SCHNEIDER, B., SCHWEITZER, R., SIEGER, R., SKJELVAN, I., SULLIVAN, K.F., SUTHERLAND, S. C., SUTTON, A. J., TADOKORO, K., TELSZEWSKI, M., TUMA, M., VAN HEUVEN, S. M. A. C., VANDEMARK, D., WARD, B., WATSON, A. J. & XU, S. 2020. A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT). Earth System Science Data, 8(2), 383-413, DOI: https://doi.org/10.5194/essd-8-383-2016
https://doi.org/10.5194/essd-8-383-2016... ). The data were downloaded from https://www.socat.info/index.php/data-access/, last access: 13 January 2022. Here we used data collected along the Brazilian continental margin, up to 400 km from the coast, in surface waters (5 m), between 1991 and 2018, when there are consistent in situ data for calculating the CO ₂ fluxes in the five ecoregions, although with some spatial or temporal gaps. Instantaneous wind speed data were obtained from the Cross-Calibrated Multi-Platform - CCMP (www.remss.com/measurements/ccmp., last ac- cess: 26 August 2022) (Atlas et al., 2011ATLAS, R., HOFFMAN, R. N., ARDIZZONE, J., LEIDNER, S. M., JUSEM, J. C., SMITH, D. K. & GOMBOS, D. 2011. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bulletin of the American Meteorological Society, 92(2), 157-174, DOI: https://doi.org/10.1175/2010BAMS2946.1
https://doi.org/10.1175/2010BAMS2946.1... ; Wentz et al., 2015WENTZ, F. J., SCOTT, J., HOFFMAN, R., LEIDNER, M., ATLAS, R. & ARDIZZONE, J. 2015. Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, Version 2.0. Santa Rosa: Remote Sensing Systems. Available at: at: http://www.remss.com/measurements/ccmp. [Accessed: 26 August 2022].
http://www.remss.com/measurements/ccmp... ; Mears et al., 2019MEARS, C. A., SCOTT, J., WENTZ, F. J., RICCIARDULLI, L., LEIDNER, S. M., HOFFMAN, R. & ATLAS, R. 2019. A Near Real Time Version of the Cross Calibrated Multiplatform (CCMP) ocean surface wind velocity data set. Journal of Geophysical Research: Oceans, 124, 6997-7010, DOI: https://doi.org/10.1029/2019JC015367
https://doi.org/10.1029/2019JC015367... ), using the geographical coordinates of the SOCAT sampling points.

Fugacity data (fCO ₂) were converted into CO ₂ partial pressure, and sea-air CO ₂ fluxes, using equa- tions widely used in many studies ( e.g. Rödenbeck et al., 2013RÖDENBECK, C., KEELING, R. F., BAKKER, D. C. E., METZL, N., OLSEN, A., SABINE, C. & HEIMANN, M. 2013. Global surface-ocean pCO2 and sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme. Ocean Science, 9(2), 193-216, DOI: https://doi.org/10.5194/os-9-193-2013
https://doi.org/10.5194/os-9-193-2013... ; Laruelle et al., 2018LARUELLE, G. G., CAI, W. J., HU, X., GRUBER, N., MACKENZIE, F. T. & REGNIER, P. 2018. Continental shelves as a variable but increasing global sink for atmospheric carbon dioxide. Nature Communications, 9, 454, DOI: https://doi.org/10.1038/s41467-017-02738-z
https://doi.org/10.1038/s41467-017-02738... ; Araujo et al., 2019ARAUJO, M., NORIEGA, C., MEDEIROS, C., LEFÈVRE, N., IBÁNHEZ, J. S. P., MONTES, M. F., SILVA, A. C. & SANTOS, M. L., 2019. On the variability in the CO2 system and water productivity in the western tropical Atlantic off North and Northeast Brazil. Journal of Marine Systems, 189, 62-77, DOI: https://doi.org/10.1016/j.jmarsys.2018.09.008
https://doi.org/10.1016/j.jmarsys.2018.0... ; Monteiro et al., 2020MONTEIRO, T., KERR, R., ORSELLI, I. B. M. & LENCINA--AVILA, J. M. 2020. Towards an intensified summer CO2 sink behaviour in the Southern Ocean coastal regions. Progress in Oceanography, 183, 102267, DOI: https://doi.org/10.1016/j.pocean.2020.102267
https://doi.org/10.1016/j.pocean.2020.10... ). For the sea-air CO ₂ flux (FCO ₂) calculation we use the equation:

[1]

F C O_{2 -} k C O_{2} \times △ p C O_{2}

seawater-air

Its components are:

[1.1]

k C O_{2} = 0.251 \times u^{2} \times (S c ⁄ 660)^{1 ⁄ 2}

Where: K = gas transfer speed; u (m s ^-1) = wind speed data CCMP (Atlas et al., 2011ATLAS, R., HOFFMAN, R. N., ARDIZZONE, J., LEIDNER, S. M., JUSEM, J. C., SMITH, D. K. & GOMBOS, D. 2011. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bulletin of the American Meteorological Society, 92(2), 157-174, DOI: https://doi.org/10.1175/2010BAMS2946.1
https://doi.org/10.1175/2010BAMS2946.1... ; Wentz et al., 2015WENTZ, F. J., SCOTT, J., HOFFMAN, R., LEIDNER, M., ATLAS, R. & ARDIZZONE, J. 2015. Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, Version 2.0. Santa Rosa: Remote Sensing Systems. Available at: at: http://www.remss.com/measurements/ccmp. [Accessed: 26 August 2022].
http://www.remss.com/measurements/ccmp... ; Mears et al., 2019MEARS, C. A., SCOTT, J., WENTZ, F. J., RICCIARDULLI, L., LEIDNER, S. M., HOFFMAN, R. & ATLAS, R. 2019. A Near Real Time Version of the Cross Calibrated Multiplatform (CCMP) ocean surface wind velocity data set. Journal of Geophysical Research: Oceans, 124, 6997-7010, DOI: https://doi.org/10.1029/2019JC015367
https://doi.org/10.1029/2019JC015367... ); Sc (Schmidt’s number) = 2039.2 − 125.62 × T + 3.6276 × T ² − 0.043219 × T ³ (Wanninkhof, 2014WANNINKHOF, R. 2014. Relationship between wind speed and gas exchange over the ocean revisited. Limnology and Oceanography Methods, 12(6), 351-362, DOI: https://doi.org/10.4319/lom.2014.12.351
https://doi.org/10.4319/lom.2014.12.351... ).

[1.2]

S C O_{2} = e x p (A 1 + A 2 \times (100 ⁄ T) + A 3 \times l o g (T ⁄ 100) + S a l (B 1 + B 2 \times (T ⁄ 100) + B 3 \times (T ⁄ 100)^{2})

Where: A1 = -60.2409; A2 = 93.4517; A3 = 23.3585; B1 = 0.023517; B2 = -0.023656; B3 = 0.0047036 and T = Kelvin temperature Sal = salinity (Weiss, 1974WEISS, R. F. 1974. Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Marine Chemistry, 2(3), 203-215, DOI: https://doi.org/10.1016/0304-4203(74)90015-2
https://doi.org/10.1016/0304-4203(74)900... ).

[1.3]

△ p C O_{2} s e a w a t e r - a i r = p C O_{2} s e a w a t e r - p C O_{2} a i r

[1.3.1]

p C O_{2} s e a w a t e r = f C O_{2} (1.00436 - 4.6691 0^{- 5} S S T)

[1.3.2]

p C O_{2} a i r = X C O_{2} (P b a r o - P s w)

Where: Psw = exp (24.4543 – 67.4509 (100/T) – 4.8489 log((T/100)) – 0.0005445 * Sal

XCO ₂ = CO ₂ concentration average dry air (xCO ₂ μmol.mol -1); *Pbaro = sea level pressure (hPa); Psw = atmospheric pressure (hPa), and *T = Kelvin temperature (Weiss and Price, 1980WEISS, R. F. & PRICE, B. A. 1980. Nitrous oxide solubility in water and seawater. Marine Chemistry, 8(4), 347-359, DOI: https://doi.org/10.1016/0304-4203(80)90024-9
https://doi.org/10.1016/0304-4203(80)900... ).

Statistical analyses

To assess the temporal trend (1990 - 2018) of CO ₂ fluxes along the Brazilian continental margin, we investigated 26 years of observa- tions available from the SOCAT. After verifying the non-parametric distribution of the data set (Shapiro-Wilk test), the Kruskal-Wallis test and a post hoc Wilcoxon rank test were applied, in order to perform the variance analysis between periods with CO ₂ fluxes data available concur- rently for the five ecoregions.

The annual trends analysis of CO ₂ fluxes was performed using linear regressions, calculated considering the time series of data for each ecore- gion separately. Mann-Kendall test was used to verify the temporal variations significance (α = 0.05). Based on the temperature variations, the following two seasons were delimited: a) warm season (i.e. temperatures above the time series mean) and b) cold season (i.e. temperatures below the time series mean). From this delimita- tion, an analysis of variance (Mann-Whitney test) was performed to verify the occurrence of signifi- cant variations in CO ₂ fluxes between these sea- sons. All statistical analyses were performed in the R environment (R Core Team 2022R CORE TEAM, 2022. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.).

RESULTS

SST, SSS and wind speed in the Brazilian coastal ecoregions

Surface seawater (5 m) temperature and salinity ( Figure 2) reflected the geographical (north-south), and the low seasonal variability, typ- ical of tropical (T = 27.4°C±1.49; S = 36.4±1.91) and subtropical (T = 22.8°C±3.41; S = 35±2.91) waters. Likewise, for wind speed a low variation pattern was observed between the different coastal regions ( Table 1). Lower salinities in some regions result from the influence of fluvial input on the coast, with a greater range of variation recorded in the Amazon ecoregion, under strong influence of the Amazon River plume, and a greater thermal amplitude in the Rio Grande ecoregion, reflecting of the La Plata River and the Patos Lagoon inputs ( Figure S1).

Considering the water average temperature throughout the time series data, periods of warmer and cooler waters were delimited, for each ecore- gion ( Figure 3). Thus, the Amazon ecoregion had higher water temperature in the period from May to September (mean = 28.76 ±0.6°C) and wa- ter, average 1°C colder, between November and April (27.76±0.42°C). In the Eastern ecoregion, the warmest period was between December and May (27.42±1.26°C), and the coldest between July and November (24.94±1.42°C). In the Northeast ecore- gion, the warmest waters period occurred between January and June (28.35±0.59°C), with slightly cooler water, between July and December (27.15±0.71°C). The period from December to April (26.06±1.41°C) corresponded to the warmest water months in the Southern ecoregion, and from May to November the lowest water temperatures were recorded (22.51±1.32°C). And in the Rio Grande ecoregion, the period corresponding from December to March (23.83±1.91°C) shows the period of warmer waters, with waters on average 5°C cooler between May and November (18.23±2.16°C).

Carbonate system (pCO ₂ and CO ₂ flux- es) in the Brazilian coastal ecoregions

Despite the large amount of SOCAT data points (171,499 observations), there are several spatial and temporal gaps. After data filtering, we achieved consistent temporal coverage for the

Figure 2.
T-S diagram. Variation of temperature and salinity in surface water (= 5m) in the ecoregions of the Brazilian continental margin. Amazon ecoregion (large salinity range under strong influence of the Amazon River plume); ortheastern, Eastern and Southeastern ecoregions (typically coastal and tropical water), and Rio Grande ecoregion (wide temperature and salinity ranges under influence of the La Plata River and the Patos Lagoon inputs).

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Table 1.
Variations (minimum, maximum, mean and standard deviation) of Sea Surface Temperature (SST*) (°C), Sea Surface Salinity (SSS*) and Wind speed** (m s-1) in each ecoregion of the Brazilian continental margin, obtained from the Surface Ocean CO ₂ Atlas (SOCAT*) and Cross-Calibrated Multi-Platform (CCMP**).

Figure 3.
Variation in surface water temperature in the ecoregions of the Brazilian continental margin, between 1991 and 2019. a- Amazon 28.23 (±0.71) °C; b- Eastern 26.45 (±1.79) °C; c- Northeastern 27.71 (±0.89) °C; d- Southeastern 24.03 (±2.22) °C; e- Rio Grande 20.71 (±3.46) °C.

Brazilian coastal ecoregions, starting in 1991. However, only in the years 2004, 2006, 2007, 2016, 2017 and 2018 data were available for all five ecoregions ( Figure S2).

The pCO ₂ values in the surface seawaters var-ied between 121.81 (Amazon) and 478.92 µatm (Eastern). The Kruskal-Wallis test showed signifi-cant differences for pCO ₂ among ecoregions (p< 0.001). In the Amazon ecoregion the variation registered for surface seawater pCO ₂ was from 121.81 to 458.62 (369.99 ± 66.64) µatm, present- ing a non-significant time trend (Kendall’s tau = -0.017; p = 0.944), with annual reduction of -0.34 µatm year ^-1 ( Figure 4). Therefore, a trend oppo- site to that of pCO ₂ atmospheric (Kendall’s tau = 1; p <0.001), whose increase was 1.87 µatm year ^-1 during the analyzed period ( Figure 5). In the Eastern ecoregion, the pCO ₂ seawater ranged be- tween 305.30 and 478.92 (390.91 ± 18.17) µatm, presenting a significant time trend (Kendall’s tau = 0.6; p <0.001), with annual increase of 1.5 µatm year ^-1 ( Figure 4). In the same way as atmospheric pCO ₂ (Kendall’s tau = 0.96; p <0.001), whose in- crease was 1.95 µatm year ^-1 ( Figure 5).

The variation of the pCO ₂ seawater in the Northeastern ecoregion was from 328.81 to 445.64 (394.96 ± 18.12) µatm, presenting a signif- icant time trend (Kendall’s tau = 0.776; p <0.001), with annual increase of 1.62 µatm year ^-1 ( Figure 4), similar to that observed for atmospheric pCO ₂ (Kendall’s

Figure 4.
Linear regressions analyses of surface seawater pCO ₂ decadal trends (1960 - 2020). a- Amazon; b- Eastern; c- Northeastern; d- Southeastern; e- Rio Grande.

Figure 5.
Linear regressions analyses of atmospheric pCO ₂ decadal trends (1960 - 2020). a- Amazon; b- Eastern; c- Northeastern; d- Southeastern; e- Rio Grande.

tau = 0.98; p <0.001), whose increase was 1.91 µatm year ^-1 during the analyzed period ( Figure 5). The temporal trend was also signifi- cant (Kendall’s tau = 0.581; p = 0.0001) in the Southeastern ecoregion, ranging from 257.33 to 299.18 (381.28 ± 25.34) µatm. With annual in- crease of 1.93 µatm year ^-1 ( Figure 5), very similar to that observed for atmospheric pCO ₂ (Kendall’s tau = 0.95; p <0.001), whose increase was 1.95 µatm year ^-1 ( Figure 5).

Rio Grande ecoregion was the only one with the highest annual increment of pCO ₂ in seawater (1.79 µatm yr ^-1), which varied between 143.86 and 437.72 (359.85 ± 35.99) µatm, than in the atmo- sphere (0.23 µatm year ^-1). Both showed significant trends (seawater pCO ₂: Kendall’s tau = 0.604; p = 0.0031 and atmospheric pCO ₂: Kendall’s tau = 0.889; p = 0.0012) ( Figure 4, Figure 5), although with very low adjustments, as recorded for temporal trends of all the other four ecoregions, in the analyzed period.

The sea-air CO ₂ fluxes along the Brazilian con- tinental margin ranged from -24.37 mmol m ^-2d ^-1 (Rio Grande) to 9.87 mmol m ^-2d ^-1 (Southeastern), differing significantly among ecoregions (p <0.05), except between Amazon and Northeastern (Wilcoxon test, p = 0.45). Most values ranged be- tween -10.0 and 10.0 mmol m ^-2d ^-1 ( Figure 6).

The largest variation amplitudes of CO ₂ fluxes along the analyzed time series were recorded in the ecoregions Rio Grande (-24.37 to 7.11 mmol m ^-2d ^-1) and Amazon (-20.64 to 6.03 mmol m ^-2d ^-1). In the ecoregions Eastern (-5.72 to 9.85

Figure 6.
Frequency distribution (density) of CO ₂ fluxes values (mmol m-2d-1) in the ecoregions of the Brazilian continental margin. (Amazon - pink area; Eastern - yellow area; Northeastern - green area; Southeastern - lilac area; Rio Grande - blue area).

mmol m ^-2 d ^-1), Southeastern (-5.97 to 9.87 mmol m ^-2 d ^-1) and, mainly Northeastern (-4.29 to 6.71 mmol m ^-2 d ^-1), the variation amplitudes of CO ₂ fluxes were smaller.

The calculated ocean-atmosphere CO ₂ fluxes were variable, non-homogeneously distributed along the Brazilian continental margin ( Figure 7). We observed that the highest means of positive CO ₂ flux occurred, respectively, in the Northeast (1.26 ± 1.56 mmol m ^-2 d ^-1), Amazon (0.72 ± 3.58 mmol m ^-2 d ^-1) and Eastern (0.66 ± 1.34 mmol m ^-2 d ^-1). The three acted, predominantly, as sourc- es of CO ₂ to the atmosphere ( Figure 8). While the Southeastern ecoregion, despite the negative mean value of CO ₂ flux (-0.16 ± 1.58 mmol m ^-2 d ^-1), showed alternation between short periods as a source, and longer periods as an atmospheric CO ₂ sink ( Figure 8). In this context, only the Rio Grande ecoregion (-2.49 ± 3.70 mmol m ^-2 d ^-1) acted pre- dominantly as an atmospheric CO ₂ sink ( Figure 8).

Temporal trends in pCO ₂ and CO ₂ fluxes along the Brazilian continen- tal margin

Decadal analysis (1990 - 2018) of pCO ₂ trends presented similar values of pCO ₂ increment in the atmosphere and seawater, observing an increase in these values throughout the time series. In the 1990s, atmospheric pCO ₂ showed a positive trend of 1.41 µatm yr ^-1 (Kendall’s tau = 0.4; p = 0.46) and the trend of pCO ₂ seawater was 1.02 µatm yr ^-1 (Kendall’s tau = 0.389; p = 0.175). In the fol- lowing decade, pCO ₂ values were 1.74 µatm year ^-1 (Kendall’s tau = 1; p = 0.0008) in the atmo- sphere, and 1.69 µatm year ^-1 (Kendall’s tau = 0.2; p = 0.474) in the seawater. And between 2011 and 2018, for the first time along the analyzed time se- ries, the pCO ₂ seawater (2.78 µatm yr ^-1; Kendall’s tau = 0.722; p = 0.009) was higher than the atmo- spheric values (2.49 µatm yr ^-1; Kendall’s tau = 1; p = 0.027) ( Figure 4, Figure 5).

Generally, the Brazilian continental margin act- ed as a CO ₂ source ( Figure 7), presented, in aver- age, trend of positive CO ₂ flux (0.05 mmol year ^-1; Kendall’s tau = 0.0714; p = 0.901) until the 2000s, changing to act as an atmospheric CO ₂ sink, in the two following decades. Between 2001 and 2010 the CO ₂ flux presented a negative trend of -0.03 mmol year ^-1 (Kendall’s tau = -0.244; p = 0.37) and in the last period of available data (2011 to 2018) this trend was of -0.05 mmol year ^-1 (Kendall’s tau = -0.214; p = 0.53). Highlighting that the temporal trends in the three decades were not significant and showed a low linear adjustment ( Figure 9).

Considering seasonal variations, we observed that there were significant differences (p< 0.001) in seawater pCO ₂ values between the warmer and colder periods, in the five ecoregions. In the

Figure 7.
CO ₂ fluxes variation indicating source (positive values) or sink (negative values) areas, along the Brazilian continental margin. *Pixel size = 1° (approximately 100 km).

Amazon ecoregion, the seawater pCO ₂ average was higher in the coldest period (350.64 ±80.67 μatm; November to April), than during the warmer period (350.64 ±80.47 μatm; May to September). In the other ecoregions, the opposite pattern was registered, with the highest seawater pCO ₂ aver- ages recorded in the warmer periods: Eastern (394.14 ±17.83 μatm; December to May/386.63 ±19.25 μatm; June to November); Northeastern (400.05 ±17.21 μatm; January to Juny/393.72 ±19.02 μatm; July to December); Southeastern (390.53 ±24.49 μatm; December to April/375.09 ±23.85 μatm; May to November) and Rio Grande (379.78 ±23.12 μatm; December to March/349.55 ±37.17 μatm; May to November).

The analysis of CO ₂ fluxes, considering this sea- sonality, presented positive average values in the Amazon ecoregion (1.81 ± 3.04 mmol year ^-1) in the coldest months. Indicating that the area acted as a CO ₂ source in this period and as an atmospheric CO ₂ sink in the warmer period (-0.50 ±3.74 mmol year ^-1) ( Figure 8). On the other hand, the Southeastern

Figure 8.
CO2 fluxes variation (1991 - 2019) in the ecoregions of the Brazilian continental margin. a- Amazon (warmest period: May - September; coldest period: November - April); b- Eastern (warmest period: December - May; coldest period: July - November); c- Northeastern (warmest period: January - June; coldest period: July - December); d- Southeastern (warmest period: December - April; coldest period: May - November); e- Rio Grande (warmest period: December - March; coldest period: May - November).

Figure 9.
Linear regressions analyses of decadal CO2 fluxes trends (μatm year-1). (a. 1991 - 2000; b. 2001 - 2010; c. 2011 - 2018).

ecoregion acted as a CO ₂ source in the warmer pe- riod (0.82 ±1.29 mmol year ^-1) and as a sink (-0. 90 ±1.36 mmol year ^-1) in the coldest period ( Figure 8). The Northeastern and Eastern ecoregions act-ed predominantly as CO ₂ sources in both seasonal periods, with an average variation of CO ₂ fluxes between 1.23 (±1.80) mmol year ^-1 (cold) and 1.29 (±1.22) mmol year ^-1 (warm) in the Northeastern ecoregion ( Figure 8). And between 0.1 (±1.37) mmol year ^-1 (cold) and 1.08 (±1.12) mmol year ^-1 (warm) in the Eastern ecoregion ( Figure 8). Only the Rio Grande ecoregion acted as an atmospher- ic CO ₂ sink throughout the entire period, with aver- age fluxes between -0.74 ±2.53) mmol year ^-1, in the warm period, and -3.17 (±3.86) mmol year ^-1, in the coldest period ( Figure 8).

Discussion

The variations observed in the dataset along the continental margin in surface temperature and salinity reflect the typical variability of this region. Around the NE, E and SE regions there is a domi- nance of Tropical Water (Stramma and Schott, 1999STRAMMA, L. & SCHOTT, F. 1999. The mean flow field of the tropical Atlantic Ocean. Deep Sea Research Part II: Topical Studies in Oceanography, 46(1-2), 279-303, DOI: https://doi.org/10.1016/S0967--0645(98)00109-X
https://doi.org/10.1016/S0967--0645(98)0... ), while at the Amazon region there is a strong influence of the Amazon River plume (low- er salinity), which may extend up to hundreds of kilometres transported by the North Brazil Current and the North Equatorial Countercurrent. Tropical precipitation also affects salinity in this region.

The northeast and eastern Brazilian continen- tal margin are narrow, receiving low riverine input, and typically oligotrophic, influenced in the inner shelf by the warm and low salinity Coastal Water (T>20°C and S<35), while on the outer shelf the saltier Tropical Water (T>20°C and S>36.4) of the Brazil Current (BC) is observed at the surface (Pereira et al., 2005PEREIRA, A. F., BELÉM, A. L., CASTRO, B. M. & GEREMIAS, R. 2005. Tide-topography interaction along the eastern Brazilian shelf. Continental Shelf Research, 25(12-13), 1521-1539, DOI: https://doi.org/10.1016/j.csr.2005.04.008
https://doi.org/10.1016/j.csr.2005.04.00... ). Further south, the continen- tal margin broadens, and the inner shelf is char- acterized by the Coastal Water (CW) while at the outer shelf the Brazil Current transports Tropical Water (TW) at surface and South Atlantic Central Water (SACW) at pycnocline level (Calado et al. 2010CALADO, L., SILVEIRA, I. C. A., GANGOPADHYAY, A. & CASTRO, B. M. 2010. Eddy-induced upwelling off Cape São Tomé (22°S, Brazil). Continental Shelf Research, 30, 1181-1188, DOI: https://doi.org/10.1016/j.csr.2010.03.007
https://doi.org/10.1016/j.csr.2010.03.00... ; Rocha et al., 2004ROCHA, C. B., SILVEIRA, I. C. A., CASTRO, B. M. & LIMA, J. A. M. 2014. Vertical structure, energetics, and dynamics of the Brazil Current System at 22°S-28°S: on the Brazil current at 22°S-28°S. Journal of Geophysical Research Oceans, 119(1), 52-69, DOI: https://doi.org/10.1002/2013JC009143
https://doi.org/10.1002/2013JC009143... ). The coastal upwelling areas around the Cabo Frio and Cabo São Tomé (22-23°S) are a source of variability to surface temperature and salinity. South of Cabo Frio, the South Brazil Bight (SBB) shows stronger stratifica- tion during summer, especially during SACW intru- sion events (Brandini et al., 2013BRANDINI, F. P., NOGUEIRA, M., SIMIÃO, M., CODINA, J. C. U. & NOERNBERG, M. A. 2013. Deep chlorophyll maximum and plankton community response to oceanic bottom intrusions on the continental shelf in the South Brazilian Bight. Continental Shelf Research, 89, 61-75, DOI: https://doi.org/10.1016/j.csr.2013.08.002
https://doi.org/10.1016/j.csr.2013.08.00... ).

During winter, the SBB water column is mixed due to winds, and at its southern portion (around 27°-28°S latitude) there may be intrusions from less saline and colder waters from the La Plata River plume and the Patos Lagoon (Piola et al., -PIOLA et al. ( 2008PIOLA, A. R., MÖLLER, O. O., GUERRERO, R. A. & CAMPOS, E. J. D. 2008. Variability of the subtropical shelf front off eastern South America: winter 2003 and summer 2004. Continetal Shelf Research, 28(13), 1639-1648, DOI: https://doi.org/10.1016/j.csr.2008.03.013
https://doi.org/10.1016/j.csr.2008.03.01... )). The buoyant La Plata plume flows north- wards close to the shore, especially during winter pushed by stronger southwestern winds, creating a lateral salinity gradient (Campos et al., 2008CAMPOS, E. J. D., PIOLA, A. R., MATANO, R. P. & MILLER, J. L. 2008. PLATA: a synoptic characterization of the southwest Atlantic shelf under influence of the Plata River and Patos Lagoon outflows. Continental Shelf Research, 28(13), 1551-1555, DOI: https://doi.org/10.1016/j.csr.2008.03.007
https://doi.org/10.1016/j.csr.2008.03.00... ; Ciotti et al., 2014CIOTTI, A. M., MAHIQUES, M. & MÖLLER, O. O. 2014. The meridional gradients of the S-SE Brazilian continental shelf: introduction to the special volume. Continental Shelf Research, 89, 1-4, DOI: https://doi.org/10.1016/j.csr.2014.08.008
https://doi.org/10.1016/j.csr.2014.08.00... ). The mixing of the La Plata River plume and the Tropical Water on the shelf forms a front called Subtropical Shelf Water (Piola et al., 2008PIOLA, A. R., MÖLLER, O. O., GUERRERO, R. A. & CAMPOS, E. J. D. 2008. Variability of the subtropical shelf front off eastern South America: winter 2003 and summer 2004. Continetal Shelf Research, 28(13), 1639-1648, DOI: https://doi.org/10.1016/j.csr.2008.03.013
https://doi.org/10.1016/j.csr.2008.03.01... ).

Despite the marked spatio-temporal gaps in the carbonate system measurements, we observed a spatial trend of pCO ₂ values increas- ing and the average CO ₂ fluxes in the south-north direction along the Brazilian coast, as well as dur- ing warmer periods. This pattern is consistent with the temperature marked influence on the dynamics of the carbonate system, affecting the CO ₂ solubility (Landschützer et al., 2014LANDSCHÜTZER, P., GRUBER, N., BAKKER, D. C. E. & SCHUSTER, U. 2014. Recent variability of the global ocean carbon sink. Global Biogeochemical Cycles, 28(9), 927-949, https://doi.org/10.1002/2014GB004853
https://doi.org/10.1002/2014GB004853... ; Heinze et al., 2015HEINZE, C., MEYER, S., GORIS, N., ANDERSON, L., STEINFELDT, R., CHANG, N., LE QUÉRÉ, C. & BAKKER, D. C. E. 2015. The ocean carbon sink – impacts, vulnerabilities and challenges. Earth System Dynamics, 6(1), 327-358, DOI: https://doi.org/10.5194/esd-6-327-2015
https://doi.org/10.5194/esd-6-327-2015... ). The Amazon ecoregion showed the opposite pattern, with increasing both pCO ₂ val- ues and average ocean-atmosphere CO ₂ fluxes during the coldest periods. Here we highlight the marked influence of Amazon River inputs, widely discussed as a determining factor in the carbon- ate system variability, between periods of high and low discharge (Körtzinger, 2003KÖRTZINGER, A., QUAY, P. D. & SONNERUP, R. E. 2003. Relationship between anthropogenic CO2 and the 13C Suess effect in the North Atlantic Ocean: ANTHROPOGENIC CO2 AND 13C SUESS EFFECT. Global Biogeochemical Cycles, 17(1), 20, DOI: https://doi.org/10.1029/2001GB001427
https://doi.org/10.1029/2001GB001427... ; Ibánhez et al., 2016IBÁNHEZ, J. S. P., ARAUJO, M. & LEFÈVRE, N. 2016. The overlooked tropical oceanic CO2 sink: overlooked tropical oceanic CO2 sink. Geophysical Research Letters, 43(8), 3804-3812, DOI: https://doi.org/10.1002/2016GL068020
https://doi.org/10.1002/2016GL068020... ; Landschützer et al., 2016; Lefèvre et al., 2017LEFÈVRE, N., MONTES, M. F., GASPAR, F. L., ROCHA, C., JIANG, S., ARAÚJO, M. C. & IBÁNHEZ, J. S. P. 2017. Net heterotrophy in the amazon continental shelf changes rapidly to a sink of CO2 in the outer amazon plume. Frontiers in Marine Science, 4, 278, DOI: https://doi.org/10.3389/fmars.2017.00278
https://doi.org/10.3389/fmars.2017.00278... ; Araújo et al., 2019ARAUJO, M., NORIEGA, C., MEDEIROS, C., LEFÈVRE, N., IBÁNHEZ, J. S. P., MONTES, M. F., SILVA, A. C. & SANTOS, M. L., 2019. On the variability in the CO2 system and water productivity in the western tropical Atlantic off North and Northeast Brazil. Journal of Marine Systems, 189, 62-77, DOI: https://doi.org/10.1016/j.jmarsys.2018.09.008
https://doi.org/10.1016/j.jmarsys.2018.0... ). The area under direct influence of the Amazon River is associated withi large surface pCO ₂ supersaturation area very close to the river mouth (Abril et al., 2013ABRIL, G., DEBORDE, J., SAVOYE, N., MATHIEU, F., MOREIRA-TURCQ, P., ARTIGAS, F., MEZIANE, T., TAKIYAMA, L. R., SOUZA, M. S. & SEYLER, P. 2013. Export of 13C-depleted dissolved inorganic carbon from a tidal forest bordering the Amazon estuary. Estuarine, Coastal Shelf Science, 129, 23-27, DOI: https://doi.org/10.1016/j.ecss.2013.06.020
https://doi.org/10.1016/j.ecss.2013.06.0... ; Cunha et al., 2013CUNHA, L. C. C. & BUITENHUIS, E. T. T. 2013. Riverine influence on the tropical Atlantic Ocean biogeochemistry. Biogeosciences, 10, 6357-6373, DOI: https://doi.org/10.5194/bg-10-6357-2013
https://doi.org/10.5194/bg-10-6357-2013... ), and ii an area strongly undersatu- rated with respect to atmospheric CO ₂ associated with the Amazon River plume (at latitude 10°N, longitude 50°W-48°W (Körtzinger et al., 2003KÖRTZINGER, A., QUAY, P. D. & SONNERUP, R. E. 2003. Relationship between anthropogenic CO2 and the 13C Suess effect in the North Atlantic Ocean: ANTHROPOGENIC CO2 AND 13C SUESS EFFECT. Global Biogeochemical Cycles, 17(1), 20, DOI: https://doi.org/10.1029/2001GB001427
https://doi.org/10.1029/2001GB001427... ; Lefèvre et al., 2017LEFÈVRE, N., MONTES, M. F., GASPAR, F. L., ROCHA, C., JIANG, S., ARAÚJO, M. C. & IBÁNHEZ, J. S. P. 2017. Net heterotrophy in the amazon continental shelf changes rapidly to a sink of CO2 in the outer amazon plume. Frontiers in Marine Science, 4, 278, DOI: https://doi.org/10.3389/fmars.2017.00278
https://doi.org/10.3389/fmars.2017.00278... ; Araujo et al., 2019ARAUJO, M., NORIEGA, C., MEDEIROS, C., LEFÈVRE, N., IBÁNHEZ, J. S. P., MONTES, M. F., SILVA, A. C. & SANTOS, M. L., 2019. On the variability in the CO2 system and water productivity in the western tropical Atlantic off North and Northeast Brazil. Journal of Marine Systems, 189, 62-77, DOI: https://doi.org/10.1016/j.jmarsys.2018.09.008
https://doi.org/10.1016/j.jmarsys.2018.0... ). This local CO ₂ sink is due to a combination of physical (mix- ing effect of river- and seawater in the plume) and biological (production in the plume) effects. The control exerted over biological processes in the area may have an opposite effect to the tempera- ture (Heinze et al., 2015HEINZE, C., MEYER, S., GORIS, N., ANDERSON, L., STEINFELDT, R., CHANG, N., LE QUÉRÉ, C. & BAKKER, D. C. E. 2015. The ocean carbon sink – impacts, vulnerabilities and challenges. Earth System Dynamics, 6(1), 327-358, DOI: https://doi.org/10.5194/esd-6-327-2015
https://doi.org/10.5194/esd-6-327-2015... ; Mu et al., 2021MU, L., GOMES, H. R., BURNS, S. M., GOES, J. I., COLES, V. J., REZENDE, C. E., THOMPSON, F. L., MOURA, R. L., PAGE, B. & YAGER, P. L. 2021. Temporal Variability of Air‐Sea CO2 flux in the Western Tropical North Atlantic Influenced by the Amazon River Plume. Global Biogeochemical Cycles, 35(6), 1-8, https://doi.org/10.1029/2020GB006798
https://doi.org/10.1029/2020GB006798... ), however those were not assessed in the present study.

Many robust data series has demonstrated the indisputable global increase in atmospheric CO ₂ over the years (Jansen et al., 2007JANSEN, E., OVERPECK, J., BRIFFA, K. R., DUPLESSY, J. C., JOOS, F., MASSON-DELMOTTE, V., OLAGO, D., OTTO-BLIESNER, B., PELTIER, W. R. & RAHMSTORF, S. 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: IPCC (Intergovernmental Panel on Climate Chance).; Khatiwala et al., 2013KHATIWALA, S., TANHUA, T., FLETCHER, S. M., GERBER, M., DONEY, S. C., GRAVEN, H. D., GRUBER, N., MCKINLEY, G. A., MURATA, A., RÍOS, A. F. & SABINE, C. L. 2013. Global ocean storage of anthropogenic carbon. Biogeosciences, 10, 2169-2191, DOI: https://doi.org/10.5194/bg-10-2169-2013
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https://doi.org/10.1016/j.csr.2021.10434... ; Carvalho et al., 2022CARVALHO, A. C. O., KERR, R., TAVANO, V. M. & MENDES, C. R. B. 2022. The southwestern South Atlantic continental shelf biogeochemical divide. Biogeochemistry, 159(2), 139-158, DOI: https://doi.org/10.1007/s10533-022-00918-8
https://doi.org/10.1007/s10533-022-00918... ).

Although the oceans act as a global CO ₂ sink, (Padin et al., 2010PADIN, X. A., VÁZQUEZ-RODRÍGUEZ, M., CASTAÑO, M., VELO, A., ALONSO-PÉREZ, F., GAGO, J., GILCOTO, M., ÁLVAREZ, M., PARDO, P. C., DE LA PAZ, M., RÍOS, A. F. & PÉREZ, F. F. 2010. Air-Sea CO2 fluxes in the Atlantic as measured during boreal spring and autumn. Biogeosciences, 7, 1587-1606, DOI: https://doi.org/10.5194/bg-7-1587-2010
https://doi.org/10.5194/bg-7-1587-2010... ; Laruelle et al., 2018LARUELLE, G. G., CAI, W. J., HU, X., GRUBER, N., MACKENZIE, F. T. & REGNIER, P. 2018. Continental shelves as a variable but increasing global sink for atmospheric carbon dioxide. Nature Communications, 9, 454, DOI: https://doi.org/10.1038/s41467-017-02738-z
https://doi.org/10.1038/s41467-017-02738... ; Roobaert et al., 2019ROOBAERT, A., LARUELLE, G. G., LANDSCHÜTZER, P., GRUBER, N., CHOU, L. & REGNIER, P. 2019. The spatiotemporal dynamics of the sources and sinks of CO2 in the global coastal ocean. Global Biogeochemical Cycles, 33(12), 1693-1714, DOI: https://doi.org/10.1029/2019GB006239
https://doi.org/10.1029/2019GB006239... ) this behavior is highly variable on continental margins ( e.g. Ito et al., 2005ITO, R. G., SCHNEIDER, B. & THOMAS, H. 2005. Distribution of surface fCO2 and air-sea fluxes in the Southwestern subtropical Atlantic and adjacent continental shelf. Journal of Marine Systems, 56(3-4), 227-242, DOI: https://doi.org/10.1016/j.jmarsys.2005.02.005
https://doi.org/10.1016/j.jmarsys.2005.0... ; Jiang et al., 2008JIANG, L. Q., CAI, W. J., WANNINKHOF, R., WANG, Y. & LÜGER, H. 2008. Air-sea CO2 fluxes on the U.S. South Atlantic Bight: spatial and seasonal variability. Journal of Geophysical Research Oceans, 113(C7), C07019, DOI: https://doi.org/10.1029/2007JC004366
https://doi.org/10.1029/2007JC004366... ; Chen et al., 2013CHEN, C. T. A., HUANG, T. H., CHEN, Y. C., BAI, Y., HE, X. & KANG, Y. 2013. Air-sea exchanges of CO2 in the world’s coastal seas. Biogeosciences, 10, 6509-6544, DOI: https://doi.org/10.5194/bg-10-6509-2013
https://doi.org/10.5194/bg-10-6509-2013... ; Carvalho et al., 2017CARVALHO, A. C. O., MARINS, R. V., DIAS, F. J. S., REZENDE, C. E., LEFÈVRE, N., CAVALCANTE, M. S. & ESCHRIQUE, S. A. 2017. Air-sea CO2 fluxes for the Brazilian northeast continental shelf in a climatic transition region. Journal of Marine Systems, 173, 70-80, DOI: https://doi.org/10.1016/j.jmarsys.2017.04.009
https://doi.org/10.1016/j.jmarsys.2017.0... ; Araújo et al., 2019ARAUJO, M., NORIEGA, C., MEDEIROS, C., LEFÈVRE, N., IBÁNHEZ, J. S. P., MONTES, M. F., SILVA, A. C. & SANTOS, M. L., 2019. On the variability in the CO2 system and water productivity in the western tropical Atlantic off North and Northeast Brazil. Journal of Marine Systems, 189, 62-77, DOI: https://doi.org/10.1016/j.jmarsys.2018.09.008
https://doi.org/10.1016/j.jmarsys.2018.0... ), similar to what we observed along the Brazilian continental margin. The as- sessment of the role of ecoregions as sources or sinks of atmospheric CO ₂, showed a spatio-tem- poral dynamics potentially justified by different re- gional processes. According to Cai et al., ( 2020CAI, W. J., XU, Y. Y., FEELY, R.A., WANNINKHOF, R., JÖNSSON, B., ALIN, S. R., BARBERO, L., CROSS, J.N., AZETSU-SCOTT, K., FASSBENDER, A. J., CARTER, B. R., JIANG, L. Q., PEPIN, P., CHEN, B., HUSSAIN, N., REIMER, J. J., XUE, L., SALISBURY, J. E., HERNÁNDEZ-AYÓN, J. M., LANGDON, C., LI, Q., SUTTON, A. J., CHEN, C. T. A. & GLEDHILL, D. K. 2020. Controls on surface water carbonate chemistry along North American ocean margins. Nature Communications, 11, 2691, DOI: https://doi.org/10.1038/s41467-020-16530-z
https://doi.org/10.1038/s41467-020-16530... ), these processes include river inputs, coastal cir- culation, and spatial and seasonal temperature variations.

In the Amazon ecoregion, our results recorded the highest seasonal range of pCO ₂ in coastal waters. This high variability results directly or in- directly from the discharge of the Amazon River, evidenced by thebroad salinity range (Figure 2), showing the mix of the Amazon River plume with the oceanic water, and the pattern of pCO ₂ su- persaturation in surface waters, close to the river mouth (Abril et al., 2014ABRIL, G., MARTINEZ, J. M., ARTIGAS, L. F., MOREIRA--TURCQ, P., BENEDETTI, M. F., VIDAL, L., MEZIANE, T., KIM, J. H., BERNARDES, M. C., SAVOYE, N., DEBORDE, J., SOUZA, E. L., ALBÉRIC, P., SOUZA, M. F. & ROLAND, F. 2014. Amazon River carbon dioxide outgassing fuelled by wetlands. Nature, 505, 395-398, DOI: https://doi.org/10.1038/nature12797
https://doi.org/10.1038/nature12797... ). Additionally, our results highlight the importance of the Amazon River plume, which creates a regional CO ₂ sink off the coast ( e.g. Lefèvre et al., 2010LEFÈVRE, N., DIVERRÉS, D. & GALLOIS, F. 2010. Origin of CO 2 undersaturation in the western tropical Atlantic. Tellus B: Chemical and Physical Meteorology, 62(5), 595-607, DOI: https://doi.org/10.1111/j.1600-0889.2010.00475.x
https://doi.org/10.1111/j.1600-0889.2010... ; Ibánhez et al., 2016IBÁNHEZ, J. S. P., ARAUJO, M. & LEFÈVRE, N. 2016. The overlooked tropical oceanic CO2 sink: overlooked tropical oceanic CO2 sink. Geophysical Research Letters, 43(8), 3804-3812, DOI: https://doi.org/10.1002/2016GL068020
https://doi.org/10.1002/2016GL068020... ; Araújo et al., 2019ARAUJO, M., NORIEGA, C., MEDEIROS, C., LEFÈVRE, N., IBÁNHEZ, J. S. P., MONTES, M. F., SILVA, A. C. & SANTOS, M. L., 2019. On the variability in the CO2 system and water productivity in the western tropical Atlantic off North and Northeast Brazil. Journal of Marine Systems, 189, 62-77, DOI: https://doi.org/10.1016/j.jmarsys.2018.09.008
https://doi.org/10.1016/j.jmarsys.2018.0... ). Considering the SST seasonality, we identified that the area alternately acted as a CO ₂ source, in the coldest periods, and as a CO ₂ sink, in the warmest periods.

In the Northeastern and Eastern ecoregions continuum, the continental shelf is narrow, and typically oligotrophic, receiving low fluvial input, predominantly Coastal Water influenced, warm (T >20°C) and low salinity (S <35), on the inner shelf. While on the outer shelf the Brazil Current carries Tropical Water (T > 20°C and S > 36.4) at the surface (Pereira et al., 2005PEREIRA, A. F., BELÉM, A. L., CASTRO, B. M. & GEREMIAS, R. 2005. Tide-topography interaction along the eastern Brazilian shelf. Continental Shelf Research, 25(12-13), 1521-1539, DOI: https://doi.org/10.1016/j.csr.2005.04.008
https://doi.org/10.1016/j.csr.2005.04.00... ; Calado et al., 2010CALADO, L., SILVEIRA, I. C. A., GANGOPADHYAY, A. & CASTRO, B. M. 2010. Eddy-induced upwelling off Cape São Tomé (22°S, Brazil). Continental Shelf Research, 30, 1181-1188, DOI: https://doi.org/10.1016/j.csr.2010.03.007
https://doi.org/10.1016/j.csr.2010.03.00... ; Rocha et al., 2014ROCHA, C. B., SILVEIRA, I. C. A., CASTRO, B. M. & LIMA, J. A. M. 2014. Vertical structure, energetics, and dynamics of the Brazil Current System at 22°S-28°S: on the Brazil current at 22°S-28°S. Journal of Geophysical Research Oceans, 119(1), 52-69, DOI: https://doi.org/10.1002/2013JC009143
https://doi.org/10.1002/2013JC009143... ). Seasonal variations are less marked, thus reflecting lower average varia- tion in CO ₂ fluxes, but increasing the positive sign (CO ₂ source) in warmer periods. In the transition zone between semi-arid and more humid areas, on the northeast coast of Brazil, Carvalho et al. -CARVALHO et al. ( 2017CARVALHO, A. C. O., MARINS, R. V., DIAS, F. J. S., REZENDE, C. E., LEFÈVRE, N., CAVALCANTE, M. S. & ESCHRIQUE, S. A. 2017. Air-sea CO2 fluxes for the Brazilian northeast continental shelf in a climatic transition region. Journal of Marine Systems, 173, 70-80, DOI: https://doi.org/10.1016/j.jmarsys.2017.04.009
https://doi.org/10.1016/j.jmarsys.2017.0... ) recorded seawater CO ₂ saturation, consid- ering the local hydrological and rainfall conditions, also showing the region’s role as a CO ₂ source to the atmosphere. The same behavior registered by Cotovicz et al. ( 2020COTOVICZ, L. C., VIDAL, L. O., REZENDE, C. E., BERNARDES, M. C., KNOPPERS, B. A., SOBRINHO, R.L., CARDOSO, R. P., MUNIZ, M., ANJOS, R. M., BIEHLER, A. & ABRIL, G. 2020. Carbon dioxide sources and sinks in the delta of the Paraíba do Sul River (Southeastern Brazil) modulated by carbonate thermodynamics, gas exchange and ecosystem metabolism during estuarine mixing. Marine Chemistry, 226, 103869, DOI: https://doi.org/10.1016/j.marchem.2020.103869
https://doi.org/10.1016/j.marchem.2020.1... ), who observed higher CO ₂ emissions in coral reef areas, when compared to regions close to the coast, and offshore.

According to Oliveira et al. -OLIVEIRA et al. ( 2019OLIVEIRA, R. R., PEZZI, L. P., SOUZA, R. B., SANTINI, M. F., CUNHA, L. C. & PACHECO, F. S. 2019. First measurements of the ocean-atmosphere CO2 fluxes at the Cabo Frio upwelling system region, Southwestern Atlantic Ocean. Continental Shelf Research, 181, 135-142, DOI: https://doi.org/10.1016/j.csr.2019.05.008
https://doi.org/10.1016/j.csr.2019.05.00... ), in the Southeastern ecoregion, ocean-atmosphere CO ₂ fluxes are highly dependent on local oceanograph- ic and meteorological conditions. In these region the upwelling system between Cabo São Tomé and Cabo Frio (22° - 23°S) stands out, which, which transports cold waters, rich in nutrients, to the surface, providing an increment in local pri- mary productivity during the summer (Moser et al., 2014MOSER, G. A. O., TAKANOHASHI, R. A., BRAZ, M., DE LIMA, D. T., KIRSTEN, F. V., GUERRA, J. V., FERNANDES, A. M. & POLLERY, R. C. G. 2014. Phytoplankton spatial distribution on the Continental Shelf off Rio de Janeiro, from Paraíba do Sul River to Cabo Frio. Hydrobiologia, 728, 1-21, DOI: https://doi.org/10.1007/s10750-013-1791-3
https://doi.org/10.1007/s10750-013-1791-... ). In these coastal upwelling regions, surface ocean pCO ₂ values are usually higher, as a result of upwelled, CO ₂-enriched subsurface waters (Ito et al. 2016ITO, R. G., GARCIA, C. A. E. & TAVANO, V. M. 2016. Net sea-air CO2 fluxes and modelled pCO2 in the southwesternsubtropical Atlantic continental shelf during spring 2010 and summer 2011. Continental Shelf Research, 119, 68-84, DOI: https://doi.org/10.1016/j.csr.2016.03.013
https://doi.org/10.1016/j.csr.2016.03.01... ). It is possible to assume, therefore, that the role of this region as a CO ₂ source in the warmer periods, as we have recorded, derives both from the input of waters that already arrive rich in CO ₂, and from the intensification o f p hy- toplankton respiration (Borges et al., 2005BORGES, A. V., DELILLE, B. & FRANKIGNOULLE, M. 2005. Budgeting sinks and sources of CO2 in the coastal ocean: diversity of ecosystemscounts: coastal CO2 sinks and sources. Geophysical Research Letters, 32(14), 1-4, DOI: https://doi.org/10.1029/2005GL023053
https://doi.org/10.1029/2005GL023053... ; Roy-Barman and Jeandeal, 2016ROY-BARMAN, M. & JEANDEL, C. 2016. Marine geochemistry: ocean circulation, carbon cycle and climate change. Oxford: Oxford University Press.).

Rio Grande ecoregion was the only one that presented atmospheric CO ₂ sink behavior through- out the analyzed time series. This region, located in the southernmost portion of the Brazilian con- tinental margin, receives large water input of the La Plata River, as well as Patos Lagon, typically colder waters, in addition to the influence of the South Atlantic Central Water (SACW). The SACW upwelling, through mesoscale processes, brings additional cooler, nutrient-rich waters to the sur- face (Pezzi et al., 2009PEZZI, L. P., SOUZA, R. B., ACEVEDO, O., WAINER, I., MATA, M. M., GARCIA, C. A. & CAMARGO, R. 2009. Multiyear measurements of the oceanic and atmospheric boundary layers at the Brazil‐Malvinas confluence region. Journal of Geophysical Research: Atmospheres, 114(D19), DOI: https://doi.org/10.1029/2008JD011379
https://doi.org/10.1029/2008JD011379... ). Thus, this region presents thermal gradients that are highly variable in time and space (Souza and Robinson, 2004SOUZA, R. B. & ROBINSON, I. S. 2004. Lagrangian and satellite observations of the Brazilian Coastal Current. Continental Shelf Research, 24, 241-262, DOI: https://doi.org/10.1016/j.csr.2003.10.001
https://doi.org/10.1016/j.csr.2003.10.00... ). Along with the fluvial plume dispersion on the continental shelf, the SACW upwelling enhances local primary productivity (Piola et al., 2000PIOLA, A. R., CAMPOS, E. J. D., MÖLLER, O. O., CHARO, M. & MARTINEZ, C. 2000. Subtropical Shelf Front off eastern South America. Journal of Geophysical Research Oceans, 105(D19), 6565-6578, DOI: https://doi.org/10.1029/1999JC000300
https://doi.org/10.1029/1999JC000300... ; Möller et al., 2008MÖLLER, O. O., PIOLA, A. R., FREITAS, A. C. & CAMPOS, E. J. D. 2008. The effects of river discharge and seasonal winds on the shelf off southeastern South America. Continental Shelf Research, 28(13), 1607-1624, DOI: https://doi.org/10.1016/j.csr.2008.03.012
https://doi.org/10.1016/j.csr.2008.03.01... ), especially in the austral winter and spring months (Ciotti et al., 1995CIOTTI, A. M., ODEBRECHT, C., FILLMANN, G. & MOLLER, O. O. 1995. Freshwater outflow and Subtropical Convergence influence on phytoplankton biomass on the southern Brazilian continental shelf. Continental Shelf Research, 15, 1737-1756, DOI: https://doi.org/10.1016/0278-4343(94)00091-Z
https://doi.org/10.1016/0278-4343(94)000... ). Both processes are potentially responsible for the increased CO ₂ sink in the area during the coldest period.

CONCLUSIONS

This study covered almost 40° in latitude along the Brazilian margin. The regional division adopted here highlights the dominant biogeographic pa- rameters such as upwelling, freshwater input, temperature, currents or coastal complexity, in the different ecoregions. The water salinity and tem- perature variations represented a characteristic pattern of north-south variation. In addition to the low seasonality, typical of tropical or subtropical waters, reflecting the greater or lesser influence of river inputs in each ecoregion.

Through the time series analysis of the sur- face water masses, we could observe the gen-eral increasing trend in pCO ₂, both in the atmo-sphere and in the seawater. Over shelf and margin areas there is a strong component of biologi-cal control in pCO ₂, especially in the inner shelf and coastal regions such as bays and estuaries. Non-homogeneous sea-to-air CO ₂ fluxes varied the marine carbonate system in the tropical and south Atlantic continental margins.

Despite the large volume of data available, temporal gaps are common in SOCAT, as data are usually collected during trade routes of opportunity vessels, which occur mainly in summer (Wang et al., 2017WANG, H., HU, X., CAI, W. J. & STERBA-BOATWRIGHT, B. 2017. Decadal f CO2 trends in global ocean margins and adjacent boundary current-influenced areas: decadal f CO2 trends in ocean margins. Geophysical Research Letters, 44(17), 8962-8970, DOI: https://doi.org/10.1002/2017GL074724
https://doi.org/10.1002/2017GL074724... ). The spatial resolution of observations in certain regions of the Atlantic is also limited, com- pared to other regions of the global ocean (Sabine et al., 2010SABINE, C., DUCKLOW, H. & HOOD, M. 2010. International Carbon Coordination: Roger Revelle’s Legacy in the Intergovernmental Oceanographic Commission. Oceanography, 23, 48-61, DOI: https://doi.org/10.5670/oceanog.2010.23
https://doi.org/10.5670/oceanog.2010.23... ). Thus, we believe it is important to highlight the need to invest in scientific cruises and more ships of opportunity to improve the sampling coverage. This will allow more robust analyses ofediting;

ACKNOWLEDGMENTS

The Surface Ocean CO ₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean – Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) programme. Our sincere thanks to the scientists and funding agencies who provided the data, and the organizers for the collection and quality-controlled data the SOCAT. This work was supported by the Brazilian Research Network on Global Climate Change - Rede CLIMA (CNPq 402832/2018- 3). H.M.J.A. acknowledges the Rede Clima for the CNPq research grant (DTI-A 381501/2019- 1). L.C.C. acknowledges the Prociência/UERJ, CNPq/PQ2 no. 309708/2021-4 and FAPERJ/CNE no. E26/201.156/2022 research grants. We acknowledge to the reviewers for their con- tributions in reviewing in this work.

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This work was supported by the Brazilian Research Network on Global Climate Change - Rede CLIMA (CNPq 402832/2018- 3). H.M.J.A. acknowledges the Rede Clima for the CNPq research grant (DTI-A 381501/2019- 1). L.C.C. acknowledges the Prociência/UERJ, CNPq/PQ2 no. 309708/2021-4 and FAPERJ/ CNE no. E26/201.156/2022 research grants. We acknowledge to the reviewers for their contributions in reviewing in this work.

Edited by

Associate Editor:

Ronald Souza

Publication Dates

Publication in this collection
09 June 2023
Date of issue
2023

History

Received
17 May 2022
Accepted
17 Jan 2023

This is an open access article distributed under the terms of the Creative Commons license.

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Ecoregion	Period	SST	SSS	Wind speed
Amazon	1993 - 2018	26.32 - 30.18 (28.23 ± 0.71)	16.57 - 36.54 (33.75 ± 4.24)	2.00 - 10.1 (6.46 ± 2.26)
Northeastern	1991 - 2018	24.98 - 30.40 (27.71 ± 0.89)	33.63 - 37.52 (36.21 ± 0.52)	2.01 - 10.7 (6.18 ± 1.72)
Southeastern	1991 - 2018	17.58 - 29.46 (24.03 ± 2.22)	24.96 - 37.44 (36.03 ± 1.35)	2.48 - 9.58 (6.04 ± 1.18)
Eastern	1991 - 2018	19.29 - 29.45 (26.45 ± 1.79)	34.23 - 37.76 (37.09 ± 0.40)	2.48 - 9.58 (6.04 ± 1.18)
Rio Grande	1991 - 2018	12.87 - 27.42 (20.71 ± 3.46)	19.70 - 37.05 (33.66 ± 3.44)	3.88 - 9.11 (7.22 ± 1.08)

Brasil