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We use observations from novel biogeochemical profiling floats deployed by the Southern Ocean Carbon and Climate Observations and Modeling program to estimate annual net community production (ANCP; associated with carbon export) from the seasonal drawdown of mesopelagic oxygen and surface nitrate in the Southern Ocean. Our estimates agree with previous observations in showing an increase in ANCP in the vicinity of the polar front (∼3 mol C m−2 y−1), compared to lower rates in the subtropical zone (≤ 1 mol C m−2 y−1) and the seasonal ice zone (<2 mol C m−2 y−1). Paradoxically, the increase in ANCP south of the subtropical front is associated with elevated surface nitrate and silicate concentrations, but decreasing surface iron. We hypothesize that iron limitation promotes silicification in diatoms, which is evidenced by the low silicate to nitrate ratio of surface waters around the Antarctic polar front. High diatom silicification increases the ballasting effect of particulate organic carbon and overall ANCP in this region. A model-based assessment of our methods shows a good agreement between ANCP estimates based on oxygen and nitrate drawdown and the modeled downward organic carbon flux at 100 m. This agreement supports the presumption that net biological consumption is the dominant process affecting the drawdown of these chemical tracers and that, given sufficient data, ANCP can be inferred from observations of oxygen and/or nitrate drawdown in the Southern Ocean.

We use observations from novel biogeochemical profiling floats deployed by the Southern Ocean Carbon and Climate Observations and Modeling program to estimate annual net community production (ANCP; associated with carbon export) from the seasonal drawdown of mesopelagic oxygen and surface nitrate in the Southern Ocean. Our estimates agree with previous observations in showing an increase in ANCP in the vicinity of the polar front (∼3 mol C m−2 y−1), compared to lower rates in the subtropical zone (≤ 1 mol C m−2 y−1) and the seasonal ice zone (<2 mol C m−2 y−1). Paradoxically, the increase in ANCP south of the subtropical front is associated with elevated surface nitrate and silicate concentrations, but decreasing surface iron. We hypothesize that iron limitation promotes silicification in diatoms, which is evidenced by the low silicate to nitrate ratio of surface waters around the Antarctic polar front. High diatom silicification increases the ballasting effect of particulate organic carbon and overall ANCP in this region. A model-based assessment of our methods shows a good agreement between ANCP estimates based on oxygen and nitrate drawdown and the modeled downward organic carbon flux at 100 m. This agreement supports the presumption that net biological consumption is the dominant process affecting the drawdown of these chemical tracers and that, given sufficient data, ANCP can be inferred from observations of oxygen and/or nitrate drawdown in the Southern Ocean.

We use observations from novel biogeochemical profiling floats deployed by the Southern Ocean Carbon and Climate Observations and Modeling program to estimate annual net community production (ANCP; associated with carbon export) from the seasonal drawdown of mesopelagic oxygen and surface nitrate in the Southern Ocean. Our estimates agree with previous observations in showing an increase in ANCP in the vicinity of the polar front (∼3 mol C m−2 y−1), compared to lower rates in the subtropical zone (≤ 1 mol C m−2 y−1) and the seasonal ice zone (<2 mol C m−2 y−1). Paradoxically, the increase in ANCP south of the subtropical front is associated with elevated surface nitrate and silicate concentrations, but decreasing surface iron. We hypothesize that iron limitation promotes silicification in diatoms, which is evidenced by the low silicate to nitrate ratio of surface waters around the Antarctic polar front. High diatom silicification increases the ballasting effect of particulate organic carbon and overall ANCP in this region. A model-based assessment of our methods shows a good agreement between ANCP estimates based on oxygen and nitrate drawdown and the modeled downward organic carbon flux at 100 m. This agreement supports the presumption that net biological consumption is the dominant process affecting the drawdown of these chemical tracers and that, given sufficient data, ANCP can be inferred from observations of oxygen and/or nitrate drawdown in the Southern Ocean.

We use observations from novel biogeochemical profiling floats deployed by the Southern Ocean Carbon and Climate Observations and Modeling program to estimate annual net community production (ANCP; associated with carbon export) from the seasonal drawdown of mesopelagic oxygen and surface nitrate in the Southern Ocean. Our estimates agree with previous observations in showing an increase in ANCP in the vicinity of the polar front (∼3 mol C m−2 y−1), compared to lower rates in the subtropical zone (≤ 1 mol C m−2 y−1) and the seasonal ice zone (<2 mol C m−2 y−1). Paradoxically, the increase in ANCP south of the subtropical front is associated with elevated surface nitrate and silicate concentrations, but decreasing surface iron. We hypothesize that iron limitation promotes silicification in diatoms, which is evidenced by the low silicate to nitrate ratio of surface waters around the Antarctic polar front. High diatom silicification increases the ballasting effect of particulate organic carbon and overall ANCP in this region. A model-based assessment of our methods shows a good agreement between ANCP estimates based on oxygen and nitrate drawdown and the modeled downward organic carbon flux at 100 m. This agreement supports the presumption that net biological consumption is the dominant process affecting the drawdown of these chemical tracers and that, given sufficient data, ANCP can be inferred from observations of oxygen and/or nitrate drawdown in the Southern Ocean.

Chlorophyll (Chl) is a distinctive component of autotrophic organisms, often used as an indicator of phytoplankton biomass in the ocean. However, assessment of phytoplankton biomass from Chl relies on the accurate estimation of the Chl:carbon(C) ratio. Here we present global patterns of Chl:C ratios in the surface ocean obtained from a phytoplankton growth model that accounts for the optimal acclimation of phytoplankton to ambient nutrient, light, and temperature conditions. The model agrees largely with observed/expected global patterns of Chl:C. Combining our Chl:C estimates with satellite Chl and particulate organic carbon (POC), we infer phytoplankton C concentration in the surface ocean and its contribution to the total POC pool. Our results suggest that the portion of POC corresponding to living phytoplankton is higher in subtropical latitudes and less productive regions (∼30–70%) and decreases to ∼10–30% toward high latitudes and productive regions. An important caveat of our model is the lack of iron limiting effects on phytoplankton physiology. Comparison of our predicted phytoplankton biomass with an independent estimate of total POC reveals a positive correlation between nitrate concentrations and nonphotosynthetic POC in the surface ocean. This correlation disappears when a constant Chl:C is applied. Our analysis is not constrained by assumptions of constant Chl:C or phytoplankton:POC ratio, providing a novel independent analysis of phytoplankton biomass in the surface ocean. These results highlight the importance of accounting for the variability in Chl:C and its application in distinguishing the autotrophic and heterotrophic components in the assemblage of the marine plankton ecosystem.

Chlorophyll (Chl) is a distinctive component of autotrophic organisms, often used as an indicator of phytoplankton biomass in the ocean. However, assessment of phytoplankton biomass from Chl relies on the accurate estimation of the Chl:carbon(C) ratio. Here we present global patterns of Chl:C ratios in the surface ocean obtained from a phytoplankton growth model that accounts for the optimal acclimation of phytoplankton to ambient nutrient, light, and temperature conditions. The model agrees largely with observed/expected global patterns of Chl:C. Combining our Chl:C estimates with satellite Chl and particulate organic carbon (POC), we infer phytoplankton C concentration in the surface ocean and its contribution to the total POC pool. Our results suggest that the portion of POC corresponding to living phytoplankton is higher in subtropical latitudes and less productive regions (∼30–70%) and decreases to ∼10–30% toward high latitudes and productive regions. An important caveat of our model is the lack of iron limiting effects on phytoplankton physiology. Comparison of our predicted phytoplankton biomass with an independent estimate of total POC reveals a positive correlation between nitrate concentrations and nonphotosynthetic POC in the surface ocean. This correlation disappears when a constant Chl:C is applied. Our analysis is not constrained by assumptions of constant Chl:C or phytoplankton:POC ratio, providing a novel independent analysis of phytoplankton biomass in the surface ocean. These results highlight the importance of accounting for the variability in Chl:C and its application in distinguishing the autotrophic and heterotrophic components in the assemblage of the marine plankton ecosystem.

Chlorophyll (Chl) is a distinctive component of autotrophic organisms, often used as an indicator of phytoplankton biomass in the ocean. However, assessment of phytoplankton biomass from Chl relies on the accurate estimation of the Chl:carbon(C) ratio. Here we present global patterns of Chl:C ratios in the surface ocean obtained from a phytoplankton growth model that accounts for the optimal acclimation of phytoplankton to ambient nutrient, light, and temperature conditions. The model agrees largely with observed/expected global patterns of Chl:C. Combining our Chl:C estimates with satellite Chl and particulate organic carbon (POC), we infer phytoplankton C concentration in the surface ocean and its contribution to the total POC pool. Our results suggest that the portion of POC corresponding to living phytoplankton is higher in subtropical latitudes and less productive regions (∼30–70%) and decreases to ∼10–30% toward high latitudes and productive regions. An important caveat of our model is the lack of iron limiting effects on phytoplankton physiology. Comparison of our predicted phytoplankton biomass with an independent estimate of total POC reveals a positive correlation between nitrate concentrations and nonphotosynthetic POC in the surface ocean. This correlation disappears when a constant Chl:C is applied. Our analysis is not constrained by assumptions of constant Chl:C or phytoplankton:POC ratio, providing a novel independent analysis of phytoplankton biomass in the surface ocean. These results highlight the importance of accounting for the variability in Chl:C and its application in distinguishing the autotrophic and heterotrophic components in the assemblage of the marine plankton ecosystem.

Information about oceanic nitrate is crucial for making inferences about marine biological production and the efficiency of the biological carbon pump. While there are no optical properties that allow direct estimation of inorganic nitrogen, its correlation with other biogeochemical variables may permit its inference from satellite data. Here we report a new method for estimating monthly mean surface nitrate concentrations employing local multiple linear regressions on a global 1° by 1° resolution grid, using satellite-derived sea surface temperature, chlorophyll, and modeled mixed layer depth. Our method is able to reproduce the interannual variability of independent in situ nitrate observations at the Bermuda Atlantic Time Series, the Hawaii Ocean Time series, the California coast, and the southern New Zealand region. Our new method is shown to be more accurate than previous algorithms and thus can provide improved information on temporal and spatial nutrient variations beyond the climatological mean at regional and global scales.

Information about oceanic nitrate is crucial for making inferences about marine biological production and the efficiency of the biological carbon pump. While there are no optical properties that allow direct estimation of inorganic nitrogen, its correlation with other biogeochemical variables may permit its inference from satellite data. Here we report a new method for estimating monthly mean surface nitrate concentrations employing local multiple linear regressions on a global 1° by 1° resolution grid, using satellite-derived sea surface temperature, chlorophyll, and modeled mixed layer depth. Our method is able to reproduce the interannual variability of independent in situ nitrate observations at the Bermuda Atlantic Time Series, the Hawaii Ocean Time series, the California coast, and the southern New Zealand region. Our new method is shown to be more accurate than previous algorithms and thus can provide improved information on temporal and spatial nutrient variations beyond the climatological mean at regional and global scales.

Information about oceanic nitrate is crucial for making inferences about marine biological production and the efficiency of the biological carbon pump. While there are no optical properties that allow direct estimation of inorganic nitrogen, its correlation with other biogeochemical variables may permit its inference from satellite data. Here we report a new method for estimating monthly mean surface nitrate concentrations employing local multiple linear regressions on a global 1° by 1° resolution grid, using satellite-derived sea surface temperature, chlorophyll, and modeled mixed layer depth. Our method is able to reproduce the interannual variability of independent in situ nitrate observations at the Bermuda Atlantic Time Series, the Hawaii Ocean Time series, the California coast, and the southern New Zealand region. Our new method is shown to be more accurate than previous algorithms and thus can provide improved information on temporal and spatial nutrient variations beyond the climatological mean at regional and global scales.

Information about oceanic nitrate is crucial for making inferences about marine biological production and the efficiency of the biological carbon pump. While there are no optical properties that allow direct estimation of inorganic nitrogen, its correlation with other biogeochemical variables may permit its inference from satellite data. Here we report a new method for estimating monthly mean surface nitrate concentrations employing local multiple linear regressions on a global 1° by 1° resolution grid, using satellite-derived sea surface temperature, chlorophyll, and modeled mixed layer depth. Our method is able to reproduce the interannual variability of independent in situ nitrate observations at the Bermuda Atlantic Time Series, the Hawaii Ocean Time series, the California coast, and the southern New Zealand region. Our new method is shown to be more accurate than previous algorithms and thus can provide improved information on temporal and spatial nutrient variations beyond the climatological mean at regional and global scales.

The widely used concept of constant ”Redfield” phytoplankton stoichiometry is often applied for estimating which nutrient limits phytoplankton growth in the surface ocean. Culture experiments, in contrast, show strong relations between growth conditions and cellular stoichiometry with often substantial deviations from Redfield stoichiometry. Here we investigate to what extent both views agree by analyzing remote sensing and in situ data with an optimality-based model of nondiazotrophic phytoplankton growth in order to infer seasonally varying patterns of colimitation by light, nitrogen (N), and phosphorus (P) in the global ocean. Our combined model-data analysis suggests strong N and N-P colimitation in the tropical ocean, seasonal light, and N-P colimitation in the Northern Hemisphere, and strong light limitation only during winter in the Southern Ocean. The eastern equatorial Pacific appears as the only ocean area that is essentially not limited by N, P, or light. Even though our optimality-based approach specifically accounts for flexible stoichiometry, inferred patterns of N and P limitation are to some extent consistent with those obtained from an analysis of surface inorganic nutrients with respect to the Redfield N:P ratio. Iron is not part of our analysis, implying that we cannot accurately predict N cell quotas in high-nutrient, low-chlorophyll regions. Elsewhere, we do not expect a major effect of iron on the relative distribution of N, P, and light colimitation areas. The relative importance of N, P, and light in limiting phytoplankton growth diagnosed here by combining observations and an optimal growth model provides a useful constraint for models used to predict future marine biological production under changing environmental conditions.

The widely used concept of constant ”Redfield” phytoplankton stoichiometry is often applied for estimating which nutrient limits phytoplankton growth in the surface ocean. Culture experiments, in contrast, show strong relations between growth conditions and cellular stoichiometry with often substantial deviations from Redfield stoichiometry. Here we investigate to what extent both views agree by analyzing remote sensing and in situ data with an optimality-based model of nondiazotrophic phytoplankton growth in order to infer seasonally varying patterns of colimitation by light, nitrogen (N), and phosphorus (P) in the global ocean. Our combined model-data analysis suggests strong N and N-P colimitation in the tropical ocean, seasonal light, and N-P colimitation in the Northern Hemisphere, and strong light limitation only during winter in the Southern Ocean. The eastern equatorial Pacific appears as the only ocean area that is essentially not limited by N, P, or light. Even though our optimality-based approach specifically accounts for flexible stoichiometry, inferred patterns of N and P limitation are to some extent consistent with those obtained from an analysis of surface inorganic nutrients with respect to the Redfield N:P ratio. Iron is not part of our analysis, implying that we cannot accurately predict N cell quotas in high-nutrient, low-chlorophyll regions. Elsewhere, we do not expect a major effect of iron on the relative distribution of N, P, and light colimitation areas. The relative importance of N, P, and light in limiting phytoplankton growth diagnosed here by combining observations and an optimal growth model provides a useful constraint for models used to predict future marine biological production under changing environmental conditions.

The widely used concept of constant ”Redfield” phytoplankton stoichiometry is often applied for estimating which nutrient limits phytoplankton growth in the surface ocean. Culture experiments, in contrast, show strong relations between growth conditions and cellular stoichiometry with often substantial deviations from Redfield stoichiometry. Here we investigate to what extent both views agree by analyzing remote sensing and in situ data with an optimality-based model of nondiazotrophic phytoplankton growth in order to infer seasonally varying patterns of colimitation by light, nitrogen (N), and phosphorus (P) in the global ocean. Our combined model-data analysis suggests strong N and N-P colimitation in the tropical ocean, seasonal light, and N-P colimitation in the Northern Hemisphere, and strong light limitation only during winter in the Southern Ocean. The eastern equatorial Pacific appears as the only ocean area that is essentially not limited by N, P, or light. Even though our optimality-based approach specifically accounts for flexible stoichiometry, inferred patterns of N and P limitation are to some extent consistent with those obtained from an analysis of surface inorganic nutrients with respect to the Redfield N:P ratio. Iron is not part of our analysis, implying that we cannot accurately predict N cell quotas in high-nutrient, low-chlorophyll regions. Elsewhere, we do not expect a major effect of iron on the relative distribution of N, P, and light colimitation areas. The relative importance of N, P, and light in limiting phytoplankton growth diagnosed here by combining observations and an optimal growth model provides a useful constraint for models used to predict future marine biological production under changing environmental conditions.

The widely used concept of constant ”Redfield” phytoplankton stoichiometry is often applied for estimating which nutrient limits phytoplankton growth in the surface ocean. Culture experiments, in contrast, show strong relations between growth conditions and cellular stoichiometry with often substantial deviations from Redfield stoichiometry. Here we investigate to what extent both views agree by analyzing remote sensing and in situ data with an optimality-based model of nondiazotrophic phytoplankton growth in order to infer seasonally varying patterns of colimitation by light, nitrogen (N), and phosphorus (P) in the global ocean. Our combined model-data analysis suggests strong N and N-P colimitation in the tropical ocean, seasonal light, and N-P colimitation in the Northern Hemisphere, and strong light limitation only during winter in the Southern Ocean. The eastern equatorial Pacific appears as the only ocean area that is essentially not limited by N, P, or light. Even though our optimality-based approach specifically accounts for flexible stoichiometry, inferred patterns of N and P limitation are to some extent consistent with those obtained from an analysis of surface inorganic nutrients with respect to the Redfield N:P ratio. Iron is not part of our analysis, implying that we cannot accurately predict N cell quotas in high-nutrient, low-chlorophyll regions. Elsewhere, we do not expect a major effect of iron on the relative distribution of N, P, and light colimitation areas. The relative importance of N, P, and light in limiting phytoplankton growth diagnosed here by combining observations and an optimal growth model provides a useful constraint for models used to predict future marine biological production under changing environmental conditions.