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Climate warming increasingly drives changes in large-scale ocean physics and biogeochemistry, and affects the kinetics of biological reactions. Together these factors govern phytoplankton productivity, thereby shaping the responses of ocean carbon and nutrient cycles to global change. Here we bring together results from experimental, observational and modelling studies to highlight how interactive feedbacks between warming and nutrient limitation can affect the responses of biogeochemically critical marine primary producers. The availability of many bioactive elements in seawater will be altered markedly in the future, thereby shifting resource deficiencies. These modifications to nutrient limitation when compounded by concurrent warming can change phytoplankton optimum growth temperatures and elemental use efficiencies in group-specific and nutrient-specific ways. The biogeochemical impacts of these nutrient and warming interactions reflect a distinction between the thermal reactivity of major cellular structural elements like nitrogen (N) and catalytic micronutrients like iron (Fe). Integrating the mechanistic feedbacks between warming, nutrient availability and primary productivity into Earth system models is necessary to improve confidence in projections of ocean biogeochemical cycle transformations in a changing climate. 

Ocean biogeochemical models have become critical tools for interpreting trace element and isotope (TEI) distributions observed during the GEOTRACES program and understanding their driving processes. Models stimulate new research questions that cannot be addressed with observations alone, for instance, concerning processes that occur over vast spatial scales and linkages between TEIs and other elemental cycles. A spectrum of modeling approaches has been applied to date, including (1) fully prognostic models that couple TEIs to broader biogeochemical frameworks, (2) simpler element-specific mechanistic models that allow for assimilation of observations, and (3) machine learning models that have no mechanistic underpinning but allow for skillful extrapolation of sparse data. Here, we evaluate the strengths and weaknesses of these approaches and review three sets of novel insights they have facilitated. First, models have advanced our understanding of global-scale micronutrient distributions, and their deviations from macronutrients, in terms of a “ventilation-regeneration-scavenging” balance. Second, models have yielded global-scale estimates of TEI inputs to and losses from the ocean, revealing, for instance, a rapid iron (Fe) cycle with an oceanic residence time on the order of decades. Third, models have identified novel links among various TEI cycling processes and the global ocean carbon cycle, such as tracing the supply of hydrothermally sourced Fe to iron-starved microbial communities in the Southern Ocean. We foresee additional important roles for modeling work in the next stages of trace element research, including synthesizing understanding from the GEOTRACES program in the form of TEI state estimates, and projecting the responses of TEI cycles to global climate change. 

Biogeochemical cycles constitute Earth’s life support system and distinguish our planet from others in this solar system. Microorganisms are the primary drivers of these cycles. Understanding the controls on marine microbial dynamics and how microbes will respond to environmental change is essential for building and assessing model-based forecasts and generating robust projections of climate change impacts on ocean productivity and biogeochemical cycles. An international community effort has been underway to create a global-scale marine microbial biogeochemistry research program to tackle gaps in this understanding. The BioGeoSCAPES: Ocean Metabolism and Nutrient Cycles on a Changing Planet program will identify and quantify how marine microbes adjust to a changing climate and assess the consequences for global biogeochemical cycles. This article summarizes the ongoing efforts to launch BioGeoSCAPES. 

Abstract Particulate phases transport trace metals (TM) and thereby exert a major control on TM distribution in the ocean. Particulate TMs can be classified by their origin as lithogenic (crustal material), biogenic (cellular), or authigenic (formed in situ), but distinguishing these fractions analytically in field samples is a challenge often addressed using operational definitions and assumptions. These different phases require accurate characterization because they have distinct roles in the biogeochemical iron cycle. Particles collected from the upper 2,000 m of the northwest subtropical Atlantic Ocean over four seasonal cruises throughout 2019 were digested with a chemical leach to operationally distinguish labile particulate material from refractory lithogenics. Direct measurements of cellular iron (Fe) were used to calculate the biogenic contribution to the labile Fe fraction, and any remaining labile material was defined as authigenic. Total particulate Fe (PFe) inventories varied <15% between seasons despite strong seasonality in dust inputs. Across seasons, the total PFe inventory (±1SD) was composed of 73 ± 13% lithogenic, 18 ± 7% authigenic, and 10 ± 8% biogenic Fe above the deep chlorophyll maximum (DCM), and 69 ± 8% lithogenic, 30 ± 8% authigenic, and 1.1 ± 0.5% biogenic Fe below the DCM. Data from three other ocean regions further reveal the importance of the authigenic fraction across broad productivity and Fe gradients, comprising ca. 20%–27% of total PFe. 

The importance of dissolved Fe (dFe) in regulating ocean primary production and the carbon cycle is well established. However, the large-scale distribution and temporal dynamics of dFe remain poorly constrained in part due to incomplete observational coverage. In this study, we use a compilation of published dFe observations (n=32,344) with paired environmental predictors from contemporaneous satellite observations and reanalysis products to build a data-driven surface-to-seafloor dFe climatology with 1°×1° resolution using three machine-learning approaches (random forest, supper vector machine and artificial neural network). Among the three approaches, random forest achieves the highest accuracy with overall R 2 and root mean standard error of 0.8 and 0.3 nmol L -1 , respectively. Using this data-driven climatology, we explore the possible mechanisms governing the dFe distribution at various depth horizons using statistical metrics such as Pearson correlation coefficients and the rank of predictors importance in the model construction. Our results are consistent with the critical role of aeolian iron supply in enriching surface dFe in the low latitude regions and suggest a far-reaching impact of this source at depth. Away from the surface layer, the strong correlation between dFe and apparent oxygen utilization implies that a combination of regeneration, scavenging and large-scale ocean circulation are controlling the interior distribution of dFe, with hydrothermal inputs important in some regions. Finally, our data-driven dFe climatology can be used as an alternative reference to evaluate the performance of ocean biogeochemical models. Overall, the new global scale climatology of dFe achieved in our study is an important step toward improved representation of dFe in the contemporary ocean and may also be used to guide future sampling strategies. 

Abstract. Supply of iron (Fe) to the surface ocean supports primary productivity, and while hydrothermal input of Fe to the deep ocean is knownto be extensive it remains poorly constrained. Global estimates of hydrothermal Fe supply rely on using dissolved Fe (dFe) toexcess He (xs3He) ratios to upscale fluxes, but observational constraints on dFe/xs3He may be sensitive toassumptions linked to sampling and interpolation. We examined the variability in dFe/xs3He using two methods of estimation, forfour vent sites with different geochemistry along the Mid-Atlantic Ridge. At both Rainbow and TAG, the plume was sampled repeatedly and the range ofdFe/xs3He was 4 to 63 and 4 to 87 nmol:fmol, respectively, primarily due to differences in plume age. To account for backgroundxs3He and shifting plume position, we calibrated He values using contemporaneous dissolved Mn (dMn). Applying thisapproach more widely, we found dFe/xs3He ratios of 12, 4–8, 4–44, and 4–86 nmol fmol−1 for the Menez Gwen, LuckyStrike, Rainbow, and TAG hydrothermal vent sites, respectively. Differences in plume dFe/xs3He across sites were not simplyrelated to the vent endmember Fe and He fluxes. Within 40 km of the vents, the dFe/xs3He ratios decreased to3–38 nmol fmol−1, due to the precipitation and subsequent settling of particulates. The ratio of colloidal Fe to dFe wasconsistently higher (0.67–0.97) than the deep N. Atlantic (0.5) throughout both the TAG and Rainbow plumes, indicative of Fe exchangebetween dissolved and particulate phases. Our comparison of TAG and Rainbow shows there is a limit to the amount of hydrothermal Fe releasedfrom vents that can form colloids in the rising plume. Higher particle loading will enhance the longevity of the Rainbow hydrothermal plume withinthe deep ocean assuming particles undergo continual dissolution/disaggregation. Future studies examining the length of plume pathways required toescape the ridge valley will be important in determining Fe supply from slow spreading mid-ocean ridges to the deep ocean, along with thefrequency of ultramafic sites such as Rainbow. Resolving the ridge valley bathymetry and accounting for variability in vent sources in globalbiogeochemical models will be key to further constraining the hydrothermal Fe flux.