Phytoplankton primary production is a crucial component of Arctic Ocean (AO) biogeochemistry, playing a pivotal role in the carbon cycling by supporting higher trophic levels and removing atmospheric carbon dioxide. The advent of satellite observations measuring chlorophyll a concentration (Chl_ a) has yielded unprecedented insights into the distribution of AO phytoplankton, enhancing our ability to assess oceanic productivity. However, the optical properties of AO waters differ significantly from those of lower‐latitude waters, and standard Chl_a algorithms perform poorly in the AO. In particular, Chl_a retrievals are challenged by interferences from other marine constituents including higher pigment packaging and higher proportion of light absorption by colored dissolved organic matter. To derive phytoplankton-originating signature as well as mitigate those effects, solar-induced chlorophyll fluorescence (SIF) emerges as a valuable tool for acquiring physiological insights into the direct photosynthetic processes in the AO. In this study, we leverage satellite-based SIF measurements to assess their correlation with a set of predictive factors influencing phytoplankton photosynthesis. We extend the temporal coverage of AO SIF data to cover the period 2004 - 2020. This novel dataset offers a pathway to monitor the physiological interactions of phytoplankton with changes in climate, promising to significantly improve our understanding of the Arctic water’s productivity. The application of this data is expected to provide insights into how phytoplankton respond to shifts in environmental changes, contributing to a more nuanced understanding of their role in High-Latitude Northern Oceans ecosystems.

While the preindustrial ocean was assumed to be in equilibrium with the atmosphere, the modern ocean is a carbon sink, resulting from natural variability and anthropogenic perturbations, such as fossil fuel emissions and changes in riverine exports over the past two centuries. Here we use a suite of sensitivity experiments based on the ECCO-Darwin global-ocean biogeochemistry model to evaluate the response of air-sea CO2 flux and carbon cycling to present-day lateral fluxes of carbon, nitrogen, and silica. We generate a daily export product by combining point-source freshwater discharge from JRA55-do with the Global NEWS 2 watershed model, accounting for lateral fluxes from 5171 watersheds worldwide. From 2000 to 2019, carbon exports increase CO2 outgassing by 0.22 Pg C yr-1 via the solubility pump, while nitrogen exports increase the ocean sink by 0.17 Pg C yr-1 due to phytoplankton fertilization. On regional scales, exports to the Tropical Atlantic and Arctic Ocean are dominated by organic carbon, which originates from terrestrial vegetation and peats and increases CO2 outgassing (+10 and +20%, respectively). In contrast, Southeast Asia is dominated by nitrogen from anthropogenic sources, such as agriculture and pollution, leading to increased CO2 uptake (+7%). Our results demonstrate that the magnitude and composition of riverine exports, which are determined in part from upstream watersheds and anthropogenic perturbations, substantially impact present-day regional-to-global-ocean carbon cycling. Ultimately, this work stresses that lateral fluxes must be included in ocean biogeochemistry and Earth System Models to better constrain the transport of carbon, nutrients, and metals across the land-ocean-aquatic-continuum.

As a marine Carbon Dioxide Removal (mCDR) approach, Ocean Alkalinity Enhancement (OAE) is emerging as a viable method for removing anthropogenic CO2 emissions from the atmosphere to mitigate climate change. To achieve substantial carbon reduction using this method, OAE would need to be widespread and scaled-up across the global ocean. However, the efficiency of OAE varies substantially across a range of space-time scales and as such field deployments must be carefully planned to maximize efficiency and minimize logistical costs and risks. Here we develop a mCDR efficiency framework based on the data-assimilative ECCO-Darwin ocean biogeochemistry model, which examines two key factors over seasonal to multi-decadal timescales: 1) mCDR potential, which quantifies the CO2 solubility of the upper ocean; and 2) dynamical mCDR efficiency, representing the full-depth impact of ocean advection, mixing, and air-sea CO2 exchange. To isolate and quantify the factors that determine dynamical efficiency, we develop a reduced complexity 1-D model, rapid-mCDR, as a computationally-efficient tool for evaluation of mCDR efficiency. Combining the rapid-mCDR model with ECCO-Darwin allows for rapid characterization of OAE efficiency at any location globally. This research contributes to our understanding and optimization of OAE deployments (i.e., deploying experiments in the real-world ocean) as an effective mCDR strategy and elucidates the regional differences and mechanistic processes that impact mCDR efficiency. The modeling tools developed in this study can be readily employed by research teams and industry to plan and complement future field deployments and provide essential Monitoring, Reporting, and Verification (MRV).

Nitrous oxide (N2O) is a greenhouse gas and an ozone-depleting agent with large and growing anthropogenic emissions. Previous studies identified the influx of N2O-depleted air from the stratosphere to partly cause the seasonality in tropospheric N2O (aN2O), but other contributions remain unclear. Here we combine surface fluxes from eight land and four ocean models from phase 2 of the Nitrogen/N2O Model Intercomparison Project with tropospheric transport modeling to simulate aN2O at the air sampling sites: Alert, Barrow, Ragged Point, Samoa, Ascension Island, and Cape Grim for the modern and preindustrial periods. Models show general agreement on the seasonal phasing of zonal-average N2O fluxes for most sites, but, seasonal peak-to-peak amplitudes differ severalfold across models. After transport, the seasonal amplitude of surface aN2O ranges from 0.25 to 0.80 ppb (interquartile ranges 21-52% of median) for land, 0.14 to 0.25 ppb (19-42%) for ocean, and 0.13 to 0.76 ppb (26-52%) for combined flux contributions. The observed range is 0.53 to 1.08 ppb. The stratospheric contributions to aN2O, inferred by the difference between surface-troposphere model and observations, show 36-126% larger amplitudes and minima delayed by ~1 month compared to Northern Hemisphere site observations. Our results demonstrate an increasing importance of land fluxes for aN2O seasonality, with land fluxes and their seasonal amplitude increasing since the preindustrial era and are projected to grow under anthropogenic activities. In situ aN2O observations and atmospheric transport-chemistry models will provide opportunities for constraining terrestrial and oceanic biosphere models, critical for projecting surface N2O sources under ongoing global warming.

As a contribution to the Regional Carbon Cycle Assessment and Processes phase 2 (RECCAP2) project, we present synthesized estimates of Arctic Ocean sea-air CO2 fluxes and their uncertainties from 8 surface ocean pCO2-observation products, 18 ocean biogeochemical hindcast and data assimilation models and 6 atmospheric inversions. For the period of 1985−2018, the Arctic Ocean was a net sink of CO2 of 116 ± 4 TgC yr−1 in the pCO2 products and 92 ± 30 TgC yr−1 in the models. The CO2 uptake peaks in late summer and early autumn, and is low in winter when sea ice inhibits sea-air fluxes. The long-term mean CO2 uptake in the Arctic Ocean is primarily caused by steady-state fluxes of natural carbon (70 ± 15 %), and enhanced by the atmospheric CO2 increase (19 ± 5 %) and climate change (11 ± 18 %). The annual mean CO2 uptake increased from 1985 to 2018 at a rate of 31 ±13 TgC yr−1dec-1 in the pCO2 products and 10 ± 4 TgC yr−1dec-1 in the models. Moreover, 77 ± 38 % of the trend in the net CO2 uptake over time is caused by climate change, primarily due to rapid sea ice loss in recent years. Both, the mean CO2 uptake and the trend, is substantially weaker in the atmospheric inversions. Uncertainties across all estimates are large, in the pCO2 products because of scarcity of observations and in the models because of missing processes.