Oceanic emissions of nitrous oxide (N2O) account for roughly one-third of all natural sources to the atmosphere. Hot-spots of N2O outgassing occur over oxygen minimum zones (OMZs), where the presence of steep oxygen gradients surrounding anoxic waters leads to enhanced N2O production from both nitrification and denitrification. However, the relative contributions from these pathways to N2O production and outgassing in these regions remains poorly constrained, in part due to shared intermediary nitrogen tracers, and the tight coupling of denitrification sources and sinks. To shed light on this problem, we embed a new, mechanistic model of the OMZ nitrogen cycle within a three-dimensional eddy-resolving physical-biogeochemical model of the ETSP, tracking contributions from remote advection, atmospheric exchange, and local nitrification and denitrification. Our results indicate that net N2O production from denitrification is approximately one order of magnitude greater than nitrification within the ETSP OMZ. However, only ~30% of denitrification-derived N2O production ultimately outgasses to the atmosphere in this region (contributing ~34% of the air-sea N2O flux on an annual basis), while the remaining is exported out of the domain. Instead, remotely-produced N2O advected into the OMZ region accounts for roughly half (~56%) of the total N2O outgassing, with smaller contributions from nitrification (~7%). Our results suggests that, together with enhanced production by denitrification, upwelling of remotely-derived N2O (likely produced via nitrification in the oxygenated ocean) contributes the most to N2O outgassing over the ETSP OMZ.

\justify Several methods have been developed to quantify the oceanic accumulation of anthropogenic carbon dioxide (CO$_2$) in response to rising atmospheric CO$_2$. Yet, we still lack a corresponding estimate of the changes in the total oceanic dissolved inorganic carbon (DIC). In addition to the increase in anthropogenic CO$_2$, changes in DIC also include alterations of natural CO$_2$. Once integrated globally, changes in DIC reflect the net oceanic sink for atmospheric CO$_2$, complementary to estimates of the air-sea CO$_2$ exchange based on surface measurements. Here, we extend the MOBO-DIC machine learning approach by \citeA{keppler_mapped_2020} to estimate global monthly fields of DIC at 1$^{\circ}$ resolution over the top 1500 m from 2004 through 2019. We find that over these 16 years and extrapolated to cover the whole global ocean down to 4000 m, the oceanic DIC pool increased close to linearly at an average rate of 3.2$\pm$0.7 Pg C yr$^{-1}$. This trend is statistically indistinguishable from current estimates of the oceanic uptake of anthropogenic CO$_2$ over the same period. Thus, our study implies no detectable net loss or gain of natural CO$_2$ by the ocean, albeit the large uncertainties could be masking it. Our reconstructions suggest substantial internal redistributions of natural oceanic CO$_2$, with a shift from the mid-latitudes to the tropics and from the surface to below $\sim$200 m. Such redistributions correspond with the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. The interannual variability of DIC is strongest in the tropical Western Pacific, consistent with the El Ni$\tilde{n}$o Southern Oscillation.

Denitrification is a key process in the global nitrogen (N) cycle, causing both nitrous oxide (N2O) and dinitrogen (N2) emissions. However, estimates of seasonal denitrification losses (N2O+N2) are scarce, reflecting methodological difficulties in measuring soil-borne N2 emissions against the high atmospheric N2 background and challenges regarding their spatio-temporal upscaling. This study investigated N2O+N2 losses in response to N fertiliser rates (0, 100, 150, 200 and 250 kg N ha-1) on two intensively managed tropical sugarcane farms in Australia, by combining automated N2O monitoring, in-situ N2 and N2O measurements using the 15N gas flux method and fertiliser 15N recoveries at harvest. Dynamic changes in the N2O/(N2O+N2) ratio (< 0.01 to 0.768) were explained by fitting generalised additive mixed models (GAMMs) with soil factors to upscale high temporal-resolution N2O data to daily N2 emissions over the season. Cumulative N2O+N2 losses ranged from 12 to 87 kg N ha-1, increasing non-linearly with increasing N fertiliser rates. Emissions of N2O+N2 accounted for 31–78% of fertiliser 15N losses and were dominated by environmentally benign N2 emissions. The contribution of denitrification to N fertiliser loss decreased with increasing N rates, suggesting increasing significance of other N loss pathways including leaching and runoff at higher N rates. This study delivers a blueprint approach to extrapolate denitrification measurements at both temporal and spatial scales, which can be applied in fertilised agroecosystems. Robust estimates of denitrification losses determined using this method will help to improve cropping system modelling approaches, advancing our understanding of the N cycle across scales.

A budget approach is used to disentangle drivers of the seasonal mixed layer carbon cycle at Station ALOHA (A Long-term Oligotrophic Habitat Assessment) in the North Pacific Subtropical Gyre (NPSG). The budget utilizes data from the WHOTS (Woods Hole - Hawaii Ocean Time-series Site) mooring, and the ship-based Hawai‘i Ocean Time-series (HOT) in the North Pacific Subtropical Gyre (NPSG), a region of significant oceanic carbon uptake. Parsing the carbon variations into process components allows an assessment of both the proportional contributions of mixed layer carbon drivers, and the seasonal interplay of drawdown and supply from different processes. Annual net community production reported here is at the lower end of previously published data, while net community calcification estimates are 4- to 7-fold higher than available sediment trap data, the only other estimate of calcium carbonate export at this location. Although the observed seasonal cycle in dissolved inorganic carbon (DIC) in the NPSG has a relatively small amplitude, larger fluxes offset each other over an average year, with major supply from physical transport, especially lateral eddy transport throughout the year and entrainment in the winter, and biological carbon uptake in the spring. Gas exchange plays a smaller role, supplying carbon to the surface ocean between Dec-May, and outgassing in Jul-Oct. Evaporation-precipitation (E–P) is variable with precipitation prevailing in the first- and evaporation in the second half of the year. The observed total alkalinity signal is largely governed by E–P, with a somewhat stronger net calcification signal in the wintertime.

River flows change on timescales ranging from minutes to millennia. These variations influence fundamental functions of ecosystems, including biogeochemical fluxes, aquatic habitat, and human society. Efforts to describe temporal variation in river flow—i.e., flow regime—have resulted in hundreds of unique descriptors, complicating interpretation and identification of global drivers of flow dynamics. Here, we used a cross-disciplinary analytical approach to investigate two related questions: 1. Is there a low-dimensional structure that can be used to simplify descriptions of streamflow regime? 2. What catchment characteristics are most associated with that structure? Using a global database of daily river discharge from 1988-2016 for 3,120 stations, we calculated 189 traditional flow metrics, which we compared to the results of a wavelet analysis. Both quantification techniques independently revealed that streamflow data contain substantial low-dimensional structure that correlates closely with a small number of catchment characteristics. This structure provides a framework for understanding fundamental controls of river flow variability across multiple timescales. Climate was the most important variable across all timescales, especially those lasting several weeks, and likely contributes as much as dams in controlling flow regime. Catchment area was critical for timescales lasting several days, as was human impact for timescales lasting several years. In addition, both methods suggested that streamflow data also contain high-dimensional structure that is harder to predict from a small number of catchment characteristics (i.e. is dependent on land use, soil structure, etc.), and which accounts for the difficulty of producing simple hydrological models that generalize well.