Marine heatwaves (MHWs) are extreme oceanic warm water events (above 90th percentile threshold) that significantly impact the marine environment. Several studies have recently explored the genesis and impacts of MHWs though they are least understood in the tropical Indian Ocean. Here we investigate the genesis and trend of MHWs in the Indian Ocean during 1982–2018 and their role in modulating the Indian monsoon. We find that the rapid warming in the Indian Ocean plays a critical role in increasing the number of MHWs. Meanwhile, the El Nino has a prominent influence on the occurrence of MHWs during the summer monsoon. The Indian Ocean warming and the El Nino variability have synergistically resulted in some of the strongest and long-lasting MHWs in the Indian Ocean. The western Indian Ocean (WIO) region experienced the largest increase in MHWs at a rate of 1.2–1.5 events per decade, followed by the north Bay of Bengal at a rate of 0.4–0.5 events per decade. Locally, the MHWs are induced by increased solar radiation, relaxation of winds, and reduced evaporative cooling. In the western Indian Ocean, the decreased winds further restrict the heat transport by ocean currents from the near-equatorial regions towards the north. Our analysis indicates that the MHWs in the western Indian Ocean and the north Bay of Bengal lead to a reduction in monsoon rainfall over the central Indian subcontinent. On the other hand, there is an enhancement of monsoon rainfall over southwest India due to the MHWs in the Bay of Bengal.

The biological oxygen (O2) saturation anomaly ΔO2/Ar is a tracer for net community production (NCP) in marine surface waters, with argon (Ar) normalization used to correct for physical effects on O2 supersaturation. Ship-board mass spectrometry has been used for ΔO2/Ar measurements, but this approach may not be accessible to many research groups. Here, we present a proof-of-concept for NCP estimates based on underway measurements of ΔO2/N2, which can be obtained from deployments of O2-Optodes and gas tension devices (GTD). We used a one-dimensional mixed layer model, validated against field observations, to evaluate divergence in ΔO2/Ar and ΔO/N2 resulting from differences in the sensitivity of Ar and nitrogen (N2) to various physical processes. Changes in sea surface temperature and responses in air-sea exchange most strongly decouple surface Ar and N2 with additional excess N2 associated with bubble-injection during high-wind conditions and vertical mixing in regions of elevated subsurface N2. In contrast, biological N2-fixation has a negligible contribution to the observed divergence between Ar and N2. Based on readily available environmental data, we present an approach to correct for Ar and N2 differences, yielding a new tracer, N2’, that is a near analog of Ar. We show that ΔO2/N2’ provides an excellent approximation to ΔO2/Ar, and that uncertainty and biases in ΔO2/N2’ are small relative to other errors in NCP calculations. Our results demonstrate the potential for ΔO2/N2’ measurements to expand NCP estimates from oceanographic research surveys, vessels of opportunity or autonomous surface vehicles.

Atlantic Water (AW) is the largest reservoir of heat in the Arctic Ocean, isolated from the surface and sea-ice by a strong halocline. In recent years AW shoaling and warming are thought to have had an increased influence on sea-ice in the Eurasian Basin. In this study we analyse 59000 profiles from across the Arctic from the 1970s to 2018 to obtain an observationally-based pan-Arctic picture of the AW layer, and to quantify temporal and spatial changes. The potential temperature maximum of the AW (the AW core) is found to be an easily detectable, and generally effective metric for assessments of AW properties, although temporal trends in AW core properties do not always reflect those of the entire AW layer. The AW core cools and freshens along the AW advection pathway as the AW loses heat and salt through vertical mixing at its upper bound, as well as via likely interaction with cascading shelf flows. In contrast to the Eurasian Basin, where the AW warms (by approximately 0.7°C between 2002 and 2018) in a pulse-like fashion and has an increased influence on upper ocean heat content, AW in the Canadian Basin cools (by approximately 0.1°C between 2008 and 2018) and becomes more isolated from the surface due to the intensification of the Beaufort Gyre. These opposing AW trends in the Eurasian and Canadian Basins of the Arctic over the last 40 years suggest that AW in these two regions may evolve differently over the coming decades.

A regional data-constrained coupled ocean-sea ice general circulation model and its adjoint are used to investigate mechanisms controlling the volume transport variability through Bering Strait during 2002 to 2013. Comprehensive time-resolved sensitivity maps of Bering Strait transport to atmospheric forcing can be accurately computed with the adjoint along the forward model trajectory to identify spatial and temporal scales most relevant to the strait's transport variability. The simulated Bering Strait transport anomaly is found to be controlled primarily by the wind stress on short time-scales of order 1 month. Spatial decomposition indicates that on monthly time-scales winds over the Bering and the combined Chukchi and East Siberian Seas are the most significant drivers. Continental shelf waves and coastally-trapped waves are suggested as the dominant mechanisms for propagating information from the far field to the strait. In years with transport extrema, eastward wind stress anomalies in the Arctic sector are found to be the dominant control, with correlation coefficient of 0.94. This implies that atmospheric variability over the Arctic plays a substantial role in determining Bering Strait flow variability. The near-linear response of the transport anomaly to wind stress allows for predictive skill at interannual time-scales, thus potentially enabling skillful prediction of changes at this important Pacific-Arctic gateway, provided that accurate measurements of surface winds in the Arctic can be obtained. The novelty of this work is the use of space and time-resolved adjoint-based sensitivity maps, which enable detailed dynamical, i.e. causal attribution of the impacts of different forcings.

It is well established that small scale cross-density (diapycnal) turbulent mixing induced by breaking of overturns in the interior of the ocean plays a significant role in sustaining the deep ocean circulation and in regulation of tracer budgets such as those of heat, carbon and nutrients. There has been significant progress in the fluid mechanical understanding of the physics of breaking internal waves. Connection of the microphysics of such turbulence to the larger scale dynamics, however, is significantly underdeveloped. We offer a hybrid theoretical-statistical approach, informed by observations, to make such a link. By doing so, we define a bulk flux coefficient, $\Gamma_B$, which represents the partitioning of energy available to an ‘ocean box’ (such as a grid cell of a coarse resolution climate model), from winds, tides, and other sources, into mixing and dissipation. $\Gamma_B$ depends on both the statistical distribution of turbulent patches and the flux coefficient associated with individual patches, $\Gamma_i$. We rely on recent parameterizations of ~$\Gamma_i$~ and the seeming universal characteristics of statistics of turbulent patches, to infer $\Gamma_B$, which is the essential quantity for representation of turbulent diffusivity in climate models. By applying our approach to climatology and global tidal estimates, we show that on a basin scale, energetic mixing zones exhibit moderately efficient mixing that induces significant vertical density fluxes, while quiet zones (with small background turbulence levels), although highly efficient in mixing, exhibit minimal vertical fluxes. The transition between the less energetic to more energetic zones marks regions of intense upwelling and downwelling of deep waters. We suggest that such upwelling and downwelling may be stronger than previously estimated, which in turn has direct implications for the closure of the deep branch of the ocean meridional overturning circulation as well as for the associated tracer budgets.

This study examines causes of the double silica maximum in the deep interior Northeast Pacific Basin using a stochastic Lagrangian tracer model based on steady-state advective fields and diapycnal diffusion established by a hydrographic inverse method that conserves potential vorticity and salinity. Lateral diffusion, unresolved by the inverse model, is adjusted for overall agreement with radiocarbon distribution. The double silica maximum in vertical profiles arises from an eastern-intensified single-maximum in the North Pacific Deep Water along the northern domain boundary (originating in the western Pacific), and a strong subarctic bottom source supplying silica to Upper Circumpolar Deep Water density surfaces that successively intersect the seafloor over a broad area east of 150°W, associated geostrophically with southward flow. The existence of the double silica maximum requires weak diapycnal transport in the deep interior, with broader implications for the conceptual picture of meridional overturning circulation in the North Pacific.

The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of the existence of dynamical coherent regions, THOR identifies these and their link to distinct currents and mechanisms such as the formation regions of deep water masses, and the location of the Gulf Stream and North Atlantic Current. Beyond a black box approach, THOR is engineered to elucidate its source of predictive skill rooted in physical understanding. A labeled dataset is engineered using an explicitly interpretable equation transform and k-means application to model data, allowing theoretical inference. A multilayer perceptron is then trained, explaining its skill using a combination of layerwise relevance propagation and theory. With abrupt CO2 quadrupling, the circulation weakens due to a shift in deep water formation regions, a northward shift of the Gulf stream and an eastwards shift in the North Atlantic Current. If CO2 is increased 1% yearly, similar but weaker patterns emerge influenced by natural variability. THOR is scalable and applicable to a range of models using only the ocean depth, dynamic sea level and wind stress, and could accelerate the analysis and dissemination of climate model data. THOR constitutes a step towards trustworthy machine learning called for within oceanography and beyond.

Soluble and total trace metals were measured in 4 size fractionated aerosol samples collected over the tropical eastern Atlantic Ocean. In samples that were dominated by Saharan dust, the size distributions of total iron, aluminium, titanium, manganese, cobalt and thorium were very similar to one another and to the size distributions of soluble manganese, cobalt and thorium. Finer particle sizes (< ~3 µm) showed enhanced soluble concentrations of iron, aluminium and titanium, possibly as a result of interactions with acidic sulfate aerosol during atmospheric transport. The difference in fine particle solubility between these two groups of elements might be related to the hyperbolic increase in the fractional solubility of iron, and a number of other elements, during the atmospheric transport of Saharan dust, which is not observed for manganese and its associated elements. In comparison to elements whose solubility varies during atmospheric transport, the stability of thorium fractional solubility should reduce uncertainties in the use of dissolved concentrations of this element in seawater as a proxy for dust deposition, although this topic requires further work.

Numerical wave models are used for a wide range of applications, from the global ocean to coastal scales. Here we report on significant improvements compared to the previous hindcast by Rascle and Ardhuin (2013). This result was obtained by updating forcing fields, adjusting the spectral discretization and retuning wind wave growth and swell dissipation parameters. Most of the performance analysis is done using significant wave heights (Hs) from the recent re-calibrated and denoised satellite altimeter data set provided by the European Space Agency Climate Change Initiative (ESA-CCI), with additional verification using spectral buoy data. We find that, for the year 2011, using wind fields from the recent ERA5 reanalysis provides lower scatter against satellite H s data compared to historical ECMWF operational analyses, but still yields a low bias on wave heights that can be mitigated by re-scaling wind speeds larger than 20 m/s. Alternative blended wind products can provide more accurate forcing in some regions, but were not retained because of larger errors elsewhere. We use the shape of the probability density function of H s around 2 m to fine tune the swell dissipation parameterization. The updated model hindcast appears to be generally more accurate than the previous version, and can be more accurate than the ERA5 H s estimates, in particular in strong current regions and for Hs greater than 7 m.

Seasonal prediction is one important element in a seamless prediction chain between weather forecast and climate projections. After several years of common development in collaboration with Universität Hamburg and Max Planck Institute for Meteorology, the Deutscher Wetterdienst performs operational seasonal forecasts since 2016 with the German Climate Forecast System, now in Version 2 (GCFS2.0). Here, the configuration of previous system GCFS1.0 and the current GCFS2.0 are described and the performance of the two systems is compared over the common hindcast period of 1990-2014. In GCFS2.0, the forecast skill is improved compared to GCFS1.0 during boreal winter, especially for the Northern Hemisphere where the Pearson correlation has doubled for the North Atlantic Oscillation index. During boreal summer, overall a similar performance of GCFS2.0 in comparison to GCFS1.0 is assessed. Future developments for climate forecasts need a stronger focus on the performance of seasonal dependent processes in a model system.

In this study, a storm surge model of the semi-enclosed Tokyo Bay was constructed to investigate its hydrodynamic response to major typhoon parameters, such as the point of landfall, approach angle, forward speed, size, and intensity. The typhoon simulation was validated for Typhoon Lan in 2017, and 31 hypothetical storm surge scenarios were generated to establish the sensitivity of peak surge height to the variation in typhoon parameters. The maximum storm surge height in the upper bay adjacent to the Tokyo Metropolitan Area was found to be highly sensitive to the forward speed and size of the passing typhoon. However, the importance of these parameters in disaster risk reduction has been largely overlooked by researchers and disaster managers. It was also determined that of the many hypothetical typhoon tracks evaluated, the slow passage of a large and intense typhoon transiting parallel to the longitudinal axis of Tokyo Bay, making landfall 25 km southwest, is most likely to cause a hazardous storm surge scenario in the upper-bay area. The results of this study are expected to be useful to disaster managers for advanced preparation against destructive storm surges.

Iron is a key micronutrient controlling phytoplankton growth in vast regions of the global ocean. Despite its importance, uncertainties remain high regarding external iron source fluxes and internal cycling on a global scale. In this study, we used a global dissolved iron dataset, including GEOTRACES measurements, to constrain source and scavenging fluxes in the marine iron component of a global ocean biogeochemical model. Our model simulations tested three key uncertainties: source inputs of atmospheric soluble iron deposition (varying from 1.4–3.4 Gmol/yr), reductive sedimentary iron release (14–117 Gmol/yr), and compared a variable ligand parameterization to a constant distribution. In each simulation, scavenging rates were tuned to reproduce the observed global mean iron inventory for consistency. The variable ligand parameterization improved the global model-data misfit the most, suggesting that heterotrophic bacteria are an important source of ligands to the ocean. Model simulations containing high source fluxes of atmospheric soluble iron deposition (3.4 Gmol/yr) and reductive sedimentary iron release (114 Gmol/yr) further improved the model most notably in the surface ocean. High scavenging rates were then required to maintain the iron inventory resulting in relatively short surface and global ocean residence times of 0.83 and 7.5 years, respectively. The model simulates a tight spatial coupling between source inputs and scavenging rates, which may be too strong due to underrepresented ligands near source inputs, contributing to large uncertainties when constraining individual fluxes with dissolved iron concentrations. Model biases remain high and are discussed to help improve global marine iron cycle models.

Waves and water level setup during storms can create overwashing flows across barrier islands. Overwashing flows can cause erosion, barrier breaching, and inlet formation, but their sediments can also be deposited and form washover fans. These widely different outcomes remain difficult to predict. Here we suggest that a breach develops when the sediment volume transported by overwashing flows exceeds the barrier subaerial volume. We form a simple analytical theory that estimates overwashing flows from storm characteristics, barrier morphology, and dune vegetation, and which can be used to assess washover deposition and breaching likelihood. Our theory suggests that barrier width and storm surge height are two important controls on barrier breaching. We test our theory with the hydrodynamic and morphodynamic model Delft3D as well as with field observations of 21 washover fans and 6 breaches that formed during hurricane Sandy. There is reasonable correspondence for natural but not for developed barrier coasts, where traditional sediment transport equations do not readily apply. Our analytical formulations for breach formation and overwash deposition can be used to improve long-term barrier island models.

The paper presents a combined numerical - deep learning (DL) approach for improving wind and wave forecasting. First, a DL model is trained to improve wind velocity forecasts by using past reanalysis data. The improved wind forecasts are used as forcing in a numerical wave forecasting model. This novel approach, used to combine physics-based and data-driven models, was tested over the Mediterranean. It resulted in ∼10% RMSE improvement in both wind velocity and wave height forecasts over operational models. This significant improvement is even more substantial at the Aegean Sea from May to September, when Etesian winds are dominant, improving wave height forecasts by over 35%. The additional computational costs of the DL model are negligible compared to the costs of either numerical models. This work has the potential to greatly improve the wind and wave forecasting models used nowadays by tailoring models to localized seasonal conditions, at negligible additional computational costs.

The effects of instrument noise on estimating the spectral attenuation rates of ocean waves in sea ice are explored using synthetic observations in which the true attenuation rates are known explicitly. The spectral shape of the energy added by noise, relative to the spectral shape of the true wave energy, is the critical aspect of the investigation. A negative bias in attenuation that grows in frequency is found across a range of realistic parameters. This negative bias decreases the observed attenuation rates at high frequencies, such that it can explain the rollover effect commonly reported in field studies of wave attenuation in sea ice. The published results from four field experiments are evaluated in terms of the noise bias, and a spurious rollover (or flattening) of attenuation is found in all cases. Remarkably, the wave heights are unaffected by the noise bias, because the noise bias occurs at frequencies that contain only a small fraction of the total energy.

The advocacy activities necessary to sustain healthy watersheds and improve impaired ones ultimately rely on the democratic process, and therefore depend on a public that values our coastal resources and understands the role that water quality plays in maintaining that value. We contend that an opportunity exists to improve the temporal and spatial density of monitoring by reducing the cost of collecting measurements, while simultaneously fostering an informed and invested public. We envision a distributed water quality monitoring sensor network, composed of low-cost ($1000-$2000) profiling devices we call TideRiders, built and operated by private citizens and local educational organizations and supported by an institution-hosted centralized data and control portal. The TideRider concept engages the public not just in the collection of data but also in the building, deployment, operation, and recovery of these robot sensors. TideRiders will carry a suite of basic water quality instrumentation (temperature, conductivity, and dissolved oxygen), transmit data and accept commands over the cellular network, and can sample surface and bottom waters by surfacing and submerging on a programmable schedule. Operators will harness tidal currents to move their TideRiders deliberately around an embayment, essentially by surfacing in a favorable tide and anchoring on the bottom in an adverse tide. A network of TideRiders deployed in tidally-dominated estuaries like Buzzards Bay and Narragansett Bay could provide basic water quality data at several-hour intervals for weeks at a time by “virtually mooring” in center-bay locations that are otherwise only accessible by boat and therefore typically sampled less frequently than shore stations. We present preliminary field results from a series of prototypes designed and built by students. The prototype devices utilize a novel low-cost semi-passive shallow-water buoyancy engine and were constructed for less than $1000 in parts.

Ocean phytoplankton play a critical role in the global carbon cycle, contributing ~50% of global photosynthesis. As planktonic organisms, phytoplankton encounter significant environmental variability as they are advected throughout the ocean. How this variability impacts phytoplankton growth rates and population dynamics remains unclear. Here, we systematically investigated the impact of different rates and magnitudes of sea surface temperature (SST) variability on phytoplankton community growth rates using surface drifter observations from the Southern Ocean (> 30oS) and a phenotype-based ecosystem model. Short-term SST variability (<7 days) had a minimal impact on phytoplankton community growth rates. Moderate SST changes of 3-5oC over 7-21 days produced a large time lag between the temperature change and the biological response. The impact of SST variability on community growth rates was nonlinear and a function of the rate and magnitude of change. Additionally, the nature of variability generated in a Lagrangian reference frame (following trajectories of surface water parcels) was larger than that within an Eulerian reference frame (fixed point), which initiated different phytoplankton responses between the two reference frames. Finally, we found that these dynamics were not captured by the Eppley growth model commonly used in global biogeochemical models and resulted in an overestimation of community growth rates, particularly in dynamic, strong frontal regions of the Southern Ocean. This work demonstrates that the timescale for environmental selection (community replacement) is a critical factor in determining community composition and takes a first step towards including the impact of variability and biological response times into biogeochemical models.

Astronomical variations in tidal magnitude can strongly modulate the severity of coastal flooding on the daily, monthly, and interannual timescales. Here, we present a new quasi-nonstationary joint probability method (qn-SSJPM) that estimates interannual fluctuations in flood hazard caused by the 18.6 and quasi 4.4-year modulations of tidal properties. We demonstrate that the qn-SSJPM provides more precise and stable storm tide probability estimates compared with the standard practice of fitting an extreme value distribution to measured storm tides, which is often biased by the largest few events within the observational period. Applying the qn-SSJPM in the Gulf of Maine, we find significant tidal forcing of flood hazard by the 18.6-year nodal cycle, whereas 4.4-year modulations and a secular trend in tides are small compared to interannual variation and long-term trends in sea-level. The nodal cycle forces decadal oscillations in the 1% annual exceedance probability storm tide at an average rate of ±13.5 mm/y in Eastport, ME; ±4.0 mm/y in Portland, ME; and ±5.9 mm/y in Boston, MA. Currently, nodal forcing is counteracting the sea-level rise-induced increase in flood hazard; however, in 2025, the nodal cycle will reach a minimum and then begin to accelerate flood hazard increase as it moves toward its maximum phase over the subsequent decade. Along the world’s meso-to-macrotidal coastlines, it is therefore critical to consider both sea-level rise and tidal non-stationarity in planning for the transition to chronic flooding that will be driven by SLR in many regions over the next century.

Reducing uncertainty in the global carbon budget requires better quantification of ocean CO2 uptake and its temporal variability. Several methodologies for reconstructing air-sea CO2 exchange from sparse pCO2 observations indicate larger decadal variability than estimated using ocean models. We develop a new application of multiple Large Ensemble Earth system models to assess these reconstructions’ ability to estimate spatiotemporal variability. With our Large Ensemble Testbed, pCO2 fields from 25 ensemble members each of four independent Earth system models are subsampled as the observations and the reconstruction is performed as it would be with real- world observations. The power of a testbed is that the perfect reconstruction is known for each of the 100 original model fields; thus, reconstruction skill can be comprehensively assessed. We find that a commonly used neural-network approach can skillfully reconstruct air-sea CO2 fluxes when and where it is trained with sufficient data. Flux bias is low for the global mean and Northern Hemisphere, but can be regionally high in the Southern Hemisphere. The phase and amplitude of the seasonal cycle are accurately reconstructed outside of the tropics, but longer-term variations are reconstructed with only moderate skill. For Southern Ocean decadal variability, insufficient sampling leads to a 39% [15%:58%, interquartile range] overestimation of amplitude, and phasing is only moderately correlated with known truth (r=0.54 [0.46:0.63]). Globally, the amplitude of decadal variability is overestimated by 21% [3%:34%]. Machine learning, when supplied with sufficient data, can skillfully reconstruct ocean properties. However, data sparsity remains a fundamental limitation to quantification of decadal variability in the ocean carbon sink.

The significant wave height (SWH) is of great importance in industries such as ocean engineering, marine resource development, shipping and transportation. Haiyang-2C (HY-2C), the 2nd operational satellite of China’s marine dynamic exploration series, can provide all-weather, all-day, global observations of wave height, wind, and temperature. The altimeter can only measure the nadir wave height and other information, and the scatterometer can obtain the wind field with a wide swath. In this paper, a deep learning approach is applied to produce a wide swath SWH data through the wind field from the scatterometer and the nadir wave height from altimeter. Two validation sets, 1-month data at 6-minute intervals and 1-day data with an interval of 10 s, are fed into the trained model. Experiments indicate that the extending nadir SWH yields a real-time wide swath grid product along track, which can be offered as support for oceanographic study, and it is superior to take the swell characteristics of ERA5 into account as the input of wide swath SWH model. In conclusion, the verification results demonstrate the effectiveness and feasibility of the wide swath SWH model.

Projecting fluid systems onto coordinates defined by fluid properties (e.g., pressure, temperature, tracer concentration) can reveal deep insights, for example into the thermodynamics and energetics of the ocean and atmosphere. We present a mathematical formalism for fluid flow in such coordinates. We formulate mass conservation, streamfunction, tracer conservation, and tracer angular momentum within fluid property space (q-space) defined by an arbitrary number of continuous fluid properties. Points in geometric position space (x-space) do not generally correspond in a 1-to-1 manner to points in q-space. We therefore formulate q-space as a differentiable manifold, which allows differential and integral calculus but lacks a metric, thus requiring exterior algebra and exterior calculus. The Jacobian, as the ratio of volumes in x-space and q-space, is central to our theory. When x-space is not 1-to-1 with q-space, we define a generalized Jacobian either by patching x-space regions that are 1-to-1 with q-space, or by integrating a Dirac delta to select all x-space points corresponding to a given q value. The latter method discretises to a binning algorithm, providing a practical framework for analysis of fluid motion in arbitrary coordinates. Considering q-space defined by tracers, we show that tracer diffusion and tracer sources drive motion in q-space, analogously to how internal stresses and external forces drive motion in x-space. Just as the classical angular momentum of a body is unaffected by internal stresses, the globally integrated tracer angular momentum is unaffected by tracer diffusion — unless different tracers are diffused differently, as in double diffusion.

This study demonstrates a link between coastal downwelling and tropical cyclone (TC) intensification. We show that coastal downwelling increases air-sea enthalpy (heat, moisture) fluxes ahead of TCs as they approach land, creating conditions conducive to intensification even in the presence of typically inhibiting factors like strong vertical wind shear. The study uses a coupled TC model (HWRF-B) and buoy observations to demonstrate that coastal downwelling developed as three TCs in 2020 approached land. Results show downwelling maintained warmer sea-surface temperatures over the ocean shelf, enhancing air-sea temperature/humidity contrasts. We found that in such cases resulting air-sea enthalpy fluxes can replenish the boundary-layer even when cool, dry air intrudes, as in sheared storms and storms approaching continental land-masses. The resulting warm, moist air is advected into the TC inner core, enhancing convective development, thus providing energy for TC intensification. These results indicate coastal downwelling can be important in forecasting TC intensity change before landfall.

Coastal nitrogen (N) enrichment is a global environmental problem that can influence acidification, deoxygenation, and subsequent habitat loss in ways that can be synergistic with global climate change impacts. In the Southern California Bight, an eastern boundary upwelling system, modeling of wastewater discharged through ocean outfalls has shown that it effectively doubles N loading to urban coastal waters. However, effects of wastewater outfalls on biogeochemical rates of primary production and respiration, key processes through which coastal acidification and deoxygenation are manifested, have not been directly linked to observed trends in ambient chlorophyll a, oxygen and pH. In this paper, we compare observations of nutrient concentrations and forms, as well as rates of biogeochemical cycling, in areas within treated wastewater effluent plumes compared to areas spatially distant from ocean outfalls where we expected minimum influence of the plume. We document that wastewater nutrient inputs have an immediate, local effect on nutrient stoichiometry, elevating ammonium and nitrite concentrations by a mean of 4 µM and 0.2 µM, respectively, increasing dissolved nitrogen: phosphorus ratios by a mean of 7 and slightly increasing chlorophyll a by a mean of 1 µg L-1 in the upper 60 m of the watercolumn, as well as increasing rates of nitrification within the plume by a mean of 17 nmol L-1 day-1 and increasing δ13C and δ15N of suspended particulate matter, an integrated measure of primary production, by a mean of 1.3 ‰ and 1 ‰, respectively. We did not observe a significant near plume effect on δ18O and δ15N of the dissolved nitrate+nitrite, an indicator of nitrate+nitrite assimilation into the biomass, instantaneous rates of primary production and respiration, or dissolved oxygen concentration, suggesting any potential impact from wastewater on these is moderated by other factors, notably mixing of water masses. These results indicate that a “reference-area” approach, wherein stations within or near the zone of initial dilution (ZID) from the wastewater outfall are compared to stations farther afield (reference areas) to assess contaminant impacts, may be insufficient to document regional scale impacts of nutrients.

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi-petabyte datasets. Datasets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud-based data science platforms, accessed through a web-browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence (AI/ML) are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data-driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?