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Oceanic dissolved inorganic carbon (TC) is the largest pool of carbon that substantially interacts with the atmosphere on human timescales. Oceanic TC is increasing through uptake of anthropogenic carbon dioxide (CO2), and seawater pH is decreasing as a consequence. Both the exchange of CO2 between the ocean and atmosphere and the pH response are governed by a set of parameters that interact through chemical equilibria, collectively known as the marine carbonate system. To investigate these processes, at least two of the marine carbonate system's parameters are typically measured – most commonly, two from TC, total alkalinity (AT), pH, and seawater CO2 fugacity (fCO2; or its partial pressure, pCO2, or its dry-air mole fraction, xCO2) – from which the remaining parameters can be calculated and the equilibrium state of seawater solved. Several software tools exist to carry out these calculations, but no fully functional and rigorously validated tool written in Python, a popular scientific programming language, was previously available. Here, we present PyCO2SYS, a Python package intended to fill this capability gap. We describe the elements of PyCO2SYS that have been inherited from the existing CO2SYS family of software and explain subsequent adjustments and improvements. For example, PyCO2SYS uses automatic differentiation to solve the marine carbonate system and calculate chemical buffer factors, ensuring that the effect of every modelled solute and reaction is accurately included in all its results. We validate PyCO2SYS with internal consistency tests and comparisons against other software, showing that PyCO2SYS produces results that are either virtually identical or different for known reasons, with the differences negligible for all practical purposes. We discuss insights that guided the development of PyCO2SYS: for example, the fact that the marine carbonate system cannot be unambiguously solved from certain pairs of parameters. Finally, we consider potential future developments to PyCO2SYS and discuss the outlook for this and other software for solving the marine carbonate system. The code for PyCO2SYS is distributed via GitHub (https://github.com/mvdh7/PyCO2SYS, last access: 23 December 2021) under the GNU General Public License v3, archived on Zenodo , and documented online (https://pyco2sys.readthedocs.io/en/latest/, last access: 23 December 2021).

For about 2 decades, oceanographers have been installing oxygen sensors on Argo profiling floats to be deployed throughout the world ocean, with the stated objective of better constraining trends and variability in the ocean's inventory of oxygen. Until now, measurements from these Argo-float-mounted oxygen sensors have been mainly used for localized process studies on air–sea oxygen exchange, upper-ocean primary production, biological pump efficiency, and oxygen minimum zone dynamics. Here, we present a new four-dimensional gridded product of ocean interior oxygen, derived via machine learning algorithms trained on dissolved oxygen observations from Argo-float-mounted sensors and discrete measurements from ship-based surveys and applied to temperature and salinity fields constructed from the global Argo array. The data product is called GOBAI-O2, which stands for Gridded Ocean Biogeochemistry from Artificial Intelligence – Oxygen (Sharp et al., 2022; 10.25921/z72m-yz67); it covers 86 % of the global ocean area on a 1∘ × 1∘ (latitude × longitude) grid, spans the years 2004–2022 with a monthly resolution, and extends from the ocean surface to a depth of 2 km on 58 levels. Two types of machine learning algorithms – random forest regressions and feed-forward neural networks – are used in the development of GOBAI-O2, and the performance of those algorithms is assessed using real observations and simulated observations from Earth system model output. Machine learning represents a relatively new method for gap filling ocean interior biogeochemical observations and should be explored along with statistical and interpolation-based techniques. GOBAI-O2 is evaluated through comparisons to the oxygen climatology from the World Ocean Atlas, the mapped oxygen product from the Global Ocean Data Analysis Project and to direct observations from large-scale hydrographic research cruises. Finally, potential uses for GOBAI-O2 are demonstrated by presenting average oxygen fields on isobaric and isopycnal surfaces, average oxygen fields across vertical–meridional sections, climatological seasonal cycles of oxygen averaged over different pressure layers, and globally integrated time series of oxygen. GOBAI-O2 indicates a declining trend in the oxygen inventory in the upper 2 km of the global ocean of 0.79 ± 0.04 % per decade between 2004 and 2022.

Accurately predicting future ocean acidification (OA) conditions is crucial for advancing OA research at regional and global scales, and guiding society's mitigation and adaptation efforts. This study presents a new model‐data fusion product covering 10 global surface OA indicators based on 14 Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), along with three recent observational ocean carbon data products. The indicators include fugacity of carbon dioxide, pH on total scale, total hydrogen ion content, free hydrogen ion content, carbonate ion content, aragonite saturation state, calcite saturation state, Revelle Factor, total dissolved inorganic carbon content, and total alkalinity content. The evolution of these OA indicators is presented on a global surface ocean 1° × 1° grid as decadal averages every 10 years from preindustrial conditions (1750), through historical conditions (1850–2010), and to five future Shared Socioeconomic Pathways (2020–2100): SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. These OA trajectories represent an improvement over previous OA data products with respect to data quantity, spatial and temporal coverage, diversity of the underlying data and model simulations, and the provided SSPs. The generated data product offers a state‐of‐the‐art research and management tool for the 21st century under the combined stressors of global climate change and ocean acidification. The gridded data product is available in NetCDF at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information: https://www.ncei.noaa.gov/data/oceans/ncei/ocads/metadata/0259391.html, and global maps of these indicators are available in jpeg at: https://www.ncei.noaa.gov/access/ocean-carbon-acidification-data-system/synthesis/surface-oa-indicators.html. Plain Language Summary A new data product, based on the latest computer simulations and observational data, offers improved projections of ocean acidification (OA) conditions from the start of the Industrial Revolution in 1750 to the end of the 21st century. These projections will support OA research at regional and global scales, and provide essential information to guide OA mitigation and adaptation efforts for various sectors, including fisheries, aquaculture, tourism, marine resource decision‐makers, and the general public. Key Points This study presents the evolution of 10 ocean acidification (OA) indicators in the global surface ocean from 1750 to 2100 By leveraging 14 Earth System Models (ESMs) and the latest observational data, it represents a significant advancement in OA projections This inter‐model comparison effort showcases the overall agreements among different ESMs in projecting surface ocean carbon variables

Hypoxia and anoxia occurred in the upper Delaware Estuary throughout much of the 20th century and diminished over the past several decades. I reviewed 30 yr of data from my laboratory's research efforts, 40 yr of consistent monitoring data from a multistate agency, results from inconsistent data collection from the past century, and anecdotal information to construct a long-time picture of the decline and increase of dissolved oxygen concentrations (DO) in the urban region of the estuary. The primary cause of the DO decline appeared to be inputs or allochthonous materials from urban sources (reduced nitrogen and carbon). In spite of extremely high nutrient concentrations, excess algal production did not influence DO anywhere along the tidal freshwater stretch or the saline portion of the well-mixed Delaware Estuary; and it does not have an influence today. The nutrient loading to the Delaware Estuary is very high, yet the typical signs of eutrophication are not obvious.Based on a model of apparent oxygen utilization, the Delaware Bay apparently had higher primary production 50 yr ago, a time when nutrient concentrations were as high or higher than today, shellfish and finfish production were apparently also higher, and DO was close to saturation. This analysis is offered as guidance in assessing and managing estuarine eutrophication, which is too often considered narrowly to be the result of inadvertent overfertilization by nutrients or a single nutrient element.

The ocean is warming, acidifying, and losing oxygen. The Global Ocean Ship-based Hydrographic Investigations Program (GO-SHIP) carries out repeat hydrographic surveys along specified transects throughout all ocean basins to allow accurate and precise quantification of changes in variables such as temperature, salinity, carbon, oxygen, nutrients, velocity, and anthropogenic tracers, and uses these observations to understand ventilation patterns, deoxygenation, heat uptake, ocean carbon content, and changes in circulation. GO-SHIP provides global, full-depth, gold-standard data for model validation and calibration of autonomous sensors, including Argo floats. The Pacific Marine Environmental Laboratory (PMEL), through sustained funding from NOAA, has developed methods to measure several of the variables routinely sampled through GO-SHIP and is a core contributor to these repeat hydrographic cruises.

Bark beetle populations have drastically increased in magnitude over the last several decades leading to the largest-scale tree mortality ever recorded from an insect infestation on multiple wooded continents. When the trees die, the loss of canopy and changes in water and nutrient uptake lead to observable changes in hydrology and biogeochemical cycling. This review aims to synthesize the current research on the effects of the bark beetle epidemic on nutrient cycling and water quality while integrating recent and relevant hydrological findings, along with suggesting necessary future research avenues. Studies generally agree that snow depth will increase in infested forests, though the magnitude is uncertain. Changes in evapotranspiration are more variable as decreased transpiration from tree death may be offset by increased understory evapotranspiration and ground evaporation. As a result of such competing hydrologic processes that can affect watershed biogeochemistry along with the inherent variability of natural watershed characteristics, water quality changes related to beetle infestation are difficult to predict and may be regionally distinct. However, tree-scale changes to soil–water chemistry (N, P, DOC and base cation concentrations and composition) are being observed in association with beetle outbreaks which ultimately could lead to larger-scale responses. The different temporal and spatial patterns of bark beetle infestations due to different beetle and tree species lead to inconsistent infestation impacts. Climatic variations and large-scale watershed responses provide a further challenge for predictions due to spatial heterogeneities within a single watershed; conflicting reports from different regions suggest that hydrologic and water quality impacts of the beetle on watersheds cannot be generalized. Research regarding the subsurface water and chemical flow-paths and residence times after a bark beetle epidemic is lacking and needs to be rigorously addressed to best predict watershed or regional-scale changes to soil–water, groundwater, and stream water chemistry.

This paper presents a novel method to couple an environmental bioremediation system with a subsurface renewable energy storage system. This method involves treating unsaturated contaminated soil using in-situ thermally enhanced bioremediation; the thermal system is powered by renewable energy. After remediation goals are achieved, the thermal system can then be used to store renewable energy in the form of heat in the subsurface for later use. This method can be used for enhanced treatment of environmental pollutants for which temperature is considered a limiting factor. For instance, this system can be used at a wide variety of petroleum-related sites that are likely contaminated with hydrocarbons such as oil refineries and facilities with above- and underground storage tanks. In this paper, a case-study example was analyzed using a previously developed numerical model of heat transfer in unsaturated soil. Results demonstrate that coupling energy storage and thermally-enhanced bioremediation systems offer an efficient and sustainable way to achieve desired temperature–moisture distribution in soil that will ultimately enhance the microbial activity.

A common strategy for calculating the direction and rate of carbon dioxide gas (CO2) exchange between the ocean and atmosphere relies on knowledge of the partial pressure of CO2 in surface seawater (pCO2(sw)), a quantity that is frequently observed by autonomous sensors on ships and moored buoys, albeit with significant spatial and temporal gaps. Here we present a monthly gridded data product of pCO2(sw) at 0.25∘ latitude by 0.25∘ longitude resolution in the northeastern Pacific Ocean, centered on the California Current System (CCS) and spanning all months from January 1998 to December 2020. The data product (RFR-CCS; Sharp et al., 2022; 10.5281/zenodo.5523389) was created using observations from the most recent (2021) version of the Surface Ocean CO2 Atlas (Bakker et al., 2016). These observations were fit against a variety of collocated and contemporaneous satellite- and model-derived surface variables using a random forest regression (RFR) model. We validate RFR-CCS in multiple ways, including direct comparisons with observations from sensors on moored buoys, and find that the data product effectively captures seasonal pCO2(sw) cycles at nearshore sites. This result is notable because global gridded pCO2(sw) products do not capture local variability effectively in this region, suggesting that RFR-CCS is a better option than regional extractions from global products to representpCO2(sw) in the CCS over the last 2 decades. Lessons learned from the construction of RFR-CCS provide insight into how global pCO2(sw) products could effectively characterize seasonal variability in nearshore coastal environments. We briefly review the physical and biological processes – acting across a variety of spatial and temporal scales – that are responsible for the latitudinal and nearshore-to-offshorepCO2(sw) gradients seen in the RFR-CCS reconstruction ofpCO2(sw). RFR-CCS will be valuable for the validation of high-resolution models, the attribution of spatiotemporal carbonate system variability to physical and biological drivers, and the quantification of multiyear trends and interannual variability of ocean acidification.

Wetlands are crucial hydrological features that provide benefits, including ecosystem services and habitat conservation, protection from flooding associated with sea level rise and extreme events, water storage and treatment, recreation, and aesthetics, among others. Peru is one of the few countries in Latin America that has not developed a national wetland inventory. While this can in part be attributed to the limited availability of peer-reviewed publications in the English literature, a larger quantity of Peruvian wetland-related publications are available in Spanish, and are less accessible to the global population. In this paper, we developed an extensive review and synthesis of the Spanish literature, including university theses, peer-reviewed articles, and government reports. Our report focuses on evaluating the state of the art of Peruvian wetlands in terms of temporal evolution, geographical distribution, vested institutions, research topics (e.g., water quality, fauna, flora, microorganisms, etc.), and advances toward the conservation of wetlands. The analysis identified 274 wetland-related publications in Spanish (188 theses, 83 peer-reviewed, and 6 government reports) and a temporal increase in dissemination over the past two decades. The reports encompassed 161 distinct wetlands distributed nationwide; however, most of the investigations focused on only a few wetland bodies with a disproportionate concentration in just three administrative regions. This reveals that wetland-related research is unevenly distributed in Peru, and highlights a need to extend this knowledge to underrepresented systems and regions. Although Peru ranks third in South America for protected (Ramsar) wetland areas, case studies have revealed that wetlands in the country are vulnerable to human activities. Recent national legislation established in 2021 should help to address this challenge, as before this time, there was a more decentralized approach, whereby each administrative region held responsibility for the protection of their own wetlands. Collective findings indicate that research activity should be increased nationally in order to better understand the function and benefits of wetlands throughout Peru, in addition to the continued development and enforcement of regulations designed to protect these valuable ecosystems. Finally, since a national Peruvian wetland inventory is urgently needed, this analysis provides a baseline for this development of, as well as identifying gaps in, knowledge needed for appropriate national representation.

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO 2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr −1 and pH decrease of 0.0014 ± 0.0004 yr −1 . RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.