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In inland water covering lakes, reservoirs, and ponds, the gas exchange of slightly soluble gases such as carbon dioxide, dimethyl sulfide, methane, or oxygen across a clean and nearly flat air‐water interface is routinely described using a water‐side mean gas transfer velocity kL‾$\\overline{{k}_{L}}$ , where overline indicates time or ensemble averaging. The micro‐eddy surface renewal model predicts kL‾=αoSc−1/2νϵ‾1/4$\\overline{{k}_{L}}={\\alpha }_{o}S{c}^{-1/2}{\\left(\\nu \\overline{{\\epsilon}}\\right)}^{1/4}$ , where Sc$Sc$is the molecular Schmidt number, ν$\\nu $is the water kinematic viscosity, and ϵ‾$\\overline{{\\epsilon}}$is the waterside mean turbulent kinetic energy dissipation rate at or near the interface. While αo=0.39−0.46${\\alpha }_{o}=0.39-0.46$has been reported across a number of data sets, others report large scatter or variability around this value range. It is shown here that this scatter can be partly explained by high temporal variability in instantaneous ϵ${\\epsilon}$around ϵ‾$\\overline{{\\epsilon}}$ , a mechanism that was not previously considered. As the coefficient of variation CVe$\\left(C{V}_{e}\\right)$in ϵ${\\epsilon}$increases, αo${\\alpha }_{o}$must be adjusted by a multiplier 1+CVe2−3/32${\\left(1+C{V}_{e}^{2}\\right)}^{-3/32}$that was derived from a log‐normal model for the probability density function of ϵ${\\epsilon}$ . Reported variations in αo${\\alpha }_{o}$with a macro‐scale Reynolds number can also be partly attributed to intermittency effects in ϵ${\\epsilon}$ . Such intermittency is characterized by the long‐range (i.e., power‐law decay) spatial auto‐correlation function of ϵ${\\epsilon}$ . That αo${\\alpha }_{o}$varies with a macro‐scale Reynolds number does not necessarily violate the micro‐eddy model. Instead, it points to a coordination between the macro‐ and micro‐scales arising from the transfer of energy across scales in the energy cascade. Plain Language Summary In inland water, the movement of slightly soluble gas molecules such as carbon dioxide, methane or oxygen across an air‐water interface is of significance to a plethora of applications in aquatic ecology, climate sciences, and limnology. The standard model, known as the micro‐eddy model, considers water packets ejected from deeper levels within an inland water body, making contact with a clean and nearly flat air‐water surface, exchanging molecules with the atmosphere, and subsequently sweeping back down. Under a set of restrictive assumptions about the statistics of contact duration and their inference from water‐side velocity statistics near the surface, prediction of the efficiency of the exchange process can be made and encoded in a so‐called gas transfer velocity. The work here demonstrates that this gas transfer velocity can be derived by assuming a turnover velocity of these water packets that follows a universal form based on a widely accepted theory of energy transfer across scales and contemporary refinements to it. Key Points The micro‐eddy model (MEM) operationally describes the air‐water gas transfer velocity kL$\\left({k}_{L}\\right)$for slightly soluble gases The MEM leads to kL${k}_{L}$being proportional to the Kolmogorov micro‐scale velocity vk$\\left({v}_{k}\\right)$Increased variability and intermittency in the turbulent kinetic energy dissipation rate act to reduce kL${k}_{L}$for the same vk${v}_{k}$

Estimating air–water gas transfer velocities (k) is integral to understand biogeochemical and ecological processes in aquatic systems. In lakes, k is commonly predicted using wind-based empirical models, however, their predictive performance under conditions that differ from their original calibration remains largely unassessed. Here, we collected 2222 published k estimates derived from various methods in 46 globally distributed lakes to (1) evaluate the predictions of a selection of six available wind-speed based k models for lakes and (2) explore and develop new empirical models to predict k over global lakes. We found that selected k models generally performed poorly in predicting k in lakes. Model predictions were more accurate than simply assuming a mean k in only 2–39% of all lakes, however, we could not identify with confidence the specific conditions in which some models outperformed others. We developed new wind-based models in which additional variables describing the spatial coverage of k estimates and the lake size and shape had a significant effect on the wind speed-k relationship. Although these new models did not fit the global dataset significantly better than previous k models, they generate overall less biased predictions for global lakes. We further provide explicit estimates of prediction errors that integrate methodological and lake-specific uncertainties. Our results highlight the potential limits when using wind-based models to predict k across lakes and urge scientists to properly account for prediction errors, or measure k directly in the field whenever possible.

Mass transfer of a range of volatile substances was studied under highly turbulent conditions. The applied setup mimicked drop structures, where the release of volatile organic carbons likely occurs at a high rate. The experiments covered several substances in a range of resistances from residing entirely in the liquid film to being fully in the gas film. The twofilm theory yielded a good prediction of the whole measured range. This allowed the experimental validation of a method where two reference substances are applied, to determine the transfer of any other substance - independently of where its resistance to mass transfer resides. One finding was that the range of dimensionless Henry's constants, where both films contributed by more than 5%, was 0.0027 to 1.05, which is over five times higher than the accepted rule of thumb (0.0005-0.18). Another finding was that the ratio between the liquid and the gas film mass transfer coefficients of the reference substances was similar for the two drop configurations studied. If this holds true over a wider range of configurations, such a ratio constitutes a valuable shortcut to the current practice of ignoring gas film resistance in the estimation of mass transfer rates.

The physical system of the Arctic is changing in profound ways, with implications for the transport of nutrients to and from the Arctic Ocean as well as the internal cycling of material on shelves and in deep basins. Significant increases in Arctic Ocean primary production have been observed in the last two decades, potentially driven by enhancements to a suite of mechanisms that increase nutrient availability to upper ocean waters, including transport from adjacent subpolar regions, storm-induced mixing, and mobilization of nutrients from terrestrial pools. The relative strength of these mechanisms varies substantially within Arctic Ocean subregions, leading to a mosaic of biogeochemical responses. Changes in primary production are also driving regional changes in the biologically mediated air-sea exchange of CO₂, while warming, enhanced stratification, and increased mobilization of carbon from terrestrial pools are also driving regionally variable trends.

Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m-2 and a bias of less than 5 W m-2. At present this accuracy target is met only at OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500 - 1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1 - 3 measurement platforms in each nominal 10° by 10° boxes. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development, product validation, and for improving satellite-based, NWP and blended flux products. In addition, some of these flux platforms will also measure direct turbulent fluxes, which can be used to improve algorithms for computation of air-sea exchange of heat and momentum in flux products and models. With these improved air-sea fluxes, the ocean’s influence on the atmosphere will be better quantified and lead to improved long-term weather forecasts, seasonal-interannual-decadal climate predictions, and regional climate projections.

Advances in L-band microwave satellite radiometry in the past decade, pioneered by ESA's SMOS and NASA's Aquarius and SMAP missions, have demonstrated an unprecedented capability to observe global sea surface salinity (SSS) from space. Measurements from these missions are the only means to probe the very-near surface salinity (top cm), providing a unique monitoring capability for the interfacial exchanges of water between the atmosphere and the upper-ocean, and delivering a wealth of information on various salinity processes in the ocean, linkages with the climate and water cycle, including land-sea connections, and providing constraints for ocean prediction models. The satellite SSS data are complimentary to the existing in situ systems such as Argo that provide accurate depiction of large-scale salinity variability in the open ocean but under-sample mesoscale variability, coastal oceans and marginal seas, and energetic regions such as boundary currents and fronts. In particular, salinity remote sensing has proven valuable to systematically monitor the open oceans as well as coastal regions up to approximately 40 km from the coasts. This is critical to addressing societally relevant topics, such as land-sea linkages, coastal-open ocean exchanges, research in the carbon cycle, near-surface mixing, and air-sea exchange of gas and mass. In this paper, we provide a community perspective on the major achievements of satellite SSS for the aforementioned topics, the unique capability of satellite salinity observing system and its complementarity with other platforms, uncertainty characteristics of satellite SSS, and measurement versus sampling errors in relation to in situ salinity measurements. We also discuss the need for technological innovations to improve the accuracy, resolution, and coverage of satellite SSS, and the way forward to both continue and enhance salinity remote sensing as part of the integrated Earth Observing System in order to address societal needs.

A month-long mesocosm study at the Institute for Chemistry and Biology of the Marine Environment (Wilhelmshaven, Germany) examined how a phytoplankton bloom and photodegradation influence the composition of colored and fluorescent dissolved organic matter (CDOM and FDOM, respectively) in the sea-surface microlayer (SML) and underlying water (ULW). The SML, a thin (1000 µm) interface between ocean and atmosphere, plays a key role in air-sea exchange processes, but temporal mechanisms behind organic matter enrichment remain unclear. To isolate biogeochemical processes from environmental variability, daily SML and ULW samples were analyzed using spectral fluorometric and photometric methods, with supporting data e.g. on irradiance, temperature, and chlorophyll-a. The study covered bloom onset, peak, and decay of two partially overlying phytoplankton blooms. Samples were taken alternatively in the morning and in the afternoon, varying the exposure time to UV-light. Changes in composition and quality of organic matter were tracked using CDOM/FDOM derived metrics. Changes in the FDOM component composition were investigated using PERMANOVA. The significant influence of the bloom phases and the layer (SML or ULW) on the component composition was confirmed, however, their interaction was not significant. Protein-like FDOM components increased in both layers during bloom progression, while humic-like FDOM components decreased throughout the study. It is likely that the change in FDOM component composition is a joint result of the influences of the phytoplankton bloom and photodegradation effects. Based on the slope ratio (SR) of CDOM absorption slopes S.sub.275-295 and S.sub.350-400, photodegradation was identified as the dominant sink of organic matter over microbial activity. While some CDOM/FDOM derived metrics indicated stronger photodegradation effects in the SML, a consistently enhanced photodegradation signal could not be conclusively confirmed due to co-occurring enrichment, passive accumulation, and degradation processes.

All exchange between the ocean and atmosphere has to cross the sea surface microlayer (SML), yet the SML impact on modulating air-sea exchange rates remains poorly understood. Surfactants, including biopolymers, can influence exchange rates by altering the rheological properties of the SML, damping surface turbulence, and capillary wave formation. We investigated the impact of wind speed on SML biopolymer enrichment, surface roughness, and interfacial surfactant coverage at the Heidelberg “Aeolotron”, a large annular wind-wave facility filled with 18 000 L seawater. Our results show that biopolymer enrichment, specifically the enrichment of polypeptides and polysaccharides, in the SML declined sharply at wind speeds above 6 m s−1, coinciding with a sudden increase in the Mean Square Slope (MSS) of waves by 1–2 orders of magnitude. At wind speed < 6 m s−1, biopolymer enrichment in the SML was accompanied by high surfactant surface coverage and strongly reduced MSS values compared to non-enriched or essentially surfactant-free clean freshwater surfaces, indicating a substantial impact of biopolymer enrichment in the SML for air-sea exchange at lower wind speed. Selective SML enrichment was observed, particularly for the amino acids arginine and glutamic acid, and the amino sugar galactosamine. Amino acid and carbohydrate monomers in the SML also exhibited significant and compound-specific wind-induced variability. Our findings suggest that biopolymers, particularly those derived from bacterial production, accumulate in the SML and act as powerful biosurfactants. Unlike artificial surfactant films, natural SML components were more susceptible to wind-induced disruption and to microbial production and decomposition. Our findings reveal that ecological processes actively regulate the chemical and physical properties of the SML, including surfactant surface coverage, and thereby potentially modulate air–sea heat and mass exchange.

Shallow coastal marine habitats are hotspots for carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere, yet these fluxes remain poorly quantified, limiting their integration into global and regional carbon budgets. Using floating chambers, this study quantified seasonal and annual CO2 and CH4 fluxes in common Baltic Sea habitats, including macroalgae-covered coarse sediments, sparsely to densely vegetated sands, submerged plant-covered mixed substrates, and reed-dominated muds. Monthly average CO2 fluxes ranged from −937 ± 161 to 3512 ± 704 mg CO2 m−2 d−1, with macroalgae and reed habitats exhibiting distinct flux ranges. Apart from macroalgae, all habitats exhibited a net annual CO2 efflux. Diffusive CH4 fluxes varied seasonally, from 0.1 ± 0.01 to 26 ± 1.5 mg CH4 m−2 d−1, with peak emissions in summer. Ebullition occurred from March to October, reaching up to 232 mg CH4 m−2 d−1 and contributed substantially to annual carbon-based greenhouse gas fluxes in the sand, mixed-substrate, and reed habitats. Contrary to previous findings that ebullition is confined to muddy, organic-rich sediments, this study found the highest CH4 ebullition in vegetated sand habitats, indicating a broader spatial extent of intense CH4 release than previously assumed. Upscaling to the shallow-water (< 6 m) zone of the Stockholm archipelago yielded total CO2-equivalent fluxes of between −0.01 and 0.2 Tg CO2-eq yr−1 (100-year timescale). For comparison, Stockholm's energy- and transport sectors emit ∼ 1.2 Tg CO2-eq yr−1, suggesting the shallow coastal zone could be a small, but non-negligible regional source for carbon-based greenhouse gases.

The sea-surface microlayer (SML) is the less than one millimeter thin interface between the surface ocean and the overlying atmosphere and plays a crucial role in sea-air gas exchange processes. However, its role in sea-air exchange processes in sea ice-influenced environments such as the Arctic Ocean remains poorly understood. Here, we present the first in situ measurements of surfactants accumulating in the SML of the Fram Strait, coupled with near-surface measurements of the climate-relevant trace gases CH4 and N2O. Sampling of the undisturbed surface ocean was conducted at leads and ice holes during the onset of sea ice melt and an early algal bloom between May and June 2023. Our results reveal that the region acted as a minor source of CH4 and N2O. Nonetheless, the algal bloom stimulated the production of surfactants, which accumulated in the SML of open leads, potentially reducing the emissions of CH4 and N2O to the atmosphere. These findings highlight the importance of resolving short-term surface processes during seasonal transitions and of integrating SML dynamics into investigating trace gas fluxes in polar regions.