Direct dimethyl sulfide (DMS) flux measurements using eddy covariance have shown a suppression of gas transfer at medium to high wind speed. However, not all eddy covariance measurements show evidence of this suppression. Processes, such as wave-wind interaction and surfactants, have been postulated to cause this suppression. We measured DMS and carbon dioxide eddy covariance fluxes during the Asian summer monsoon in the western tropical Indian Ocean (July and August 2014). Both fluxes and their respective gas transfer velocities show signs of a gas transfer suppression above 10 m/s. Using a wind-wave interaction, we describe a flow separation process that could be responsible for a suppression of gas transfer. As a result we provide a Reynolds number-based parameterization, which states the likelihood of a gas transfer suppression for this cruise and previously published gas transfer data. Additionally, we compute the difference in the gas transfer velocities of DMS and CO 2 to estimate the bubble-mediated gas transfer using a hybrid model with three whitecap parameterizations. Plain Language SummaryInvestigating the air-gas transfer of dimethyl sulfide (DMS) and CO 2 , we estimate the influence of bubble-mediated gas transfer.Furthermore, we explore the phenomena of gas transfer suppression. The gas transfer between atmosphere and ocean should increase with increasing wind speed. At certain wind speed the amount of gas transferred flattens. We provide a wind-wave interaction as possible explanation of this phenomenon.

The air-sea gas transfer velocity (K660) is typically assessed as a function of the 10-m neutral wind speed (U10n), but there remains substantial uncertainty in this relationship. Here K660 of CO2 derived with the eddy covariance (EC) technique from eight datasets (11 research cruises) are reevaluated with consistent consideration of solubility and Schmidt number and inclusion of the ocean cool skin effect. K660 shows an approximately linear dependence with the friction velocity (u*) in moderate winds, with an overall relative standard deviation (relative standard error) of about 20% (7%). The largest relative uncertainty in K660 occurs at low wind speeds, while the largest absolute uncertainty in K660 occurs at high wind speeds. There is an apparent regional variation in the steepness of the K660-u* relationships: North Atlantic ≥ Southern Ocean > other regions (Arctic, Tropics). Accounting for sea state helps to collapse some of this regional variability in K660 using the wave Reynolds number in very large seas and the mean squared slope of the waves in small to moderate seas. The grand average of EC-derived K660(−1.47 + 76.67u*+ 20.48u*2 or 0.36 + 1.203U10n+ 0.167U10n2) is similar at moderate to high winds to widely used dual tracer-based K660 parametrization, but consistently exceeds the dual tracer estimate in low winds, possibly in part due to the chemical enhancement in air-sea CO2 exchange. Combining the grand average of EC-derived K660 with the global distribution of wind speed yields a global average transfer velocity that is comparable with the global radiocarbon (14C) disequilibrium, but is ~20% higher than what is implied by dual tracer parametrizations. This analysis suggests that CO2 fluxes computed using a U10n2 dependence with zero intercept (e.g., dual tracer) are likely underestimated at relatively low wind speeds.

During the summer monsoon, the western tropical Indian Ocean is predicted to be a hot spot for dimethylsulfide emissions, the major marine sulfur source to the atmosphere, and an important aerosol precursor. Other aerosol relevant fluxes, such as isoprene and sea spray, should also be enhanced, due to the steady strong winds during the monsoon. Marine air masses dominate the area during the summer monsoon, excluding the influence of continentally derived pollutants. During the SO234‐2/235 cruise in the western tropical Indian Ocean from July to August 2014, directly measured eddy covariance DMS fluxes confirm that the area is a large source of sulfur to the atmosphere (cruise average 9.1 μmol m−2 d−1). The directly measured fluxes, as well as computed isoprene and sea spray fluxes, were combined with FLEXPART backward and forward trajectories to track the emissions in space and time. The fluxes show a significant positive correlation with aerosol data from the Terra and Suomi‐NPP satellites, indicating a local influence of marine emissions on atmospheric aerosol numbers.

Abstract. Eddy covariance measurements show gas transfer velocity suppression at medium to high wind speed. A wind–wave interaction described by the transformed Reynolds number is used to characterize environmental conditions favoring this suppression. We take the transformed Reynolds number parameterization to review the two most cited wind speed gas transfer velocity parameterizations: Nightingale et al. (2000) and Wanninkhof (1992, 2014). We propose an algorithm to adjust k values for the effect of gas transfer suppression and validate it with two directly measured dimethyl sulfide (DMS) gas transfer velocity data sets that experienced gas transfer suppression. We also show that the data set used in the Nightingale 2000 parameterization experienced gas transfer suppression. A compensation of the suppression effect leads to an average increase of 22 % in the k vs. u relationship. Performing the same correction for Wanninkhof 2014 leads to an increase of 9.85 %. Additionally, we applied our gas transfer suppression algorithm to global air–sea flux climatologies of CO2 and DMS. The global application of gas transfer suppression leads to a decrease of 11 % in DMS outgassing. We expect the magnitude of Reynolds suppression on any global air–sea gas exchange to be about 10 %.

Abstract. River temperature is an important parameter for water quality and an important variable for physical, chemical and biological processes. River water is also used by production facilities as cooling agent. We introduced a new way of calculating a catchment-wide air temperature using a time-lagged and weighed average. Regressing the new air temperature vs. river water temperature, the meteorological influence and the anthropogenic heat input could be studied separately. The new method was tested at four monitoring stations (Basel, Worms, Koblenz and Cologne) along the river Rhine and lowered the root mean square error of the regression from 2.37 ∘C (simple average) to 1.02 ∘C. The analysis also showed that the long-term trend (1979–2018) of river water temperature was, next to the increasing air temperature, mostly influenced by decreasing nuclear power production. Short-term changes in timescales < 5 years were connected with changes in industrial production. We found significant positive correlations for the relationship.

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Printer-friendly version Discussion paper for the ACC*w calculation method. The reasons for the ACC*w resulting in lower RMSE compared to ACC or w only, is now described in detail in the methods section. Overall, the results (correlations, RMSE, NCS, ∆RBTcalc) changed only so slightly, that the scientific conclusion and the key messages were not influenced. This also visible in the attached track changes version of the manuscript. GENERAL COMMENTS The manuscript presents a study of short term and long term changes in river temperature and investigates the influence of natural and anthropogenic drivers of these changes which is interesting and generally within the scope of HESS. River temperatures at various monitoring locations along the river Rhine as well as industrial production and nuclear power plant activities are analyzed. The authors further develop a novel approach of calculating a catchment-wide average air temperature which is used in the linear regression relationship between air and river temperature. Overall, the scientific approach and the methods appear to be valid. However, there are some points which need further clarification: (1) The relationships between river temperature and its drivers are investigated using multiple linear regressions separating the so-called Rhine base temperature (i.e. the river temperature without influences of air temperature and discharge) and air temperature and discharge influences on river temperature. More information on the multiple linear regressions for each location is required for the reader to be able to evaluate the robustness of this approach Comment: The RMSE and the NCS information is provided for every measurement station. In addition, the data is now included in the supplement. We used the year 2001 as a test year and regressed Tw using Tc and Q just for this year. Then the 2001 regression coefficients were used to calculate a modelled Tw for the years 2000 to 2018. The RMSE and NCS show better results compared to the long term regression, which is sensible for a shorter regression period.