Comparison of OH reactivity measurements in the atmospheric simulation chamber SAPHIR

Hydroxyl (OH) radical reactivity (kOH) has been measured for 18 years with different measurement techniques. In order to compare the performances of instruments deployed in the field, two campaigns were conducted performing experiments in the atmospheric simulation chamber SAPHIR at Forschungszentrum Jülich in October 2015 and April 2016. Chemical conditions were chosen either to be representative of the atmosphere or to test potential limitations of instruments. All types of instruments that are currently used for atmospheric measurements were used in one of the two campaigns. The results of these campaigns demonstrate that OH reactivity can be accurately measured for a wide range of atmospherically relevant chemical conditions (e.g. water vapour, nitrogen oxides, various organic compounds) by all instruments. The precision of the measurements (limit of detection  < 1 s−1 at a time resolution of 30 s to a few minutes) is higher for instruments directly detecting hydroxyl radicals, whereas the indirect comparative reactivity method (CRM) has a higher limit of detection of 2 s−1 at a time resolution of 10 to 15 min. The performances of the instruments were systematically tested by stepwise increasing, for example, the concentrations of carbon monoxide (CO), water vapour or nitric oxide (NO). In further experiments, mixtures of organic reactants were injected into the chamber to simulate urban and forested environments. Overall, the results show that the instruments are capable of measuring OH reactivity in the presence of CO, alkanes, alkenes and aromatic compounds. The transmission efficiency in Teflon inlet lines could have introduced systematic errors in measurements for low-volatile organic compounds in some instruments. CRM instruments exhibited a larger scatter in the data compared to the other instruments. The largest differences to reference measurements or to calculated reactivity were observed by CRM instruments in the presence of terpenes and oxygenated organic compounds (mixing ratio of OH reactants were up to 10 ppbv). In some of these experiments, only a small fraction of the reactivity is detected. The accuracy of CRM measurements is most likely limited by the corrections that need to be applied to account for known effects of, for example, deviations from pseudo first-order conditions, nitrogen oxides or water vapour on the measurement. Methods used to derive these corrections vary among the different CRM instruments. Measurements taken with a flow-tube instrument combined with the direct detection of OH by chemical ionisation mass spectrometry (CIMS) show limitations in cases of high reactivity and high NO concentrations but were accurate for low reactivity (< 15 s−1) and low NO (< 5 ppbv) conditions

Process verification of a hydrological model using a temporal parameter sensitivity analysis

To ensure reliable results of hydrological models, it is essential that the models reproduce the hydrological process dynamics adequately. Information about simulated process dynamics is provided by looking at the temporal sensitivities of the corresponding model parameters. For this, the temporal dynamics of parameter sensitivity are analysed to identify the simulated hydrological processes. Based on these analyses it can be verified if the simulated hydrological processes match the observed processes of the real world. <br><br> We present a framework that makes use of processes observed in a study catchment to verify simulated hydrological processes. Temporal dynamics of parameter sensitivity of a hydrological model are interpreted to simulated hydrological processes and compared with observed hydrological processes of the study catchment. The results of the analysis show the appropriate simulation of all relevant hydrological processes in relation to processes observed in the catchment. Thus, we conclude that temporal dynamics of parameter sensitivity are helpful for verifying simulated processes of hydrological models

A new marine biogenic emission: methane sulfonamide (MSAM), dimethyl sulfide (DMS), and dimethyl sulfone (DMSO₂) measured in air over the Arabian Sea

We present the first ambient measurements of a new marine emission methane sulfonamide (MSAM: CH5NO2S), along with dimethyl sulfide (DMS) and dimethyl sulfone (DMSO2) over the Arabian Sea. Two shipborne transects (W → E, E → W) were made during the AQABA (Air Quality and Climate Change in the Arabian Basin) measurement campaign. Molar mixing ratios in picomole of species per mole of air (throughout this paper abbreviated as ppt) of DMS were in the range of 300–500 ppt during the first traverse of the Arabian Sea (first leg) and 100–300 ppt on the second leg. On the first leg DMSO2 was always below 40 ppt and MSAM was close to the limit of detection. During the second leg DMSO2 was between 40 and 120 ppt and MSAM was mostly in the range of 20–50 ppt with maximum values of 60 ppt. An analysis of HYSPLIT back trajectories combined with calculations of the exposure of these trajectories to underlying chlorophyll in the surface water revealed that most MSAM originates from the Somalia upwelling region, known for its high biological activity. MSAM emissions can be as high as one-third of DMS emissions over the upwelling region. This new marine emission is of particular interest as it contains both sulfur and nitrogen, making it potentially relevant to marine nutrient cycling and marine atmospheric particle formation

Identifying the connective strength between model parameters and performance criteria

In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria. To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated. With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter. In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling

Hydrogen isotope fractions of long-chain alkenones produced by Emiliania huxleyi during experiments

Over the last decade, hydrogen isotopes of long-chain alkenones have been shown to be a promising proxy for reconstructing paleo sea surface salinity due to a strong hydrogen isotope fractionation response to salinity across different environmental conditions. However, to date, the decoupling of the effects of alkalinity and salinity, parameters that co-vary in the surface ocean, on hydrogen isotope fractionation of alkenones has not been assessed. Furthermore, as the alkenone-producing haptophyte, Emiliania huxleyi, is known to grow in large blooms under high light intensities, the effect of salinity on hydrogen isotope fractionation under these high irradiances is important to constrain before using dDC37 to reconstruct paleosalinity. Batch cultures of the marine haptophyte E. huxleyi strain CCMP 1516 were grown to investigate the hydrogen isotope fractionation response to salinity at high light intensity and independently assess the effects of salinity and alkalinity under low light conditions. Our results suggest that alkalinity does not significantly influence hydrogen isotope fractionation of alkenones, but salinity does have a strong effect. Additionally, no significant difference was observed between the fractionation responses to salinity recorded in alkenones grown under both high and low light conditions. Comparison with previous studies suggests that the fractionation response to salinity in culture is similar under different environmental conditions, strengthening the use of hydrogen isotope fractionation as a paleosalinity proxy

Improving Information Extraction From Simulated Discharge Using Sensitivity‐Weighted Performance Criteria

Due to seasonal or interannual variability, the relevance of hydrological processes and of the associated model parameters can vary significantly throughout the simulation period. To achieve accurately identified model parameters, temporal variations in parameter dominance should be taken into account. This is not achieved if performance criteria are applied to the entire model output time series. Even when using complementary performance criteria, it is often only possible to identify some of the model parameters precisely. We present an innovative approach to improve parameter identifiability that exploits the information available regarding temporal variations in parameter dominance. Using daily parameter sensitivity time series, we construct a set of sensitivity-weighted performance criteria, one for each parameter, whereby periods of higher dominance of a model parameter and its corresponding process are assigned higher weights in the calculation of the associated performance criterion. These criteria are used to impose constraints on parameter values. We demonstrate this approach by constraining 12 model parameters for three catchments and examine ensemble hydrological simulations generated using these constrained parameter sets. The sensitivity-weighted approach improves in particular the identifiability for parameters whose corresponding processes are dominant only for short periods of time or have strong seasonal patterns. This results overall in slight improvement of model performance for a set of 10 contrasting performance criteria. We conclude that the sensitivity-weighted approach improves the extraction of hydrologically relevant information from data, thereby resulting in improved parameter identifiability and better representation of model parameters.Deutsche ForschungsgemeinschaftOpen access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]