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A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters >50 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations (<0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13 % and −22 % for updraft velocities 0.3 and 0.6 m s−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

In the field of ocean observing, the term of “observatory” is often used without a unique meaning. A clear and unified definition of observatory is needed in order to facilitate the communication in a multidisciplinary community, to capitalize on future technological innovations and to support the observatory design based on societal needs. In this paper, we present a general framework to define the next generation Marine OBservatory (MOB), its capabilities and functionalities in an operational context. The MOB consists of four interconnected components or “gears” (observation infrastructure, cyberinfrastructure, support capacity, and knowledge generation engine) that are constantly and adaptively interacting with each other. Therefore, a MOB is a complex infrastructure focused on a specific geographic area with the primary scope to generate knowledge via data synthesis and thereby addressing scientific, societal, or economic challenges. Long-term sustainability is a key MOB feature that should be guaranteed through an appropriate governance. MOBs should be open to innovations and good practices to reduce operational costs and to allow their development in quality and quantity. A deeper biological understanding of the marine ecosystem should be reached with the proliferation of MOBs, thus contributing to effective conservation of ecosystems and management of human activities in the oceans. We provide an actionable model for the upgrade and development of sustained marine observatories producing knowledge to support science-based economic and societal decisions. Refereed 14.A Manual (incl. handbook, guide, cookbook etc) 2018-09-07

Frisbee is a next generation platform designed to unify chaos testing and perfomance benchmarking. We address the key pain points developers and QA engineers face when testing cloud-native applications in the earlier stages of the software lifecycle. We make it possible to: Write tests: for stressing complex topologies and dynamic operating conditions. Run tests: provides seamless scaling from a single workstation to hundreds of machines. Debug tests: through extensive monitoring and comprehensive dashboards

Organic compounds represent a significant fraction of submicrometer atmospheric aerosol mass. Even if most of these compounds are semi-volatile in atmospheric concentrations, the ambient organic aerosol volatility is quite uncertain. The most common volatility measurement method relies on the use of a thermodenuder (TD). The aerosol passes through a heated tube where its more volatile components evaporate, leaving the less volatile components behind in the particulate phase. The typical result of a thermodenuder measurement is the mass fraction remaining (MFR), which depends, among other factors, on the organic aerosol (OA) vaporization enthalpy and the accommodation coefficient. We use a new method combining forward modeling, introduction of "experimental" error, and inverse modeling with error minimization for the interpretation of TD measurements. The OA volatility distribution, its effective vaporization enthalpy, the mass accommodation coefficient and the corresponding uncertainty ranges are calculated. Our results indicate that existing TD-based approaches quite often cannot estimate reliably the OA volatility distribution, leading to large uncertainties, since there are many different combinations of the three properties that can lead to similar thermograms. We propose an improved experimental approach combining TD and isothermal dilution measurements. We evaluate this experimental approach using the same model, and show that it is suitable for studies of OA volatility in the lab and the field.

The possibility of measuring aerosol optical absorption properties in the UV spectral range such as single scattering albedo (SSA), using remote sensing techniques, is currently an open scientific issue. We investigate the limitations on calculating column average SSA using a combination of global UV spectral measurements (that are comon in various UV monitoring stations worldwide) with radiative transfer modeling. To point out the difficulties in such a retrieval we have used the travelling reference spectroradiometer QASUME (Quality Assurance of Spectral Ultraviolet Measurements in Europe) results from 27 visits to UV monitoring stations around Europe. We have used the QASUME instrument as relative reference, analyzing absolute differences and also temporal and spectral deviations of UV irraidances, that are used as basic input for the SSA retrieval. The results comparing the mean SSA derived by all instruments, measuring synchronous UV spectra, showed that 5 were within ± 0.02 difference from the SSA calculated from the QASUME instrument, while 17 were within ± 0.04, for the Solar zenith angle of 60 degrees. As for the uncertainty that has been calculated using the 2σ standard deviation of the spectral measurements, a mean 0.072 and 0.10 (2σ) uncertainties have been calculated for 60° and 30°, respectively. Based on the fact that additional uncertainties would be introduced in the SSA retrieval from AOD model input accuracy, assymetry parameter assumptions, we show that only very few instrumnents could be able to detect long term SSA changes. However, such measurements/results ar useful in order to retrieve SSA at UV wavelengths, a product needed for various applications such as, inputs for modeling radiative forcing studies and satellite retrieval algorithms.

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

Glider vehicles are now perhaps some of the most prolific providers of real-time and near-real-time operational oceanographic data. However, the data from these vehicles can and should be considered to have a long-term legacy value capable of playing a critical role in understanding and separating inter-annual, inter-decadal, and longterm global change. To achieve this, we have to go further than simply assuming the manufacturer’s calibrations, and field correct glider data in a more traditional way, for example, by careful comparison to water bottle calibrated lowered CTD datasets and/or “gold” standard recent climatologies. In this manuscript, we bring into the 21st century a historical technique that has been used manually by oceanographers for many years/decades for field correction/inter-calibration, thermal lag correction, and adjustment for biological fouling. The technique has now been made semi-automatic for machine processing of oceanographic glider data, although its future and indeed its origins have far wider scope. The subject of this manuscript is drawn from the original Description of Work (DoW) for a key task in the recently completed JERICO-NEXT (Joint European Research Infrastructure network for Coastal Observatories) EU-funded program, but goes on to consider future application and the suitability for integration with machine learning. Refereed 14.A Sea surface salinity Subsurface salinity TRL 8 Actual system completed and "mission qualified" through test and demonstration in an operational environment (ground or space) Manual (incl. handbook, guide, cookbook etc) Standard Operating Procedure 2019-12-03

Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

The extracellular concentration of H2O2 in surface aquatic environments is controlled by a balance between photochemical production and the microbial synthesis of catalase and peroxidase enzymes to remove H2O2 from solution. In any kind of incubation experiment, the formation rates and equilibrium concentrations of reactive oxygen species (ROSs) such as H2O2 may be sensitive to both the experiment design, particularly to the regulation of incident light, and the abundance of different microbial groups, as both cellular H2O2 production and catalase–peroxidase enzyme production rates differ between species. Whilst there are extensive measurements of photochemical H2O2 formation rates and the distribution of H2O2 in the marine environment, it is poorly constrained how different microbial groups affect extracellular H2O2 concentrations, how comparable extracellular H2O2 concentrations within large-scale incubation experiments are to those observed in the surface-mixed layer, and to what extent a mismatch with environmentally relevant concentrations of ROS in incubations could influence biological processes differently to what would be observed in nature. Here we show that both experiment design and bacterial abundance consistently exert control on extracellular H2O2 concentrations across a range of incubation experiments in diverse marine environments. During four large-scale (>1000 L) mesocosm experiments (in Gran Canaria, the Mediterranean, Patagonia and Svalbard) most experimental factors appeared to exert only minor, or no, direct effect on H2O2 concentrations. For example, in three of four experiments where pH was manipulated to 0.4–0.5 below ambient pH, no significant change was evident in extracellular H2O2 concentrations relative to controls. An influence was sometimes inferred from zooplankton density, but not consistently between different incubation experiments, and no change in H2O2 was evident in controlled experiments using different densities of the copepod Calanus finmarchicus grazing on the diatom Skeletonema costatum (<1 % change in [H2O2] comparing copepod densities from 1 to 10 L−1). Instead, the changes in H2O2 concentration contrasting high- and low-zooplankton incubations appeared to arise from the resulting changes in bacterial activity. The correlation between bacterial abundance and extracellular H2O2 was stronger in some incubations than others (R2 range 0.09 to 0.55), yet high bacterial densities were consistently associated with low H2O2. Nonetheless, the main control on H2O2 concentrations during incubation experiments relative to those in ambient, unenclosed waters was the regulation of incident light. In an open (lidless) mesocosm experiment in Gran Canaria, H2O2 was persistently elevated (2–6-fold) above ambient concentrations; whereas using closed high-density polyethylene mesocosms in Crete, Svalbard and Patagonia H2O2 within incubations was always reduced (median 10 %–90 %) relative to ambient waters.

Atmospheric new particle formation (NPF) is a common phenomenon all over the world. In this study we present the longest time series of NPF records in the eastern Mediterranean region by analyzing 10 years of aerosol number size distribution data obtained with a mobility particle sizer. The measurements were performed at the Finokalia environmental research station on Crete, Greece, during the period June 2008–June 2018. We found that NPF took place on 27 % of the available days, undefined days were 23 % and non-event days 50 %. NPF is more frequent in April and May probably due to the terrestrial biogenic activity and is less frequent in August. Throughout the period under study, nucleation was observed also during the night. Nucleation mode particles had the highest concentration in winter and early spring, mainly because of the minimum sinks, and their average contribution to the total particle number concentration was 8 %. Nucleation mode particle concentrations were low outside periods of active NPF and growth, so there are hardly any other local sources of sub-25 nm particles. Additional atmospheric ion size distribution data simultaneously collected for more than 2 years were also analyzed. Classification of NPF events based on ion spectrometer measurements differed from the corresponding classification based on a mobility spectrometer, possibly indicating a different representation of local and regional NPF events between these two measurement data sets. We used the MALTE-Box model for simulating a case study of NPF in the eastern Mediterranean region. Monoterpenes contributing to NPF can explain a large fraction of the observed NPF events according to our model simulations. However the adjusted parameterization resulting from our sensitivity tests was significantly different from the initial one that had been determined for the boreal environment.