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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).

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.

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.

This paper synthesizes the available scientific information connecting atmospheric nucleation with subsequent cloud condensation nuclei (CCN) formation. We review both observations and model studies related to this topic, and discuss the potential climatic implications. We conclude that CCN production associated with atmospheric nucleation is both frequent and widespread phenomenon in many types of continental boundary layers, and probably also over a large fraction of the free troposphere. The contribution of nucleation to the global CCN budget spans a relatively large uncertainty range, which, together with our poor understanding of aerosol-cloud interactions, results in major uncertainties in the radiative forcing by atmospheric aerosols. In order to better quantify the role of atmospheric nucleation in CCN formation and Earth System behavior, more information is needed on (i) the factors controlling atmospheric CCN production and (ii) the properties of both primary and secondary CCN and their interconnections. In future investigations, more emphasis should be put on combining field measurements with regional and large-scale model studies.

We present a 3-D climatology of the desert dust distribution over South and East Asia derived using CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) data. To distinguish desert dust from total aerosol load we apply a methodology developed in the framework of EARLINET (European Aerosol Research Lidar Network). The method involves the use of the particle linear depolarization ratio and updated lidar ratio values suitable for Asian dust, applied to multiyear CALIPSO observations (January 2007–December 2015). The resulting dust product provides information on the horizontal and vertical distribution of dust aerosols over South and East Asia along with the seasonal transition of dust transport pathways. Persistent high D_AOD (dust aerosol optical depth) values at 532 nm, of the order of 0.6, are present over the arid and semi-arid desert regions. Dust aerosol transport (range, height and intensity) is subject to high seasonality, with the highest values observed during spring for northern China (Taklimakan and Gobi deserts) and during summer over the Indian subcontinent (Thar Desert). Additionally, we decompose the CALIPSO AOD (aerosol optical depth) into dust and non-dust aerosol components to reveal the non-dust AOD over the highly industrialized and densely populated regions of South and East Asia, where the non-dust aerosols yield AOD values of the order of 0.5. Furthermore, the CALIPSO-based short-term AOD and D_AOD time series and trends between January 2007 and December 2015 are calculated over South and East Asia and over selected subregions. Positive trends are observed over northwest and east China and the Indian subcontinent, whereas over southeast China trends are mostly negative. The calculated AOD trends agree well with the trends derived from Aqua MODIS (Moderate Resolution Imaging Spectroradiometer), although significant differences are observed over specific regions.

We present a theoretical study investigating the cloud activation of multicomponent organic particles. We modeled these complex mixtures using solubility distributions (analogous to volatility distributions in the VBS, i.e., volatility basis set, approach), describing the mixture as a set of surrogate compounds with varying water solubilities in a given range. We conducted Köhler theory calculations for 144 different mixtures with varying solubility range, number of components, assumption about the organic mixture thermodynamics and the shape of the solubility distribution, yielding approximately 6000 unique cloud condensation nucleus (CCN)-activation points. The results from these comprehensive calculations were compared to three simplifying assumptions about organic aerosol solubility: (1) complete dissolution at the point of activation; (2) combining the aerosol solubility with the molar mass and density into a single effective hygroscopicity parameter κ; and (3) assuming a fixed water-soluble fraction ϵeff. The complete dissolution was able to reproduce the activation points with a reasonable accuracy only when the majority (70–80%) of the material was dissolved at the point of activation. The single-parameter representations of complex mixture solubility were confirmed to be powerful semi-empirical tools for representing the CCN activation of organic aerosol, predicting the activation diameter within 10% in most of the studied supersaturations. Depending mostly on the condensed-phase interactions between the organic molecules, material with solubilities larger than about 0.1–100 g L−1 could be treated as soluble in the CCN activation process over atmospherically relevant particle dry diameters and supersaturations. Our results indicate that understanding the details of the solubility distribution in the range of 0.1–100 g L−1 is thus critical for capturing the CCN activation, while resolution outside this solubility range will probably not add much information except in some special cases. The connections of these results to the previous observations of the CCN activation and the molecular properties of complex organic mixture aerosols are discussed. The presented results help unravel the mechanistic reasons behind observations of hygroscopic growth and CCN activation of atmospheric secondary organic aerosol (SOA) particles. The proposed solubility distribution framework is a promising tool for modeling the interlinkages between atmospheric aging, volatility and water uptake of atmospheric organic aerosol.

In this paper we provide an overview of new knowledge on oxygen depletion (hypoxia) and related phenomena in aquatic systems resulting from the EU-FP7 project HYPOX ("In situ monitoring of oxygen depletion in hypoxic ecosystems of coastal and open seas, and landlocked water bodies", http://www.hypox.net). In view of the anticipated oxygen loss in aquatic systems due to eutrophication and climate change, HYPOX was set up to improve capacities to monitor hypoxia as well as to understand its causes and consequences. Temporal dynamics and spatial patterns of hypoxia were analyzed in field studies in various aquatic environments, including the Baltic Sea, the Black Sea, Scottish and Scandinavian fjords, Ionian Sea lagoons and embayments, and Swiss lakes. Examples of episodic and rapid (hours) occurrences of hypoxia, as well as seasonal changes in bottom-water oxygenation in stratified systems, are discussed. Geologically driven hypoxia caused by gas seepage is demonstrated. Using novel technologies, temporal and spatial patterns of water-column oxygenation, from basin-scale seasonal patterns to meter-scale sub-micromolar oxygen distributions, were resolved. Existing multidecadal monitoring data were used to demonstrate the imprint of climate change and eutrophication on long-term oxygen distributions. Organic and inorganic proxies were used to extend investigations on past oxygen conditions to centennial and even longer timescales that cannot be resolved by monitoring. The effects of hypoxia on faunal communities and biogeochemical processes were also addressed in the project. An investigation of benthic fauna is presented as an example of hypoxia-devastated benthic communities that slowly recover upon a reduction in eutrophication in a system where naturally occurring hypoxia overlaps with anthropogenic hypoxia. Biogeochemical investigations reveal that oxygen intrusions have a strong effect on the microbially mediated redox cycling of elements. Observations and modeling studies of the sediments demonstrate the effect of seasonally changing oxygen conditions on benthic mineralization pathways and fluxes. Data quality and access are crucial in hypoxia research. Technical issues are therefore also addressed, including the availability of suitable sensor technology to resolve the gradual changes in bottom-water oxygen in marine systems that can be expected as a result of climate change. Using cabled observatories as examples, we show how the benefit of continuous oxygen monitoring can be maximized by adopting proper quality control. Finally, we discuss strategies for state-of-the-art data archiving and dissemination in compliance with global standards, and how ocean observations can contribute to global earth observation attempts.

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.

The particulate matter source apportionment technology (PSAT) is used together with PMCAMx, a regional chemical transport model, to estimate how local emissions and pollutant transport affect primary and secondary particulate matter mass concentration levels in Paris. During the summer and the winter periods examined, only 13% of the PM2.5 is predicted to be due to local Paris emissions, with 36% coming from mid-range (50–500 km from the center of the Paris) sources and 51% from long range transport (more than 500 km from Paris). The local emissions contribution to simulated elemental carbon (EC) is significant, with almost 60% of the EC originating from local sources during both summer and winter. Approximately 50% of the simulated fresh primary organic aerosol (POA) originated from local sources and another 45% from areas 100–500 km from the receptor region during summer. Regional sources dominated the secondary PM components. During summer more than 70% of the simulated sulfate originated from SO2 emitted more than 500 km away from the center of the Paris. Also more than 45% of secondary organic aerosol (SOA) was due to the oxidation of VOC precursors that were emitted 100–500 km from the center of the Paris. The model simulates more contribution from long range secondary PM sources during winter because the timescale for its production is longer due to the slower photochemical activity. PSAT results for contributions of local and regional sources were compared with observation-based estimates from field campaigns that took place during the MEGAPOLI project. PSAT simulations are in general consistent (within 20%) with these estimates for OA and sulfate. The only exception is that PSAT simulates higher local EC contribution during the summer compared to that estimated from observations.

The second Cabauw Intercomparison of Nitrogen Dioxide measuring Instruments (CINDI-2) took place in Cabauw (the Netherlands) in September 2016 with the aim of assessing the consistency of multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements of tropospheric species (NO2, HCHO, O3, HONO, CHOCHO and O4). This was achieved through the coordinated operation of 36 spectrometers operated by 24 groups from all over the world, together with a wide range of supporting reference observations (in situ analysers, balloon sondes, lidars, long-path DOAS, direct-sun DOAS, Sun photometer and meteorological instruments). In the presented study, the retrieved CINDI-2 MAX-DOAS trace gas (NO2, HCHO) and aerosol vertical profiles of 15 participating groups using different inversion algorithms are compared and validated against the colocated supporting observations, with the focus on aerosol optical thicknesses (AOTs), trace gas vertical column densities (VCDs) and trace gas surface concentrations. The algorithms are based on three different techniques: six use the optimal estimation method, two use a parameterized approach and one algorithm relies on simplified radiative transport assumptions and analytical calculations. To assess the agreement among the inversion algorithms independent of inconsistencies in the trace gas slant column density acquisition, participants applied their inversion to a common set of slant columns. Further, important settings like the retrieval grid, profiles of O3, temperature and pressure as well as aerosol optical properties and a priori assumptions (for optimal estimation algorithms) have been prescribed to reduce possible sources of discrepancies. The profiling results were found to be in good qualitative agreement: most participants obtained the same features in the retrieved vertical trace gas and aerosol distributions; however, these are sometimes at different altitudes and of different magnitudes. Under clear-sky conditions, the root-mean-square differences (RMSDs) among the results of individual participants are in the range of 0.01–0.1 for AOTs, (1.5–15) ×1014molec.cm-2 for trace gas (NO2, HCHO) VCDs and (0.3–8)×1010molec.cm-3 for trace gas surface concentrations. These values compare to approximate average optical thicknesses of 0.3, trace gas vertical columns of 90×1014molec.cm-2 and trace gas surface concentrations of 11×1010molec.cm-3 observed over the campaign period. The discrepancies originate from differences in the applied techniques, the exact implementation of the algorithms and the user-defined settings that were not prescribed. For the comparison against supporting observations, the RMSDs increase to a range of 0.02–0.2 against AOTs from the Sun photometer, (11–55)×1014molec.cm-2 against trace gas VCDs from direct-sun DOAS observations and (0.8–9)×1010molec.cm-3 against surface concentrations from the long-path DOAS instrument. This increase in RMSDs is most likely caused by uncertainties in the supporting data, spatiotemporal mismatch among the observations and simplified assumptions particularly on aerosol optical properties made for the MAX-DOAS retrieval. As a side investigation, the comparison was repeated with the participants retrieving profiles from their own differential slant column densities (dSCDs) acquired during the campaign. In this case, the consistency among the participants degrades by about 30 % for AOTs, by 180 % (40 %) for HCHO (NO2) VCDs and by 90 % (20 %) for HCHO (NO2) surface concentrations. In former publications and also during this comparison study, it was found that MAX-DOAS vertically integrated aerosol extinction coefficient profiles systematically underestimate the AOT observed by the Sun photometer. For the first time, it is quantitatively shown that for optimal estimation algorithms this can be largely explained and compensated by considering biases arising from the reduced sensitivity of MAX-DOAS observations to higher altitudes and associated a priori assumptions.