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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).

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.

To determine the level of salt tolerance of the medicinal plant Echinacea, the physiological and biochemical characteristics of E. purpurea, E. pallida and E. angustifolia exposed to different NaCl levels (0, 50, 75, and 100 mM) were evaluated under hydroponic culture. Dry weights of shoots and roots were not affected by salinity; however E. purpurea and E. pallida exhibited higher survival rate than E. angustifolia, which also showed high salt injury index and electrolyte leakage compared to the other two species. Gas exchange (photosynthetic rate, stomatal conductance, and transpiration rate) showed a decline with increasing salt concentrations in all species with a more pronounced reduction in E. angustifolia. E. purpurea was able to retain more Na+ in the roots than the other two species showing its capacity to regulate Na+ translocation to shoots (Na+ exclusion). Moreover, the activities of two major antioxidant enzymes; superoxide dismutase (SOD) and ascorbate peroxidase (APX) were increased by salinity in E purperea, while the activities were decreased in E. angustifolia. The characteristic phytochemical profiles of caffeic acid derivatives (CADs) and alkamides/ketones were obtained for the three species, and quantitative changes were determined. Cichoric acid, the major CAD in E. purpurea, was increased with salinity up to 75 mM NaCl. A relative increase in alkamides and CADs was recorded in E. angustifolia, while in E. pallida, the level of echinacoside and major ketones (22 and 24) decreased, suggesting that the medicinal value of this species was compromised by salt stress. First evidence of salt-induced changes in alkamides and ketones in Echinacea was demonstrated in this study. Activity of phenylalanine ammonia-lyase (PAL), the major enzyme involved in the biosynthesis of CADs, was increased only in the roots of E. purpurea, further reflecting the differences in salt tolerance between species. It can be concluded that Echinacea species exhibited a limited degree of salt tolerance; however, E. purpurea showed a higher tolerance than E. pallida and E. angustifolia. This tolerance was mainly attributed to the increase in Na+ exclusion capacity, antioxidant activities and PAL activity.

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.

Publisher Copyright: © 2015 John Wiley & Sons Ltd. The hepatitis C virus (HCV) epidemic was forecasted through 2030 for 15 countries in Europe, the Middle East and Asia, and the relative impact of two scenarios was considered: increased treatment efficacy while holding the annual number of treated patients constant and increased treatment efficacy and an increased annual number of treated patients. Increasing levels of diagnosis and treatment, in combination with improved treatment efficacy, were critical for achieving substantial reductions in disease burden. A 90% reduction in total HCV infections within 15 years is feasible in most countries studied, but it required a coordinated effort to introduce harm reduction programmes to reduce new infections, screening to identify those already infected and treatment with high cure rate therapies. This suggests that increased capacity for screening and treatment will be critical in many countries. Birth cohort screening is a helpful tool for maximizing resources. Among European countries, the majority of patients were born between 1940 and 1985. A wider range of birth cohorts was seen in the Middle East and Asia (between 1925 and 1995). publishersversion Peer reviewed