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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 work aims at characterising the seamount physiography and biology in the OSPAR Convention limits (north-east Atlantic Ocean) and Mediterranean Sea. We first inferred potential abundance, location and morphological characteristics of seamounts, and secondly, summarized the existing biological, geological and oceanographic in situ research, identifying examples of well-studied seamounts. Our study showed that the seamount population in the OSPAR area (north-east Atlantic) and in the Mediterranean Sea is large with around 557 and 101 seamount-like features, respectively. Similarly, seamounts occupy large areas of about 616 000 km2 in the OSPAR region and of about 89 500 km2 in the Mediterranean Sea. The presence of seamounts in the north-east Atlantic has been known since the late 19th century, but overall knowledge regarding seamount ecology and geology is still relatively poor. Only 37 seamounts in the OSPAR area (3.5% of all seamounts in the region), 22 in the Mediterranean Sea (9.2% of all seamounts in the region) and 25 in the north-east Atlantic south of the OSPAR area have in situ information. Seamounts mapped in both areas are in general very heterogeneous, showing diverse geophysical characteristics. These differences will likely affect the biological diversity and production of resident and associated organisms.

Ground-based spectroscopy measurements acquired systematically within the Toolik Vegetation Grid in the 2016 growing season. All data were collected in a subset of 1 x 1 m long-term monitoring plots representing three distinct vegetation communities three times representing early, peak and late season. Spectral data were acquired using a GER 1500 field spectrometer (350-1050 nm; 512 bands, spectral resolution 3 nm, spectral sampling 1.5 nm, and 8! field of view). Spectra were collected under clear weather conditions at the highest solar zenith angle between 10:00 and 14:00 local time. Data were collected at nadir approximately 1 m off the ground resulting in a Ground Instantaneous Field of View (GIFOV) of approximately 15 cm in diameter. Nine point measurements of upwelling radiance (Lup) were collected in each plot and averaged to characterize the spectral variability and to reduce noise. Downwelling radiance (Ldown) was measured as the reflectance from a white Spectralon© plate. Surface reflectance (R) was processed as Lup/Ldown x 100 (0-100%). Reflectance spectra were preprocessed with a Savitzky-Golay smoothing filter (n = 11) and subset to 400-985 nm to remove sensor noise at the edges of the radiometer detector. Digital camera data were acquired using a consumer-grade camera (Panasonic DM3 LMX, Japan) approximately 1 m off the ground with a white frame for registration of off nadir images. For detailed definitions of the RGB indices see metadata.docx. Leaves and stems of the dominant vascular species in a subset of the sampled plots were collected at early, peak, and late season for chlorophyll and carotenoid analysis.Samples were placed in porous tea bags and preserved in a silica gel desiccant in an opaque container for up to 3 months until pigment extraction (Esteban et al. 2009, doi:10.1007/s11120-009-9468-5). Each sample was homogenized by grinding with a mortar and pestle. Approximately 1.00 mg (+/- 0.05 mg) of homogenized sample was placed into a vial with 2 ml of dimethylformamide (DMF). Vials were then wrapped in aluminum foil to eliminate any degradation of pigments due to UV light and stored in a fridge (4C) for 24 hrs. Samples were measured into a cuvette prior to spectrophotometric analysis. Bulk pigments concentrations were then estimated using a spectrophotometer measuring absorption at 646.8, 663.8 and 480 nm (Porra et al. 1989, doi:10.1016/S0005-2728(89)80347-0) . Absorbance (A) values at specific wavelengths were transformed into µg/mg concentrations of chlorophyll a, Chla, chlorophyll b, Chlb, total chlorophyll, Chl, carotenoids, Car (for equations see metadata.docx). Pigment concentration was calculated as the average concentration of the dominant species in each plot. mean_"pigment" represents the mean of all biomass from each vegetation community and sd_"pigment" represents the standard deviation of each vegetation community.

In June 2009, 22 spectrometers from 14 institutes measured tropospheric and stratospheric NO2 from the ground for more than 11 days during the Cabauw Intercomparison Campaign of Nitrogen Dioxide measuring Instruments (CINDI), at Cabauw, NL (51.97° N, 4.93° E). All visible instruments used a common wavelength range and set of cross sections for the spectral analysis. Most of the instruments were of the multi-axis design with analysis by differential spectroscopy software (MAX-DOAS), whose non-zenith slant columns were compared by examining slopes of their least-squares straight line fits to mean values of a selection of instruments, after taking 30-min averages. Zenith slant columns near twilight were compared by fits to interpolated values of a reference instrument, then normalised by the mean of the slopes of the best instruments. For visible MAX-DOAS instruments, the means of the fitted slopes for NO2 and O4 of all except one instrument were within 10% of unity at almost all non-zenith elevations, and most were within 5%. Values for UV MAX-DOAS instruments were almost as good, being 12% and 7%, respectively. For visible instruments at zenith near twilight, the means of the fitted slopes of all instruments were within 5% of unity. This level of agreement is as good as that of previous intercomparisons, despite the site not being ideal for zenith twilight measurements. It bodes well for the future of measurements of tropospheric NO2, as previous intercomparisons were only for zenith instruments focussing on stratospheric NO2, with their longer heritage.