ALBERT

Description: The Green Edge initiative was developed to investigate the processes controlling the primary productivity and the fate of organic matter produced during the Arctic phytoplankton spring bloom (PSB) and to determine its role in the ecosystem. Two field campaigns were conducted in 2015 and 2016 at an ice camp located on landfast sea ice southeast of Qikiqtarjuaq Island in Baffin Bay (67.4797N, 63.7895W). During both expeditions, a large suite of physical, chemical and biological variables was measured beneath a consolidated sea ice cover from the surface to the bottom at 360 m depth to better understand the factors driving the PSB. Key variables such as temperature, salinity, radiance, irradiance, nutrient concentrations, chlorophyll-a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, carbon stocks and fluxes were routinely measured at the ice camp. Here, we present the results of a joint effort to tidy and standardize the collected data sets that will facilitate their reuse in other Arctic studies. The dataset is available at http://www.seanoe.org/data/00487/59892/ (Massicotte et al., 2019a).

Description: The Green Edge initiative was developed to investigate the processes controlling the primary productivity and fate of organic matter produced during the Arctic phytoplankton spring bloom (PSB) and to determine its role in the ecosystem. Two field campaigns were conducted in 2015 and 2016 at an ice camp located on landfast sea ice southeast of Qikiqtarjuaq Island in Baffin Bay (67.4797∘ N, 63.7895∘ W). During both expeditions, a large suite of physical, chemical and biological variables was measured beneath a consolidated sea-ice cover from the surface to the bottom (at 360 m depth) to better understand the factors driving the PSB. Key variables, such as conservative temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured at the ice camp. Meteorological and snow-relevant variables were also monitored. Here, we present the results of a joint effort to tidy and standardize the collected datasets, which will facilitate their reuse in other Arctic studies. The dataset is available at https://doi.org/10.17882/59892 (Massicotte et al., 2019a).

Description: An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. While obtaining reliable forecasts is a key issue, it is a challenging task, especially when forecasting high flows in an extrapolation context, i.e. when the event magnitude is larger than what was observed before. In this study, we present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. The experiment set-up is based on (i) a large set of catchments in France, (ii) the GRP rainfall–runoff model designed for flood forecasting and used by the French operational services and (iii) an empirical hydrologic uncertainty processor designed to estimate conditional predictive uncertainty from the hydrological model residuals. The variants of the uncertainty processor used in this study differ in the data transformation they use (log, Box–Cox and log–sinh) to account for heteroscedasticity and the evolution of the other properties of the predictive distribution with the discharge magnitude. Different data subsets were selected based on a preliminary event selection. Various aspects of the probabilistic performance of the variants of the hydrologic uncertainty processor, reliability, sharpness and overall quality were evaluated. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows in an extrapolation context and considerable variability among catchments and across lead times. The increase in statistical treatment complexity did not result in significant improvement, which suggests that a parsimonious and easily understandable data transformation such as the log transformation or the Box–Cox transformation can be a reasonable choice for flood forecasting.

Description: Four summer seasons of nitrogen oxide (NO) concentrations were obtained at the South Pole (SP) during the Sulfur Chemistry in the Antarctic Troposphere (ISCAT) program (1998 and 2000) and the Antarctic Tropospheric Chemistry Investigation (ANTCI) in (2003, 2005, 2006–2007). Together, analyses of the data collected from these studies provide insight into the large- to small-scale meteorology that sets the stage for extremes in NO and the significant variability that occurs day to day, within seasons, and year to year. In addition, these observations reveal the interplay between physical and chemical processes at work in the stable boundary layer of the high Antarctic plateau. We found a systematic evolution of the large-scale wind system over the ice sheet from winter to summer that controls the surface boundary layer and its effect on NO: initially in early spring (Days 280–310) the transport of warm air and clouds over West Antarctica dominates the environment over the SP; in late spring (Days 310–340), the winds at 300 hPa exhibit a bimodal behavior alternating between northwest and southeast quadrants, which is of significance to NO; in early summer (Days 340–375), the flow aloft is dominated by winds from the Weddell Sea; and finally, during late spring, winds aloft from the southeast are strongly associated with clear skies, shallow stable boundary layers, and light surface winds from the east – it is under these conditions that the highest NO occurs. Examination of the winds at 300 hPa from 1961 to 2013 shows that this seasonal pattern has not changed significantly, although the last twenty years have seen an increasing trend in easterly surface winds at the SP. What has also changed is the persistence of the ozone hole, often into early summer. With lower total ozone column density and higher sun elevation, the highest actinic flux responsible for the photolysis of snow nitrate now occurs in late spring under the shallow boundary layer conditions optimum for high accumulation of NO. This may occur via the non-linear HOX–NOx chemistry proposed after the first ISCAT field programs and NOx recycling to the surface where quantum yields may be large under the low-snow-accumulation regime of the Antarctic plateau. During the 2003 field program a sodar made direct measurements of the stable boundary layer depth (BLD), a key factor in explaining the chemistry of the high NO concentrations. Because direct measurements were not available in the other years, we developed an estimator for BLD using direct observations obtained in 2003 and step-wise linear regression with meteorological data from a 22 m tower (that was tested against independent data obtained in 1993). These data were then used with assumptions about the column abundance of NO to estimate surface fluxes of NOx. These results agreed in magnitude with results at Concordia Station and confirmed significant daily, intraseasonal and interannual variability in NO and its flux from the snow surface. Finally, we found that synoptic to mesoscale eddies governed the boundary layer circulation and accumulation pathways for NO at the SP rather than katabatic forcing. It was the small-scale features of the circulation including the transition from cloudy to clear conditions that set the stage for short-term extremes in NO, whereas larger-scale features were associated with more moderate concentrations.

Description: In this paper we present the continued development of the miniDOAS, an active differential optical absorption spectroscopy (DOAS) instrument used to measure ammonia concentrations in ambient air. The miniDOAS has been adapted for use in the Dutch National Air Quality Monitoring Network. The miniDOAS replaces the life-expired continuous-flow denuder ammonia monitor (AMOR). From September 2014 to December 2015, both instruments measured in parallel before the change from AMOR to miniDOAS was made. The instruments were deployed at six monitoring stations throughout the Netherlands. We report on the results of this intercomparison. Both instruments show a good uptime of ca. 90 %, adequate for an automatic monitoring network. Although both instruments produce 1 min values of ammonia concentrations, a direct comparison on short timescales such as minutes or hours does not give meaningful results because the AMOR response to changing ammonia concentrations is slow. Comparisons between daily and monthly values show good agreement. For monthly averages, we find a small average offset of 0.65 ± 0.28 µg m−3 and a slope of 1.034 ± 0.028, with the miniDOAS measuring slightly higher than the AMOR. The fast time resolution of the miniDOAS makes the instrument suitable not only for monitoring but also for process studies.

Description: The mountain cryosphere is recognized to have important impacts on a range of environmental processes. This paper reviews current knowledge on snow, glacier, and permafrost processes, as well as their past, current and future evolution in mountain regions in mainland Europe. We provide a comprehensive assessment of the current state of cryosphere research in Europe and point to the different domains requiring further research to improve our understanding of climate–cryosphere interactions, cryosphere controls on physical and biological mountain systems, as well as related impacts. We highlight advances in the modeling of the cryosphere, and identify inherent uncertainties in our capability of projecting changes in the context of a warming global climate.

Description: Information on snow depth and its spatial distribution is crucial for many applications in snow and avalanche research as well as in hydrology and ecology. Today snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been done using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 opto-electronic scanner with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward looking sensor data. We compare these products with the following independent datasets acquired simultaneously: (a) manually measured snow depth plots (b) differential Global Navigation Satellite System (dGNSS) points (c) Terrestrial Laser Scanning (TLS) and (d) Ground Penetrating Radar (GPR) datasets, to assess the accuracy of the photogrammetric products. The results of this investigation demonstrate the potential of optical scanners for wide-area, continuous and high spatial resolution snow-depth mapping over alpine catchments above tree line.

Description: Information on snow depth and its spatial distribution is crucial for numerous applications in snow and avalanche research as well as in hydrology and ecology. Today, snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been successfully performed using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 optoelectronic scanner, acquiring stereo imagery with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward-looking sensor data. In order to assess the accuracy of the photogrammetric products, we compare these products with the following independent data sets acquired simultaneously: (a) manually measured snow depth plots; (b) differential Global Navigation Satellite System (dGNSS) points; (c) terrestrial laser scanning (TLS); and (d) ground-penetrating radar (GPR) data sets. We demonstrate that the method presented can be used to map snow depth at 2 m resolution with a vertical depth accuracy of ±30 cm (root mean square error) in the complex topography of the Alps. The snow depth maps presented have an average accuracy that is better than 15 % compared to the average snow depth of 2.2 m over the entire test site.

Description: Switchgrass (Panicum virgatum L.) is a C4, perennial grass that is being developed as a bioenergy crop for the United States. While aboveground biomass production is well documented for switchgrass ecotypes (lowland, upland), little is known about the impact of plant belowground productivity on microbial communities down deep in the soil profiles. Microbial dynamics in deeper soils are likely to exert considerable control on ecosystem services, including C and nutrient cycles, due to their involvement in such processes as soil formation and ecosystem biogeochemistry. Differences in root biomass and rooting characteristics of switchgrass ecotypes could lead to distinct differences in belowground microbial biomass and microbial community composition. We quantified root abundance and root architecture and the associated microbial abundance, composition, and rhizodeposit C uptake for two switchgrass ecotypes using stable-isotope probing of microbial phospholipid fatty acids (PLFAs) after 13CO2 pulse–chase labeling. Kanlow, a lowland ecotype with thicker roots, had greater plant biomass above- and belowground (g m−2), greater root mass density (mg cm−3), and lower specific root length (m g−1) compared to Summer, an upland ecotype with finer root architecture. The relative abundance of bacterial biomarkers dominated microbial PLFA profiles for soils under both Kanlow and Summer (55.4 and 53.5 %, respectively; P  =  0.0367), with differences attributable to a greater relative abundance of Gram-negative bacteria in soils under Kanlow (18.1 %) compared to soils under Summer (16.3 %; P  =  0.0455). The two ecotypes also had distinctly different microbial communities process rhizodeposit C: greater relative atom % 13C excess in Gram-negative bacteria (44.1 ± 2.3 %) under the thicker roots of Kanlow and greater relative atom % 13C excess in saprotrophic fungi under the thinner roots of Summer (48.5 ± 2.2 %). For bioenergy production systems, variation between switchgrass ecotypes could alter microbial communities and impact C sequestration and storage as well as potentially other belowground processes.

Description: The distribution of the maximum annual three day snow fall depth H72, used for avalanche hazard mapping according to the Swiss procedure (Sp), is investigated for a network of 124 stations in the Alpine part of Switzerland, using a data set dating back to 1931. Stationarity in time is investigated, showing in practice no significant trend for the considered period. Building on previous studies about climatology of Switzerland and using an iterative approach based on statistical tests for regional homogeneity and scaling of H72 with altitude, seven homogenous regions are identified. A regional approach based on the index value is then developed to estimate the T-years return period quantiles of H72 at each single site i, H72i(T). The index value is the single site sample average μH72i. The dimensionless values of H*72i=H72i / μH72i are grouped in one sample for each region and their frequency of occurrence is accommodated by a General Extreme Value, GEV, probability distribution, including Gumbel. The proposed distributions, valid in each site of the homogeneous regions, can be used to assess the T-years return period quantiles of H*72i. It is shown that the value of H72i(T) estimated with the regional approach is more accurate than that calculated by single site distribution fitting, particularly for high return periods. A sampling strategy based on accuracy is also suggested to estimate the single site index value, i.e. the sample average μH72i, critical for the evaluation of the distribution of H72i. The proposed regional approach is valuable because it gives more accurate snow depth input to dynamics models than the present procedure based on single site analysis, so decreasing uncertainty in hazard mapping procedure.