Metabolism of 5-Fluro-2-Oxindole by Human Cytochrome P450s

Undergraduate Basi

How forward‐scattering snow and terrain change the Alpine radiation balance with application to solar panels

Rough terrain in mid- and high latitudes is often covered with highly reflective snow, giving rise to a very complex transfer of incident sunlight. In order to simplify the radiative transfer in weather and climate models, snow is generally treated as an isotropically reflecting material. We develop a new model of radiative transfer over mountainous terrain, which considers for the first time the forward scattering properties of snow. Combining ground-measured meteorological data and high resolution digital elevation models, we show that the forward scattering peak of snow leads to a strong local redistribution of incident terrain reflected radiation. In particular, the effect of multiple terrain reflections is enhanced. While local effects are large, area-wide albedo is only marginally decreased. In addition, we show that solar panels on snowy ground can clearly benefit from forward scattering, helping to maximize winter electricity production

Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model

The development of stream temperature regression models at regional scales has regained some popularity over the past years. These models are used to predict stream temperature in ungauged catchments to assess the impact of human activities or climate change on riverine fauna over large spatial areas. A comprehensive literature review presented in this study shows that the temperature metrics predicted by the majority of models correspond to yearly aggregates, such as the popular annual maximum weekly mean temperature (MWMT). As a consequence, current models are often unable to predict the annual cycle of stream temperature, nor can the majority of them forecast the inter-annual variation of stream temperature. This study presents a new statistical model to estimate the monthly mean stream temperature of ungauged rivers over multiple years in an Alpine country (Switzerland). Contrary to similar models developed to date, which are mostly based on standard regression approaches, this one attempts to incorporate physical aspects into its structure. It is based on the analytical solution to a simplified version of the energy-balance equation over an entire stream network. Some terms of this solution cannot be readily evaluated at the regional scale due to the lack of appropriate data, and are therefore approximated using classical statistical techniques. This physics-inspired approach presents some advantages: (1) the main model structure is directly obtained from first principles, (2) the spatial extent over which the predictor variables are averaged naturally arises during model development, and (3) most of the regression coefficients can be interpreted from a physical point of view – their values can therefore be constrained to remain within plausible bounds. The evaluation of the model over a new freely available data set shows that the monthly mean stream temperature curve can be reproduced with a rootmean-square error (RMSE) of +/-1.3 °C, which is similar in precision to the predictions obtained with a multi-linear regression model. We illustrate through a simple example how the physical aspects contained in the model structure can be used to gain more insight into the stream temperature dynamics at regional scales

Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

Snow distribution in complex alpine terrain and its evolution in the future climate is important in a variety of applications including hydropower, avalanche forecasting and freshwater resources. However, it is still challenging to quantitatively forecast precipitation, especially over complex terrain where the interaction between local wind and precipitation fields strongly affects snow distribution at the mountain ridge scale. Therefore, it is essential to retrieve high-resolution information about precipitation processes over complex terrain. Here, we present very-high-resolution Weather Research and Forecasting model (WRF) simulations (COSMO–WRF), which are initialized by 2.2&thinsp;km resolution Consortium for Small-scale Modeling (COSMO) analysis. To assess the ability of COSMO–WRF to represent spatial snow precipitation patterns, they are validated against operational weather radar measurements. Estimated COSMO–WRF precipitation is generally higher than estimated radar precipitation, most likely due to an overestimation of orographic precipitation enhancement in the model. The high precipitation amounts also lead to a higher spatial variability in the model compared to radar estimates. Overall, an autocorrelation and scale analysis of radar and COSMO–WRF precipitation patterns at a horizontal grid spacing of 450&thinsp;m show that COSMO–WRF captures the spatial variability normalized by the domain-wide variability in precipitation patterns down to the scale of a few kilometers. However, simulated precipitation patterns systematically show a lower variability on the smallest scales of a few hundred meters compared to radar estimates. A comparison of spatial variability for different model resolutions gives evidence for an improved representation of local precipitation processes at a horizontal resolution of 50&thinsp;m compared to 450&thinsp;m. Additionally, differences of precipitation between 2830&thinsp;m above sea level and the ground indicate that near-surface processes are active in the model.</p

Thermodynamics in the hydrologic response: Travel time formulation and application to Alpine catchments

This paper presents a spatially-explicit model for hydro-thermal response simulations of Alpine catchments, accounting for advective and non-advective energy fluxes in stream networks characterized by arbitrary degrees of geomorphological complexity. The relevance of the work stems from the increasing scientific interest concerning the impacts of the warming climate on water resources management and temperature-controlled ecological processes. The description of the advective energy uxes is cast in a travel time formulation of water and energy transport, resulting in a closed form solution for water temperature evolution in the soil compartment. The application to Alpine catchments hinges on the boundary conditions provided by the fully-distributed and physically-based snow model Alpine3D. The performance of the simulations is illustrated by comparing modeled and measured hydrographs and thermographs at the outlet of the Dischma catchment (45 km2) in the Swiss Alps. The Monte Carlo calibration shows that the model is robust and that a simultaneous fitting of stream ow and stream temperature reduces the uncertainty in the hydrological parameters estimation. The calibrated model also provides a good fit to the measurements in the validation period, suggesting that it could be employed for predictive applications, both for hydrological and ecological purposes. The temperature of the subsurface flow, as described by the proposed travel time formulation, proves warmer than the stream temperature during winter and colder during summer. Finally, the spatially-explicit results of the model during snowmelt show a notable hydro-thermal spatial variability in the river network, owing to the small spatial correlation of infilltration and meteorological forcings in Alpine regions

Author Correction: A Database of Snow on Sea Ice in the Central Arctic Collected during the MOSAiC expedition

Correction to: Scientific Data, published online 22 June 2023 The original version showed the wrong image for Figure 3, with the image for Figure 4 used for both. This has been corrected in the pdf and HTML versions of the article, with the correct version of Figure 3 replacing the duplicated figure. The dates in the figure captions were also incorrect and have been amended as follows: Figure 3 caption: “from 2019-10-25 - 2020-07-30” modified to “from 2019-10-25 - 2020-05-15” Figure 4 caption: “from 2020-02-25 - 2020-07-30” modified to “from 2020-06-13 - 2020-07-30”

Biogenic Volatile Organic Compound and Respiratory CO2 Emissions after 13C-Labeling: Online Tracing of C Translocation Dynamics in Poplar Plants

Globally plants are the primary sink of atmospheric CO(2), but are also the major contributor of a large spectrum of atmospheric reactive hydrocarbons such as terpenes (e.g. isoprene) and other biogenic volatile organic compounds (BVOC). The prediction of plant carbon (C) uptake and atmospheric oxidation capacity are crucial to define the trajectory and consequences of global environmental changes. To achieve this, the biosynthesis of BVOC and the dynamics of C allocation and translocation in both plants and ecosystems are important.We combined tunable diode laser absorption spectrometry (TDLAS) and proton transfer reaction mass spectrometry (PTR-MS) for studying isoprene biosynthesis and following C fluxes within grey poplar (Populus x canescens) saplings. This was achieved by feeding either (13)CO(2) to leaves or (13)C-glucose to shoots via xylem uptake. The translocation of (13)CO(2) from the source to other plant parts could be traced by (13)C-labeled isoprene and respiratory (13)CO(2) emission.In intact plants, assimilated (13)CO(2) was rapidly translocated via the phloem to the roots within 1 hour, with an average phloem transport velocity of 20.3±2.5 cm h(-1). (13)C label was stored in the roots and partially reallocated to the plants' apical part one day after labeling, particularly in the absence of photosynthesis. The daily C loss as BVOC ranged between 1.6% in mature leaves and 7.0% in young leaves. Non-isoprene BVOC accounted under light conditions for half of the BVOC C loss in young leaves and one-third in mature leaves. The C loss as isoprene originated mainly (76-78%) from recently fixed CO(2), to a minor extent from xylem-transported sugars (7-11%) and from photosynthetic intermediates with slower turnover rates (8-11%).We quantified the plants' C loss as respiratory CO(2) and BVOC emissions, allowing in tandem with metabolic analysis to deepen our understanding of ecosystem C flux

Myelin insulation as a risk factor for axonal degeneration in autoimmune demyelinating disease

Axonal degeneration determines the clinical outcome of multiple sclerosis and is thought to result from exposure of denuded axons to immune-mediated damage. Therefore, myelin is widely considered to be a protective structure for axons in multiple sclerosis. Myelinated axons also depend on oligodendrocytes, which provide metabolic and structural support to the axonal compartment. Given that axonal pathology in multiple sclerosis is already visible at early disease stages, before overt demyelination, we reasoned that autoimmune inflammation may disrupt oligodendroglial support mechanisms and hence primarily affect axons insulated by myelin. Here, we studied axonal pathology as a function of myelination in human multiple sclerosis and mouse models of autoimmune encephalomyelitis with genetically altered myelination. We demonstrate that myelin ensheathment itself becomes detrimental for axonal survival and increases the risk of axons degenerating in an autoimmune environment. This challenges the view of myelin as a solely protective structure and suggests that axonal dependence on oligodendroglial support can become fatal when myelin is under inflammatory attack