Recent structural evolution of forni glacier tongue (Ortles-Cevedale Group, Central Italian Alps)

Structural glaciology yields important details about the evolution of glacier dynamics in response to climate change. The maps provided here document the occurrence and evolution of brittle and ductile structures on the tongue of Forni Glacier, Ortles-Cevedale Group, Central Italian Alps, between 2003 and 2014. Through the remote sensing-based analysis of structures, we found evidence of brittle fractures such as crevasses, faults and ring faults, and ductile structures such as ogives at the base of the icefall in the eastern glacier tongue. Although each of the three glacier tongues have evolved differently, a reduction in flow-related dynamics and an increase in the number of collapse structures occurred over the study period. Analysis of the glacier structural evolution based on the numbers and the locations of different structures, suggest a slowdown of glacier flow on the eastern tongue. The recent evolution of the glacier also suggests that the occurrence of a disintegration scenario is likely to worsen over the next decades

Short GRBs at the dawn of the gravitational wave era

We derive the luminosity function and redshift distribution of short Gamma Ray Bursts (SGRBs) using (i) all the available observer-frame constraints (i.e. peak flux, fluence, peak energy and duration distributions) of the large population of Fermi SGRBs and (ii) the rest-frame properties of a complete sample of Swift SGRBs. We show that a steep $\phi(L)\propto L^{-a}$ with a>2.0 is excluded if the full set of constraints is considered. We implement a Monte Carlo Markov Chain method to derive the $\phi(L)$ and $\psi(z)$ functions assuming intrinsic Ep-Liso and Ep-Eiso correlations or independent distributions of intrinsic peak energy, luminosity and duration. To make our results independent from assumptions on the progenitor (NS-NS binary mergers or other channels) and from uncertainties on the star formation history, we assume a parametric form for the redshift distribution of SGRBs. We find that a relatively flat luminosity function with slope ~0.5 below a characteristic break luminosity ~3$\times10^{52}$ erg/s and a redshift distribution of SGRBs peaking at z~1.5-2 satisfy all our constraints. These results hold also if no Ep-Liso and Ep-Eiso correlations are assumed. We estimate that, within ~200 Mpc (i.e. the design aLIGO range for the detection of GW produced by NS-NS merger events), 0.007-0.03 SGRBs yr$^{-1}$ should be detectable as gamma-ray events. Assuming current estimates of NS-NS merger rates and that all NS-NS mergers lead to a SGRB event, we derive a conservative estimate of the average opening angle of SGRBs: $\theta_{jet}$~3-6 deg. Our luminosity function implies an average luminosity L~1.5$\times 10^{52}$ erg/s, nearly two orders of magnitude higher than previous findings, which greatly enhances the chance of observing SGRB "orphan" afterglows. Efforts should go in the direction of finding and identifying such orphan afterglows as counterparts of GW events.Comment: 13 pages, 5 figures, 2 tables. Accepted for publication in Astronomy & Astrophysics. Figure 5 and angle ranges corrected in revised versio

Technical Specification for the CLIC Two-Beam Module

A high-energy (0.5-3 TeV centre-of-mass), highluminosity Compact Linear Collider (CLIC) is being studied at CERN [1]. The CLIC main linacs, 21-km long each, are composed of 2-m long two beam modules. This paper presents their current layout, the main requirements for the different sub-systems (alignment, supporting, stabilization, cooling and vacuum) as well as the status of their integration

Venice lagoon chlorophyll-a evaluation under climate change conditions: A hybrid water quality machine learning and biogeochemical-based framework

Climate change presents a significant challenge to lagoon ecosystems, which are highly valued coastal environments known for their provision of unique ecosystem services. As important as fragile, lagoons are vulnerable to both natural processes and anthropogenic activities, and this vulnerability is exacerbated by the impacts of climate change, which are likely to result in severe ecological consequences. The complexity of water quality (WQ) processes, characterized by compounding and interconnected pressures, highlights the importance of adequate sophisticated methods to estimate future ecological impacts on lagoon environments. In this setting, a hybrid framework is introduced where Machine Learning (ML) and biogeochemical (BGC) models are integrated in a sequential modelling approach. This integration exploits the unique strengths offered by both models. The ML model allows capturing and learning linear and nonlinear correlations from historical data; the BGC interprets and simulates complex environmental systems subject to compounded pressures, building on identified causal relationships. Multi-Layer Perceptron (MLP) and Random Forest (RF) ML algorithms are trained, validated and tested within the Venice lagoon case study to assimilate historical WQ data (i.e., water temperature, salinity, and dissolved oxygen) and spatio-temporal information (i.e., monitoring station location and month), and to predict changes in chlorophyll-a (Chl-a) conditions. Then, projections from the BGC model SHYFEM-BFM for 2019, 2050, and 2100 timeframes under RCP 8.5 are integrated into the ML model (composing the hybrid ML-BGC model) to evaluate Chl-a variations under future biogeochemical conditions forced by climate change projections. Moreover, the SHYFEM-BFM standalone Chl-a projections are also used to compare the hybrid and the BGC scenarios. Annual and seasonal Chl-a predictions are developed by classes based on two classification modes (median and quartiles) established on the descriptive statistics computed on historical data. Results from the case study showed as the RF successfully classifies Chl-a with an overall model accuracy of about 80% for the median and 61% for the quartiles modes. Concerning future climate change scenarios, results revealed a decreasing trend for the lowest Chl-a values (below the first quartile, i.e. 0.85 µg/l) moving to the far future (2100), with an opposite rising trend for the highest Chl-a values (above the fourth quartile, i.e. 2.78 µg/l). On the seasonal level, summer remains the season with the highest Chl-a values in all scenarios, although in 2100 a strong increase in higher Chl-a values is also expected during the springtime one. The proposed hybrid framework represents a valuable approach to strengthen both multivariate Chl-a modelling and scenarios analysis, by placing artificial intelligence-based models alongside biogeochemical models