University of Valencia

About: University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Population & Neutrino. The organization has 27096 authors who have published 65669 publications receiving 1765689 citations. The organization is also known as: Universitat de València & UV.

Compensatory water effects link yearly global land CO2 sink changes to temperature

Abstract: Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance. Our study indicates that spatial climate covariation drives the global carbon cycle response.

Quantifying the heart of darkness with GHALO – a multibillion particle simulation of a galactic halo

Abstract: We perform a series of simulations of a Galactic mass dark matter halo at different resolutions: our largest uses over 3 billion particles and has a mass resolution of 1000 M⊙. We quantify the structural properties of the inner dark matter distribution and study how they depend on numerical resolution. We can measure the density profile to a distance of 120 pc (0.05 per cent of Rvir), where the logarithmic slope is −0.8 and −1.4 at (0.5 per cent of Rvir). We propose a new two-parameter fitting function that has a linearly varying logarithmic density gradient over the resolved radii which fits the GHALO and VL2 density profiles extremely well. Convergence in the halo shape is achieved at roughly three times the convergence radius for the density profile at which point the halo becomes more spherical due to numerical resolution. The six-dimensional phase-space profile is dominated by the presence of the substructures and does not follow a power law, except in the central few kpc which is devoid of substructure even at this resolution. The quantity, ρ/σ3, which is often used as a proxy for the six-dimensional phase-space density should be used with caution.

Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data

Abstract: This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm-2. For values higher than 3 g middotcm-2, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.

Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo

Abstract: We present results on the mass, spin, and redshift distributions with phenomenological population models using the 10 binary black hole (BBH) mergers detected in the first and second observing runs completed by Advanced LIGO and Advanced Virgo. We constrain properties of the BBH mass spectrum using models with a range of parameterizations of the BBH mass and spin distributions. We find that the mass distribution of the more massive BH in such binaries is well approximated by models with no more than 1% of BHs more massive than 45 M and a power-law index of (90% credibility). We also show that BBHs are unlikely to be composed of BHs with large spins aligned to the orbital angular momentum. Modeling the evolution of the BBH merger rate with redshift, we show that it is flat or increasing with redshift with 93% probability. Marginalizing over uncertainties in the BBH population, we find robust estimates of the BBH merger rate density of R= (90% credibility). As the BBH catalog grows in future observing runs, we expect that uncertainties in the population model parameters will shrink, potentially providing insights into the formation of BHs via supernovae, binary interactions of massive stars, stellar cluster dynamics, and the formation history of BHs across cosmic time.