Colliding Wolf-Rayet (WR) winds produce thermal X-ray emission widely observed by X-ray telescopes. In wide WR+O binaries, such as WR 140, the X-ray flux is tied to the orbital phase, and is a direct probe of the winds’ properties. In the Galactic center, ~30 WRs orbit the super massive black hole (SMBH) within ~10”, leading to a smorgasbord of wind-wind collisions. To model the X-ray emission of WR 140 and the Galactic center, we perform 3D hydrodynamic simulations to trace the complex gaseous flows, and then carry out 3D radiative transfer calculations to compute the variable X-ray spectra. The model WR 140 RXTE light curve matches the data well for all phases except the X-ray minimum associated with periastron, while the model spectra agree with the RXTE hardness ratio and the shape of the Suzaku observations throughout the orbit. The Galactic center model of the Chandra flux and spectral shape match well in the region r ≤ 3”, but the model flux falls off too rapidly beyond this radius.
The gas cloud G2 is currently being tidally disrupted by the Galactic Centre super-massive black hole, Sgr A*. The region around the black hole is populated by ∼ 30 Wolf-Rayet stars, which produce strong outflows. Here we explore the possibility that gas clumps like G2 originate from the collision of stellar winds via the non-linear thin shell instability.
An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 degrees C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models.