Fast motions of galaxies in the Coma I cloud: a case of Dark Attractor?

We notice that nearby galaxies having high negative peculiar velocities are distributed over the sky very inhomogeneously. A part of this anisotropy is caused by the "Local Velocity Anomaly", i.e. by the bulk motion of nearby galaxies away from the Local Void. But a half of the fast-flying objects reside within a small region RA = [11.5h, 13.0h], Dec. = [+20\circ, +40\circ], known as the Coma I cloud. According to Makarov & Karachentsev (2011), this complex contains 8 groups, 5 triplets, 10 pairs and 83 single galaxies with the total mass of 4.7\star10^13M\odot. We use 122 galaxies in the Coma I region with known distances and radial velocities VLG < 3000 km/s to draw the Hubble relation for them. The Hubble diagram shows a Z-shape effect of infall with an amplitude of +200 km/s on the nearby side and -700 km/s on the back side. This phenomena can be understood as the galaxy infall towards a dark attractor with the mass of \sim 2\star10^14M\odot situated at a distance of 15 Mpc from us. The existence of large void between the Coma and Virgo clusters affects probably the Hubble flow around the Coma I also.Comment: Accepted for publication in ApJ, 23 pages, 4 figure

Scientific and human errors in a snow model intercomparison

International audienceTwenty-seven models participated in the Earth System Model - Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modelling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables, and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modelling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parametrizations are problematic and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behaviour and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for15 some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the communit

Snow cover duration trends observed at sites and predicted by multiple models

The 30-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models, but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting