ABSTRACT We study the physical drivers of slow molecular cloud mergers within a simulation of a Milky Way-like galaxy in the moving-mesh code arepo, and determine the influence of these mergers on the mass distribution and star formation efficiency of the galactic cloud population. We find that 83 per cent of these mergers occur at a relative velocity below 5 km s−1, and are associated with large-scale atomic gas flows, driven primarily by expanding bubbles of hot, ionized gas caused by supernova explosions and galactic rotation. The major effect of these mergers is to aggregate molecular mass into higher-mass clouds: mergers account for over 50 per cent of the molecular mass contained in clouds of mass M > 2 × 106 M⊙. These high-mass clouds have higher densities, internal velocity dispersions and instantaneous star formation efficiencies than their unmerged, lower mass precursors. As such, the mean instantaneous star formation efficiency in our simulated galaxy, with its merger rate of just 1 per cent of clouds per Myr, is 25 per cent higher than in a similar population of clouds containing no mergers.
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Lifting the haze off the cloud: a consumer-centric market for database computation in the cloud
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Coyotes (Canis latrans) exist throughout North America and increasingly thrive in dense urban spaces; they also cause controversies when they eat small pets or seem to pose a threat. Based on fieldwork in Los Angeles, and an archive of over 400 conversations collected from the online application Nextdoor (2015–2019), we theorize the emergence of what we call the cloud coyote. Cloud coyotes are not representations but lively actors in coyote politics animated by discussion, debate, and a settler logic of property relations in places like Los Angeles. They do this by performing a threat and justifying a response that includes various attempts at extermination, containment, and assimilation, all of which—even supposedly humane alternatives—further sediment forms of settler colonialism in urban Los Angeles. We diagnose this process, show how it works, and argue that anticolonial practices—in both Los Angeles and its cloudy territories like Nextdoor—are needed to escape from perpetuating its violence.more » « less
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Abstract. The role of clouds in the Arctic radiation budget is not well understood. Ground-based and airborne measurements provide valuable data to test and improve our understanding. However, the ground-based measurements are intrinsically sparse, and the airborne observations are snapshots in time and space. Passive remote sensing measurements from satellite sensors offer high spatial coverage and an evolving time series, having lengths potentially of decades. However, detecting clouds by passive satellite remote sensing sensors is challenging over the Arctic because of the brightness of snow and ice in the ultraviolet and visible spectral regions and because of the small brightness temperature contrast to the surface. Consequently, the quality of the resulting cloud data products needs to be assessed quantitatively. In this study, we validate the cloud data products retrieved from the Advanced Very High Resolution Radiometer (AVHRR) post meridiem (PM) data from the polar-orbiting NOAA-19 satellite and compare them with those derived from the ground-based instruments during the sunlit months. The AVHRR cloud data products by the European Space Agency (ESA) Cloud Climate Change Initiative (Cloud_CCI) project uses the observations in the visible and IR bands to determine cloud properties. The ground-based measurements from four high-latitude sites have been selected for this investigation: Hyytiälä (61.84∘ N, 24.29∘ E), North Slope of Alaska (NSA; 71.32∘ N, 156.61∘ W), Ny-Ålesund (Ny-Å; 78.92∘ N, 11.93∘ E), and Summit (72.59∘ N, 38.42∘ W). The liquid water path (LWP) ground-based data are retrieved from microwave radiometers, while the cloud top height (CTH) has been determined from the integrated lidar–radar measurements. The quality of the satellite products, cloud mask and cloud optical depth (COD), has been assessed using data from NSA, whereas LWP and CTH have been investigated over Hyytiälä, NSA, Ny-Å, and Summit. The Cloud_CCI COD results for liquid water clouds are in better agreement with the NSA radiometer data than those for ice clouds. For liquid water clouds, the Cloud_CCI COD is underestimated roughly by 3 optical depth (OD) units. When ice clouds are included, the underestimation increases to about 5 OD units. The Cloud_CCI LWP is overestimated over Hyytiälä by ≈7 g m−2, over NSA by ≈16 g m−2, and over Ny-Å by ≈24 g m−2. Over Summit, CCI LWP is overestimated for values ≤20 g m−2 and underestimated for values >20 g m−2. Overall the results of the CCI LWP retrievals are within the ground-based instrument uncertainties. To understand the effects of multi-layer clouds on the CTH retrievals, the statistics are compared between the single-layer clouds and all types (single-layer + multi-layer). For CTH retrievals, the Cloud_CCI product overestimates the CTH for single-layer clouds. When the multi-layer clouds are included (i.e., all types), the observed CTH overestimation becomes an underestimation of about 360–420 m. The CTH results over Summit station showed the highest biases compared to the other three sites. To understand the scale-dependent differences between the satellite and ground-based data, the Bland–Altman method is applied. This method does not identify any scale-dependent differences for all the selected cloud parameters except for the retrievals over the Summit station. In summary, the Cloud_CCI cloud data products investigated agree reasonably well with those retrieved from ground-based measurements made at the four high-latitude sites.more » « less
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