The impact of resolution on the adjustment and decadal variability of the Atlantic Meridional Overturning Circulation in a coupled climate model

Variations in the Atlantic Meridional Overturning Circulation (MOC) exert an important influence on climate, particularly on decadal time scales. Simulation of the MOC in coupled climate models is compromised, to a degree that is unknown, by their lack of fidelity in resolving some of the key processes involved. There is an overarching need to increase the resolution and fidelity of climate models, but also to assess how increases in resolution influence the simulation of key phenomena such as the MOC. In this study we investigate the impact of significantly increasing the (ocean and atmosphere) resolution of a coupled climate model on the simulation of MOC variability by comparing high and low resolution versions of the same model. In both versions, decadal variability of the MOC is closely linked to density anomalies that propagate from the Labrador Sea southward along the deep western boundary. We demonstrate that the MOC adjustment proceeds more rapidly in the higher resolution model due the increased speed of western boundary waves. However, the response of the Atlantic Sea Surface Temperatures (SSTs) to MOC variations is relatively robust - in pattern if not in magnitude - across the two resolutions. The MOC also excites a coupled ocean-atmosphere response in the tropical Atlantic in both model versions. In the higher resolution model, but not the lower resolution model, there is evidence of a significant response in the extratropical atmosphere over the North Atlantic 6 years after a maximum in the MOC. In both models there is evidence of a weak negative feedback on deep density anomalies in the Labrador Sea, and hence on the MOC (with a time scale of approximately ten years). Our results highlight the need for further work to understand the decadal variability of the MOC and its simulation in climate models

Experimental L-band SST satellite communications/surveillance terminal study. Volume 1 - Study summary

Study of design for experimental L band supersonic transport communications/surveillance termina

Excitation of methyl cyanide in the hot core of Orion

The excitation of CH_3CN in the hot core of Orion is examined using high-sensitivity observational data at 1.3 mm. Observed line fluxes are analyzed by means of multilevel statistical equilibrium (SE) calculations which incorporate current theoretical values of the collisional excitation rates. The analysis is applied to both optically thin models of the hot core region and models with significant optical depths. Trapping is found to play a critical role in the excitation of CH_3CN. An optically thin analysis yields a kinetic temperature of 275 K and a cloud density of 2 x 10^6 cm^(-3). Unequal column densities are deduced in this case for the two symmetry species: N_A = 1.4 x 10^(14) cm^(-2) and N_E = 2.0 x 10^(14) cm^(-2). The deduced cloud density and temperature are lowered to 1.5 x 10^6 cm^(-3) and 240 K. The model with trapping is favored because of the agreement with measured sizes of the hot core source and the more plausible N_A/N_E ratio. Analysis of radiative excitation in the hot core indicates it is unlikely to significantly affect the ground vibrational state populations of CH_3CN. It most likely is significant for excitation of the V_8 band

On the Interpretation of the broad-band millimeter-wave flux from Orion

Spectral observations of the core of Orion A at wavelengths around 1.3 mm show a high density of strong, broad emission lines. The combined flux in lines with peak antenna temperatures stronger than 0.2 K accounts for approximately 40 percent of the broad-band millimeter-wave flux from the region. Thus the broad-band flux from Orion A is in large part due to sources other than dust emission

Molecular abundances in OMC-1: The chemical composition of interstellar molecular clouds and the influence of massive star formation

We present here an investigation of the chemical composition of the various regions in the core of the Orion molecular cloud (OMC-1) based on results from the Caltech Owens Valley Radio Observatory (OVRO) millimeter-wave spectral line survey (Sutton et al.; Blake et al.). This survey covered a 55 GHz interval in the 1.3 mm (230 GHz) atmospheric window and contained emission from over 800 resolved spectral features. Of the 29 identified species 14 have a sufficient number of detected transitions to be investigated with an LTE "rotation diagram" technique, in which large numbers of lines are used to estimate both the rotational excitation and the overall abundance. The rotational temperatures and column densities resulting from these fits have then been used to model the emission from those remaining species which either have too few lines or which are too weak to be so analyzed. When different kinematic sources of emission are blended to produce a single feature, Gaussian fits have been used to derive the individual contributions to the total line profile. The uniformly calibrated data in the unique and extensive Caltech spectral line survey lead to accurate estimates of the chemical and physical parameters of the Orion molecular cloud, and place significant constraints on models of interstellar chemistry. A global analysis of the observed abundances shows that the markedly different chemical compositions of the kinematically and spatially distinct Orion subsources may be interpreted in the framework of an evolving, initially quiescent, gas-phase chemistry influenced by the process of massive star formation. The chemical composition of the extended Orion cloud complex is similar to that found in a number of other objects, but the central regions of OMC-1 have had their chemistry selectively altered by the radiation and high-velocity outflow from the young stars embedded deep within the interior of the molecular cloud. Specifically, the extended ridge clouds are inferred to have a low (subsolar) gas-phase oxygen content from the prevalence of reactive carbon-rich species like CN, CCH, and C_3H_2 also found in more truly quiescent objects such as TMC-1. The similar abundances of these and other simple species in clouds like OMC-1, Sgr B2, and TMC-1 lend support to gas-phase ion-molecule models of interstellar chemistry, but grain processes may also play a significant role in maintaining the overall chemical balance in such regions through selective depletion mechanisms and grain mantle processing. In contrast, the chemical compositions of the more turbulent plateau and hot core components of OMC-1 are dominated by high-temperature, shock-induced gas and grain surface neutral-neutral reaction processes. The high silicon/sulfur oxide and water content of the plateau gas is best modeled by fast shock disruption of smaller grain cores to release the more refractory elements followed by a predominantly neutral chemistry in the cooling postshock regions, while a more passive release of grain mantle products driven toward kinetic equilibrium most naturally explains the prominence of fully hydrogenated N-containing species like HCN, NH_3 , CH_3CN, and C_2H_5CN in the hot core. The clumpy nature of the outflow is illustrated by the high-velocity emission observed from easily decomposed molecules such as H_2CO. Areas immediately adjacent to the shocked core in which the cooler, ion-rich gas of the surrounding molecular cloud is mixed with water/oxygen rich gas from the plateau source are proposed to give rise to the enhanced abundances of complex internal rotors such as CH_30H, HCOOCH_3 , and CH_30CH_3 whose line widths are similar to carbon-rich species such as CN and CCH found in the extended ridge, but whose rotational temperatures are somewhat higher and whose spatial extents are much more compact

The rotational emission-line spectrum of Orion A between 247 and 263 GHz

Results are presented from a molecular line survey of the core of the Orion molecular cloud between 247 and 263 GHz. The spectrum contains a total of 243 resolvable lines from 23 different chemical species. When combined with the earlier survey of Orion from 215 to 247 GHz by Sutton et al. (1985), the complete data set includes over 780 emission features from 29 distinct molecules. Of the 23 molecules detected in this survey, only NO, CCH, and HCO^+ were not identified in the lower frequency data. As a result of the supporting laboratory spectroscopy performed to supplement existing millimeter-wave spectral line catalogs, only 33 of the more than 780 lines remain unidentified, of which 16 occur in the upper frequency band. A significant chance remains that a number of these unidentified lines are due to transitions between states of either isotopically substituted or highly excited abundant and complex molecules such as CH_3OH, CH_3OCH_3, and HCOOCH_3, whose rotational spectra are poorly known at present. The very small percentage and weak strength of the unidentified lines implies that the dominant chemical constituents visible at millimeter wavelengths have been identified in the Orion molecular cloud

Regret Bounds for Reinforcement Learning with Policy Advice

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinforcement learning task at hand. We prove that RLPA has a sub-linear regret of \tilde O(\sqrt{T}) relative to the best input policy, and that both this regret and its computational complexity are independent of the size of the state and action space. Our empirical simulations support our theoretical analysis. This suggests RLPA may offer significant advantages in large domains where some prior good policies are provided

A pragmatic cluster randomized controlled trial of an educational intervention for GPs in the assessment and management of depression

Background. General practitioners (GPs) can be provided with effective training in the skills to manage depression. However, it remains uncertain whether such training achieves health gain for their patients. Method. The study aimed to measure the health gain from training GPs in skills for the assessment and management of depression. The study design was a cluster randomized controlled trial. GP participants were assessed for recognition of psychological disorders, attitudes to depression, prescribing patterns and experience of psychiatry and communication skills training. They were then randomized to receive training at baseline or the end of the study. Patients selected by GPs were assessed at baseline, 3 and 12 months. The primary outcome was depression status, measured by HAM-D. Secondary outcomes were psychiatric symptoms (GHQ-12) quality of life (SF-36), satisfaction with consultations, and health service use and costs. Results. Thirty-eight GPs were recruited and 36 (95%) completed the study. They selected 318 patients, of whom 189 (59%) were successfully recruited. At 3 months there were no significant differences between intervention and control patients on HAM-D, GHQ-12 or SF-36. At 12 months there was a positive training effect in two domains of the SF-36, but no differences in HAM-D, GHQ-12 or health care costs. Patients reported trained GPs as somewhat better at listening and understanding but not in the other aspects of satisfaction. Conclusions. Although training programmes may improve GPs' skills in managing depression, this does not appear to translate into health gain for depressed patients or the health service

Predicting Future Instance Segmentation by Forecasting Convolutional Features

Anticipating future events is an important prerequisite towards intelligent behavior. Video forecasting has been studied as a proxy task towards this goal. Recent work has shown that to predict semantic segmentation of future frames, forecasting at the semantic level is more effective than forecasting RGB frames and then segmenting these. In this paper we consider the more challenging problem of future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of fixed-sized convolutional features of the Mask R-CNN instance segmentation model. We apply the "detection head'" of Mask R-CNN on the predicted features to produce the instance segmentation of future frames. Experiments show that this approach significantly improves over strong baselines based on optical flow and repurposed instance segmentation architectures