The Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite provides robust and global direct measurements of the cloud vertical structure. The GCM‐Oriented CALIPSO ...Cloud Product is used to evaluate the simulated clouds in five climate models using a lidar simulator. The total cloud cover is underestimated in all models (51% to 62% vs. 64% in observations) except in the Arctic. Continental cloud covers (at low, mid, high altitudes) are highly variable depending on the model. In the tropics, the top of deep convective clouds varies between 14 and 18 km in the models versus 16 km in the observations, and all models underestimate the low cloud amount (16% to 25%) compared to observations (29%). In the Arctic, the modeled low cloud amounts (37% to 57%) are slightly biased compared to observations (44%), and the models do not reproduce the observed seasonal variation.
Key Points
To evaluate the cloud vertical structure of models using CALIPSO satellite
Five GCMs underestimate the total cloud cover at all latitudes except in Arctic
Discrepancies are more pronounced in tropics and poles, and over continents
From a traditional low‐, middle‐, and high‐cloud “layered” perspective as well as a more detailed “level” perspective (40 levels), we compare the vertical distribution of clouds in 12 general ...circulation models (GCMs) against the GCM‐Oriented Cloud‐Aerosols Lidar and Infrared Pathfinder Satellite Observations Cloud Product (CALIPSO‐GOCCP) using a satellite simulator approach. The layered perspective shows that models exhibit the similar regional biases: an overestimate (underestimate) of high clouds over oceans (continents) in the tropics and a strong underestimate of low clouds over stratocumulus regions. Although high clouds are too infrequent on average, the level perspective reveals that high‐level clouds fill too many upper levels of the column when present (geometrically too thick), suggesting an overestimation of the cloud overlap. Compositing by dynamical regimes and large‐scale relative humidity shows that the models tend to have too many high‐level clouds in moist environments and too few boundary layer clouds in dry environments regardless of dynamical regimes.
Key Points
Detailed vertically resolved observations, such as CALIPSO‐GOCCP, are crucial to expose all cloud biases in climate models
High‐layered clouds are too infrequent while at the same time fill too many column upper levels, likely mostly due to overlap assumption
GCMs have too many high‐level clouds in moist environments and too few low‐level clouds in dry environments
Clouds cover about 70% of Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere ...over the entire globe and across the wide range of spatial and temporal scales that compose weather and climate variability. Satellite cloud data records now exceed more than 25 years; however, climate data records must be compiled from different satellite datasets and can exhibit systematic biases. Questions therefore arise as to the accuracy and limitations of the various sensors and retrieval methods. The Global Energy and Water Cycle Experiment (GEWEX) Cloud Assessment, initiated in 2005 by the GEWEX Radiation Panel (GEWEX Data and Assessment Panel since 2011), provides the first coordinated intercomparison of publicly available, standard global cloud products (gridded monthly statistics) retrieved from measurements of multispectral imagers (some with multiangle view and polarization capabilities), IR sounders, and lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature, or altitude), cloud thermodynamic phase, and cloud radiative and bulk microphysical properties (optical depth or emissivity, effective particle radius, and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by the inherent instrument measurement capability for detecting and/or identifying optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. The monthly gridded database presented here facilitates further assessments, climate studies, and the evaluation of climate models.
We compare the cloud detection and cloud phase determination of three independent climatologies based on Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) to airborne in ...situ measurements. Our analysis of the cloud detection shows that the differences between the satellite and in situ measurements mainly arise from three factors. First, averaging CALIPSO Level l data along track before cloud detection increases the estimate of high‐ and low‐level cloud fractions. Second, the vertical averaging of Level 1 data before cloud detection tends to artificially increase the cloud vertical extent. Third, the differences in classification of fully attenuated pixels among the CALIPSO climatologies lead to differences in the low‐level Arctic cloud fractions. In another section, we compare the cloudy pixels detected by colocated in situ and satellite observations to study the cloud phase determination. At midlatitudes, retrievals of homogeneous high ice clouds by CALIPSO data sets are very robust (more than 94.6% of agreement with in situ). In the Arctic, where the cloud phase vertical variability is larger within a 480 m pixel, all climatologies show disagreements with the in situ measurements and CALIPSO‐General Circulation Models‐Oriented Cloud Product (GOCCP) report significant undefined‐phase clouds, which likely correspond to mixed‐phase clouds. In all CALIPSO products, the phase determination is dominated by the cloud top phase. Finally, we use global statistics to demonstrate that main differences between the CALIPSO cloud phase products stem from the cloud detection (horizontal averaging, fully attenuated pixels) rather than the cloud phase determination procedures.
Key Points
Comparison of the cloud and cloud phase of three CALIPSO climatologies with in situ measurements
Cloud detection differences due to vertical/horizontal averaging and fully attenuated pixels
Very high agreement for midlatitude ice clouds, more disagreement with the mixed‐phase clouds
We take advantage of climate simulations from two multimodel experiments to characterize and evaluate the cloud phase partitioning in 16 general circulation models (GCMs), specifically the vertical ...structure of the transition between liquid and ice in clouds. We base our analysis on the ratio of ice condensates to the total condensates (phase ratio, PR). Its transition at 90% (PR90) and its links with other relevant variables are evaluated using the GCM‐Oriented Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation Cloud Product climatology, reanalysis data, and other satellite observations. In 13 of 16 models, the PR90 transition height occurs too low (6 km to 8.4 km) and at temperatures too warm (−13.9°C to −32.5°C) compared to observations (8.6 km, −33.7°C); features consistent with a lack of supercooled liquid with respect to ice above 6.5 km. However, this bias would be slightly reduced by using the lidar simulator. In convective regimes (more humid air and precipitation), the observed cloud phase transition occurs at a warmer temperature than for subsidence regimes (less humid air and precipitation). Only few models manage to roughly replicate the observed correlations with humidity (5/16), vertical velocity (5/16), and precipitation (4/16); 3/16 perform well for all these parameters (MPI‐ESM, NCAR‐CAM5, and NCHU). Using an observation‐based Clausius‐Clapeyron phase diagram, we illustrate that the Bergeron‐Findeisen process is a necessary condition for models to represent the observed features. Finally, the best models are those that include more complex microphysics.
Key Points
Cloud phase intermodel differences are very large
Prognostic cloud phase scheme is necessary to reproduce realistic cloud phase
These results could lead to an improvement of the next generation of GCMs
Ground‐based observations show that persistent liquid‐containing Arctic clouds occur frequently and have a dominant influence on Arctic surface radiative fluxes. Yet, without a hemispheric multi‐year ...perspective, the climate relevance of these intriguing Arctic cloud observations was previously unknown. In this study, Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used to document cloud phase over the Arctic basin (60–82°N) during a five‐year period (2006–2011). Over Arctic ocean‐covered areas, low‐level liquid‐containing clouds are prevalent in all seasons, especially in Fall. These new CALIPSO observations provide a unique and climate‐relevant constraint on Arctic cloud processes. Evaluation of one climate model using a lidar simulator suggests a lack of liquid‐containing Arctic clouds contributes to a lack of “radiatively opaque” states. The surface radiation biases found in this one model are found in multiple models, highlighting the need for improved modeling of Arctic cloud phase.
Key Points
New CALIPSO‐GOCCP observations show ubiquitous liquid‐containing Arctic clouds
Insufficient liquid‐containing Arctic cloud leads to radiation biases in models
Reproducing observed cloud phase is an important target for model improvement
This article presents the GCM‐Oriented Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Cloud Product (GOCCP) designed to evaluate the cloudiness simulated by general ...circulation models (GCMs). For this purpose, Cloud‐Aerosol Lidar with Orthogonal Polarization L1 data are processed following the same steps as in a lidar simulator used to diagnose the model cloud cover that CALIPSO would observe from space if the satellite was flying above an atmosphere similar to that predicted by the GCM. Instantaneous profiles of the lidar scattering ratio (SR) are first computed at the highest horizontal resolution of the data but at the vertical resolution typical of current GCMs, and then cloud diagnostics are inferred from these profiles: vertical distribution of cloud fraction, horizontal distribution of low, middle, high, and total cloud fractions, instantaneous SR profiles, and SR histograms as a function of height. Results are presented for different seasons (January–March 2007–2008 and June–August 2006–2008), and their sensitivity to parameters of the lidar simulator is investigated. It is shown that the choice of the vertical resolution and of the SR threshold value used for cloud detection can modify the cloud fraction by up to 0.20, particularly in the shallow cumulus regions. The tropical marine low‐level cloud fraction is larger during nighttime (by up to 0.15) than during daytime. The histograms of SR characterize the cloud types encountered in different regions. The GOCCP high‐level cloud amount is similar to that from the TIROS Operational Vertical Sounder (TOVS) and the Atmospheric Infrared Sounder (AIRS). The low‐level and middle‐level cloud fractions are larger than those derived from passive remote sensing (International Satellite Cloud Climatology Project, Moderate‐Resolution Imaging Spectroradiometer–Cloud and Earth Radiant Energy System Polarization and Directionality of Earth Reflectances, TOVS Path B, AIRS–Laboratoire de Météorologie Dynamique) because the latter only provide information on the uppermost cloud layer.
Abstract
Background
Sleep disorders are highly prevalent among university students. In particular, the symptoms of sleep disorders are more prevalent among healthcare students.
Aims
To assess the ...prevalence of risk factors of insomnia and sleep disorders and to examine the correlations between them among nursing and medical students. We also compared the effects of shift work during internship.
Methods
The sample was 417 healthcare students; 202 of them were nursing students, and the remaining 215 were medical students. We used a self-administered questionnaire to assess the risk factors for insomnia (i.e. age, BMI, tobacco consumption, physical activity and perceived stress, using the General Health Questionnaire-12). We also used the Sleep and Daytime Habits Questionnaire and Epworth Sleepiness Scale to assess the prevalence of sleep disorders and daytime sleepiness.
Results
A higher percentage of nursing students than medical students were aged 25 years or older, engaged in inadequate levels of physical activity and consumed tobacco. With the exception of tobacco consumption among nursing students, high scores on the GHQ-12 were the only risk factor associated with daytime and nighttime symptoms and poor sleep quality. There was no significant association between the symptoms of sleep disorders and shift work including night shifts.
Conclusions
Since sleep disorders are highly prevalent among healthcare students, early detection and management is recommended. This will decrease the risk of harm to students and patients, due to medical mistakes.
Incisional hernia is a common complication of abdominal surgery, and it is often a source of morbidity and high costs for health care. This is a case-control study to compare laparoscopic versus ...anterior-open incisional hernia repair.
170 patients with incisional hernia were enrolled in this study between September 2001 and December 2004. Of these, 85 underwent anterior-open repair (open group: OG), and 85 underwent laparoscopic repair (laparoscopic group: LG). The clinical outcome was determined by a median follow-up of 24.0 months for LG and OG.
No difference was noticed between the two groups in age, American Society of Anesthesiologists (ASA) score, body mass index (BMI), and incisional hernia diameter. Mean operative time was 61.0 min for LG patients and 150.9 min for OG patients (p < .05). Mean hospitalization was 2.7 days for LG patients and 9.9 days for OG patients (p < .05). Mean return to work was 13 days (range, 6-15 days) in LG patients and 25 days (range, 16-30 days) in OG patients. Complications occurred in 16.4 % of LG patients and 29.4 % of OG patients, with a relapse rate of 2.3% in LG and 1.1% in OG patients.
Short-term results indicate that laparoscopic incisional hernia repair is associated with a shorter operative time and hospitalization, a faster return to work, and a lower incidence of wound infections and major complications compared to the anterior-open procedure. Further studies and longer follow-up are required to confirm these findings.
Two different cloud climatologies have been derived from the same NASA-Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)-measured attenuated backscattered profile (level 1, version 3 ...dataset). The first climatology, named Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations-Science Team (CALIPSO-ST), is based on the standard CALIOP cloud mask (level 2 product, version 3), with the aim to document clouds with the highest possible spatiotemporal resolution, taking full advantage of the CALIOP capabilities and sensitivity for a wide range of cloud scientific studies. The second climatology, named GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP), is aimed at a single goal: evaluating GCM prediction of cloudiness. For this specific purpose, it has been designed to be fully consistent with the CALIPSO simulator included in the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) used within version 2 of the CFMIP (CFMIP-2) experiment and phase 5 of the Coupled Model Intercomparison Project (CMIP5). The differences between the two datasets in the global cloud cover maps-total, low level (P > 680 hPa), midlevel (680 < P < 440 hPa), and high level (P < 440 hPa)-are frequently larger than 10% and vary with region. The two climatologies show significant differences in the zonal cloud fraction profile (which differ by a factor of almost 2 in some regions), which are due to the differences in the horizontal and vertical averaging of the measured attenuated backscattered profile CALIOP profile before the cloud detection and to the threshold used to detect clouds (this threshold depends on the resolution and the signal-to-noise ratio).
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