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
Even though the diurnal cycle of solar forcing on the climate system is well defined, the diurnal evolutions of water vapor and clouds induced by the solar forcing are not yet established across the ...tropics. Here we combine recent satellite observations of clouds profiles and relative humidity profiles to document the diurnal variations of the water vapor and clouds vertical distributions over all the tropics in June-July-August. While the daily mean water vapor and cloud profiles are different between land and ocean, their diurnal variations with respect to their daily means exhibit similar features. Relative humidity profiles and optically thin cloud fraction profiles vary together which maximize during night-time in the entire troposphere and a minimize in day-time. The fraction of optically opaque clouds peak in the free troposphere in the early afternoon, transforms into a high altitude positive anomaly of optically thin clouds from nightfall to sunrise. In addition, land regions exhibit a daily low thin cloud positive anomaly, while oceanic regions exposed to subsidence air motions exhibit positive anomalies of opaque clouds in the lower atmosphere during the second half of the night, which grow until sunrise.
Previous generations of climate models have been shown to under‐estimate the occurrence of tropical low‐level clouds and to over‐estimate their radiative effects. This study analyzes outputs from ...multiple climate models participating in the Fifth phase of the Coupled Model Intercomparison Project (CMIP5) using the Cloud Feedback Model Intercomparison Project Observations Simulator Package (COSP), and compares them with different satellite data sets. Those include CALIPSO lidar observations, PARASOL mono‐directional reflectances and CERES radiative fluxes at the top of the atmosphere. We show that current state‐of‐the‐art climate models predict overly bright low‐clouds, even for a correct low‐cloud cover. The impact of these biases on the Earth' radiation budget, however, is reduced by compensating errors. Those include the tendency of models to under‐estimate the low‐cloud cover and to over‐estimate the occurrence of mid‐ and high‐clouds above low‐clouds. Finally, we show that models poorly represent the dependence of the vertical structure of low‐clouds on large‐scale environmental conditions. The implications of this ‘too few, too bright low‐cloud problem’ for climate sensitivity and model development are discussed.
Key Points
Low clouds too optically thick; particularly shallow cumulus clouds
Compensating errors: underestimate low‐cloud & overestimate high‐cloud fraction
Relative frequency of stratocumulus & shallow cumulus clouds not captured well
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.
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.
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
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
Climate models predict that the geographic distribution of clouds will change in response to anthropogenic warming, though uncertainties in the existing satellite record are larger than the magnitude ...of the predicted effects. Here we argue that cloud vertical distribution, observable by active spaceborne sensors, is a more robust signature of climate change. Comparison of Atmospheric Model Intercomparison Project present day and +4 K runs from Coupled Model Intercomparison Project Phase 5 shows that cloud radiative effect and total cloud cover do not represent robust signatures of climate change, as predicted changes fall within the range of variability in the current observational record. However, the predicted forced changes in cloud vertical distribution (directly measurable by spaceborne active sensors) are much larger than the currently observed variability and are expected to first appear at a statistically significant level in the upper troposphere, at all latitudes.
Key Points
Cloud vertical distribution is sensitive to climate warmingSpaceborne lidar can measure forced cloud changeForced trends in cloud profiles occur at altitudes above 5 km
COSP Bodas-Salcedo, A.; Webb, M. J.; Bony, S. ...
Bulletin of the American Meteorological Society,
08/2011, Letnik:
92, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Errors in the simulation of clouds in general circulation models (GCMs) remain a long-standing issue in climate projections, as discussed in the Intergovernmental Panel on Climate Change (IPCC) ...Fourth Assessment Report. This highlights the need for developing new analysis techniques to improve our knowledge of the physical processes at the root of these errors. The Cloud Feedback Model Intercomparison Project (CFMIP) pursues this objective, and under that framework the CFMIP Observation Simulator Package (COSP) has been developed. COSP is a flexible software tool that enables the simulation of several satellite-borne active and passive sensor observations from model variables. The flexibility of COSP and a common interface for all sensors facilitates its use in any type of numerical model, from high-resolution cloud-resolving models to the coarser-resolution GCMs assessed by the IPCC, and the scales in between used in weather forecast and regional models. The diversity of model parameterization techniques makes the comparison between model and observations difficult, as some parameterized variables (e.g., cloud fraction) do not have the same meaning in all models. The approach followed in COSP permits models to be evaluated against observations and compared against each other in a more consistent manner. This permits a more detailed diagnosis of the physical processes that govern the behavior of clouds and precipitation in numerical models. The World Climate Research Programme (WCRP) Working Group on Coupled Modelling has recommended the use of COSP in a subset of climate experiments that will be assessed by the next IPCC report. In this article we describe COSP, present some results from its application to numerical models, and discuss future work that will expand its capabilities.
New space‐borne active sensors make it possible to observe the three‐dimensional structure of clouds. Here we use CALIPSO lidar observations together with a lidar simulator to evaluate the cloudiness ...simulated by a climate model: modeled atmospheric profiles are converted to an ensemble of subgrid‐scale attenuated backscatter lidar signals from which a cloud fraction is derived. Except in regions of persistent thick upper‐level clouds, the cloud fraction diagnosed through this procedure is close to that actually predicted by the model. A fractional cloudiness is diagnosed consistently from CALIPSO data at a spatio‐temporal resolution comparable to that of the model. The comparison of the model's cloudiness with CALIPSO data reveals discrepancies more pronounced than in previous model evaluations based on passive observations. This suggests that space‐borne lidar observations constitute a powerful tool for the evaluation of clouds in large‐scale models, including marine boundary‐layer clouds
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